Kss >«*# sion of Agricultural Sciences UNIVERSITY OF C A 1 IF OR N DIVERSIFICATION FORNIA CROPS H. O. CARTER ROBERT JENSEN G. W. DEAN CALIFORNIA AGRICULTURAL Experiment Station Extension Service CIRCULAR 503 REVISED Certain unpredictable factors, such as weather, cause important variations in crop yields, prices, and income. In the past the farmer has had to rely primarily upon his own experience in estimating the risks associated with different crops and cropping systems. This circular supplements the grower's experience by presenting indexes of California crop variabilities in yields, prices, and income. Variabilities are expressed as relative percentages of variation from the average over a period of many years, and are based on a detailed study of California state and county agri- cultural records. The information presented here will enable the California grower to better estimate the relative variations to be expected from year to year in yields, prices, and incomes of individual crops. Possibili- ties for reducing risks through crop diversification are also con- sidered for several major farming areas of California. SEPTEMBER, 1968 THE AUTHORS: H. O. Carter is Professor of Agricultural Economics in the Experiment Station and on the Giannini Foundation, Davis. Robert Jensen is a Graduate Student, Department of Agricultural Economics, Davis. G. W. Dean is Professor of Agricultural Economics in the Experiment Station and on the Giannini Foundation, Davis. This circular is a revision of circular 503 of the same title authored by Harold O. Carter, Gerald W. Dean, and A. Doyle Reed. [2] RISK AND DIVERSIFICATION FOR CALIFORNIA CROPS INTRODUCTION i? avorable soil and climatic conditions in California enable most farmers to select from many possible alternatives the crops they grow in any one season. The crop- ping pattern followed depends on many considerations — such as access to markets, specialized abilities and experiences, availability of labor and mechanical equipment, and relative product and re- source prices. An additional factor is the farmer's attitude towards the uncertainty associated with different crops. New farm- ers have limited experience, and the ex- perience of longer-established farmers may be based on a "biased" sample of un- usual crop years. Therefore, this circular provides additional information in the form of indexes of crop variability for yields, prices, and income of the main commercial crops and cropping systems. Starting with the assumption that past variability can be used to project future variability, measurements more objective than individual experience are used to provide supplemental information for making choices among crops. For exam- ple, the grower can choose: (1) high-in- come crops having high risk; or (2) low- income crops having low risk; or (3) a mixture of (1) and (2). The choice is usu- ally relevant to the farmer's present situa- tion — for example, farmers without much equity would probably prefer a crop plan which minimizes risk, while other farmers may choose high-risk crops because high income generally associated with them may, in their minds, offset probabilities of large losses. MEASUREMENT OF VARIABILITY Variability of crop production generally can be separated into two parts: the pre- dictable part, which includes technologi- cal advances and long-run economic trends, such as inflation and price cycles; and the unpredictable part, which is nor- mally associated with weather. When planning for the current year's crop pro- gram, farmers are probably more inter- ested in the unpredictable variability af- fecting current levels of prices, yields, and income. It is only this unpredictable or random component which we try to meas- ure in this study. Variability measures used were derived mainly from California state yield, price, and income data over a period of 20 to 48 years. Since climate, resources, and eco- nomic conditions differ in various re- gions, we have derived variability meas- ures for field-crop yields in agricultural regions wherever data were available. California data are not as limiting for