This book gives an account of supply response for major crops during pre and post reform periods using Nerlovian adjustment cum adaptive expectation model. Estimation is based on dynamic panel data approach using pooled cross section - time series data across states for India for the period 1980-81 to 2004-2005. The significant feature of the specification used in the study is both main and substitutable crops are jointly estimated by a single set of equations and by introducing varying slope coefficients to capture different responses. The analysis is a useful additional contribution to the existing empirical literature in supply response.

Climate change is amplifying the threat of drought to food security. While drought can cause significant agricultural production loss through both crop yield and acreage, previous studies have mostly overlooked the impacts of drought on crop acres. To address this gap, we study corn and soybean production in U.S. counties from 2001 to 2019, examining drought impacts on planned planting acres, prevented planting, and crop abandonment as climate adaptation responses. Our findings show that 28% (26%) of drought impacts on corn (soybean) production can be attributed to the net effect of farmers' decisions on acres. Moreover, drought affects production throughout the crop year, not solely during the growing season. Specifications that omit pre-planting and planting-season drought underestimate the adverse impacts of growing-season drought on the harvested ratio, yield, and production of corn and soybeans by between 11 and 29 percent. Our findings highlight the role of adaptive behavioral responses in quantifying the extent of climate-related damage in agriculture.

Estimates of crop supply response to price have a long history in agricultural economics. The primary contribution of this dissertation is to estimate the short-run (1-year) and long-run (5- to 10-year) acreage response to price for corn and soybeans. I characterize the dynamics of aggregate supply response by aggregating a field-level conceptual model of crop decisions with rotation incentives across heterogeneous fields. The model indicates that the long-run response is likely to be smaller than the short-run response. The econometric model exploits crop data, in the form of pixels, derived from satellite imagery for Illinois (1999-2010) and Iowa and Indiana (2000-2010). To approximate fields as the unit of analysis, I use Common Land Unit boundaries from the Farm Service Agency. Expected per bushel crop revenues are the sum of a futures price, an expected loan deficiency payment, and an expected basis obtained from 93 market locations. Soil and precipitation data are spatially merged to the crop data. The field-level dataset contains approximately 1 million observations per year. I argue that previous estimates of aggregate acreage response to price have been biased due to the use of aggregate data and pooled estimators (i.e., estimating a heterogeneous coefficient with a single parameter). The bias from aggregation occurs because aggregating a dynamic model results in a different dynamic structure at the aggregate level, thus the model is misspecified with a single lagged dependent variable. The pooling bias occurs because the coefficient on the lagged dependent variable captures the variation in the price response. When the price of corn increases, fields that have a larger price response are more likely to plant corn in the current year, but they are also more likely to have planted corn the previous year since prices are positively autocorrelated. I avoid these sources of bias by using disaggregate crop data and estimating coefficients that differ by soil regimes. Using the field-level data, I estimate the own-price acreage elasticity for corn as 0.52 in the short run and 0.34 in the long run. I estimate the own-price acreage elasticity for soybeans as 0.31 in the short run and 0.23 in the long run. In contrast, estimates with county-level data for the same region and time period indicate that the long-run elasticities are larger than the short-run elasticities.

With reference to India.