Academic journal article Journal of Agricultural and Applied Economics

Using a Climate Index to Measure Crop Yield Response

Academic journal article Journal of Agricultural and Applied Economics

Using a Climate Index to Measure Crop Yield Response

Article excerpt

Using principal component analysis, a climate index is developed to estimate the linkage between climate and crop yields. The indices based on three climate projections are then applied to forecast future crop yield responses. We identify spatial heterogeneity of crop yield responses to future climate change across a number of U.S. northern and southern states. The results indicate that future hotter/drier weather conditions will likely have significant negative impacts on southern states, whereas only mild impacts are expected in most northern states.

Key Words: climate change, crop yields, principal component analysis

JEL Classifications: Q1, Q54

(ProQuest: ... denotes formulae omitted.)

Contemporary Global Climate Models (GCMs), including the Australian CSIRO 3.5, Canadian CGCM 3.1, and Japanese MIROC 3.2, all predict that average temperature will keep rising with modest changes in precipitation for most states in the continental United States for the rest of the century (Coulson et al., 2010). This is assuming that greenhouse gas emissions follow the IPCC SRA1B scenario.1 Although agricultural technologies continue to improve, previous studies have indicated that temperature and precipitation variations have significant impacts on crop yields (Lobell, Cahill, and Field, 2007; Almaraz et al., 2008; Schlenker and Roberts, 2009).

Environmental conditions such as soil properties are expected to result in spatially varying climate change impacts. To date, there are a limited number of studies that have attempted to compare the effects of climate variations on crop yields across regions. Tao et al. (2006) studied data from sample stations located in various geographic and climatic zones in China and found that temperature was negatively correlated with crop yield at all stations except Harbin in northeastern China. McCarl, Villavicencio, and Wu (2008) found that the effects of temperature on crop yields vary across U.S. regions. In the appendix of Schlenker and Roberts (2009), the United States was divided into three regions: the northern, the interior, and the southern to explore how the temperatureyield relationship varies over different regions. They found that the threshold where temperature negatively affects yield is slightly lower in warmer areas, and the southern region has a lower sensitivity to extreme heat. Using a crop growth model, Butterworth et al. (2009) found that climate change would increase the productivity of oilseed rape in the United Kingdom but with the greatest benefits in Scotland in the north rather than England in the south.

Understanding how crop yield responses vary across regions can help predict the price and welfare impacts of climate change and aid in planning mitigation strategies related to food production. As an attempt to test the hypothesis of spatially varying climate change impacts, this study develops a set of climate indices to measure crop yield response across regions. The yield response model based on the climate indices, which are mutually orthogonal, should generate more stable coefficient estimates and yield predictions than models using highly correlated climatic variables.

Literature Review

Two major methodologies have been used to study the relationship between weather and crop yields: crop growth models and regression models. Crop growth modeling is a computerbased simulation approach based on a mathematical integration of biology, physics, and chemistry (Hoogenboom, 2000; Jones et al., 2003). It incorporates weather information- temperature, precipitation, solar radiation, and humidity-with other factors such as planting and harvest dates, fertilizer and irrigation applications, and soil properties to simulate crop yields. Although useful in examining how weather conditions affect crop growth, crop growth models are typically complex and require extensive, detailed information (Walker, 1989), which makes them less applicable in studies with large spatial scales. …

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