333 Sj)ACKS GlANNINI FOUNDATIO N ■> > ■ / * i;:-;:vr::-c_!ir / oi^ c- emand for lAlfalfa Hayi in California JUM i I' !)86 Li BRARV The authors are: Kazim Konyar Postdoctoral Research Associate University of California Riverside Keith Knapp Assistant Professor of Resource Economics University of California Riverside Giannini Foundation Research Report No. 333 i^l^apf Agriculture and Natural Resources UNIVERSITY OF CALIFORNIA t PRINTED MAY 1986 TABLE OF CONTENTS I. INTRODUCTION j Purpose of the Study I Normative versus Positive Approaches to Demand Estimation 1 Plan of Study 2 II. NORMATIVE ESTIMATES OF ALFALFA DEMAND BY MILK COWS 2 Characteristics of the California Dairy Industry 2 Optimum Alfalfa Consumption per Cow 3 Regional Alfalfa Consumption by Milk Cows 6 Regional Alfalfa Demand by Milk Cows 12 III. ESTIMATES OF ALFALFA FED TO NON-MILK LIVESTOCK AND THE AGGREGATE NORMATIVE DEMAND FUNCTION 14 Alfalfa Consumption per Head 14 Livestock Numbers 16 Total Alfalfa Consumption by Region 18 Aggregate Normative Demand Function 18 rV. ECONOMETRIC ESTIMATES OF ALFALFA DEMAND IN CALIFORNIA 20 Regression Models 20 Data 21 Regression Results 21 V. COMPARISONS OF THE TWO APPROACHES TO ESTIMATING ALFALFA DEMAND 26 VI. CONCLUSIONS 28 REFERENCES 29 APPENDIX TABLES 32 t The Giannini Foundation Research Report Series is designed to communicate research results to specific professional audiences interested in applications. The first Research Report was issued in 1961 as No. 246, continuing the numbering of the GF Mimeograph Report Series which the Research Report replaced. Other publications of the Foundation and all publications of Foundation members are listed in the Giannini Reporter issued periodically. Single copies of this Research Report or the most recent Giannini Reporter may be requested from Agriculture and Natural Resources Publications, 6701 San Pablo Avenue, Oakland CA 94608. I. INTRODUCTION PURPOSE OF THE STUDY Alfalfa is one of the most important crops in California, averaging some 1 5 percent of total field crop acreage and generating 17 percent of total value of field crop production in California between 1979-82 (CaUfomia Crop and Livestock Reporting Service (CCLRS), Field Crop Review, Federal-State Market News Service (MNS), Alfalfa Hay: California Market Summary. In 1980 alfalfa and other hay ranked first among field crops in California in terms of acreage and second in value of production (Nuckton and Johnston, 1983). Alfalfa is also a substantial water user accounting for approximately 18 percent of water applied in irrigated agriculture (California Department of Water Resources, 1982) or roughly 15 percent of all water use in the state. Nationwide, alfalfa represented about 8 percent of all field crop acreage between 1979-82 (U.S. Department of Agriculture, 1983) Despite its importance there are relatively few studies of the market for alfalfa, either in California or in other parts of the country. In a classic study Schuhz (1938) provides estimates of nationwide demand for hay and other agricultural commodities for several time periods ending in 1929. Shumway, in a 1983 paper on Texas field crops, formulates the quantity of alfalfa supplied as a function of expected own- and competing-crop prices, input prices and fixed input quantities. In a 1974 study investigating the effects of government inter- vention in California field crop production desicions. Just uses the Nerlovian adaptive expectation - partial adjustment hypothesis for annual crops to formulate an alfalfa supply function. Blake and Clevenger (1984) combine an annual supply and demand model with a system of monthly equations to forecast alfalfa prices in New Mexico. Myer and Yanagida (1984) provide an econometric estimate of the demand for alfalfa in 1 1 western states and combine that demand relation with an ARIMA model to generate forecasts for quarterly alfalfa prices. The purpose of this paper is to estimate the demand for alfalfa in California. The first approach is normative and is based on a detailed linear programming (LP) model developed at the University of California (UC), Davis for feeding dairy cattle. This LP model can be used for estimating alfalfa demand on an individual milk cow basis, and has the further advantage that it is actually being used by dairy farmers in their feeding decisions. Aggregate normative demand is then esti- mated using an extensive set of data on milk cow characteristics provided by the dairy industry and data on the numbers and quantities fed to other livestock. The second approach is positive in that historical data are used to estimate alfalfa demand schedules econometrically as in Schultz (1938), Blake and Clevenger (1984), and Myer and Yanagida (1984); however, some alternative specifications are con- sidered. The demand estimates have a number of potential applications: forecasting alfalfa prices given production levels, forecasting long-run equilibrium levels of alfalfa production given production costs and cattle numbers, analyzing the effects of dairy price supports and water policies on the alfalfa market. The estimates are currently being used in a spatial equilibrium model of the California alfalfa market (Konyar, 1985; Konyar and Knapp, 1985). The procedures employed here could be used to estimate alfalfa demand in other areas and the derived demand for other inputs to the dairy industry. The empirical comparison of the normative and positive approaches to demand estimation may also be of interest. NORMATIVE VERSUS POSITIVE APPROACHES TO DEMAND ESTIMATION Shumway and Chang (1977) review the advantages and disadvantages of normative and positive ap- proaches to estimating supply functions for agricultural commodities. The normative approach makes use of detailed data and knowledge of the industry which may not be capturable in an econometric analysis. The normative approach is also useful for analyzing conditions significantly different from those experi- enced historically — conditions which one would hesitate to extrapolate from more general statistical relations. But the normative approach cannot incorporate all the relevant decisions, constraints, and options of the underlying behavioral model. For this econometrics is better suited being based more closely on observed behavior. Normative models often must use unverified behavioral assumptions and information sets, though in this study the dairy feeding model or a similar one is actually used by dairy farmers. A disadvantage of the econometric approach to demand estimation is that a relatively long time series is needed to obtain an adequate sample size. Changes in technology, manage- ment practices, or structure during this period may make forecasting the estimated demand curves un- reliable. Time trends and other information used to 1 adjust for these changes are likely to be imperfect in practice. Ideally, all available information should be used in estimating demand relations, suggesting a combination PLAN OF Normative dairy cow demand functions for alfalfa are estimated in Section II. The demand for alfalfa by other livestock is assumed to be perfectly inelastic. Estimates of consumption are given in Section III, as is the aggregate normative demand function. Econometric of the two approaches. But as a practical matter, mosi economic studies estimating demand or supply re- lations choose one or the other. This study which uses both provides a direct comparison between them. THE STUDY estimates of statewide demand for alfalfa are given in Section IV. Section V compares the results from the two approaches. Section VI provides a summary and conclusion. II. NORMATIVE ESTIMATES OF ALFALFA DEMAND BY MILK COWS The dairy industry is the single largest user of alfalfa in California. Approximately 70 percent of California's alfalfa crop is fed to dairy livestock and about 71 percent of that amount or 50 percent of all alfalfa is consumed by milk cows. In this section we provide normative estimates of alfalfa demand by milk cows in four different regions and then generalize them for the remainder of milk cows in the state. Regional demand estimates reflect the differences in dairy management CHARACTERISTICS OF THE Background and Terminology In 1982, the California dairy industry with its 940 thousand milk cows and close to 2 billion sales, ranked second in the nation to Wisconsin in both animal numbers and revenue. California's dairy industry is the nation's most productive with an average milk output of 15, 446 pounds per cow. The size of a dairy farm varies from a few cows to over 4,000. There were 2,845 dairy farms in the state in 1983, with an average herd size of 330 cows (Siebert, 1983). While the number of farms has been decUning over the years, the decrease has been more than offset by the increase in the average herd size, resulting in an increased number of dairy cows in the state. The diversity across the state in climates, topog- raphy, soils, cropping patterns, etc., results in sub- stantial variation in dairy famring methods. However, the State's dairy herd is relatively homogeneous biologically. Its components are: — cows, females who have calved, — calves, animals under one year old of either sex, — heifers, female calves between age one and two who have not yet calved, — bulls, uncastrated males of any age, — steers, castrated males of any age. practices, milk prices, and prices of other feeds. First, general characteristics of the state's dairy industry and of the four regions are described. Alfalfa demand on an individual milk cow basis is estimated using an LP model developed at UC Davis and resuhs are presented. The procedure for aggregating the individual-cow demand relations is then described, and the regional estimates given. IFORNIA DAIRY INDUSTRY Of 1.65 miUion dairy cattle and calves in 1982, 57 percent were cows, 28 percent were replacement heifers and 15 percent were bulls, calves, or steers (calculated from CCLRS, California Livestock Statistics, 1982). Heifers kept as replacements are bred when they are two years old; after giving birth they begin their lactation period which lasts an average of 305 days. Towards the end of the second month of the lactation period they are bred again; about two months before giving birth, milking stops. These dry cows make up 15-20 percent of a herd at any given time. In general, a cow reaches her highest output at her third lactation, after which average cows are culled (Peterson, personal communication). Feeding Dairy Cattle Nutrition is of fundamental importance to milk production, maintenance, growth, reproduction, and health of a dairy cow. Feed costs typically comprise about 50-65 percent of total dairy production costs and are a major factor affecting the profitability of milk production. Feeds for dairy cattle can be divided into (1) concentrates, feeds that are high in energy and usually low in fiber content, and (2) roughages, feeds that are 2 bulky, relatively high in fiber, and low in energy. (Feeds 81 to 102 in Appendix Table 1, are roughages.) Cows need a certain amount of roughage to maintain a healthy existence, proper digestive functioning, and production of milk with acceptable fat content. Hay, pasture, and silage are forage crops commonly used as roughage feeds for dairy cows. Alfalfa hay is the most widely used forage crop in California, making up approximately 85 percent of the hay fed to dairy cattle. Alfalfa is rich in protein, vitamins, calcium, potassium, and trace minerals and is slightly higher in energy value than most other forages. Because of its high nutritive value and its palatability, alfalfa is the main source of protein for California dairy herds and provides almost half the energy they consume (Pelissier and Bath, 1977, p. 21). Alfalfa is also fed to dry cows, calves, replacement heifers, and bulls. During the time that cows are dry, they need energy and protein to replenish body reserves which are critical to their performance in the succeeding lactation period. The amount of alfalfa a dry cow should consume depends, among other things, on her condition at the end of lactation, desired weight gain, and other feeds provided. This study follows Pelissier and Bath (1977) in its assumption that a dry cow consumes the same amount of alfalfa as at the end of lactation. QuaUty of Alfalfa Hay QuaUty is an important determinant of the price dairy farmers are willing to pay for alfalfa. Alfalfa quality is measured by its nutrient content, i.e., energy, protein, minerals, and vitamins. Because energy is the nutrient most limiting to milk production, energy content is the most important parameter in evaluating alfalfa quality. High energy alfalfa is also high in protein. In measuring the quality of alfalfa, two analyses are conducted: determination of percent dry matter (% DM) and of modified crude fiber (MCF). From these two tests, estimated net energy (ENE), digestible protein (DP), and total digestible nutrient (TDN) values of alfalfa hay can be reliably predicted (Bath, Marble, Smith, 1978). (See also Pelissier and Bath, 1977, pp. 5-1 1, for a definition and discussion of the above nutrients. Table 2 in the Appendix lists the average nutrient analyses of various types of alfalfa hay and of other feedstuffs.) Given soil and weather conditions, the quality of alfalfa depends largely on harvesting and postharvest- ing practices. The stage of maturity at which alfalfa is cut strongly influences its quality. Compared to other parts of the plant, the leaves are higher in protein and lower in fiber, so a pre-bud or early bud harvest has the most leaves and therefore highest quality. As alfalfa matures, it starts to lose the lower leaves, stems get thicker, protein declines, and fiber increases. However, continuously harvesting alfalfa in the early bud stage shortens the life of the stand resulting in reduced annual yields. Thus there is a tradeoff between yield and quality. Postharvest practices, including curing, baling, and handling, can greatly affect alfalfa quality. For this study alfalfa number 82, characterized in Appendix Table 2, is chosen as representative of the various qualities of alfalfa hay fed to dairy cows. Consumption Regions Four consumption regions are specified. Each is formed by several counties with large dairy cow populations surrounding a traditional alfalfa market center. The regions were defined to reflect differences in prices and availability of feedstuffs, in the price of milk, and in dairy management practices. These four regions contain 93 percent of California's milk cows (Table 1). Demand for alfalfa by the remainder of the state's milk cows is assumed to be similar to that in one of the four defined consumption regions.' OPTIMUM ALFALFA CONSUMPTION PER COW Model Alfalfa demand on an individual milk cow basis is estimated with a normative model. The model was first developed by Dean et al. (1972), extended by Bath and Bennett ( 1 980), and designed as a computer program by the Animal Science Extension unit at UC Davis, under the title "California Dairy Ration Program." The program is accessible through county dairy farm advisors for a small fee, and most of the state's dairy farmers use this or similar programs when formulating their rations. Therefore, it is believed that the results of the model are a very close approximation to dairy farmers' cattle feeding behavior. The LP model maximizes income above feed costs: ""m = Pm • f(qi, ■ • ■ , qn) - 2: Piqi 1 where: TTm = profit per cow, $/day 'The mountain counties of Siskiyou, Modoc, Shasta, and Lassen and the Sacramento Valley counties of Yuba, Tehema, Glenn, Butte, Colusa, Sutter, Yolo, and Solano were assumed to be similar to the North Valley region. The northern coastal counties of Del Norte, Humbolt, Mendocino, and Lake, and Trinity County were assumed to be similar to the Petaluma region. The Sierra counties of Plumas, Sierra, Nevada, Placer, Eldorado, Amador, Alpine, Calaveras, Tuolumne, Mariposa, Mono, and Inyo, and Imperial County were assumed similarto the Southern California region. The central coastal counties of Alameda, San Mateo, Santa Clara, Santa Cruz, San Benito and Monterey and the southern coastal counties of San Luis Obispo, Santa Barbara, and Ventura were assumed similar to the South Valley region. 