fruit and vegetable crops which are con- centrated in localized areas, but yield fluctuation on individual farms may be lessened in compiling state yield series — or, to a lesser extent, in compiling local data. The major limitation of the price data is the wide fluctuation of crop prices dur- ing a shipping season. However, if we assume that farmers ship throughout the season, the price misrepresentation may not be too drastic. [3] Variability Index The variability index presented in this circular shows the degree of random vari- ability in crop prices, yields, or income relative to the current level of these items. For example, the random variation (vari- ation other than long-term trends) in rice yields over the years has been about 310 pounds per acre. The current level of yield averages about 4,900 pounds per acre. Accordingly, the yield variability in- dex for rice is 310-^4,900 = 0.06, or 6 per cent. Variability indexes for price and income are computed similarly. VARIABILITY OF SELECTED CALIFORNIA CROPS Yield Variability The following indexes measure only yield fluctuations not associated with the long- run trends resulting from technological developments and other production fac- tors. Field Crops In general, the yield variability of major field crops is quite low compared to other crops ( table 1). Those crops in the upper half of the rankings in table 1 are gener- ally considered stable irrigated field crops ■ — they have a shorter growing season than most of the other crops, thus they are less likely to show wide fluctuations. Crops at the bottom of the rankings are generally affected more by seasonal weather, insects, and disease. TABLE 1. Yield Variability Indexes for Selected Field Crops* Variability index Table 1 continued Product ( by area) State Region per cent Grain sorghum 3 Northern Sacramento Valley .... 5 Yolo County 5 Fresno-Madera 5 Tulare-Kings 6 Kern County 5 Imperial Valley 5 Field corn 3 Yolo County 2 Fresno-Madera 2 Tulare-Kings 2 Alfalfa 3 Northern Sacramento Valley .... 8 Yolo County 3 Fresno-Madera 2 Tulare-Kings 3 Kern County 7 Imperial Valley 3 Potatoes, late summer 5 Variability index Product ( by area) State Region per cent Barley 6 Northern Sacramento Valley .... 9 Yolo County 17 Fresno-Madera 5 Tulare-Kings 6 Kern County 5 Imperial Valley 8 Sugar beets 6 Yolo County 13 Fresno-Madera 17 Tulare-Kings 15 Kern County 15 Imperial Valley 11 Rice 6 Northern Sacramento Valley .... 7 Yolo County 8 Fresno-Madera 9 Cotton lint 7 Fresno-Madera 7 Tulare-Kings 7 Kern County 5 Imperial Valley 5 Cottonseed 7 Oats 7 Potatoes, fall 11 Potatoes, late spring 11 Potatoes, early summer 11 Wheat 15 Northern Sacramento Valley .... 12 Yolo County 20 Fresno-Madera 16 Tulare-Kings 11 Kern County 11 Safflower 18 Potatoes, winter 18 Flaxseed 18 * Based on yield per acre. Basic methodology for variability measures used in tables 1—9 will be found in: Carter, H. O., and G. W. Dean, Income, Price, and Yield Variability for California Crops and Crop- ping Systems. Hilgardia 30(6):175-218. Regional indexes appear to support the hypothesis that considerable individual county yield variability is averaged out when county series are used. It is hard to [4] ascertain how much of the measured vari- ability in these farming areas is related to uncertainty and how much to varying levels of factor inputs, managerial ability, farm- tenure situation, and other factors. Sugar beets show this relationship most clearly. On the whole, the range of re- gional indexes is small; most of the varia- tion between regions is due to the adapt- ability of the crop to the region's climate. Barley and wheat are much higher in the Yolo area because of the amount of nonirrigated range land area included in computing the area's index. Vegetables Table 2 summarizes 35 California veg- etable crops. Because of economic condi- tions, some planted acreage of certain crops may not be harvested or only a small portion of the total yield per acre may be harvested. The most variable crops are those harvested in spring — win- TABLE 2. Yield Variability Indexes for Selected Vegetables* Product