No. hio r-*4 4 UNIVERSITY OF CALIFORNIA' DAVIS UNIVERSITY OF CALI DIVISION OF AGRICl JUN 2 0 1974 JURAL SCIENCES ■MiifiiJ- REC. LIBRARY Water Supplies and Cost in Relation to Farm Resource Use Decisions and Profits on Sacramento Valley Farms Trimble R. Hedges CALIFORNIA AGRICULTURAL EXPERIMENT STATION GIANNINI FOUNDATION OF AGRICULTURAL ECONOMICS Giannini Foundation Research Report No. 320 MARCH 1974 by University of California, Davis Department of Agricultural Economics WATER SUPPLIES AND COSTS IN RELATION TO FARM RESOURCE USE DECISIONS AND PROFITS ON SACRAMENTO VALLEY FARMS 1. Enterprise Choices, Resource Allocations, and Earnings on 1,280-Acre Rice Farms in the Central Sacramento Valley By Trimble R. Hedges March 21, 1974 TABLE OF CONTENTS Page FOREWORD iiiv SUMMARY X THIS STUDY ANALYZES HOW VARIATIONS IN IRRIGATION WATER QUANTITIES AND/OR COSTS AFFECT FARM DECISIONS AND RESOURCE USE, HENCE EARNINGS, ON 1,280-ACRE RICE FARMS 1 The Analysis Involves a Broad Range of Resource Allocation and Technological Decisions 1 Earlier Work Provides Essential Background Data for This Inves- tigation 3 PHYSICAL RESOURCES, PARTICULARLY SOIL AND WATER, STRONGLY INFLUENCE CROP PROFITS 6 Land and Soil Characteristics are Critical 6 Climatic Factors Exert Important Influence 10 Low-Cost Water Favors Rice Production 10 THE 1,280-ACRE FARM SIZE PERMITS EFFICIENT RESOURCE COMBINATION ... 14 Farmers, Federal and State Agencies, Farm Suppliers and Indi- viduals Provided Information 14 The 1,280-Acre Farm Studied Is a Common Size for Rice Farms .. 15 High-Capacity Machines and Heavy Investments Lower Costs Per Product Unit and Lessen Time-Related Uncertainty 21 Alternative Crops Vary Widely in Output, Revenue, Costs and Net Returns Per Acre 23 THE METHODOLOGY FOCUSES ON AN ECONOMIC ANALYSES OF PHYSICAL, BIOLOG- ICAL, AND ECONOMIC RELATIONSHIPS 26 Crop Yield Estimates Vary According to Irrigation Treatments on Each Soil 26 Yield Estimates Reflect Mean Soil Moisture Availability Ratios for Crops Other Than Rice 27 -ii- TABLE OF CONTENTS (Con't.) Page Rice Irrigation Practice Definitions are Distinct From Row Crop Practices 28 Estimated Plant Growth and Yields Reflect Irrigation Practices and Moisture Availability During the Growing Season 31 Net Returns Per Acre Determine Profits Rankings for Individual Crops 32 Linear Programming Analyzes Alternative Resources Use Opportu- nities and Identifies Optimum Choices Under Specified Assump- tions and Constrjiints 33 Constraints Reflect Limits Set by Resource Availability, Tech- nology, Market Conditions, and Institutional Factors 36 Budgeted Total Farm Earnings Statements Determine Profits and Returns to Various Resource Categories 38 SHIFTS IN NET RETURNS AND OPTIMUM RESOURCE USE ACCOMPANY VARIATIONS IN WATER PRICES AND QUANTITIES 39 Net Returns-Over-Variable Expenses Drop Sharply As Water Costs Rise 39 Initial Water Increments Above Zero Bring High Added Net Returns Per Acre-Foot: Basin Soils Use the Least, Recent Alluvium, the Most Irrigation Water 46 WATER QUANTITIES AND PRICES GOVERN OPTIMUM MANAGEMENT DECISIONS, RESOURCE ALLOCATIONS, AND CROPPING SYSTEMS 50 Water Quantities Sharply Limit Crop Choices, Resource Alloca- tion, and Net Returns on Irrigated Farms 50 WATER PRICE VARIATIONS REGULATE QUANTITIES USED, CROP CHOICES, AND RESOURCE ALLOCATIONS 53 WATER PRICES AND SOIL ADAPTABILITY GOVERN FARM WATER DEMAND 56 EXPENSIVE OR LIMITED QUANTITIES OF WATER SHARPLY REDUCE FARM PROFITS 58 FARM PRODUCT PRICES STRONGLY INFLUENCE WATER USE, CROP CHOICES AND ACRES, AND PROFITS 67 -iii- TABLE OF CONTENTS (Con't.) Page Rice Prices Dominate Decisions on Sacramento Valley Rice Farms 67 Changing Economic Conditions Bring New Price Production and Price Relationships for California Growers 73 Up-to-Date Technology and Efficient Resource Use Are Essential to Profitable Rice Farming 76 Higher Acreages Could Bring Greater Profits at Recent Rice Prices 76 CONCLUSIONS 77 -iv- LIST OF TABLES Table Page 1 Real Farm Estate and Operating Equipment Inventories and Invest- ments; 1,280-Acre Rice Farm, 196A-1966 Average Prices 18 2 Summary of Fixed Costs, 1,280-Acre Rice Farm, 1964-1966 Average Prices 19 3 Irrigation Water Budget; Rice on Basin Clay Adobe Soil, Continual Deep-Flooding (45 Days), Followed by Shallow-Flooding, Calculated from Physical Data 29 4 Variations in Farm Net Returns and Irrigation Water Variable Costs for Three Soils, 1964-1966 Average Prices 40 5 Farm Net Returns Per Acre-Foot of Water Varying Quantities of Irrigation Water on Three Soils, 1964-1966 Average Prices 47 6 Farm Earnings and Profits (Capital and Management Income) at Varying Water Quantities and Costs, Rice Yields, and Allotments, 1964-1966 Average Prices, Except as Indicated 61 7 Rice Production and Net Returns at Varying Rice Prices Without Allotments, 1964-1966 Average Prices, Except for Rice 68 -V- LIST OF FIGURES Figure Page 1 Sacramento Valley Study Area and Generalized Soil Map 7 2 Precipitation, Temperatures, and Growing Season Colusa, California 11 3 Net Returns and Variable Costs Per Acre for Specified Crops by Soils and Irrigation Treatments 24 4 Production Possibility Chart, Saf flower and Rice 35 5 Farm Net Returns at Varying Irrigation Water Costs for Three Soils 41 6 Farm Net Returns at Varying Quantities of Irrigation Water and Fixed Costs for Three Soils (Water Variable Expense; $1.25 per acre-foot) 48 7 Changes in Net Farm Returns, Crop Acres, and Marginal Value Products Per Acre-Foot of Water at Varying Quantities of Irrigation Water 51 — On Old Alluvium Soils — On Basin Soils — On Recent Alluvium Soils. 8 Optimum Cropping Plans for Critical Price Ranges of Irrigation Water 54 — On Basin Soils — On Old Alluvium Soils — On Recent Alluvium Soils. 9 Farm Demand for Irrigation Water on Three Soils 57 10 Optimum Cropping Plans and Rice Production 69 — On Basin Soils at Varying Critical Rice Prices — On Old Alluvium Soils at Varying Critical Rice Prices — On Recent Alluvium Soils at Varying Critical Rice Prices. 11 Rice Production And Farm Net Returns for Varying Rice Prices Without Allotments 71 -vi- LIST OF APPENDIX TABLES Table Page A-1 Growth Rates for Crops Other Than Rice on Various Soils by Five Percent Intervals for Available Soil Moisture Depletion and Combined Averages; Three Irrigation Practices 84 A-2 Condensed Basic Computational Form for Linear Programming Calculations; 1,280-Acre Farm; Basin Soil; Variable Water Prices 85 A-3 Calculation Methods for Determining Annual Fixed Costs on Farm Property or Capital Goods, (illustrated by 85 draw-bar horsepower tracklayer tractor) 86 A-4 Estimated Field Irrigation Efficiency Under Furrow Irrigation for Different Application Depths by Soil Type 87 A-5 Irrigation Water Added to Soil, Irrigation Efficiency, and Total Seasonal Applications by Soils, Irrigation Practices, and Crops, 1,280-Acre Farm 88 A-6 Quantities and Costs of Irrigation Water for Rice by Soils and Irrigation Practices 89 A-7 Calendar of Operations and Physical Inputs Per Acre 1,280- Acre Farm; Rice on Basin Soil Irrigation Under Deep Flooding — Lowered Practice 90 A-8 Variable Input Expenses Per Acre 1,280-Acre Farm; Rice Accord- ing to Soils and Irrigation Practices, 1964-1966 Average Prices 91 A-9 Variable Input Expenses and Net Returns Per Acre of Rice According to Soils and Irrigation Practices, 1964-1966 Average Prices 92 A-10 Summary of Variable Input Costs and Net Returns Per Acre for all Crops According to Soil and Irrigation Practices , 1964- 1966 Average Prices 93 -vil- FOREWORD This report focxises on the rice fairmlng phase of an investigation into how water quantities and costs affect enterprise choices, resource alloca- tions, and profits in the Sacramento Valley. The investigation was author- ized under California Agricultural Experiment Station Project Number 1321- 07-10. Support for the research leading to this report came from the OFFICE OF WATER RESOURCES RESEARCH, USDI, under the program of Public Law 88-379, as amended, and by the University of California, Water Resources Center. It is a part of the Office of Water Resources Research Project No. B-068 CAL as well as the California Water Resources Center Project UCAL-WRC-W-111. This over-all investigation, under the title, "On-Farm Irrigation Water Supplies and Costs in Relation to Cropping Systems and Production Adjust- ments in the Sacramento Valley," also includes a second phase that centers on the southern Sacramento Valley. A report on this additional research, now nearing completion, will bear the title. Water Supplies and Costs in Relation to Farm Resource Use Decisions and Profits on Sacramento Valley Farms ; 2. General Crop Farms in the Southern Sacramento Valley . The author acknowledges his debt to the many individuals and organi- zations who contributed importantly to the success of the research that led to this report. Ralph Hanan and Raul Fiorentino, at the time of their contributions Research Assistants in the Department of Agricultural Eco- nomics at Davis, bore primary responsibility for the statistical work. Hanan aided in Collecting and processing the field and secondary data, and did the planning and operation of the programming and other analytical procedures. Fiorentino assisted in completing the statistical work on this report, and, particularly, in final refinements of both the data and the exhibits that appear herein. Craig Boyer also shared in the statis- tical analysis . Many people provided data, viewpoints and/or advice and judgments that were essential for pursuing and completing the analyses reported here. I also drew heavily on work published by researchers and other personnel in the California Agricultural Experiment Station and Agricultural Extension Service, the Department of Water Resources, the United States Department -viii- of Agriculture, the Agricultural Stabilization and Conservation offices in the rice-producing counties, the County Agricultural Commissioners' offices and other state experiment stations, as well as on some unpublished data that became available to me. I am particularly grateful to W. 0. Pruitt for making available exper- imental results on evapotranspiration rates and water use for crops, to Milton Miller for his valuable counsel and suggestions. Thanks, too, to many other individuals in County Agricultural Extension, Irrigation Dis- trict, County Assessor's, and individual business firm office for a great deal of Information and many suggestions. The farmers who furnished infor- mation in formal interviews, and on other occasions, merit special thanks; it is only through their cooperation that it was possible to obtain crit- ical technical farm organization and operating information. -ix- SUMMARY This study of 1,280-acre rice farms in the Butte-Colusa subarea of the Sacramento Valley focuses on the economic impacts of variations in available water quantities and costs on farm earnings and profits (see pages 10-22). It examines three farm models each representing an important rice-growing soil and reflecting the dominant organization and operating characteristics of 1,280-acre rice farms in the study. Total irrigation water available approximated 5.75 acre- feet per acre for 60,000 acres of basin land, and 6.75 acre-feet per acre for 104,000 acres of alluvial soils. Cost rates per acre varied from about $10.00 to approximately double that level for rice and usually ranged from $4.00 to $5.00 per acre for other crops, except pasture rates for which crops were $1.00 to $1.50 higher. Price levels, acreage allotments, and other politico-economic aspects of the context for the investigation reflect the middle 1960's (1964-1966). The analysis draws on latest research information concerning irrigation practices for rice to evaluate differences in water quantities and costs, yields, and net returns for each of these varying irrigation practices. It undertakes to relate these irrigation practice phenomena to total farm earnings and prof- its for each of three major categories of soils commonly used to produce rice. Growers in Butte and Colusa counties normally produce 45 to 50 percent of all the rice produced in the Sacramento Valley, and 40 to 45 percent of California's total production. The rice acreage concentrates on the basin and old alluvium soils, but extends onto the more recent alluvium soils to some extent. Differences among these three soils in soil structure, water permeability, and adaptability to crops other than rice made it necessary to include three models in the analyses, one for each of the basin, old alluvium, and new alluvium soils, in order to reflect prop- erly the physical and economic results of these variations. The study analyzed three rice irrigation practices: (1) deep flooding, not lowered; (2) deep flooding, lowered; (3) shallow flooding. The first practice was standard for rice in California while land remained unlevelled, dikes contoured, and checks irregular in shape. Both deep flooding lowered and shallow floodings, however, expanded during the 1960 's as farmers leveled their fields, established uniform slopes and rectangular checks. -X- Knowledge accumulated through their own experience and experimental research has encouraged rice growers to introduce and expand those practices . Deep flooding remains the most general practice in growing rice, however, at the time of this study. The three practices for irrigated crops other than rice differ according to the percentage of available soil moisture deple- tion permitted before reirrigating: (1) dry, 100 percent; (2) medium, 80 percent; (3) wet, 60 percent. Total average investments for 1,280-acre rice farms range from nearly $700,000 for the basin model to over $800,000 units on recent alluvium. Capital represented by land dominates these total investments. These rela- tively high capital investments also mean large annual fixed costs, whether expressed on the total farm or the per acre basis. Such costs of owning and maintaining the capital range from about $81,000 total farm and $68.00 per acre for the basin, to over $91,000 total farm and $77.00 per acre for the recent alluvium models. The high original and average investments required for power units, and for dikers, harvesters, and other machinery large enough to permit operators to use power and labor efficiently, largely explain why this study focuses on 1,280-acre rice farms; the 400 to 550 acres of rice possible on a unit of this size (depending on acreage allot- ment regulations) , constitute enough acreage to use most of the unit capac- ity of such efficient machinery. An analysis of net returns-over-variable expenses for rice, and other adapted crop alternatives showed that, with fixed costs ignored, rice yielded net returns per acre at double or greater the level of crops ranking next highest in earnings on all three of the soils studied. These returns range from $219.00 per acre for the basin soils under a deep-shallow treat- ment to $184.00 for the deep flooding irrigation practice. The same rela- tionships, with somewhat higher per-acre returns, held for rice on the other two soils. These results, particularly for rice, reflect results of applying latest research knowledge and technology under careful water management and control on levelled land with rectangular checks. The physical inputs, pro- duction expenses, and prices for rice are those in effect during the late 1960 's. Acreage allotments, however, represent 40 percent of tillable land on these 1,280-acre analysis models. Rice yields reflect superior management. -xl- as well as the advantages of latest research technology; they range from 60 hundredweight per acre for the deep flooded irrigation to 68 hundredweights per acre for the other two methods on basin soils to a spread from 65 to 72.5 hundredweights per acre on the two alluvial models. These yields com- pare with the state average for each of the 1969 and the 1970 seasons at 55 hundredweights, per acre. The differential between this statwide average at 55 hundredweights, and the yields used in the analysis of these three soils, represent the premium on up-to-date technology based on the latest research, optimal water control and management, and sound decisions and management by the operator. A series of linear programming analyses within the framework of 28 constraints evaluated the potential effect on total farm net returns-over- variable expenses of varying water quantities, water prices, and prices for rice. The constraints relate to seasonal totals and intraseasonal water quantities available, total tillable land, and the maximtim acreages of individual crops within this total, and harvester hours per season. This analysis yielded the total farm net retums-over-variable expenses, ignoring fixed costs. A comparison of this total farm net receipt figure, under varying conditions of water quantity and price as well as rice prices, with total farm fixed costs identifies the "breakeven" level at which these farm receipts exactly cover fixed costs. This level includes interest on investment at a market rate but leaving no income or profit to management. Total farm net retums-over-variable expenses decline sharply as water prices rise from zero to the highest price tested in the analysis. The highest water prices at which total farm net income would cover fixed costs and leave a postive return to management are $15.00 per acre-foot for basin, $14.00 for old alluvium and $21.00 for recent alluvium income in the linear program analyses under these specified optimal management, technology and allotment conditions. These analyses apply the high performance yields and 40 percent acreage allotments used in this study. Rice growers would find production quite profitable, however, at prices in the vicinity of $7.00 per acre-foot, provided their rice yields and acreage allotments remain at these high performance levels. Total farm net returns drop sharply on basin soils for each dollar of rise in the earlier increments of water -xii- prices. A drop of nearly $22,000 in net returns accompanies a rise in water costs from zero to about $8.00 per acre-foot, this means about $2,800 decline in net returns per dollar rise in water prices. Another analysis examined the effects on total farm net returns-over- variable expenses of increasing total seasonal water quantities available from zero to the maximum level associated with increases in net returns. The results clearly show that rice has a high advantage over other irrigated crops on all three of the soils in the Butte-Colusa rice subarea. The results also indicate that the increases in total farm net returns per acre- foot of irrigation water added is extremely high for the initial increments up to about 2,500 acre-feet on the basin, 4,200 acre-feet on the two allu- vial soils. The greater adaptability of these latter two soils as compared with the basin, soil with acreage allotments in effect for rice, explain the larger quantities of water that the alluvial soils can use effectively in expanding total farm net returns. It is due to this same crop adaptability advantage, that the two alluvial soils are able to use profitably 4,300 and about 5,000 acre-feet of water, respectively, for the old and recent allu- vium, as compared with about 3,000 acre-feet for the basin soil at maximum total farm net returns with irrigation water price at $1.25 per acre-foot. An analysis of shifts in land use and cropping patterns as quantities of water available rise progressively from the zero level, further confirms the economic advantage of rice over other crops on farms in this subarea. The cropping system on 1,280-acre non-irrigated farms would include barley 354, wheat 354, saf flower 236, fallow 118, and idle land 118 acres; this pattern would apply on all three farm models, regardless of soils. Rice would yield the highest returns for all water increments on basin soils until the entire 472 acre allotment is reached. There would be some shifts in rice acreages among irrigation practices as water quantities increase. Grain sorghum would enter the basin cropping system after water quantities exceed rice requirements. The two alluvial soils reflect their greater range of crop adaptability as additional increments of irrigation water become available, beginning with zero quantities. Beans, a crop with minimal water requirements as compared with rice, appears before rice, and occupies the 177-acre maximum -xiii- within the acreage constr£ilnt applying to this crop at maximum water avail- ability on both of these soils. Sugar beets, again subject to its own con- straint, comes into the cropping system at maximum water availability levels on the recent alluvium. The pattern of change in land use and cropping patterns as water prices drop from levels initially so high as to prohibit use for irrigation is quite similar on all three soils. These relationships resemble those exliib- ited as water quantities rise at the fixed price of $1.25 per acre-foot. The first shift from the non-irrigated cropping system brings in rice on the basin, and dry beans on the two alluvial soils; the alternate cropping system with water prices at $7.00 per acre-foot or lower is the same for all three soils as at maximum water use with prices at $1.25 per acre-foot. Budget analysis conqparisons demonstrate clearly that operators pro- ducing rice on each of the three 1,280-acre models, according to soils, in this study would earn quite satisfactory profits under the cost and price conditions of the late 1960 's provided that they have acreage allotments representing 40 percent or more of the tillable land^ and that they can maintain yields at the high performance levels used in this study. Thus the basin soil model would return its operator nearly $100,000 in net farm income. This amount would represent nearly $92,000 of profit to this oper- ator after he has allowed himself $7,700 of wages (the same figure that he pays his hired employees). This total profit would represent $44,600 as interest on the capital that the farm employs calculated at the market rate of 6.5 percent, plus $46,900 as a return to the operator for risking this capital and for performing other management functions. This operator, alter- natively, can express his $92,000 profit figure as a percentage of his total farm capital; the result is a 13.4 percent rate of return. Similar earnings at satisfactory levels would accrue to operators on the two alluvium soils under the same set of cost, price, and acreage allotment conditions, combined with high performance yields. Unfortunately these high performance yields, reflecting a combination of technology based on the latest research knowledge, efficient water appli- cation and control, and optimum management, are not typical of California -xiv- rice operations. The 40 percent acreage allotment for rice also is dis- tinctly higher than the level typically available to California growers during recent seasons.—^ The statewide average rice yields of 50 hundred- weights per acre, at an acreage allotment representing about 30 percent of the tillable land on the rice producing farms represent a much more typi- cal condition for most basin soil rice farms. The total profit of $38,700 on such an operation would lack $5,900 of covering the entire 6.5 percent interest allowance on farm capital. Thus the operator would lack $5,900 of getting any return at all for assuming capital risks and would receive nothing to pay him for performing management functions! Similar analyses of farm earnings and profit for the two alluvial soils yield similar results. The rice grower on such soils would receive positive returns to risk capital and management with a lower (30 percent) acreage allotment and rice yields at 55 hundredweights per acre. This total manage- ment income would amount to only about $14,000, after setting aside allow- ances of $48,800 for interest on his capital invested at an assumed market rate of 6.5 percent. Such a level of management income for managing and operating a three-quarters of a million dollar business, and for £issuming the uncertainties and risks involved in using capital for this purpose, is a decidedly more favorable return to the manager than the negative income to the operator on the basin farm. This $16,000 management income, how- ever, probably is not comparable with the level of income that managers of three-quarters of a million dollar nonfarm businesses commonly expect to receive. This analysis of earnings indicates clearly, first, that the typical California rice grower with acreage allotments and yields below the 40 percent and 60 to 72 hundredweight levels receives decidedly lower incomes to capital and management than those indicated in these analyses J./ An announcement by the Secretary of Agriculture on 6 August 1971 but later rescinded on 20 December 1971, would have reduced 1972 allotments 10 percent under those for 1971. Total U. S. allotments had been set at 1,652,600 acres, and California (estimated) at 299,800 acres by this announced cut. California's Butte and Colusa County growers would have been able to plant in 1972 only about 35 percent of their total rice land, had this 6 August rule stood. The cut would have been even more serious for many individual growers, probably to about 30 percent of their suitable cropland. -XV— for the models with high performance yields and 40 percent acreage allot- ments. It also is evident, in the second place, that farmers could better afford to pay in the range of $7.00 per acre-foot for their water, if this price is accompanied by a 40 percent acreage allotment, than to operate with a 30 percent allotment and water prices at $1.25 per acre-foot. A final linear programming analysis established the effects of varying rice prices on, first, the acreage and production of rice on these 1,280- acre models, and, second, total farm net retums-over-variable expenses. It would be profitable for farmers to introduce rice into their cropping systems in the Butte-Colusa subarea at prices of $2.55 to $2.62 per hundred- weight under the conditions of this study. This analysis shows, further- more, that under the conditions of this study only shortages of water would keep rice from preempting practically all of the tillable acres on these farms as rice prices rise further to a rcinge between $3.00 and $4.00 per hundredweight. Physical and biological factors whose effects this study does not identify nor measure probably would intervene to check rice acreage much short of levels considering only price rises for rice. These data do suggest, however, that rice acreage at somewhere in the range of 40 to 50 percent of total tillable land would represent a sound cropping system in this area, provided water supplies are adequate, and, that farmers apply the most efficient technology based upon up-to-date research. WATER SUPPLIES AND COSTS IN RELATION TO FARM RESOURCE USE DECISIONS AND PROFITS ON SACRAMENTO VALLEY FARMS 1. Enterprise Choices, Resource Allocations, and Earnings on 1,280-Acre Rice Farms in the Central Sacramento Valley Trimble R. Hedges* THIS STUDY ANALYZES HOW VARIATIONS IN IRRIGATION WATER QUANTITIES AND/OR COSTS AFFECT FARM DECISIONS AND RESOURCE USE, HENCE EARNINGS, ON 1,280-ACRE RICE FARMS The Analysis Involves a Broad Range of Resource Allocation and Technological Decisions This study has as its primary over-all objective to establish economic guidelines that farmers may use effectively when making decisions on crop choices, land and other resource allocations, and related production tech- nology and methods . The actual procedures and analysis center on three specific objectives underlying the broad over-all objective of the study. 1. To identify the physical attributes of irrigation water supplies in the Sacreunento Valley. This information is esential to estab- lish current cost structures, flexibility components, uncertainty elements, and long- terra trends in water supplies, and to determine the physical and economic characteristics of supply schedules for irrigation water. 2. To establish: (a) the physical input-output relationships for water and crop yields in producing adapted crops, within relevant output ranges; (b) the impact of prices for products and costs for input factors upon water allocations among such crops; (c) the effects of alternative irrigation practices on yields and production input costs; and (d) combinations in which different crops will fit together to form cropping systems imder varying water supply conditions. 3. To establish appropriate criteria and effective analysis to: (a) guide choices for particular cropping systems; (b) evaluate oppor- tunities for adjustments and limitations on such adjustments, including irrigation practices; (c) maximize earnings and profits. *Trimble R. Hedges is Professor of Agricultural Economics and Agricultural Economist in the Experiment Station and on the Giannini Foundation. -2- The analytical approach centers attention on irrigation water quan- tities and costs as dominant issues in farm operations for an area where precipitation during the summer months is totally inadequate to permit economic production of any nonirrigated crop. Thus the analysis undertakes to establish and measure, on the one hand, how variations in water quan- tities and irrigation practices affect physical output and net dollar returns under constant price conditions for rice and other alternative crops. It also examines on the other hand the impact on such net returns that varying water prices (costs) exert. The analysis could not ignore other important phenomena and problems in the physical-institutional-eco- nomic context within which the rice farmer must operate. The complete anal- ysis considers, therefore, such additional profit-affecting factors as prices for inputs other than water, and also for rice and other farm outputs. It also considers the more important production-and-market-regulating forces such as allotments imder the Federal Agricultural Stabilization and Conser- vation Act, institutional and other informal marketing constraints, and historical evidence as to market capacity for certain products. Not only farmers planning and managing rice production operations, but also many agencies, firms, and individuals providing farmers with goods, services or other production and/or marketing needs should find these results useful. These findings relate most closely to the prices, produc- tion technology, and general condition that prevailed during the mid- to late 1960 's, the time period to which most of the data used in the analysis apply. Concerned users should find, however, that with appropriate adjust- ments the results of this investigation will aid in decisions, and in planning and executing rice farm operations, under conditions that differ from those of the latter 1960's. This same comment is appropriate con- cerning the farm model to which these findings relate. The analysis focuses on a 1,280-acre farm size with characteristics that reflect those most common or typical in the geographic area studied. But, with certain adjust- ments, farmers or others concerned with other sizes of operation should find these results useful. The analysis centers on this particular size because the 400 to 550 acres of rice normally associated with such a total farm acreage fit well within the unit capacities of certain equipment, notably -3- the diker and the harvesters (see page 22). Here again, relatively simple adjustments can bring the analytical results into a framework geared to farms of differing sizes. Another feature of this study is the attempt that it makes to evaluate the possible range of gains in profits that farmers may obtain by putting latest research findings and technology into use. Thus the analysis compares the relative returns per acre and in total farm profits under alternative rice irrigation technology and practices. The two major analytical tools that this investigation employs are 1) linear programming, and 2) budget analysis. It was possible with these tools to accomplish five specific steps essential to the study's ultimate objective: 1. Construct a farm model that will typify modal characteristics for a specific farm organization and size under specified conditions, in order to identify and measure how varying water supply and cost conditions affect total farm performance and profits . 2. Construct complete input-output models for all production materials and services; determine total revenue, aggregate variable expenses, and net returns-over-variable expenses for each alternative crop; relate these basic facts to relevant resource, economic, and institutional conditions for the farm model. 3. Identify economic choice criteria governing crop selection and resource allocations; develop effective measurement techniques for the purpose of maximizing enterprise and, ultimately, total farm net profits. 4. Establish the relationships between irrigation water supply and costs, and seasonal availability characteristics, on the one hand, and the critical resource use and earning features of the total farm business, on the other; consider the influence of varying supply and price conditions for other critical resources and for important farm products. 5. Explore the opportunities for adjusting the farm organization to variations in availability and cost of water, and to changes in other major institutional and economic forces affecting farm organization and earnings. Earlier Work Provides Essential Background Data for This Investigation Rice research in California has emphasized variety selection and breeding, fertilization, irrigation, pest control; all are important subject -4- matter areas. The work on irrigation has particular relevance to this study, however; this is because problems of water quantities and prices represent the central issues in this analysis. Some recent research on weed control problems and fertilization under varying irrigation practices also has special significance to this study, due to the impact of such variations in weed control and fertilization practices on irrigation requirements and costs. Other research on biological problems and pro- duction technology in rice has also contributed heavily to the data used, the methods of organization, and the analytical approaches in this Inves- tigation. But the Impact of this biologically-oriented research expresses Itself primarily in the choices and quantities of specific Inputs, and In the technology that the input-output analysis in this study reflects . We do not undertake to evaluate the economic implications of research findings other than those concerned expressly with irrigation. Adams [1] reported some of the pioneer work on rice irrigation in California. He concluded from irrigation experiments during the years 1914 through 1919 that about five feet of Irrigation water is an adequate sea- sonal total to produce rice on the principal rice soils in the Sacramento Valley (clays and clay adobes of the Willows, Stockton, Sacramento, Capay, and Yolo series). He also found that, "The previous loam soils require an excessive amount of irrigation water and, from a water standpoint are not suitable for rice growing." More recent experience, and current prac- tice in the Sacramento Valley, substantiate these findings as well as Adams' other observations that about one-third of the water applied to rice evap- orates into the atmosphere, and that farm operators vary widely in the quantities of water that they use in growing rice. Adams reports, on this latter issue, that 43 full-season measurements from 1914 to 1918 revealed a range In quantities applied from 3.91 to 18.70 feet per acre. Wide ranges In water use still exist in the Sacramento Valley rice area. Oelke [23] and [24] cites evidence from experiments in 1963 through 1967 that a combination of shallow irrigation along with herbicide appli- cations to control weeds, particularly water grass, results in higher rice yields than those possible from deep irrigation. He indicates, also, that reduced fertilizer applications tend to Increase the yields under the shallow -5- irrigation (one to two inches deep) , as compared with the customary deeper flooding practices (six to eight inches average depth). Both Adams' and Oelke's work provided highly important technical information for the anal- ysis in this study; it is basic to the assumptions and the alternative production technologies and resource combinations that our analysis employs. Our analysis of comparative profits according to different levels of rice yields and acreage allotments provides some evidence as to the economic importance of yield-increasing technology for rice. Much of the research concerned with rice varieties, fertilization, pest control, and other technical aspects of rice production appears in circulars, leaflets, and the monthly periodical, California Agriculture . Thus Davis' [7] circular represents one of the earlier general reports on rice and its production in California. Leaflets by Mikkelsen, Finfrock, and Miller [21]; Finfrock, Raney, Miller, and Booher [10]; Thysell, Miller, and Booher [29]; and by Burton, Grigarik, Hall, Lange, Swift, and Webster [4] include recommendations on rice technology in the areas of fertiliza- tion, water management, varieties and seed selection, and pest control, respectively. Studies concerned with the economic aspects of rice production range from the (processed) " Sample Rice Costs " that Lindt [19] prepared for grow- ers in Placer, Sacramento, Sutter, and Yuba counties in 1966 to Grant's [12] sophisticated analysis on evalioating government program costs for rice, the latter being a cooperative study in 1969 involving the U.S.D.A. and Texas A. & M. University. Sitton's [27] Sacramento Valley study in 1958, a systematic analysis of organization, costs, and returns, provides basic Information on resource use, technology, production costs, and returns during the late 1950 's for Sacramento Valley rice farms, including a range of from 150 to 600 acres of rice. A more recent series of analytical reports dealing with problems of farm organization, risk, and economics of size in tractor and labor combinations under Arkansas conditions was released in 1969 by Hottel, Grant, and Mullins [16 and 17]. A leaflet by Sitton, with Reed and Davis [26], considers possible adjustments to controls, and one by Mehren [20] examines the broader questions relating to government -6- policy for rice. All of these studies deal with economic questions and issues important to farmers and others involved in the rice industry. None seeks to attain the specific objectives of this investigation, how- ever, although individually and collectively they do contribute to such goals . PHYSICAL RESOURCES, PARTICULARLY SOIL AND WATER, STRONGLY INFLUENCE CROP PROFITS Land and Soil Characteristics are Critical That section of the Mid-Sacramento Valley that the Chico, Colusa County [18], and Oroville [34] soil surveys include in their reports provides the physical setting for this investigation of Sacramento Valley rice farming. This study does not undertake, however, to examine the economic aspects of profitable farm operation for all soil types in this over-all area. It focuses, instead, on the heavier and more poorly drained soils on which rice enjoys unique advantages over other alternative crops, and on only two of the several counties in the Sacramento Valley that are important rice producers. These three surveys provide soil inventory and classification data for western Butte County (Chico and Oroville surveys) , and Colusa County (Colusa County survey) (see Figure 1) . Sources in Butte and Colusa counties provided most of the data for the analysis in this study, but some information on irrigation water supplies did come from Glenn County. The three reports, in combination, furnish information on about 1,33,000 acres of which slightly more than half is in Colusa County. The remaining acreage divides almost equally between the Chico and Oroville survey reports. Alluvial soils account for about 55 percent of the total land in the general area that these surveys cover. The proportion of such land in the total for Butte County, nearly 60 percent, slightly exceeds that for the Colusa survey; the latter reports shows approximately one-half of all land that is alluvial. This study centers primarily on the soils that farmers commonly use to produce rice; this means it largely excludes the Grades I and II soils according to the Storie Index. It includes, instead. Grades III, IV, and V soils in the basin, older alluvial, and, to a limited extent, more recent alluvial soils in the aforementioned western portion of Butte County and eastern portion of Colusa County. -7- FIGURE 1 SACRAMEHTO VALLEY STUDY AREA AHD GEMERALIZED SOIL MAP -8- Butte County has slightly fewer than 160,000 acres of such soils, pre- dominantly in the Storie Index Grade IV classification, and of Stockton or Landlow clay or clay adobe series (see references 16 and 18 above). These grades III, IV, jind V soils represent about 25 percent of the total acreage covered by the two Butte County surveys combined. During recent years, siibject to government acreage limitations that vary somewhat from one year to another, rice usually has occupied from 33.33 to 40 percent of this acreage, with other grains and safflower accounting for most of the other land in crops. Actually, the total acreage in other crops tends to be somewhat less than that in rice, while approximately an equal acreage usu- 2/ ally is in fallow or lying idle.— The alluvial soils on which farmers usually grow rice in Colusa County show a wider range of Storie Index Grades (III, IV, V) than those in Butte County. They also represent a larger proportion of the total alluvial soil resources in the Colusa County survey, about one-third of all land 3/ as compared with about 25 percent in Butte County.— In Colusa, as in Butte County, other grains and fallow occupy most of the land not in rice during any particular season. Here again, during recent years, rice has accounted for about one-third of the crop acreage for this rice-growing area west of the Sacramento River. In summary, this investigation centers on the rice-growing, predomi- nantly basin and older alluvial soils in Central California and draws most of the data used in the analysis from Butte and Colusa counties. These soils, lying on either side of the Sacramento River, are quite similar in IJ Grades I and II soils, approximating a total of 200,000 acres, accounted or about one-third of all land in the two Butte County surveys combined. Such lands lie largely to the north, but include some acreage to the east, of the Grade IV (basin) soils that farmers commonly use to produce rice. These latter rice soils occupy about 156,000 acres in the county, or approximately 25 percent of the land in the Chico and Oroville soil surveys combined. "hj Grades I and II soils in Colusa County include about 133,000 acres of land or approximately 20 percent of all soils included in the report; they lie largely to the west of the rice growing area. The basin and old alluvium soils are mostly in Storie Grade IV through V (about 225,500 acres) but include about 23,200 acres of Grade VI basin soil in their total area of 248,700 acres. -9- physical characteristics (Figure 1 and references 6, 18, 34). They range in texture from clay loam through clays to clay adobe. Along with heavy textures, these soils manifest the characteristics of limited water per- colation, poor drainage, and problems in management [33]. These charac- teristics tend to limit the number of profitable crops adapted to the area, hence the range of choices that farmers have in crop selection. It was noted above that other grains, plus saf flower, occupy most of the land in crops other than rice in the area studied; also that an acre- age about equal to either that in rice or in the other crops combined, usually lies fallow during any given season. This holds for these soils in both Butte and Colusa counties. Limited crop adaptability on the so-called rice soils, plus the restrictions on rice acreage imposed under government income support programs, and a number of other less specific problems, all combine to present a relatively unique set of farm problems to growers operating on these soils. Nor is this problem limited to rice growers on such soils in the two counties cited here. On the contrary, these two counties combined in 1968 included 48 percent of the rice acre- age in Sacramento Valley and 43 percent of the total in California. They accounted for 185,100 acres of rice harvested (with a total production of 9,841,200 cwt.) out of a total Sacramento Valley acreage of 383,600 acres (20,848,200 cwt.) and a State total of 432,000 acres (23,328,000 cwt. 4/ [5]).