3 Table 1. Consumption Regions, Counties Included, and Number Of Dairy Cows In Each, 1982 Region name Number of milk (alfalfa market centers) Counties included cows that have calved Pptaliima Sonoma 36,000 rpptaliima^ Marin 13,000 Contra Costa 2,500 Napa 2,000 53,500 North Valley Sacramento 21,600 (Escalon-Modesto-Turlock) San Joaquin 61,000 Stanislaus 107,000 Merced 90,000 iViaUCia 17 000 296,600 South Vfillf*v Fresno 54,700 (Tulare- Visalia-Hanford) Tulare 115,000 Kings 56,000 Kern 20,000 245,700 Southern California Los Angeles 4,300 (Chino Valley) San Bernardino 166,000 Riverside 90,000 San Diego 17,000 Orange 300 277,600 TOTAL: 873,400 (93% of state total of 940,000) Source: California Crop and Livestock Reporting Service, Livestock Statistics, 1982. 4 Pm = blend price of milk, $/ cwt f( ) = milk production function Pi = price of feed i, $/lb Qi = quantity of feed i, lb/ day. This maximization is subject to various types of constraints including nutrient and maintenance re- quirements for cows of various body weights, pro- duction requirements at various levels of milk pro- duction and fat tests, maximum voluntary roughage intake as concentrate intake is increased, minimum fiber and roughage levels to maintain normal milk-fat tests, palatability restrictions and a segmented pro- duction function. Given the price of milk and feed and the nutrient content of the available feeds, the model is solved for the profit maximizing combination of feedmix that makes up the daily ration. Other factors affecting the optimum ration, such as the cow's daily production ability, weight and lactation stage, and average milk fat, are imposed on the model externally at predetermined levels. Running the California Dairy Ration Program The California Dairy Ration Program formulates the optimum feed intake on a daily basis. A cow's abiUty level is specified as pounds of milk per day. The program is restricted to a 30-100 pound range in 10- pound intervals. The prices of other feeds used in the model are given in Table 2. Types of feed available and Table 2. Available Feeds and Prices in Each Region, 1982 Average Prices reed Number Feed Name Petaluma North South Valley Valley Southem California — dollars per hunderweight — 2 Almond hulls 3.15 3 19 3 19 7 Barley 6.45 6 50 asci 6 40 9 Beet pulp, dried 6.18 6 27 6 27 6 15 yj. 1 J 15 Brewers grain, wet 1.59 21 Com grain 6.18 6 50 24 Cottonseed meal, 41 E 7.95 7 86 7 60 25 Cottonseed meal, 41 S 9.00 8.64 8.25 26 Cottonseed meal, 50S 7.00 27 Cottonseed, whole 8.64 7.50 7.50 8.65 29 Dicalcium phosphate 15.66 18.00 15.66 19.00 36 Hominy feed, 5% fat 6.14 6.14 37 Hominy feed, low fat 7.00 39 Limestone, ground 1.10 2.72 42 Malt, barley 6.14 46 Molasses, cane 6.36 48 Monoammonium phosphate 29.00 50 Oats, lightweight 5.23 51 Oats, Pes 5.45 52 Orange pulp, dried 5.90 58 Rice bran 5.90 4.77 68 Soybean meal 10.59 74 Wheat, mill run 4.68 5.90 6.45 75 Wheat, soft 6.66 82 Alfalfa hay 3.00-10.00 3.00-10.00 3.00-10.00 3.00-10.00 89 Com silage 1.50 1.50 90 Cottonseed, hulls 2.95 2.95 2.00 93 Oat silage, boot 1.40 1.50 1.50 94 Oat silage, dough 1.40 1.50 1.50 Blend price of milk 13.07 12.88 12.96 13.24 Sources: U.S Department of Agriculture, Federal-State Market News Service, Feedstuff Prices in California Monthly and Yearly Averages, 1982. Personal communication with feed suppliers, county dairy farm advisors and dairy farmers. See Appendix Table 1 for a detailed nutrition composition of each feed. 5 their prices vary from region to region. The feeds listed in Table 2 are available most of the time; their average 1982 prices are given. A blend price of milk is calculated for each of the four regions as a weighted average of quota, base and overbase prices of milk. Quota, base, and overbase prices are determined statewide and depend on the shares of various grades and prices of milk that go into each. (For an explanation of this complex pricing scheme, see California Department of Food and Agriculture, 1982a, 1982b, 1981, 1974; Milligan, 1978; and Shanbazian, 1981.) These prices differ for each dairy farmer depending on location differentials and assigned milk quota levels. The blend price for a region is calculated as an average of the quota, base, and overbase prices from CCLRS, Dairy Information Bulletin, 1982, weighted by the aggregate quota, base and overbase milk in each respective region. These aggregate regional quota and base assignments are calculated from county assignments by the California Department of Food and Agriculture (1982c), which are aggregates of all milk producers' quota and base in a given county. The overbase is the difference between total milk output and the sum of quota and base. The resulting blend prices are given in the last row of Table 2. A cow's output varies during lactation, with its highest level early in the period, then a steady decline thereafter. Table 3 shows the distribution of output, over a typical 10-month lactation, expressed as a percentage of total output. Dairy farmers, especially ones with large herds, generally separate their milking cows into several strings according to level of produc- tion or stage of lactation, and feed each group a different combination of roughage and concentrate that closely meets the nutritional needs of each string. This practice has been shown to be very cost-effective and dairy farmers are strongly urged to follow it (Pelissier and Bath., 1977, pp. 71-80; Bath, 1982; Pearson, 1979). A cow is moved from one string to another throughout a given lactation as her milk output and hence nutrition requirements change. In this study, dairy farmers are assumed to feed their cows in four separate production strings, producing 40, 60, 80 and 100 pounds of milk a day, respectively. For instance, a typical cow that produces 16,000 pounds of milk in a 10-month lactation will be fed in the 80-pound producing string during the first three months of the lactation, in the 60-pound string for the next four months , and in the 40-pound string for the last three months. Given the input prices and blend prices of milk, the Table 3. Monthly Milk Output as a Percentage of Total Output Month 1 2 3 4 5 6 7 8 9 10 Percentage of total output 13.6 13.4 12.3 11.3 — Percent — 10.3 9.4 8.5 7.7 7.0 6.5 Source: Nyles Peterson, Dairy Farm Advisor, San Bernardino County. model is solved for various prices of alfalfa for each production string. The alfalfa price is varied from $3 to $10 at $1 intervals. Alfalfa consumption is constrained to a minimum of 7 pounds per day in the first three regions and to 10 pounds per day in Southern California.2 The minimum constraint is higher for Southern California because there are fewer substitutes for alfalfa there. There are several other predetermined specifications for the model, such as the average cow weight and milk fat, which are assumed to be the same for each region. (See Appendix Table 2 for these other specifications in a typical computer printout of the program.) The optimum daily alfalfa consumption in the four regions, for four production strings at various alfalfa prices is given in Table 4. Results show that at low prices more alfalfa is fed to the lower production strings than to the higher ones. High-producing cows need more energy than the low producers, but since the maximum voluntary intake is a constraining factor, the energy requirement of the high producers is initially met by specified levels of concentrates, supplemented by alfalfa; where as, low producers can obtain most or all of their energy needs from alfalfa alone if its price is sufficiently low. REGIONAL ALFALFA CONSUMPTION BY MILK COWS The results of the normative model are on a per cow per day basis. To obtain regional aggregate demand curves daily alfalfa consumption per cow must be converted to yearly consumption per cow and then aggregated over all cows in a given region. To accomplish this, three sources of information are needed. First, information on the distribution of cows among yearly production groups must be obtained. ^These constraints were imposed after consulting with dairy farm advisors (Bennett, Peterson). 6 Table 4. Optimum Daily Alfalfa Consumption Per Cow By Region, Alfalfa Price, and Level of Milk Production Alfalfa price Milk production (lbs/ day) 40 60 80 100 dollars per hundredweight — pounds of alfalfa — Petaluma 9 JJ.60 ZO. 10 27.17 21.00 A H /l.UU 7 I AA zl.UU 25.00 21.00 « J 7 fV\ /.UU 7 AA /.UU •7 CA 7.54 21.00 O /.uu 7 AA 7.0U T AA 7.00 15.50 / 7 riA /.uu T AA 7.00 n AA 7.00 8.47 8 0 7 /.UU 7.00 T AA 7.00 7.00 9 7.00 7.00 7.00 7.00 10 7.00 7.00 7.00 7.00 North Vallley 3 7A 28.00 24.00 A zl.UU 1 Q 07 lo.o / 7/1 70 21.43 1A CO 1 ^ ^7 18.42 1 T CA 17.50 £ P 7 /.UU n AA /.uu 7 AA 7.00 1 £ '^A 16.20 / 7 nr\ /.UU ^ AA /.UU 7 AA 7.00 1 A O >4 10.84 8 o 7 Ar» /.UU AA /.UU 7 AA 7.00 T AA 7.00 9 7.00 7.00 7.00 7.00 10 7.00 7.00 7.00 7.00 South ValUey ■» J jo.jU 31.59 28.00 A AA 24.00 A •t zl.UU 18.87 24.28 '^'^ AA 22.00 e J 1 7 HA Iz.UU 15.85 21.40 21.71 £ V 1 1 7>l J 1.24 15.80 19.04 20.80 7 7 HA /.UU 9.92 13.41 1A CC 20.55 o o 7 AA /.UU 8.13 13.03 15.38 0 y /.UU 8.13 13.03 1 C 1 A 15.10 10 7.00 8.13 13.03 15.10 Southern California 3 36.92 30.90 27.73 23.83 4 25.76 29.48 24.87 21.00 5 14.27 16.97 20.16 20.39 6 10.00 10.00 13.97 17.93 7 10.00 10.00 11.72 17.70 8 10.00 10.00 11.72 16.08 9 10.00 10.00 11.72 15.05 10 10.00 10.00 11.72 14.83 Source; Calculated 7 Second, the distribution of the yearly milk output per cow within a given production group is needed for a typical ten-month lactation period. Third, the eco- nomic break-even point for cows in each production group must be determined. When the price of alfalfa increases, some categories of cows become unprofitable to keep and are culled. Distribution of Cows Among Production Groups The production data needed to calculate production group assignments are available on tapes at two data processing firms located in Tulare, California, and Provo, Utah. These firms provide computer services for the California Dairy Herd Improvement Association (DHIA), a joint venture sponsored by California dairy farmers, UC Cooperative Extension Service, and the Dairy Cattle Research Branch of the U.S. Department of Agriculture. Dairy farmers, through their local, state and national DHIA organizations, are responsible for the conduct of the program in compliance with established policies, rules and standards. The members of this voluntary association send detailed information on most of their cows to one of these two firms including the production and feed ration data discussed above. The information further includes such things as the cow's age, physical traits, lactation stage, monthly and complete lactation milk output and fat content of the milk. The two data firms compile this information and provide it to dairy industry concerns. It becomes part of the national data base, aiding research on herd management and genetic improvements, benefiting the entire dairy industry in the long run. Data on computer tapes from the Animal Science extension, UC Davis, show that some cows milk well beyond the desired 305 day lactation and some go dry sooner than 305 days. Thus, a cow's output depends not only on her ability to produce but also on the length of the lactation. To assign the cows into production groups, each cow's total output is adjusted to 305 days by using adjustment formulas and factors obtained from Thompson. The resulting 305-day production is then grouped into production categories at 1000-pound intervals. A further adjustment needs to be made to the production group assignments before they can be generalized regionally. Dairy farmers who are members of DHIA tend to be more efficient operators with greater average milk output than non-DHIA dairymen. Given the total milk output and number of dairy cows in DHIA member farms in the demand regions, average output per DHIA cow is calculated and compared with the residual output and cow numbers assumed to be non-DHIA. Results show that the average output per DHIA cows is 2,000 pounds greater than for a non-DHIA cow in Petaluma, 1,00 pounds greater in North Valley and in Southern California; there is no appreciable difference in the South Valley region. The regional distributions including total cows in taking the weighted average of DHIA and non-DHIA distributions, where non-DHIA distributions are assumed to have the same shape as DHIA distributions but a different mean. The weights used are the percentage of cows in DHIA and non-DHIA farms. Results are given in Table. 