Variability index per cent Beans, green lima 4 Strawberries, total crop 5 Tomatoes, processing 5 Bell peppers, late summer 5 Celery, late fall 5 Tomatoes, early fall 5 Sweet corn, early summer 6 Onions, late summer 6 Artichokes 6 Lettuce, early fall 6 Asparagus, total production 6 Garlic, summer 6 Cauliflower, early spring 7 Carrots, late fall 7 Celery, winter 7 Carrots, early summer 8 Honeydews, late summer 8 Lettuce, winter 8 Tomatoes, early summer 9 Snap beans, early fall 9 Watermelon, early summer 9 Lettuce, early spring 9 Watermelon, late spring 10 Carrots, winter 10 Cantaloupes, midsummer 10 Cabbage, early spring 11 Cauliflower, late fall 11 Lettuce, summer 12 Sweet corn, late spring 13 Tomatoes, early spring 13 Onions, late spring 13 Broccoli, early spring 15 Celery, spring 16 Cantaloupes, spring 16 Sweet corn, fall 18 * Based on yield per acre [5 ter weather conditions contribute to their high yield uncertainty. In general, vege- table crops show somewhat greater yield variability than do field crops because of the specialized skills, soils, and climatic conditions required for successful vege- table production. Fruits and Nuts Table 3 shows yield variabilities for 19 major California fruit and nut crops. Fruit and nut yield-variability coefficients aver- age considerably higher than those ob- served in vegetable and field crops. Many of the high-variability coefficients can be explained by the alternate-bearing ten- dency associated with fruit and nut crops. The tendency is particularly strong with avocados, cherries, apricots, and olives. Although freestone peaches (which are TABLE 3. Yield Variability Indexes for Selected Fruits and Nuts* Product Variability index Per cent Peaches, freestone 7 Figs 8 Grapes 8 Peaches, clingstone 9 Grapefruit 9 Lemons 11 Plums 12 Walnuts 15 Prunes 15 Dates 18 Apples 19 Oranges, Valencia 20 Pears, all 21 Oranges, navel 24 Almonds 24 Olives 24 Apricots 26 Sweet cherries 33 Avocados 34 * Based on yield per acre lowest in yield variability) exhibit an al- ternate-bearing tendency, the magnitude of year-to-year changes is small relative to recent yield levels. Most of the other crops in the upper half of the rankings in table 3 show less alternate-bearing tendency. Price Variability Price variability exerts an important in- fluence on planting decisions. In this study, year-to-year fluctuations in prices are treated as the relevant variability for such decisions. ] Field Crops Table 4 presents the price variability indexes for 17 field crops in California. Government price controls play the largest part in determining the size of the vari- ability coefficient and the order of the rankings. The majority of the crops in the upper half of the rankings are those which have been subjected to a considerable de- gree to direct government control. At the TABLE 4. Price Variability Indexes for Selected Field Crops* Product Variability index per cent Oats 3 Sugar beets 6 Barley 6 Cotton lint 6 Field corn 8 Flaxseed 10 Safflower 11 Alfalfa 12 Wheat 13 Rice 14 Grain sorghum 25 Potatoes, fall 27 Potatoes, late summer 32 Cottonseed 32 Potatoes, winter 34 Potatoes, late spring 37 Potatoes, early summer 48 * Based on yield per acre other extreme, the price of early potatoes is very uncertain for the grower — even though California is the major supplier of potatoes in the United States from about Mav 15 to June 30. The great price vari- ability can be partly explained by the producers' tendencies to overrespond to the previous year's potato prices. Vegetables Year-round growing conditions in Cali- fornia make California a major vegetable producing state. Manv California vege- tables are produced for specific and often limited markets, and these markets are generally determined by the supply from competing areas. As a result, California vegetable prices depend to an important extent on the supply conditions elsewhere. Table 5 gives the price variabilities for 35 selected California vegetables. Processing vegetables usually rank lower in price [ TABLE 5. Price Variability Indexes for Selected Vegetables* Product Variability index per cent