— These same two counties, in 1972, included 138,300 acres or 42 percent of California's total rice acreage. Most of the total California production is in the Sacramento Valley; other important counties in this valley, with major areas of similar soils and sizable rice acreages during 1968, included Sutter 80,200 acres; Glenn, 57,800; Yolo, 30,900; Yuba, 16,800; and Sacramento County 12,800 acres. Comparable acreages for 1972 were 55,700, 45,000, 21,600, 15,400, and 9,900, respectively. In Colusa County, and also to a considerable degree among various of the other counties cited in the preceeding paragraph, farmers produce rice kj The California Crop and Livestock Reporting Service reported in California Field Crop Statistics . 1959-68, issued June 1969, that Butte County had 66,000 acres in rice during 1967 and 77,100 acres during 1968. The comparable data for Colusa County were 89,900 in 1967 and 180,000 dur- ing 1968. -ic- on several different types and Storie Index grades of soil. Thus, Colusa County during most seasons includes a sizable acreage of rice grown on older alluvial and, to a limited extent, newer alluvial soils, as well as that on basin soils. Soils of these different types and Storie Index grades differ, sometimes importantly, in physical characteristics and adaptability for profitable crop production. It is necessary, in this study therefore, to analyze separately the basin, older alluvial, and recent alluvial soils when studying the impact of variations in irrigation water prices and quantities available on optimum decisions and resource allocations . Climatic Factors Exert Important Influence Sitton has pointed out that, in general, climatic characteristics favor rice production in the Sacramento Valley [7]. Butte and Colusa and the other rice-growing counties of the valley (as well as those in the San Joaquin Valley to the south) all have long growing seasons with equable temperature levels. This subarea of the valley has only two climatic limitations; first, normal precipitation during the growing months is entirely inadequate to produce rice (or other summer growing crops) with- out irrigation, using ground or surface water; second, inclement weather during the period from late September through November may lower quality, reduce yields and/or increase harvesting costs (see Figure 2). The 242- day normal growing season, beginning in April and ending in early September, is long enough for rice to develop and mature. Farmers drain and dry their fields during September in normal years and complete harvest by the end of October. Unseasonable or heavier-than-usual storms during October may delay harvest sufficiently that storms normal for November and later months cause losses or increased cost. Low-Cost Water Favors Rice Production Surface water from the Sacramento and Feather rivers represents by far the major source of irrigation water for rice farmers in the study area. Irrigation districts, organized for this purpose, furnish the greater part of this water. Some farmers also obtain additional irrigation -11- FIGURE 2 PRECIPITATION, TEMPERATURES, AND GROWING SEASON COLUSA, CALIFORNIA 3.0. 2.5. 2.0. 1.5. 1.0. 0.5. 0 Mean Maximum Temperature . 100.0 - 75.0 Mean Minimum Temperature Growing Season 269 Days H re 3 -3 re i-i (11 pr C 1-1 n 50.0 _ - 25.0 JY 0 N J F M A M J 1/ Annual 15.16 inches Sources: Climatalogical Data, U.S. Weather Bureau; Period Averages -12- water from Incorporated or unincorporated mutual water companies and from private firms. The State Department of Water Resources reports the follow- ing California water districts together with the acres of Irrigated crop- land served according to counties [8]: Butte County: Blggs-West Grldley Water District, 29,000 acres: Butte Water District, 14,000 acres; Rlchvale Irrigation District, 25,000 acres. Colusa County: Colusa County Water District, 30,000 acres; Glenn-Colusa Irrigation District, 150,000 acres (Colusa and Glenn counties); Princeton-Codora-Glenn Irrigation District, 11,700 acres. Water quantities available to some of these districts are relatively large in relation to acreage served. This is particularly true for irrigated land on the western side of the Sacramento River. Thus water diversions for irrigation purposes by the Glenn-Colusa Irrigation District averaged about 960,000 acre-feet during the four years, 1963 through 1966.—^ Of this total, about 180,000 to 185,000 acre- feet represented recaptured drain water while the remainder came directly from the Sacramento River. On the east side of the Sacramento River the Rlchvale Irrigation District reported about 127,000 acre-feet of water diverted in 1964. This district delivered 114,000 acre-feet of this water to farmers for irrigation purposes [9]. The Butte Water District reported total diversions of about 118,000 acre-feet of which 116,000 was for Irrigation purposes during 1966.—^ Both these latter districts purchased small amounts of these reported totals. Information available for the Glenn-Colusa (Colusa and Glenn counties) and Rlchvale (Butte County) districts during 1964 provide a clear indica- tion of both the crops using this water, and the approximate amount of water that growers applied per acre on these crops. Thus in Colusa County 106,200 acres of crops received Irrigation water and rice acreage represented almost 65,000 acres (about two-thirds) of this total. In addition, 32,350 acres of normally Irrigated land remained idle or fallow as did nearly 4,000 acres of land normally dry- farmed.—'' The Glenn-Colusa Irrigation District reports 5^/ Information available through courtesy Glenn-Colusa Irrigation District, Willows, California. 6_l Information by courtesy Butte Water District, Grldley, California. Jj Information by courtesy Glenn-Colusa Irrigation District, o£. cit. -13- 955,400 acre-feet of water diverted during this season for irrigation pur- poses, or an average of almost 9 feet for each of the 160,200 acres irri- gated. Farmers did not apply this water at uniform rates to all of the crops. An estimate of how they did allocate their total water supply, assuming usual irrigation practices for crops other than rice, indicates that these farmers applied 11 or more feet of water per acre to their rice and approximately 4-1/2 feet to the other irrigated crops. During this season, farmers in Richvale District (Butte County) diverted nearly all of about 114,000 acre-feet of water to 12,810 acres of rice; other irrigated crops accounted for only 25 irrigated acres [9]. Thus, rice growers in this district applied about 9 acre-feet of water for each acre of rice during 1964. The same fortuitous circumstances making relatively ample water quan- tities available to farmers in these Central Sacramento Valley rice growing counties also explain low water costs. Mountain-origin streams with gen- erous flow throughout the season, plus early-established water rights for the districts involved, enable these suppliers to make water available to farmer patrons at quite nominal prices. The water service agencies estab- lish their rates on a per-acre basis with some variation among crops and with or without a minimal assessment (also on an acre basis). The rates for irrigation water to produce rice in the 1964-1966 period varied among the several districts from about $10.00 per acre to approximately double 8/ that level.— Rates for other crops usually range from about $4.00 to $5.00 per acre for crops other than pastures; rates for the latter use 9 / typically are $1.00 or $1.50 higher .- Data also are available from one major irrigation district to indicate how total annual water diversion for irrigation purposes varies among the ^/ Irrigation water variable expenses (costs) in this study include only tolls paid for surface water plus a minimal acre assessment. In actual practice, water tolls in the study area are levied on a per acre basis and vary according to the crop. It was necessary for purposes of this analysis, to express these water costs on a per-acre-foot basis; hence, the range of charges indicated here. 9^/ Information by courtesy of various water service agencies and farmers. -14- irrigation season months from April through October. In percentages, the proportions by months are as follows: April May June July August September October 11.1 20.3 18.2 20.1 18.5 7.9, and 3.9 percent These diversion percentages are useful in estimating monthly quantities of water applied on the several soil categories used to grow rice. We applied these percentages by months to total annual diversions corresponding to the approximate acreages involved for land according to major categories (basin, older alluvium, and recent alluvivim) . The results represent esti- mates of the total and montly amounts of water available for irrigation on these major soil types. Considering basin soils in the Glenn-Colusa and Richvale districts, combined, it appears that about 350,000 acre-feet of water were available during the 196A-1966 period for approximately 60,000 acres of basin land and that total water available per acre-foot represented about 5.75 acre-feet per acre. The comparable data for the alluvial soils show about 700,000 acre-feet of water for approximately 104,000 acres of land, or approximately 6.75 acre-feet of water per acre. THE 1,280-ACRE FARM SIZE PERMITS' EFFICIENT RESOURCE COMBINATION Farmers, Federal and State Agencies, Farm Suppliers and Individuals Provided Information Growers of rice, the dominant crop in this Butte- Colusa County study area, received acreage allotments allocated by the U. S. Department of Agriculture under the Agricultural Stabilization and Conservation Act. Local farm program administrative officers provided factual information regarding total farm acreages, rice allotments, and other farm operating information for individual growers under this program. The same agency also furnished information on support prices, production goals, and other administrative features of the rice program. The California Department of Water Resources and various irrigation districts and water districts in the Butte-Colusa area supplied specific information on water quantities, and acres irrigated, plus conditions and costs for watpr delivered. Such information came through both official releases and interviews or corre- spondence. The California Crop and Livestock Reporting Service also provided -15- much data for this study. Their official releases containing historical data on crop acreages, yields, and sales prices for farm products proved essential. The three soil survey reports cited in the previous section provided detailed information on soil resources, their classification and character- istics that influence crop adaptation and yields. We used official cli- matological data, as published in various reports of the U. S. Weather Bureau, to identify weather characteristics and patterns for the study area and to evaluate how such phenomena affect seedbed preparation, the growing season, and the critical harvesting period. Evapo-transpiration and the influence of soil characteristics on water- holding capacity and percolation were particularly important to this inves- tigation. Researchers in the area of soil-water-plant relationships were most helpful concerning these questions. They furnished all available information and gave generously of their time and counsel. Fundamental research in this field has progressed well and experimental work has yielded some important quantitative results. Unfortunately, these results do not provide all detailed and complete quantiative data needed for the crops, soils, and climatic conditions included in this analysis. We used estimates to compensate for such deficiencies. These reflect the available experi- mental data plus the suggestions and judgments of researchers in soil-water- plant relationships. The author assumes full responsibility, however, for any deficiencies in these estimates. Interviews with farmers, machinery dealers, other supply agencies, agricultural researchers, and extension specialists and farm advisors provided basic information on farm organiza- tion, resource availability and use patterns, and production technology, practices, and input patterns. The 1.280-Acre Farm Studied Is a Common Size for Rice Farms— ^ This operation, including two sections of land, is large enough to use economically tractors and field machinery with characteristics required to 10 / Major terms relating to farm models appearing in this report, and their definitions, are as follows: (Continued on next page.) -16- meet the physical conditions in the area. The distribution of total soil resources in the study area among three general soil groups (basin, older alluvium, and recent alluvium) made it necessary to analyze each of these three soil situations. Thus the study includes three models for the 1,280- acre unit, one for each of three soil groups. (Footnote 10 continued from page 15.) Stibarea - a segment of a major geographic area, such as the Sacramento Valley, selected for study. Irrigation Practice - technique or method used in irrigation, identified . in this study by the depth of applications for rice and by the deple- tion level for available soil moisture prior to irrigation for other crops . Variable Expenses (Costs) - sum of annual cash operating expenses, plus unpaid family (operator's) labor (see Appendix Tables A-8, A-18) . This item may appear as Variable Expenses (Costs) per Acre for a sin- gle crop, or as Fann Variable Expenses (Costs) representing the total for an entire farm. Fixed Costs - sum of annual cash and noncash for using capital items and for general costs not readily allocated to specific enterprises (see Appendix Table A-3) . Gross Receipts - sum of annual receipts from sales of farm crops. Net Returns-Over- Variable Expenses (Costs) - Gross Receipts minus Variable Expenses . (Costs) (See Appendix Tables A-8 and A- 10). This item may appear as Net Returns -Over- Variable Expenses (Costs) representing the total for an entire farm. NET FARM INCOME - Net Cash income plus (or minus) inventory changes on noncapital items and minus noncash fixed costs (not including interest on investment) . Any unpaid labor contributed by the farm operator is not included in the farm expenses. PROFIT (Capital and Management Income ) - Net Farm Income minus the value of any unpaid labor (including operator's). MANGEMEOT INCOME - PROFIT less 6.5 percent on the total farm capital. The residual (and it may well be negative) is payment for the operator's managerial ability and services. RATE EARNED - PROFIT (Capital and Management Income) expresses as per- centage of the farm capital. -17- Water quantities available for irrigation differ as between the model for the basin soils and those for the other two categories. These water quantities, distributed according to half-month irrigation periods during the growing season, reflect the variation in total water available in ratio to total irrigated land according to the soil differences in the area. Soils and land use on the three models reflect the patterns established by the producer survey, and confirmed by the ASC data for Butte and Colusa counties . They are the same for each farm as far as over-all use is con- cerned. About eight percent of the 1,280-acre total in each model is in farmsteads and headquarters sites, easements for public roads, and drainage ditches, farm roads, and wasteland. Of the remaining 92 percent — 1,180 acres — rice normally occupied slightly over one-third during the latter 1960 's with the remainder divided between, (a) other crops, and (b) fallow or idle land. The specific crops other than rice differ and vary in acreage among the three different soil groups as will be evident in later sections. The basin group shows the narrowest range of adaptation for crops other than rice. The submodel for this soil group essentially represents Storie Grade IV soil in Butte County and a combination of Grades IV and V in Colusa County. The Stockton clay adobe series dominates in the former, while Colusa County basin soils includes Grimes, Marvin, Marmon, Sacramento, and Willows series, [6, 18, 34]. The recent alluvium and older alluvium soils, largely in Colusa County, include mostly Storie Index, Grade III soils but also some of higher grades, largely intermixed. With Grade III the chief soil series involved are Genevra, Harrington, and I^ers. These alluvial soils, particularly the recent alluvium group, have a wider range of crop adaptation than the basin soils. The analysis recognizes this difference in the choice of crops for testing on these two soils, as compared with those in the basin group. Land dominates inventory and inves tment values for all three models in this study (see Tables 1 and 2). Thus land values, including the cost of leveling for the 1,180 acres of cropland, account for 77 percent of the original Investments and 85 percent of the average Investments (814,470) for the recent alluvium model, all prices reflecting 1964-1966 price levels. Similar relationships for the other two were 75 and 85 percent, respect- tively, for the older alluvium ($750,470 average investment) and 73 and -18- Real Farm Estate and Operating Equipment Inventories and Investments; 1,280-Acre Rice Farm, 1964-1966 Average Prices Size or Useful life Initial Salvage Average Total Item capacity Nunber on farm cost value value depreciation 1 2 3 4 6 7 years dollars LAND Rflw L find a/ 576,000 Recent alluvium $A50/acre 1,280 - 576,000 N.A.-' 0 Old alluvium $400/acre 1,280 512,000 N.A." 512,000 0 Basin $350/acre 1,280 — 448,000 N.A." 448,000 0 Leveling $100 /acre 1,180 -- 118,000 N.A." llSjOOO 0 TOTAL (R.A.) $542/acre 1,280 — 694 ,000 N.A." 694,000 0 TOTAL (O.A.) $'i92/acre 1,280 — 630,000 N.A." 630,000 0 TOTAL (Basin) $4A2/acre 1,280 — 566 ,000 N.A." 566 ,000 0 IMPROVEMeJTS Shop-storage 4,000 ft.' 1 30 10,400 1,000 5,700 9,400 Machinery shed 2,400 ft. , n.aV 20 3,200 500 1,850 2,700 Shop equipment N.A.i' 10 2,500 0 1,250 2,500 Fuel storage (gas) 2,000 gal. 1 10 500 60 280 440 Fuel storage (diesel) 2,000 gal. 1 10 375 50 213 325 TOTAL 16 ,975 9 293 15 365 EQUIPMENT Irrigation 450 900 Siphons 3" 200 4 900 0 Power ( t raclc— lay er tractor) D— 7 10 33,640 5,050 19,345 28,590 D-6 i 10 26,074 3,911 14,993 22,163 D-it 10 16,149 2,422 9,286 13,727 (row~crop tractor) W-3 1 6 7,900 1,580 4,740 6,320 TOTAL 83,763 12,963 48,364 70,800 j 1 Transport 6,660 Truck 2- ton 2 6 8,320 1,660 4,990 Pickup 1/2-ton 2 4 6,450 2,580 4,515 3,870 Pickup 1/2- ton 1 6 3,225 645 1,935 2,580 TOTAL 17,995 4,885 11,440 13,110 Rice Machinery 2,046 3,348 Landplane 12' X 60' 10 3,720 372 Plows 6 X 16" 10 5,720 572 3,146 5,148 Plows 5 X 14" 10 2,525 0 1,263 2,525 Field cultivator 12' 10 936 0 468 926 Disk 21' 10 3,744 0 1,872 3,744 Disk 15' 10 2,445 0 1,223 2,445 1 Harrow 24' 15 624 0 312 624 ! Float 12' 15 300 0 150 300 Drill (grain) 14' 10 1,200 0 600 1,200 Fertilizer disk 12' 10 470 0 235 470 Bulldozer blade 10' 10 1,460 0 730 1,460 Harvester (S.P.) 16' 5 45,000 13,500 29,250 31,500 Bankout wagon (S.P.) 140 cwt. 8 5,720 572 3,146 5,148 Bankout wagon 140 cwt. 10 2,500 0 1,250 2,500 Grease wagon 250 gal. 10 1,000 0 500 1,000 Grease wagon 350 gal. 10 1,500 0 750 1,500 Lowbed trailer 350 gal. 10 1,600 0 800 1,600 Equipment carrier 25' 10 1,040 0 520 1,040 Weed sprayer 200 gal. 10 832 0 416 832 Rice boxes 200 4 1,000 0 500 1,000 TOTAL 83,336 15,016 49,177 68,320 Other croDS Machinery 598 1,196 Planter 6 bed 1 10 1,196 0 Cultivator 6-R 1 10 1,404 0 702 1,404 Ditcher 4' 1 10 364 0 182 364 Mower 7' 1 10 520 0 260 520 TOTAL 3,484 0 1,742 3,484 EQUIPMENT TOTAL 189,478 32,864 111,173 156,614 ALL PROPERTY TOTAL Recent alluvium 900,453 37,474 814,466 171,979 Old alluvium 836,453 34,474 750,466 171,979 j Basin 1 772,453 34,474 686,466 171,979 &/ N.A. - Not applicable TABLE 2 Summary of Fixed Costs, 1,280-Acre Rice Farm, 1964-1966 Average Prices Noncash fixed costs Cash fixed costs Interest on average in- vestment Annual depreciat ion Taxes Insurance^'' Other Basis 6.5 percent Years life Total Assessment @ 25 percent of value X levy rate Varies N.A. Total Total all fixed costs 2 3 4 5 6 7 8 dollars PROPERTY Land - 1,280 acres Recent alluvium Old alluvium Basin 45,110 40,950 36,790 45,110 40,950 36,790 11,278 10,238 9,198 11.278 10,238 9,198 56,388 51,188 45,988 Improvements 604 775 1,379 151 74 225 1,604 Equipment Irrigation Power Transport Machinery Rice Other crops 29 3,144 744 3,192 113 225 7,501 2,508 10,230 348 254 10,645 3,252 13,422 461 7 415^^ 799 28 1.462^/ 7 786 415 2,261 28 261 11,431 3,667 15,683 489 Total property Recent alluvium Old alluvium Basin 52,936 48,776 44,616 21,587 21,587 21,587 74,523 70,363 66,203 13,464 12,424 11,384 1,536 1,536 1,536 15,000 13,960 12,920 89,523 84,323 79,123 GENERAL OVERHEAD Electric & other services Accounting Dues , f ees Office 445 600 300 300 445 600 300 300 ALL FIXED Recent alluvium Old alluvium Basin 91,168 85,968 80,768 aI Insurance calculated 0 1 percent on 80 percent of average value for improvements; 5 percent on average value of equipment. W Also includes 2 percent of market value for motor vehicle tax. £/ Harvesters only. -20- 82 percent, respectively, for the basin ($686,470 total average investment soils. The amounts that inventory and investments for improvements rep- resent are only nominal; repair and storage space for machinery plus stor- age for fuel accounted for all of these investments. Field power and rice machinery, roughly equivalent in investment values, accounted for most of the equipment inventory and investments on all three submodels. Transpor- tation equipment also requires a sizable investment for trucks and pickups (bankout wagons appear under rice machinery), but machinery for other crops is minimal. It facilitated the analysis in this study to include only planting and cultural machinery for these other crops, and to include prac- tically all harvesting machinery costs under contracted services. This procedure permits a stable level of total farm fixed costs, regardless of changes in the cropping system to maximize returns. Total fixed cos ts include noncash and cash overhead on farm property, plus general farm overhead (see Table 2) . The land valuations do not reflect precise market or sales values; they do reflect the influence of a combination of price-indicating phenomena, including tax assessments, basic physical productivity, and farm products prices computed on a period-normal basis. Such estimates are useful to suggest the relationship between total farm earnings and capital investments, and to make comparisons among farm units differing in basic characteristics, such as our three farm units in this study. The various elements that affect farm costs, such as useful life for structures and equipment, salvage values if any, prices and cost rates, and tax assessments and levies all reflect existing or normal levels at the time of the study. Data to identify such levels came from official reports and interviews. Aggregate farm costs for the three 1,280-acre submodels varied from $91,200 on the recent alluvium to $80,800 for the basin units; thus, they ranged from a high of $77.00 per tillable acre on the former to $68.00 per tillable acre on the latter submodel (see Table 2). Quantities of irrigation water differed between the basin soil model and the other two farms. The 6,78A acre-foot total for the former repre- sents about 5-3/4 acre-feet per acre for the season, or about one acre- foot per acre less than the 7,964 acre-feet available for each of the two other models. The seasonal distribution of these aggregate quantities in -21- acre Inches among half-month irrigation periods from April through September appear in the accompanying text table. Old Recent Old Recent Basin Alluvium Alluvium Basin Alluvium Alluvium April 1-15 4.512 5.304 5.304 Augus t 1- -15 7.524 8.844 8.844 16-30 4.524 5.304 5.304 16- ■31 7.524 8.844 8.844 May 1-15 8.256 9.696 9.696 Sept. 1- •15 3.216 3.768 3.768 16-31 8.268 9.708 9.708 16- •30 3.216 3.780 3.780 June 1-15 7.404 8.688 8.688 Oct . 1- •15 3.168 3.720 3.720 16-30 7.404 8.700 8.700 July 1-15 8.184 9.600 9.600 16-31 8.184 9.612 9.612 Farm labor requirements for these three analytical models of 1,280-acre rice farms represent full-time work for two workers assumed to be hired plus one-half time of the operator (management functions occupy the rest of the operator's time). Such a labor force, supplemented as necessary by contract operations and temporary workers to meet seasonal peak requirements, can prepare the seedbed, plant, and irrigate, and perform the cultural operations, and also provide a part of the harvest labor for a cropping system including approximately 472 acres of rice, 177 acres of dry edible beans, 295 acres of wheat, and 118 acres of fallowed land or reasonable modifications of it. Variations in the cropping system, such as substi- tuting barley, saf flower, or milo in varying proportions for the beans and wheat also would be manageable with this labor force. The farm supply of regular labor will have to expand, however, if shifts in the cropping system are toward substituting such crops as com, alfalfa, sugar beets (where the two latter crops are adapted) , or other crops with high cultural labor requirements for the crops supplementary to rice. High-Capacity Machines and Heavy Investments Lower Costs Per Product Unit and Lessen Time-Related Uncertainty Farmers who produce rice have discovered that heavy-duty equipment, and corresponding high-powered tractors, are essential to obtain optimum -22- rice yields. These requirements reflect the unique characteristics of the heavy soils in the Central Sacramento Valley, and the irrigation require- ments for rice. The limited-duration harvesting period also places a premium on timeliness in this operation. All three of these 1,280-acre models include in their power comple- ment one D-7 and one D-6 tracklayer tractors, plus two 16-foot self-pro- pelled rice harvesters on tracks. These four equipment items, alone, represent an initial cost of more than $120,000. Their average value exceeds $70,000. Such heavy investment outlays are acceptable for this kind of equipment only if rice farm operations require such equipment for effective performance, and if total annual use is heavy enough to permit reasonable total costs per unit of product. These conditions hold for these power units on the 1,280-acre rice farms. They have the capacity to operate large-scale seedbed machinery on the heavy soils and under the sometimes difficult moisture conditions on these ricelands. The two trac- tors, in combination, also are capable of operating one-time-over diking machines for building rice levees. It is because farmers can build levees in this manner that it is feasible to plan operations to remove the levees following each rice crop, and rebuild them for those that follow. The necessity .to establish a balance between operating capacity for such large power units and their total cost for ownership and operation is one of the basic reasons for choosing the 1,280-acre models for this analysis. Farmers on smaller operations find it difficult to justify such equipment in terms of per-unit of product cost. Uncertainty is a major element determining the choice of two 16-foot self-propelled rice harvesters for these analytical models. The two in combination include enough daily harvesting capacity to permit harvesting the potential maximum rice acreage on units of this size within the time normally available for such harvests. Weather conditions, primarily rainy spells with heavy amounts of precipitation in early November, plus the time required to produce rice and drain and dry the fields, establish an effec- tive limit of some four to six weeks for completing rice harvest. Increased costs are almost certain, and some reduction in either quantity or quality or both are probable, for rice remaining unharvested after about 10 November. -23- Alternatlve Crot)s Vary Widely In Output. Revenue , Costs, and Net Returns Per Acre A preliminary step In the analysis for this study was to rank the var- ious possible alternative crops according to net returns-over-variable expense per acre, using 1964-1966 average prices for both inputs and outputs. We included, in calculating these net returns, certain items that in other circumstances might appear under fixed costs or "overhead". Thus irrigation district assessment fees and fringe costs for labor were deducted from gross receipts per acre as variable expenses to determine net returns -over-variable expenses . The results of these calucations and crop rankings show a sharp varia- tion among crops in relative earning capacity under the conditions of this study . The basin soils show the narrowest range of crop adaptation of any of the three soil categories. Rice provides the highest net returns; only small grains, grain sorghum, and saf flower also appear in the later analysis. Soils in the old alluvium group show a wider range of adapation with beans and com added to the list of crops considered, while the recent alluvium soils also include sugar beets and alfalfa (see Figure 3). Rice, with three alternative irrigation treatments considered in this study, is decidedly the most profitable crop that farmers in the study area can produce under the conditions for this analysis. Net re turns-over- variable expenses ranged from $185.00, to $219.00 per acre on the basin soils, and from $205.00, to $232.00 on the old alluvium and recent alluvium soils (see Appendix Table A-10) . These net returns to rice ranged from about 2.3 to 2.8 times the level of those for the next ranking alternative crop, depending on the soil and the irrigation treatment. The relative rankings of the alternative crops differed somewhat among the three soils studied. There was little difference in net returns per acre among unirrigated wheat ($65.00 per acre) and grain sorghum irrigated under either the 60- or 80-percent soil moisture depletion ratio on the basin soils ($61.00 per acre and $58.00 per acre respectively). Grain sorghum at the 100-percent ratio followed next in the order of net returns with safflower, barley, oats and vetch seed, oats, and grain hay following in that order. Data already presented indicate that fixed costs -24- FIGURE 3 NET RETURNS AND VARIABLE COSTS PER ACRE FOR SPECIFIED CROPS BY SOILS AND IRRIGATION TREATMENTS. 240 180 120 60 0 60 120 180 240 240 180 120 60 0 60 120 180 240 240 180 120 60 0 60 120 180 240 BASIN NET RETURNS -Og|}|}&|}&&&o- VARIABLE COSTS OLD ALLUVIUM NET RETURNS VARIABLE COSTS RECENT ALLUVIUM NET RETURNS VARIABLE COSTS , Oat 3 1 60% 80% 100%Min. Re 1 - 60% 80% 100% 60% 80% 100% 60% 80% 100% 60% 100% Wheat Grain Sofflower | 4 Hoy Be ^<"9h""' Borlev Oats -( >- -<>- -(>- Sugar Beets Corn RICE, DEEP; 2 - RICE, DEEP/SHALLOW; 3 - RICE, SHALLOW -< Alfalfa -25- for the basin soil model amount to over $85,000, or $68.00 per acre; thus rice is the only one of the alternative crops with net ret urns -over-variable expenses sufficient to cover all fixed costs (see Table 2). Wheat and grain sorghum come closest to this net returns level, while the remaining crops tested fall distinctly short of it. Dry edible beans irrigated at the 60- and 80-percent soil moisture depletion levels ($101.00 per acre and $99.00 per acre, respectively) ranked next to rice in net earnings-over- variable expenses on old alluvium soils (see Figure 3). Both of these net returns, however, were less than half those for the least profitable rice irrigation practice. The advantage to the operator on the old alluvium lies in the fact that these returns levels, plus those for wheat, are higher than fixed costs per acre ($73.00 per acre). Thus they will cover such costs and leave some margin to help pay for management. The remaining crops ranked in about the same order as for the basin soils, but showed somewhat higher levels of returns. In addition, com, not included in the basin group, showed net returns-over- variable expenses somewhat above safflower but below those for grain sorghum. Dry edible beans and sugar beets showed similar levels of net returns- over-variable expenses on recent alluvium soils in this analysis. Again, both crops showed lower earning capacities than rice grown under any one of the three irrigation practices, but did show net returns-over-variable expenses that exceed fixed costs. Wheat showed net returns equal to fixed costs with grain sorghum and com following closely on these soils. Alfalfa ranked lowest of any of the alternatives tested (see Figure 3 and Appendix Table A- 10) . It is evident from these data that rice holds a distinct economic advan- tage for production in the area studied, and under the conditions of the analysis. It also is clear that both differences in soil quality and in irrigation practices affect net retums-over-variable expenses. Relatively high variable expenses, as compared with gross receipts, placed the row crops and alfalfa at a disadvantage as compared with rice whose input cost- vers US-receipts ratio is highly favorable. -26- THE METHODOLOGY FOCUSES ON AN ECONOMIC ANALYSES OF PHYSICAL, BIOLOGICAL, AND ECONOMIC RELATIONSHIPS Crop Yield Estimates Vary According to Irrigation Treatments on Each Soil An important step in this analysis was to estimate crop yields for each soil considered, according to specified irrigation treatments for each crop. The procedure used to compare these estimates for crops other than rice was the same as that developed and used for earlier similar investigations in the San Joaquin Valley by Hedges and Moore [15].—^ This Central Sacramento Valley study is, however, the first such investigation that includes rice as one (here, the principal) alternative crop. We define and explain in a subsequent section the procedure used to relate rice yeilds to irrigation practices. First, however, we will review the procedure for row crops and close-grown crops not requiring submergence irrigation. The purpose of the procedure for preparing yield estimates is to eval- uate how irrigation practices interact with soil-water-plant relationships to regulate yields. Two definitions are important in analyzing relation- ships, field capacity (FC) and permanent wilting percentage (PWP) . The first, FC, represents all the water that a particular soil will hold follow- ing a thorough wetting, but after allowing enough time for free water to drain out by gravity. PWP refers to the soil moisture content below which the plants cannot obtain water readily. Plants wilt at this moisture level and do not recover unless water is added immediately to the soil, Vlehmeyer and Hendrickson [30], Vlehmeyer and Hendrlckson [31], and Berlnger [2]. Questions regarding profitable irrigation practices therefore concern the amounts of water to be added, and their proper timing, in order to maintain soil moisture within the range between FC and PWP that will enable the operator to maximize net dollar returns. We base our analysis in this study on the concept that in general the relative rate of plant growth depends upon the mean soil moisture stress 11 / See Appendix Table A-1 for procedure in relating moisture avail- ability to growth rates. -27- in the active root zone; that is, that the tension with which moisture adheres to the soil particles near the active roots regulates the amount of moisture available to the plant, and, hence, its growth rate Hagan [13], Wadleigh [32]. Not all scientists fully accept this view of soil-water-plant relation- ships. Some researchers of long standing in the field hold that variations in soil moisture content between FC and PWP have little bearing on plant development and yield. Some among those who support the mean moisture- stress concept, moreover, concede that brief periods of high stress can have an exaggerated impact upon plant growth. They hold, nonetheless, that the moisture-stress theory represents the best approximation for a wide range of crops under varying soil and climatic conditions. Yield Estimates Reflect Mean Soil Moisture Availability Ratios for Crops Other Than Rice We use the mean soil moisture availability-stress theory as the basis to analyze how irrigation affects growth and yields for row and close-grown crops except rice. Irrigation practices represent an important influence regulating profits on the individual farm because they affect soil moisture stress, Moore [22], We assume, in applying this concept that growth is a completely reliable indicator of yield; that a yield reduction in the same proportion acconqjanies any given departure of growth rate from the maximum potential. The starting point for estimating yields associated with each irrigation practice is an estimate of potential yields under optimum soil moisture conditions; these estimates reflect the research findings, experi- ence, and judgments of researchers and specialists working on irrigation problems. The subsequent procedure involved six steps for the crops studied on each soil type categories according to a given set of climatic conditions. 1. Determining amounts of water, days between each successive pair of irrigations (length of cycle, and timing for applications, under each of three specified irrigation treatments. These represent for crops other than rice different percentage depletions of available soil moisture (100, 80, and 60 percent, respectively) permitted before applying water. -28- 2. Measuring changes in soil moisture depletion levels throughout each irrigation cycle during the season. We obtained soil mois- ture releases curves representative of each of the soils studies. With these data, we constructed relative growth rate curves. 3. Estimating plant growth (hence under the assumed relationships, yields) according to levels of available soil moisture for each irrigation cycle. Relative growth curves provide the basis for these estimates. 4. Establishing the mean growth rates for each crop during each cycle according to soils and irrigation treatments and expressing each as an index of the potential yield possible under physically optimum moisture conditions. 5. Cumulating the growth rates (and yields) for the several cycles into a seasonal yield index for each crop, according to soils and irrigation treatments. 6. Applying the seasonal yield indices from (5) to the potential yields estimated to obtain yields associated with each of the various irrigation treatments for crops involved on each soil series type. This approach uses the (1) dry, 100-percent, (2) medium, 80-percent, and (3) wet, 60-percent available soil moisture depletion levels, respect- - ively, to define the three major irrigation practices for analyzing irri- gation practices in relation to yields for crops other than rice. Rice Irrigation Practice Definitions are Distinct From Row Crop Practices The fact that California rice growers use the submergence method in irrigating rice makes necessary a different approach for this crop than other crops in measuring the relationship between irrigation practices and yields. Both research investigations and observations indicate that growers' irrigation practices vary widely and, consequently, that the total quantities of water that they apply to produce rice also differ. Adams' 1914-1919 study reported variations from slightly less than 4 feet to almost 19 feet in the total depth of water applied in the Sacramento Valley [1]. Total water applications for rice must be adequate to meet three requirements: (a) fill the root zone to holding capacity, (b) supply the water transpired during the growing season, and (c) provide quantities TABLE 3 Irrigation Water Budget; Rice on Basin Clay Adobe Soil, Continual Deep-Flooding (45 Days), Followed by Shal low- Flood ing , Calculated From Physical Data 1/ S""2,TandM/" " ''''' 1 3-foot depth of root zone), c/ Evapo transpiration rate per day times number of days in time period, d/ Water flowing through and out of the checks, e/ Col. 7 + Col. 8. f/ Not applicable. °"p%rsonner' ' ' ""'^^"^'^ ""^"^ ^ - -^'^ - agronomists, irrigation personnel. Aval lable water (inches) Consumntive usp 1 Total water with- , drawn— at end of period Per-f oot 1 Root-' zone 2 Surface 3 Water added 4 Total , ^ D/ water— Per-day cl For period— Out- , , flow^/ April 1-15 1.50 4.50 0 17.3 5 21.80 6 .14 7 2.10 8 0 9 2. 10 10 19.70 April 16-30 3.00 9.00 10.7 8.4 28.10 .18 2.70 6.40 9.10 19.00 May 1-15 3.00 9.00 10.0 5.6 24.60 .21 3.15 2.45 5.60 19.00 May 16-31 3.00 9.00 10.0 5.6 24.60 .24 3.84 1.76 5.60 19.00 June 1-15 3.00 9.00 10.0 5.6 24.60 .28 4.20 3.40 7.60 17.00 June 16-30 3.00 9.00 8.0 5.6 22.60 .32 5.15 2.45 7.60 15.00 July 1-15 3.00 9.00 6.0 5.6 20.60 .33 4.95 1.65 6.60 14.00 July 16-31 3.00 9.00 5.0 5.6 19.60 .31 4.96 1.64 6.60 13.00 Aug. 1-15 3.00 9.00 4.0 5.6 18.60 .27 4.05 2.55 6.60 12.00 Aug. 16-31 3.00 9.00 3.0 5.6 17.60 .22 3.52 5.08 8.60 9.00 Sept. 1-15 3.00 9.00 0 0 9.60 .17 2.55 0 2.55 6.45 Sept. 16-30 2.15 6.45 0 0 6.45 .14 2.10 0 2.10 4.35 TOTAL tl tl tl 70.5 j f/ f/ 43.10 27.38 70.65 f/ and irrigation distric -32- plant growth rates inversely with percentages of available soil moisture depletion to adjust estimated "ideal" yields, obtained from these qualified agricultural authorities. The result was to relate variations in growth rates and yields to available soil moisture under varying irrigation treat- • , 12/ ments according to soils. — Net Returns Per Acre Determine Profit Bankings for Individual Crops Most farmers in the Central Sacramento Valley have two or more alter- native choices in deciding what crop to produce on a particular piece of land, although these alternatives may vary sharply in net returns. This choice situation presents operators with decision problems. These farmers almost always must consider how variations in physical, economic, and insti- tutional conditions affect such choices and their financial outcomes. Gov- ernmental statutes and regulations (e.g., acreage allotments) may seriously limit freedom of decision. Rice is the principal profit-returning crop in the Central Sacramento Valley. All three of the soils examined in this study also can grow winter grains, grain sorghum, saf flower, and dry edible beans. Field com also is a possible alternative on the old and recent alluvium soils . This analysis includes detailed summaries of production requirements and costs, outputs, and revenue, and net returns-over- variable expenses for each of these crops, usually under two or more sets of conditions. Intervi«/s with famers, commerical agencies serving farmers, and public officials, as well as published reports from available secondary sources yielded data for these summaries. Procedures for preparing summaries involved five steps for each crop under each unique set of conditions: 1. Determining the cultural and harvest operations involved, the timing for each one according to caldendar dates, and the equip- ment, power, labor, and materials involved. 