5. Distribution of Output per Cow over a Ten-Month Period Given the production groups in Table 5, the expected monthly ouput for each group is calculated by applying the percentages in Table 3. To make the monthly output levels adaptable to the normative model, output is divided by 30.5 for the average daily output for a given cow in a given month. Results range from 21 pounds of milk per day in the tenth month for a 10,000- pound producer to 133 pounds of milk per day in the first month for a 30,000-pound producer. Recall that dairy farmers are assumed to separate their cows into four production strings each of which is fed a different ration. Those with averge daily output between 0-40 pounds are assumed to be fed in a 40 pound string; 41-60 pound producers in a 60 pound string; 6 1 -80 pound producers in a 80 pound string; and 81 pounds or more in a 100 pound string. Table 6 shows the total number of months a given production group is fed in a given feeding string. Yearly alfalfa consumption by each production group is calculated from Tables 4 and 6, using 30.5 to convert daily consumption in Table 4 to monthly consumption. Recall that cows are assumed to be fed the same ration during their two dry months as at the end of their lactation. Annual alfalfa consumption by production group is then aggregated using the data in Table 5 to determine regional quantities demanded for various prices of alfalfa. Break Even Point Before the calculated price-quantity combinations can be used to estimate demand curves, adjustments in aggregate quantities consumed must be made for cows culled as the price of alfalfa increases. Currently a dairy cow producing 16,000 pounds in her second or later lactations is considered a marginal cow: Any cow producing less is culled. The rationality of this management practice can be checked by calculating yearly profit per cow for the 15,000-17,000 pound categories. The yearly profit per cow is calculated as the difference between total revenue and total variable cost. The feed portion of the variable cost is obtained from the Dairy Ration Program results (see Appendix Table 2). Added to that are other variable costs such as labor and variable operating expenses per cow (California Department of Food and Agriculture, 1982d). Table 7 shows the yearly net revenues for the three production groups at various prices of alfalfa. At an alfalfa price of $5 per hundred weight (cwt), the 15,000 pound category is unprofitable to keep. Net 8 Table 5. Distribution of Cows By Level of Milk Production and By Region Southern Milk output Petaluma North Valley South Valley California per 305 day lactation llUiilL^Cl llLXillL/Cl pClL^lll Ul UUIIlUCI pCICCIlL Ul IlUIllDCI (pounds) tntnl tntal Ul CtJWa total tntal lUldi Oi CUWa 10,000 or less 7.30 3904 7.32 21719 5.76 14152 2.82 7840 11,000 5.11 2734 5.24 15529 3.37 8280 2.94 8162 12,000 454 3501 6.69 19846 4.96 12187 4.47 12412 13,000 7.95 4251 8.26 24505 7.06 17346 6.47 17958 14,000 8.57 4587 8.88 26329 8.85 21744 8.61 23895 15,000 9.57 5122 9.63 28568 10.12 24865 10.24 28423 16,000 9.96 5326 9.85 29228 10.73 26364 11.27 31289 17,000 9.50 5083 9.44 27987 10.48 25749 11.30 31357 18,000 8.73 4671 8.52 25283 9.62 23636 10.44 28991 19,000 7.67 4104 7.20 21350 8.05 19779 9.07 25188 20,000 6.01 3218 5.77 17108 6.54 16069 7.36 20428 21,000 4.60 2460 4.40 13053 4.92 12088 5.46 15152 22,000 3.29 1758 3.16 9363 3.60 8845 3.88 10776 23,000 2.10 1125 2.20 6514 2.33 5723 2.50 6953 24,000 1.37 735 1.43 4227 1.48 3636 1.46 4064 25,000 .79 421 .89 2634 .91 2236 .85 2305 26,000 .46 247 .51 1525 .58 1425 .45 1251 27,000 .25 132 .29 862 .29 713 .20 548 28,000 .13 69 .16 475 .16 393 .09 254 29,000 .06 33 .10 297 .09 221 .08 210 30,000 or more .04 24 .07 199 .10 246 .03 96 TOTALS 100.00 53,500 100.00 296,600 100.00 245,700 100.00 277,600 Percentage of cows (76) (67) (57) (43) that are in DHIA Source: See text 9 Table 6. Number of Months Cows are Fed in a Production String, Depending on Milk Output Milk output per 305 Feeding strings by daily output of milk (lbs/ day) day lactation (pounds) 40 60 80 100 Months 10,000 or less 10 2 — 11,000 9 3 ' — ' — * 12,000 8 4 — 13,000 7 5 — — 14,000 6 6 — — 15,000 5 5 2 16,000 5 4 3 — 17,000 4 4 4 — 18,000 4 3 5 — 19,000 4 3 3 2 20,000 — 6 4 2 21,000 6 3 3 22,000 5 4 3 23,000 5 3 4 24,000 4 4 4 25,000 4 3 5 26,000 4 3 5 27,000 3 3 6 28,000 3 3 6 29,000 6 6 30,000 5 7 Sources: See text 10 Table 7. Net Revenue Per Cow Per Year for 15,000-17,000 Pound Producing Cows, by Region Alfalfa price Pound of Milk Production 15,000 16,000 17,000 dollars per hundredweight — dollars of — Petaluma 3 101.42 231.42 344.43 4 46.52 176.52 288.30 5 - 81.50 48.42 158.98 6 -107.21 22.80 133.36 7 -132.83 - 2.83 107.74 -158.45 - 28.45 82.12 9 -184.07 - 54.07 50.50 m -209.69 - 79.79 30.88 North Vallley 3 134.64 263.64 367.32 4 13.86 142.86 255.08 5 - 61.79 67.22 175.78 6 -107.23 21.77 125.76 7 -132.85 - 3.85 100.14 8 -158.47 - 29.47 74.52 9 -184.09 - 55.09 48.90 10 -209.71 - 80.71 23.28 South Vallley 3 161.28 291.28 394.96 4 41.72 171.72 283.94 5 - 33.93 96.08 204.64 6 - 70.26 59.48 161.94 7 -118.41 11.59 111.92 8 -153.18 - 28.18 75.32 9 -185.51 - 55.51 41.16 10 -217.84 - 87.84 7.00 Southern California 3 38.88 170.88 280.70 4 11.43 143.43 251.42 5 - 59.94 72.06 178.22 6 -106.00 26.01 130.64 7 -134.36 - 2.36 105.02 8 -172.79 - 40.79 65.98 9 -203.90 - 71.90 34.26 10 -241.42 -109.42 - 3.45 Regional milk prices are those used in the normative model. Source: Calculated 11 revenue for the 16,000 pound category becomes negative with an alfalfa price of $7/ cwt. For the 17,000 pound category, net revenue is positive except in Southern California with alfalfa at $ 10 / cwt. The results in Table 7 are consistent with the current practice of culling the 15,000-pound milk producers and treating the 16,000-pound category as marginal. Table 7 does not show the final break-even point, since a fixed blend price of milk is used in calculating the net revenues. California law governing the dairy industry requires bimonthly adjustments in the price of market milk with changes in the cost of production. When an upward adjustment is made, some of the negative profits shown would be offset by an increased blend price. Increases in the cost of production lead to an increase in the minimum price of Class 1 milk via an automatic formula. (See California Department of Food and Agriculture, 1982e, p.5.) The ingredients of this formula are the Class 4 price, the cost of milk production, and the average weekly earnings for manufacturing produc- tion workers. An increase in the Class 1 price then increases the quota price, which in turn increases the blend price. Class 1 milk makes up about 75 percent of the quota milk, and the quota's share in the blend price is around 60 percent. Net revenues are then recalculated to reflect the change in blend price, given the increases in the cost of production. Results show that losses are offset up to an alfalfa price of $8/ cwt for Petaluma, South Valley, and Southern California, and up to $9/ cwt for North Valley. The 15,000 pound category is unprofitable at any alfalfa prices above $5 / cwt, while the 1 7,000 pound category generates profits at all prices. The break-even price of alfalfa for the 16,000 pound category and the expected reduction in the number of cows in each region are given in Table 8. It is possible that, as low producers are culled, there will be genetic improvement in a herd. This would increase the mean of the regional distributions which, in turn, would affect the aggregate alfalfa consumption. However, in the short run, the improvement in average milk output is negligible; including it in the calculations would unduly compli- cate the analysis. Percentages used to calculate the number of cows culled is the difference between the percentage of cows in the 16,000 pound group (see Table 5) and the percentage of 16,000 pound producers in their first lactation (see Appendix Table 3). Cows are not usually culled until after this first lactation, when they reach maturity. The aggregate optimum alfalfa consumption is adjusted downward for the quantity that would have been consumed by culled cows. The resulting price- quantity combinations, converted to tons, are given in Table 9. The fifth column of Table 9 gives alfalfa demand by milk cows not included in the four regions. Since the number of these milk cows is small (7 percent of all milk cows in the state), no attempt was made to estimate separate alfalfa demand relations for them. They are grouped under "other"; demand is estimated using the results from the major consumption region closest to each particular county or subregion (for details see the description of the consumption regions in Section II). REGIONAL ALFALFA DEMAND BY MILK COWS The data in Table 9 are fitted using ordinary least squares to a log-linear demand function with the following form: Q = aP'' where: P is the price of alfalfa Q is the quantity consumed a, b are parameters Several other functional forms were estimated (e.g., linear and quadratic) but the log-linear form gave the best results statistically. Parameter estimates and summary statistics are given in Table 10. Demand for alfalfa is apparently less elastic in the southern regions — an outcome not unexpected since there are fewer substitutes for alfalfa there. Table 8. Break-even Price of Alfalfa for 16,000 Pound Milk Producers and Number of Cows Culled Number of cows Break-even price Number of culled as a percentage Regions of alfalfa cows culled of the total dollars per cwt number percent Petaluma 8 3,602 6.73 North Valley 9 18,629 6.28 South Valley 8 16,776 6.83 Southern California 8 19,893 7.17 Source: Calculated 12 Table 9. Aggregate Annual Alfalfa Consumption by Milk Cows by Price of Alfalfa and Region Alfalfa Price North Vallev South Vallev Southern California Other Total $/ton 66 — — — 301,813 — — — — — _ 1,742,008 tons 1,443,616 — — — — — 1,615,981 — — — - 387198 — — — — 5490616 88 212,970 1,132,941 942,663 1,350,496 259259 3898329 110 77,981 777,467 716,312 861,713 162796 2596269 132 73,678 410,687 678,632 580,333 126726 1870056 4fil 145 554 826 102287 1580462 176 63,925 379,945 384,897 509,611 91708 1430086 198 63,925 356,081 384,102 506,243 89703 1400054 220 63,925 KiC AO 1 356,081 ^A^ Afii 1 loom Source: Calculated Table 10. Estimated Alfalfa Demand by Milk Cows, by Region Constant Price Coefficient Region In a b IN. Petaluma 18.009 -1.338 81 (1.283) (0.261) iNortn valley 20.202 -1.419 91 (0.885) (0.180) ^outn Valley 18.978 -1.159 .yyj (0.453) (0.092) Southern 18.807 -1.085 91 1 aliir^mia \^aliiui ma (0.681) (0.139) Other 18.172 -1.291 .95 (0.592) (0.121) Total 20.585 -1.227 .95 (0.568) (0.116) Standard errors are in parentheses. All estimated coefficients are significant at the 1 percent level. Sources: Estimated from data in Table 9. 13 III. ESTIMATES OF ALFALFA FED TO NON-MILK LIVESTOCK AND THE AGGREGATE NORMATIVE DEMAND FUNCTION In this section alfalfa demand by non-milk dairy and non-dairy livestock are estimated on a per-animal basis and then are aggregated over all respective animals in 25 regions. Calculations made here show that in 1982, 41 percent of total alfalfa consumption in California is by milk cows, 18 percent by non-milk dairy animals, 17 percent by beef cattle, and 24 percent by horses. Alfalfa consumption demand by animals other than milk cows is assumed to have zero elasticity for several reasons. For beef cattle the share of alfalfa in the daily ration is small compared to other feeds. It is mostly used to supplement pasturage, usually through the winter when grazing is not available, or in emergencies when natural feed is late or less than expected. Therefore a change in consumption by beef cattle is not likely to affect aggregate alfalfa consumption substantially.^ Alfalfa provides energy more efficiently than other feeds, especially for growing calves and heifers and to a certain extent for fattening cattle in the feedlots. Therefore, a change in the price of alfalfa is expected to bring about a relatively small change in the amount fed per animal. Alfalfa is a preferred horse feed because of its high nutritional value and palatability, so horseowners are expected to show a very small response to changes in alfalfa price. Alfalfa is an important part of the non-milk (replacement heifers, calves, and bulls) dairy livestock ration. It is needed for its rich nutrient content in aiding the healthy growth of young animals. Young cattle and calves are grazed on pasture and fed other forages as available; alfalfa supplements these other feeds. There- fore, dairy farmers are not expected to greatly alter the amount of alfalfa in the rations of non-milk dairy cattle with changes in the alfalfa price. Historical and regional data on alfalfa consumption by non-milk livestock are not available, but it seems reasonable to assume in the short run that alfalfa demand by these animals stays constant as the price changes. First, alfalfa consumption per head by animals other than milk cows is discussed. Then, regional cattle and horse numbers are estimated, and their aggregate alfalfa consumption is calculated. ALFALFA CONSUMPTION PER HEAD Dairy Cattle The regional quantity of alfalfa consumed per milk cow at the 1982 average alfalfa price is calculated from the demand equations in Table 10 and is given in Table 1 1. The source for other (statewide) estimates in Table 11 isKingetal., 1980. Beef Cattle The three main stages in raising beef cattle for slaughter are breeding herd to weamer calf, stocker (weaner calf to yearUng feeder), and feedlot finishing. These three stages take approximately 19 to 24 months; feed requirements change throughout the process. In the first stage the newborn calf is kept with the mother for up to 8 months where it is sustained with her milk and by grazing on pasture. After reaching 400-500 pounds it is weaned and considered a yearling. Some of these calves are kept as replacements and remain on grazing; the rest become stocker cattle and are fed a high forage ration of pasture and hay. According to Dunbar, two-thirds of this hay is alfalfa. This second stage lasts about 5 months when yearlings weigh as much as 700 pounds. The amount of alfalfa consumed at this stage is estimated to be .18 ton per year per animal and is reported under lightweight calves fed in Table 11. (Most ofthe yearlings are under 1 year of age and thus are still considered calves.) The third stage of beef production is performed in a feedlot where the ration is based largely on concentrates. The purpose is to fatten feeder cattle to a grade of Good or Choice beef to meet market demand, but they are also fed hay for proper digestion. Cattle can stay in the feedlot from 2-5 months and sometimes longer, depending on weight at arrival, daily weight gain and desired weight gain. On average, feedlot cattle consume .2 ton of alfalfa hay per year. Alfalfa consumption by the remaining beef cattle is given in Table 11. Horses The amount and type of feed a horse needs varies according to its weight and the work it performs. Like cows, horses require energy, protein and vitamins for maintenance, growth, work and reproduction. Many different feeds can supply the necessary nutrients for horses, but alfalfa is the most nutritious of hays available in California. It can make up all or part of a ration, and it is the most common roughage fed to horses in the state. There is no reUable estimate of there are computer programs for forniulating beef rations, they are not used uniformly and are not as accessible as the California Daiiy Ration Program. 