Historically, from 1950-2002, alfalfa and cotton have been among California’s top commodities in terms of total value . In 1950 cotton was ranked third in terms of value of production in California with a value of $202 million. By 2001, cotton had slipped to the eighth most valuable commodity in California in value of production. The trend has been downward during the period 1950-2002. Hay was ranked fifth in 1950 in California with a value of production of $121 million. In 2001, hay was ranked seventh in value of production just ahead of cotton. Models are developed for California alfalfa and cotton acreage, production, and consumption. Both single equation and systems of equations are estimated. The data consist of 33 annual observations from 1970 to 2002. In some models, there were slightly fewer observations due to lags in the specifications. A brief description of the alfalfa market is given prior to reporting the estimations of the models. In addition, some issues related to the nature of the data are discussed. Alfalfa hay acreage in California has averaged about a million acres per year during the past 30 years . Alfalfa contributes about 85 percent of the value of all hay production in California. Alfalfa is influenced by profitability of alternative annual crops such as cotton, tomatoes, trees, and vines. The demand for alfalfa hay is determined to a large degree by the size of the state’s dairy herd, which consumes about 70 percent of the supply. Horses consume about 20 percent. Alfalfa is a perennial crop with a three to five-year economic life. Since it is a water intensive crop,grow table its profitability is strongly influenced by water and water costs. In addition, alfalfa is important in crop rotations because of its beneficial effects on the soil .

Alfalfa production in California has been increasing annually since the mid nineties . It reached a peak in 2002 at 8.1 million tons. The increase in production has been primarily due to the upward trend in yields and not to increases in acreage. Alfalfa real grower price in California, using a 1983/84 base, has exhibited a downward trend since the early eighties . In 2002 the real grower price was about $60 per ton. A three-equation system for alfalfa was developed and estimated. Iterative three stage least squares are used to estimate a model consisting of acreage, production, and demand relationships for alfalfa. We assume that the market for alfalfa is in equilibrium, that is, that quantity demanded is equal to production. We further assume that stocks are included in the demand for alfalfa. Thus, the three endogenous variables are: acreage, production, and alfalfa price. The estimators will be asymptotically efficient given that the model is specified correctly. The gain in efficiency is due to taking into account the correlation across equations. And three-stage least squares will purge the correlation that exist between endogenous variables on the right hand side of the equations in the model with the error terms. Cotton is the most important field crop gown in California. Growers in California grow two types of cotton: Upland, or Acala and Pima. Upland cotton makes up about 70 to 75 percent of the California cotton market and is the higher-quality cotton. Upland has a worldwide reputation as the premium medium staple cotton, with consistently high fiber strength useful in many apparel fabric applications. Export markets are important, attracting as much as 80 percent of California’s annual cotton production in some years making it California’s second highest export crop . Historically, California cotton, in terms of value of production, was the third highest ranking crop in California in 1950 below cattle and calves and dairy products.

In 2001 cotton was ranked the eighth highest valued crop below milk and cream, grapes, nursery products, cattle and calves, lettuce, oranges, and hay . There has been a downward trend in cotton acreage and production in California since 1979. California growers produced 3.4 million bales of cotton on l.6 million acres in 1979. In 2002 they produced about 2 million bales of cotton on 700,000 acres . Cotton yields have experienced an upward trend since 1979 . Nominal producers’ prices in California for cotton exhibit an upward trend since the 1970s, but real producers’ prices in California has exhibit a downward trend since the mid seventies . Recently the World Trade Organization ruled against U.S. cotton subsidies. U.S. cotton subsidies totaled about $10 billion in 2002 and the WTO ruled that the subsidies created an unfair competition for Brazil, which filed the complaint. California producers received about $1.2 billion in subsidies in 2002. California cotton is not as subsidized as cotton in other states, such as Texas, because subsidies are based on price and California’s higher-quality cotton is more expensive . Acreage, production, and demand equations are estimated for California cotton. Single equation and system of equations models are developed and estimated. In this report we aggregated the different cotton varieties. Disaggregated models of cotton were also estimated because of changes in the cotton industry and to allow for different impacts for subsidized and unsubsidized varieties. The number of observations in the disaggregated models present in the next section are limited due to the relatively recent introduction of Pima in California. The estimated models indicate that the short-run own-price elasticity of alfalfa acreage is inelastic but more elastic when ample water is available. By applying water marginally through out the growing period, a producer can obtain more cuttings of alfalfa. Alfalfa yields are also responsive to increases in prices. The own price elasticity of yields is 0.08 and highly significant.