Strawberries, total crop 3 Asparagus, total production 6 Beans, green lima 7 Snap beans, early fall 8 Artichokes 8 Cantaloupes, midsummer 10 Sweet corn, late spring 12 Cantaloupes, spring 12 Carrots, early summer 12 Sweet corn, fall 13 Honeydews, late summer 14 Tomatoes, early fall 14 Broccoli, early spring 14 Tomatoes, early spring 14 Watermelon, late spring 15 Cauliflower, late fall 16 Sweet corn, early summer 16 Celery, late fall 16 Celery, winter 16 Lettuce, winter 17 Lettuce, early fall 17 Tomatoes, processing 17 Bell peppers, late summer 17 Tomatoes, early summer 17 Cauliflower, early spring 20 Onions, late summer 20 Watermelon, early summer 21 Lettuce, summer 24 Carrots, winter 26 Lettuce, early spring 30 Carrots, late fall 30 Cabbage, early spring 30 Onions, late spring 31 Garlic, summer 37 Celery, spring 41 * Based on yield per acre variability than do those grown mainly for specialized markets, because canneries and freezers provide a stable outlet for processed vegetables and prices are often determined before the growing season starts. Fruits and Nuts Table 6 ranks 19 fruit and nut crops according to price variability. Much of the fluctuation of those crops in the upper range of price variability can be correlated with wide fluctuations of production. Avo- cados ranked highest in yield variability among the fruits (table 3), and olives and Valencia oranges also ranked high. Raisin and wine outlets for grapes are highly in- terrelated; an oversupply or decline in demand for either affects prices for all 6] TABLE 6. Price Variability Indexes for Selected Fruits and Nuts* TABLE 7. Gross Income Variability Indexes for Selected Field Crops* Product Variability index Peaches, freestone . Walnuts Oranges, navel Grapefruit Peaches, clingstone Figs Almonds Sweet cherries . . . . Apricots Dates Plums Apples Pears Lemons Grapes Prunes Olives Oranges, Valencia . Avocados per cent 13 13 13 14 15 18 19 19 20 20 21 23 24 27 27 27 32 32 35 * Based on yield per acre grapes. Because variability for some of the fruit and nut crops may be influenced by marketing orders, the possibility of initiat- ing or discontinuing price-stabilizing mar- keting orders and agreements should be considered when estimating future price variabilities. Fruit and nut crops in the low price- variability range have one or more stabil- izing influences. As examples, grapefruit and freestone peach prices are stabilized because grapefruit and freestone peach production changes little from year to year, and walnuts, although fairly high in yield variability, tend to be stabilized in price by a federal marketing order and a strong grower-owner cooperative. Gross Income Variability Ultimately, growers are interested in the net income variability of alternative crops and cropping systems. Net income variability results from the interaction of vield, price, and cost, but the impossibil- ity of obtaining complete and accurate cost data over a 48-vear period necessi- tated the use of gross income (price x quantity) data in computing crop income variabilities. However, net income vari- ability is closely related to gross income variability because costs tend to be stable or to change onlv gradually. Gross income per acre is computed Product (by area) Variability index State Region per cent Barley 5 Northern Sacramento Valley .... 16 Yolo County 16 Fresno-Madera 5 Tulare-Kings 6 Kern County 6 Imperial Valley 8 Safflower 9 Sugar beets 11 Yolo County 15 Fresno-Madera 22 Tulare-Kings 17 Kern County 15 Imperial Valley 12 Grain sorghum 13 Northern Sacramento Valley .... 13 Yolo County 13 Fresno-Madera 11 Tulare-Kings 14 Kern County 9 Imperial Valley 5 Alfalfa 14 Northern Sacramento Valley .... 15 Yolo County 14 Fresno-Madera 16 Tulare-Kings 14 Kern County 14 Imperial Valley 13 Wheat 14 Northern Sacramento Valley .... 20 Yolo County 19 Fresno-Madera 16 Tulare-Kings 26 Kern County 11 Oats 14 Field corn 14 Yolo County 5 Fresno-Madera 5 Tulare-Kings 5 Flaxseed 18 Rice 18 Northern Sacramento Valley .... 