2. Calculating physical quantities for all inputs, including services such as labor, power and machinery hours, plus seed, fertilizer, irrigation water, and other materials . 12 / See Appendix Tables A-4 and A-5 for crop irrigation water require- ments according to soils . -33- 3. Estimating yields according to the relevant determinants. Thus -71-.;::; for each irrigation treatment considered (deep, deep-shallow, and shallow for rice; 100, 80, and 60 percent available soil moisture depletion, respectively, between irrigations for other crops) it _^ was necessary to estimate the appropriate yield, as well as all associated inputs that vary with irrigation practice or yield. 4. Applying relevant cost rates and prices to express all inputs and yields in dollar values. These calculations included only variable expense items; depreciation, taxes on equipment, and other fixed costs were omitted in this initial accounting. 5. Summing total variable costs and revenues, according to appro- priate classifications, in order to obtain gross receipts, total variable expenses, and net returns above variable expenses for each crop. This procedure excludes fixed costs for this step because the specific purpose at this step is to afford a basis to compare crops, and to use the resulting data in developing criteria to choose crops and allocate resources. Comparisons within a cons tant fixed cost structure for the entire farm are entirely feasible for many crucial decisions, and require only minor modi- fications for others. Thus it simplifies calculations and saves time to omit the fixed costs and to concentrate on variable inputs and costs for this step in the analysis. Linear Programming Analyzes Alternative Resource Use Opportunities and Identifies Optimum Choices Under Specified Assumptions and Constraints Limited water quantities and relatively wide price variations generate irrigation water use problems on many California farms. Such constraints require farm operators to choose among several competing uses for available water. The result is that these operators must make decisions involving complicated interrelationships among these several enterprises, as well as with other necessary resources, within a framework of shifting and uncertain prices. Linear programming offers important advantages as a technique for analyzing such problems. In the words of Heady and Candler [14], "A linear programming problem has three quantitative components: an objective, alter- native methods or processes for attaining the objective, and resource or 13/ other restrictions. — Garvin [11], in a more technical definition states 13 / See page 2, Heady and Candler. -34- that, "... linear progranming deals with the minimization of a linear function, subject to the subsidiary conditions that the variables are non- 14/ negative and must satisfy a set of linear equations. — We use linear programming techniques in this analysis to obtain answers under specified sets of conditions to three types of questions: (a) what enterprises should the operator include in the total farm business (what to produce?) ; (b) how should he allocate available water and other resources among enterprises (how much to produce?); and (c) in what proportion should he combine irrigation water with other materials and services used for each product or enterprise (what irrigation treatments [practices] should he use)? A simple problem including two alternative crops, rice and safflower, and two resource restrictions (constraints), 400 acres of land and 1,000 hours of tractor power illustrates the linear programming method (see Figure 4). The 400 acres of land, at alternative yields of 30 hundredweights of safflower sorghum or 60 hundredweights of rice per acre, can produce 12,000 hundredweights of safflower or 24,000 hundredweights of rice. Available tractor hours will operate 600 acres of safflower producing 18,000 hundred- weights, or 200 acres of rice producing 12,000 hundredweights. The farmer is limited, therefore, to producing 12,000 hundredweights of safflower (point A) due to the land limitation, or 12,000 hundredweights of rice (point C) due to the tractor hour limitation, or to some combination of the two crops that is consistent with both constraints, as defined by the line ABC (see Figure 4). His problem is to decide which crop or crops, and how many acres of each, to produce in order to maximize income. Sales prices are $4.00 per hundredweight for safflower and $5.00 per hundredweight for rice. When we draw a constant revenue (isorevenue) line that just touches (is tangent to) the heavy crooked line ABC and has a slope to the ratio of the two product prices $5.00 rice i 9';'» $4.00 safflower " '"^ find the combination of production that maximizes income. This combination. 14/ See page 3, Garvin. -35- FIGURE 4 1,000 Cwts. of Rice -36- for our example, includes 9,000 hundredweights of saf flower and 6,000 15/ hundredweights of rice (point B) with total revenue of $66,000. This problem is quite simple with two enterprises (crops) and two restrictions. But our problem with 12 enterprises under three differing irrigation practices, and 28 restrictions, does not lend itself to solu- tion by this method of simple charts and budget calculations. Maximum restrictions (on recent alluvium) include formal or informal acreage con- straints [14], limits on water quantities available in different time periods [13], and rice harvester hour limits (see Figure 2). These plus the 12 enterprises and three irrigation practices, present a problem that is too unwieldy for the graphic method. Linear programming allows simul- taneous consideration of all these factors, however, and yields optimum solutions that maximize net farm income under a varying range of condi- tions. Machine computation makes it more manageable, and speeds the analysis . Constraints Reflect Limits Set By Resource Availability, Technology. Market Conditions, and Institutional Factors Growers seeking to obtain maximum profits from their operations usually try to put as many acres as possible into the crop offering the highest net returns-over- variable expenses. If no constraints exist to interfere, therefore, we would expect an operator on our 1,280-acre farm model to plant all his irrigable acres in rice. He would divide these acres between rice and his next most profitable crop if, for some reason, it is not pos- sible to plant all land to rice. He would extend this principle to bring in other crops in order of profitability as further constraints might require. Constraints do exist in this problem; they include physical resource limitations, economic conditions, and institutional forces. We have attempted to recognize such limitations on freedom of management decision through defining a set of 28 constraints that reflect conditions on the farm and in the study area: 15/ Rice produced to the limit of tractor hours available would return $607000; saf flower expanded to the limit of land resources would produce $48,000 in revenue. Land Resource Constraints Acres Percent Total farm 1,280 I'laximum tillable land 1,180 Maximum tillable land in crops 940 Maximum tillable land in irrigated crops 826 Maximum tillable land in rice (40 percent allotment) 472 Maximum tillable land in other crops 354 Maximum tillable land in beans (dry edible) 177 Maximum tillable land in grain hay 118 Maximum tillable land in saf flower 236 Maximum tillable land in sugar beets 236 Maximum tillable in wheat 354 Minimum tillable land idle 118 Minimum tillable land in fallow (25 percent of rice) R/4 Miximum tillable land in oats and vetch seed 118 100 80 70 40 30 15 (recent and old alluvium) 10 20 20 (recent alluvium only) 30 10 R/4 10 (old and recent alluvium) Water Resource Constraints (13) Seasonal totals April 1-15 16-30 Acres Inches Basin Soils May June July August September October 1-15 16-31 1-15 16-30 1-15 16-30 1-15 16-31 1-15 16-30 1-15 95,568 5,304 5,304 9,696 9,708 8,688 8,700 9,600 9,612 8,844 8,844 3,768 3,780 3,720 $1,384 4,512 4,524 8,256 8,268 7,404 7,404 8,184 8,184 7,524 7,524 3,216 3,216 3,168 Rice Harvestor Hour Constraint (1) Hours September (5 days @ 9 hours x 2) 90 October 1-15 (15 days @ 8 hours x 2) 240 October 16-31 (15 days @ 7.5 hours x 2) 225 November 1-15 (13.5 days (§ 5.75 hours x 2) 155^ Total Hours (Seasonal) 710 -38- Our analysis includes 12 crops; rice, sugar beets, dry edible beans, saf flower (two cultural methods), alfalfa hay, grain sorghum (milo) , field corn, barley, wheat, oats, oats and vetch seed, plus grain hay, each adapted to one or more of the three soil categories. The 12 crops expand in number to 27 income activities, or "processes" in linear programming terminology (a maximum of 23 on any one soil) since a single irrigated crop is listed once for each irrigation treatment, or other input combination. These 28 constraints and 27 income activities establish the framework for the anal- ysis in the following sections. They define the range within which the forces regulating optimum crop choices and resource allocations for max- imizing profits under specified conditions must operate. Important variations exist among farms in the study area, according to the soil characteristics. Our analysis recognizes these variations. Thus we examine each of three different soils with its own properties and range of adapted alternatives. We identify these three soil-crop pattern models by capital letters in the tables and figures accompanying the main body of this report: A — The analysis for basin soils and adapted crops . B — The analysis for old alluvium soils and adapted crops. C — The analysis for recent alluvitmi soils and adapted crops. Budgeted Total Farm Earnings Statements Determine Profits and Returns to Various Resource Categories Linear programming analysis in this study identified the optimum resource pattern and indicated the total farm net returns-over- variable expenses imder each set of assumptions and conditions examined. This approach, however, did not determine total Net Farm Income , nor measure Profit and the respec- tive earnings shares to capital, management, or operator labor.— ^ Further analysis is necessary in order to calculate these measures of farm business success under varying water quantity and cost condition. We used budget analysis for this purpose: this method combines the Cross Receipts, Variable Expenses , and Net Returns-Over- Variable Expenses yielded by the linear 16 / See pages 15 and 16, above. -39- programming analysis with data reflecting capital investments, and related Fixed Cos ts . It is possible through this approach, therefore, to calculate the necessary earnings measures and, evaluate (a) , the effect of a given set of conditions on farm resource use, total farm profits, and the returns to various farm resources, and/or (b) , how various plans associated with the respective sets of conditions compare in financial returns and resource earnings . SHIFTS IN NET RETURNS AND OPTIMUM RESOURCE USE ACCOMPANY VARIATIONS IN WATER PRICES AND QUANTITIES A linear programming analysis effectively identified and measured the relationship between, first, varying water prices and, second, varying water quantities on the one hand, and total farm net returns-over-variable expenses on the other. It was necessary to make a separate analysis for each of these relationships and for each of the three soils situations in the study (basin, old alluvium, and recent alluvium) . Net Re turns-Over- Variable Expenses Drop Sharply As Water Costs Rise Water costs were allowed to vary from zero to $34.00 per acre-foot in the analysis for the basin soil and from zero to $26.00 per acre-foot for the other two soil situations (see Table 4) . Maximum quantities of water used and maximum net retums-over-variable costs for all three soil situa- tions are with water price at the zero level. This total use was 3,000 acre-feet for the basin soils, compared with 4,300 on old alluvium and 5,200 on recent alluvium soils (see Table 4). The greater amounts of water used on the latter two soil categories reflect the wider range of irrigated crops adapted to these two soils. Both net returns-over-variable expenses and the amount of water applied declined sharply on all three soils as water costs rose. Total net returns- over-variable expenses at zero water prices ranged from $136,600 for the basin soils to $160,300 for the recent alluvium with the old alluvium soil at $154,400; falling only slightly behind the recent alluvium. As water costs rose to about the $15.00 level, the net returns figure dropped to TABLE 4 Variations In Farm Net Returns and Irrigation Water Variable Costs for Three Soils, 196A-1966 Average Prices Basin Old alluvium Recent alluvium Net returns Cost per acre- foot Quantity' Net returns Cost per acre- foot Quantity Net returns Cost per acre- foot Quantity 1 2 3 4 5 6 7 8 9 dollars acre- feet dollars acre-feet dollars acre-feet 136,605 0 3,017 154,423 0 4,313 160,336 0 5,193 114,660 7.90 3,017 123,563 7.20 4,313 126,144 6.60 5,193 95,214 14.90 2,773 93,480 14.20 4,256 123,563 7.20 4,313 51,664 32.60 2,451 64,845 21.10 4,156 93,480 14.20 4,256 48,789 34.20 1,838 63,348 21.50 3,761 64,845 21.10 4,156 48,789 100.00 0 62,278 22.10 1,783 63,334 21.50 3,761 61,096 26.10 299 62,271 22.10 1,783 61,096 100.00 0 61,089 61,089 26.10 100.00 299 0 o -41- FIGURE 5 FARM NET RETURNS AT VARYING IRRIGATION WATER COSTS FOR THREE SOILS FIXED COSTS 40 — a BASIN b OLD ALLUVIUM c RECENT ALLUVIUM 30 - 20 - 10 - I I I I I I I I I I I L 0 4 8 12 16 20 24 28 32 36 40 44 COST OF IRRIGATION WATER PER ACRE-FOOT (DOLLARS) -42- $95,200 on the basin soils and, at this level, afforded only about $14,400 above the amount of net returns required to cover fixed costs ($80,800) on this basin soil model (see Figure 5) . Further rises finally to $30.00 per acre-foot, reduced net returns to levels markedly below total farm fixed costs for this basin soil. Reduc- tions in water quantities that maximized net returns-over- variable expenses also dropped simultaneously. The total amount stood at 2,800 acre-feet with water prices at about $15.00 per acre-foot, but dropped to 1,800 acre- feet at a $34.00 price (see Table 4). Total farm fixed costs, as calculated in this study, include two major categories of overhead costs. First, and by far the largest, of these cat- egories is that group of costs incurred by owning property. This category, in turn, includes two major types of costs: (a) noncash fixed costs, including interest on investments and annual depreciation costs and, (b), cash fixed costs, including taxes and insurance. Total fixed costs for this first major category (costs incurred by owning property) ranged from $79,000 on basin soils to $89,500 on the recent alluvium (see Table 2). Land costs, including only interest on investments and taxes (no depre- ciation), represented by far the largest item here. Equipment accounted for most of the rest of these property-oriented fixed costs, with improve- ments accounting for only $1,600. Cost items in the second major category, general overhead, were of minor importance in this study; they accounted for about $1,600. Total farm fixed costs are highly important to an individual farm oper- ator; they represent an overhead charge that his net returns-over-variable expenses must cover before any net profit remains to pay for his own manage- ment and risk assumption. Thus the point at which net returns-over-variable expenses exactly equals these total farm fixed costs represents a "break- even" financial result for the operator's total farm business, including wages at going rates for his own labor. At this point, net returns from his income enterprises exactly cover the total farm fixed costs or overhead — the "burden" that the farm must carry in order to operate at all. To the extent that his net retums-over-variable expenses exceed this breakeven point, the farm operator receives a surplus or profit to pay him for -43- his management and for the risk that he runs in carrying on his farming operations. The land value and interest-on-investments data in this report represent estimates based on "going rates," as determined from public records and interviews. Depreciation costs, likewise, represent approx- imations rather than precise determinations. This is because the latter values, representing annual costs for "using up" property, depend heavily upon accurate judgments as to length of useful life. These life-expectancy figures, in turn, can only be estimates or human judgments; at best they will correspond closely with reality, due to the background of experience of the person(s) making this judgment. In summary, the amounts that this report presents as total farm fixed costs may or may not agree closely with reality. Their greatest usefulness lies in providing a basis to compare results from each of the several dif- erent farm models included in the study with each other , and to provide perspective on the net earnings significance of the total farm figure net returns-over-variable expenses. Even if land values estimated for this study are precisely accurate, the interest component of noncash fixed costs will vary with any changes in the assumed going rate (6.5 percent per annum in this study). It is evident from this analysis of net retums-over-variable expenses, and the quantities of water used that farmers cannot profitably pay prices greater than $15.00 per acre-foot under the yield, price, costs and other conditions specified in this analysis. It is reasonably certain, further- more, that the conditions used in this analysis are much more favorable than those that most Butte-Colusa area rice growers have faced during recent seasons. The assumed price ($4.90 per hundredweight) is in line with those of the 1967-1969 seasons, but both yields (60-72 hundredweights per acre, depending upon soils group and cultural practices), and acre allotments for rice exceed the usual levels for rice growers in the Butte-Colusa area during these seasons. A water price of about $11.00 per acre-foot would associate with tlie break-even point for the basin soil model in this study at a rice yield of 50 hundredi^eights per acre — a typical yield level for many rice farmers in the Butte-Colusa subarea, and the three-year (1967-69) average yield for -44- Butte County. Rice allotments at 30 percent of tillable land also would result in drastically lowering prices for water that farmers could pay and still break-even on their total farm variable and fixed costs. The impact would be about the same as for the 10-hundredweight yield reduction. A combination of reduced allotments plus the yield reduction would mean that farmers could not pay more than about $5.00 per acre-foot for irrigation water and still hope to cover total farm variable and fixed costs, with no allowance for management or risk. Similar analyses indicates that reducing the yields or the allotments to typical levels would sharply lower the water prices that rice growers on old or recent alluvium soils could afford to pay and still recover all costs except returns to management. Our analytical approach uses better- than- typical yields and allotments, however, because we believe that so doing will make the results more mean- ingful for future reference. The technology and "know-how" already exist to accomplish such yields on a broad basis in this subarea, as is evidenced by the performance that some growers now are achieving. As for allotments, we believe the 40 percent used is more realistic than a lower figure would be in terms of population growth, both at home and abroad, and the pressure of world food needs. Certainly, sound resource use in terms of alternative choices for rice growers on all three of the soil categories in this study strongly support acreage allotments for rice not lower than the 40 percent level. The wide economic advantage of rice over alternative crops indicates that higher-than-40 percent allotment levels for rice would permit even more effective use of resources in the Butte-Colusa rice-producing area. Growers on old and recent alluvium soils show little difference in the water price and the associated net returns-over-variable expenses asso- ciated most closely with the break-even point. Both these units, however, would use about 1,500 acre-feet more water at this break-even point than the basin soil model (see Table 4) . These two alluvium soil units show more capacity to maintain both irrigation water use and the level of net returns-over- variable expenses as water prices increase further, at least up to levels in the $20.00 range. The reasons for these differences are evident in the data showing the net returns-over-variable expenses for the various adapted crops presented in an earlier section (see Table 4). We do -45- not anticipate that rice growers with rights in surface water obtained from irrigation districts in the Butte-Colusa subarea will have to face water prices running up to the levels tested here. We believe, nonethe- less, that the results obtained in this test constitute conclusive evidence that rice production has a preferred position as compared with alternative choices under any reasonable set of conditions to be anticipated in this subarea. This analysis demonstrates, further, as is evident in the accompanying text table based on 1964-1966 prices, that relatively sharp drops in net retums-over-variable expenses accompany water price rises for all three soil situations tested. Net Returns Amount Change Water Price Amount Change Net Returns Per Dollar Price Change BASIN SOILS 136,605 0.00 114,660 -$21,945 $ 7.90 + $ 7.90 95,214 - 19,446 14.90 + 7.00 51,664 - 43,550 32.60 + 17.70 -$2,778 - 2,735 - 2,460 OLD ALLUVIUM SOILS 154,423 0.00 123,563 -$30,860 $ 7.20 + 7.20 123,563 - 30,083 14.20 + 7.00 64,485 - 28,995 21.10 + 6.90 -$4,286 - 4,293 - 4,202 160,336 126,144 93,480 -$34,193 - 30,083 RECENT ALLUVIUM SOILS 0.00 $ 6.60 + $6.60 14.20 .60 5,176 4,302 Thus, a drop of nearly $22,000 in net returns accompanied a rise in water cost from zero to $7.90 per acre-foot on basin soils; this drop rep- resents $2,780 decline per $1.00 rise in price per acre-foot of irrigation water. Further declines of similar magnitude accompanied water price -A6- rises from $7.90 to $14.90 and on to $32.60 per acre-foot. Tlie comparable changes on old alluvium and recent alluvium soils were still greater, those on the recent alluvium soils being almost twice as large per dollar of water price rise as were the drops in net returns-over-variable expenses for the basin soils. On the old alluvium soils, the drop in net returns was nearly $31,000 for the initial rise in water prices to $7.20 per acre-foot or $4,300 per acre-foot. The change on recent alluvium soils was a $34,200 drop for a water price rise from zero to $6.60 — a reduction of nearly $5,200 for each rise of $1.00 in water price. Initial Hater Increments Above Zero Bring High Added Net Returns Per Acre-Foot: Basin Soils Use the Least, Recent Alluvium , the Most Irrigation Water A linear programming analysis indicates, for each of the three soils studied, the effects on net returns-over-variable expenses as irrigation water quantities vary from zero to the amount that maximizes net returns. Small grains, graiin hay, and saf flower represent the principal economically adapted crops from among which farmers must choose in this subarea if they do not have water for irrigation. Tlie minimum net returns-over- variable expenses associated with zero water quantities in this linear programming analysis, therefore, reflect one or a combination of these adapted crops for each soil category (see Figure 6). The level of these net returns at zero water applications ranged from 58 percent of total farm fixed costs (net returns $47,000 versus fixed costs of $30,800) for basin soils to 70 percent ($61,000 versus $86,000) for old alluvium soils. Sharp gains in net returns accompanied the initial water additions for all three soils. This water enabled farmers to divert land from the nonirrigated crops to rice production. Thus the first 1,800 feet of irrigation water available to operators on basin soils returned $33.00 per acre-foot applied, with a resulting gain of about $60,000 in net returns-over-variable expenses (see Table 5). The analyses for the two alluvium soils show net returns of $25.00 and $24.00 respectively, per acre-foot for the old and the new alluvium soils . Increases in total farm net returns continue to accompany additional increments of irrigation water. The addition net returns per acre-foot -1*7- TABLE 5 Farm Net Returns Per Acre-Foot of Water Varying Quantities of Irrigation Water on Three Soils, 196A-1966 Average Prices Net Returns Irrigati on water Net returns Total Change Total Change Per acre-foot 1 2 3 4 5 dol] .ars acre -feet dollars Basin 47,012 0 0 0 0 107,592 60,570 1,838 1,838 32.96 126,828 19,236 2,451 613 31.38 131,217 4,389 2,773 322 13.63 132,830 1,613 3,017 244 6.61 Old alluvium 61,096 0 0 0 0 68,519 7,423 299 299 24.82 99,475 30,956 1,783 1,484 20.85 139,562 40,087 3,761 1,978 . 20.26 147,406 7,844 4,156 395 19.85 148,699 1,293 4,256 100 12.93 149,034 335 4,313 57 5.87 Recent alluvium 61,096 0 0 0 0 67,237 6,141 250 250 24.56 98,193 30,956 1,733 1,483 20.87 138,280 40,087 3,712 1,979 20.25 146,551 8,271 4,123 411 20.12 147,629 1,078 4,194 71 15.18 149,034 1,405 4,313 119 11.80 152,643 3,609 4,973 660 5.46 153,706 1,063 5,172 199 5.34 FIGURE 6 FARM NET RETURNS AT VARYING QUANTITIES OF IRRIGATION WATER AND FIXED COSTS FOR THREE SOILS (WATER, VARIABLE EXPENSE; $1.25/ACRE FOOT) NET RETURNS A BASIN B OLD ALLUVIUM C RECENT ALLUVIUM L L I I 1 1 1 0 1,000 2,000 3,000 4.000 5,000 6,000 QUANTITIES OF IRRIGATJON WATER (ACRE-FEE T) -A9- (marginal returns) for each added water increment, however, decline steadily after the initial applications (see Table 5). Thus basin soils returned about $6.60 per acre-foot for the last increment (244 acre-feet) of the 3,000 acre-feet total associated x^ith the maximum net returns-over-variable expenses ($132,800). Similar declines in marginal net returns per acre-foot accompanied added increments of water up to 4,300 acre-feet on the old allu- vium soils. These reductions were from the $25.00 level to slightly less than $6.00 (see Table 6). Total net returns at this maximum water applica- tion level were $149,000 on the old alluvium soils. The analysis for recent alluvium soils revealed a drop in marginal returns from over $25.00 per acre-foot for the initial increments to slightly over $5.00 for the final ones as total water applications reached 5,200 acre- feet, and total net returns $154,000 for these soils. The increases in total water quantities from 3,000 acre- feet for the basin to 4,300 for old alluvium, and to 5,200 for recent alluvium soils reflects the adaptability of irrigated crops other than rice to these latter two soils. The correspondingly greater aggregate totals for net returns- over-variable expenses indicates the greater profit potential for soils with a greater range of adaptability for irrigated crops. This analysis also reveals considerable variation among the three soil categories in the level of marginal net returns before and after total water applications reach the level accompanying the break-even point for total farm net returns versus fixed costs. Net returns from the basin soils reach this break-even point at water applications of about 1,050 acre-feet. The comparable quantities were 1,200 acre-feet for the old alluvium and 1,400 acre-feet for the basin soils (see Figure 6) . Average marginal net returns per acre-foot of irrigation water applied were $32.00 per acre-foot for the basin soils up to the break-even point, and about $27.00 for the remain- ing applications up to the 3,000 acre-foot total. Comparable figures for the other two soils were $21.00 per acre-foot, and $20.00 per acre-foot for the old alluvium and $21.00 and slightly over $15.00 per acre-foot of water before and after the break-even point on the recent alluvium soils. -50- WATER QUANTITIES AND PRICES GOVERN OPTIMUM MANAGEMENT DECISIONS, RESOURCE ALLOCATIONS, AND CROPPING SYSTEl-IS Water Quantities Sharply Limit Crop Choices, Resource Allocation, and Net Returns on Irrigated Farms Water is a limited, if not actually a scarce, resource on many rice farms. This is particularly true for much of the basin soil area east of the Sacramento River in Butte County (see Figure 1) . We used a linear programming analysis to determine what specific land allocations would optimize total farm net returns-over- variable expenses for the three farm models in this study. We pointed out earlier that without irrigation water, farmers have no economic alternative other than to plant their crop land to winter and spring crops adapted for dry farming. The constraints, farm input and product prices, and other conditions established for this study indicate that without irrigation water, farmers on all three soil categories studied would maximize their total returns with the following cropping system: barley, 354; wheat, 354; safflower, 236; fallow, 118; and idle land 118 acres (see Tables 4 and 5, and Figure 7). Rice, the crop with the highest net returns-over- variable expenses per acre, would claim all water increments on the basin soil until the entire 472 acres permitted under assumed government allotments is in operation (see Figure 7) . The shallow irrigation treatment would be the optimum practice until after water increments reach 2,450 acre-feet; the deep- shallow practice would optimize net returns for the total farm cropping system at water quantities exceeding this quantity. Our earlier analysis identified grain sorghum as the only irrigated crop other than rice that appears to be an economic alternative on the basin soils (see Figure 3). This analysis confirms the earlier indication; actually grain sorghum appears in the farm cropping system only at water quantities exceeding about 2,800 acre- feet. The order in which irrigated crops other than rice enter the cropping system that yields maximum net retums-over-variable expenses is quite different for the two alluvial soils than for the basin soils (see Figure 7) . Dry beans appear in the cropping system for the old alluvium category -51- ricuu 7 CHANGES IN NET FARM RETURNS, CROP ACRES, AND MARGINAL VALUE PRODUCTS PER ACRE FOOT OF WATER AT VARYING QUANTITIES OF IRRIGATION WATER ON OLD ALLUVIUM SOILS J/ 0 t.OOO 2,m 3,000 d.OOO 5.000 6,000 QUANTITIES OF IRRIGATION WATER (ACRE-FEETt 2/ AT O WATEB OUANTlTr BARlEi' 354 SAFFLOWEB 236, WHEAT 354,F*llOW lie ACHES CHANGES IN NET FARM RETURNS, CROP ACRES, AND MARGINAL VALUE PRODUCTS PER ACRE FOOT OF WATER AT VARYING QUANTITIES OF IRRIGATION WATER OH RECENT ALLUVIUM SOILS BEAMS 1100%! BARLEY SAFFL0WE8 iR.g ) WHEAT FALLOW BICE (DP SH) SCANS (imi SAFFLOWCRIH., WHEAT FALLOW I RICE (DP SH) TTT BEANS 160%) 1 18 WHEAT 354 FALLOW RICE IDP SH) 1 BEAHilim) I WHEAT ] FALLOW 1 RICE [OP SH) 411 A BEANS (loot) 354 A WHEAT MB A FALLOW 177 A RICE (DP SH) 472 A BEANS , I6DS) 177 A [wheat 295 a FALLOW I IB A iftlCE I (DP SHI tlj A I BEANS , I60\] .SUGAR k BEETS .SUGAR BEETS J80SJ 59 A IWHEAT 59 A iFAi inn IIH 4 177 A 0 1,000 2,000 1,000 J, 000 5,000 QUANTITIES OF IRRIGATION WATER (ACRE FEET) _!/ AT O WATER OUANTI Tf BARLEY JW, SAFFLOWEB 226. WHEAT J54, FAllOW 11BACBES -52- with the initial water increment. Irrigation requirements for this crop are sufficiently low as compared with rice, that a water addition insuffi- cient to bring in rice acreage does make it possible to include 177 acres of dry beans. PJ.ce (177 acres irrigated under the deep-shallow practice) appears v^^ith the second water increment, expands to 413 acres with the third, and reaches its maximum 472 acres permitted under the allotment program with the fourth water increment (see Figure 7) . The cropping pattern on recent alluvium as successive increments of irrigation water bring quantities to the 5,200 foot maximum, is similar for beans to the pattern on the old alluvium soils. This low-water-require- ment row crop (177 acres) appears in the optimum cropping system before rice and continues to retain a place in the maximum net returns cropping system for all successive water increments. In contrast with its un- changing position in the acreage for each cropping system on the old allu- vium soils, however, the dry bean acreage on recent alluvium varies among the optimum net returns cropping systems. Beans appear in the initial irrigated system at 177 acres, continue at this acreage under the second water addition, expand to 354 with the third increase in water quantity increments by which x^ater quantity finally reaches its maximum 5,200 acre- foot total for recent alluvium soils (see Figure 7). Rice, appears in the cropping system at the second water increment with 177 acres under deep- shallow irrigation. This crop requires two more increments to reach its maximum 472-acre allotment level. The deep-shallow irrigation practice is the most profitable for this maximum return crop for all water quantities in v/hich the highest net total farm return cropping system includes rice. The range of crop adaptation for the recent alluvium soils makes it profit- able for sugar beets to appear in the optimum cropping system at the final two water additions. This crop, irrigated at the 60 percent soil deple- tion level, occupies 177 acres at the next- to-last water increment, and a total of 236 acres (177 at 60 percent and 59 at 80 percent available soil moisture depletion levels) under the final water increment. This was at maximum water availability of 5,200 acre-feet for the entire farm. -53- WATER PRICE VARIATIONS REGULATE QUANTITIES USED, CROP CHOICES, AND RESOURCE ALLOCATIONS A farmer seeking to maximize his profits, will use smaller quantities of irrigation water at relatively high prices per acre- foot. It is self- evident, also, that he will make every effort to apply the quantities that he does use to the crops or other enterprises that will yield the highest net returns to the water applied. We wanted to go beyond these general guidelines in this analysis; we undertook, also, to identify as precisely as possible the effects of water price variations on quantities used and the choice of crops to which water is applied. We set up to accomplish such identification a linear programming analysis in which water prices begin at levels uneconomic for irrigation use, and then lower, progres- sively, until further reductions bring no expansion in quantities that optimize total farm net retums-over-variable expenses. The result was a series of water prices and the cropping systems (crops and acreages of each) associated with each price (see Figure 8) . The cropping system on basin soils with water prices at levels that prohibit irrigation, again, include the winter grains, safflower, and fal- low (cultivated but unplanted) land. Acreages in each of these used were barley, 354; wheat, 354; safflower, 236; and fallow land, 118 acres; these land allocations among crops and fallow land apply not only to basin, but also to the two alluvium soils under the prices, constraints, and condi- tions of this study. The highest water price at which the linear program- ming analysis shows an irrigated crop coming into the farming system was at $34.20 per acre-foot. Rice, under the shallow irrigation treatment, replaced the 354 acres of barley, and used the entire first increment of irrigation water (about 1,840 acre-feet — see Figure 8). Rice acreage con- tinued to expand, and the quantities of water applied to increase, as water prices decline, successively, to about $33.00, and then to about $15.00 per acre-foot. Rice reached its maximum acreage permitted under the allot- ment program at the $33.00 price. The third water increment, at a price of about $15.00 per acre-foot, brought, therefore, not added rice acreage but a shift from the shallow to the deep-shallow irrigation practice. Tlie -54- FIGURE 8 OPTIMUM CROPPING PLANS FOR CRITICAL PRICE RANGES OF IRRIGATION WATER ON BASIN SOILS BARLEY 354 A SAFFLOWER (DP SH) 472 A BEANS I60M WEAT 59 A FALLOW 1 18 A 2.000 3,000 TTIES OF IRBlOATtON WATER 4.000 (ACfiE-FEETI -55- result of this shift was to utilize an additional 325 acre-feet of water on the rice enterprise. The final adjustment on the basin soils as water prices lowered came with the drop from $15.00 to $8.00 per acre-foot; 118 acres of grain sorghum irrigated under the 60 percent available soil mois- ture depletion practice replaced the saf flower. This shift accounted for the 255 acre-foot expansion in water use (see Figure 8) . Only at the lat- ter two irrigation water price levels of $15.00 and $18.00 per acre-foot, respectively, did total farm net returns-over-variable-expenses exceed the break-even point under this analysis, by about $14,000 and about $34,000, respectively . The two alluvium soils shew a wider range of adjustments to declining water prices than the basin soil. This difference reflects the greater range of crop adaptability characterizing these two soils. Thus the old alluvium requires lesser price declines to stimulate shifts in the cropping pattern and, naturally, displays a greater number of such cropping pattern changes (see Figure 8). The initial water use (about 300 acre-feet) came at the $26.00 per acre-foot price and brought into the cropping system 177 acres of beans under the medium irrigation practice. Rice (177 acres under the deep-shallow irrigation practice) came in with the second water increment of about 1,500 acre-feet, and at the $22.00 water price. Rice acreage continued to expand to its 472-acre allotment level, while dry beans dropped to 118 acres, as water prices declined in two steps to $21.00 per acre-foot (see Figure 8). Further drops in water prices, finally to about $7.00 per acre-foot, resulted in acreage shifting from wheat to beans, thus increasing the latter back to the 177-acre level, and water use to a total of about 4,200 acre-feet. The general relationship among water prices, quantities used, and cropping patterns for the recent alluvium soil was similar to that on the old alluvium. Beans appear in the cropping system at a water price of about $26.00 per acre-foot, and continue to occupy crop acres for all of the remaining systems associated with successively lower water prices. This crop, however, does, first lose acreage to rice, and, later, regain it at the expense of wheat, as water prices drop from $22.00 to $21.00 per acre-foot (see Figure 8). Rice (177 acres deep-shallow irrigated) -56- appears in the cropping system with the second water increment, and con- tinues to increase until it reaches the 472 acre maximum with the fourth water addition. Sugar beets also find a place in the cropping system on the recent alluvium soils, but only at the lowest water price appearing in this analysis, about $7.00 per acre-foot, at which price they occupy 236 acres . The old alluvium soil model reaches its maximum total water use at about 5,100 acre-feet at this $7.00 water price xvith three irrigated crops, rice, sugar beets, and beans accounting for 885 acres with the rest of the tillable area in wheat (59 acres) and fallow land (118 acres). It is true for the recent alluvium model, as for the other two, that total farm net returns-over-variable expenses equal or exceed total farm fixed costs only at relatively low water prices. These net returns, at $93,500, approxi- mately equal fixed costs at the $14.00 water price; they increase to show net profits at prices below this level. WATER PRICES AND SOIL ADAPTABILITY GOVERN FARM WATER DEMAITO It was evident in the preceding section, as well as in earlier ones, that the total amount of water used at maximum total farm net returns- over-variable expenses varies widely among the three farm models. The actual range of this variation is from slightly over 3,000 to 5,200 acre- feet (see Figure 9). This initial water increment of about 1,800 acre- feet on the basin soil meets requirements of 354 acres of rice, and dis- places a like acreage of barley at an initial price of $34.00 per acre- foot, as shown in the preceding section (see Figures 8 and 9). No gain in net returns accompanies this shift from a non-irrigated to an irrigated crop, however, due to the extremely high price of water. Gains in net returns do accompany the next three water increments, however; net returns exceed the break-even point with the third, and reach a maximum (at about $115,00) with the fourth and final increment (3,000 acre-feet). The analysis in the preceding section traces the shift from law to higher water use, and accompanying expanded acreage for rice plus the appearance of grain sorghum in the cropping system, as these water prices FIGURE 9 FARM DEMAND FOR IRRIGATION WATER ON THREE SOILS DEMAND A BASIN B OLD ALLUVIUM C RECENT ALLUVIUM 1 1 B 1 1,000 2,000 3,000 4,000 QUANTITIES OF IRRIGATION WATER (ACRE-FEET) 5,000 -58- decline. The relatively narrow range of choice among profitable uses for irrigated land on the basin soils explains why only rice and grain sorghum appear in the cropping systems on this type of soil. This same fact, also, explains the relatively small water quantity (3,000 acre-feet) associated with maximum net returns on this soil. The elasticity of demand for irri- gation water on rice farms of this size on basin soils, in other words, was quite low; relatively sharp drops in water prices generated only small increases in total water use. In contrast, the other two soils with a wider range of adaptability to crops show much higher demand elsisticity for irrigation water. The recent alluviimi model with the widest range of crop adaptability, furthermore, shows the highest demand elasticity level of any of the three soil category models (see Figure 9). This is evident in the relatively small drop in prices required to stimulate expanded water use, and in the wide range in quantities between the low initial increment of about 300 acre-feet and the 5,200 acre-foot maximum usage. EXPENSIVE OR LIMTED QUANTITIES OF WATER SHARPLY REDUCE FARM PROFITS Sacramento Valley rice growers apply relatively large quantities of irrigation water to rice; this holds even for producers with leveled fields of basin or other soils the structure of which largely prevents downward percolation, and who control their water applications effectively. The requirement standards on the basin model in this study range from 5.2 to 5.9 acre-feet per acre of rice, depending upon the irrigation technology. The standard for growers producing rice on alluvial soils with higher percolation rates ranges from 8.2 to 8.9 acre-feet. We chose to use these standards deliberately, although available information from previous stud- ies, records, and interviews indicate that nuany farmers use quantities con- siderably greater than these to produce rice in the Sacramento Valley. We chose. these water use rates on the basis of research information and actual performance by some growers in order that our results may reflect the pro- duction and returns possible for growers applying the most up-to-date research knowledge who also use superior water management practices. Water prices and quantities exert important influence on rice farmers ' profits . -59- The quantities of water required to produce rice under conditions specified for the three models in this study, though less than many farmers spply, still are large enough to leave no doubt on this conclusion. One of the marked advantages accruing to rice growers planting and producing the bulk of the rice grown in the Sacramento Valley is the relatively large supplies of water available, and the nominal price of this water. Both of these favorable conditions would tend to disappear with any major increase in the total Sacramento Valley rice acreage. Such acreage expan- sion would soon exhaust water supplies, while additional quantities of water, if available at all, could be had only at sharply increased prices. Water quantities and prices represent only two of the major forces that jointly regulate the profits that Sacramento Valley farmers obtain from growing rice. These farmers must control and use large amounts of capital; a combination of inflationary price rises plus new technology and increased mechanization during the 1960 's and early 1970 's stimulated an upward trend in these capital requirements. Thus the average investments for the three models in this study ranged from slightly less than $700,000 for the basin to over $800,000 for the recent alluvium unit (see Table 1). Investments at such levels as these entail heavy fixed costs obligations. Interest on investment, alone, at an assumed rate of 6.5 percent per annum amounts of nearly $45,000 for the basin, nearly $49,000 for the old allu- vium, and nearly $53,000 for the recent alluvium model in this study. Total fixed cost, including depreciation, taxes, insurance, and other items for these three models were $81,000, $36,000, and $91,000 per year, respectively, for these three units. We have related these fixed costs to total farm net returns-over-vari- able expenses in our several analyses prior to this section in this report. We pointed out that the break-even point, at which the total farm net return exactly covers the fixed cost, represents a critical economic balance in the farm operations. It is at this balance that the operator is able to recover all of his costs, both variable and fixed and including pay for his own in field work at the same pay rate that he pays his hired employees. This break-even point, however, leaves nothing above this equated balance -60- either to pay him as manager for perfoirming the functions of planning, organizing, directing, and otherwise managing the business, or to reward him for risking the capital, either owned or borrowed, in this operation. A farmer, like any other businessman, does not willingly operate at this level; he expects his farm operations to pay him something more for his capital and management than these two resources can earn in the market if he hires them out. We include in this report an analysis to show on a comparative basis the level of arm earnings under the conditions specified in this study, and under some other sets of conditions. The basin model offers a useful starting point for these comparisons. This model (Case 1) , with no restric- tions on the typical 3,017 acre-feet of water available under conditions of this study, a water price of $1.25 per acre-foot, and at rice yields of 68 hundredweights per acre under the deep flooding- lowered irrigation prac- tice, would result in gross receipts over variable expenses of $132,834 for the total farm operation (see Table 6). The cropping system at this level of net returns would include 472 acres of rice, 118 acres of grain sorghum (irrigated at the 60-percent level of soil moisture depletion) , 354 acres of wheat, and 118 acres in each fallow and idle land. This total net returns figure lends itself to analysis according to standard farm earnings measures. We subtract fixed costs ($80,300) from the net returns, to obtain $52,034 as Net Returns over fixed costs . We then add to this latter item the Value of Operator's work in field opera- tions at $2,575 (estimated at one-third the total annual cost for a full- time employee), plus $44,616, representing Interest on capital , to deter- mine that NET FARM INCOME is $99,225 (see Table 6, column 1). This is the figure from which the farmer must allocate the proper shares in earn- ings to all resources that he uses. We estimate his own Operator's Wages (full-year basis) at the same rate that he pays his hired employees, $7,725, and subtract this amount from NET FARM INCOrlE . The result, $91,500 is PROFIT ; this is what the farm pays the operator under the operating con- ditions of this study as a reward for using capital and his management, including risk assumption. Farm Earnings and Profits (Capital and Management Income) at Varying Water Quantities and Costs, Rice Yields, and Allotments, 1964-1966 Average Prices, Except as Indicated Basin soils Old alluvium soils Water: quantity price yield Case 1-^ Case 2 Case 3 Case 4 Case 6^ Case 7 3,017 A-feet $1.25/A-foot 40 percent 68 cwt/A 1,838 A-feet $1.25/A-foot 30 percent 50 cwt/A 1,838 A-feet $1.25/A-foot 40 percent 68 cwt/A 3,017 A-feet $7.90/A-foot 40 percent 68 cwt/A 4,300 A-feet $1.25/A-foot 40 percent 71 cwt/A 3,603 A-feet $1.25/A-foot 30 percent 55 cwt/A dollars c/ Total farm capital- Gross receipts less variable expenses 1 2 3 4 5 6 $686,470 132,834 $686,470 80,036 $686,470 109,351 $686,470 112,771 $750,466 149,032 $750,466 106,811 Total fixed costs 11 ^ I.