Inter-annual Weather Variation and Crop Yields

While the effects of rising mean temperatures on agricultural output have been studied extensively, there is limited discussion of the impact of inter-annual weather variation on crop yields. This paper estimates the link between weather and crop yields separating the influence of (i) mean weather outcomes (i.e., climate) to which a farmer can adapt from (ii) unpredictable year-to-year weather fluctuations to which a farmer can only partially adapt as crops are planted before the weather shock is realized. We find that corn in extreme climates, both hot and cold, are more sensitive to interannual weather variation than the ones in moderate climates. Global warming has two effects on corn yields: first, warming will induce farmers in moderate-temperate climates to plant varieties that are less robust to weather fluctuations, while farmers in cool climates will plant varieties that are more robust to weather fluctuations. Second, the elasticity of reductions in expected corn yields with respect to an increase in the standard deviation of weather fluctuations is -0.4. Since most farmers are eligible for subsidized crop insurance, an increase in weather variation also directly translates into added government payments. ♣ Department of Economics and School of International and Public Affairs, Columbia University, 420 West 118th Street, Room. 1308, MC 3323, New York, NY 10027. Email: ws2162@columbia.edu. Phone: (212) 854-1806. Agriculture is the sector of the economy most directly linked to weather, as precipitation and temperature directly enter the production function. Most of the existing literature that addresses the potential consequence of climate change focuses exclusively on the effects of a shift in mean weather outcomes (i.e., climate). This paper examines the influence of the second moment of the temperature distribution, i.e., year-to-year variance, on crop yields. The economic implications of mean shifts and changes in inter-annual variance are quiet distinct: farmers can adapt to shifts in mean weather outcomes by switching to various varieties of the same crop, by using various planting practices (e.g., sowing densities), or switching to a completely different crop. For example, corn varieties are often classified by the required degree days, i.e., the optimal sum of daily temperatures above a certain baseline. However, while mean weather (climate) is known, actual weather outcomes are random and unpredictable at the time of planting in spring. The time lag between the time of planting and weather realization is the crucial component that distinguishes a shift in mean weather outcomes from year-to-year variations. Once a farmer has committed to a certain crop by choosing a particular seed and planting practise, the weather outcome is realized and, in retrospect, it might have been better to grow a different variety or use a different planting practice. Variation in weather hence has two dimensions: Average weather (or climate) varies in space, i.e., between different location. Year-to-year weather variation adds a time dimension, as weather outcomes at a given location vary between years. This paper utilizes a panel data set of corn yields to simultaneously estimate the outer envelope of corn yields attainable by adapting to various climates as well as the effect of inter-annual variation that reduce expected yields below the highest attainable one if the weather outcome would have been known in advance. The drop inside the outer envelope occurs because a suboptimal variety was grown for the particular weather outcome. The curvature or robustness of individual crop varieties determines how much weather fluctuations reduce observed yields below the outer envelope. If the curvature was zero or the plant was completely robust to inter-annual variations, one would remain on the outer envelope. However, if a crop is very sensitive, fluctuations will push observed yields within the outer envelope. We allow these additional productivity losses due to weather fluctuations to vary by crop variety. For example, corn species bread in the highlands of Peru show much more resistance to weather variation than the ones used in the Mediterranean. In the preferred model estimate below, the curvature of For a more elaborate discussion of degree days, see the data section below.