Alfalfa yields are negatively related to the previous year’s cotton price. Production is positively related to own price with an estimated elasticity of 0.44 and significant. Production was negatively related to risk with an elasticity of risk equal to -0.75. Demand for alfalfa is a derived demand and is positively related to the number of cows and milk price support and negatively related to its own price. The estimated own-price elasticity of cotton acreage is 0.53 and highly significant. Cotton acreage decreases with an increase in risk in growing cotton and as price of alfalfa increases. The short-run own-price elasticity of cotton production is 0.497 and the long-run estimate is 0.503. The own-price elasticity of cotton demand is – 0.684. Rayon is a substitute for cotton. The empirical results support the fact that alfalfa and cotton are rotating crops in California. In recent years there has been an increase in Pima acreage relative to the traditional Upland variety in California. Upland cotton prices have a positive impact on acres planted to Upland. When Pima prices increase, the acres planted to Upland decrease. A similar situation applies to Pima acreage. That is, an increase in Upland prices causes a decrease in Pima acreage. Thus, the empirical results support that hypothesis that relative prices of Upland and Pima have a significant impact on the adoption of the two varieties. Future research needs to focus on the collection of more data related to the consumption of California cotton and alfalfa, stocks and inventories, and interstate trade of alfalfa between California and Oregon and Nevada. California is one of the major producers of rice in the US. The other most important states are Arkansas, Louisiana, Mississippi, Missouri and Texas. The market in California appears to be fully integrated with the southern states, as suggested by an empirical check of the law of one price. This conclusion is hardly surprising,growing lettuce hydroponically given that rice is a storable and easily transportable commodity. Figure 1 illustrates the law of one price between California and Arkansas. A simple ordinary least squares regression of California rice price on Arkansas price gives an R2 of 0.939 and an estimated slope coefficient between 0.80 and 0.94 . Moreover, a simple cointegration test suggests the absence of unit roots in the disturbances. Thus, California price and Arkansas rice prices move together over the long run. Market integration suggests that a US level model can be useful to describe California rice production. In this study, however, we present both national and state models. Prior to reporting the estimated production function for rice, a brief discussion of some aberrations of the rice market will be explained. Around 1976-77 there was a price collapse that caused producers to rotate to other crops or not plant rice at all. This lead to decreases in rice production. In the early eighties rice prices collapsed again and this caused many growers to forfeit their crop to the government because the price was below the value of the government loan. This was not only the case with rice, but other program crops such as wheat and corn. In an attempt to reduce acreage and sell off the rice that the government had claimed, the government implemented the 50/92 plan. Subsides were directly linked to production. Thus, if a grower did not produce he was not paid. The 50/92 program allowed the grower to produce on 50% of his acreage and receive 92% of the subsidies that he would receive if he had produced on 100% of his land. This reduced production allowed the government to reduce the stocks of commodities that they had to claim in 1981-82. The 50/92 program ran until about 1988. Since then subsidies have been decoupled from production to prevent problems like this from happening again. The 50/92 program was popular in the south, especially in Texas where their production was lower and they had low fixed costs of land, but in California it was only widely used for a few years.

Policy variables are incorporated into some of the models below. The estimated US price expectation elasticity of supply is 0.45 which is also inelastic but is not significant. The estimated coefficient of Thailand price of rice is 0.27 with a t-ratio of about two. The index for price support is positive but not significant. There is also a positive and significant time trend in the supply of rice. According to the estimated price coefficient , the elasticity of demand of US rice implies that an increase of 1% in price results in a decrease of 0.36% change in the quantity demanded. As the price of Thailand rice increases, the demand for US rice increases, but again the estimated coefficient is not significant. The income elasticity is 0.33 and the estimated coefficient of Japanese income is 0.34 as expected. Both coefficients are not significant, however. Rice producers in California and throughout the United States respond positively to increases in rice prices. The short-run price elasticity of production, based on a partial adjustment model, for the US was estimated to be 0.23. When policy variables were included in the production equation the price elasticity dropped to 0.18 .The production equation was an aggregated one. For a disaggregated approach that estimates how rice producers respond to different support programs, see McDonald and Sumner. The estimated own-price elasticity of demand for rice was found to be inelastic for a SUR system. The income elasticity for rice was estimated to be 0.74 in a single-equation demand function . US rice producers export less when the US price increases . They export more when the Thailand rice price increases since Thailand is a major competitor in the world market. California is the second leading producer of fresh tomatoes in the US, after Florida. Figures 1-3 compares fresh tomatoes planted acreage, production and nominal price for US, Florida and California. California accounts for about 95 percent of the area harvested for processing tomatoes in the United States—up from 79 percent in 1980 and 87 percent in 1990. The other major producers are Texas, Utah, Illinois, Virginia, and Delaware and Florida. In Figure 1, total U.S. fresh tomato acreage has declined over the period 1960 to 2002, but acreage in California and Florida has remained steady. The declined in acreage has come from the states of Texas, Utah, Illinois, Virginia, and Delaware . Figures 4-6 illustrates the trends for California and US planted acreage, production and nominal prices for processed tomatoes.Tomato growing is based on grower-processor contract agreements. The majority of production is traded this way with the spot market playing a marginal role. Most initial processing is by firms that manufacture tomato paste, a raw ingredient.