* i- w ^ U L i i O \J V w L. fixed costs Add ^ Value operator's work- Interest on capital 80,800 $ 52,034 2,575 44,616 80,800 $(-) 764 2,575 44,616 80,800 $ 28,551 2,575 44,616 80,800 $ 31,971 2,575 44,616 85,968 $ 63,064 2,575 48,776 85,968 $ 20,843 2,575 48,776 NET FARM INCOME $ 99,225 $ 46,427 $ 75,742 $ 79,162 $114,415 $ 72,194 Operators wage— PROFIT (return to capital and man- agement) Interest on farm capital (? 6.5 percent 7,725 $ 91,500 44,616 7,725 $ 38,702 44,616 7,725 $ 68,017 44,616 7,725 S 71,437 44,616 7,725 $106,690 48,776 7,725 $ 64,469 48,776 MANAGEMENT INCOME-^ $ 46,884 $(-) 5,914 $ 23,401 $ 26,821 $ 57,914 $ 15,693 RATE EARNED 13.3 5.6 9.4 10.4 14.2 8.6 a/ Rice yield (? 68 cwt., unlimited water and 30 percent allotment would result in PROFIT $73,342, MANAGEMENT INCOME $28,726, and RATE EARNED 10.7 percent. b/ Yield reductions, alone (Case 9), would result in PROFIT of $72,040, MANAGEMENT INCOME of $23,467, and a RATE EARNED of 9.6 percent, a 30 percent allotment, alone (Case 9), in PROFIT of $90,304, MANAGEMENT INCOME of $41,518 a RATE EARNED of 12.0 percent. c_/ Average investment in farm property. d/ Calculated « $2.60 per hour for 16.5 60-hour weeks. e/ Full year wages for operators time at hired workers rates. f_/ Reward for risk assumption, decision making, and other management functions. -62- Tlils profit figure has its usual meaning as a measure of earnings; it constitutes an undivided return that belongs jointly to capital and manage- ment. But the farmer can identify a market, or "competitive," rate of return for the capital that his business employs. That figure, representing the rate the money could earn in alternative investments and calculated at 6.5 percent in this study, amounts to $44,616. This amount, subtracted from the PROFIT ($91,500) yields $46,884 as MANAGEMENT INCOME , the oper- ator's return for managing, and for assuming the risks involved in the farm business. Another way to evaluate these farm returns is to express the $91,500 PROFIT as a percentage of the $686,470 Total Farm Capital investment; the result of this calculation is 13.3 percent representing the RATE EARNED by this farm model (Case 1) on the capital investment under the conditions of this study (see Table 6, column 1). This RATE EARNED figure represents a joint return to capital plus management. Most rice farmers in the Sacramento Valley would consider the earnings at the levels determined by the analysis in the preceding paragraph as highly favorable. By far, the great majority of such farmers who operate basin farms under water quantity and price, and other conditions specified in this study — except for government allotments and rice yields per acre — would add that their own operations do not yield returns at these levels, that they receive lower earnings! One reason is because their yields do not come up to the 68 hundredweights per acre specified for Case 1, the 1,280-acre basin model analyzed in the preceding paragraph (Table 6, col- umn 1). Thus the statewide average yield for rice during the five seasons, 1966 through 1970, according to the California Crop and Livestock Reporting Service , ranged from 49 to 55 hundredweights per acre. The latter yield figure applies to the 1969 and 1970 crops. The 40 percent level assumed for the rice acreage allotment, as a percentage of total tillable land, also exceeds the acreage that farmers were permitted to plant if they elected to comply with the Federal Acreage Allotment Program during the early years of the 1970 's. -63- Our analysis Includes the better-than-typical conditions and earnings example represented by the Case 1, basin soil, unit because this example reflects a level of performance possible to many growers applying presently known technology effectively, and planting the proportion of their available total crop land in rice that reflects good land use practice for these basin soils in this subarea. It also is useful, however, to examine the effects on farm production and earnings that result when farmers are unable to apply, or for other reasons fail to apply these relatively optimum produc- tion conditions. Case 2 among our comparisons will serve our needs for this purpose. These earnings data reflect rice yields of 50 hundredweights per acre, a 30 percent allotment, and water supplies at $1.25 per acre-foot in sufficient quantities to irrigate the 354 acres of rice representing the 30 percent allotment (see Table 6, column 2). Our Case 2 basin farm oper- ated under these conditions, falls short by nearly $800 of yielding enough net returns-over-variable expenses to cover all fixed costs. The other earnings measures also show to a sharp disadvantage, as compared with those highly favorable ones applying to Case 1, in which both yields and acreage allotments exceed the typical in the area (see Table 6, column 1). Thus NET FARM INCOME , which earnings measure includes no allowance for the Oper- ator's own unpaid labor nor for the interest on the capital he uses, barely exceeds the return that this capital could earn if invested in an alternative use at a rate of 6.5 percent. The total PROFIT at $38,702 lacks $5,900 of equalling this interest return at the competitive rate of 6.5 percent. RATE EARNED , is 5.6 percent, and thus falls nearly one percent short of eqtialling the "market" rate. Farmers who operate under conditions such as these definitely are at an earnings disadvantage! The Case 3 comparison for the basin soil model also features the 30 percent allotment but its rice yields are at 68 hundredweights per acre (see Table 6, column 3). Those growers who have been successful in attain- ing comparable yields during recent seasons should find this a useful basis of comparison with their own operations in the earnings data for this par- ticular variation of the basin soil model. The extra 18 sacks add over $80.00 per acre to gross returns ($88.00 less minimal added expense, pri- marily for drying and hulling extra rice) . The result is to establish -64- total farm Net Returns over Fixed Costs at $28,550; this gain is enough to put all the remaining earnings measxires in a favorable position. NET FAR}I INCOME is almost $76,000, PROFIT slightly over $68,000, and ^^ANAGEMENT INCOME $23,400 (see Table 6, column 3). PROFIT , expressed as a percentage of total farm capital, comes out at 9.4 as the RATE EARNED on investment, or nearly three percent above the assumed market rate. We hold that most managers would not view these earnings as unreasonably high for a business with $700,000 capital investment. The $44,600 interest on capital at a 6.5 percent rate of return represents a relatively low percentage, as com- pared with such virtually risk- free alternatives as insured savings and loan accounts, mortgages, and high grade bonds. We believe, furthermore, that most business owners would consider the $23,400 MANAGEME^TT INCOME as a bargain price at which to hire a competent manager for a business requiring a $700,000 Investment! We show a fourth variation of the basin soil model in this study; this Is Case 4, In which all the standard conditions and restraints of the study apply except that water prices are at $7.90, instead of at the standard $1.25 per acre-foot water cost (see Table 6, column 4). This modification of the basin soil model, as might be expected, shows sharply lower earnings than Case 1 in which water prices are at the standard $1.25 per acre-foot. The earnings on this Case 4 basin model, however, are more favorable, than for the Case 3 example, with the same (68 hundredweights per acre) yield, but a 30-percent acreage allotment (354 acres of rice). Thus MANAGEMENT INCOME , at ($26,820 exceeds that for Case 3 ($23,400) by $3,400), while RATE EARNED (10.4 percent), is one percentage point higher than the 9.4 percent earnings for the example with a 68 hundredweights yield, but a 30-percent allotment (see Table 6, columns 1 and 4). Rice growers, in other words, could better afford to pay nearly $8.00 an acre- foot for water than take a cut in allotments from 40 to 30 percent of tillable basin land, provided they are able to obtain rice yields of 68 hundredweights per acre. A final example for the basin soil model, Case 5 (not shown), differs from the Case 3 only in that the limitation on rice acreage reflects a reduction from 40 down to 30 percent in acreage allotment for this crop. -65- as compared with the Case 3 example in which the limitation on rice reflected a shortage of water. The earnings measures for this Case 5 variation of the basin model ( NET FARM INCOME $81,070, PROFIT $73,340 and RATE EARNED 10.7 percent) come out somewhat higher from those for Case 3. This differential reflects the advantage of irrigated grain sorghum over dry-farmed saf flower on the basin soil. The remaining earnings comparisons in this analysis indicate the results from varying water quantity, water price, and acreage allotments on the old alluvium model. The first example. Case 6, representing effi- cient use of research technology combined with a 40 percent allotment, shows highly satisfactory earnings. The PROFIT at a dollar magnitude of nearly $107,000 affords nearly $58,000 ?4ANAGEMENT INCOME after covering the cost of interest on capital investment at the 6.5 percent assumed rate (Table 6, column 5). Or this same PROFIT expressed as a percentage of total farm capital investment ($750,470) represents a RATE EARiNED of 14.2 percent. Here, again, as for the basin soil model, these optimum manage- ment and earning performance levels do not reflect the typical performance that farmers obtain on this type of soil in the Sacramento Valley rice producting area. In contrast, the example (Case 7) using a 55 hundred- weights rice yield, combined with a 30 percent acreage allotment, shows P^QFJ^T at slightly under $64,500, MANAGEMENT INCOME at about $16,000, and 8.6 percent as the RATE EARNED on capital investment (see Table 6, column 6). The same comments with reference to the adequacy of returns for the basin soil Case 2 apply here in an even greater degree. It is equally true for this Case 7, old alluvium, example that the level of earnings much more nearly reflects actual performance than do those earnings indicated for the Case 6 example under optimum use of research technology, and a 40 percent acreage allotment. Our analysis included two other comparisons for the old alluvium model. Cases 8 and 9 (not shown). Each of these represents a modification of Case 6; Case 8 includes a 30 percent acreage allotment but a 71 hundred- weights per acre rice yield; Case 9 includes a 40 percent acreage allotment and the 55 hundredweights per acre rice yield. The yield reduction, from 71 to 55 hundredweights per acre for rice, would have the most unfavorable effect upon earnings. The result (Case 9) would be to cut PROFIT to about -66- $72,240, MANGEMENT INCOME to $23,470, and the RATE EARNED percentage to 9.6 percent, on the total capital investment ($750,470). The results with yields remaining at the 71 hundredweight level, but under a 30 percent, instead of a 40 percent, allotment (Case 8), would be to reduce these earnings measures to $90,300 for PROFITS , about $41,500 for MANAGEMENT INCOME , and to 12 percent for RATE EARNED . One or another of these last three cases; (7) a yield sharply below the 71 hundredweights level plus a 30 percent (not a 40 percent) acreage allotment; (8) a sharply reduced yield (here represented by the 55 hundred- weights figure) plus a 40 percent allotment, or (9) a relatively high yield (71 hundredweights per acre), but a 30 percent allotment, will include most rice growers and their operations during most seasons on the old or recent alluvium soils in the central Sacramento Valley. Minimal numbers of growers and acreage were under the 40 percent allotment during the 1969 and 1970 seasons. The comparative farm earnings data presented above indicate that the old alluvium soils hold a definite earnings advantage over the basin type under the conditions considered in this study. This advantage reflects higher rice yields, a greater range of adaptation for producing crops other than rice profitably, and some yield advantages on crops other than rice. These same findings also apply and, in somewhat greater degree, to the recent alluvium soils as is evident from data presented in other sections of this report (see Figure 3 and Appendix Table A-10) . The basin soils, however, hold one advantage over both categories of alluvium soil in rice production, if water quantities are limited. This is because the relatively low percolation losses on these basin soils reduce the total water requirements for producing rice, as compared with the alluvium soils on which, percolation losses are greater. This difference and, its impact on profit levels for the basin, as compared with the two alluvium soils, is not clearly evident in these farm earnings comparisons. These differ- ences do manifest themselves, however, in the following section that centers upon the profit impacts of varying prices for rice with no acreage allot- ments in force. -67- FARM PRODUCT PRICES STRONGLY INFLUENCE WATER USE, CROP CHOICES AND ACRES, AND PROFITS Rice Prices Dominate Decisions on Sacramento Valley Rice Farms Most farmers in the Butte-Colusa subarea of the Sacramento Valley tra- ditionally have looked to rice as their principal source of farm earnings. The analytical results presented in the earlier portions of this study fully substantiate the soundness of this viewpoint; these results identify rice as the most profitable crop for this subarea. But all of our results from analysis of water quantity and water price-versus farm earnings relationships reflect the $4.90 per hundredweight rice price, and the 40 percent of cropland specified as the government acreage allotment in the conditions of this study. The earlier linear programming results leave unanswered, therefore, questions concerning optimum rice production pol- icies on these farm models in the absence of government acreage allotments, and at prices differing from the $4.90 per hundredweight (but the preceding section on profits does examine these questions). A linear programming analysis in which prices varied from zero to $4.90 per hundredweight of rough rice, and with no government acreage allotments specified, throws some added light on these questions. The effect of thus modifying the conditions assumed for this study varied importantly among the three soil categories considered. There was little variation among the three soils, however, in the rice price neces- sary to cause farmers to allocate a substantial part of their land to this crop; operators on the basin soil would find it advantageous to substitute rice for nonirrigated crops at a price of $2.55 per hundredweight while a slightly higher price, $2.62 per hundredweight, would have the same effect for operators on the two alluvium soils (see Table 7). At this $2.55 price, and with no acreage allotments in force, farmers on the basin soils would maximize net returns-over- variable expenses by planting 546 acres of rice (see Figure 10) . They would substitute rice for a like acreage of grain sorghum, thus leaving only 44 acres in the latter crop along with 354 acres of dry-farmed wheat, 100 acres in fallow, and 118 acres idle. A further price rise to $2.98 per hundredweight would displace the remainder TABLE 7 Rice Production and Net Returns at Varying Rice Prices Without Allotments, 1964-1966 Average Prices Except for Rice Basin Old alluvium Recent alluvium Net returns Price per cwt. Quantities Net returns Price per cwt. Quantities Net returns Price per cwt. Quantities 1 2 3 4 5 6 7 8 9 do: .lars cwt. dollars cwt. dollars cwt. 57,952 0 0 73,155 0 0 79,553 0 0 57,952 2.55 36,855 73,155 2.62 29,323 79,553 2.62 12,567 73,680 2.98 39,887 75,155 2.72 35,500 80,743 2.72 29,323 78,936 3.11 53,924 102,222 3.46 42,050 89,164 3.01 35 ,500 111,290 3.71^/ 53,924 112,734 3.71^/ 42,050 114,101 3.71 42,050 121,535 3.90 53,924 120,724 3.90 42,050 114,101 3.90^^ 42,050 148,497 4.40 53,924 141,749 4.90 42,050 122,020 4.40 42,050 175,459 4.90 53,924 162,774 4.90 42,050 164,140 4.90 42,050 a/ No change in cropping system at this or higher prices. -69- FIOIU 10 OPTIMUM CROPPING PLANS AND RICE PRODUCTION ON BASIN SOILS AT VARYING CRITICAL RICE PRICES 5 - RICE (SHALLOW) 793 A. RICE (DP SH) Wi A WHEAT 3M A FALLOW ua A GRAIN SORGHUM (MV) 590 A > WHEAT 3S4 A FALLOW na A 1 I I I I i_ 0 10,000 20,000 30,000 40,000 50,000 60,000 RICE PRODUCTION (CWT) OPTIMUM CROPPING PLANS AND RICE PRODUCTION ON OLD ALLUVIUM SOILS AT VARYING CRITICAL RICE PRICES RICE (DP BEANS WHEAT FALLOW 'SMt SOO A / 0%) 177 A I M A < III A I RICE (SHALLOW) saO A . BEANS {60%) 177 A > WHEAT 117 A FALLOW I IB A RICE (DP SH) 413 A BEANS (60%) 177 A. WHEAT 267 A. FALLOW 111 A GRAIN SORGHUM 160%) . BEANS WHEAT FALLOW 177 A. 3S4 A III A. 30.000 40,000 RICE PRODUCTION (CWT) OPTIMUM CROPPING PLANS AND RICE PRODUCTION ON RECENT ALLUVIUM SOILS AT VARYING CRITICAL RICE PRICES RICE(DP/SH) 413 A BEANS (40%) 177 A SUGAR BEETS (60%) 236 A SlWAft BEETS (60%) 69 A •MEAT USA FALLOW ng A RICE (DP SH) SOOA BEAHS (60%) 177 A SUGAR BEETS (60%) UV A WHEAT USA FALLOW na A. CRAJN SORGHUM 160%) 177 A BEANS (60%) 177 A SUGAR BEETS (60%) 236 A WHEAT 3M A FALLOW nt A RICE (OP SH) 177 A- BEANS (60%) )77 A SUGAR BEETS (60%) Z36 A ■HEAT 3S4 A FALLOW liav 30,000 40.000 RICE PROOUCTIOH (CWT) -70- of the grain sorghum as well as over half of the wheat (see Figure 10) . Rice acreage would climb to 570 acres, with 446 under the deep-shallow, and the rest under the shallow irrigation. A slight increase in rice prices (to $3.11 per hundredweight) would bring rice plantings to 793 acres, the maximum potential for the basin model, considering water supplies, harvesting equipment, and the rest of the power and machinery complement of this 1,280-acre unit (see Table 7). This analysis does not consider yield reductions resulting from rice following rice in the cropping system, nor their rotational or biological factors that might check rice acreage or reduce yields. Total farm net returns at this maixmum rice acreage in the absence of acreage allotments and at a $3.11 per hundredweight price would fall slightly short of total farm fixed costs ($80,800). Further gains in rice prices would have no effect toward increasing rice acreage and production, but would greatly improve total farm net returns-over- variable expenses. The $32,000 gain in net returns that would accompany a 20 cent gain in rice prices (to the $3.71 level) would bring total farm net returns to $30,500 above the level of total farm fixed costs (see Table 7). This surplus over the brealc-even level for total farm fixed costs versus net returns would represent the return to the operator for assuming capital risk, making decisions and directing the farm operations, and other manage- ment functions. Further gains in total farm profit would accompany addi- tional price rises. A price equal to the $4.90 per hundredweight of rough rice assumed in this study, coupled with unrestricted freedom for farmers to plant rice without acreage allotments, would result in total farm net returns of $17,550, if rice yields and all costs per unit remained unchanged, or almost double the level of total farm fixed costs (see Table 7 and Figure 11). This figure, however, again reflects rice planting at nearly 800 acres and no yield reductions; neither of these conditions appears realistic (see Figures 10 and 11). It is highly likely, also, that rice yields would fall below the 68 hundredweights per acre level if rice occupied 800 acres (68 percent) of 1,180 tillable acres, of which only 944 are in crops. The effect of increasing rice prices, in the absence of government acreage allotments, would be similar for the two alluvium soils to that FIGURE 11 RICE PRODUCTION AND FARM NET RETURNS FOR VARYING RICE PRICES WITHOUT ALLOTMENTS Rice Prices Per COT (Dollars) -72- for the basin soils (see Figure 10). The 580-acre maximum rice acreage under these conditions on each of these two farms, however. Is sharply below the maximum for the basin model. The old alluvium model would Include the 177 acres of dry beans, a low water requirement crop, at the maximum rice acreage (see Figure 10). The recent alluvium soil also would include a like acreage of dry beans and, in addition, 69 acres of sugar beets. Thus sugar beets substitute for wheat on the recent alluvium model with maximum rice acreage, but not on the old alluvium soil (see Figure 10) . Rice acreage, reaches its 580-acre maximum at a price of $3.71 per hundredweight on the old alluvium, and at a price of $3.90 per hundred- weight on the recent alluvium soil (see Figure 10) . Added gains in price would increase the total net returns for the entire farm because they would expand the net returns-over- variable expenses to the rice enterprise. Such price gains would exert no Influence, however, toward stimulating growers on these alluvium models to expand rice acreage beyond the 580-acre maximum. Water supplies available during the pre-seedlng flooding stage would not permit any such expansion due to the relatively high percolation losses on these soils. This situation constrasts to that for the basin soil model, with minimal losses from percolation. Available water quantities would permit rice acreage to expand to nearly 800 acres on this latter soil type. Actually there is room for serious doubt as to whether it would be advantageous for rice growers on the alluvium soils to expand acreage beyond the 580-acre level at which it represents about 50 percent of all tillable land. Here, again, as with respect to the hypothetical 800-acre seeding on the basin model, it is entirely likely that biological and economic factors not evaluated in this study would make it unprofitable to expand rice acreage to such levels. Growers on the alluvium soils have certain positive forces operating to limit the profitability of excessively high rice acreage. The greater range of crop adaptation on these better-drained soils also makes some of the alternative crops more profitable relative to rice as rice expands on the alluvium soils, as compared with the basin soils. These forces would operate on the alluvium soils in addition to reduced rice yields and other negative forces that would tend to reduce rice profits as acreage expands. -73- The data showing comparable rice production in total hundredweights and net returns at varying rice prices in the absence of government acre- age allotments require some evaluation (see Table 7 and Figure 11) . The comparisons among the three models reflecting the three soil situations appear valid within the analytical framework up to the point that rice acreage ceases expanding as prices increase (see Table 7, columns 2, 3, 5, 6, 8 and 9). Total farm net returns-over-variable expenses at this point of maximum rice acreage and production on the three farms would be highest for the recent alluvium model ($114,101) and lowest for the basin model ($111,209). This relationship shifts, however, as prices rise to the $4.90 maximum for this analysis, which price also is the standard one for the analyses previously presented in this study. Total farm net returns at this $4.90 price would be greatest for the basin model ($175,459) and lowest for the old alluvium ($162,774). This shift in net returns levels among the three soils reflects the greater acreage and production of rice at the maximum level for the basin soil. The result is that the basin soil model would have more total rice on which to gain the advantage of price increases (the 93,924 hundredweights for the basin soil model repre- sents 28 percent more rice than the 42,050 hundredweights totals for the other two models). We do not consider these total farm net returns com- parisons valid for prices higher than those associated with 580 acres of rice for these three models, based on a total of 1,180 acres of tillable land. This is for reasons already stated in terms of rice yields, feasible acreages, and available water supplies. Changing Economic Conditions Bring New Price Production and Price Relationships for California Growers The $4.90 per hundredweight maximum rice price in the preceding anal- ysis is consistent with the econcmic context prevailing during the 1960 's and early 1970's. California rice production and prices during this period ranged from 19.1 million hundredweights (produced on 360 thousand acres) and $4.30 per hundredweight in 1966, to 17.2 million hundredweights (from 331 thousand acres) and $5.24 in 1971. The average production during the three year period 1966-1968 was 19.9 million hundredweights (from 384 thousand acres) and the weighted average price was $4.78 per hundredweight. -74- The average, combined Butte and Colusa County rice acreage for these three years was 165 thousand acres or about 40 percent of the land available for rice growing in these counties. Acreage limitations and price supports applied during these years. Changes in the overall economic situation, both in the U. S. and abroad, and in the foreign demand for rice after 1971, brought an economic context markedly different after that year from the one during the period ending with 1971. General price rises, unusual foreign demand that drained away accumulated grain stocks of all types, and sharp increases in off- shore demand for rice all combined to create new relations between prices and production of California rice.— ^ Farmers dare not assume that the unusual price-supporting influences, and this uniquely high price for rice in 1973, represent a permanent shift, so that similarly high prices will continue during the remainder of the 1970' s. They may expect, however, that new market and price conditions will prevail, and that rice prices will be higher than during the 1960 's unless production and supply completely outrun demand. The 1973 demand and price situation should favor some increases in California rice acreages and production during the latter half of the 1970 's if the U. S. Government relaxes or suspends acreage limita- 18/ tions. — The problem that rice growers would face under such circumstances is to decide how much they safely can expand acreage and production and 17 / The California Crop and Livestock Reporting Service reported that U. S. rice prices averaged $13.70 on October 15, 1973 as compared with $6.78 per hundredweight one year earlier. U.S.D.A. purchase and loan prices per hundredweight for rice were $5.27 in 1972, $6.07 in 1973 (U.S.D.A. 2374-73) and are announced at $6.23 in 1974 (U.S.D.A. 3275-73). 18 / The Secretary of Agriculture announced in November 1973 (California Rice-77) that 1974 rice allotments will be 1,652,296 and 299,766 acres respectively for the U. S. and California. The latter acreage compares with an 332,990-acre allotment for California in 1973, thus represents a reduction. A later announcement (Rice 222-74) , however increased these allotments to 2,100,000 and 380,921 acres respectively, for the U. S. and California. This later figure for California represents an increase of 81,000 acres over 1973 allotments. -75- still be able to sell at prices that will permit profitable operation. There are no precise advance answers to this question. Information is available, however, to relate Butte and Colusa County rice acreage both to the land available to produce rice in these counties and to total California acreage. Data presented earlier in this study indicate that land available, and adapted for rice growing in Butte and Colusa counties combined, totals about 405 thousand acres. This figure provides a basis to examine how changes in total California acreages might affect Butte and Colusa counties assuming that these two counties continue to account for the same proportion (about 43 percent) of this state total as during the three years 1966-1968. California's 400,000 acres in rice for harvest in 1973 represents only a slight increase above the three-year average for 1966-1968 (384 thousand acres). The accompanying increase in Butte and Colusa counties, combined, to maintain the acreage in those tWo counties at the 43 percent level of the state total, however would mean an 16.5 thousand acre, or 10 percent acreage gain. We already h ave pointed out that the 1973 October price level is double that of one year earlier in spite of the fact that rice acreage in California is the highest since 1968. Further expansion in California's total rice acreage to 450 thousand acres, with Butte and Colusa counties continuing to maintain their same percentage of the total, would mean 193.5 thousand acres for Butte and Colusa counties, or almost 48 percent of the rice land adapted primarily to this crop. An increase to the 500 thousand acre level for California under the same assumptions would increase the combined Butte and Colusa Counties' acreage to 215 thousand acres, or about 25 percent above its 1973 level (see text table). California acreage Butte-Colusa Counties combined (43 percent of California total) (Acres) Percent adapted land in rice 400,000 450,000 500,000 550,000 600,000 172,000 193,500 215,000 236,500 258,000 42.0 47.8 53.1 58.4 63.7 -76- Similar percentage acreage Increases for our analytical models with 1,180 acres of tillable land would indicate rice acreage increases from the 472 acres representing the 40 percent allotment to 525, and 590 acres, respect- ively. We have already indicated that biological and management problems may well make it difficult to maintain the 68 and 71 hundredweights per acre yield levels used in this analysis if acreage expands to the latter level. The other question, that farmers know from past experience to be critical, is whether aggregate California rice production at the level accompanying such acres expansion would lead to disastrous price declines. Up-to-Date Technology and Efficient R.esource Use Are Essential to Profitable Rice Farming This analysis of rice acreage, production and prices in the absence of allotments, as is true of that in earlier sections, assumes much above state average yields (68 to 71 cut as compared with 50 to 60) . Farmers obtaining average yields, and particularly those with less than 55 hundred- weights rice yields, would face quite different earnings situations. The earlier section on "FABII PROFITS " compares PROFIT , I'lMAGEI-lENT INCOME , and RATE EARNED under varying levels for yield, allotments, water supplies and costs (see Table 6, pages 15 and 16). This comparison shows farmers with the 50 or 55 hundredweights yields at a sharp earnings disadvantages . Higher Acreages Could Bring Greater Profits at Recent Rice Prices Our main conclusions from the price-acreage analysis centers on the earlier adjustments of rice acreage to the price increases at lower initial prices for this crop. 1. Rice would come into the cropping system on the central California rice farms at relatively low prices; actually these prices barely exceed 50 percent of the $4.90 standard price used in this study. 2. The acreage response to rising rice prices in this analysis clearly confirms the marked economic advantage rice has over other alter- native crops in this rice growing subarea. 3. Rice acreage would reach its maximum, at relatively low prices on farms in the central Sacramento Valley, within the framework of resources available and economic conditions of the 1960 's. -77- 4. Yield reductions for technical reasons, plus limited water sup- plies, would operate to set limits on rice far short of the total amount of cropland on these farms. We believe that the 580-acre maximim indicated for the two alluvium soils by a water quantity restraint certainly represents an upper limit for rice acreages on all soils in this subarea. Such rice acreages on these models with 1,180 acres of tillable land represent 49 percent of all tillable land and 61 percent of the total land seeded to all crops. The practicable maximum rice acreages for all three of these soils actually may be lower than 580-acres on the 1,280- acre rice farms. 5. Water shortages would limit rice acreage to lower levels than these as a percent of available land for the Butte-Colusa sub- area as a whole. Unless farmers improve water management and irrigation practices, the standards used in this study are lower than the amounts farmers in the subarea consistently use to pro- duce rice. 6. Rice holds a strong production and earnings advantage in the cen- tral Sacramento Valley. Artificial constraints to restrict this crop to acreages lower than the levels indicated by technical and resource factors are definitely against the economic interest of farmers in this part of the Sacramento Valley. CONCLUSIONS The results of this study point to conclusions that farmers and those who serve faraers in the central Sacramento Valley, and particularly in the Butte-Colusa County subarea, should find useful. These conclusions focus on the major issues that farmers must face in allocating and using land, water and other resources: 1) the place that rice holds in the cropping patterns and farming systems in this subarea; 2) the relative rank of alternative crops as profit makers — or as fixed cost payers; 3) the physical and economic relationships involved in using water to irrigate rice and other crops; 4) the impact of variations in water quantities and costs on optimum farm organization and resource allocation; 5) the influence of Agricultural Stabilization and Conservation Act acreage allotments and price supports on rice and other crops with respect to sound use of land and other resources and, importantly, farm earning levels; finally, 6) some of the more important technological and managerial choices that offer possibilities to farmers seeking to increase -78- resource productivity and farm earnings. The more Important of these con- clusions relate specifically to rice, the primary source of gross and net farm income for farmers in the two counties: 1. Rice shews relatively high net returns-over-variable expenses per acre for rice on the basin, old alluvium, and recent alluvium soils in the Butte-Colusa subarea studied. These net returns for rice range from slightly below, to more than $200 per acre for each of the three soil situations (ignoring fixed costs), depending upon the irrigation practice used. These high returns give rice a distinct economic advantage over alternative economically adapted crops. They represent double or more the net returns possible from the strongest competitor among alternative crops with the margin of advantage for rice somewhat greater on the basin than on the old or recent alluvium soils. 2. Farmers who successfully apply the latest research knowledge and technology may obtain rice yields that range from nearly, to slightly over 70 hundredweights per acre during normal seasons. These excellent yields enable such growers to gain the maximum financial advantage from rice's strong competitive position, as compared with other crops in this subarea. The 12 to 15 hundred- weights per acre (or greater) yield advantage of such farmers, as compared with state average yields of 55 hundredweights per acre during the latter 1960's and the 1970 season, clearly establishes the importance of this advantage. 3. Relatively generous water supplies, available for rice irrigation in the past at the nominal total flat rate cost of $10.00 to $12.50 (or, at the most $15.00) per acre, including assessments, have constituted one of the important advantages of many rice growers in this subarea. Government acreage limitations and allotments during recent years have restricted rice acreage to such extent that most growers, particularly on the west side of the Sacramento River, have experienced no shortage of water to irrigate their total rice seedings . 4. Many rice growers commonly use more water than necessary to obtain optimum rice yields, according to research findings, particularly at the Biggs Experiment Station, and empirical evidence, both for individual farm operations and on aggregate use in the subarea. Both controlled experiments and the experience of some growers indicate that better field layout and leveling, improved land preparation, more effective water control, and irrigation practices featuring lower water applications can increase rice yields as compared with the water management policies and practices many grcwers commonly follow. 5. Aggregate water quantity data for the subarea, coupled with research findings on rice irrigation requirements, suggest that if all farmers were to adopt improved rice irrigation practices. -79- farmers could expand rice acreage in the subarea to a level rep- resenting between 40 and 50 percent of the tillable land prima- rily adapted for rice production. This would mean that growers would need to limit total seasonal water applications to between 5 and 6 acre- feet for the basin adobe soils, and to not over 8 acre-feet for the alluvium soils producing rice. 6. A farmer on a typical 1,280-acre rice operation with 1,180 acres of tillable land and about 950 acres in crops essentially can grow only barley, wheat, or safflower, if he has no irrigation water available. Rice will yield him the highest net returns for initial water increments if, as, and when supplies become available for irrigation. His marginal increases in total farm net returns-over-variable expenses would be about $33.00 per acre- foot of water for the first 1,800 acre-feet on basin soil, $21.50 per acre-foot for the first 2,200 acre-feet on the old alluvium, and about $21.40 for the first 1,700 acre-feet on the recent alluvium soil. The rates of return for subsequent increments of water would decline sharply as rice acreage reaches its maximum allotment level and it becomes necessary to introduce other irri- gated crops. 