14 Table 11. Estimated Annual Quantity Fed Per Head, Milk Cows, 1982, and Other Livestock, 1974-1976 Share of Feed Other Alfalfa alfalfa in grains concentrates Hay Pasture^ hay" total ration Dairy tons — pcrccni Milk cows Petaluma 2.33 North Valley 2.47 south Valley 3.51 I'aliTVimiQ J. /j \j. lyyj Z.DDJ Af- 'V3 U.JO / C\ 4.08 U.HZo 1 7 Bulls c H. J il Oj Beef Cows 0.013 0.045 1.00 4.65 0.67 12 Heifers and bulls 0.70 2.24 0.47 10 Lightweight calves fed 0.215 0.115 0.27 0.18 20 Feedlot finishing 1.12 0.48 0.30 0.20 11 "The estimates of pasture consumption are reported in Animal Unit Months (AUM) in the above source and are converted into tons, multiplying by .4 ton/ AUM. ""King et al. do not distinguish between alfalfa and other hay types in their estimates of hay fed per head. Estimates for alfalfa are derived by assuming that the share of alfalfa in total hay consumption is 95 percent for dairy and 67 percent for beef livestock. •^Dashes indicate not applicable. Source: Estimates for milk cows are calculated from the demand equations in Table 10 assuming an avergage 1982 alfalfa price of $1 13.03 in Petaluma, $1 14.02 in North Valley, $99.03 in South Valley, and $98.75 in Southern California. Estimates for non-milk livestock are calculated from King et al., Trends in California Livestock and Poultry Production, Consumption, and Feed Use: 1961-1978, Tables 6 and 7. 15 horses, but alfalfa is the most nutritious of hays available in Califonria. It can make up all or part of a ration, and it is the most common roughage fed to horses in the state. There is no reliable estimate of alfalfa consumption per horse; however, various studies suggest amounts of 5-15 pounds per 1,000 pound horse at medium work (Albaugh et al., 1983; Baer, 1982). Another difficulty involves the total number of horses reported statewide. Specifically, the various estimates show large discrepancies. In the light of these un- certainties total statewide alfalfa consumption by horses is taken to be the difference between available supply of alfalfa and the estimated consumption by all other livestock net of horses. Aggregate alfalfa con- sumption by all dairy and beef cattle is estimated; the resulting quantity is subtracted from available supply (including net imports) to arrive at total consumption by horses. The regional consumption by horses then depends on the number of horses in each region. Even though regional horse numbers may not be precise, they are nevertheless expected to reflect the relative shares of the horse population among the regions. LIVESTOCK NUMBERS Livestock numbers are estimated for 25 regions in California. All types of cattle and horses are considered in order to estimate the regional breakdown. Con- sumption by milk cows is then netted out to determine alfalfa consumption by non-milk livestock. Dairy Cattle The available data provide county-level numbers of milk cows, but only statewide numbers for the rest of the dairy livestock. The CCLRS, California Livestock Statistics gives Statewide data for heifers, calves, bulls, and steers. Expressing these aggregates as percentages of milk cows in each county could give county-level estimates for the remaining categories of dairy animals. But CCLRS in 1982 reports dairy calves, bulls, and steers together with the same categories for beef. Earlier livestock reports (1955 to 1970), however, separate the data by dairy and beef. Thus, the average of the historical allocation between beef and dairy was used to separate them in 1982. About 75 percent of the dairy farmers in Riverside and San Bernardino counties send their weaned female calves to other parts of the state (two-thirds) or out of state (one-third) to be raised on pasture until they are ready to give birth to their first calf. Smaller percentages are used for heifer and calf numbers in these two regions to account for the outshipments. The resulting estimates are given in Table 12. Beef Cattle Basically the same procedure is used to derive regional beef heifer, calf and bull numbers as was used for dairy cattle. However, estimating regional numbers for beef steers requires some additional calculations. Steers are castrated males more than 1 year old and over 500 pounds. Most of them are either in feedlots (73 percent in 1982) or destined for feedlots to be fattened for market. They are not, therefore, equally allocated among regions in proportion to beef cow population since feedlots tend to be concentrated in certain areas of the state. Availablility, price of feed grains, and to some extent the weather determine feedlot location. Most are located in Southern California and the San Joaquin Valley. To derive the regional beef steer numbers, all dairy and beef cattle categories estimated to this point are summed for each region and the results are subtracted from the regional "all cattle and calves" figures in CCLRS Livestock Statistics to yield an estimate for beef steer and feedlot cattle. For those regions without feedlot operations, the net figures are negative because they included steer calves sold to feedlots right after weaning. Since individual cattle categories have to add up to the all cattle and calves total for each region, negative amounts were forced to zero by subtracting them from the "beef calves" number. The estimated regional beef steer numbers correspond closely with the more aggregate regional feedlot cattle numbers reported in CCLRS. The final figures are in Table 12. Horses Estimates of the California horse population vary widely. One estimate by Collins (1978) reported 900,000 horses for 1975. A 1978 estimate by the U.S. Department of Commerce gave the California horse population as 112,000, while an American Horse Council estimate for 1980 was 843,000. The California State Horsemen's Association provides county esti- mates for 1980 that total 1,040,000. In this study estimates of the regional shares of the horse population rather than absolute numbers will have to suffice. Precise numbers would not be that much help anyway because of the uncertainty about alfalfa consumed per horse. County estimates by the California State Horsemen's Association are used for this study. Table 12 gives the estimated regional horse numbers. 16 Table 12. Regional Livestock Numbers, 1982 Dairy Beef Beef County All calves Dairy calves or cattle ^heifers bulls CUlU region and MUk Dairy buUs, and Beef Beef buUs, Beef feedlot No. calves cows heifers steers) steers cows heifers steers) buUs cattle Horses Pctfllums 1 Petaliima* 907000 17844 VOOJ jKy*yy 197S U 17^70 J / J /U North VaDey 2. Sacramento ziooo lUo// 506 37000 6698 39050 2265 0 50404 3. San Joaquin 215000 61000 30152 20346 1428 46000 8328 44931 2816 0 34428 ^ ^taniQlfiiic 315000 107000 J JUOO 9S04 49000 9S71 Z J / 1 ZZ73Z QOOOO 44488 ■?00I8 f>8000 19T10 00000 4169 8007 990S9 ZZ7JZ 130000 17000 8403 5670 398 31000 41444 1807 I oy 1 18S7/^ 0181 7 1 O 1 South Valley 7. Fresno 372000 54700 27039 18244 1280 53000 9595 70856 3244 134043 39018 8. Tulare 325000 115000 56846 38356 2691 34000 6155 45455 2081 24416 12898 9. Kings 128000 56000 27681 18678 1311 6000 1086 8021 367 8855 5738 10. Kern 240000 20000 9886 6671 468 61000 11043 81551 3734 45648 29837 Southern California 1 1 . Los Angeles 4ZU0U 430U ZiZb I A1A 1434 1 A 1 iui jUUU 9Uj DD64 Zl 144 I jU J 34 k3all OdllOiUlllU 980000 I^^^OOO 41040 3885 7000 1267 4005 428 0 58527 1 ^ U t\7f>r*ci/l9 U. IXlVCIolUC IQOOOO QOOOO 90894 9106 9000 12032 23338 82626 It. OCUI t-ZlCgU S7000 17000 ( /VAA/ 840 1Q8 9000 1699 190'^9 551 2317 35690 1 S Ctr'nnnf^ I -J . \J\ ul IgC 14000 '100 148 1 to 100 7 3500 634 4679 214 4418 24523 Other 16. Mountain'' 314000 3850 1903 1284 90 182000 32948 80785 11140 0 58759 17. North Coast" 128000 16950 8379 5653 397 57000 10319 25813 3489 0 44725 18. Trinity 7000 0 0 0 0 3000 543 3273 184 0 3835 19. Yuba 38000 1700 840 567 40 16000 2897 14977 979 0 10646 20. Sacto. Valley'' 255000 20900 10331 6971 489 106000 19190 84631 6488 0 63153 21. Solano 4/UUU I lUU jO/ ZD 1 soon Z/ ID 018 6977 oz / / ■loss 22. Sierra" 192000 1250 618 417 29 104000 18828 60493 6366 0 98394 23. Central Coast 328000 10150 5017 3385 238 102500 18556 137032 6274 A AO AO 44846 /800Z 24. South Coast^ 256000 10300 5091 3435 241 106500 19280 104633 6519 0 55017 25. Imperial 450000 400 198 133 9 3000 543 4011 184 441522 6886 State Totals: 5,000,000 940,000 425,000 290,000 22,000 1,160,000 210,000 1,090,000 71,000 792,000 1,040,913 *Sonoma, Marin, Contra Costa, Napa counties ""Siskiyou, Modoc, Shasta, Lassen counties "Del Norte, Humbolt, Mendocino, Lake counties ''Tehema, Glenn, Butte, Colusa, Sutter, Yolo counties "Plumas, Sierra, Nevada, Placer, El Dorado, Amador, Alpine, Calaveras, Tuolumne, Mariposa, Mono, Inyo counties ^Alameda, San Mateo, Santa Clara, Santa Cruz, San Benito, Monterey counties *San Luis Obispo, Santa Barbara, Ventura counties Sources: Calculated using CCLRS, California Livestock Statistics and California Dairy Industry Statistics, various issues. 17 TOTAL ALFALFA CONSUMPTION BY REGION Tables 11 and 12 contain the necessary information to calculate the regional aggregate alfalfa consumption by dairy and beef cattle. Per-head consumption figures for milk cows in Table 11 are used to estimate the aggregate alfalfa consumption by milk cows in the four major dairy regions. For the remaining regions alfalfa consumption per milk cow is assumed to be the same as that in the closest major consumption region as discussed in Section II. Dairy steers (missing in Table 1 1) are assumed to consume the same amount of alfalfa as dairy bulls. These steers number only 8,000 statewide so this assumption is not critical. No estimate for per head alfalfa consumption by beef steers is given in Table 1 1 . Twenty-seven percent of the "Steers and Bulls" category in Table 12 are steers that are not yet in feedlots. Lacking data to accurately estimate their consumption, they are assumed to consume the same amount of alfalfa as feedlot cattle. This may result in a slight underestimation of alfalfa consumption because an animal's ration outside a feedlot tends to contain more roughage and hence more alfalfa than a feedlot ration. The alfalfa supply in 1982 was 7,362,159 tons. This includes production of 6,863,387 tons plus 38 1 ,772 tons of net imports. Subtracting the aggregate alfalfa consumption by dairy and beef cattle (column 2 plus column 3 in Table 3) from the total supply gives statewide consumption by horses. (This also includes a very small amount consumed by sheep.) Dividing total alfalfa consumed by horses by the estimated horse population gives 1.67 tons per year of 9.15 pounds of alfalfa per day per horse. This estimate is within the range of 5-15 pounds per day estimated by Albaugh et al. (1983) and Baer (1982). The regional aggregate alfalfa consumption estimates are given in Table 13. AGGREGATE NORMATIVE DEMAND FUNCTION Estimated alfalfa demand by milk cows was given in Table 10. Table 13 gives estimated alfalfa consumption by all livestock in 1982 in each of 25 regions. Summing the totals of columns 1, 3 and 4 gives estimated alfalfa consumption by all non-milk livestock. This value is 4.295 million tons and is assumed to be invariant with respect to alfalfa prices for the reasons given earlier. Using the estimated equation in Table 10 for all milk cows, taking antilogs, and adding 4.295 for all other livestock, yields the normative demand function for alfalfa consumption in California: Q = 870.9 + 4.295 where Q is total alfalfa consumption in California (million tons) and P is the price of alfalfa in 1982 dollars ($/ton). The own-price elasticity of alfalfa demand is -.49 when evaluated at an average price paid by dairy farmers in 1982 of $104.88/ ton. 18 Table 13. Regional Annual Alfalfa Consumption By Livestock Type Consumption County or Dairy (except All All region No. region^ milk cows) dairy beef Horses Total Pf*t nil 1 m Q I^CloiUIIla 1 rciaiuuid. 