20 Yolo County 20 Fresno-Madera 17 Cotton lint 20 Fresno-Madera 20 Tulare-Kings 20 Kern County 20 Imperial Valley 8 Cottonseed 22 Potatoes, late summer 26 Potatoes, fall 26 Potatoes, late spring 35 Potatoes, winter 37 Potatoes, early summer 40 * Based on yield per acre simply as the product of yield per acre and price. Thus, the year-to-year relation- ship between price and yields is im- [7] portant. If high prices tend to be associ- ated with low yields and vice versa, gross income variability is reduced. This rela- tionship is observed for many fruits where, with acreage relatively constant from year to year, changes in total pro- duction and prices depend primarily on changes in yields per acre. Field Crops Table 7 presents gross income variabil- ity indexes by state and region for 17 Cal- ifornia field crops. The two factors con- tributing to gross income variability are yield variability and price variability. Yield variability for field crops is relatively low (table 1), and the most important fac- tor contributing to gross income variabil- ity is price variability. Potatoes are by far the most variable field crop with respect to price and gross income. Regional indexes that are lower than state indexes for various crops can be ex- plained bv the adaption of the crop to the climatic conditions in the regions and farmer specialization to the crop. Field corn is a good example of this: the state index is 14, but all three regional indexes are 5. This is because corn growing has been highly specialized in these three regions. Vegetables Table 8 shows the gross income vari- abilities of 35 major California vegetable crops. Ranking of the crops is consistent with yield and price variability results de- rived earlier (tables 2 and 5). Vegetables low in price and yield variability are con- centrated at the lower end of the gross income variability scale; crops high in price variability tend to fall in the upper gross income variability range. As with field crops, price variability apparently outweighs yield variabilitv as the major determinant of gross income variability. Fruits and Nuts Table 9 ranks 19 major California fruit and nut crops in order of gross income variabilitv. The correlation between price and vield for fruits and nuts is important in determining gross income variability. The reasoning is as follows: California is [ TABLE 8. Gross Income Variability Indexes for Selected Vegetables* Product Variability index per cent Strawberries, total crop 4 Artichokes 5 Asparagus, total production 7 Broccoli, early spring 9 Tomatoes, early fall 10 Tomatoes, processing 11 Beans, green lima 11 Watermelon, late spring 12 Tomatoes, early spring 13 Honeydews, late summer 14 Cantaloupes, midsummer 15 Cantaloupes, spring 16 Onions, late summer 16 Sweet corn, early summer 16 Snap beans, early fall 17 Sweet corn, late spring 18 Celery, winter 19 Lettuce, summer 19 Lettuce, early fall 20 Cauliflower, early spring 21 Cauliflower, late fall 21 Bell peppers, late summer 21 Sweet corn, fall 22 Garlic, summer 22 Celery, late fall 23 Carrots, early summer 24 Watermelon, early summer 25 Carrots, late fall 25 Lettuce, winter 25 Tomatoes, early summer 27 Carrots, winter 32 Lettuce, early spring 33 Cabbage, early spring 34 Onions, late spring 39 Celery, spring 50 * Based on yield per acre the largest producer of most types of fruits and nuts consumed in the United States, and the bearing acreages of these crops change only gradually from year to year. Therefore, major year-to-year changes in total national production re- sult primarily from changes in California yields. Because total national production and price tend to move in opposite direc- tions, vield and price in California tend to do likewise. Thus, crops which have individual price and yield variabilities may be relatively stable in income — if vield is low, prices are high, and vice versa. Avocados, Valencia oranges, and cherries provide excellent examples of such price-yield relationships. These crops displav relatively high yield and price variabilities yet they are relatively stable in terms of gross income. ] Dates are one of the most variable fruit crops in terms of gross income (table 9), yet the individual yield and price