7. Farmers with a 40-percent rice allotment, and using latest research knowledge and technology to obtain yields in the 70 hundredweights per-acre range, could pay up to about $15.00 per acre- foot for irrigation water on the basin, and up to about $14.00 on either of the two alluvium soils, on a non-profit (but break-even) basis for the balance between total farm net returns and fixed costs. But a fanner who operates at this break-even point under conditions of this study will earn no return whatever for his managerial efforts, nor will he receive any earnings above the "going market rate" on his capital as a reward for risking it in the farm oper- ation. A water price of between $7.00 and $8.00, however, would permit the farmer who succeeds in producing under these optimum conditions to receive substantial total farm net returns-over- variable costs in excess of those required to cover his fixed costs; he thus would receive a profit on his management and capital. 8. Optimum product ion conditions for the 1,280— acre farm models in this study include a 40 percent acreage allotment, combined with a 68 hundredweights yield per acre for rice, and an economic com- bination of supplementary crops to use planted land not in rice. A farmer on basin soils operating under such conditions could earn about $92,000 total profit, which amount would pay him $44,600 as interest on his capital investments and the remaining $47,200 for his own management and for risking his capital. But, unfor- tunately, most farmers do not produce under these optimum condi- tions. A farmer on basin soil with a 50 hundredweights per acre rice yield and a 30 percent acreage allotment can obtain only $38,700 as profit. This amount lacks $5,900 of equalling what his capital investment would earn him if he put it into a savings account, or other use, at 6.5 percent interest. Such an earnings -80- level means that the operator gets nothing whatever to pay for his own managerial efforts, nor for running the risk of imparing or losing his capital. A grower with this earnings level, further- more, lacks $5,900 of receiving the market rate on his investment, x^hile all he can point to for his year's work as an individual is $7,700. This latter amount equals what he pays one of his employ- ees, and the operator presumably could have earned it by working in the fields for someone else. 9. The earning opportunities for the operator on old alluvium soil with rice yields at 55 hundredweights per acre and, again, a 30 percent allotment are somewhat more favorable. Such an operator would have nearly $16,000 left over as reward for his own manage- ment and risk assumption, after allocating $48,800 to pay interest on his capital at the competitive rate assumed to be 6.5 percent per annum. The slightly higher yields for rice, plus the greater range of crop adaptability and somewhat higher yields for supple- mentary crops, explains why the operator on an old alluvium would fare better than one on basin soil. The 6.5 percent rate of return on capital, plus $16,000 management income for organizing, managing, and assuming risks on a business representing a three- quarter of a million dollars investment likely would not attract very many managers from nonfarm business of similar dollar mag- nitude. 10. A final, and conclusive, fact to confirm the economic advantage rice in this subarea is the low rice prices at which farmers would find it profitable to substitute rice for other crops in their cropping systems; a price of $2.55 per hundredweight on basin and $2.62 per hundredweight on the two alluvium soils would have this effect under the yield and cost conditions of this study. Presently available water quantities would limit rice acreage to not more than one-half the total tillable acres on the alluvium soils. Quite possibly such an upper limit also would apply to the basin soils as well, if not because of water short- ages, then due to soil and biological factors. 11. The final, and obvious, conclusion from these analyses is that government acreage allotments or other restrictions, tend to pre- vent growers from using their land and other resources most effec- tively in order to maximize farm profits, since such restrictions limit acreage at levels below those that farmers normally would . find it profitable to plant. Such restrictions also result in less than optimum resource allocation and use from the standpoint of society as a whole. This study did not undertake to determine precisely the optimum proportion of tillable land that farmers should plant in rice. The analyses suggest, nevertheless, that this level may be somewhere between 40 and 50 percent, of these tillable acres, considering quantities of water available to the farmers in the subarea and the relative per acre earnings of other adapted crops. A more precise determination of optimum rice acreage, as a percent of all tillable land, will require biolog- ical, soils, and economic information not available for this study. 4/3/74 Irw -81- REFERENCES [1] Adams, Frank, Rice Irrigation Measurements and Experiments in Saa- ramento Valley, 1914-19 19, Berkeley: University of California, Agricultural Experiment Station Bulletin No. 325, September 1920, 67 pp. [2] Beringer, Christoph, An Eoonanio Model for Determining the Produc- tion Function for Water in Agriculture, Berkeley: University of California, Agricultural Experiment Station, Giannini Foundation Research Report No. 240, 1961. [3] Booher, L. J., and Clyde E. Houston, Water Holding Characteristics of Some California Soils, Davis: University of California, Depart- ment of Water Science and Engineering, Processed, 1958. [4] Burton, V. E. , A. A. Grigarick, Hall, W. H. Lange, J. E. Swift, and R. K. Webster, 1970 Pest and Disease Control for Rice, Berkeley: University of California, Agricultural Experiment Station and Exten- sion Service. [5] California Crop and S. S. Reporting Service, California Field Crop Statistics 1959-1968. [6] Carpenter, Oroville, Soil Survey, see Storie Index Bulletin 599, Rept. 4:1-48, 1929. [7] Davis, Loren L. , California Rice Production, Berkeley: University of California, Agricultural Extension Service Circular 163, April 1950. [8] Department of Water Resources, Directory of Water Service Agencies in California, State of California, Resources Agency of California, Bulletin No. 114, June 1962. [9] Department of Water Resources, Irrigation and Water Storage Districts in California, 1964, State of California, Resources Agency, Bulletin No. 21-54, December 1965. [10] Finfrock, D. C. , F. C. Raney, M. D. Miller, and L. J. Booher, Manage- ment in Rice Production, Berkeley: University of California, Agri- cultural Experiment Station, Extension Service Leaflet 131, December 1960. [11] Garvin, Walter W. , Introduction to Linear Programning, New York: McGraw-Hill Book Company, 1960. [12] Grant, Warren R. , Application of an Economic Model for Evaluating Government Program Costs for Rice, Texas, Department of Agricultural Economics and Sociology, Texas Agricultural Experiment Station, College Station, July 1969. -82- REFERENCES (Con't.) [13] Hagan, Robert H. , Factors Affecting Soil Moisture-Plant Grcuth Relations^ Netherlands, Report of the XIV International Horti- cultural Congress, 1955, 86 pp. [14] Heady, Earl 0., and Wilfred Candler, Lineajc Progrcsnming Methods^ Ames: Iowa State College Press, 1958. [15] Hedges, Trimble R. and Charles V. Moore, Economics of On-Fam Irrigation Water Availability and Costs ^ and Related Adjustments: I. Enterprise Choices , Resource Allocations ^ and Earnings on 640-Aare General Crop, Farms on the San Joaquin Valley Eastside^ Berkeley: University of California, Agricultural Experiment Station, Giannini Foundation Research Report, No. 257, (see also reports 261, 263, and 256 in this series). [16] Hottel, J. B., Warren R. Grant, and Troy Mullins, Equipment Technology and Weather on Rice Farms in the Grand Prairie, Arkansas Part II: Economics of Size for Specified Tractor and Labor Combinations, Fayetteville: Arkansas University, Agricul- tural Experiment Station Bulletin 748, October 1969. [17] Hottel J. B., Warren R. Grant, and Troy Mullins, Equipment Technology and Weather on Rice Farms in the Grand Prairie, Arkansas Part I: Farm Organization and Risk, Fayetteville: Arkansas University, Agricultural Experiment Station Bulletin 734, December 1968. [18] Lapham, Colusa Soil Survey. [19] Lindt, John H., California Rice Growing, Berkeley: University of California, Agricultural Extension Service, April 1966. [20] Mehren, George L. , Crisis in Rice, Berkeley: University of California, Agricultural Experiment Station, Extension Service Leaflet 34, September 1954. [21] ItLkkelsen, D. S., D. C. Finfrock, and M. D. I^iller, Rice Fertil- ization, Berkeley: University of California, Agricultural Exper- iment Station, Extension Service Leaflet 96, January 1958. [22] Moore, Charles V., "A General Analytical Framework for Estimating the Production Function for Crops Using Irrigation Water," Journal of Farm Economics, Vol. XLIII, Part 1, No. 4 November 1961, pp. 876-888. [23] Oelke, E. A., "Influence of Water Management on Rice Yields and Plant Development," paper presented to the United States Dele- gation to the Tenth Session of the Working Party on Soils, Water and Fertilizer Practices, International Rice Commission, FAO, on July 18, 21, 1966 at Lake Charles, Louisiana. -83- REFERENCES (Con't.) [24] Oelke, Ervin, "Biggs Researchers Examine all Phases of Rice Culture," Sacramento Bee, March 14, 1968, P. CL12. [25] Pruitt, W. 0., "Evaporation — A Guide to Irrigation," California Turgrass Culture, Vol. 14, No. 4, October 1964, pp. 24-32. [26] Sitton, G. R. , A. D. Reed, and Loren L. Davis, Adjusting to Rice Controls, Berkeley: University of California, Agricultural Experiment Station, Extension Service Leaflet 47, March 1955. [27] Sitton, Gordon R. , Sacramento Valley Rice Farms: I. Organization, Costs, and Returns, Berkeley: University of California, Agri- cultural Experiment Station, Giannini Foundation Mimeo Report No. 207, July 1958. [28] Storie, R. Earl and Walter W. Weir, Generalized Soil Map of California, Rept. 4:1-63, Ser. 1926. [29] Thysell, Joseph, Milton D. Miller, and L. J. Booher, Management in Rice Production, Berkeley: University of California, Agri- cultural Experiment Station, Extension Service Leaflet 131, December 1960. [30] Viehmeyer, F. J., and A. H. Hendrickson, Essentials of Irrigation and Cultivation of Orchards, Berkeley: University of California, Agricultural Extension Service Circular 50, Revised 1950, pp. 4-9. [31] Viehmeyer, F. J., and A. H. Hendrickson, The Effects of Soil Moisture on Deciduous Fruit Trees, Report of the XIII International Horticultural Congress, 1952, Vol. 1, pp. 306-319. [32] Wadleigh, Cecil H. , "Soil Moisture in Relation to Plant Grw^th," Yearbook of Agriculture , Washington, D. C, U. S. Department of Agriculture, 1955, pp. 358-361. [33] Watson, Oroville Soil Survey. [34] Weir, Walter W. and R. Earl Storie, A Rating of California Soils, Berkeley: University of California, Bulletin 599, 1936. -8U- a?pf-;;-;"Ia tabi,k a-j Growth UaLes for Crops Other Than Rice on Various :-;oils by Five Percent Intervals for AvailaV-le vSoiJ Moisture neptt'ti.on and Combinfti Avera?^es; Vbree I rr J ^atioii Practices a/ f'er centaee intervals available soil moisture r.rov;th rates bv soil tvnes Jenlet ion Clay Clav loam Silt loam 1 2 3 percentap;es of potential 0-15— -too 0^^ b/ -inn 15 .1-20 100.0 99.0 100.0 20 . 1-25 99.0 98.5 100.0 25 . 1- JO 98.5 98.5 100.0 30.1-35 98.0 98.0 99.0 35 . 1-40 97.5 98 . 0 98 . n 40.1-4 5 96.5 98.0 93.0 45.1-50 96.0 97.5 98.0 50.1-55 95.5 97.0 98.0 55.1-60 94.0 96.0 97.0 60.1-65 92.0 94.0 96.0 65.1-70 89.0 92.0 95.0 70.1-75 86.0 88.0 93.0 75.1-80 82.0 84.0 90.0 80.1-85 /6 . ') /o , • ) CS J « u 85.1-90 70.0 72.0 75.0 90.1-95 62.0 64. n 65.0 95.1-100 50.0 50.0 50.0 Sum at 100 nercent level 1,782.00 1,798.50 1,835.00 . 05 Sum X (percent) 89.10 89.92 91.75 Sum at 80 nercent level 1,524.00 1, 538.00 1,562.00 Sura X ~~r (nercent) o!) 95.25 96.12 97.62 Sum at 60 percent level 1,175.00 1,180.00 1,188.00 Sum X (percent) 60 97.91 93.33 99.00 a/ Irrigation nractices Include (1) 100 percent, (2) 80 percent, and (3) 60 percent deoletion. b/ The first three five-percent intervals have been consolidated for brevity . APPENDIX TABLE A-2 Condensed Basic Computaclonal Form for Linear Programming Calculaclons; 1,280-Acre Farm; Basin Soil; Variable Water Prices a/ Crop activities. through y Resource of activity at non-zero level Supply of activity level Rice 1 c/ =23 Rice 2 c/ S4 Rice 3 c/ Ss Grain sorghum 1 d/ =26 Grain ] Grain sorghuni sorghum 2 d/ 3d/ =27 =28 Saf- f lower D c/ =29 Saf- f lower E d/ =30 Barley =31 Wheat =32 Oats =33 Oats/ vetch seed =34 Grain hay S5 Fallows =36 C f/ 184.83 219.43 210.13 60.79 57.88 49.79 44.63 47.12 39.00 64.90 22.26 37.18 19.26 -7.53 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 C, Land crops 1 1062 1 1 1 1 1 1 1 1 1 1 1 1 1 1 C_ Irrigation 2 crops 876 1 1 1 1 1 1 Rice 472 1 1 1 Grain hay 118 1 Cj Oats/vetch 118 1 Safflower 236 1 1 Cj Vi'heat 354 1 1 Cg Fallow lie 1 1 Water 4/1-5/15 17,292A" 28.5 31.3 21.8 4.63 4.63 4.63 Water b/16-31 8.268A" 5.6 5.6 6.0 3.0 3.69 0 C^^ Water 6/1-15 7,404A" . 7.6 5.6 6.0 4.15 5.54 7.5 C^^ Water 6/16-30 7,404A" 7.6 5.6 7.0 5.54 0 0 Cj^j Water 7/1-15 8,184A" 6.6 5.6 7.0 7.5 10.71 13.89 C, , Water 7/16-31 14 8,184A" 6.6 5.6 7.0 0 0 0 C,, Water 8/1-15 15 7,524A" 6.4 5.6 6.0 0 0 0 C,^ Water 8/16-31 lb 7,524A" 0 5.6 1.5 0 0 0 Cj^ Water 9/1-15 3,216A" 0 0 0 0 0 Water 9/16-30 3,216A" Water 10/1-5 3,168A" C^Q Harv. hrs. 710 .5 .5 .5 i/ 4/ s/ Cjj^ TOTAL IljO 0 68.9 70.5 62.3 24.82 24.57 26.02 a/ See also Heady, Earl 0., and Wilfred Cwidler , o£. cit . . p. 273. W Disposal activities (C^ through ^22^ omitted. These serve in calculations to account for resources not used in optimum crop combinations at various prices. c/ Rice irrigation practices include (1) deep, (2) deep, lowered, and (3) shallow. d/ Grain sorghum irrigation pra^-tices include (1) 60 percent, (2) 80 percent, and (3) 100 percent levels of available soil moisture depletion prior to irrigation, e/ Saf flower production practicfs Include (D) regular, and (E) minimal. O C line includes net returns-over-varlable expenses per acre for each of the 14 Income (or negative income) acttvlties (farm enterprises). £/ Not applicable. -86- APPENDIX TABLE A-3 Calculation Methods for Determining Annual Fixed Costs on Farm Property or Capital Goods 85 drawbar horsepower tracklayer tractor^/ NON-CASH COSTS Interest (6.5 percent of averaee investment^ 1 Original cost + salvage value! 6.5/100 - [$33,640 + S.OSn 6.5/100 » $1 257 L 2 J L 2 ' J Depreciation Original cost - salvage value = $33,640 - $2,402] L years on farm 10 J 3,124 TOTAL $4,381 CASH COSTS Taxes Assessment @ 35 percent of average investment] « p6,771 x 6.5| $ 440 Insurance ^Estimated @ 0.75 percent of average investmentj- j$19,345 X 0.75%j $ 146 TOTAL $ 586 ALL FIXED COSTS $4,967 a/ Fixed costs in this report include "overhead" costs that the farm operator incurs largely regardless of variations in the scope of his annual opera- tions. A heavy proportion of these costs relate directly to land, machinery and other capital goods; some refer to such overhead as "cost of owning" such property, or, simply, as "capital costs." Another important category of fixed costs are those administrative expenses that are unavoidable in the function of managing, but that are difficult if not impossible to allo- cate to specific income-producing activities, or enterprises. Among this latter group are office expenses, and organization dues. -87- APPENDIX TABLE A-4 Estimated Field Irrigation Efficiency Under Furrow Irrigation for Different Application Depths by Soil Type Desired application ucuLii xn mcriGS a / Soil type Recent alluvium Old alluvium Basin percent < 2 0.50 0.50 0.60 2 55.00 55.00 63.00 2 1/2 60.00 58.00 65.00 3 62.00 60.00 68.00 3 1/2 64.00 63.00 65.00 4 65.00 65.00 62.00 4 1/2 66.00 65.00 60.00 5 67.00 66.00 60.00 5 1/2 68.00 67.00 58.00 6 69.00 66.00 56.00 7 70.00 63.00 54.00 8 70.00 60.00 52.00 a/ Assumes tail water system. Source: Estimated by research and Agricultural Extension workers in irrigation problems and methods. APPENDIX TABLE A-5 Irrigation Water Added to Soil, Irrigation Efficiency, and Total Seasonal Applications by Soils, Irrigation Practices, and Crops, 1,280-Acre Farm Depletion levels for available soil moisture 100 percent 80 percent 60 percent Crop Water added Effi- , ciency— Total water Water added Effi- , ciency— Total water Water added Effi- - ciency— Total water 1 2 3 4 5 6 7 8 9 inches percent inches percent inches percent inches Basin Grain sorghum 15.2 58.5 26.0 15.2 61.8 24.6 15.8 63.7 24.8 Old alluvium Grain sorghum 17.5 62.3 28.1 19.3 61.9 31.3 16.3 57.0 28.7 Corn 27.8 62.5 44.5 26.0 63.9 40.7 26.8 61.9 43.3 Beans 10.5 62.1 16.9 12.5 61.6 20.3 13.9 57.7 24.1 Recent alluvium Grain sorghum 17.5 62.3 28.1 19.3 61.9 31.3 16.3 57.0 28.7 Corn 27.8 62.5 44.5 26.0 63.9 40.7 26.8 61.9 43.3 Beans 10.5 62.1 16.9 12.5 61.9 20.3 13.9 57.7 24.1 Sugar beets 25.3 61.4 41.2 25.5 63.1 40.4 29.1 65.0 44.8 Alfalfa b/ 49.0 70.0 70.0 c/ c/ c/ 46.2 65.0 71.1 a/ Irrigation efficiencies are seasonal weighted averages o^ Individual water applications. hj An established stand. Sj Not applicable. -89- APPENDIX TABLE A-6 Quantities and Costs of Irrigation Water for Rice by Soils and Irrigation Practices Irrigation practices Basin clay adobe ' Recent and old alluvium clay loam 1 Deep, not lowered Quantity of water 68.90" (5.74') 106.6" (8.88') Cost of water (a$1.25/A' $ 7.18 $ 11.10 Labor $ 5.20 $ 5.20 TOTAL irrigation cost $12.38 $ 16.30 2 Deep , lowered Quantity of water 70.5" (5.88') 100.6" (8.17') Cost of water @$1.25/A* $ 7.35 $ 10.48 Labor $ 5.20 TOTAL irrigation cost $12.55 y X.J • O O 3 Shallow I Quantity of water 62.30" (5.19') 99.0" (8.25') Cost of water @$1.25 $ 6.49 1 $ 10.31 Labor $ 5.20 $ 5.20 TOTAL irrigation cost $11.69 $ 15.51 -90- APFENUIX TABLE A-7 Calendar of Operations and Physical Inputs Per Acre 1,280-Acrc farmi Rice on Basin Soil Irrigated Under Deep Flooding — Lowered Practice Crew and equipment Acres per 9-hour Hours per acre Dates and operations Men Power Equipment day Man Tractor Materials 1 2 ■3 4 5 6 7 PREPLANT March Plow Disc (2X) Landplane (2X) 1 1 1 D-7 D-6 D-6 6 X 16" bottoms 21' disc harrow (offset) 12' X 60' landplane 17 60 37 .52 .15 .24 .52 .15 .24 April Survey Plow contours Plow cliecks Check Plowing borrow Discing pits, harrowing Placing boxes Closing checks Fertilizing 1 1 1 1 1 2 1 D-4 D-7 D-7 D-7 D-6 D-4 0-l> 4 X 14" bottoms 6 X 16" bottoms 14' checker 6 X 16" bottoms 21' disc and 21' harrow Dozer Dozer 2 applications (50# and 40*) air @ $3.10/acre 250 130 104 190 150 280 82 .04 .07 .10 .05 .06 .06 .11 .04 .07 .10 .05 .06 .03 .11 Box - 4 acres/box 905 (Urea i Amm. sulphate) May Flood 2 .5 CULTURAL May Plant Air @ S2.00/acre 160« @ S7.30/100S ;iay-September Irrigate 1 2.0 May-June Weed control Insect control September Draining Opening checks 1 1 D-4 Air-propanil @ $2.00/acre, -M.C.P.A. @ $1.50/acrc Alr-parathlon @ $1.25/acre Dozer 180 .05 .05 M.C.P.A. - 10 gallons/ acre = $1.52 Propanil - 12 gallons/ acre = S16.59 Faratliion 1/5 pints/ acre - 5.32 October Remove boxes Knock checks Knock checks Knock checks 2 1 1 1 D-4 D-7 D-4 D-6 Dozer 6 X 16" bottoms Dozer 14' float 280 130 190 246 .06 .03 .05 .04 .03 .03 .05 .04 HARVEST October Harvesting Banking out Haul to drier 2 2 2 D-4 2 J.D. "105's" (16' cut) 2 BanUout wagons 2-2 ton trucks 16^/ 16 16 1.0 1.0 1.0 .5 TOTAL 7.13 1.57 a/ Harvesting: harvest . 16 acres/8-hour day from 1-15 October; 12 acres/6-hour day from 16-31 October; 8 acres/4-hour day for remainder of -91- APPENDIX TABLE A-8 Variable Input Expenses Per Acre 1,280-Acre Farm; Rice According Co Soils and Irrigation Practices, 1964-1966 Average Prices Basin _ — Old alluvium Recent alluvium T f 4.. xnput xteins I a/ 2 3 2 1 3 1 1 2 3 4 5 I 6 7 8 9 dollars dollars dollars PREHARVEST Power D"* 7 Track, lay er 3. 49 3. 49 2 . 69 3. 49 3 . 49 2.69 3 . 49 3 . 49 2 . 69 D— 6 Tracklayer 2.28 2 . 28 2 . 43 2 . 28 2.28 2.43 2 . 28 2.28 2.43 D-^A Tracklay e r .47 .47 . 45 .47 .47 . 45 .47 .47 . 45 TOTAL 6.24 _ 6.24 5.57 6 . 24 6.24 5.57 6,24 6.24 5.57 Transport 4.13 4, 13 4.13 4.13 4.13 4,13 ; 4.13 4, 13 4, 13 Machinery Plnu (ft V ^ft"'S 1.04 1.04 1.04 1.04 1.04 1.04 1,04 1,04 Plow checks (6 x 16*' p low J . 14 . 14 . 10 . 14 . 14 . 10 , 14 . 14 . 10 Plow borrow (A X 1 A " J . 10 . 10 .03 . 10 . 10 , 03 . 10 . 10 . 03 Disc (21') and harrow (24') . 16 . 16 .09 .16 . 16 . 09 , 16 . 16 .09 Knock checks (plow) .06 .06 .06 .06 .06 . 06 , 06 .06 . 06 Disc (21') - 2x . 80 . 80 . 80 . 80 . 80 . 80 , 80 .80 . 80 Landplane 1.33 1. 33 1.33 1 . 33 1.33 1.33 1. 33 1.33 1.33 KnocK cnecKS (iloat) .004 . 004 . 004 . 004 . 004 . 004 . 004 . 004 Plow contours (A x 14 ) .03 .03 .03 03^ .03 .03 , 03 .03 .03 Close checks^ (dozer) ^""^ "^"^ . 55 .55 .55 . 55 .55 . 55 ,55 .55 .55 .002 .002 .002 .002 .002 .002 ,002 ,002 .002 Open checks (dozer) .001 .001 .001 .001 .001 .001 ,001 ,001 ,001 Remove boxes (dozer 2nd man ) . 006 . 006 . 006 . 006 .006 . 006 .006 ,006 ,006 '?«^ .001 W .001 .001 . 001 .001 b/ Grease wagon and low bed . 10 . 10 . 10 .10 . 10 . 10 . 10 . 10 ,10 trailer TOTAL 4.32 4.32 4.14 4 . 32 4.32 4.14 4.32 4.32 4,14 Labor General (excluding Irrigation) 5.62 5.62 5 . 25 5.62 5 . 62 5 . 25 5 . 62 5.62 5,25 Irrigation 5. 20 5.20 5 . 20 5 . 20 5 . 20 5 . 20 5, 20 TOTAL 10.82 10.82 10.45 10 . 82 10.82 10.45 10.82 10.82 10.45 Cont rac ted Per 1 11 i zer ^ mater i als 10 . 32 10. 32 10. 32 10. 32 10, 32 10.32 10 . 32 10,32 10.32 - application 3.10 3.10 3.10 3.10 3.10 3.10 3,10 3.10 3.10 Weed control - materials 5.67 5.67 18. 11 5.67 5.67 18. 11 5.67 5.67 18.11 - application 2.00 2.00 3.50 2.00 2.00 3.50 2,00 2,00 3.50 Pes t cont rol — mater ial s -.32 .32 .32 .32 .32 ,32 .32 .32 .32 - application 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1,25 1,25 Seed ~ application 2.00 2.00 2.00 2.00 2,00 2.00 2,00 2.00 Checker 1.30 1.30 .52 1.30 1.30 ,52 1.30 1.30 .52 TOTAL 25.96 25.96 39.12 25.96 25.96 39,12 25.96 25,96 39.12 Materials Seed 12.26 12.26 12.26 12.26 12.26 12.26 12.26 12,26 12.26 Irrigation water 7.18 7.35 6.49 11.10 10.48 10.31 11.10 10.48 10.31 19.44 19.61 18.75 22.74 22.57 23.36 22.74 22,57 Interest Operating capital (excluding 1.70 1.70 1.94 1.70 1.70 1.94 1.70 1. 70 1,94 water) Water and i r r 1 ga t ion labo r .31 .31 .30 40 .40 ,39 .40 .40 .39 TOTAL 2.01 2.01 2.24 2.10 2.10 2.33 2.10 2,10 2,33 TOTAL PREHARVEST COSTS j 72.92 73.09 84.40 76.93 76.31 88,31 76.93 76.31 88.31 HARVEST Machinery Combine (2 x 16') 4.31 4.31 4.31 4.31 4.31 4.31 4.31 4.31 4.31 S.P. banlcout wagon .73 .73 .73 .73 .73 .73 .73 ,73 .73 Bankouc wagon 1.00 1.00 1.00 1.00 1.00 1.00 1,00 ' 1.00 1,00 Hauling to drier .98 1.18 1.18 1.08 1.27 1,27 1,08 1,27 1.27 TOTAL 7.02 7.22 7.22 7.12 7.31 7,31 7.12 7,31 7,31 Concracted Drying 17.78 19.56 20.00 19.26 21.04 21.48 17.78 21.04 21.48 Hire truck (2T) 3.00 3.00 3.00 3.00 3.00 3,00 3,00 3.00 3.00 TOTAL 20.78 22.56 23.00 22.26 24.04 24.48 22.26 24,04 24,48 Labor 8.45 8.45 8.45 8.45 8.45 8.45 8.45 8,45 8,45 TOTAL HARVEST COSTS 36.25 38.23 38.67 37.83 39.80 40,24 37.83 39,80 40,24 TOTAL VARIABLE COSTS 109.17 111.32 123.07 114.76 L16.il 128,55 114.76 116,11 128.55 a/ 1 - deep, not lowered; 2 - deep, lowered; 3 - shallow. bj Not applicable. APPENDIX TABLE A-9 Variable Input Expenses and Net Returns Per Acre of Rice According to Soils and Irrigation Practices, 196A-1966 Average Prices Inputs by maior sroup Basin Old alluvium Recent alluvium a/ 1-' 2 3 1 2 3 1 2 3 1 2 3 4 5 6 7 8 9 dollars dollars dollars PREHARVEST Power 6.24 6.24 5.57 6.24 6.24 5.57 6.24 6.24 5.57 Transport 4.13 4.13 4.13 4.13 4.13 4.13 4.13 4.13 4.13 Machinery 4.32 4.32 4.14 4.32 4.32 4.14 4.32 4.32 4.14 Labor 10.82 10.82 10.45 10.82 10.82 10.45 10.82 10.82 10.45 Contracted 25.96 25.96 39.12 25.96 25.96 39.12 25.96 25.96 39.12 Materials 12.26 12.26 12.26 12.26 12.26 12.26 12.26 12.26 12.26 Interest (excluding 1.70 1.94 irrigation) 1.70 1.70 1.94 1.70 1.70 1.94 1.70 Water 7.18 7.35 6.49 11.10 10.48 10.31 11.10 10.48 10.31 Interest (water and irrigation labor) .31 .31 .30 .40 .40 .39 .40 .40 .39 TOTAL Preharvest Costs 72.92 73.09 84.40 76.93 76.31 88.31 76.93 76.31 88.31 HARVEST Machinery 7.02 7.22 7.22 7.12 7.31 7.31 7.12 7.31 7.31 Labor 8.45 8.45 8.45 8.45 8.45 8.45 8.45 8.45 8.45 Contracted 20.78 22.56 23.00 22.26 24.04 24.48 22.26 24.04 24.48 TOTAL Harvest Costs 36.25 38.23 38.67 37.83 39.80 40.24 37.83 39.80 40.24 TOTAL VARIABLE COSTS Yields, cwt. per acre Price per cwt. TOTAL GROSS RECEIPTS NET RETURNS 109.17 60.00 4.90 111.32 67.50 4.90 123.07 68.00 4.90 114.76 65.00 4.90 116.11 71.00 4.90 128.55 72.50 4.90 114.76 65.00 4.90 116.11 71.00 4.90 128.55 72.50 4.90 294.00 184.83 330.75 219.43 333.20 210.13 318.50 203.74 347.90 231.79 355.25 226.70 318.50 203.74 347.90 231.79 355.25 226.70 &I 1 - deep, not lowered; 2 - deep, lowered; 3 ■ shallow. -93- APPElfDIX TABLE A-10 SuMoary of Variable Input Costa and Bet Retiirna Per Acre for All Cropa AccoriUng to Soil and Irrigation Practleea, 19614-1966 Average Prices Total Price Ret returns Irrigation Preharvest Harvest variable per Gross Bet plus water Crops Practice!/ costs costs costs Yields unit receipts returns cost*/ 1 2 i 5 6 7 8 9 code dollars quantity dollars Basin 2 73 09 38 23 111 32 67 50 Cvt. 330 75 219 k3 226.78 3 8l4 1.0 38 67 123 07 68 00 Cvt . . k.90 333 20 210 13 216.62 1 T2 92 36 25 109 17 60 00 Cwt. 29k 00 I8k 83 192.01 c/ Crain SorghuB-' 60 1.8 59 57 57 57 57 53 80 Cwt. 2.20 118 36 60 79 63.38 BO 1.8 1.5 57 ko 57 ko 52 ko Cwt. 2.20 115 28 57 88 60. kk 100 W 18 58 Ok 58 Ok k9 00 Cwt. 2.20 107 80 k9 76 52. k7 Safflower^' D 31. 06 6 57 ko 63 19 60 Cwt. k.35 85 26 kk 63 kk.63 E 11 1.6 6 67 18 13 15 00 Cwt. k.35 65 25 k7 12 k7.12 Barley 20 15 6 85 ■ 27 00 27 50 Cwt. 2.k0 66 00 39 00 39.00 Wheat 20 21. 8 95 29 19 39 20 Cwt. 2.k0 9k 08 6k 90 6k. 90 Grain Hay 20 1.8 13 16 33 6k 2 30 Cwt. 23.00 52 90 19 26 19.26 Oats 21 21 6 53 27 71 20 00 Cwt. 2.50 50 00 22 26 22.26 Oats , vetch seed 21. 73 18 86 30 95 3 50 Cwt. 23.00 80 50 37 18 37.18 Old AllUTlUB Rice 2 76 31 39 80 116 11 71 00 Cwt . 3k7 90 231 79 2k2.27 3 88 31 1.0 2k 128 55 72 50 Cwt. • k.90 355 25 226 70 237.01 1 76 93 37 83 Ilk 76 65 00 Cwt. 318 50 203 7k 21k. 8k Grain Sorghum 60 50 1.6 9 15 59 61 59 00 Cwt. 129 80 " 70 19 73.18 80 51 77 9 15 60 92 57 70 Cwt . 2.20 126 9k 66 02 69 .28 100 50 17 8 98 59 15 5k 00 Cwt. 118 80 59 65 62.58 Com 60 75 81 37 77 113 58 63 90 Cwt. 166 Ik 52 56 57.20 80 7k 52 37 11 111 63 62 50 Cwt. ) 2.60 162 50 50 87 55.11 100 76 38 31. 91 111 29 58 00 Cwt. 150 80 39 51 kk.02 Beans 60 1.0 56 ko 98 81 50 19 70 Cwt. 182 23 100 73 103.2k 80 38 68 1.0 03 78 76 19 20 Cwt . 9.25 177 60 98 100 .95 100 36 98 37 92 7k 90 18 00 Cwt. 166 50 91 60 93.36 Safflower D 31. 06 6 67 ko 73 23 60 Cwt. k.35 102 66 61 93 61.93 Barley 20 15 6 95 27 10 35 00 Cwt. 2,k0 ek 00 56 90 56.90 Wheat 20 21. 9 05 29 29 kk 25 Cwt. 2.k0 106 20 76 91 76.91 Oats 21 21 6 62 27 80 26 60 Cwt. 2.50 66 50 38 67 38.67 Grain Hay 20 1.8 15 06 35 5k 2 70 tons 23.00 62 10 26 56 2^.56 decent Alluvium Hice 2 76 31 39 80 116 11 71 00 Cwt. 3k7 90 231 79 2k2.27 3 88 31 1.0 2k 128 55 72 50 Cwt. ) k.90 355 25 226 70 237.01 1 76 93 37 63 Ilk 76 65 00 Cwt. 318 50 203 7k 21k. 8k Grain Sorghum 60 50 1.6 9 15 59 61 59 00 Cwt. 129 80 70 19 73.18 80 51 77 9 15 60 92 57 70 Cwt. > 2.20 126 9k 66 02 69.28 100 50 17 8 96 59 15 5k 00 Cwt. 118 80 59 65 62.58 Com 60 75 81 37 77 113 58 68 80 Cwt. 178 88 65 30 69.81 80 71. 52 37 11 111 63 67 30 Cwt. 2.60 17k 98 63 35 67.59 100 76 38 31. 91 111 29 62 90 Cwt. J 163 5k 52 25 56.89 Beans 60 1.0 56 1.0 98 81 50 19 70 Cwt. 182 23 100 73 103.2k 80 38 68 ko 08 78 76 19 20 Cwt. . 9.25 177 60 98 8k 100.95 100 36 98 37 92 7k 90 18 00 Cwt. 166 50 91 60 93.36 Sugar Beets 60 111. 91. k9 56 16k 50 18 70 Cwt . 261 80 97 30 101 .96 80 112 78 k8 50 161 28 18 30 Cwt. . Ik. 00 256 20 9k 92 99.10 100 112 26 k5 05 157 31 17 00 Cwt. 238 00 80 69 8k. 98 Alfalfa 60 50 15 33 87 8k 02 k 29 tons 2k. 50 105 11 21 09 28.05 100 1.9 23 33 87 83 10 3 9k tons 2k. 50 96 53 13 k3 20.21 Safflower D 31. 06 6 67 ko 73 23 60 Cwt. k.35 102 66 61 93 61.93 Barley 20 15 6 95 27 10 35 00 Cwt. 2.k0 8k 00 56 90 56.90 Wheat 20 21. 9 05 29 29 kk 25 Cwt. S.kO 106 20 76 91 76.91 Oats 21 21 6 62 27 80 33 ko Cwt. 2.50 83 50 55 70 55.70 Grain Hay 20 1.8 15 06 35 5k 2 70 tons 23.00 62 10 26 56 26.56 a/ Het returns assuming zero water cost . b/ 2 ■ deep-lowered; 3 = shallow; 1 = deep, not lowered. £/ 60, 80, 100 = percent soil moisture depletion before irrigation. d/ D » regular practice; E = minimal practice. 3 DIVISION OF AGRICULTURAL SCIENCES UNIVERSITY OF CALIFORNIA Water Supplies and Costs in Relation to Farm Resource Decisions and Profits on Sacramento Valley Farms By TRIMBLE R. HEDGES CALIFORNIA AGRICULTURAL EXPERIMENT STATION GIANNINI FOUNDATION OF AGRICULTURAL ECONOMICS Giannini Foundation Research Report No. 322 June, 1977 University of California, Davis Department of Agricultural Economics WATER SUPPLIES AND COSTS IN RELATION TO FARM RESOURCE DECISIONS AND PROFITS ON SACRAMENTO VALLEY FARMS 2. Enterprise Choices, Resource Allocations, and Earnings on 640-Acre General-Crop Farms in the Lower Sacramento Valley. The author is Trimble R. Hedges, Professor Emeritus, Agricultural Economics, Davis. -i- TABLE OF CONTENTS Page FOREWORD "^i SUMMARY viii THIS STUDY ANALYZES THE EFFECTS OF WATER QUANTITIES AND COSTS ON RESOURCE USE AND PROFITS 1 The Analysis Centers Upon Criteria for Profitable Water Use Decisions 1 Earlier Research Provided Information 7 FARMERS MUST CONSIDER PHYSICAL, INSTITUTIONAL AND ECONOMIC FACTORS IN MANAGING IRRIGATED FARMS 7 Irrigation Water is an Increasingly Scarce Resource 7 Yolo County Soils Favor Many Crops 9 Growing Seasons Are Long But Dry 11 Laws, Regulations, and Government Programs Affect Farm Operations 12 Farmers Faced Unfavorable Price Relationships During the Late 1960's 12 THE 640-ACRE GENERAL CROP FARM REFLECTS TYPICAL FARl-1 ORGANI- ZATION AND OPERATIONS 1^ Farmers, Government Agencies, Research and Extension Personnel, and Business Firms Provided Data 15 The Synthesized 640-Acre Analysis Models Reflect Yolo County Farms -^^ YOLO FARMS HAVE HIGH INVESTMENTS AND FIXED COSTS 23 Land Dominates Farm Inventories and Investments 23 Only for Tomatoes Did Net Returns Equal Fixed Costs 27 PHYSICAL AND BIOLOGICAL FACTORS AND YIELDS ARE CRITICAL IN ECONOMIC ANALYSIS 31 Irrigation Treatments Affect Crop Yields 31 Crop Net Returns Vary Widely 36 -ii- Page Linear Programming Identifies Optimum Crop Choices for Profits 37 Constraints Set Limits According to Resource Availability, Technology, Markets, and Institutional Factors 38 OPTIMUM RESOURCE USE AND NET RETURNS VARY WITH WATER PRICES AND QUANTITIES 40 Net Returns Drop Sharply as Water Costs Rise 40 Marginal Net Returns Per Acre-Foot Decline With Increases in Quantities Used 46 FARMERS MUST EVALUATE THE EFFECTS OF WATER QUANTITY AND PRICE CHANGES TO IDENTIFY OPTIMUM CROPPING SYSTEMS AND RESOURCE ALLOCATIONS 50 Water Quantities Sharply Limit Crop Choices, Land Use and Net Returns 50 Water Price Variations Bring Changes in Quantities Used, Crops and Resource Allocations 55' WATER PRICES AND SOIL ADAPTABILITY INFLUENCE WATER DEMAND 58 FARM EARNINGS WERE LOW AT 1966-1968 PRICES 61 Budgeted Total Farm Earnings Statements Indicate Returns and Profits 61 High Productivity Soils Yielded Greater Profits at 1966- 1968 Prices 62 Higher 1972-73 Estimated Profits Reflect Greater Gains in Farm Product Than in Input Prices 67 CROP CHOICES, WATER USE AND PROFITS REFLECT FARM PRODUCT PRICES 71 Tomato Prices Dominate Decisions and Earnings on Sacramento Valley General Crop Farms 71 CONCLUSIONS 77 REFERENCES 81 -iii- LIST OF TABLES Table ' Pages 1 Farm Real Estate, Operating Equipment Investor ies, and Investments, 640-Acre Farm — High and Medium Productivity Soils, 1966-1968 Prices 22 2 Summary of Fixed Costs, 640-Acre Farm, High and Medium Productivity Soils, 1966-1968 Prices 25 3 Irrigation Water Budget; Tomatoes on Yolo Silty Loam Soil, Irrigation Practice 2 (Medium) Reirrigation at 80 Percent Depletion of Available Soil Moisture 35 4 Variations in Farm Net Returns and Irrigation Water Variable Costs, High and Medium Productivity Soils, 1966- 1968 Prices 41 5 Farm Net Returns Over Variable Costs at Varying Quantities of Irrigation Water, 1966-1968 Prices 47 6 Capital and Management Income — Farm Earnings and Profits at Varying Tomato Yields, 1960-1964 Price Levels and at 1972-1973 Price Levels 63 7 Farm Net-Returns-Over-Variable Costs at Varying Tomato Prices With A Fixed Tomato Acreage; 640-Acre Farms on High Productivity Soils 76 -iv- LIST OF FIGURES Figure Page 1 Yolo County Study Area and Generalized Soil Map 10 2 Precipitation, Temperatures, and Growing Season; Davis .. 13 3 Net Returns per Acre for Specified Crops, by Irrigation Treatments, High Productivity Soils, 1966-1968 Prices ... 28 4 Net Returns per Acre for Specified Crops, by Irrigation Treatments, Medium Productivity Soils, 1966-1968 Prices . 29 5 Farm "Net" Returns and Irrigation Water Variable Costs, High and Medium Productivity Soils, 1966-1968 Prices 45 6 Farm Net Returns at Varying Quantities of Irrigation Water, High and Medium Productivity Soils, 1966-1968 Prices 49 7 Changes in Net Farm Returns, Crop Acres, and Marginal Value Products Per Acre-Foot of Water at Varying Quantities of Irrigation Water; High Productivity Soils, 1966-1968 Prices 52 8 Changes in Net Farm Returns, Crop Acres, and Marginal Products Per Acre-Foot of Water at Varying Quantities of Irrigation Water; Medium Productivity Soils, 1966- 1968 Prices 53 9 Optimum Cropping Plans for Critical Ranges of Irrigation Water Variable Costs, Medium Productivity Soils, 1966- 1968 Prices 56 10 Optimum Cropping Plans for Critical Ranges of Irrigation Water Variable Costs, Medium Productivity Soils, 1966- 1968 Prices 59 11 Fam Demand for Irrigation Water, 1966-1968 Prices 60 12 Tomato Production Under Varying Contract Prices With No Acre On Tonnage Quotas, 640-Acre Farm on High Produc- tivity Soil, 1966-1968 Prices 74 -V- LIST OF APPENDIX TABLES Table Page A-1 Growth Rates for Crops on Various Soils by Five Percent Intervals for Available Soil Moisture Depletion and Combined Averages; Three Irrigation Practices 84 A- 2 Condensed Basic Computational Form for Linear Program- ming Calculations, 640-Acre General-Crop Farm Yolo High Productivity Soils, Variable Water Prices 85 A-3 Estimated Field Irrigation Efficiency Under Furrow Irrigation for Different Application Depths by Soil Type on Deep Well-Drained Soils 86 A-4 Irrigation Water Added to Soil, Irrigation Efficiency, and Total Seasonal Applications by Soils, Irrigation Practices, and Crops, 640-Acre General-Crop Farms 87 A-5 Calendar of Operations and Physical Inputs Per Acre, 640-Acre Farm 1966-1968 Conditions; Tomatoes on High Productivity Soils Irrigated Under Practice 1 (60 Per- cent Depletion) 88 A-6 Variable Inputs, Expenses, and Net Returns Per Acre of Tomatoes According to Soils and Irrigation Practices, 1966-1968 Average Prices 89 A-7 Summary of Variable Input Costs and Net Returns Per Acre for All Crops According to Soils and Irrigation Practices 1966-1968 Average Prices 90 -vi- FOREWORD This report examines the general-crop phase of an investigation into how water quantities and costs affect enterprise choices, re- source allocations, and farm profits in the Sacramento Valley. California Agricultural Experiment Station Project Number 1321-07-10 authorized this project. The OFFICE OF WATER RESOURCES RESEARCH, USDI, under the program of Public Law 88-379, as amended, provided partial support for this research, as did, also, the University of California, Water Resources Center. This investigation, thus, is a part of the Office of Water Resources Research Project Number B-068CAL, as well as the California Water Resources Center Project UCAL-l-JRC-W-111. An earlier report under this overall investigation, Giannini Founda- tion Research Report Number 320, bears the title. Water Supplies in Relation to Farm Resource Use Decisions and Profits on Sacramento Valley Farms; 1. Enterprise Choices, Resource Allocations, and Earnings on 1,280-Acre Rice Farms in the Central Sacramento Valley . The author is grateful for the assistance from various individuals and agencies. Carlos Benito, Raul Fiorentino, and Ralph Hanan, research assistants, had primary responsibility for the statistical analysis. Many individuals provided factual information, viewpoints, and advice, particularly with respect to biological and physical relationships of Yolo County's agricultural industry. I drew heavily upon published work by researchers and others in the California Agricultural Experiment Station and Agricultural Extension Service, the Department of Water Resources, the United States Department of Agriculture, the Yolo County Agricultural Stabilization and Conservation -vii- Office, Yolo County Agricultural Coinmi'ss loner's reports, and other State Experiment Stations, as well as certain unpublished data made available to me. Many individuals in County Agricultural Extension, Irrigation District, County Assessor, and individual business firm offices provided vital information as well as helpful suggestions. Farmers who furnished information in formal interviews and on other occasions deserve special thanks. I also am grateful to my colleagues. Pro- fessors Harold 0. Carter and Gerald W. Dean (now deceased) for allow- ing me access to their earlier survey materials collected in Yolo County. -viii- SUMMARY This svimmary of research on two 640-acre general-crop farm models in the Southern Sacramento Valleys, Yolo Coimty, examines the economic impacts of variations in water quantities and costs on farm profits. This second study in the Sacramento Valley irrigation economics series considers two analysis models: The high productivity soils unit includes Storie Index Grades I and II soils; the soils for the medium productivity unit are predominantly Storie Index Grade III, but range into Grade IV. Interview data for the mid-1960's from a Yolo County sample indicate that farmers using groundwater would face no shortages of irrigation water if water, pumping lifts, and well outputs maintained their early season levels throughout the year. They do experience serious shortages, however, due to groundwater levels lowering, pumping lifts increasing, and well outputs falling. Thus water quantities available for 16-hour pumping schedules vary from 279 acre-feet for the 1-15 April period to 212 acre-feet for the 16-31 July and 1-16 August periods. These mid-summer quantities do represent shortages that limit farmer decisions on crop choices. Yolo County farmers depending upon groundwater and wells also face a downward trend from year to year in water levels with associated increases in pumping lifts and water costs. Some Yolo County farmers, however, depend upon surface water for all or part of their irrigation requirements. Those living in eastern Yolo County with rights to Sacramento River water had ample supplies under the conditions during 1966-1968. Many farms in western Yolo -ix- County, however, were not so favorably situated. They experienced serious water shortages In years with low rainfall. Thus shortages of Irrigation water currently present problems to farmers depending upon groundwater, and to western Yolo County operators. Maximum potential water quantities available for the analysis models in this study total nearly 2,800 acre-feet, according to the number of wells and their potential pumping capacity. Actual use according to conditions during the 1966-1968 period was about 1,680 acre-feet for the high productivity unit and about 1,180 acre-feet for the unit on the medium productivity soil. The differences between potential and actual use reflect the Impact of seasonal shortages in limiting crop acres to the number that quantities during the minimum well output periods can Irrigate adequately. Water prices were approximately $2.50 to $3.00 per acre-foot, depending on quantities pumped, for variable expenses only, or a total of about $7.00 to $9.00 per acre-foot for variable expenses and fixed costs combined. The study analyzed three general crop irrigation practices: (1) relrrigatlng when the depletion percentage for available soil moisture reaches 60 percent — "wet"; (2) relrrigatlng at the 80 percent deple- tion level — "medium"; and (3) relrrigatlng at the 100 percent deple- tion level~"dry". The two Yolo County analysis models require large total average Investments in land. Irrigation, facilities, power equipment, and machinery. The average for the high productivity unit was $720,446, land alone accounting for $604,000 of this total. Comparable figures for the medium productivity unit were $494,321, land accounting for -X- $380,000. High annual fixed costs "burdens" for these Yolo County operators accompany these investments. The totals were $76,992 for the high and $60,117 for the medium productivity analysis units or $128 and $100 per tillable acre, respectively. Average investments in power, transport, and field operating equipment total $69,003 for each unit; accoirpanying annual fixed costs amount to $20,446. Canning tomatoes lead the crops in amount of net returns under all three irrigation practices, with sugar beets and alfalfa hay following in that order. The wet irrigation practice shows higher net returns for each crop with the medium and dry following in that order. Wet and medium irrigation practice alfalfa hay displaces dry practice sugar beets on the medium productivity soils, with the dry alfalfa hay following. The above ranking of crops does not indicate profit levels, because expenses do not include fixed costs. Profit arises only when net returns to a crop are high enough to exceed fixed costs per acre, $128 and $100, respectively for the two soil grades. Tomatoes did yield high enough net returns under all three irrigation practices to show profits and to offset lower returns on some of the other crops. Sugar beets, alfalfa hay, dry beans, saf flower, barley, and wheat all had positive, but not large enough, net returns to cover fixed costs, hence earned no profit. A series of linear programming analyses within the framework of 22 constraints evaluated the potential effects of water quantities, water prices, and tomato prices on total farm net returns. The constraints included total and Intraseasonal total water quantities, total tillable land, and acreages for individual crops. -xi- Yolo County fanners operating 640-acre units on both high and medium productivity soils can expect net returns to drop sharply as irrigation water variable expenses rise from zero up to a range of $10.00 to $15.00 per acre-foot. Profitable quantities to use do not drop as sharply as the drop in net returns. They do drop sharply, however, as water variable costs continue a rise to $20.00 or more per acre-foot. This latter price level is the practical limit on irriga- tion water variable expenses if fanners hope to obtain total farm net returns over all variable expenses. Fanners, to avoid losses at 1966-1968 conditions, should cut water use from 40 to 50 percent as water variable costs rise from $10.00 to about $20.00 per acre-foot. Water quantities available at a constant price of $2.53 per acre-foot for variable expenses also relate closely to net returns. Both farm units would have minimal net returns using zero quantities of irrigation water. Net returns then rise sharply as farmers in- crease irrigation water use up to totals of 1,676 acre- feet and 1,172 acre-feet, respectively, for the high and medium productivity soils. Total farm net returns at these maximum water usages were $82,528 for the high and $54,689 for medium productivity soil units. The added total farm net returns per acre-foot varied from $176 per acre-foot for the first increment to $8.20 on the final increments with total use at 1,676 acre-feet on high productivity soil. Changes for the medium productivity soil ranged from $91.00 per acre-foot for the first addition to $16.60 for the last when quantity used was 1,172 acre-feet. Farmers wuld find it profitable to shift from the unirrigated grain crops to tomatoes, sugar beets, alfalfa hay, and beans as -xii- available water quantities Increase. The unit on high productivity soils could maximize its net returns at the 1,676 acre-foot level for available irrigation water by including 150 acres each of tomatoes and sugar beets, 47 acres of alfalfa hay, 65 acres of dry edible beans, 30 acres of safflower, and 150 acres of wheat in the cropping system. The medium productivity soil units should include 150 acres of toma- toes, 140 irrigated under the wet practice, 127 acres of sugar beets irrigated under the dry practice, and 323 acres divided between wheat, safflower, and barley with the first two crops accounting for 270 acres of this total. Optimum crop choices and land allocations would vary for both analysis units as available water quantities increase from zero to the maximum profitable levels. An additional linear program analysis determined the impact on crop choices and land allocations for these two analysis units as water costs rise from zero price to over $20.00 per acre-foot. Tomatoes would be the only profitable irrigated crop for both analysis units at the final maximum price; the unirrigated crops, barley, safflower, and wheat should occupy all other crop land. Farmers should eliminate other irrigated crops with lower earning capabilities than tomatoes from the cropping system as prices rise. These analyses also indicate that farmers on the better soils can profit by expanding water use faster as prices drop, as compared vrith operators on the medium soil. Differences in yields between the soils in these two units, plus the fact that operators of farms on both soil . grades must pay the same prices for inputs, explain this greater water use response on the higher grade soil units. -xiii- We summarized these various analyses, finally, in an overall farm earnings analyses. The medium productivity unit has net returns of $54,689 and fixed costs of $60,117; it lacks $5,428 of meeting all fixed costs. The $19,076 PROFIT for this unit under 1966-1968 con- ditions lacks nearly $10,600 of covering interest at the assumed 6.0 percent market rate, with no allowance for MANAGElffiNT INCOME. This PROFIT figure represents only 3.86 on farm investments, with no income for management, as compared with the market rate. The high productivity soil unit yields more favorable returns; PROFIT at $43,532, exceeds the market rate Interest on Investments by $310. This $310 represents a token MANAGEMENT INCOME. We con^jared the same two models with tomato yields increased from 19.6 to 23.5 tons per acre for the medium and from 22.6 to 27.1 tons for the high productivity unit. These yield increases with prices and costs remaining the same, result in a PROFIT of $36,626, MANAGEMENT INCOME of $6,972, and RATE EARNED 7.41 percent to the medium produc- tivity analysis unit. A similar comparison for the high produc- tivity soil shows PROFIT of $62,432, MANAGEMENT INCOME $19,210, and RATE EARNED at 8.7 percent. A third earnings analysis evaluates the impact of price change upon farm earnings. It uses average 1972-1973 prices for both farm products and production inputs, but makes no changes in investments or fixed costs. The result is $43,064 PROFIT, $13,400 MANAGEMENT INCOME, and an 11 percent RATE EARNED on total farm capital investment for the medium productivity unit. Conqiarable figures for the high productivity unit are $77,461 PROFIT, $34,039 MANAGEMENT INCOME, and 10.8 percent RATE EARNED. Both absolute and relative prices certainly affect farm earnings importantly. -xiv- An additional budget analysis drops the 25 percent (150 acre) constraint on canning tomato acreage and exainines the results if prices are allowed to range from zero to the study period level of $30.00 per ton. Tomatoes would come into the cropping system at a price of $20.50 per ton, reach the 150 acre restraint limit at $21.00 per ton, and 349 acres at $22.50 per ton. Such a combination of prices and production for tomatoes would result in a $62,000 figure for total farm net returns on the high productivity soils. It is sharply below the $82,000 figure for 150 acres of tomatoes and a contract price of $30.00 per ton. These results dramatize two im- portant facts: a) tomatoes are the strongest competitor among Yolo County crops studied and b) tomato prices exert a dominant influence on profits to both this crop and the total farm operations. WATER SUPPLIES AND COSTS IN RELATION TO FARM RESOURCE DECISIONS AND PROFITS ON SACRAME!