704717 ZLrt / IZ — tons — 4asnA OZOoJ 1 1 ^oni Jl JWI North Valley 2. Sacramento 32279 85631 36032 84097 205759 J. oall JUaC|Uin 7l IJO 1 oZo 4414^ S744f» 1414 1 1 A f , olanisiaus 1 cQonn 1 jVtUU 494 1 on 18904 Sn79^ 1 JU / Zj 1 C J. ivierccQ 1 7I9fi7 / IZQ/ 18904 6. Madera 25405 67395 35474 15317 118187 ooum V alley 7 rresno Ol /Hj Z / J /tl/ 81 in7 \jj\jyy o o. Tulare 1 / lOJJ jy 1 ID zi J 1 7 \JJ\J l*\\J n V. Kmgs ojOoo Z6UZ40 701 8 /7I0 7077*^8 V ar-n iv.ern 7Q00C luUViOO 71^94 77I4Q'^ Southern 11. Los Angeles 6426 22551 9351 250824 282726 California 1 0 IZ. San Bernardino 1 7AQ 1 O 6908 07640 001970 li. Riverside o<;iQ') yjiyz 419^09 IJOOO 1 178 S7 'i84417 IH. oan LJiego 80 155 9684 59547 158385 1 c Ij. Orange 'Wo 13 Other 16. Mountain 5753 19267 157203 98036 274505 17. North Coast 25330 64857 49326 74621 188804 18. Trinity 0 0 2941 6399 9340 19. Yuba 2540 6739 15238 17763 39739 20. Sacramento Valley 31233 82856 98322 105367 286545 21. Solano 1644 4361 16623 6604 27587 Li.. oierra iviouniain 1 ou / 6555 92409 164165 263130 23. Central Coast 15168 50795 113980 131577 296352 24. South Coast 15392 51545 102314 91792 245651 25. Imperial 598 2098 91378 11488 104964 State Totals: 1,294,268 4,244,587 1,263,870 1,736,700 7,245,159 *For regional breakdowns by county see footnotes in Table 12. Sources: Estimated as explained in the text. 19 IV. ECONOMETRIC ESTIMATES OF ALFALFA DEMAND IN CALIFORNIA This section provides econometric estimates of alfalfa demand in California. Alfalfa production data are available on a countywide basis from agricultural commissioner reports. The Federal-State Marketing News Service (MNS) provides price data for the major dairy livestock producing regions. However, no data on consumption of alfalfa are available by county or region. Therefore, econometrically estimated demand schedules for alfalfa can only be provided for the state as a whole. REGRESSION MODELS The quantity of alfalfa demanded in a given year is determined by the price of alfalfa, the price of other feeds, the price of various livestock products, and the number and type of livestock animals. Four different regression models are used in estimating statewide alfalfa demand. The following variables are used in each: CONSt = alfalfa consumption in California, year t (1000 tons/year); PALF, = price of alfalfa hay, year t ($/ton); PFEED, = cost index for other feeds used in livestock production, year t; LPINDX, = price index for livestock products, year t; CATt = number of beef and dairy cattle, year t (1000 head). The first demand function is specified as an aggregate linear relation between alfalfa consumption and the other variables plus a trend. Linear I: (1) CONS, = ao + 1 PALF, + az PFEED, + aj LPINDX, + a4 CAT, + a^t + a^t^ + e, Time series data are not available for Uvestock other than beef and dairy cattle. The intercept and time trends in (1) allow for changes in consumption of these other animals over time as well as changes in feeding practices for dairy and beef cattle unrelated to prices. Next we allow for the separate effects of changes in consumption per head and in numbers of cattle. Muhiplying per head consumption by the number of head gives consumption by each livestock type and summing gives total consumption: (2) CONS, = CS • CAT, + Cti • HORS, where denotes per head consumption of dairy and beef cattle, Q*| denotes per head consumption by horses and other non-cattle livestock, and HORS, denotes numbers of horses and other non-cattle livestock. As noted previously, time series data on horses are unavailable so we assume (3) HORS, = bo + b,t and similarly, (4) = b2 + bjt The quantity of alfalfa consumed per head of cattle depends on the price of alfalfa, other feeds, and livestock products. A time trend may also be appro- priate to account for technical change. Three different functional forms were used to specify per head cattle consumption: Linear: (5) = Co + c, PALF, + C2 PFEED, + C3 LPINDX, + C4t Deflated Unear: (6) Q^ = Co.c. PALF, PFEED, , LPINDX, LPINDX, Generalized Leontief: (7) 0^ = .^.. / LPINDX, y/^, / PFEED, ^ PALF, ^ V PALF, ^ Substituting (3) and (4) and one of (5)-(7) into (2) results in three alfalfa demand regressions to be estimated. It is easily confirmed that these regressions are linear in the parameters for all three functional forms. These demand functions are referred to as Linear II, Deflated Linear, and generalized Leontief, respectively. Holding other variables constant, we expect quantity demanded to be inversely related to the price of alfalfa and positively related to livestock numbers and prices. The effect of a change in the price of other feeds is indeterminate: An increase will tend to increase alfalfa consumption through the substitution effect but will tend to decrease alfalfa consumption to the extent that milk or beef output per cow is lowered. Time series data are available for beef and dairy cattle separately so (2) was also estimated by spUtting the CAT, variable into dairy and beef cattle numbers 20 and specifying per head consumption for each using however, were unsatisfactory and are not reported here, one of the three functional forms in (5)-(7). The results, DATA The regressions were estimated using data from 1945-1982. The data sources are given in the references. As described below, some additional calculations were performed before estimation. The cattle numbers used here are for all cattle and calves in California. The reported data are as of January 1. Since alfalfa is consumed throughout the year, we also used a time series for average number of cattle and calves during the year. This series was obtained by averaging the January 1 data. The livestock price index (LPINDXt) was calculated as the weighted average of milk and beef prices with weights of .7 and .3, respectively. These weights represent the typical share of dairy and beef in alfalfa consumption. Milk and beef prices are both measured in dollars per hundred weight. Alfalfa consumption (CONSt) is calculated as alfalfa production plus carryin stocks and imports of alfalfa products to California minus carryout stocks and exports from California. Alfalfa represents by far the largest share of hay produced in the state. The CCLRS reports data for both total hay and alfalfa hay. While alfalfa hay production is used in estimating alfalfa hay consumption, total hay production is used in estimating alfalfa hay stocks. The data for hay stocks are stocks of all hay on farms, January 1 and May 1. Alfalfa hay stocks were estimated from this series by assuming that alfalfa stocks were the same fraction of total stocks as alfalfa production was of total hay production in the preceding year. Both January 1 and May 1 stocks data were used in the regressions. Data on imports and exports of alfalfa products are problematical. There are no such data before 1962 (Lehigh, 1985). A considerable portion of alfalfa imports to California comes from Arizona; however, import data do not include shipments from Arizona between 1973-81 and part of 1982 when the border agricultural inspection stations were closed. Cube imports are also significant; however, data are available only for 1980-83 and only 1983 includes all shipments from Arizona. Inshipments of alfalfa typically represent a small portion of the overall supply (Cothem, 1982, suggests from 2 to 4 percent). Cothem also indicates that inshipments are not likely to increase much due in part to substantial transport costs. Because alfalfa imports and exports from California apparently represent a small part of the market and because of the severe data limitations, the main regression results reported here do not include trade data. We did, however, attempt to construct a time series for import/ export data and reestimate the regressions. The results were generally poor and are not reported here. Alfalfa price data are available directly for the years 1945-78. For the remaining years price was estimated by dividing the value of alfalfa production by the quantity produced. The price index for other feeds used in livestock production (PFEEDj) was estimated as a weighted average of the prices for barley, com, and oats where the weights are the total quantities produced in California in each year. Two other feed cost indices are provided by the U. S. Department of Agriculture in its Agricultural Statistics: a feed cost index and a dairy feed cost index," both for the United States. However, the correlation coefficients of these two indices with the constmcted feed cost index are .97 and .96, respectively, precluding their joint use in least squares regressions. REGRESSION RESULTS There are four basic regression models (equation 1 separately and equation 2 in combination with each of the three functional forms (5)-(7), and four com- binations of hay stocks and cattle numbers. Each of the four regression models was estimated for each of the four combinations of hay stocks and cattle numbers. The first regression model contains two terms involving time while the other three models each contain three time terms. For each model and hay stock/ cattle combination we first estimated the regression with all time terms included. Those that were insignificant at the 5 percent level were eliminated sequentially until the remaining time terms were either significant at the 5 *The 16 percent protein dairy cost index was considered. percent level or there were none remaining. Thus we have one regression for each model and each hay stock/ cattle number combination for a total of 16. Results are reported in Tables 14-17. The values are quite high (.87-.9 1) and vary only sUghtly among the various regressions. The F values are significant at the .01 percent level or better in all cases. The positive intercepts indicate consumption by livestock other than beef and dairy. The derivatives with respect to cattle numbers and prices were evaluated at the sample mean for each of the regressions. In all cases the derivative with respect to cattle numbers was positive as expected. The derivative with respect to the price of alfalfa was 21 Table 14. Estimated Alfalfa Demand Functions, Linear Model I (Equation 1) Cattle series Hay stock series Intercept CAT, PALP, LPINDX, PFEED, R2 F value Jan Jan -ID. 601 77 77n T Q7A QQ ,oo jy.oo (4 32")** (U 7)** (-1 27")* (2 52)** Jan May -JZ. n't /'t.ooo Q lf\< y. IKJj 01 .y 1 Q 1 Q1 (3.963)** (14.326)** (-3.024)** (2.845)** (1.598) Avg. Jan 1614.058 1.002 -3.940 52.065 -6.755 .89 66.97 (4.702)** (12.46)** (-.338) (1.756)* (-1.009) Avg. May 1308.199 1.032 -19.396 48.738 5.474 .91 82.68 (4.276)** (14.394)** (-1.870)* (1.844)* (.918) Dependent variable = CONS,, 38 observations t - statistics are in parentheses * - significant at 10% level ** - significant at 5% level Table 15. Estimated Alfalfa Demand Functions, Linear Model II (Equation 5) Cattle Hay stock series series Intercept CAT, PALF,»CAT, LPINDX,* CAT, PFEED,»CAT, R2 F value Jan Jan 1721.953 .944 -.004 .017 -.000 .88 59.40 (5.285)** (9.966)** (-1.456) (2.506)** (-.130) Jan May 1629.953 .932 -.007 .016 .002 .91 81.97 (5.896)** (11.6)** (-3.093)** (2.805)** (1.754)* Avg. Jan 1618.614 .995 -.001 .012 -.001 .89 66.68 (5.148)** (10.769)** (-.480) (1.778)* (-.822) Avg. May 1563.722 .974 -.004 .011 .001 .91 82.54 (5.587)** (11.843)** (-1.870)* (1.838)* (.958) Dependent variable = CONS,, 38 observations t - statistics are in parentheses * - significant at 10% level ** - significant at 5% level n Table 16. Estimated Alfalfa Demand Functions, Deflated Linear Model (Equation 6) i^attie series Hay stock series Intercept CAT, ' PALF, \ ) . CAT, ^ LPINDX, ^ ( ) . CAT, ^ T PTisinv Year r2 F value Jan Jan 1804.672 1.141 -.010 -.025 .87 75.59 (4.843)** (10.368)** (-.278) (-1.145) Jan May 817.307 .865 -.102 .038 29.478 .91 84.73 (2.097)** (5.948)** (-3.01)** (1.793)* (2.700)** Avg. Jan 1733.838 1.113 .011 -.029 .89 89.10 (4.993)** (11.090)** (.327) (-1.432) Avg. May 930.647 .893 -.074 .027 23.787 .91 86.91 (2.397)** (6.113)** (-2.123)** (1.293) (2.071)** Dependent variable = CONS,, 38 observations t - statistics are in parentheses * - significant at 10% level ** - significant at 5% level Table 17. Estimated Alfalfa Demand Functions, Generalized Leontief Model (Equation 7) Cattle series Hay stock series Intercept \ CAT, r™^^')"'-cAT. ^ PALF, .PFEED, yi2,^^j^ ^ PALF, Year r2 F value Jan Jan 1842.214 .931 .595 -.233 .87 76.34 (4.923)** (5.068)** (2.014)** (-1.302) 3m May 856.046 .160 .414 .268 28.753 .91 83.76 (2.186)** (.607) (1.694)* (1.590) (2.644)** Avg. Jan 1772.682 1.065 .460 -.265 .89 90.77 (5.118)** (6.324)** (1.667)* (-1.627) Avg. May 982.869 .400 .313 .178 22.702 .91 86.20 (2.533)** (1.411) (1.283) (1.062) (1.989)** Dependent variable = CONS,, 38 observations t - statistics are in parentheses * - significant at 10% level ** - significant at 5% level 23 generally negative, the only exceptions being with the average cattle /January stocks regression for the de- flated linear and generalized Leontief models. (The alfalfa price coefficient is insignificant in the deflated linear case. Significance is not determined in the generalized Leontief case.) The derivatives with respect to livestock prices are positive in all regressions as expected. As mentioned, the sign of the feed price derivative is indeterminate apnon. Generally, we found the feed price derivatives to be negative with January stocks and positive with May stocks; however, the coefficients are insignificant at the 10 percent level in almost all cases. In general, the intercept and cattle number terms are highly significant (5 percent or better) in all regressions, the only exceptions being two regressions in the Leontief model. The alfalfa price coefficients are significant (10 percent or better) with May stocks, but insignificant with January stocks. However, in the Leontief demand model the alfalfa price coefficient is significant (10 percent or better) in the first three regressions and marginally significant in the fourth. The livestock price coefficient is generally significant at the 10 percent level or better and, as noted, the feed price terms are generally insignificant. Time trends are significant and were retained in only four regressions (May stocks for the deflated linear and Leontief models). Based on the statistical evidence presented in Tables 14-17, it is not clear which of the regressions should be used for further economic analysis. The values are all quite similar and the F values are very significant so these measures do not provide a useful criterion. Only two regressions can be eliminated because of im- plausible signs: the average cattle/ January stocks regressions for the deflated linear and Leontief models. In terms of significance of the estimated parameters, inspection of Tables 14-17 suggests that the May stocks series outperformed the January stocks series and the January cattle series generally outperformed the average cattle series. The exception is the Leontief model where January stocks gave better results. Similar conclusions apply when evaluating the regressions on the basis of the significance of the alfalfa price terms alone. Although some preference may be given to the January cattle/ May stocks regressions due to the significance of the individual terms, it does not seem possible to select the "best" model based only on the results in Tables 14-17. Comparisons with the normative results will provide additional criteria as will forecasting tests to be described in the next section. The own-price elasticities for alfalfa demand eval- uated at the sample mean, presented in Table 18, range from -. 