This paper presents findings from an analytical scheme that offers a promising alternative to traditional procedures of modeling acreage response. The scheme addresses the two-step decision process in which program and nonprogram planting decisions are modeled separately, conditional on the decision to participate. This provides a more realistic and intuitive portrayal of producers' decision making process. The model is applied at the regional level to assess the impact of farm programs on acreage response for corn in the Cornbelt and Lake States, and for wheat in the Northern Plains. The impacts of policy variable changes on participation and planted acreage are also analyzed.

This study reports results of an acreage supply equation using individual farm data and develops implications for bias introduced by use of aggregate data. The analysis shows that use of aggregate acreage response models underestimates the absolute value of price elasticities and the lagged adjustment process. Further, aggregated analyses cannot incorporate some variables found to be important in explaining from level acreage response.

Southeast drought conditions have accentuated the demand for irrigation in the face of restricted water supply. For allocating this supply, Georgia held an auction for withdrawing irrigated acreage. This auction withdrew 33,000 acres from irrigation, resulting in a physical estimate of a 399 acre-feet daily increase in water flow. The actual reduction is driven by crop distributional changes on the basis of economic substitution and expansion effects. In contrast to the physical estimates, an econometric model that considers these effects is developed. The differences between the physical and econometric models result in an increase in the estimate of water savings of around 19% to 24%.

An expected utility model that includes output price and yield uncertainty was used to estimate cotton, com, and soybean acreage response equations for the Southeast. The model appeared to fit the soybean and corn data well, resulting in own-price elasticity estimates of 0.317 for com and 0.727 for soybeans. When applied to cotton acreage, however, the model did not yield satisfactory results. When elasticity was allowed to change over time, however, statistical results for the cotton equation improved, yielding an own-price elasticity of 0.915 at data means.

Elasticities of import demand and supply often drive economic models, butfew empirical estimates of these elasticities exist for vegetables andfruits. For those that do exist, most are outdated. Because elasticitieschange over time as income, prices, and market conditions change, outdatedelasticity estimates may not be representative of changes in importquantities demanded or in acreages, yield, and quantities supplied.Moreover, import demand elasticities by country of origin for mostvegetables and fruits are nonexistent. This article presents research thatupdates elasticity estimates for each of the selected product categories andincludes production and trade implications.