^TO VALLEY FARMS 2. Enterprise Choices, Resource Allocations, and Earnings on 640-Acre General-Crop Farms in the Southern Sacramento Valley Trimble R. Hedges* THIS STUDY AJJALYZES THE EFFECTS OF WATER QUANTITIES AND COSTS ON RESOURCE USE AND PROFITS The Analysis Centers Upon Criteria for Profitable Water Use Decisions This study has as its primary overall goal to establish economic guidelines that will aid farmers in making management decisions to maximize profits. They need such guidelines for crop choices, land, and other resource allocations. They also need criteria to decide related production, technology, and operating method issues, particu- larly in reference to changing prices and price relationships. The first report on this investigation of irrigation economics on Sacramento Valley farms examined how water quantity and price rela- tionships affect optimum resource allocations and earnings on rice farms in the Central Valley.—^ This part of the study centers on the lower (southern) valley. Physical characteristics and economic capabilities of soil resources, and related farm organization and *Trimble R. Hedges is Professor of Agricultural Economics and Agricultural Economist Emeritus in the Experiment Station and on the Giannini Foundation. \j Hedges, Trimble R., Water Supplies and Costs in Relation to Farm Resource Use Decisions and Profits on Sacramento Valley Fatros, 1. Enterprise Choices, Resource Allocations, and Earnings on 1,280- Acre Rice Farms in the Central Sacramento Valley , California Agri- cultural Experiment Station, Giannini Foundation Research Report No. 320, Davis: March 1974. -2- operational patterns, vary widely in the lower valley. The relation- ships and results for the Central Sacramento Valley have little appli- cation, therefore, to this general-crop area. The analysis in this, as in the prior report, focuses on three specific objectives that underlie the broad overall goals of the investigations : 1. To determine the quantities of irrigation water available, seasonally and in total, and the fixed, variable, and total costs for this water. 2. To analyze (a) how variations in water quantities and costs affect changes in relative profitability of alternative crops and land use within the relevant technology and price structure and (b) how these changes, in turn, relate to opti- mum resource allocation and affect profits earned. 3. To establish criteria and analytical guides for farmers to use in allocating resources and in making adjustments to such allocations within a context of varying water quantities and prices.—^ The analytical approach in this Southern Sacramento Valley phase of the Irrigation Economics Investigation focuses on irrigation water quantities and costs. These are dominant concerns for farm operators in an area where irrigation is essential to produce summer crops. Precipitation in this valley falls far short of that required to produce any nonirrigated summer crop profitably. Our analysis under- takes, first, to establish and measure how variations in water quantities and irrigation practices affect physical output and net dollar returns under specified cost and price conditions. It considers the principal crops adapted to the area; processing tomatoes, sugar beets, alfalfa hay, and a range of feed grains and specialty crops. The analysis 1/ See also Giannini Foundation Research Reports Numbers 261, 263, and 286 in the Economics of On-Farm Irrigation Water Availability and Costs, and Related Farm Adjustments series. -3- also examines, secondly, the impact that varying water prices (costs) exert on such net returns. Other phenomena and problems also are important in the total physical- institutional-economic context within which a general-crop farmer in the Southern Sacramento Valley makes his decisions. The investigational approach is to specify additional profit-governing influences of major importance within a time-related context. These include: (a) prices for inputs other than water, (b) price relations among the principal products, such as tomatoes and sugar beets, (c) alternate levels for tomatoes, and (d) the more important production and market regulating forces. These latter include allotments under the Federal Agricultural Stabilization and Conservation Act, institutional, and other informal marketing constraints. Relative profitability is the criterion that identifies crop choices and optimum resource use in this analysis. The conventional farm management definition of profit applied in this study refers to Capital and Management Income or the net return to the farm firm above all fixed and variables costs, including an allowance for unpaid family labor.— ^ The initial procedure in this study, however, considers only the variable expenses in relation to (cash) receipts and resultant net returns in all preliminary analysis of alternative resource uses. It then evaluates total farm returns and profits in a special farm earnings analysis based on optimum crop choices and resource allocations. 1/ See footnote lU pages 17 and 18, for definitions of terms. -4- The relationships and guidelines established in this study should aid faraers to make their plans and manage their farm operations. Other agencies, firms, and individuals in agricultural production or marketing, or concerned with the interrelations between agriculture and other sectors of the total econoTsy, should find the results of this analysis useful. Yolo County in the Southern Sacramento Valley provides the locale for this investigation. The basic data on prices for farm inputs and farm products reflect price levels during the period 1966-1968. A section of the analysis, however, applies later price levels in order to evaluate possible changes under certain assumptions. The analysis focuses upon two sjmthesized 640-acre General Crop farms. One has "high productivity" (Storie Index Grade I and II) soil; the other is on "medium productivity" (Storie Index Grade III and IV) soils. Data that farmer interviewees supplied in surveys during the 1960's and the latter 1950*s provided the basic framework to specify and define organizational and operational characteristics for these two 640-acre general-crop farm models. Other data come from a range of official and private sources. Important among these are Yolo County officials, the Yolo County Agricultural Stabilization and Conservation Offices, farm suppliers, and service agencies, California Crop and Livestock Reporting Service-United States Department of Agriculture Statistical Services and both Experiment Station and Agricultural Extension personnel on the University of California staff. This investigation uses a synthetic approach based on modal concentration of data for relevant farm characteristics. It does not represent an analysis of a specific sample of farming units. -5-* Budget analysis and linear programming are the primary analytical tools used in this investigation. Detailed budgets for one acre of each crop tested provide comparative data for gross receipts, variable expenses, and net returns. A linear programming analysis then analyzed and identified optimum profit levels for various combinations of these crops and resource allocations under varying water quantities and prices. Such analytical results for each soil type — farm provide representative guides for resource use decisions for specified input and output price, technological, and resource conditions. The findings of this study reflect the prices, production tech- nology and practices, and general conditions that prevailed during 1966-1968. This is the time period to which most data used in the basic analysis apply. We do, however, make appropriate adjustments to crop acreages, yields, and prices for inputs and farm products to change the context and apply the general relationships established in this investigation according to varying assumptions. We choose a 640-acre farm unit as the appropriate size for our analysis models in this investigation because we believe that this size represents one economic unit for the crops, technology, and practices appropriate in the Southern Sacramento Valley. This is not to say that farm opera- tors in command of adequate resources will not find it advantageous to operate larger units. Earlier investigations in this and other parts of California do indicate, however, that much of the cost savings per unit of product are to be obtained by increasing the size of operation up to the 640-acre farm unit.— ^ 1/ See, for example, the following Giannini Foundation Research (Footnote 1/ continued on page 6.) ' -6- We followed five specific steps in using budget analysis to establish the framework of gross receipts, operating costs, and earnings for the different crops. Linear programming then was the method used to explore the impacts of a wide range of variations in water quantities, water costs, and prices for inputs other than water, and for various farm products. These five steps were as follows: 1. Construct by budgeting methods farm models that typify modal characteristics for two 640-acre farm-size organization models under specified conditions. These models, once we have specified and defined their characteristics, provide the framework that we use to identify and measure how varia- tions in water quantity and cost conditions, as well as in other cost- and receipt-affecting farm phenomena, affect total farm performance and profits. 2. Construct complete cost and receipts budgets for all pro- duction materials and services and associated yields; de- termine total revenues, aggregate variable expenses and net returns-over-variable expenses for each alternative crop; relate these basic facts to relevant resource, eco- nomic, and institutional conditions for each of the two farm models. 3. Use linear progranming to measure the impacts of variations in irrigation water quantities and costs on enterprise net returns and on total farm profits. 4. Establish, by linear programming, the relationships between irrigation water quantities and costs and seasonal avail- ability characteristics to critical resource use and earn- ings feature of the total farm business on the other. Ex- amine how varying supply and price conditions for other (Footnote 1/ continued from page 5.) Reports: Sitton, Gordon R. , Number 207, Sacramento Valley Rice Farms . 1. Organization Costs and Returns . 1958; Dean, Gerald W. and H. 0. Carter, Number 238, Cost-Size Relationships for Cash Crop Farms in Yolo County, California , 1960; Carter, Harold 0. and G. W. Dean, Number 253, Cost-Size Relationships for Cash Crop Farms in Imperial Valley, California . 1962; Paris, J. Edwin and D. L. Armstrong, Number 269, Economies Associated with Size, Kern County Cash Crop Farms . 1963; Moore, Charles V., Number 285, Economies Associated with Size, Fresno County Cotton Farms . 1965; Johnston, Warren E., Number 314, Economies of Size and Imputed Values of Farmland in the Imperial Valley of California . 1971 -7- critical resources and for important farm products affect resource use and enterprise earnings and the total farm operation. 5. Explore the opportunities to adjust the farm organization to variations in water quantities and costs and to changes in other major institutional and economic forces affecting farm organization and earnings. Earlier Research Provided Information A number of analyses dealing specifically with problems of plant-soil-water relationships were particularly useful in this study. We relied upon the work of Booher, Hagan, Pruitt, Viehmeyer and Hendrickson, and Wadleigh for standards and guidelines in establishing the water quantity relationships in producing irrigated farm crops.— ^ We drew heavily on production technology bulletins and circulars for individual crops in determining the technology, practices, and input requirements for producing these several crops. We also assem- bled information regarding both published and unpublished research by Experiment Station and Agricultural Extension personnel on economic problems and relationships in the southern portion of the Sacramento Valley. FARIIERS MUST CONSIDER PHYSICAL, INSTITUTIONAL AND ECONOMIC FACTORS IN MANAGING IRRIGATED FARMS Irrigation Water is an Increasingly Scarce Resource Both quantities and costs are important in irrigation water decisions. Farmers in Yolo County obtain most of their irrigation water from wells. The Sacramento River and Clear Lake also provide ll See pages 32 through 34 . -8- water for irrigation purposes. Thus the Sacramento River furnishes virtually all irrigation water required for a limited number of farmers with established rights to this source. Certain farmers depend almost entirely upon Clear Lake water. This dependence works to the disadvantage of some during seasons when water supplies from this source are inadequate to meet all needs. V7ells, originally at pumping lifts of less than 100 feet, meet irrigation water requirements for the majority of Yolo County farms. But pumping lifts have tended to increase, with accompanying rises in pumping costs, as well numbers have expanded. The result is that lifts of 100 feet or more, and total (including both fixed and varia- ble) costs of $7.50 per acre-foot, or higher, were typical in the Yolo County general crop area at the end of the 1960 's. Inadequate supplies for some growers, coupled with a trend towards steadily increasing pumping lifts and costs, have accentuated the need for careful water management. Further increases in well numbers will tend to aggravate problems arising from reduced quantities and higher costs. Continuing increases in costs per acre-foot for irrigation water certainly are in the future for Yolo County farmers if the present trend toward greater pumping depths persists. Various United States Bureau of Reclamation water projects promise, ultimately, to provide new water supplies to help alleviate the problems arising from present limited supplies of irrigation water in Yolo County. Among these, the West Sacramento Valley and the Yolo-Zamora canals are of prime importance. Water districts such as Yolo County Flood Control and Water Conservation District, will provide and operate facilities to deliver this new water to farm -9- headgates. These projected sources of surface water to supplement that from irrigation wells, when completed, should greatly ease the problems of limited water in the Southern Sacramento Valley even though these added quantities may come at higher than existing prices. This hopeful outlook for increased quantities, however, still remains uncertain; no definite time schedule exists in mid-1974 for completing these Bureau of Reclamation facilities. Yolo County farmers also must recognize the fact that, even when added surface water becomes availa- ble, they must apply effective water management practices and make sound decisions in allocating water and other resources among alter- native crops. Yolo County Soils Favor ^^any Crops Most soils in the eastern two-thirds of Yolo County rank high or medium in productivity. The County's soil resources range from alluvial basins bordering the Sacramento River to hilly-to-mountainous area of residual soils lying west of an irregular line connecting the boundary of Sections 4 and 5 of Township 12 North, Range 2 West on the northern boundary of the county, and Sections 25 and 26, Township 8 North, Range 2 West at the southern boundary. Recent to old alluvial soils of high or medium productivity occupy most of the area east of this line and extend almost to the Sacramento River [2]. The Yolo-Brentwood and Marvin-Rincon- Tehama Associations, predominently of Grades I and II ranking according to the Storie Index, account for most of the high productivity soils in this central and eastern area (see Figure 1) . -11- The medium productivity (Grades III and IV) soils include basin soils, primarily of the Sycamore-Laugenour and Capay-Sacramento Associations, and older terrace soils lying north of Woodland, west of Interstate 5, which route their western boundary roughly parallels, and smaller scattered bodies. The basin soils extend in a narrow strip bordering the Sacramento River from Yolo County's northern border with Colusa to its southern boundary with Solano County ex- tending almost to the northern boundary of the county plus smaller scattered areas. The terrace includes a number of associations: Sehorn, Balcom-Sehorn, Corning-Hillgate, and Hillgate-San Ysidro Associations account for most of the area [2], Both the high and the medium productivity soils in Yolo County will produce grain, grain hay, and other winter crops not requiring summer irrigation. These two productivity classes, under irrigation and effective technology and management, also will produce a wide range of close-grown and row crops: tomatoes, sugar beets, alfalfa, dry beans, grain, sorghum, and others. Much of the land in the high productivity soil category also is capable of producing climatically- adapted tree crops such as the stone fruits, almonds, and walnuts. Yields vary markedly for all these crops, depending upon specific soil characteristics as well as climatic and other variations. Rice is profitable on some of the basin and other more heavily-textured soils, provided adequate quantities of irrigation water are available at acceptable prices. Growing Seasons Are Long But Dry Yolo County's 242-day growing season, combined with a temperature pattern that is moderate during the winter months and not excessively -12- hot during the sununer, permits a raaximiara range of crop adaptability (see Figure 2) . The extremely dry summers (total precipitation for the months of May through September amounts normally to barely one inch), however, operate to limit crop production and/or profitable yields based only on precipitation. Farmers in Yolo, as in most other California counties, compensate for this seasonal water shortage by irrigating to meet most of the moisture requirements for summer crops. Laws, Regulations, and Government Programs Affect Farm Operations Federal policies and programs under Agricultural Stabilization and Conservation legislation had directly affected sugar beet acreage, price levels, and labor management practices for an extended period prior to this study. Farmers who produce grains such as barley, rice, and wheat also had found it mandatory to consider relevant government programs when making their decisions. More generally, federal statutes and policies governing irrigation water development projects, such as the West Sacramento Valley and Yolo-Zamora Canals, will affect all agricultural crops and land uses. Farmers should anticipate that they will have to accommodate their plans to Federal policies and regulations to the extent that the Federal Government finances and controls future Yolo County water development. Farmers Faced Unfavorable Price Relationships During the Late 1960 *s Yolo County farm operators, in common with most other farmers, experienced serious price disadvantages during the late 1960 's. These -13- JL/ Annual, 16.43 Inches Sources: (1) U.S. Department of Commerce, Cllmatologlcal Data for California. (2) Climatic Summary of the United States, Supplement, Years 1931- 1960. -14- disadvantages reflected the fact that farm product prices declined or moved sideways during most of these years while prices that farmers had to pay rose at a rapid rate. Thus the index of farm prices on a 1910-1914 base stood, in 1950, at the favorable ratio of 108 percent of the level for the comparable index of prices paid by farmers (for production goods, family living, farm wages, taxes, and interest — the "parity index") during that year. The divergent trends between prices received and the parity index brought the ratio between the two price series—the "Parity Ratio" — to a level of 79 during 1970 [29]. The farm price index actually reached its peak (302 percent of the 1910-1914 base) during 1951, and stood at 285 percent of the base period during 1970. Meanwhile, the prices that farmers paid moved from 251 in 1950 to a high of 387 in 1970 [29], Wages, taxes, and interest rates rose much more sharply during the 1960's than the prices that farmers had to pay for production goods and family living. Yolo County farmers, as did others, strove during these years to adjust to unfavorable prices by cutting costs; they substituted cheaper mechanical power and new technology for human energy and labor. They expanded capital investments and, consequently, fixed costs and operating expenses on power and machinery units in their effort to reduce labor requirements and cut total operating expenses. They tried particularly to cut costs per unit of product. Yolo growers achieved spectacular success during the 1960's, for example, in cutting labor use and wage outlays for producing processing toma- toes; they did so largely by mechanizing the harvest. Materials such as fertilizers and pesticides, and a wide range of services, are readily available under competitive price conditions in -15- thls general crop area. Loan capital also Is available In adequate amounts but, as indicated above, the cost of this capital increased sharply during the latter 1960's. Yolo County fanners have access to effective marketing institu- tions and channels to move farm products into processing and distri- bution channels. Contracts are available to sugar beet and tomato growers from a local sugar beet processing factory and from tomato processors operating both within and outside Yolo County. Rice dryers, warehouses, packing sheds, and other facilities for handling and marketing grains, oil seeds, seed crops, melons, and other pro- ducts, also are available in or near Yolo County. The major earnings problem that Yolo County farmers had to cope with during the 1960 's, as indicated above, reflected the unfavorable relationships between farm prices and the prices of goods and services that farmers buy. Their problem was to produce at low enough costs per unit so that sales prices received would defray such costs and leave an earnings margin for growers. THE 640-ACRE GENERAL CROP FARJI REFLECTS TYPICAL FARM ORGANIZATION AND OPERATIONS Farmers, Government Agencies, Research and Extension Personnel, and Business Firms Provided Data This study- applied a synthetic approach to establish structural and operational characteristics for two 640-acre farm models. It drew heavily in this approach on information obtained from interviews with Yolo County farmers during earlier investigations. Surveys during the first half of the 1960 's and the latter part of the 1950 's were particularly useful sources of information on farm organization and -16- operatlons. Carter's and Dean's published reports were valuable for this purpose. We also benefited from having access to their inter- view materials for unpublished research still in progress. Still other reports by these same two analysts also were helpful, even though these deal with California areas outside the Sacramento Valley. Carter and Dean had published two major analyses based on Yolo County prior to 1965. One examined relationships between farm size in acres and costs per dollar of revenue [7], [9] and the other dealt with the interrelationships among enterprises and how they affect optimum farming systems in Yolo County [10], A more recent field interview survey in the late 1960's, directed by these same two researchers, assembled more up-to-date information (including changes subsequent to the earlier studies) with respect to farm organization, operation, and other factors affecting farm earnings. This latter study has particular significance for the present analysis because it provided essential information concerning the characteristics of wells, pipelines, and other facilities used in farm irrigation, including performance rates for the wells. This study also was useful to update earlier farm structure and performance data. A series of survey reports by Parsons and others on physical and dollar costs inputs for Yolo County crops enterprises yielded useful information concerning farm technology, practices, and production costs [36], More specialized information focusing on requirements, performance, and costs in mechanized tomato harvesting also were available as a result of a series of studies by Parsons and Zobel on this new harvesting technology [37]. Many agencies at the Federal, State, and County level contributed to this study published -17- and unpublished studies, Tliis analysis also depended to a great extent on such sources for prices of both farm products and those goods and services that farmers must purchase. Farm machinery dealers and farm product processors, and/or handlers, furnished much informa- tion concerning technical requirements of equipment and the conditions, procedures, and costs in handling both farm supplies purchased and farm products sold in Yolo County. The Synthesized 640-Acre Analysis Models Reflect Yolo County Farms— ^ Variations in drainage characteristics underlie the major differ- ences in land capability between the high and the medium productivity 640-acre farm models in this analysis [1]. Soils in both of these categories are alluvial in origin (some new, some older) , relatively deep, and medium to moderately fine in texture except that some yj 1-lajor terms relating to farm models appearing in this report, and their definitions, are as follows: Cropping System - detailed cropping organization for a Farm Model . Farm Model - synthesized Farming System , based on typical farm characteristics data for a particular geographic subarea. Farming System - Detailed organization, methods of operation, and practices used on a Farm Model (see Appendix Tables A-5, A-6, and A-7). Subarea - a segment of a major geographic area, such as the Sacramento Valley, selected for study. Irrigation Practice - technique or method used in irrigation, identified in this study by the depletion level for available soil moisture prior to irrigation. (Footnote \J continued on page 18.) -18- moderately coarse-textured soils occur in the Storie Index Grade IV category. The 640-acre total for each of the two models in this analysis include 600 acres of cropland plus 40 acres in farmsteads and operating centers, roads, and drainage ditches. All cropland in these two models is irrigable, but annual cropping systems include 480 acres, or 80 percent of the total, in irrigated crops with the remain- ing 120 acres in nonirrigated or dry-farmed crops. The following section will include further and more precise information on the range of crops and the ranges and limitations in acreage for each. (Footnote \J continued from page 17.) Variable Expenses (Costs) - sum of annual cash operating expenses, plus unpaid family (operator's) labor (see Appendix Tables A-6, A-7) . This item may appear as Variable Expenses (Costs) per Acre for a single crop or as Farm Variable Expenses (Costs) representing the total for an entire farm. Fixed Costs - sum of annual cash and noncash costs for using capital items and for general "overhead" costs not readily allocated to specific enterprises (see Table 2) . Gross Receipts - sum of annual receipts from sales of farm crops. Net Returns-Over-Variables Expenses (Costs) - Gross Receipts minus Variable Expenses (Costs) (see Appendix Tables A-6 and A-7) . This item may appear as Net Returns-Over-Variable Expenses (Costs) for a single crop acre, or as Farm Net Returns-Over-Variable Expenses (Costs) representing the total for an entire farm. Net Farm Income - Net Cash Income plus (or minus) inventory changes on noncapital items and minus noncash fixed costs (not in- cluding interest on investment). Any unpaid labor contributed by the farm operator is not included in the farm expenses. Profit (Capital and Management Income) - Net Farm Income minus the value of any unpaid labor (including operator's). Management Income - Profit , less six percent on the total farm capital. The residual (and it may well be negative) is payment for the operator's managerial ability and services. Rate Earned - Profit (Capital and Management Income) expressed as a percentage of farm capital investments. -19- Five wells provide the entire amount of irrigation water a val la- ble to each of the two analysis models. Both the high and the medium productivity analysis models include underground concrete pipelines, as well as siphons, in their irrigation set ups; the high productivity unit has 3,000 feet and the medium productivity farm 1,500 feet of 16-inch diameter pipe. Each unit includes 1,000 irrigation siphons ranging from 1.5 to 6- inches in diameter. The five wells vary In pumping capacity measured near their maximum water levels and minimum pumping lifts at the beginning or early in the irrigation season. This variation is from 1,050 to 1,350 gallons per minute. They have a total output capacity during this maximum performance period of 5,800 gallons per minute. Maximum total water output is 2.8-acre inches per hour or about 307-acre inches (25.6 acre- feet) in a 24-hour period — enough water to irrigate about 50 acres once at an application rate of 6 inches. Such a combination of wells and water production capacity reflects the typical irrigation facilities and quantities available on farms with soils of the qualities included in this study according to field survey data (Carter's and Dean's survey in 1966). These quantities, if available throughout the season, would be adequate to irrigate the 480 acres of irrigated crops included in the 640-acre models at the 6-inch application rates three times during the month or with a total of 18 acre-inches of water per acre per month. An operator who irrigates on a 16-hour daily schedule, could make a 6-inch application every 15 days, or 12 acre- inches per month, at this maximum water output. This study uses this 16-hour irrigation practice and 15-day totals to specify quantities of irrigation water for the 640-acre farms analyzed. -20- But wells do not maintain throughout the irrigation season their output levels as cited above. These outputs are near their peak capacity. Instead, their output in gallons per minute begins to decline when groundwater levels drop and pumping lifts increase due to heavy water withdrawals at the time that major summer irrigation schedules get underway. Such declines continue until this heavy water use ceases, usually in the latter half of August. We used an average available water quantity of 250 acre-feet for each 15-day period from April through to mid-September, or a total of 2,750 acre-feet for the season. The estimated amounts available during each 15-day irrigation period vary from 279 acre-feet for each of the first four periods to 212 acre-feet during the first half of August. The 279 acre-feet represent 111 percent of the 250 acre-feet seasonal average for the eleven 15 periods. The 212 acre-feet for the first 15 days in August represents 85 percent. It is these reduced quantities available during certain irrigation periods, rather than inadequate quantities for the season as a whole, that set primary limits on farmers choices in selecting crops and allocating resources to them. Variable costs for these modal farm analysis units approximate $2.50 to $3.00 per acre-foot. Fixed costs add $4.50 to $6.00 per acre-foot so that total water costs range from $7.00 to $9.00 per acre-foot. Water quantities and potential shortages still demand attention in Yolo County, however, in spite of the relatively favor- able water situation, at least for seasonal totals, reflected by these data. First, these data indicate shortages during certain periods during the season. Second, many Yolo farmers do not have -21- quantitles available equal to these and do face higher costs for irrigation water. Third, falling water tables and increasing pumping lifts during recent years indicate that many Yolo County farmers who now have ample quantities of irrigation water at reasonable prices will face less favorable conditions in the future, unless additional surface water becomes available from new sources. Well production inevitably will decline as water tables continue to lower from year- to-year, while pumping costs per acre-foot will increase. Projected Bureau of Reclamation surface water irrigation projects ultimately should relieve such potential water shortages. Farmers have no assurance, however, that they will be able in the future to obtain water from a combination of pumping and surface sources at costs comparable to those of the present. They face great uncertainty regarding the time when new surface water will arrive at farm headgates. Equipment inventories include high capacity machinery. A tomato harvester and the field power units dominate in the machinery and equipment inventory for each of the 640-acre units (see Table 1). One tomato harvester has ample unit capacity for processing tomato plant- ings up to 150 acres or more. Additional harvesting capacity by custom harvesting service also is readily available in Yolo County. The full line of equipment includes a landplane, seedbed equipment, row crops, machinery and transport equipment ranging from a grease wagon for servicing field machinery to a two-ton truck. These in- ventories do not include grain, hay, or beet harvesting equipment; such machinery is available on a custom or contract basis. Total farm labor requirements for 640-acre units such as these two models include essentially all of the operators time, plus full -22- TABLE 1 Farm Real Estate, Operating Equipment Inventories, and Investments, 6^0-Acre Farm — High and Medium Productivity Soils, 1966-1968 Prices Useful Numner life Inl t 1 al Salvage Average iocax ue Size or capacity on farm on farm cost value value preclation S2 1 <} z J 5 6 dolla rs dollars Rflw Isnd Hij^h proXuU/acre 600 60,000 60,000 Total $9A4/acre 640 604,000 604,000 MedluiD producclvlcy acre 640 320,000 — 320,000 Leveling $100/acre 600 60,000 — 60,000 Total AAA 380,000 Structures Shop* and storage 4,000 sq. ft. 1 30 10,400 1,000 5,700 9,400 Machinery shed 2,400 sq. ft. 1 20 3,200 500 1,850 2.700 Shop equipment N.A. N.A. 10 2,500 0 1,250 2,500 Gasoline storage 2,000 gal. 1 10 500 50 275 450 Diesel oil storage 2,000 gal. 1 10 375 38 206.5 337 oUD tocax 16 ,975 1 , JOO 9,281.5 15,387 Irrigation Equipment Wells I 400 ft. X 14 In. 3 20 16,800 0 8,400 16.800 II 400 ft. X 16 in. 2 20 12,800 • 0 6,400 12.800 Pump plus motor I 40 hp. 1 20 5,030 503 2,767 4.527 TT ■ 50 hp. 3 20 18,300 1,830 10,065 16,470 III ou np . 1 20 6,825 682 3,753 6,143 59,755 3,015 31,385 Concrete pipes High productivity 3,000 ft. X 16 in. — 40 8,500 g 4.250 8,500 Medium productivity 1,500 ft. X 16 In. — 40 4,250 0 2,125 4,250 Siphons (aluminum) 1.5 in. - 6 in. 1,000 5 5,000 Q 2,500 5,000 Subtotals High productivity 73,255 38,135 70,240 Medium productivity 69,005 3,015 36,010 65,990 Mac nine rv T^n/fn 1 o r> ^ lU It • X 4U t C • 1 15 4 ,860 490 2.675 4,370 Ditcher 4 f t . 1 10 1,510 0 760 1,510 iU 1 1 . 1 10 1,770 0 885 1,770 o f t . 1 10 760 0 380 760 Plow 4 It. X It) in. 1 10 1,110 0 S5S 1,100 Disc 1 1 c» 1 10 1,900 0 950 1,900 Splice harrow 21 ft . 1 20 700 0 3S0 700 'Liifj L w Lii na t tow It • 1 10 2,920 0 1 ,460 2,920 Roller ZX It • 1 20 2,380 0 1.190 2,380 12 ft . 1 10 160 Q SO 160 Liste 4 -row 1 10 920 A U «60 920 Fertilizer spreader 12 ft. 1 5 490 0 245 490 O L U au a o L U L 1 ^rtrt IK- 1,jUU XDS . 1 10 320 0 160 320 Sled planter 6 ft. X 30 in. 1 10 3,930 0 1.965 3,930 Rolling cultivator 6-row 1 10 1,460 0 730 1,460 Thinner 6-row 1 10 760 0 380 760 Tine harrow 35 ft. 1 10 1,190 0 590 1,190 Fertilizer attachment 4 -row 1 10 1,060 0 530 1,060 Sled cultivator 6 ft. X 30 in. 1 10 2,250 0 1.125 2.250 Weed sprayer » p.t.o. 35 f t . 1 10 1,460 0 730 1.460 1 vUlo llaLVcaLCrL 1 5 24 ,850 2,500 13.675 22,350 Fork lift 4,000 lbs. 1 8 6,500 650 3.575 5,850 Tomato harvest cleaning 1 8 1,620 300 960 1,320 Subtotal 64,880 3,940 34,410 60,940 Power D-60 60 hp. (752) 1 10 26,075 3,910 15,000 22,165 31 hp. (507) 2 10 17,800 3,560 10,680 14,240 Subtotal 43,875 7,670 25.680 36,405 Transport Truck 2 ton 1 5 4,495 < 906 2.700 3,600 Pickup ,75 ton 2 6 6,970 1,400 4.180 5,570 Trailer 2 ton 1 10 1,510 0 755 1,510 Low bed trailer 20 ft. 1 10 1,510 0 755 1,500 Grease wagon 250 gal. 1 IC 1,100 0 550 1,100 Subtotal 15,565 8,940 13.280 TOTALS High productivity 818,550 18,539 720,446.5 196,252 Medium productivity 590,300 18,539 494,321.5 192,002 -23- tlme by three employees, during at least eight months of the year. The operator also must bring in additional part-time hourly-wage workers at certain critical seasons, such as during harvest. He normally would contract labor needed for major harvesting operations such as tomatoes, in addition to occasional part-time workers. YOLO FARMS HAVE HIGH INVESTMENTS AND FIXED COSTS Land Dominates Farm Inventories and Investments Total farm property inventories, at average investment values for the latter 1960's, were $720,446 for the high and $494,322 for the medium productivity model in this study (see Table 1) . Land values account for the bulk of these investments for each of the farm units, $604,000 (84 percent) for the high and $380,000 (77 percent) for the medium productivity farm. These estimated average land value figures remain the same as initial cost; land is a nondepreciable item in this analysis. Average total investments for other farm property, including structures, irrigation installations, machinery, power and transport equipment, are substantially lower than their initial costs. This is because these investment items do depreciate with age and use. Thus initial outlays for property other than land on the high productivity model totaled $214,550, while average values, at $116,300 are almost $100,000 lower; comparable data for the medium productivity unit are $210,300 and $114,320, respectively (see Table 1). The magnitude of these investments for 640-acre general crop farms in Yolo County at late 1960's price levels, underscores emphat- ically one of the serious management problems for the family farm -24- operator in this county; the fixed costs "burden" on the farm capital that he requires for efficient operation demand a large share of his total net returns-over-variable expenses. Costs of owning property dominate the fixed costs for both of the 640-acre models in this analysis.