1 1 to .03 for January stocks and from -.2 1 to -. 1 1 for May stocks. The elasticities are remarkably consistent across models for a given hay stock/ cattle number series. The own-price elasticities calculated at 1982 values, presented in Table 19, range from -.2 to .03 with January hay stocks data and from -.45 to -. 14 with May hay stocks data. These results suggest that the demand for hay in California is quite inelastic. Table 18. Own-Price Elasticities for Alfalfa Demand, Evaluated at the Sample Mean Hay Linear Linear Deflated GeneraUzed Cattle stock demand I, demand II, linear demand. Leontief demand. series series equation 1 equation 5 equation 6 equation 7 January January -,1 -.u -.02 -.01 January May -.21 -.19 -.21 -M Average January -.03 .02 .03 Average May -.13 -.11 -.15 -.15 Source: Computed from the regression results in Tables 14-17. 24 Table 19. Own-Price Elasticities for Alfalfa Demand, Evaluated at 1982 Prices and Cattle Numbers Hay Linear Linear Deflated Generalized Cattle stock HpmanH T HfmmiH TT ilIlCd.1 UClUallU, ijCUllllCi uciTianu, series series equation 1 equation 5 equation 6 equation 7 January January -.20 -.24 -.02 -.02 January May -.40 -.45 -.25 -.20 Average January -.05 -.06 .03 .02 Average May -.25 -.25 -.18 -.14 Source: Computed from the regression results in Tables 14-17. 25 V. COMPARISONS OF THE TWO APPROACHES TO ESTIMATING ALFALFA DEMAND The normative demand has an own-price elasticity of -.49 when evaluated at 1982 prices. The regression elasticities range from -.45 to .03 when evaluated at 1982 prices and cattle numbers. In general the linear I and II models yielded more elastic demand estimates than the other two models. Likewise the January cattle/ May stocks series yielded more elastic demands than the other data series. In particular, 1982 elasticities for the linear I and II models with the January cattle/ May stocks series were -.4 and -.45, respectively — about the same as the estimated normative elasticity. The intercept terms and time trends in the regression models with per head demands predict alfalfa con- sumption by livestock other than beef and dairy cattle. (This separation is not possible in the linear I model because both cattle and non-cattle livestock enter into the intercept and/ or time trend terms.) The predicted consumption levels of non-cattle livestock (primarily horses) are given in Table 20.' Predicted non-cattle consumption ranges from 1.6 to 1.8 million tons per year for those regressions without time trends. These estimates are remarkably similar to the 1.7 million ton estimate for horses and other non-cattle livestock in the normative analysis. One regression with time trend had a plausible value for non-cattle livestock (1.9 million tons per year); however, the other regressions had implausible values (2.4 to 3.2 million tons per year). The derivative of consumption with respect to cattle numbers in the regression models provides estimates of average annual alfalfa consumption per head of cattle. The predicted 1982 levels, given in Table 21, range from .98 to 1.1 tons per year for the aggregate cattle regressions without time trends. These are quite close to an average of 1.1 tons per animal per year estimated from Table 13 in the normative analysis. However, the regressions with time trends predict per head of cattle consumptions of .71-.78 tons per year, which are implausible results, based on the normative analysis. The normative and positive demand relations were tested by forecasting 1983 consumption given 1983 prices and cattle numbers. Actual alfalfa consumption in 1983 was estimated as alfalfa production plus net imports minus change in carryover stocks using MNS data. This amounted to 6.98 or 7.29 miUion tons depending on whether May or January carry-over stocks are used. Livestock prices, feed prices and cattle numbers were obtained from the same sources used in estimating the demand relations. Consumption was forecast using the 16 regressions reported in Tables 14-17. In all cases predicted consumption was less than actual consumption. The magnitude of the errors ranged from 4.4 percent to 12 percent of actual consumption and the root mean squared percentage error was 8.5 percent for the 16 regressions (Pindyck and Rubinfeld, 1981). In contrast to the statistical results, the average cattle regressions generally per- formed better than the January cattle regressions. The May hay stocks regressions performed slightly better than the January stocks regressions. Generally the linear I model was the poorest in forecasting con- sumption and the linear II model was only slightly better. The deflated linear and generalized Leontief models performed about the same. Alfalfa price data for testing the normative demand model were obtained from MNS for 1983 for each of the four consumption regions and then deflated to 1982 dollars using a USDA production cost index. Alfalfa consumption by milk cows was estimated from Table 9 and then adjusted for the change in milk cow numbers in 1983 in these regions. Alfalfa consumption by non- milk livestock was then estimated from Table 13 after adjusting for changes in numbers of beef and non-milk dairy cattle in 1983. The resulting consumption was estimated as 6.97 million tons. This underestimated actual consumption by 0. 1 - 4.4 percent depending on whether actual consumption is measured using changes in January hay stocks or changes in May carryovers. 'These predicted consumption levels are the regression intercepts except for the deflated linear and Leontief models which include linear trends under the May stock series. 26 Table 20. Econometric Estimates of Consumption By Livestock Other Than Beef and Dairy Cattle in 1982 Cattle series Hay stock series Linear II, equation 5 Deflated linear, equation 6 Generalized Leontief, equation 7 Jan. Jan. Avg. Avg. Jan May Jan May 1722 1630 1618 1564 1,000 tons 1804 3235 1734 2881 1842 2358 1772 1862 Source: Computed from regression results, Tables 15-17. Table 21. Econometric Estimates of Annual Cattle Consumption Per Head, 1982 Cattle series Regression Models Hay stock series Linear demand I, equation 1 Linear demand II, equation 5 Deflated linear demand, equation 6 Generalized Leontief demand, equation 7 Jan Jan Avg Avg Jan May Jan May .98 1.02 1.00 1.03 — tons per animal — 1.04 .99 .99 .71 1.10 1.02 1.04 .77 .98 .71 1.00 .78 Source: Computed from regression results, Tables 14-17. 27 VI. CONCLUSIONS Demand for alfalfa in California was estimated using both normative and positive approaches. The norma- tive analysis yielded an own-price elasticity of -1.23 for alfalfa consumption by milk cows and an elasticity of -.49 for aggregate statewide alfalfa demand. The elasticity for milk cow consumption appears to be a reliable estimate given (1) the level of detail in the LP model used to estimate demand on an individual milk cow basis, (2) the fact that this model (or similar models) is currently used by producers in making feeding decisions, and (3) the extensive data set available for aggregating individual demand relations. The elasticity of total demand is a lower bound on the true elasticity since the demand by non-milk livestock was assumed to be perfectly inelastic. The error involved in the perfectly inelastic assumption is likely to be small because (1) alfalfa consumption makes up a small share of the diet of these non-milk animals relative to the milk cow share and (2) econometric demand functions all produced 1982 own-price elasti- cities less than -.49. When the normative demand model was tested by estimating 1983 consumption using 1983 prices and livestock numbers, the forecast error ranged between 0. 1 and 4.4 percent depending on the hay stock series used to estimate actual consumption in 1983. The demand regressions generally performed well. They had high values, significant F statistics, and correct signs in almost all cases. Predicted levels of per-head cattle consumption and total non-cattle consumption (mainly horses) matched normative esti- mates of the same quantities remarkably well. The exceptions were the regression with time trends which generally produced implausible values for per-head cattle consumption and total non-cattle consumption.* When the cattle regressions were tested by fore- casting 1983 consumption, consumption was under- estimated in all cases. The magnitude of the error ranged from 4.4 percent to 12 percent of actual consumption with a root mean square percentage error of 8.5 percent. Thus the normative forecast was better than the positive forecast in light of the smaller forecast error of the former. Ignoring the two regressions with positive own-price elasticities, the regressions yielded elasticities ranging from -.45 to -.02 depending on the functional forms and the data set used to estimate consumption and cattle numbers. While these estimates are all consistent with an inelastic demand for alfalfa, the range of estimates significantly reduces their usefulness for further quanti- tative analysis. Note that it does not seem possible to distinguish among these estimates based on per- formance over the sample period, or a single year's forecasting test. Further, the statistically estimated elasticities are generally less than the normative elasticity which we take to be a reliable lower bound on the true elasticity. These problematic results seem noteworthy given that demand relations are typically estimated econometrically for the purpose of deter- mining elasticities. ^ese regressions considered linear and quadratic time trends in both per-head cattle consumption and total non-cattle consumption. The implausible results provide some evidence for the point earlier that ad hoc time trends may be a very imperfect means of capturing technical change. 28 REFERENCES Albaugh, R., J. P. Hughes, E. D. Loomis and K. Baer. The Light Horse. Cooperative Extension, Leaflet No. 2338, University of California, February 1983. Auslam and Associates. Crop Price Forecasting Equations San Joaquin Hydrological- Economic Model (Revised). Prepared for California De- partment of Water Resources, Sacramento, July 1981. Baer, K. Feeding Horses. Division of Agricultural Sciences, Leaflet No. 21134, University of California, June 1982 (revised). Bath, D. L. Dairy Specialist, Cooperative Extension, Animal Science, University of California, Davis. Personal communication. Bath, D. L. Will Drop in the Milk Support Price Affect Optimum Feeding programs? University of California, Davis, Cooperative Extension, Animal Science, Unpublished Dairy Newsletter, Vol. 12, No. 5, May 1982. Bath, D. L. and L. F. Bennett. "Development of a Dairy Feeding Model for Maximizing Income above Feed Cost with Access by Remote Computer Terminals." you wa/o/ Dairy Sciences, Vol. 63, No. 8, August 1980. Bath, D. L., V. L. Marble, and F. F. Smith. Alfalfa Hay Testing. Division of Agricuhural Sciences, Leaflet No. 2323, University of California, December 1978. Bennett, Richard. Dairy Farm Adviser, University of California, Cooperative Extension, Sonoma, Napa County. Personal communications. Bishop, Shirl. Dairy Farm Advisor, University of California, Cooperative Extension, Riverside County. Personal communications. Blake, M. J. and T. Clevenger. "A Linked Annual and Monthly Model for Forecasting Alfalfa Hay Prices." Western Journal of Agricultural Economics, Vol. 9, No. 1, July 1984, pp. 195-9. California Agricultural Resources Model. A joint statewide agricultural modeling effort by the Giannini Foundation of Agricultural Economics, the California Department of Food and Agri- culture and the U.S. Department of Agriculture. California Crop and Livestock Reporting Service. Dairy Information Bulletin. Sacramento, October 1982. California Crop and Livestock Reporting Service. California Dairy Industry Statistics. Sacramento, 1980-1982. California Crop and Livestock Reporting Service. California Livestock Statistics. Sacramento, 1945-1983. California Crop and Livestock Reporting Service. Field Crop Statistics. Sacramento, 1945-1978. California Crop and Livestock Reporting Service. Field Crop Review. 1978-1982. California Dairy Herd Improvement Association. Annual Report, Tulare, Stanislaus, and Southern Counties, 1981. California Department of Food and Agriculture, Bureau of Milk Pooling. Major Changes in the Milk Plan Effective February 1, 1982. Sacramento, January 1982a. California Department of Food and Agriculture, Bureau of Milk PooUng. The Pooling Plan for Market Milk, as Amended. Sacramento, January 1982b. California Department of Food and Agriculture, Bureau of Milk Pooling. Production Base and Pool Quota by County Location of Producer's Ranch. Unpublished report, Sacramento, January 1982c. California Department of Food and Agriculture, Bureau of Milk Pooling. History of Milk Pooling Program. Sacramento, January 1981. California Department of Food and Agriculture, Bureau of Milk Stabilization. Milk Production Cost Index. September-October, 1982d. California Department of Food and Agricuhure, Bureau of Milk Stabilization. Stabilization and Marketing Plan, as Amended, for Market Milk for the Southern California Marketing Area. Sacramento, August 1982e. Cahfornia Department of Food and Agriculture, The California Milk Marketing Program. A Special Report to the Senate Committee on Agriculture and Water Resources Pursuant to the Provision of Senate Resolution No. 98 of the 1973-1974 Regular Session. Sacramento: Cali- fornia State Senate, 1974. California Department of Water Resources. Outlook for Water Consumption by California 's Feed and Forage Industry through 2010. State of 29 California, The Resources Agency, Bulletin 212, February 1982. Cothern, James. Processing, Transporting and Pricing California Alfalfa Hay. University of California, Division of Agricultural Services, Leaflet No. 2890, June 1977. Cothern, James. "Alfalfa Production and Prices in an Unstable Economy." In Proceedings of the 12th California Alfalfa Symposium, Fresno, California, 1982. Dean, G. W., H. O. Carter, H. R. Wagstaff, S. O. Olayide, M. Ronning, and D. L. Bath. Production Functions and Linear Programming Models for Dairy Cattle Feeding. 