variabil- ity for dates (tables 3 and 6) are only moderately high. The reason lies in a posi- tive year-to-year yield-price relationship since 1940. Date imports were cut off during World War II, and California producers increase yields in response to record prices. Although inclusion of these "abnormal" years in the sample of annual observations possibly tends to provide an overestimate of gross income variability, the same positive year-to-year correlation between yields and prices has continued in general since World War II. In most instances, individual yield and price variabilities of California fruit and nut crops are rather poor indicators of in- come variabilities because they ignore significant yield-price correlations. TABLE 9. Gross Income Variability Index for Selected Fruits and Nuts* Product Variability index per cent Oranges, navel 7 Prunes 8 Lemons 10 Apples 12 Oranges, Valencia 13 Walnuts 13 Plums 15 Avocados . 15 Grapefruit 16 Sweet cherries 17 Peaches, freestone 19 Peaches, clingstone 19 Figs 19 Pears 21 Dates 24 Grapes 25 Apricots 25 Almonds 29 Olives 32 * Based on yield per acre CROP DIVERSIFICATION AS A MEANS OF LESSENING INCOME VARIABILITY An ideal combination of crops is one wherein the low income from one crop is offset by a high income from a second crop, and vice versa. But even though in- come can often be stabilized through di- versification, the level of income may be lower than that obtained by specializa- tion. Thus, the farmer may have to make a choice between an unstable income at a high average level and a more stable income at a lower level. A cropping system cannot always be selected entirely on the basis of income stability. Farmers diversify for various reasons. Certain crops may be included in cropping systems partly for soil-building properties, and some crops may help con- trol disease or weed problems. For lower- ing income variability, an ideal combina- tion of crops is one wherein low income from one crop is offset by high income from a second crop, and vice versa. Un- fortunately, even though income can often be stabilized through diversification the level of income may be lower than that obtained by specialization. Thus, the farmer may have to make a choice be- tween an unstable income at a high aver- age level and a more stable income at a lower level. The following analysis provides esti- mates of the income variability from a number of common cropping systems in the northern Sacramento Valley, Yolo County, Fresno-Madera, Tulare-Kings, Kern County, and Imperial Valley. As the number of crops and cropping systems considered in this section is rela- tively small, production costs were budg- eted to allow use of net income data. Net income per acre is defined as gross income (yield per acre x annual average price) minus operating costs. Depreciation, taxes, and other fixed charges would have to be deducted in each case to arrive at net profits. Individual county yields or weighted average county yields (where more than one county is located in an area) were used for most crops. Prices were based on California state data. Op- erating costs for each crop in each area were obtained by simple budget and cost studies. Where harvesting costs consti- tuted a major cost item, costs were ad- justed for the yield level. [9] For purposes of comparison the varia- bility indexes presented in table 10 are based on cropping systems for 560-acre farms, with the 560 acres divided equally among the respective crops. Northern Sacramento Valley Area This is primarily a rice-growing area, and normally the farmer's main decision is what to grow in combination with rice. The crop combination with the lowest variability index is R-R-R-M (420 acres of rice and 140 acres of milo). As the milo TABLE 10. Net Income Variability Comparisons Between Selected Crop Combinations in Six Areas (Based on a 560-Acre Farm) Table 10 continued. Crop combination* ( by area) Average net income Variability 1962-1966f index* dollars Northern Sacramento Valley: Barley 14,000 Rice 58,000 Alfalfa 22,000 R-R-R-M 48,000 R-R-R-W 48,000 R-R-R-B 47,000 R-R-R-A-A-A-W 37,000 