—^ The totals for farm fixed costs, hov/ever, do include "General Overhead" in the amounts of $3,138 for each of the two farming units (see Table 2), The major fixed costs arising from property ownership, including depreciation, interest on investment, taxes, and insurance, totaled $73,834 for the high pro- ductivity and $56,979 for the medium productivity units (see Table 2). Thus the resulting annual fixed costs total $76,972 for the high and $60,177 for the medium productivity farm or $128 and $100 per crop acre, respectively (see Table 2). Fixed costs for owning land ($45,000) account for over one-half of the total for the high productivity unit and just short of fifty percent ($28,000) for the medium productivity analysis model (see Table 2). This is despite the fact that land bears no charge for depreciation. These land fixed costs reflect the investment values ($944 per acre for the high, $594 per acre for the medium productivity unit) and accompanying high costs for interest and ad valorem taxes. These land values reflect assessment practices and data, as reported in detail by the Yolo County Assessor's Office. Total land values, as the assessment procedures establishes them, are the data that provided the basis for land values as this study reports them for 1966-1968 ( assessed values represent a predetermined fraction of these y See heading. Table 2, page 25. -25- Swury FXxsd CosO, 640-Acr* Pan* High «nd Hcdlia Producclvlcy Soil*, 1966-1960 frlcaa iMla or AaaeiaBeiit taCe or levy Noncash fixed cost-i Caah fixed coses Total •11 filed costs Interest on In- vestment Depre- clat Ion Total T.ix^s Insur- ance '>ther Total Assessnent Average va lue Orlpinal cost 0.014 a average value Aversae vs lue 6 percent V.irles Varies 1 2 3 4 5 6 7 8 dollars tand Land total High productivity 3(,240.O0 36.240.00 8,456.00 8,456.00 44,696.00 HadluM productivity 22,800.00 22.800.00 5,320.00 5,320,00 28,120.00 structures 342.00 313.35 655.35 79.80 42.7; 122.55 777.89 nacnincry anaa 111.00 135.00 246.00 25.90 13.88 39.78 285.78 C^ulpMdt ahop 75.00 250.00 325.00 17.50 9.38 26.88 351.88 Caaollna atorajie 16.30 45.00 61.50 3.85 2.06 3.91 67.41 PimI oil storage 12.40 33.70 46.09 2.89 1.54 — 4.43 50.52 Subtotal 556.90 777.05 1,333.95 129.94 69.61 — Mt.5S 1,533.48 Irrlitation EtTulpTTient Valla aoo It* s 14 tn. 304.00' 840.00 1.344.00 117.60 63.00 180.60 1,524.60 400 ft. X 16 in. 314.00 640.00 1.024.00 89.60 48.00 137.60 1,161.60 Pwpa plua Botors 163.99 226.35 392.34 38.73 20.75 59.48 451.82 90 hp. 603.90 823.90 1.427.40 140.91 73.49 216.40 1,643.80 M tip. 225.21 307.15 532.36 52.55 28.15 80.70 613.06 CoDcrata pip* Ugh productivity (3.000 ft. X 16 In.) 255.00 212.50 467.50 59.30 31.88 n.38 358.88 Hadlua productivity (1(300 tt. X Id In.) 127.50 106.25 233.75 29.75 15.94 — 49.69 279.44 Slphona (alun inua ) 150.00 1.000.00 1.150.00 35.00 18.75 — 53.75 1,203.75 Subtotal Bigh productivity 2,288.10 4.049.90 6.337.60 533.89 286.02 — JI9.91 7,157.51 Kadlua productivity 2,160.60 3,943.65 6.103.85 504.14 270.08 774.22 6,878.07 Kachinery Landplana 160.00 291.00 451.00 17.00 37.00 488.00 DlKchar 45.00 151.00 196.00 11.00 — — 11.00 207.00 Dosar blade 53.00 177.00 230.00 12.00 — — 12.00 242.00 Qilael plow 22.00 76.00 98.00 5.00 — — J. 00 103.00 riow 33.00 110.00 143.00 8.00 — — ■ (.00 151.00 Dlac 37.00 190.00 247.00 13.00 — 13.00 260.00 Splka harrow 21.00 35.00 56.00 Sprlngtooth harrow 87.00 292.00 379.00 20.00 20.00 399.00 ■oiler 71.00 119.00 190.00 16.00 16.00 206.00 Float 5.00 16.00 21.00 1.00 _ 1.00 22.00 Uotar 27.00 92.00 119.00 6.00 — — 6.00 125.00 Fartlllzer spreader 15.00 198.00 113.00 3.00 3.00 116 .00 Broadcaster 10.00 32.00 42.00 2.00 — — 1.00 44.00 Slad planter 118.00 393.00 511.00 28.00 — — 28.00 539.00 lolling cultivator 44.00 146.00 190.00 10.00 — 10.00 200.00 Thinner 23.00 76.00 99.00 5.00 — 3.00 104.00 Tina harrow 35.00 119.00 154.00 8.00 — 8.00 162.00 Fartlllter attachnent 32-.00 106.00 138.00 7.0O 7.00 145.00 Slad cultivator 66.00 225 .00 16.00 309 . 00 Veed aprayer, p.t.o. 44.00 146.00 190.00 10.00 10.00 200.00 Xcioiato harvester 820.00 4.470.00 5.290.00 191.00 102.56 293.56 5.583.56 rorkllfc 214.00 731.00 945.00 50.00 26.81 — 76.81 1.021.81 Touto harvest cleaning a^utpncnt 58.00 165.00 223.00 13.00 13.00 Subtotal 2,062.00 8.256.00 10.318.00 477.00 129.32 606.32 10.924.32 yowT 0-60 900.00 2.216.00 3.116.00 210.00 750.00 960.00 4.076.00 W-31 (2) 640.00 1.424.00 2,064.00 150.00 534.00 684.00 2.748.00 Subtotal 1.540.00 3.640.00 5,130.00 360.00 1.284.00 1.644.00 6,824.00 Transport 855.00 Trvck 162.00 600.00 762.00 31.00 55.00 93.00 riekup (2) 250.00 928.00 1,178.00 58. OO 45.00 103.00 1,281.00 Trallar 45.00 151.00 196.00 10.00 10.00 206.00 Low bed trallar 45.00 150.00 195.00 10.00 10.00 205.00 Craaaa wattoo 33.00 110.00 143.00 8.00 8.00 151.00 Subtotal 535.00 1.939.00 2,474.00 124.00 100.00 224.00 2,698.00 TOTAL PROPERTY High productivity 43,222.00 18.661.93 61,883.95 10,080.83 1,868.95 11,949.78 73,811.50 Madlua productivity 29.654.50 18.555.70 48,210.20 ' 6,915.08 1,853.01 8.768.09 56,978.50 General Overhcnd Electricity and other aervlcea 445.00 445.00 445.00 Deaand chargea t.493.50 1.493.50 1,491.50 Accounting 600.00 600.00 600.00 Duea, fcaa 300.00 300.00 300.00 Office 300.00 300.00 300.00 Total 3,138.50 3,138.50 3,138.30 TOTALS High productivity 43.222.00 18.661.9! 61 ,1181.9! 10.080.81 1,868.95 3,118.50 13, OAS. 28 76,972.00 Hadtu. productlvty 2'). 654. JO 18.555. 70 48,210.20 6,915.08 1.851. 01 1,138.50 11.906.59 40,117.00 -26- total values). These total values relate closely to current market values for farmland, according to soil quality, location, and other price-affecting factors. They do not, hov;ever, represent precise market values for farmland of the two land productivity levels that this study considers. This is because current assessment guides, once established, remain in effect for two or more years before being reexamined and revised. Some variations exist, therefore, between assessed and market values for any specific year, except immediately following a revision. We use these land values, and the fixed costs arising from them, in this analysis in two ways: (a) to compare final net earnings from the soils of two different productivity levels; (b) as a basis, though admittedly inexact, to evaluate how important fixed costs are as claimants upon farm net receipts-over-variable expenses. The high level of farm fixed costs ($128 and $100 per crop acre for the high and the medium productivity farms, respectively) put in sharp focus the problem that Yolo County farmers face in attempting to earn returns for their managements and profits on their investments. The net excess of gross receipts-over-variable expenses must exceed these per-acre fixed costs before the farmer receives any residual income to cover management earnings or profits. But biological and other conditions make it mandatory for farmers to include in their cropping plans certain crops that do not meet these minimum $128 or $100 per acre net receipts criteria. Farm operators must look to the acreage planted in the higher- returns crops, to provide such earnings for the farming operations as a whole. -27- Only for Tomatoes Did Net Returns Equal Fixed Costs We used budget analysis procedures to determine gross receipts, total expenses, and net returns-over-varlable expenses (henceforth "net returns") for the several crops that Yolo County farmers produce. We based our calculations on typical production technology and prac- tices, normal Input quantities and yields, and typical prices for both farm products and the inputs during the late 1960 's. The resulting values for net returns varied widely among these several crops for both the high and the medium productivity analysis units. All crops, however, showed positive net returns except corn on the medium productivity soil (see Figures 3 and 4). Only for tomatoes, however, was the level of these net returns adequate to equal, or exceed, the fixed cost or overhead (burden) per crop acre. The actual level of net returns for the 640-acre high productivity model varied from $311 for tomatoes at the (1) "wet" Irrigation practice to only $22 per acre for corn produced with the (3) "dry" irrigation practice (see Figure 3 and Appendix Table A-7). Thus each acre in tomatoes irrigated with the wet treatments shows a return of $183 above the combined cost for both variable expenses and fixed cost. This posi- tive return may offset deficits between net returns and fixed costs for other less-profitable crops. Any surplus for the farm as a whole remaining above such deficits for individual crops represents a return to the operator for assembling resources, making decisions, assuming risks, and other management services. This excess of total farm net returns is the amount against which the farmer must weigh total fixed costs in calculating profits or other farm earnings measures. The farm operator must, of course, consider his farm as a whole when -28- FIGURE 3: Net Returns Per Acre for Specified Crops, By Irrigation Treatments, High Productivity Soils, 1966-1968 Prices 1. Tomatoes 2. Tomatoes 3. Tomatoes 4. Sugar beets 5. ' Sugar beets 6. Alfalfa hay 7. Alfalfa hay 8. Sugar beets 9. Alfalfa hay 10. Alfalfa seed 11. Alfalfa seed 12. Beans 13 . Beans 14. Alfalfa seed 15. Beans 16. Wheat 17 . Mile 18. Milo 19. Milo 20. Saf flower 21. Barley 22. Corn 23. Corn 24. Com Depletion (percent) 60 80 100 60 80 80 60 100 100 60 80 60 80 100 100 60 60 80 100 60 60 80 100 Variable expenses, in dollars _L _L CD Net returns, in dollars _1_ 600 500 400 300 200 100 0 100 200 300 400 Dollars -29- FIGURE 4: Net Returns Per Acre for Specified Crops, By Irrigation Treatments, Medium Productivity Soils, 1966-1968 Prices Depletion (percent) I. Tomatoes 60 2. Tomatoes 80 3. Tomatoes 100 4. Sugar beets 60 5. Sugar beets 80 6. Sugar beets 100 7. Alfalfa hay 60 8. Alfalfa hay 80 9. Alfalfa hay 100 10. Beans 60 11. Beans 80 12. Wheat 13. Alfalfa seed 60 14. Alfalfa seed 80 15. Beans 100 16. Saf flower 17. Alfalfa seed 100 18. Mile 60 19. Barley 20. Milo 80 21. Milo 100 22. Corn 60* 23. Corn 80* 24. Corn 100* Variable expenses, in dollars Net returns, in dollars _L _L -1- _l_ J- 600 500 400 300 200 100 0 Dollars 100 200 300 400 Black areas indicate negative returns. r -30- evaluating receipts, costs, and net returns. But he also needs facts on physical output and dollar receipts per acre, man year, dollar in- vested and dollar of variable input for individual crops and other enterprises, if any, to identify differences among them in physical and economic efficiency and in earnings. He requires such information to guide his decisions in making adjustments to improve total farm earnings. Processing tomatoes and sugar beets ranked highest among alter- native crops in net returns for Yolo County farmers on general crop farms during the late 1950' s. Various constraints, including those arising from economic, institutional, and biological factors, limited the proportion of total acreage that Yolo County growers could plant to these crops. Thus farmers had to consider regulations under the Federal Agricultural Stabilization and Conservation policies and pro- grams, the need for market outlet contracts, and the necessity to limit acreage in order to control nematodes effectively, as well as earnings capacity, when deciding how many acres to plant in sugar beets. Decisions about tomatoes, likewise, involve contracting outlets plus biologically-appropriate cultural practices. Yolo County farmers commonly, therefore, produce, in addition to these two high-earnings enterprises, such additional crops as alfalfa hay (sometimes alfalfa seed), barley, dry edible beans, field corn, and grain sorghums, as well as various other not-so-commonly planted alternatives. Double cropping is not a regular practice extensively used in Yolo County. Sugar beets rank next to tomatoes in order of net returns on the high productivity soils. Alfalfa hay, alfalfa seed, beans, wheat, milo, saf flower, barley, and corn follow in this general order. -31- Exceptions to the listing according to crops occurred when the dry- treatment sugar beets ranked below the wet and medium-treatment alfalfa hay (see Figure 3 and Appendix Table A-7). Sugar beets, alfalfa hay, alfalfa seed, and dry edible beans, as a group, rank decidedly higher in net returns than the grains and safflower. The ranking order according to net returns on the medium produc- tivity model under conditions of the late 1960's closely resembles that for the high productivity unit. The actual level of such returns was lower, however, for all crops (see Figures 3 and 4). Thus, the maximum level of net returns for tomatoes was $224 per acre as com- pared with $311 on the high productivity unit. Gross receipts to corn for all three irrigation treatments totaled less than variable expenses, with negative net returns as the result. Returns to sugar beets and alfalfa hay rank next in magnitude after those to tomatoes. Beans yield somewhat more favorable net returns than alfalfa seed for this medium productivity model. PHYSICAL AND BIOLOGICAL FACTORS AND YIELDS ARE CRITICAL IN ECONOMIC ANALYSIS Irrigation Treatments Affect Crop Yields Our analysis to determine how crops rank according to earnings on each soil required us, as a first step, to estimate crop yields according to irrigation treatments. We used for this purpose the same procedure as that developed and used for earlier similar investiga- tions in the San Joaquin Valley by Hedges and Moore [15, 17].—^ 1/ See pages 29-32 in Hedges and Moore [15, 1961] for a complete explanation of this procedure. -32- We prepared crop yield estimates according to how irrigation practices interact with soil-water-plant relationships within a range between Field Capacity (FC) and Permanent Wilting Percentage (PW) . The first term represents all the water that a particular soil will hold following a thorough wetting, but after allowing enough time for free water to drain out by gravity. The PWP refers to the soil moisture content below which the plants cannot obtain sufficient water. Plants wilt when available moisture drops below this critical level and do not recover unless the soil receives enough additional water immediately to raise the available moisture percentage above PWP [30, 31, 5]. Questions regarding profitable irrigation practices for making these additions, therefore, concern the amounts of water added and the proper timing for these additions in order to maintain soil moisture within the range between FC and POT that will enable the operator to maximize net dollar returns. We base our analysis in this study on the concept that, in general, the relative rate of plant growth depends upon the mean soil moisture stress in the active root zones; that is, that the tension with which moisture adheres to the soil particles near the active roots regulates the amount of moisture available to the plant, and hence, its growth rate [13, and 32]. Not all scientists fully accept this view of soil-water-plant relationships. Some re- searchers of long standing in the field hold that variations in soil mositure content between FC and PWP have little bearing on plant development and yield. Some among those who support the mean moisture- stress theory, moreover, concede that brief periods of high stress can have an exaggerated impact upon plant growth. They hold, nevertheless. -33- that the moisture-stress theory represents the best approximation for a wide range of crops under varying soil and climatic conditions. Pruitt's work on transpiration and consumptive use of water by growing plants [19] provided valuable information on water use in crop production. We drew on Booher's and Houston's 1958 report [A] for data on water-holding capacity and other soil characteristics that affect irrigation in California. Storie's and Weir's 1933 Index [27] was the basis for our soil grade classifications. Preliminary infor- mation from Andrews e_t al . 1972 Soil Survey [2] and their earlier (1968) General Soil Map of Yolo County, provided the essential infor- mation to identify and inventory soils in Yolo County. We assume in applying the mean moisture-stress theory that growth is a completely reliable indicator of yield; that a yield reduction in the same proportion accompanies any given departure of growth rate from the maximum potential. We also assume that each irrigation brings water in the plant root zone to soil field capacity and that for each soil quality, the time lapse between successive irrigations governs the percentage of available soil moisture depletion, growth rates, and yields. We used six steps in estimating yield: (1) determining irrigation practices (amounts of water, days between irrigation, and timing for applications); (2) establishing variations among soil types in rates of soil moisture depletion; (3) estimating the variations in plant growth rates; (A) determining seasonal mean growth (yield) rates; (5) calculating a seasonal yield index for each crop on each soil according to three irrigation treatments; and (6) estimating yields for each of the irrigation treatments for each crop on each crop on each soil [15]. -34- Our approach uses the (1) wet, 60 percent; (2) medium, 80 per- cent; and (3) dry, 100 percent available soil moisture depletion levels, respectively, to define the 3 major irrigation practices in relation to yield for crops on the two Yolo County analysis models. We undertook in this study to establish and measure as precisely as possible the exact relationship between irrigation practices (and soil moisture available to the growing crop), on the one hand, and crop yield (indicated by growth rates) on the other. The time between successive irrigation (the length of the irri- gation cycle) varies according to soil characteristics, the stage of plant growth, and other factors that affect available soil moisture. We use an irrigation budget to identify irrigation cycles and to specify amounts of water required to meet the soil moisture availa- blility conditions specified for wet, medium, and dry soil moisture levels prior to irrigation at the end of each cycle (see Table 3) . Ex- perimental data on growth rates and yields for various crops in relation to variations in soils and in irrigation practices were not adequate to provide the data required in this study. We obtained through personal consultation and interviews with scientists, farm operators, and farm advisors, however, estimates to fill in these gaps. We use a mathematical procedure that identifies plant growth rates inversely with percentages of available soil moisture depletion to adjust estimated "Ideal" yields, obtained from these agricultural scientists. We then relate these adjusted yields, according to soil moisture availability, to growth rates and yields under varying irrigation treatments for the two soil productivity levels. TABLE 3 Irrigation Water Budget; Tomatoes on Yolo Sllty Loan Soil, Irrigation Practice 2 (Medium) Relrrlgatlon at 80 Percent Depletion of Available Soil Moisture (All Quantities in Acre Inches) Depth root Available water (inches) New root Fron new Moisture at start Total water Water at Month zone (feet) per foot soil root , a/ zone— zone , . In feet^' root zone— Carry^/ over — of , period^ Addltlon»^-' Total , available^' with- , drawn*^ end of period Irrigation dates 1 2 3 4 5 6 7 8 9 10 11 April 1-15 preirrlgatlon 5.0 1.15 5.75 — 5.75 5.75 2.50 8.25 8.25 April 5 April 16-30 plant 0.5 1.65 0.825 0.82 0.82 0.57 0.25 May 1-15 1 1.65 1.65 0.5 0.82 0.25 1.07 1.30 2.37 1.04 1.33 May 12 May 16-31 2 1.65 3.30 1.0 1.65 1.33 2.98 2.98 1.95 1.03 June 1-15 3 1.65 4.95 1.0 1.65 1.03 2.68 4.00 6.68 3.59 3.09 June 9 June 16-30 A 1.65 6.60 1.0 1.65 3.02 4.74 5.20 9.94 4.36 5.58 June 29 July 1-15 5 1.65 8.25 1.0 1.65 5.58 7.23 7.23 5.56 1.67 July 15-31 5 1.65 8.25 0 0 1.67 1.67 6.60 8.27 5.05 3.22 July 19 August 1-15 5 1.65 8.25 0 0 3.22 3.22 6.60 9.82 4.26 5.56 August 9 August 16-31 5 1.65 8.25 0 0 5.56 5.56 6.60 12.16 4.02 8.14 August 15 TOTAL 7.42 32.80 30.40 a/ Moisture available in root zone when soil is at field capacity (column 2 x column 3). b/ Addition to root zone due to expansion of roots into new soil. c/ Added moisture available in new root zone. d/ Moisture left In root zone at end of time period (amount in column 11 for last time period). e/ Moisture now available to plant (column 5 + column 3). i/ Moisture added to soil by irrigations to bring soil back Co field capacity (column 9 ■•■ colum 10). Evapo-transpiratlon rate per day times number of days in time period (column 7 'f column 8). Note: Data in columns 1, 2, and 5 must be obtained from outside sources such as agronomists and irrigation personnel. -36- Crop Net Returns Vary Widely Farmers managing irrigated operations in the Southern Sacramento Valley usually must consider two or more alternative crops plus variations in physical, economic, and institutional conditions when choosing crops and allocating resources. We used budget analysis in this study to prepare detailed sum- maries on production requirements and costs (inputs), yields (outputs), gross receipts (revenues), and net returns-over-variable expenses for each of the crops that Yolo County farmers produce. These summaries reflect not one, but commonly two or more irrigation conditions. Two or more such production, cost, and returns summaries were necessary for each crop evaluated according to more than one irrigation prac- tices. The calculation procedures involved five steps for each crop under each unique set of conditions: 1. Determining the cultural and harvest operations involved the timing for each one according to calendar dates and the power, equipment, labor, and materials involved. 2. Calculating physical quantities for all inputs, including services such as labor, power, and machinery hours, plus seed, fertilizer, irrigation water, and other materials . 3. Estimating yields according to irrigation practices (60, 80, and 100 percent available soil moisture depletion) inputs that vary with irrigation practice or yields. 4. Applying relevant cost rates and prices to express all inputs and yields in dollar values. These calculations include only variable expense items; depreciation, taxes on equipment, and other fixed (overhead) costs do not enter this initial accounting. 5. Summing total variable expenses and revenues, each by categories, to obtain total variable costs, gross receipts, and net returns for each crop. This first analytical step ignores fixed costs. It does establish the basis to compute earning capacity of individual crops. The -37- resulting data provide a criterion to choose crops and allocate re- sources. Such comparisons and choices iirithin a constant fixed cost structure for the entire farm are adequate for many crucial decisions and contribute importantly to others. Linear Programming Identifies Optimum Crop Choices for Profits Limited water quantities and either year to year variations or rising trends in water prices for many of them, require farm operators to choose among competing uses for available irrigation water. These operators must make decisions that involve complicated interrelation- ships among these several enterprises, as well as with other necessary resources, within a price relationship framework. Linear programming has important advantages as a technique for analyzing such problems. Heady and Candler [14] state "A linear programming problem has three quantitative components: an objective, alternative methods or processes for obtaining the objective, and resource or. other restrictions." We use the linear programming method in this analysis to obtain answers under specified sets of conditions to three types of questions: (a) what enterprises should the operator include in the total farm business (what to produce?) ; (b) how should he allocate among these enterprises his available water quantities and other resources (how much to produce?); and (c) in what proportions should he combine irrigation water with other materials and services that he uses to pro- duce each product or enterprises (how should he produce — how to irrigate?) . -38- The problem in our Yolo County analysis involves ten enterprises under three differing (possible) irrigation practices. It also includes, as the maximum restrictions (constraints), 9 formal or informal acreage constraints and 13 water quantity limits in different time periods. A decision problem involving 22 constraints plus the 10 enterprises and three irrigation practices is highly complex. A simple approach based upon charts and budget calculations is not adequate to solve a problem involving so many interrelated processes. The linear programming method, however, will consider all of these factors simultaneously and will yield optimum solutions that maximize net farm income under a varying range of conditions. Jlachine compu- tation, using a computer, makes linear programming a manageable and speedy procedure. Constraints Set Limits According to Resource Availability, Technology, Markets, and Institutional Factors Growers who seek maximum profits usually try to put as many acres as possible into the crop yielding the highest net returns. If no constraints interfere, therefore, we would expect an operator on our 640-acre farm model to plant all irrigable acres to processing toma- toes. He would divide these acres between tomatoes and his next most profitable crop (sugar beets), if, for some reason, he finds it impossible to plant all land to tomatoes. He would extend this principle to add other crops, in order of profitability, as further constraints might dictate. Constraints do establish limits on farm operator decisions in this Yolo County analysis; the total list of restrictions includes physical resource limitations, economic con- ditions, and institutional forces. The following list of 22 constraints -39- reflect conditions in the study area and on the high productivity farm model in the Southern Sacramento Valley: A. Land Resource Constraints Acres Percent of Cropland Total farm 640 Maximum cropland 600 100 Maximum land in crops 600 100 Maximum land in irrigated crops 480 80 Maximum land in dry edible beans 90 15 Maximum land in safflower 120 20 Maximum land in sugar beets 150 25 Maximum land in processing tomatoes 150 25 Maximum land in wheat 150 25 Minimum land in barley or wheat 120 20 B . Water April 1-15 16-30 May 1-15 16-31 June 1-15 16-30 July 1-15 16-31 August 1-15 16-31 September 1-15 Total April-September Total June- August 279.0 acre-feet 279.0 acre-feet 279.0 acre-feet 279.0 acre-feet 262.2 acre-feet 240.9 acre-feet 234.0 acre-feet 212.0 acre-feet 212.0 acre-feet 234.3 acre-feet 240.0 acre-feet 2,750.0 acre-feet 1,394.0 acre-feet -40- Our analysis, for each of the 2 farm models, Includes 10 crops; alfalfa hay, alfalfa seed, barley, dry edible beans, corn, grain sorghum (mile), saf flower, sugar beets, processing tomatoes, and wheat on each of the two farm models. The analysis procedure employs 24 "processes" (linear programming terminology) to include all variations in these 10 crops. Each irrigated crop requires three processes, one for each irrigation treatment, to reflect its performance within the conditions of this study; hence the total of 24 processes for 10 crops. Constraints number a total of 22; nine reflect land availability or limitations upon how the farmer may use it; the other 13 relate to quantities of irrigation water available, the June through August and April through September totals, and the amounts for half-month periods beginning with the first 15 days of April and ending with the last 15 days of August. These 22 constraints and 24 income activities establish the framework for the analysis in the following section (see Appendix Table A-2) . They define the context within which operate the forces that regulate optimum crop choices and resource allocations for these two 640-acre Yolo County general crop farms. ♦ OPTIflUM RESOURCE USE AND NET RETURNS VARY WITH WATER PRICES AND QUANTITIES Net Returns Drop Sharply as Water Costs Rise The analysis of water costs in relationship to net returns allowed variable water costs to range from 0 to $74 per acre-foot on the high and from 0 to $58 on the medium productivity soils (see Table 4). The highest level of net returns, and maximum quantities of -41- TABLE 4* Variations In Farm Net Returns and Irrigation Water Variable Costs, High and Medium Productivity Soils, 1966-1968 Prices •.T a/ Net returns— Expense/acre-foot 1 2 3 dollars acre-feet High productivity soils 86,647 .00 1 fi7fi X , o / o 68,952 10.56 J. ,00/ 67,951 11.16 1,557 65,021 13.08 1,511 64,460 13.44 1,395 61,002 15.84 1,323 52,728 21.20 754 13,328 74.28 184 57,615 44,662 34,504 7,575 Medium productivity soils .00 11.04 20.16 58.20 1,173 1,122 707 641 aJ Gross receipts less variable expenses. Fixed costs are not subtracted. -42- % irrigation water used, on both soil productivity models accompany the zero cost per acre- foot for water. The first grade soil model used 1,676 acre-feet and the medium grade model used 1,173 acre-feet at the zero price (see Table 4). These irrigation water "costs" include only variable expenses; cash outlays for power, repairs, and service. Fixed costs on wells, pumps, motors, and other irrigation equipment appear along with those for other property (see Tables 1 and 2). Both net returns and the amount of water applied decline sharply on both soils as water costs rise. Total net returns at zero water prices were $86,647 for the first grade and $57,615 for the medium grade soil (see Table 4). The optimum quantity of water for the first grade soil dropped only slightly as irrigation water prices rose to $10.56 (14 acre-feet), but a total quantity decline of 119 feet did accompany the rise in water prices from zero to $11.16 per acre- foot. The drop in net returns was proportionately much more drastic; almost $18,700 or about 20 percent; this contrasts with a 7 percent reduction in water. The total farm net returns for the first grade farm unit was $52,728 at a water price of $21.20 per acre-foot. This unit would use only 754 acre-feet of water at this price. Total net farm returns on the medium grade model dropped by $12,953 as water prices rose from zero to $11.04 per acre- foot, but, again, water quantities used dropped only slightly — 50 acre-feet (see Table 4). Here again, the proportionate drop in net returns greatly exceeded the negligible decline in water use. Declines in total farm net returns continued as water prices rose on both these Yolo County farm models. -43- The above results deraonstrate how rising water costs associate with both reduced water use and declining net returns for farms on both high and medium productivity soils. They also indicate that even at zero water prices farmers on 640-acre farms with medium productivity soils did not obtain total farm net re turns -over-variable expenses adequate to cover all fixed costs, given the technology, output, and price context used in this analysis. The deficit was $2,502 on these farms with medium productivity soils. Net returns decrease sharply as irrigation water variable costs rise from zero up to somewhere in the range between $10.00 and $15.00 per acre-foot. The quantities of irrigation water that yield most favorable returns, however, do not decline in proportion with the drop in net farm returns-over-variable expenses. Quantities of water that farmers can afford to use drop sharply, however, as water variable costs reach the price range around $20.00 per acre-foot (see Table 4). This water price level represents the practicable limit on irrigation water prices to Yolo County farmers, if they are to cover variable ex- penses under 1966-1968 price conditions as the sharp reduction in water use at such prices suggests. The preceding discussion focuses upon total farm net returns as the measure of farm earnings as, first, water prices per acre-foot and, second, quantities of irrigation water available, vary within specified ranges. But this total farm net returns figure ignores farm overhead, or fixed costs, that farm net income must cover if the farm operator is to remain in business. This total represents a heavy cost to operators on both of the farm analysis models in Yolo County. The farming unit on high productivity soils has total fixed costs of -44- $76,972, the comparable figure for the other unit, on medium pro- ductivity soils, is $60,117 (see Table 2). The break-even point for total farm fixed and variable cost versus total farm receipts is at the magnitude for total farm net returns that exactly equals total fixed costs, $76,972 for the high productivity model and $60,117 for the one on medium productivity soils. This break-even point or balance between total farm net returns and total fixed costs, however, does no more than exactly cover total farm costs, fixed and variable. It provides no surplus above these costs to pay the operator for making decisions, assuming the risks of investing his capital in the farm business, and other managerial services. The two farm analysis models in this investigation differ sharply in their capacity to return managerial income to the operator. The 640-acre unit on medium grade soil, under price and other conditions in the latter 1960's, in fact, lacks $2,500 of covering all the fixed costs, after paying variable expenses, even at zero irrigation water prices! An operator on the high grade soil unit fares considerably better. His break-even point falls someplace between $6.00 and $7.00 per acre-foot price for irrigation water (see Figure 5) . He would have positive net returns for his management services at water prices below approximately $6.75 per acre-foot. Differences in yield levels for the various crops between these two soil qualities explain these differences in earning capacity for the soils under the price and other conditions that prevailed during the late 1960's. FIGURE 5: Farm "Net" Returns and Irrigation Water Variable Costs, High and Medium Productivity Soils, 1966-1968 Prices -46- Marglnal Net Returns Per Acre-Foot Decline With Increases in Quantities Used A second linear program analysis examines the effects on water use, total farm net returns, and net returns per added acre-foot of water (marginal net returns) as water quantities on these two analysis models rose from zero to 1,676 acre-feet on the high and to 1,172 acre-feet on the medium grade soils (see Table 5) . This analysis underscores the importance of irrigation water to profitable farm operations in Yolo County. Thus the farming unit on high grade soil realizes $4,960 in net returns-over-variable expenses without irriga- tion water and $13,790 when only 50 acre-feet of irrigation water are available; the comparable results for the medium grade soil unit are $3,265 with zero water and $9,000 with Irrigation water quantities at 50 acre-feet (see Table 5) . Both of these 640-acre farm units showed sharp responses in total farm net returns to relatively small incre- ments of Irrigation water for the first several additions. The high grade soil unit adds $8,829, $13,104, $40,660, and $11,226, respec- tively, to total farm net returns-over-variable expenses with success- ive increments of 50, 138, 566, and 569 acre-feet. These data indi- cate that the gain in net income per acre-foot of added water for these 3 Increments amounted to $175.50, $94.50, $71.80, and $19.70 per acre-foot, respectively (see Table 5). Similar relations exist for the medium productivity soil unit. Three successive Increments in irrigation water, beginning at the 64 acre-feet level for total, included additions of 568, 76, and 465 acre-feet. Total net farm income also gained sharply, with these water increments, as water use rose to a total of 1,172 acre- feet. -A7- TABLE 5 Farm Net Returns Over Variable Costs at Varying Quantities of Irrigation Water, 1966-1968 Prices Net returns Irrigation water Net returns added per acre-foot Total i Change Total Change 1 i 2 3 4 dollars acre-feet dollars High productivity soils 4,957 0.0 0.0 — IJ, /oo 8,829 50.3 50.3 175.50 26,887 13,101 188.0 137.7 94.50 67,547 40,660 754.0 566.0 71.80 78,773 11,226 1,323.0 569.0 19.70 79,686 913 1,393.9 70.9 12.90 80,299 613 1,450.0 56.1 10.93 81,151 852 1,528.0 78.0 10.92 81,457 306 1,556.1 28.1 10.89 82,405 948 1,661.0 104.9 82,528 123 1,676.0 15.0 8.20 Total Changes from Net Returns of $80,299 to $82,528 80,299 613 1,450.0 56.1 10.93 (82,528) (2,842) (1,676.0) (282.1) (8.20) Medium productivity soils 3,265 0.0 0.0 9,046 5,786 63.5 63.5 91.06 42,714 33,668 631.0 567.5 56.80 46,958 4,244 707.0 76.0 55.80 54,689 7,731 1,172.0 465.0 16.60 -48- The marginal gain In dollars per acre-foot of xjater Increments ranged from $57 for the 568 addition dawn to $16.60 for the 465 acre-foot addition (see Table 5). Maximum levels for water usage and net farm returns were at 1,676 acre-feet and $82,528 for the high grade and 1,172 acre-feet and $54,689 for the medium grade unit. The marginal net return to the final water addition was $8 and $16.60, respectively (see Table 5), Variable water costs (prices) are at $2.53 per acre-foot for both analysis units in this analysis of the relation between varying amounts of irrigation water and net farm returns. Fixed costs, not included in the water costs (prices) for this analysis, are a com- ponent of total farm fixed costs (see Tables 1 and 2) . These overhead costs, $5.27 per acre-foot for the high and $6.89 for the medium soil productivity unit, would, if included, bring total water costs to $8.00 and $9.42, per acre-foot, respectively for these two analysis models. Total net farm returns for the high grade soil unit in this study reach the break-even point when approximately 1,250 acre-feet of water are available for irrigation (see Figure 6) . Surplus net farm income to pay for management decisions and risks accumulate as water quantities expand beyond this break-even quantity, and to the 1,676 acre-foot maximum usage. Net income above combined fixed costs plus variable expenses, for the medium soil grade analysis unit, however, responds no more favor- ably to increasing water quantities than to low water prices. The maximum total farm net returns is approximately $54,700, associated with the 1,172 acre-feet of water used. This figure falls short by FIGURE 6: Farm Net Returns at Varying Quantities of Irrigation Water, High and Medium Productivity Soils, 1966-1968 Prices Quantities of Irrigation Water (Acre-Feet) -50- $4,428 of equaling variable and fixed costs combined for this medium productivity level under production and price conditions of the late 1960*s (see Figure 6 and Table 5). These two linear programming analyses Indicate clearly how Important It Is for profitable farm operation In Yolo County that the operators have adequate supplies of Irrigation water at reasonable prices. Tlie quantities accompanying maximum total net Income and the highest level of water used do not appear excessive. The 1,676 acre-foot total for the high grade soil represents only about 2.8 acre-feet per crop acre; the 1,172 acre-feet on the medium grade unit, about 1.9 acre-feet. The fact that both units Included sizeable acreages of nonlrrigated crops, such as barley and wheat explains these low total water use figures. FARIIERS MUST EVALUATE THE EFFECTS OF WATER QUANTITY AITO PRICE CHANGES TO IDEt^TIFY OPTIMUM CROPPING SYSTEfIS AND RESOURCE ALLOCATIONS Water Quantities Sharply Limit Crop Choices . Land Use and Net Returns But evidence now Indicates that farmers may expect in the future to encounter added problems in managing irrigation water. These anticipated problems will concern both price levels and quantities available. The analysis in this section focuses on how water prices and quantities affect farm operator decisions in choosing specific crops and in allocating available land and other resources to such crops. Our results reflect operator goals to maximize net farm earnings and represent optimum decisions under the conditions specified for this study. -51- We used a linear programming analysis to determine what specific crop choices and land allocations would optimize total farm net returns for the two farm models in this study. The constraints, farm input and product prices, and other conditions for this study indicate that without irrigation water farmers on both soil grades would plant only safflower and a small grain (barley or dwarf wheat) according to the conditions in this study. They well might leave a sizeable percentage of their total cropland fallow idle in order to store soil moisture from one season to a following one. Our assumption on input requirements for the various crops included a minimum irrigation for both barley and wheat, since farmers in the study area do find irrigation required to obtain assumed yields at least one year in three or four. Our analyses, therefore, for both soil grades show safflower as the only crop that farmers would produce on the two models if they had no water available (see Figures 7 and 8). Farmers with no irrigation water at all, of course, would plant both barley and wheat, as well as safflower.—^ The analysis for the high grade soil model indicates that farmers would make important changes in the crops that they choose, and, in the acreage of land to which they would allocate these crops, as the quantities of water available increase by relatively small increments. Thus approximately 50 acre-feet of water would enable farmers to add 150 acres of wheat to the 120 acres of safflower that they would grow with zero quantities of irrigation water available (see Figures 7 and 8). 