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Fresno, California, Decem- ber 1979. Pelissier, C. L. and D. L. Bath. Feeding Dairy Cattle. University of California, Cooperative Extension, Publication No. 4064, June 1977. Peterson, Nyles. Dairy Farm Adviser, University of California, Cooperative Extension, San Bernar- dino County. Personal communications. Pindyck, R. S. and D. L. Rubinfeld. Econometric Models and Economic Forecasts. New York: McGraw-Hill, Inc., 1981. Schultz, H. Theory and Measurement of Demand, Chicago: University of Chicago Press, 1938. Schumway, C. Richard. "Supply, Demand, and Technology in a Multiproduct Industry: Texas Field Crops." American Journal of Agricultural Economics, Vol. 65, No. 4, November 1983, pp.748-760. Shahbazian, V. Current Affairs in Dairy Marketing. 30 Paper presented at the Dairy Farm Advisers' Training Conference, University of California, Davis, March 1981. Siebert, J. W. California Dairy Statistics for 1982. University of California, Davis, Cooperative Extension, Agricultural Economics, Unpublished Dairy Newsletter, Vol. 13, No. 6, June 1983. Siebert, J. W. How a Federal Milk Assessment of $0.50 per Hundred- Weight Might Affect Cali- fornia Dairymen. Unpublished report. University of California, Davis, Cooperative Extension, Agricultural Economics, September 1982. Thompson, J. R. Department of Animal Science, University of California, Davis. Personal com- munications. U.S. Department of Agriculture. Agricultural Statistics. Washington D.C., 1983 University of California. Alfalfa Production Costs in California. Division of Agricultural Sciences, Leaflet 2765, 1977. Van Riet, W. J. Beef Production in California. University of California, Division of Agricultural Sciences, Leaflet No. 21184, November 1980. DATA SOURCES FOR THE ECONOMETRIC ANALYSIS Cattle Numbers, Price of Milk, Price of Beef 1945-83: California Crop and Livestock Reporting Service, California Livestock Statistics, various issues. Hay Production 1945-78: California Crop and Livestock Reporting Service, Field Crops Statistics, various isssues. 1978-82: California Crop and Livestock Reporting Service, Field Crop Review, various issues. 1980-83: Federal-State Market News Service, Alfalfa Hay: California Market Summary, mi. Alfalfa Hay Prices 1945-78: California Crop and Livestock Reporting Service, Field Crops Statistics, various issues. 1979-83: Calculated from alfalfa production and value data, Federal-State Marketing News Service, Alfalfa Hay: California Market Summary, various issues. Hay Stocks 1945-61: California Crop and Livestock Reporting Service, Field Crops Statistics, various issues. 1962-79: Bob Hettinger, California Crop and Live- stock Reporting Service, personal com- munication. Barley, Corn, Oats Production and Prices 1945-78: California Crop and Livestock Reporting Service, Field Crops Statistics, various issues. 1978-82: California Crop and Livestock Reporting Service, Field Crop Review, various issues. 31 APPENDIX Table Al. Average Nutrient Analyses of Feeds 04/23/84 <, 1 0 0 2 D M - FEED FEED ZEFF MAX MAX MAX t NAME 2DM NE(L) ENE TDN 2CP ZFAT CF ZADF 2ASH 2CA ZP 2NPN 2CDM ZCDM ZTDM MLK DRY HFR 1 ALMOND HULLS, 132 CF 91 0.57 547 56 4.4 4.0 7 27 6.6 0.23 0,11 0,00 20,0 100.0 45.0 2 ALMOND HULLS* 15Z CF 91 0.53 516 53 4.2 4.0 9 31 6.6 0.23 0,11 0,00 20.0 100.0 45.0 3 ALMOND HULLS I SHELL 91 0.45 453 45 3.3 2.2 11 40 9,9 0.22 0,11 0.00 20,0 100.0 45.0 4 APPLE POMACE J DRIED 89 0.71 672 69 4.9 5.1 17 26 2,2 0.13 0,12 0.00 20.0 100.0 45.0 5 BAKERY HASTE, DRIED 92 0.94 880 89 11.9 14.9 1 1 1,7 0.07 0.11 0.00 15.0 15.0 15,0 6 BARLEY, 49* 89 0.88 828 84 10.7 2.1 4 5 2,3 0.05 0.37 0.00 80.0 100,0 100,0 7 BARLEY »46-48t 89 0.86 807 82 10.7 2.1 7 9 2,9 0.05 0,37 0,00 80.0 4 AA A 100.0 100.0 8 BARLEY, LIGHT HT 89 0.80 755 77 13.4 2.1 8 11 3,6 0.05 0.37 0.00 80.0 100.0 100.0 9 BEET PULP, DRIED 91 0.81 765 78 8.0 0.7 22 34 3,9 0.75 0.11 0,00 40.0 100.0 40.0 10 BEET PULP,MOL. DRIED 92 0.81 765 78 9.9 0.6 17 26 6.4 0.61 0.11 0.00 40.0 100.0 40.0 11 BONE MEAL, STEAMED 95 0.12 120 16 12.7 1.3 3 86.0 30.51 14.31 0.00 100.0 100.0 100.0 12 BREAD,DRIED 92 0.94 880 89 13,3 3.1 1 1 2.0 0.09 0.16 0.00 15.0 15.0 25.0 13 BREWERS GRAIN, DR 25P 92 0.68 640 66 26,0 7.2 16 23 4.1 0.29 0.54 0.00 25.0 100.0 30.0 14 BREUERS GRAINjCALIF 92 0.61 578 60 22.2 6.3 20 29 4.1 0.29 0.54 0.00 25.0 100.0 30.0 15 BREUERS GRAIN, WET 24 0.69 651 67 26,0 7.2 16 23 4.1 0.29 0.54 0.00 25.0 100.0 30.0 16 CITRUS PULP, DRIED 90 0.80 755 77 6.9 3.8 14 23 7.0 2.07 0.13 0.00 25,0 100.0 30.0 17 COCONUT MEAL, EXP 93 0.85 796 81 21,9 7.4 13 20 7.2 0.23 0.66 0.00 50,0 100.0 30,0 18 COCONUT MEAL, SOL 92 0.77 724 74 23.1 2.7 16 24 7.3 0.18 0.66 0.00 50,0 100,0 30,0 19 CORN EARSjGROUND 87 0.84 786 80 9.3 3.6 9 12 1,4 0.05 0.26 0.00 80.0 100.0 100,0 20 CORN BRAIN, CRACKED 89 0.84 786 80 10.0 4,3 2 3 1.3 0,03 0.31 0.00 80.0 100,0 100.0 21 CORN GRAIN, GR OR RLD 89 0.92 869 88 10.0 4.3 2 3 1.3 0,03 0.31 0.00 80,0 100.0 100,0 01 mow ni iiTciki rrcn yo O.OO OA", 82 2o»I 2»8 9 12 8.6 0.33 A n / 0.36 0.00 25.0 100.0 15.0 07 rnDU n i itcu ucai li LUKN uLUIbN nbflL 91 A GO 0.88 828 84 65.9 2.4 3 4 3.9 0.18 0.51 0.00 25.0 100.0 15.0 24 COTTONSEED MEAL, 41 E 94 0.80 755 77 43.6 6.7 13 20 7.2 0.17 1.28 0.00 25.0 100.0 30.0 25 COTTONSEED MEAL, 41 S 92 0.78 734 75 44.8 2.3 13 20 6.9 0.17 1.31 0.00 25,0 100.0 30.0 26 COTTONSEED MEAL, 50 S 92 0.78 734 75 54.0 2.3 9 12 6.9 0.17 1,09 0.00 25.0 100.0 30.0 27 COTTONSEED, WHOLE 93 1.04 973 98 24,9 21.1 18 29 3.9 0.15 0.73 0.00 20,0 100.0 30.0 28 DIAMMONIUM PHOSPHATE 96 0.00 0 0 142.2 0.0 0 0 23.8 0.00 25.56 22.76 1.0 1.0 1.0 29 DICALCIUN PHOSPHATE 96 0.00 0 0 0.0 0.0 0 0 86.8 23.70 18.84 0.00 100.0 100.0 100,0 30 DISODIUM PHOSPHATE 96 0.00 0 0 0.0 0.0 0 0 83.9 0.00 22.78 0.00 100,0 100.0 100,0 31 DIST CORN GRAINS, DR 92 0.88 828 84 29.5 9.9 13 20 2.7 0.10 0.40 0,00 25.0 100.0 25,0 32 FAT, ANIMAL 99 1.97 1847 182 0.0 96.9 0 0 0.0 0.00 0,00 0,00 3.0 3.0 3.0 33 FERMENTATION SOLUBLE 93 0.92 869 88 28.9 5.7 4 6 7.2 0.38 1,47 0.00 10.0 10.0 10,0 34 GRAPE POMACE, DRIED 91 0.28 266 30 12.7 7.6 17 54 5.5 0.01 0.01 0.00 5.0 5.0 15.0 35 HEGARI GRAIN 88 0.84 786 80 11.7 2.9 9 1.8 0.03 0.33 0,00 50.0 100.0 30.0 36 HOMINY FEED, 52 FAT 91 0.97 911 92 11.8 7.2 6 12 3.0 0,06 0.58 0,00 50.0 100.0 30.0 37 HOMINY FEED, LOU FAT 91 0.95 890 90 11,3 5.2 5 11 2.6 0.06 0.58 0,00 50.0 100.0 30.0 38 KELP, DRIED 91 0.30 287 32 7,1 0.5 7 10 38.6 2.72 0.31 0,00 5.0 5.0 30.0 39 LIMESTONE, GROUND 100 0.00 0 0 0,0 0.0 0 0 95.8 36.07 0.02 0,00 100,0 100.0 100.0 40 LINSEED MEAL, 35 EXP 91 0.85 796 81 38,8 5.9 10 17 6.3 0,43 0.93 0,00 25,0 100.0 30.0 41 LINSEED MEAL ,35 SOL 91 0,79 744 76 38,6 1.1 10 17 6.4 0,43 0.91 0.00 25,0 100.0 30.0 42 HALT, BARLEY 91 0.92 869 88 15.8 1.8 2 3 2.5 0.09 0.52 0.00 20,0 100,0 30.0 UAtT ftAnt^\# kill 43 MALT, BARLEY, NU 91 0.80 755 77 32,2 7.2 18 24 4.0 3.22 0.57 0.00 20.0 100.0 30.0 44 MILO,CAL OR MIDUEST 88 0.84 786 80 11,7 3.3 2 9 2,3 0.03 0.33 0.00 50.0 100.0 30.0 45 MILO,SOUTHUEST 88 0.79 744 76 10.6 3.1 3 9 2,3 0.03 0.33 0.00 50.0 100.0 30.0 46 MOLASSES, CANE 75 0.75 703 72 4.3 0,0 0 0 12,6 1,19 0,11 0.00 8,5 8.5 10,0 47 MOLASSES, CANE, 32 P04 75 0.72 682 70 4.2 0,0 0 0 12,2 1,15 0,95 0.00 8.5 8.5 10,0 48 MONOAMMONIUH PHOSPHT 98 0.00 0 0 70,2 0,0 0 0 23,8 0,53 24,49 11.84 1.0 1.0 1.0 49 MONOSODIUM PHOSPHATE 87 0.00 0 0 0.0 0,0 0 0 88.9 0,00 25,80 0,00 100.0 100.0 LOO.O 50 OATS, LIGHT UT 91 0.66 620 64 13.3 5,0 17 25 5,2 0,10 0,36 0.00 80,0 100.0 90.0 (Continued) '4/23/84 Table Al. Average Nutrient Analyses of Feeds (Continued) 1 0 0 2 D H ■EED FEED 2EFF HAy nHA , * NAHE 2DH NE(L) ENE TDN 2CP 2FAT CF 2ADF 2ASH VP Ail Ml If 51 OATS. PCS 91 0.80 755 77 10.1 6.0 12 17 4.1 0.10 V 4 X V 0.36 W • WU 0.00 RO.O Ov.v 100 0 90 0 52 ORANGE PULP .DRIED 88 0.81 765 78 8.5 V 1.7 10 16 3.8 0,71 0.11 0,00 25.0 100.0 X vv • V 30.0 wv » y 53 OYSTERSHELL FLOUR 100 0.00 0 0 1.0 0.0 0 0 89.8 38,22 0,07 0,00 100.0 X VV ♦ V 100.0 X vU » V 100.0 xw ¥ V 54 PEAS.CULL 90 0.87 817 83 26.5 1.2 6 9 3.1 0.13 0,47 0.00 25.0 100.0 30,0 55 PINEAPPLE BRAN 87 0.76 713 73 4.6 1.9 18 28 3.4 0.24 0,12 0,00 25.0 100.0 30,0 56 POTATOES. DRIED 90 0.80 770 77 8.7 0.3 X. 7 w 4.R 0.07 0.91 0.00 v,vv 9'i 0 100 0 4*1 0 57 RICE BRAN AND HULLS 91 0.31 297 33 6.7 5.6 17 53 19.1 X / » X 0.08 0.59 0.00 5.0 W » V 100.0 10.0 58 RICE BRAN.13Z FAT 91 0.79 744 76 14.0 15.1 19 16 Xw 14.8 XT 0.07 1.A9 U 4 vv 90.0 100.0 70.0 59 RICE BRAN. SOL 90 0.64 599 62 15.9 3.4 13 17 15.1 0.07 1.62 0.00 V * V V 20.0 i>v * V 100.0 X V V t V 30.0 WV « V 60 RICE GRAIN.POLISHED 89 0.88 828 84 8.2 2.0 1 2 5.8 0.03 0.12 0,00 25.0 100.0 30.0 61 RYE GRAIN 90 0,83 786 80 13.8 1.9 3 4 2.1 0,07 0,36 0,00 10,0 100.0 100.0 62 SAFFLOUER SEEDS 92 0.94 880 89 19.5 32.0 16 40 3.1 0.25 0.67 V to/ 0.00 20.0 100.0 X vV \ V 30.0 wV U 63 SAFFLOUER HEAL .20 S 92 0.56 526 55 23.9 1.1 17 39 4.3 0.37 0.80 0.00 20.0 100.0 30.0 64 SAFFLOUER HEAL. 42 S 90 0.79 744 76 46.5 1.1 13 20 7.1 0.44 1,41 0,00 20.0 100.0 30.0 65 SCREENINGS. GOOD GR 90 0.72 682 70 13.5 5.2 9 12 9.8 0.46 0,32 0,00 20.0 100.0 45.0 66 SCREENINGS. REFUSE 90 0.57 536 56 11.5 4.3 16 40 10.6 0.46 0,32 0,00 20.0 100.0 45.0 67 SOD TRIPOLYPHOSPHATE 96 0.00 0 0 0.0 0.0 0 0 0.0 0.00 25,98 0,00 100.0 100,0 100,0 68 SOYBEAN HEAL. 44 SOL 89 0.35 796 81 49.6 1.4 7 10 6.8 0.36 0,75 0,00 50.0 100,0 30,0 69 SOYBEAN HEAL. 48 SOL 89 0.85 796 81 54.0 1.4 3 5 6.8 0.36 0.75 0,00 50.0 100.0 30,0 70 SUNFLOWER HEAL.EXP 93 0.72 682 70 44.1 5.2 13 33 6.3 0.46 1.12 0,00 25.0 100.0 30,0 71 SUNFLOWER HEAL. SOL 93 0.67 630 65 50.3 1 .2 12 30 6.3 0.40 1.10 0,00 25.0 100.0 30,0 72 UREA.46Z N 90 0.00 0 0 287.5 0.0 0 0 10.0 0.00 0.00 46,00 1.5 1.5 1,0 73 WHEAT BRAN 89 0.72 682 70 18.0 5.0 11 12 6.8 0,12 1.32 0,00 25,0 100,0 30,0 74 WHEAT HILL RUN 90 0.77 724 74 17.0 4.8 9 10 5.8 0,10 1.13 0,00 25,0 100,0 30,0 75 WHEAT. SOFT. PCS 86 0.92 869 88 12.0 2.2 3 4 2.1 0,06 0.41 0,00 50.0 100,0 30,0 76 WHEY.LIQUID 7 0.81 765 78 14.0 4.3 0 0 11.0 0.98 0.81 0.00 10,0 10,0 10,0 77 WHEYiCOND.421 SOLIDS 42 0.81 765 78 14.0 4.3 0 0 11.0 0.98 0.81 0,00 10,0 10,0 10,0 78 WHEY PRODUCT. DRIED 93 765 / WW 78 17.0 1.4 0 0 16.7 1.67 1.11 0,00 10,0 10,0 10,0 79 YEAST. BREWERS.DRIED 93 0.81 765 78 48.3 0.8 3 4 7.7 0.14 1.54 0,00 5.0 5,0 5,0 80 SALT 90 0.00 0 0 0.0 0.0 0 0 100.0 0.00 0.00 0,00 100,0 100,0 100,0 81 ALFALFA HAY. 212 HCF 90 0.64 511 62 22.0 2.7 22 29 9.3 1.60 0.30 0.00 100,0 82 ALFALFA HAY .24% HCF 90 0.59 478 58 19.5 2.7 26 33 9.3 1.40 0.25 0.00 100,0 83 ALFALFA HAYt?SZ HCF 90 7 V 0.55 428 54 wT 15.0 2.7 30 37 9.3 1.20 0,20 0.00 100,0 84 ALFALFA SILAGE .36 DH 36 0.59 467 58 19.5 2.7 26 33 9.3 1,40 0,25 0,00 100.0 85 BARLEY HAY WW vnitL.b I 1 in > 87 0.58 433 57 8.9 26 33 7.6 0,21 0,30 0,00 100.0 23 0.65 505 63 8.8 2.7 22 29 5.9 0,34 0,63 0,00 100.0 87 CORN. CANNERY SILAGE 29 0.65 505 63 8.8 2.7 27 34 5.9 0,34 0,63 0.00 100,0 88 CORN SILAGE. 252 DH WW bUlXJl WA^nUlv 7 tfi 1 25 0.67 520 65 8.0 2.7 24 31 5,7 0.27 0,20 0,00 100.0 89 CORN SILAGE 1 302 DM 30 0.67 520 65 8.0 2.7 24 31 5.7 0.27 0,20 0,00 100.0 90 COTTONSEED HULLS # V wW 1 1 WIIWW^ V 1 IWWItoW 90 0.37 254 38 4.3 1.0 50 71 2.9 0.16 0,10 0,00 100.0 91 OAT HAY f A un 1 fin 1 88 0.55 405 54 9.2 3.0 31 36 7,7 0.26 0.24 0,00 100.0 9? nAT fiWn UFTPH HAY 7^ Unl nnu VLlUn nnt 449 14.1 3.0 W * V 31 40 9.1 7 • X 0.72 0,29 0,00 100,0 93 DAT SILAGE I BOOT 22 0.64 499 T 70 62 11.0 4.0 26 33 8.3 0.47 0,10 0,00 100.0 94 OAT SILAGE. DOUGH 30 0.60 475 59 9.7 4.0 34 42 8.3 0.47 0.33 0,00 100.0 95 PINEAPPLE GREENCHOP 18 0.57 433 56 7.6 2.2 27 35 6.4 0.28 0,08 0,00 50.0 96 PINEAPPLE PRESSCAKE 21 0.74 636 71 5.3 0.7 26 34 2.6 0.28 0.08 0.00 30.0 97 PINEAPPLE STUHPMEAL 46 0.66 541 64 3.0 0.8 22 30 1.9 0.28 0,08 0.00 30.0 9fl c;n&RMIIM c^TI TIM 70 aUnunUn DILHUI. 7 Ov vri ft V.JO 44S «;<; jj W » w 2.7 26 £w 33 WW 9.0 0.32 0,18 0,00 100.0 99 SliBANriRARR HAY 7 7 dUi^riMUixno^ nn I 89 W7 405 54 li.O 1.8 29 42 9.0 0.56 0.31 0,00 100.0 100 SUGARCANE BAGASSE 55 0.38 202 39 1.5 0.4 49 56 5.5 0.35 0.27 0.00 50.0 101 SUGARCANE STRIPPINGS 45 0.44 270 44 3.6 0.9 45 51 10.3 0.35 0,27 0,00 30,0 102 IREA-CORN SILAGE 30 0.67 520 65 12.7 2.7 24 31 5,7 0.27 0,20 0.77 100,0 Source: Natural Research Council, Committee on Animal Nutrition. Nutrient Requirements of Domestic Animals, No. 3: Nutrient Requirements of Dairy Cattle. 5th Revised Edition, National Academy of Sciences, Washington, 1978. Note: Feed numbers are the identification numbers for the Dairy Ration Program and are assigned by Animal Science Extension, UC Davis. Table A2. A Sample Print-Out of the California Dairy Ration Program CALIFaRNIA DAIRY RATION (HAXIMUM INCOnC ABOUE FEED COSTS) SOUTHERN CALIFORNIA 1/18/83 21:43:09 HOURS specifications: production curve maxihuh average nilk fat average cou weight sleno price ne(l) for activity first lactation heifers in group., second lactation heifers in group. 60 LBS 3.6 X 1400 LBS » 13.24/CUT 10 X OF MAINTENANCE 30 X 25 X LB/DAY XROUQHAGE FEEDS USED IN RATION: AS FED AS FED DM CONSTRAINTS AS FED lOOZ DM PRICE RANGE -POUNDS- -ZROUGH- «/CUT LOUER UPPER niN MAX MIN MAX COTTONSEED HULLS ALFALFA HAY>24Z MCF 11.00 10.00 S2.4 47.6 52.4 47.6 2.00 6.00 1.99 2.71 5.29 ««««* 10.0 TOTAL ROUGHAGE. 21.00 (18.90 LBS DM) XCONCENTRATE AS FED DM BEET PULPf DRIED 13. IS 39.1 38.9 6.1s 6.08 6.55 COTTONSEED MEAL > 41 E 8.19 24.4 2S.0 7.60 5.68 7.70 ALMOND HULLStlSX CF 6.77 20.1 20.0 3.1s 0.83 3.28 CORN GRAIN>GR OR RLD 3.87 11. S 11.2 6. SO 6.25 6.51 COTTONSEEDfUHOLE 1.54 4.6 4.6 8. 65 8.63 9.07 DICALCIUM PHOSPHATE 0.09 0.3 0.3 16.00 3.07 18.32 TOTAL CONCENTRATE. 33.60t (30.78 LBS DM) AS FED lOOX DM -POUNDS- -XCONC- MIN MAX MIN MAX 40.0 25.0 20.0 80.0 20.0 ♦note: PROVIDE SALT FREE CHOICE OR AS 0.5X OF CONCENTRATE MIX. PROVIDE OTHER ESSENTIAL MINERALS NOT SUPPLIED IN ADEQUATE AMOUNTS «Y FEEDS IN RATION LISTED ABOVE. roughage: CONCENTRATE RATIO • 38:62 (DM) LB/COU ♦/COU PRICE PER CUT LOUER RANGE UPPER RANGE OPTIMUM DAILY MILK TOTAL FEED COST production: 56.0 7.41 2.86 13.24 10.81 14.41 TOTAL DAILY INCOME ABOVE FEED COST: 4.SS ( AS FED ) CONCENTRATE ROUGHAGE TOTAL RATION -CONSTRAINTS— MIN MAX DRY MATTER PCT NE(L> TDM CRUDE PROTEIN CRUDE FAT EFF. CF ADF ASH CALCIUM PHOSPHORUS Ca:PH0S RATIO NPN COST AS FED COST DRY MATTER 91.61 X 90.00 X 90.99 X 0.71 MCAL/LB 0.43 MCAL/LB 0.60 MCAL/LB 68.50 X 42.77 X 58.60 X 15.69 X 10.38 X 13.65 X 3.86 X 1 .63 X 3.00 X 13.43 X 34.71 X 21 .62 X 23.91 X 47.61 X 33.03 X 4.78 X 5.35 X 5.00 X 0.43 X 0.68 X 0.52 X 0.47 X 0.15 X 0.35 X 0.90 4.38 1 .50 0.00 X 0.00 X 0.00 X * 6.08 /CUT * 3.90 /CUT ♦ 5.24 /CUT * 6.64 /CUT » 4.34 /CUT » 5.76 /CUT 35.00X 13.65X 15.47X 0.49X 0.35X 1 .50 0.4SX FEEDS NOT USED IN RATION: BARLEY>46-48« COTTONSEED MEALf41 5 HOMINY FEED. LOU FAT MONOAMMONIUM PHOSPHT UHEAT MILL RUN UHEAT.SOFTfPCS PRICE AT FORMULATION OPPORTUNITY 6.40 8.2s 7.00 29.00 6.45 6.66 6.10 7.35 6.98 4.38 5.88 6.40 Table A3. Percentage of Cows in Different Production Groups at Different Lactation Stages Petaluma Production-- -LACTATION NUMBER- TOT AL Group 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 21000 22000 23000 24000 25000 26000 27000 28000 29000 30000 31000 32 000 33000 34000 35000 36 000 37000 38000 39000 40000 41000 42 000 43000 44000 4 5000 46000 4 7000 48000 49000 50000 1 0.17 0.21 0.03 0.02 0.05 0.09 0.27 0.47 0.68 l.ll 1.60 2.42 2.77 3.36 3.47 3.15 2.41 I .78 1.16 0.63 0.34 0.15 0.08 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.15 0.07 0.01 0.01 0.03 0.38 0.19 0.27 0.47 0.76 0.84 1.26 1 .58 1.96 2.34 2.51 2.31 2.15 1.70 1 .26 0.85 0.58 0-31 0.18 0 .07 0.04 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.05 0.07 0. 