R-R-R-A-A-A-B 36,000 A-A-A-W-M 21,000 A-A-A-B-M 20,000 Yolo County : Barley 14,000 Alfalfa 31,000 Tomatoes 96,000 A-A-A-Co-M-W 26,000 A-A-A-Co-M-B 26,000 A-A-A-SB-Co-M 29,000 A-A-A-R-R-W 38,000 A-A-A-R-R-B 37,000 A-A-A-R-R-R 44,000 A-A-A-W-SB-T 40,000 A-A-A-SB-T-SB 42,000 A-A-A-B-SB-T 39,000 R-R-R-M 49,000 R-R-R-W 48,000 R-R-R-B 47,000 A-A-A-T-Co-T 52,000 A-A-A-T-M-T 51,000 Fresno-Madera : Barley 13,000 Alfalfa 36,000 Cotton 80,000 Cantaloupe 94,000 Sugar beets 22,000 A-A-A-Co-C 42,000 A-A-A-C-C-W 47,000 A-A-A-C-C-B 47,000 per cent 23 35 51 32 33 33 34 35 51 52 30 39 67 24 25 26 27 28 30 32 33 34 34 35 36 46 47 32 33 38 75 121 20 23 23 Crop combination ' ( by area) Average net income Variability 1962-19661 index! A-A-A-C-C-SB . . A-A-A-C-C-Ca . . A-A-A-C-SB . . . A-A-A-R-R-R-W A-A-A-R-R-R-B A-A-A-C-M-Ca . Co-C-M-C-Co-SB C-B-SB-M-Ca . . A-A-A-Co-SB . . . R-R-R-M A-A-A-Ca A-A-A-Ca-SB . . . Ca-Co-SB-W-M . Tulare-Kings: Barley Alfalfa Cotton Sugar beets .... A-A-A-C-C-Co . A-A-A-C-Co . . . A-A-A-C-C-M-M A-A-A-C-C-B . . . A-A-A-C-C-W . . C-C-M-Co-Co-SB A-A-A-C-C-SB . A-A-A-C-SB . . . A-A-A-SB-SB-C . A-A-A-Co-SB . . . Kern County : Barley Cotton Alfalfa Sugar beets Potatoes (late spring) A-A-A-C-C-B A-A-A-C-C-W A-A-A-C-C-SB A-A-A-C-C-C A-A-A-C-C-Pls A-A-A-SB-M C-C-W-Pls-SB-M . . . A-A-A-C-C-Pw A-A-A-B-C-Pls C-C-W-Pw-SB-M . . A-A-A-B-C-Pw A-A-A-SB-Pvv A-A-A-SB-Pls Imperial Valley: Cotton Barley Alfalfa Sugar beets Lettuce A-A-A-C-B-SB A-A-A-C-C-B A-A-A-C-C-M A-A-A-C-C-SB A-A-A-C-M-SB A-A-A-C-C A-A-A-C-C-C A-A-A-B-SB-M 48,000 24 60,000 24 42,000 25 39,000 25 39,000 25 50,000 26 40,000 28 30,000 29 30,000 32 41,000 34 51,000 41 45,000 41 33,000 49 15,000 26 44,000 30 72,000 34 23,000 81 50,000 16 46,000 16 45,000 16 48,000 17 48,000 19 40,000 21 50,000 21 45,000 24 42,000 29 37,000 30 15,000 21 98,000 37 38,000 41 29,000 64 126,000 139 54,000 22 54,000 23 57,000 24 68,000 25 56,000 36 32,000 37 47,000 44 73,000 46 42,000 47 64,000 51 59,000 54 50,000 73 46,000 76 116,000 22 18,000 36 33,000 40 33,000 45 76,000 157 44,000 15 58,000 15 59,000 15 60,000 15 45,000 16 66,000 16 74,000 16 28,000 25 ( Continued on next page) [10] Table 10 continued. income Crop combination* 1962—19661 Variability (by area) Average net index $ A-A-A-LB-M-C-C 63,000 28 C-C-C-LB-M 93,000 29 A-A-A-LB-C-C 70,000 34 A-A-A-C-LB-SB 57,000 35 A-A-A-LB-LB 57,000 84 LB-M-LB-SB-LB-M 59,000 101 * Assumes equal proportions of the 560-acre farm devoted to each crop in the crop combination. For example, "Rice" refers to 560 acres of rice alone; "R-R-R-M" refers to 420 acres of rice and 140 acres of milo. Symbols are defined as: A-alfalfa, B— barley, C— cotton, Ca-cantaloupe, Co-field corn, LB-lettuce barley double crop, M-milo, Pis-late spring potatoes, Pw-winter potatoes, R-rice, SB- sugar beets, W— wheat. f Net income refers to gross income minus operat- ing costs (excluding depreciation, taxes, and interest on investment). t The variability index measures relative varia- bility. Absolute or dollar variability can be obtained by multiplying average net income by the variability index expressed decimally. For example, the absolute variability of barley in the northern Sacramento Val- ley area is $3,220 ($14,000 X 0.23). land is planted to wheat, the index in- creases from 32 to 33, and when the alter- nate crop is changed to barley net income (gross income minus operating costs only) decreased from $48,000 to $47,000. Add- ing alfalfa to the rotation as in the fourth rotation R-R-R-A-A-A-W (240 acres rice, 240 acres alfalfa, and 80 acres wheat) lowers the net income by $11,000 and increases the variability index to 34. Approximately the same result is obtained when barley is added in place of wheat. The last two crop combinations exclude rice as a possibility. Here, we see that the net income greatly decreases and the variability index increases. Yolo County Area The high income crop in most of this area is tomatoes, but in certain regions crop combinations revolve around rice. In table 10, 14 rotations are listed in order of their variability index. Alfalfa (3 years) was considered the mainstay of all