1/ Experience indicates that yields of nonirrigated grain in occasional low precipitation seasons (1 July-30 June) would drop sharply from those for other seasons, particularly when precipitation in such low-total seasons falls sharply below normal for March, April, and May. FIGURE 7: Clianges" In Net Farm Returns, Crop Acres, and Marginal Value Products Per Acre-Foot of Water at Varying Quantities of Irrigation Water; High Productivity Soils, 1966-1968 Prices 120A Safflower 150A Wheat 330A Idle 120A Safflower 130A Whieat 330A Barley 120A Safflower 150A Wheat ISOA Tomatoes (60Z) 180A Barley 120A Safflower 150A Wheat 150A Tomatoes (60X) 30A Barley 150A Sugar beets (60Z) 120A Safflower 150A Wheat 150A Tomatoes (60Z) 150A Sugar beets (60%) 30A Beans (60X) 38A Safflower 15f)A Wheat , 150A Tomatoes (60Z) 150A Sugar beets (60%) 65A Beans (lOOZ) 47A Alfalfa (80Z) -L FIGURE 8 : Changes In Net Farm Returns, Crop Acres, and Marginal Products Per Acre-Foot of Water Varying Quantities of Irrigation Water; Medium Productivity Soils, 1966-1968 Prices 120A Safflower 150A Wheat 330A Idle 120A Safflower 150A Wheat 150A Tomatoes (60Z) 180A Idle 120A Safflower 150A Wheat ISOA Tomatoes (60Z) 180A Barley 120A Safflower 150A Wheat 140A Tomatoes (60X) lOA Tomatoes (80Z) 53A Barley 127A Sugar beets I I n « c u 60 - Quantities of Irrigation Water (100 Acre-Feet) -54- Only when total water available reaches about 750 acre-feet for the high and 630 for the medium grade soils, would farmers find quantities adequate to bring processing tomatoes into their farm crop systems. An operator on the high grade soil unit with 750 acre-feet of water could optimize his total farm net returns-over-variable expenses (at $67,500), by including 180 acres of barley, 120 acres of safflower, 150 acres of tomatoes, irrigated at the "wet" irrigation practice or 60 percent available soil moisture depletion level (practice 1) , and 150 acres of wheat (see Figure 7). The comparable cropping system for the operator on medium grade soil with 630 acre-feet of water available would include 180 acres idle, instead of the barley (see Figure 8). Further changes in crop choices and land allocations occur for both analysis models as water quantities continue to expand. The operator on high grade soil could maximize his total farm net income at $80,300, by bringing in 30 acres of dry edible beans and 150 acres of sugar beets (wet) when he has about 1,450 acre-feet of water available. His highest-return cropping systems, when water quantities available reach 1,660 acre-feet, would include 150 acres each of tomatoes and sugar beets (both wet), 47 acres of alfalfa hay (medium), 65 acres of dry edible beans, 38 acres of safflower, and 150 acres of wheat (see Figure 7). His total farm net returns would be at its maximum ($82,528) under the conditions of this study with this quantity of water and this cropping system. The operator on the medium grade soil unit would find his optimum cropping system and maximum total farm net returns ($54,689) with 1,172 acre-feet of water available. His optimum cropping pattern under this situation would include 140 acres of tomatoes at the 60 percent soil -55- moisture depletion level (wet), 10 acres at the 80 percent level (medium), and 127 acres of sugar beets under irrigation practice 1. Other crops would include 53 acres of barley, 150 acres of wheat, and 120 acres of saf flower (see Figure 8). Water Price Variations Bring Changes in Quantities Used, Crops and Resource Allocations The price level at which a farmer is able to obtain his irrigation water will influence importantly both how much he uses and to which crops he applies it. He can reach his goal of maximizing total farm net returns only if he applies the quantities that he has available to the crops or other enterprises that will yield the highest possible net return per acre-foot or other unit that he uses. But this generali- zation is not an adequate basis on which farm operators or others can make decisions. We undertook, therefore, to determine as precisely as possible how optimum crop choices and acre allocations vary as prices rise, step-by-step, over a range wide enough to include all likely prices for irrigation water in the Southern Sacramento Valley. VJe set up, to accomplish this aim, a linear programming analysis in which initial water variable expenses are at zero and then increase them, progressively, until they reach $30.00 per acre-foot or higher. The result of this analysis, for each of the two major Yolo County soil grades, was a series of step-by-step water price levels (1 through 7 and 1 through 3) and associated cropping systems with irrigated crop acreages decreasing as water costs rise at each price step (see Figure 9) . The highest price at which farmers on the high grade soil model in this study could use irrigation water economically to minimize losses was $21.50 per acre-foot. FIGURE 9: Optlaua Cropping Flans for Critical Ranftes of Irrigation Uater Variable Costa; High Productivity Soils at 1966-1968 Prlcea (7) (6) (4) (2) (1) Barley 180A. Safflower 120A. Tomatoes (60Z) 150A. Wheat 150A. Barley 30A. Safflower 120A. Sugar beets (60Z) 150A. Tomatoes (60Z) 150A. Wheat 150A. Beans (60Z) 72A. Safflower 78A. Sugar beets (60Z) 150A. Tomatoes (60Z) 150A. Wheat 150A. Alfalfa (80Z) Beans (60Z) Beans (lOOZ) Safflower 40A. 14A. 56A. 40A. Sugar beets (60Z) 150A. Tomatoes (60Z) ISOA. Wheat ISOA. Alfalfa (80Z) 47A. Beans (100%) 65A. Safflower 38A. Sugar beets (60Z) ISOA. Tomatoes (60Z) ISOA. Wheat ISOA. (7) ($21.20 (6) ($15.84) Beans (60Z) 30A. Safflower 120A. (5) Sugar beets (60Z) ISOA. Tomatoes (60Z) 150A. Wheat ISOA. Alfalfa (80Z) 13A. Beans (60Z) 66A. (3) Safflower 71A. Sugar beets ISOA. Tomatoes (60Z) ISOA. Wheat ISOA. (5) ($13.44) 1 (4)^ ($13.08) I I (3) ($11. 16) LX^) ($10. S6) (1) ($0.00) IT Quantities of Irrigation Water (100 Acre-Fert) -57- Farmers would find It profitable to make major shifts in crop choices and acreages as water prices rise from the (unrealistic) level of zero price to a maximum of $30.00 per acre-foot. The optimum cropping system at zero price would include 150 acres each of tomatoes (wet), sugar beets (wet), and wheat, plus 47 acres of alfalfa (medium), 65 acres of beans, and 38 acres of safflower (see "1" Figure 9). A rise in water variable costs to $10.50 per acre- foot would not indicate major changes for these crops, but an additional rise to $11.16 would favor reductions in alfalfa (from 40 to 13 acres) and both reduce beans slightly (from 70 to 66 acres) and shift irrigation practice from the dry on most of the planting (56 acres) to the wet on the entire 66 acres. Safflower also would increase from 40 to 71 acres (see "3" Figure 9). ITie next level at which rising water prices indicate crop and acreage shifts is at $13.08 per acre-foot. Alfalfa would drop out, safflower would expand from 71 to 78 acres, and beans (wet) would gain the other six acres no longer in alfalfa (see "4" Figure 9). A further slight rise to ^13.44 for water would associate with a switch of 42 acres from beans to safflower. A water price rise of $2.40 per acre foot, to $15.84, would indicate that the 30 acres of beans should shift to barley. A final rise, to $21.20 per acre-foot for water, would eliminate the sugar beets, barley adding the 150 acres withdrawn from sugar beets. The overall pattern of crop acreage shifts, as water prices increase step-by-step, is much the same for the medium grade soil model as for the analysis unit on the high grade soil. Crop acreages at -58- the artificial zero variable cost per acre-foot for irrigation water would include 127 acres of sugar beets (wet) , 150 acres of tomatoes (wet), 120 acres of saf flower, 150 acres of wheat, and 53 acres of barley (see "1" Figure 10). The indicated optimum crop acreages following a rise to $11.04 per acre-foot for water variable costs would be the same as at zero prices except that 12 acres would shift from sugar beets to barley. These acreages would remain the same following a further rise in water variable costs to $20,16 per acre-foot.—^ WATER PRICES AND SOIL ADAPTABILITY INFLUENCE WATER DEMAND The preceding analyses have indicated a wide difference between the two analysis models in the total quantities of water that they would use at price levels that would optimize total farm net returns. The high grade soil unit would use almost 50 percent more water than the medium grade soil unit (see Table 4, and Figures 6, 10, and 11). The constraints established for sugar beets and processing tomato acreage also, within the price context of the latter 1960's, set practical limits on the amounts of irrigation water that associate with optimum total farm net returns on both these units. These two crops are the ones that yield substantial net return margins: the margins are definitely narrower for all other alternative crops adapted to the Southern Sacramento Valley under the conditions of Ij Gross receipts from both tomatoes and wheat would exceed total variable expenses, hence would result in positive total farm net re- turns at water variable expenses in the $50.00 to $60.00 per acre-foot range. This total of $7,575, however, falls far short of covering total farm fixed costs at $60,180 (see Table 4). FIGURE 10: Optimum Cropping Plans for Critical Ranges of Irrigation Water Variable Costs, Medium Productivity Soils, 1966-1968 Prices $ 27.00- 22.50- 18.00 — Barley 65A. Saf flower 120A. 13.50 — (3) Sugar beets (60Z) 115A. Tomatoes (602) 150A. Wheat 150A. Barley 65A. Saf flower 120A. 9.00— (2) Sugar beets (60) 115A. Tomatoes (60) 150A. Wheat 150A. Barley 53A. Saf flower 120A. A.50— (1) Sugar beets (60) 127A. Tomatoes (60) 150A. Tomatoes (80) lOA. Wheat 150A. 1 (3) ($20.16) I (2) ($11.04) 8 -t- 10 (1) ($0.00) 12 14 16 Quantities of Irrigation Water (100 Acre-Feet) FIGURE U: Farm Demand for Irrigation Water, 1966-1968 Prices $21.20 (754A') ,High productivity soils $20.16 (707A') "1 Medium productivity soils L 1 ] i r S 8 10 12 Quantities of Irrigation Water (100 Acre-Feet) 1 r 14 16 18 -61- this analysis. Thus the operator who has allocated the maximum allow- able acreage to these two crops, along with the irrigation water quantities (wet) that will maximize yields, given the intraseasonal variations in water quantities, has limited opportunities remaining to expand his total farm net income by increasing the amount of irri- gation water that he uses. The operator on the medium grade soil is at a greater disadvantage in this dilemma than his first grade soil competitor. Variable costs do not decline in proportion to his lower yields, as compared with those the operator on high grade soil obtains. The result is to narrow his margin of net returns and his total farm net returns, as compared with overall costs. His per-acre fixed costs are sharply lower, as compared with the high productivity unit, but not enough to offset the net returns disadvantage. Operators on the better soils find it profitable to expand water use in greater degree as prices drop than those operators on medium grade soils. These differences in yields and associated economic gains and in operator responses reflects the higher productivity level for the first grade soil. It shows as a higher ratio of change in the horizontal (water use) to the vertical (water cost) curve for high than for medium grade soil (see Figure 11). FARM EARNINGS WERE LOW AT 1966-1968 PRICES Budgeted' Total Farm Earnings Statements Indicate Returns and Profits Linear programming analysis identified the optimum resource pattern and indicated the total net returns under each set of assump- tions and conditions examined in this study. This approach, however -62- does not determine total NET FARM INCOME , nor measure PROFIT and the respective earnings shares to capital, management, or operator labor.— ^ A further step in the analysis is necessary to provide these measures of farm business success under varying water quantity and cost con- ditions. We use budget analysis in this step. The method serves to combine Gross Receipts, Gross Expenses and Net Returns-Over-Variable Expenses . yielded by the linear programming analysis, with data re- flecting Capital Investments and related Fixed Costs . The result is to determine the necessary earnings measures and to evaluate both: a) the effect of a given set of conditions on farm resource use, total farm profits, and the returns to various farm resources and/or, b) how various plans associated with the respective sets of conditions compare in financial returns and resource earnings. The earnings analysis in this study provide comparisons between the high, and the medium productivity soils plus additional comparisons within each of these two soil qualities. A further comparison under- takes to evaluate the impact of sharply different price levels upon farm earnings. High Productivity Soils Yielded Greater Profits at 1966-1963 Prices The medium productivity model offers a useful starting point for earnings comparisons (see Table 6). This model (Case 4), with no restrictions on the typical 1,173 acre-feet of water available under conditions of this study, would result in Net Returns-Over-Variable Expenses of $54,689 for the total farm operation (see Table 5). Other 1/ See Footnote 1, pages 15 and 16. Table 6 Capital and Management Income— Farm EaminRs and Profits at Varying Tomato Yields 196 -64 Price Levels and at 1972-73 Price Levels Hieh oroductivity soils Medium productivity soils Case 1— Case 2^/ Case 3^' /a/ Case 4— Case Case 6- Water: quantity (A-) price Tomatoes : acre yield 2.53 150.0 22.6 1,676 2.53 150.0 27,1 1,676 2.53 150.0 22.6 1,173 2.53 150.0 19.6 1,173 2.53 150.0 23.5 1,173 2.53 150.0 19.6 dollars 1 2 3 4 5 6 Total farm capital 720, 4A6 720,446 720,446 494,322 494,322 494,322 Net retums-over-variable expenses 82,528 101,332 119,250 54,689 72,239 81,573 Total fixed costs 76,972 76,972 76,972 60,117 60,117 60,117 Net retums-over-flxed cos ts 5,A60 24,360 42,278 (-)5,428 12,122 21,450 Add ^1 Value operator's worlc~ Interest on capital 2,575 43.222 2,575 4,010 43.223 2,575 29-65^ 2,575 29.654 4,010 NET FARM INCOME 51,257 70,157 89,511 26,801 44,351 55,114 Subtract . — ~~~~~~'~* e / Operator's wage— 7,725 7.725 12,050 7,725 7,725 12,050 PROFIT (return to capital and management) 43,532 62,432 77,461 19,076 36,626 43,064 Less interest on farm capital @ 6 percent 43,222 43,222 43,222 29,654 29,654 29,654 MANAGEMENT INCOME-^ 310 19,210 34,039 (-)10,578 6,972 13,410 RATE EARNED 6.0 8.7 10.8 1 3.86 7.41 11.1 a/ Cases 1 and 4 reflect study conditions in Yolo County during the late 1960*8. b/ Cases 2 and 5 show the impact of higher tomato yields on earnings. cl Cases 3 and 6 represent the effects of 1972-73 price levels with other conditions unchanged. d/ Calculated @ $2.60 per hour for 16.5 60-hour weeks (columns 3 and 6 @ $4.05 per hour). ¥/ Full year wages for operator's time at hired workers rates. f/ Reward for risk assumption decision making and other management functions. -64- conditlons include a water price of $2.53 per acre-foot (variable expenses only) and tomato yields of 19.6 tons per acre. The total farm fixed costs summary includes those for irrigation. The cropping system at this level of net returns would include 150 acres of tomatoes (irrigated at the 60 percent level of soil moisture depletion), 150 acres of wheat, 127 acres of sugar beets in each 60 percent irrigation practice, 120 acres of safflower, and 53 acres of barley. This total net returns figure lends itself to analysis according to standard farm earnings measures. We subtract fixed costs ($60,117) from the net returns, to obtain minus $5,428 as Net returns over fixed costs . We then add to this latter item the Value of operator's work in field operations at $2,575 (estimated at one-third the total annual cost for a full-time employee), plus $29,654, representing Interest on capital , to determine that NET FARM INCOME is $26,801 (see Table 6, column 4). This is the figure from which the farmer must allocate the proper shares in earnings to all resources that he uses. We estimate his own Operator's wages (full-year basis) at the same rate that he pays his hired employees, $7,725, and subtract this amount from NET FAR14 INCOME. The result, $19,076 is PROFIT: this is what the farm pays the operator under the operating conditions of this study as a reward for using capital and his management, including risk asstomption. This profit figure has its usual meaning as a measure of earnings; it constitutes an undivided return that belongs jointly to capital and management. But the farmer can identify a market, or "competitive," rate of return for the capital that his business employs. That figure, representing the rate the money could earn in alternative investments and calculated at 6,0 percent in this study, amounts to $29,654. This -65- aniount, subtracted from the PROFIT ($19,076) yields minus $10,578 as MANAGEMENT INCOME, the operator's return for managing, and for assuming the risks involved in the farm business. Thus the operation lacks over $10,500 of paying the operator any return at all for management, in addition to interest on the capital he uses. Another way to evaluate these farm returns is to express the $19,076 PROFIT as a percentage of the $^i94,322 Total farm capital investment; the result of this calculation is 3.86 percent representing the RATE EARNED by this farm model (Case 4) on the capital investment under the conditions of this study (see Table 6, column 4). This RATE EARNED figure represents a joint return to capital plus management; we note it falls 2.16 percent below our assumed market rate in 1966-1968 period. The earnings level for general-crop farms on medium produc- tivity soils, as determined by the analysis in the preceding paragraph, is distinctly unfavorable. Farmers operating under such conditions were at a marked disadvantage within the 1966-1968 operating and price contexts. The earnings performance record is more satisfactory for the high productivity land unit example represented by Case 1. This example reflects the higher yield performance possible to growers applying presently known technology effectively on Yolo and other soils of quality grading I or II according to the Storie Index. The Case 1 example uses 1,676 acre-feet of irrigation water with no constraints and at a variable cost of $2.53 per acre-foot. Tomatoes (wet) occupy 150 acres and yield 22.6 tons per acre (see column 1, Table 6). Total capital investments and associated fixed costs are sharply higher for this unit on high productivity soil than for the one on medium -66- productivity soil. These totals, $720,446 for capital requirements and $76,972 for total fixed costs, conpare with $494,322 total investments and $60,117 fixed costs for the medium productivity soils (see columns 1, 4, Table 6). All earnings measures greatly exceed the levels for comparable measures on the medium productivity unit. Comparisons are as follows: a) PROFIT $43,500 versus $19,100; b) IIAI^AGEMENT INCOME $310 versus minus $10,600; and c) RATE EARIJED on capital investment 6 percent versus 3.86 percent. Higher Yields for Tomatoes , sharply increase profits. Cases 1 and 4 show tomato yields at 22.6 and 19.6 tons per acre, respectively, for the high and medium productivity soils. These yields were typical for this crop during the 1966-1968 period of this study. But many farmers obtained higher yields, vjhile, of course, some failed to reach this typical level. Cases 2 and 5 indicate how important it is to growers to apply technology that results in high level yields. The 4.5 ton difference for Case 2 (27.1 tons) as compared with Case 1, results in gains of almost 50 percent in both PROFIT and RATE EARIIED and a rise from $310 to $19,210 in management income for the high productivity unit (see Table 6, columns 1 and 2). Gains resulting from a rise of almost 4 tons on the medium productivity soils are equally dramatic. Again, PROFIT and RATE EARNED levels almost doubled; management income shifts from a minus $10,600 to a plus $7,000, a gain of $17,600! Crops other than tomatoes also Indicate how the yield advantages on the high productivity soils operate to strengthen earnings. Land allocations among crops and increased water use on the high productivity soils show this fact clearly. Thus the cropping system for this higher income -67- unit includes 150 acres each of tomatoes, sugar beets, and wheat, with the remaining land in crops divided among alfalfa, dry edible beans, and saf flower (see Figure 9). This high productivity soil unit also uses 500 acre-feet of water more than the unit with medium productivity soils (see Table 6, columns 1 and 4). Higher 1972-73 Estimated Profits Reflect Greater Gains in Farm Product Than in Input Prices Drastic price rises in both farm and nonfarm commodities occurred between 1966-68, upon which period this study focuses, and the years 1971-73. These changes carried the index of farm prices received for the U.S. as a whole during 1972-73 to 149 percent of the 1967 average. The comparable shift for prices of production goods and services that farmers buy was a rise to 128 percent of 1967. These price rises reflected the combined influence of several major price- influencing forces. A general inflationary price trend, still continuing as we prepare this report, was important among these forces. Increases in gross national product and personal income, measured in either current or 1967 dollars, also exerted major influence. Thus gross national product (GNP) rose by about 50 percent between 1967 and the average for 1972 and 1973, while personal incomes rose by about 60 percent. The third major price-affecting force was the huge rise in exports of American grains and oil seeds. Tliese shipments, primarily to USSR and the Peoples Republic of China, removed accumulated grain surpluses from the U.S. market, greatly reduced the quantities of grain relative to livestock consumption requirements, and stimulated a strong upward force affecting first grain, then livestock and livestock products, and finally all food prices on the U.S. market. -68- A further influence at the international level is the continuing relatively greater rise in world population than in food production and supplies. The upward surge in prices, particularly the relatively greater increase in farm product selling prices than in prices for production inputs, dictates the need to reexamine the farm earnings results of this study, based as they are on 1966-1968 prices. The earnings results for Case 3 and Case 6 show the results of our analy- sis for this purpose. This supplementary analysis does not include an adjustment for Investments and Fixed costs; they remain on the same 1966—1968 basis as presented for the basic analysis. Prices for both the products Yolo County farmers produce, and the production input items making up their variable expenses, however, do reflect the 1972-1973 price situation.—^ Both Cases 3 and 6 show sharply increased earnings, as a result of recalculating according to 1972-1973 price conditions. A conparison between Case 1 and Case 3 for the high productivity soils indicates \J Prices for farm products in 1972-1973 ranged from 137 to 175 percent of their 1967 levels while input prices in September 1973 ranged by groups from 117 to 165 percent of 1967 levels: Farm products prices Production Inputs prices 1966-1968 1972-1973 September 1973 Crop Actual Actual a/ Percent- Inputs Percent— Alfalfa hay (ton) $27.87 $42.00 150 Motor supplies 122 Barley (cwt) 2.38 3.99 168 Motor vehicles 136 Wheat (cwt) 2.37 4.15 175 Farm machines 149 Beans (dry) (cut) 8.52 14.55 171 Fertilizer 117 Saf flower 4.25 7.00 165 Supplies 121 Sugar beets 13.17 18.40 152 Interest 165 C. tomatoes 30.00 37.55 137 Taxes 161 Wages 156 a/ 1967 = 100 percent. -69- that the increased PROFIT represents a 78 percent increase. MANAGE- MENT INCOME and RATE EARNED likewise show sharp increases; the former by almost $34,000 and the latter by about 5 percentage points. More favorable prices had an even greater impact on the medium productivity soils (Cases 4 and 6) , gross receipts showed a relatively smaller margin over variable expense at 1966-1968 conditions for this model unit. Thus PROFIT at $43,000 is more than double its level at the 1966-1963 price level (see Table 6). MANAGEMENT INCOME a residual claimant on net farm income after deducting a market rate of return to invested capital gained from a minus $10,600 to a positive return of $13,400. The net gain is almost $24,000! The RATE EARl^ED measure shows comparable gains; from 3.9 percent to 11 percent (see Table 6). These comparisons between farm earnings on the Yolo County general- crop analysis models in this study sharply define the critical question to which Yolo County farmers need an answer: "Does this change in absolute and relative prices for farm products versus the cost of production inputs mean a new era of much more favorable earnings?" An answer must recognize that analysis includes no adjustment for invest- ments or associated fixed costs. This omission is realistic because the time interval between the two sets of price conditions is so short that changes in investments and fixed cost have been relatively small. But fixed costs will rise in response to rising general price levels if the latter continue. The critical question then is, "Will price relationships between what Yolo County farmers sell and what they buy continue to be as favorable as in 1972-1973, compared with 1966-1968, or at least more favorable then they were during the base period?" We -70- believe that the answer to the first part of this question is defin- itely "Ko"; farra prices for production inputs will continue to rise. Farmers should expect that these cost price rises will narrow the gap, so that the favorable advantage of farm prices will reduce as time goes on. The sharp impact of the tightened supplies of grain relative to consumption requirements in the United States, mentioned above, should give way to a more normal balance between grain supplies and use in forthcoming crop years. Only a similar drastic and unusual drop in available, grain quantities in the United States, due either to another unusually heavy foreign demand or a drought, or some other influence that sharply cuts production, would bring such a price-stimulating in- fluence.—^ The farm earnings analysis in this section indicate that Yolo County farmers, under reasonably stable price conditions normally were not able during the latter 1960 's to obtain profits, management income, or rates on capital investment at levels offering any advantage over alternative uses for their resources, whether these be land, capital, or their own time and management capacity. The comparison between the analysis models on the high and medium productivity units indicates that the farmers operating the medium productivity soil units have a relatively less favorable opportunity for competitive earnings then those on the high productivity soils. Land values, and consequently the total magnitude of the investment of this resource in this analysis, are not absolutely precise. They do reflect assessment practices, and, 1/ Unfavorable weather conditions in 1974 did reduce U.S. corn and total grain production sharply below early season expectations with resulting price-stimulating effects. -71- according to data available, agree reasonably well with market value in the late 1960's.— ^ We believe, furthermore, that the comparisons between the two soil grades, both resulting from the same approach, are more meaningful than either in absolute terms. Our comparison between returns for the model units on the two soil grades indicate that the land values for the medium productivity soils are high relative to production and earning capacity, as compared with those for the higher productivity soils. The relatively low levels of return on investments and to manage- ment on these analysis models are consistent with results from similar analyses in other parts of California, and in the Nation as a whole, over extended periods of time. These results indicate that the rela- tively lower prices that farmers receive for their products, as compared with those for the materials and services that they must buy to pro- duce them, make it difficult for farmers to obtain competitive rates of return to capital investments or management capacity. The more favor- able price relationships of the early 1970 's have given farmers a respite from the "price-cost squeeze" relationship, but farmers have no assurance that these more favorable earnings opportunities will persist for more than a few years. CROP CHOICES, WATER USE AND PROFITS REFLECT FARM PRODUCT PRICES Tomato Prices Dominate Decisions and Earnings on Sacramento Valley General Crop Farms The previous section identifies the importance of soil adapta- bility and productivity in governing farmer decisions intended to 1^/ See page 24. -72- maximize farm earnings. Results of the analyses reflect the advant- ages of the first grade soils model over the medium grade unit in farm yields, and in both gross and net returns to the total farm, as well as per acre. Prices that farmers receive for their products also are critically important in fann earnings. Any appreciable differences among enterprises over time in how farm product prices change will en- courage resources shifts among enterprises. Earlier sections of this report identified processing tomatoes and sugar beets as the two crop enterprises that contribute the most to total farm net income on irrigated operations in Yolo County. Each of these crops, however, is limited by acreage specified constraints to not over 150 acres. But would these constraints necessarily apply under some set of conditions other than those that prevailed in the late 1960's? If not, how would relaxing these constraints operate to affect farmer decisions in reallocating their resources and, ulti- mately, the level of total farm earnings? The ansx^er for sugar beets appears to be that the 25 percent limitation probably would continue to apply within the framework of known technology and price relationships. This reflects the seriousness of the sugar beet nematode problem, combined with costs of control under methods other than crop rotation. The evidence is not so clear, however, for processing tomatoes. Perhaps operators on irrigated Yolo County farms could expand tomato acreage beyond the 25 percent (150 acre) maximum included in the constraints for the 640-acre farm. We shall assume for analytical purposes that this possibility does exist. The analysis in this section undertakes to find answers to the question, "What adjustments in land and other resource allocations would accompany maximum total farm net income if we remove constraints on total tomato acreage?." -73- A linear program analysis for the high productivity soil units indicates that under 1966-1968 conditions tomatoes would come into the cropping system when prices reach about $20.50 per ton (see Figure 12). The optimum acreage would reach the 150-acre level established by the constraints when tomato prices reach about $21.00 per ton. Each further price increase for tomatoes would bring an added net return per acre, and an increase in acreages until prices reach $22.60 per ton.—'' Area planted, at this price under 1966-1968 prices (349 acres) would produce 78,850 tons of fruit (see Figure 12). Total net farm returns at this combination would be about $62,000. This analysis, undoubtedly, presents a completely unrealistic and exaggerated perspective on the possibility for increasing tomato production in the absence of acreage constraints or contract prices. It does serve, however, to emphasize the importance of tomato prices in affecting total farm net income during the late 1960's. The contract tomato price in this study ($30.00 per ton) is an important factor affecting total farm net income. The fact that tomatoes would come into the farming system at a price slightly over $20.00 a ton, if no constraints apply, does not indicate that farmers could obtain profits by producing at such prices under 1966-1968 conditions. It merely indicates that tomatoes would have a net earnings advantage over alternative crops at this price, with other crop prices remaining at the levels in the preceding analyses. This relative advantage for tomatoes does demonstrate, therefore, the strong competitive position of tomatoes as farmers review alternatives in choosing their crops and allocating their resources. 1/ An operator would find it necessary to contract custom har- vesting service in most years if he expanded acreage more than five to ten acres beyond 150 acres. FIGURE 12: Tomato Production Under Varying Contract Prices With No Acre On Tonnage Quotas, 6A0-Acre Farm on High Productivity Soil, 1966-1968 Prices Prices (dollars per ton) -75- This same conclusion becomes evident from another analysis. The approach in this latter instance is to relax the acreage constraint to allow 185 acres as the area planted to tomatoes at the $20.50 minimum price, and to increase prices by $1.00 intervals from $21.00 to $30.00 per ton. The other crops included in the cropland allocations for this analysis, by irrigation practice, include 20 acres of alfalfa (medium), 30 acres of dry edible beans (dry), 87 acres of saf flower (dry), 150 acres of beets (wet), and 120 acres of wheat (none) (see Table 7). We assume in this analysis that the 185 acres of tomatoes indicated to ac- company $20.50 price for this crop, without constraints, would be constant at all prices above $20.50. This assumption certainly is more realistic than that for the preceding analysis in which tomato acreage continued up to the 350-acre level. Prices for the other crops, again, remain unchanged at their levels for the late 1960's. Each successive $1.00-per-ton increment in tomato prices within this framework would add $22.60 to net returns per acre of tomatoes, or to a total increment of $4,181 for the entire 185 acres in tomatoes. The result, when these price increments have brought the price per ton to the $30.00 level contract price used in this study would be a total of $89,560 for total net farm returns (see Table 7). This amount exceeds by $7,030 the maximum for the high productivity soils unit in this study under conditions that prevailed in the late 1960's. Thus this earnings level indicates, by its greater magnitude, the advantage to Yolo farmers that could accompany a slightly over 12 percent in- crease in acres planted to processing tomatoes. Prices for processing tomatoes were slower than the feed crop prices to respond during the unusual price rises of the early 1970' s. Prices for this crop averaged -76- TABLE 7 Farm Net Returns at Varying Tomato Prices With A Fixed Tomato Acreage; 640-Acre Farms on High Productivity Soils A. Crops, acres and net returns with tomato @ $20.50 per ton Acres per acre iocax net return 1 2 3 Alfalfa (80%) 20.0 91.90 $ 1,838.00 Beans (100%) 38.0 62.34 2,369.00 Saf flower — 87.0 41.31 3,594.00 Sugar Beets (60%) 150.0 117.27 17,590.00 Tomatoes (60%) 185.0 96.07 17,461.00 Wheat 120.0 58.22 6,986.00 Total $49,838.00 B. Net returns at varying tomato prices, 185 acres of tomatoes Tomato price Added net per acre Total added net Total farm net return 1 2 3 $20.50 $49,838.00 21.00 $ 11.30 $ 2,090.00 51,982.00 22.00 33.90 6,272.00 56,110.00 23.00 56.50 10,452.00 60,290.00 24.00 79.10 14,634.00 64,472.00 25.00 101.70 18,814 00 68,652.00 26.00 124.30 22,996.00 72,834.00 27.00 146.90 27,176.00 77,014.00 28.00 169.50 31,357.00 81,196.00 29.00 192.10 35,538.00 85,377.00 30.00 214.70 39,719.00 89,558.00 -77- $34 per ton during 1972 and $41 in 1973; not until 1974 did contract prices reach the $50 level, representing 167 percent of their prices during the 1966-1968 period. CONCLUSIONS Shortages of irrigation water already present problems to many Yolo County farmers who depend primarily upon surface water. This problem is most serious to farmers in the western portion of Yolo County and varies from year to year, depending upon levels of pre- cipitation and water accumulation in reservoirs. The trend toward increasing pumping lifts during the 1960 's indicates that operators pumping from groundwater supplies also may expect to meet shortages in the future. Certainly their pumping costs will rise as pumping lifts increase. Economically-adapted crops tested here vmder Yolo County con- ditions vary widely in net returns. Processing tomatoes yield by far the widest margin, with sugar beets and alfalfa hay trailing. Data for the study period, 1966-1968, indicate that no other crop has sufficient margin of net returns to cover fixed costs per acre for 640-acre general-crop farms on either high or medium productivity soils. One or both, biological or market constraints, limit the acreage that farmers can plant to tomatoes or other high net return crops. Thus they must allocate a major proportion of their farm land to crops for which the net return is not adequate to cover fixed costs. Positive farm profits under these conditions depend upon net returns from canning tomatoes or some other high return crop being adequate to leave a surplus after helping pay fixed costs for the relatively low net returns crops. -78- High land values largely explain the relatively high fixed costs — $128 and $100 per tillable acre, respectively, for operators on high and medium productivity soils. Yolo County land values are relatively high as compared with the annual income earning capacity of farm operating units. The result is relatively low earnings measured as the rate earned on capital investments; the imbalance of land value versus earning capacity is more serious for the medium than for the high productivity soils. Our analysis of the effect of varying water quantities on optimum crop choices and land allocation indicates that Yolo County farmers on 640-acre general-crop farms would find optimum water usage to be about 1,700 acre-feet for units on high productivity soils (Storie Index grades I and II) , given the intraseasonal variations in water quanti- ties. The medium productivity (Storie grades III and below) under price and other conditions of the study period (1966-1968) should use sharply less water, about 1,200 acre- feet per year. Here, again, intraseasonal variations in water quantities impose limits on farmer decisions regarding crop choices and acreages. Variations in water prices also affect optimum crop choices and acre allocations. Farmers must reduce irrigation water use sharply as variable expenses per acre-foot rise above $7.00 or $8.00. They find tomatoes to be the only crop that will pay the cost of the irrigation water as these variable expenses reach the $20.00 level per acre— foot. Our earnings analysis indicate, as suggested above, that under the study period conditions (1966-1968) Yolo County farmers on 640-acre farms received low incomes to both their capital investments and their management. Those on the high productivity soils show a percentage RATE -79- EARNED on capital investments about equal to the 6 percent market rate of interest, but receive only a token return above this market rate to pay for their own management services. Tliose on the medium productiv- ity units fared decidedly worse; they not only had minus returns for their management, but found their RATE EARNED figure more than 2 percent short of the 6 percent assumed market rate. The earnings data demonstrate the impact of the "price-cost squeeze" that plagued farmers during the latter 1950' s and the 1960's. Price movements during the early 1970 's brought both higher price levels and changes in price relationships between the products farrors sell and the goods and services they buy. We use average prices during the 1972-1973 period to evaluate how these price changes might effect farm earnings. More favorable farm prices enable Yolo County farmers to obtain decidedly higher income. This analysis leaves investments and fixed costs at the 1966-1968 level, but considers the changes in both farm product prices and prices that farmers buy for production purposes. The results indicate that the more favorable price situation brings MANAGEMENT INCOME to $13,400 and RATE EARNED on capital investments to 11 percent for the 640-acre farm on medium productivity soil; the comparable figures for high productivity soils are $37,000 for MANAGEI1ENT INCOlffi and almost 11 percent for RATE EARNED. Yolo County farmers should not expect these favorable price relationships of 1972-1973 to continue indefinitely. Farmers again will find their earnings opportunities progressively less favorable if and when price relationships similar to those of the 1966-1968 period return. Some of the forces operating to generate the overall price levels and relationships during 1972-1973 suggest, however, that -80- faraers may have a somewhat more favorable set of price relation- ships during the rest of the 1970 's than they experienced during the 1966-1968 study periods. The overall surplus of grains has disappeared and appears unlikely to return. Meanwhile, on the world-wide scene, the greater rate of growth in population than in food supplies continues, and now has reached such levels that chronic shortages are likely. A major drop in production for any one of several important nations or areas could precipitate a world crisis in food supplies. Inflation continues, of course, in both the United States and in other countries; this force tends to maintain or stimulate farm prices as well as those for other commodities. Our final conclusions are two: 1) Yolo County farm operators will need increased amounts of irrigation water from surface sources in the future; if the trend toward reduced well output and higher pumping costs continue; 2) Individual farmers should recognize that applying new scientific knowledge is the most effective technology that they can apply to increase farm profits. The first of these conclusions re- flects the bulk of the analysis reported in earlier sections of this report. A special analysis of how changes in tomato yields affect profits provides the basis for the second one. Operators of 640-acre units on both high and medium productivity soils could increase MANAGE- MENT INCOME and PROFITS importantly by increasing yields 10 percent at zero cost. This analysis, as reported in the section on earnings, simply reflects the advantage that superior management already has. Many farmers included in our county average are already obtaining such output levels. bf:jma (3/1/77) -81- REFERENCES [1] Anderson, L. D., et al.. Pest and Disease Control Program for Field Corn and Sorghums y California Agricultural Experiment Station Extension Service, 1967. [2] Andrews, Wells, Charles Goudey, George Staidl, and Leland A. Bates, Soil Survey of Yolo County^ California^ United States Department of Agriculture Soil Conservation Service, 1972. [3] Armstrong, David L. and J. Edwin Paris, Fojm Machinery: Costs ^ Performance RateSy and Combinations, Southern San Joaquin Valley, California, California Agricultural Experiment Station, Giannini Foundation of Agricultural Economics, Giannini Founda- tion Research Report No. 273, March 1964. [4] Booher, L. J. and Clyde E. Johnson, Water Holding Characteris- tics of Some California Soils, Department of Water Science and Engineering, University of California, Davis, Processed, 1958. [5] Beringer, Christoph, An Economic Model for Determining the Pro- duction Function for Water in Agriculture, Berkeley: University of California, Agricultural Experiment Station, Giannini Founda- tion Research Report No. 240, 1961. [6] California Crop and Livestock Reporting Service, California Field Crop Statistics, 1959-1963. [7] Carter, Harold 0. and CTerald W. Dean, "Cost-Size Relationships for Cash Farms in a Highly Commercialized Agriculture," Re- printed from Journal of Farm Economics, Vol. ' 149.05 51.57 210.62 24.00 13.17 316.08 105,46 115. 15 Sugar beets ton 3 146 . 86 61.60 208 . 46 22.00 13 . 17 289 . 74 81, 28 91 . 36 Tomatoes ton 1 195.40 171.83 367.23 22.50 T 30.00 678.00 310,77 321.17 Tomatoes ton 2 194.82 171.82 356.54 22.00 30.00 650.00 293,35 303.62 Tomatoes ton 3 189.56 171.54 351 . 20 20.50 in nn ju • uu All; nn ox J . uu 9 ^ fin J , ou Wheat cwt. 1 30.19 27.33 57.52 49.00 2.37 116.38 58,22 59.20 Medium productivity soils Alfalfa hay ton 1 85.54 50.51 136.05 6. 80 27.87 189.52 53,47 69.54 Alfalfa hay ton 2 88 . 7 7 50. 99 139 . 76 6. 70 27.87 185.73 46.97 53.89 Alfalfa hay ton 3 79.68 49 . 03 128.71 6.20 27.87 172. 79 44 .08 58.52 Alfalfa seed cwt . 1 114.92 26.05 140.97 4 . 40 40.23 177.01 36.04 48.94 Alfalfa seed cwt. 2 112 . 86 25.50 138 . 36 4.30 40. 23 172.99 34.53 45.98 Alfalfa seed cwt. 3 110.09 23.85 133.94 4.00 40.23 150.92 25.98 38.52 Barley cwt 27.42 18.97 46.39 9 Qn 2.38 69.97 23.58 24.63 Beans cwt. 1 75.51 32.54 108.05 17.60 8.52 149.95 41.00 48.58 2 72.39 32.70 105.09 17 . 10 8.52 145.69 40,60 47.36 Beans cwt' 3 71.64 31.03 102.67 16.00 8.52 135.32 33,65 40.22 Corn cwt. 1 128.34 31.84 160.18 58.70 2.59 152.03 -8 , 15 3.14 Corn cwt . 2 130.52 31.12 161.54 57.20 2.59 148.15 -13,49 -1.36 Corn cwt . 3 124.59 29.40 154.09 53.40 2.59 138.31 -15,78 -5.18 Milo cwt. 1 78.78 30.80 109.58 59.00 2.30 135.20 26,12 34.12 Milo cwt. 2 78.40 30.17 108.57 57.20 2.30 131.56 22,99 30.89 Milo cwt. 3 75.32 28.87 104.19 53.50 2.30 123.05 18,85 25.95 Saf flower cwt. 23.54 17.25 40.79 15.00 4.25 58.00 27,21 27.21 Sugar beets ton 1 146.36 61.60 207.95 21.50 1.37 294.55 86.59 96.58 Sugar beets ton 2 146.21 61.60 207.81 21.00 1.37 287.70 79,89 89.80 Sugar beets ton 3 144.01 61.50 205.61 19.50 1.37 268.52 52.91 72.28 Tomatoes ton 1 192.99 170.56 363.55 19.60 30.00 588.00 224.45 233.89 Toma toes ton 2 203.35 170.55 373.91 30.00 570.00 196.09 208.22 Tomatoes ton 3 197.47 170.56 368.03 17.80 30.00 534.00 165.97 176,57 Wheat cwt . 54.36 39.20 2.37 92.90 38.54 39,59 &I 1-60 percent, 2 = 80 percent, 3 = 100 percent available soil moisture depletion before reirrigating. 1 96 9 8 ^^