01 0.03 0.02 0.04 0.09 0. 16 0.28 0.42 0.57 0. 79 1.02 1.37 1.62 1.66 1.83 1.81 1.45 1.2B 0.89 0.51 0.42 0.19 0.12 0.06 0,03 0.01 0. 01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.oo 0.00 O.Ol 0.04 0.02 0.01 0.01 0. 01 0.03 0.07 0.10 0.12 0.28 0.39 0.49 0.61 0.82 1.06 1.16 1.29 1.26 1 .02 0.86 0, 79 0.46 0.33 0.21 0.15 0. 07 0.05 0.02 0. 01 00 00 00 00 00 00 00 00 00 00 00 00 0.00 0.00 0.00 0.00 O.OQ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.03 0.01 0.01 0.01 0.02 0.06 0.07 0. 12 0.20 0.28 0. 39 0.46 0.61 0.69 0- 80 0.81 0.86 0. 75 0.64 0.47 0.39 0.27 0. 18 0.09 0.06 0.02 0.02 0.00 0.01 0-00 0.00 0-00 0.00 0-00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 .00 0.02 0.02 0.02 0.01 0.01 0.03 0.05 0.09 0.10 0.18 0.23 0.29 0-37 0.44 0 .49 0.51 0.54 0.57 0.54 0.42 0.35 0.22 0.13 0.08 0.07 0.04 0.03 0.01 0.00 0.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0-00 0-00 0-00 0.00 0.01 0.02 0.00 0.01 0.01 0.02 0.03 0.03 0.09 0.11 0- 12 0-16 0-22 0-23 0.31 0.34 0.29 0.34 0.29 0.33 0.22 0.17 0. 12 0-10 0-06 0-OZ 0.01 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8 0.00 0. 01 0.00 0. 00 0.01 0.01 0.02 0. 03 0.04 0.08 0.09 0. 14 0. 17 0.22 0.21 0.24 0.24 0.24 0.20 0. 17 0. 14 0.08 0. 08 0.04 0.02 0.02 0.00 0. 00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.02 0.03 0.05 0.07 0.09 0.10 0.10 0.16 0.10 0.13 0.16 O.IO 0.08 0.06 0.04 0.02 0.01 0.01 0.01 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.oo 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10 0.00 0 .00 0.00 0.00 0.01 0.01 0.01 0.00 0.03 0.04 0.04 0.04 0.05 0.05 0.08 0.06 0 .08 0.05 0.02 0.02 0.03 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 .00 0.00 0.00 0 .00 0 .00 0.00 0.00 0.00 0 .00 0.00 0.00 0.00 0.00 0 .00 0.00 0.00 0.00 0-00 0 .00 11 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.02 0.01 0.02 0.02 0.02 0.02 0.03 0.03 0.03 0.02 0.01 0.01 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 coo 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12 0.00 0.00 0.00 0.00 0-00 0-00 0-01 O-Ol 0-00 0-01 O-Ol 0-01 0-01 0.02 0.01 0.01 0.02 0.01 0.01 0.00 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0-00 0-00 0-00 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 14 3 -00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-CO 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 18 0.00 0.00 0- 00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19 0.00 0.00 0.00 0.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.30 0.00 0- 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 3.00 0.00 0.00 20 0.00 0. 00 0.00 0.00 0. 00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.50 0.24 0.08 0.14 0.22 0.55 1.05 1.58 2.52 3.78 5.20 6.60 8.08 9.49 10.14 9.84 9.37 8.43 6.71 5.27 3.81 2.47 1.63 0.94 0.56 0.30 0.15 0.08 0.03 0.02 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.00 TOTAL 26.43 22.05 16.81 11.77 8.34 5.88 3.67 2-47 1-37 0-64 0-27 0-13 0-05 0-02 0-00 0-00 0-00 0.00 0.00 0.00100.00 (Continued) Table A3. Percentage of Cows in Different Production Groups at Different Lactation Stages (Continued) North Valley Production — Group ^ -LACTATION NUMBER- TOTAL 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 21000 22000 23000 24000 25000 26000 2 7000 28000 29000 30000 31000 32 000 33 000 34000 35000 36 000 37000 38000 39000 40000 41000 42 000 43000 44000 45000 46000 47000 48 000 49 000 50000 0.18 0.13 0.03 0.04 0.10 0.15 0.31 0.53 0.86 1.37 2.02 2.61 3.26 3.69 3.68 3.48 2.88 2.23 1.52 0.96 0.58 0.29 0.16 0.08 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.10 0.04 0.02 0.04 0.08 0.12 0.23 0.35 0.58 0.84 1.18 1.56 1.85 2.19 2.32 2.35 2.32 2.01 1.68 1.32 0.92 0.66 0.43 0.27 0.14 0.07 0.04 0.01 O.Ol 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.03 0.07 0. 02 0.02 0.03 0.05 0.08 0- 12 0.19 0.30 0.48 0.63 0.88 1.10 1- 38 1.55 1.67 1.62 1.55 1.30 1.10 0.82 0.61 0.40 0.25 0. 15 0.08 0. 05 0.03 0. 01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.02 0.04 0.02 0.01 0.02 0.03 0.05 0.07 0.12 0.19 0.29 0.37 0.51 0.69 0.81 1 .02 1.06 1.09 1.05 0.94 0.78 0.64 0.44 0.30 0.20 0.14 0.08 0.04 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0 J 0.00 0.02 0.03 0.01 0.01 0.02 0.02 0.04 0.06 0.07 0. 14 0. 19 0.30 0.36 0.46 0.53 0.64 0.70 0.73 0.66 0.60 0.49 0.39 0.30 0.21 0. 16 0.08 0.05 0.03 0.02 0.01 0.00 0-00 0.00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.01 0.02 0.03 0.04 0.06 0.10 0-14 0-18 0.24 0-32 0-34 0-42 0.45 0-42 0 .43 34 32 21 16 10 06 05 03 0.01 0-01 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0-00 0.00 0.03 0.00 0.00 o.oo 0.00 0.00 0.00 00 00 00 00 00 0.01 0.01 0.00 0.00 0-01 0-01 0.02 0-04 0-05 0.08 0. 10 0.13 0.15 0.20 0.21 0.24 0.25 0.24 0.20 0.19 0.14 O.IO 0-07 0.06 0-03 0.01 0.01 0.01 0-00 0-00 0-00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8 0.01 0.01 0.00 0.00 0.00 0.01 0-01 0-02 0.03 0- 04 0.07 0.07 0. 10 0. 12 0. 13 0.14 0. 12 0. 13 0.10 0.08 0. 06 0. 05 0.02 0.02 0.01 0.01 0.00 0.00 0. 00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0-01 0.01 0.02 0.02 0.04 0.05 0.05 0.07 0.06 0.06 0.06 0.06 0.05 0.03 0.03 0.02 0.01 0.00 O.OO 0.00 0.00 0.00 0.00 O.OP 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.oc 0.00 0.00 0.00 0-00 0.00 0-00 0-00 0.00 0-00 0-00 0-00 10 0-00 0.00 0.00 0.00 0.00 0.00 0 .01 0.01 0.01 0.01 0.01 0.03 0.02 0.03 0.03 0-02 0.02 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0-01 0.01 0.01 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0 -00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.oc 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.co 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 14 0 .00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 9.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20 0. 00 0.00 0.00 0. 00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0-00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.35 0.40 0.15 0.12 0.24 0.38 0.68 1.13 1.78 2.86 4.20 5.60 7.20 8.59 9.41 9.92 9.61 8.88 7.62 6.16 4.84 3.46 2.46 1.60 1.02 0.59 0.34 0.19 0.10 0.04 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 TOTAL 31.21 23.81 16.59 11.07 7.33 4.53 2.60 1.39 0.67 0.28 0.11 0.04 0.02 0-00 0-00 0.00 0.00 0.00 0.00 0. 00100.00 (Continued) Table A3. Percentage of Cows in Different Production Groups at Different Lactation Stages (Continued) South Valley Production -LACTATION NUMBER- Group 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 21000 22000 23000 24000 25000 26000 2 7000 28000 29000 30000 31000 32000 33000 34000 35000 36000 37000 38000 39000 40000 41000 42000 43000 44000 45000 46000 47000 48000 49000 50000 0.07 0.05 0.03 0.04 0.07 0.10 0.22 0.33 0.63 1.07 1.65 2.45 3.39 4.04 4.13 3.73 3.08 2.18 1.41 0.87 0.53 0.27 0.15 0.08 0.04 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0. 02 0.02 0.02 0.03 0.05 0.09 0.17 0.28 0.45 0.68 0.99 1.44 1.91 2.32 2.61 2.68 2.58 2.25 1.80 1.31 0.93 0.58 0.35 0.21 0.15 0.08 0.04 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.02 0.02 0.03 0.04 0.06 0.09 0.14 0.26 0.37 0.58 0.88 1.18 1.49 1.74 1.89 1.94 1.72 1.47 1.15 0.88 0.59 0.35 0.24 0,14 0.07 0.04 0.03 0.01 0.00 0.00 00 00 00 00 .00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.02 0.03 0.04 0.07 0.09 0. 16 0.20 0.35 0.53 0.70 0.89 1.06 1.22 1.22 1.19 1.06 0.86 0.70 0.46 0.31 0.20 0.12 0.06 0.03 0.02 0.01 0.01 0 .00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.01 0.02 0.02 0.05 0. 06 0. 11 0. 16 20 33 39 0.52 0.66 0. 70 0. 77 0.71 0.65 0.52 0.41 0.26 0.20 0.12 0.08 0.03 0.02 0.01 0.01 0.01 0.00 O.OC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.02 0.03 0.04 0.06 0.12 0.16 0.21 0.27 0.36 0.42 0.45 0.46 0.39 0.35 0.28 0.21 0.17 0.12 0.07 0.04 0.03 0.01 0.00 0.00 0.00 0 .00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.02 0.03 0.05 0.07 0.10 0.12 0. 16 0.20 0.26 0.21 0.24 0.20 0. 19 0.16 0.11 0.08 0.04 0.03 0.02 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8 0.00 0. 00 0.00 0.00 0.00 0. 01 0.01 0.02 0.02 0.04 0. 04 0.07 0.09 0.11 0.10 0. 14 0. 13 0. 13 0.11 0.09 0.07 0.04 0.03 0.03 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 o.oo 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.02 0.03 0.03 0.05 0.06 0.07 0.07 0.07 0.06 0.04 0.04 0.03 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 O.OC 0.00 0.00 0.00 0.00 0.00 0.00 10 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0-01 0.02 0.01 0-02 0-02 0.03 0.03 0.02 0.03 0.02 0.01 0-01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0 .00 0.00 0.00 0.00 0.00 11 0.00 0 .00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.co 0.00 0.00 O.PO 0.00 0.01 0.00 0.00 o.co 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 14 0. 00 0. CO 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.co 0.00 0.00 0.00 0.00 0.00 0. 00 15 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 O.OC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0.00 0.00 0.00 0-00 0-00 0-00 0-00 0-00 0-00 0.00 0.00 0.00 0.00 0-00 0-00 0-00 0-00 0.00 0-00 0-00 17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0-00 0-00 0.00 0.00 0.00 18 0.00 0.00 0.00 0. 00 0.00 0.00 0. 00 0-00 0-00 0.00 0.00 0-00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0-00 0-00 0.00 0-00 0.00 0.00 0.00 0-00 0-00 0.00 0.00 0-00 0-00 0-00 0-00 0.00 TOTAL 19 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20 0.00 0. 00 0.00 0.00 0-00 0.00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.30 0-00 0-00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0-00 0.00 0.00 0-00 0-00 0-00 0-00 0-12 0-12 0.09 0.11 0.19 0.28 0.49 0.80 1.33 2.23 3.37 4.96 7.06 8.85 10.12 10-73 10.48 9.62 8.05 6.54 4-92 3-60 2.33 1.48 0.91 0.58 0-29 0-16 0-09 0-05 0.03 0.01 0.00 0.00 0-00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 TOTAL 30.61 24.09 17.45 11.67 7.07 4.30 2.36 1.30 0.66 0.27 0.08 0.04 0.01 0.01 0.00 0-00 0-00 0.00 0.00 0.00100.00 ( Continued) Table A3. Percentage of Cows in Different Production Groups at Different Lactation Stages (Continued) Southern California Production Group 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 KfOOO 15000 16000 17000 18000 19000 20000 21000 22000 23000 24000 25000 26000 27000 28000 29000 30000 31000 32000 33000 34000 35000 36000 37000 38000 39000 40000 41000 42000 43000 44000 45000 46000 4 7000 48000 49000 50000 0.05 0.07 0.05 0.05 0.04 0.06 0.11 0.20 0.32 0.57 0.98 1.79 2.55 3.53 4.16 4.03 3.38 2.49 1.63 0.96 0.49 0.30 0.13 0.07 0.04 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.03 0.02 0.01 0.02 0.03 0 .06 0.09 0.12 0.22 0.42 0.69 1.07 1 .67 2.14 2.65 3.13 3.24 2.79 2.47 1.79 1.17 0.82 0.45 0.24 0.10 0.05 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.01 0.02 0.01 0.01 0.02 0.03 0.05 0.05 0.07 0.10 0.19 0.30 0.51 0.79 1.25 1.60 1.96 2.33 2.12 1.96 1.62 1.21 0.82 0.50 0.27 0.17 0. 09 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.02 0.03 0.03 0.04 0.08 0.12 0.17 0.29 0.44 0.74 0.90 1.23 1.39 1.43 1.34 1.15 0.85 0.68 0.41 0.25 0.16 0.07 0.03 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.01 0.02 0.04 0.03 0.08 0.11 0.21 0.29 0.41 0. 65 0.78 0.86 0.90 0.90 0. 75 0. 58 0.45 0.26 0. 19 0. 11 0.05 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.01 0.03 0.04 0.05 0.08 0.12 0. 15 0.23 0.31 0.42 0.46 0.50 0.48 0.37 0.33 0.21 0.15 0.09 0.06 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 0.01 0.03 0.03 0.04 0.07 0.13 0.14 0.18 0.23 0.28 0.22 0.21 0.19 0.15 0. 10 0.05 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8 0.00 0.00 0.00 0.00 0.00 0. 00 0.01 0.01 0.01 0.01 0.02 0.03 0.05 0.07 0.07 0. 11 0. 10 0. 11 0. 11 0. 10 0.07 0.05 0.03 0. 02 0.01 0.01 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0. 00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0. 00 0. 00 0.00 0.00 -LACTATION NUMBER 9 10 11 12 TOTAL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.02 0.03 0.04 0.05 0.04 0.05 0.05 0.04 0.04 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.oc 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.oo 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.co 0.00 0.00 o.co 0.00 0.00 0.00 0.01 0.01 0.01 0.02 0.01 0.03 0.03 0.02 0.02 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 .00 0.00 0.00 0.00 0 .00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 .00 0 ,00 0.00 0.00 0.00 0.00 0.00 0 .00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.oc 0.00 13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 14 0.00 0.00 0.00 0.00 0.00 o.co 0.00 0.00 0.00 0.00 0.00 o.co 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.co 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 o.co 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 15 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0, 00 0.00 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 17 0.00 0.00 0.00 0.00 0.00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0. 00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00 18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 19 0.00 0.00 0.00 0.00 0,00 0.00 0.00 0.00 0.00 0.00 0.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 O.DO 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 20 0 .00 0.00 0.00 0.00 0. 00 0. 00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.12 0.15 0.11 0.12 0.13 0.17 0.31 0.44 0.66 1.11 1.92 3.24 4.93 7.16 9.23 10.57 11.33 11.27 9.82 8.51 6.49 4.68 3.28 1.92 1.12 0,65 0.30 0.12 0.07 0.04 O.Ol 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 TOTAL 28.07 25.60 18.13 11.92 7.79 4.17 2.1b 1.02 0.46 0.22 0.11 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00100.00 Note: Calculated from DHIA data.