rota- tions except where rice was grown. Be- sides tomatoes and rice, some of the cash crops commonly grown with alfalfa are sugar beets, barley, wheat, milo, and corn. Combinations of these crops usually lower the relative income variability, and the relative variability index usually increases as expected net income increases. [ Fresno-Madera Area Although there is not as much rice grown here as in the preceding two areas, several crop combinations containing rice are included in the listing for comparative purposes. Rotations of rice grown with alfalfa and wheat or barley have both a higher expected net income and a lower variability index than in the preceding two areas. This is due to the more stable and higher yields obtained from alfalfa in this area. However, when rice is grown in rotation with only milo expected net in- come is lower and the variability index is higher. Cantaloupes are a high-risk crop, as well as a high-value crop; the inclusion of cantaloupes into most cropping systems tends to increase both income levels and income variability. Tulare-Kings Area Table 10 shows 6 of the major field crops grown in this area. Alfalfa is the mainstay of most rotations and its inclu- sion with cotton makes up the majority of crop combinations. Cotton is the high- income crop, but it also has the most absolute income variability associated with it. There is a small range of expected income ($42,000-$50,000) associated with all rotations that include cotton and alfalfa. The main reason the variability index increases is due to the inclusion of sugar beets in the rotation — sugar beets show high, absolute, and relative, income variability. Corn, however, is lower in relative variability and not a great deal higher in absolute variability than milo, wheat, and barley. Kern County Area Crop production in Kern County gen- erally shows greater income variability than in the other five areas. Sugar beets, which have an unstable yield record, play a large part in this high variability. Potatoes are the most risky crop: late spring potatoes have a variability index of 186 per cent, and winter potatoes have a variability index of 139 per cent. The high net income variability of potatoes in Kern County is dramatized further by the fact that net income per acre in recent in years has varied from -$203 to $715. Therefore, the addition of potatoes to any rotation system (especially late spring potatoes) increases both the absolute and relative income variability. In general, the addition of cotton to the rotation tends to decrease the variability index. Imperial Valley Area Table 10 shows that this is the most stable of the six areas. Alfalfa, cotton, barley, milo, and sugar beets, are all rela- tively stable, while lettuce is a high-risk vegetable crop. As in most of the other areas cotton has the greatest dollar-income variability of the field crops, but because it is a high-value crop its relative varia- bility is low. Expected income in all of the rotation is higher as more cotton is in- cluded. Of the cropping patterns without lettuce selected, no substantial differences in relative net income variability were observed except in the rotation where no cotton was included. The addition of let- tuce into the cropping systems substan- tially alters this situation. The 20-year average net income of let- tuce is $135 and the net income variance is $212. Although in recent years the aver- age net income has increased, the chance of losses is still high. The last six rotations listed contain a lettuce-barlev double crop. Again, we note that the addition of cotton is a stabilizing influence as well as being a major contribution to net income. The last two rotations without cotton have the highest variability of all the rotations attempted. Co-operative Extension work in Agriculture and Home Economics, College of Agriculture, University of California, ond United States Department of Agriculture co-operatinp Distributed in furtherance of the Acts of Congress of May 8, and June 30, 1914. George B. Alcorn, Director, California Agricultural Extension Service. 5m-9,'68(Jl905s)VL [12