The Effects of Climatic Variability on US Irrigation Adoption

This paper contributes to the literature underscoring the importance of climatic variance by developing a framework for incorporating the means and tails of the distributions of rainfall and temperature into empirical models of agricultural production. The methodology is applied to estimate the impact of climate change on the discrete choice decision to adopt irrigation since it is an important adaptation to climate change. We develop a discrete choice model for the decision to install irrigation capacity that captures the effects of both climate means and extremes. Climatic means and frequencies of climatic events in the upper tails of the temperature and precipitation distributions are used to estimate the parameters of a normal distribution for temperature and a Weibull distribution for precipitation. Using estimates from a probit model, we examine the independent effects of changing climatic mean and variance on the probability of adopting irrigation. Increasing the mean temperature, holding variance constant, shifts the entire distribution toward warmer temperatures—increasing the frequency of extreme temperatures. For precipitation, the specification captures the separate effects of mean rainfall, frequency of rainfall, and frequency of extreme events. The results show that the tails of the temperature and precipitation distributions, not the means, are the dominant climatic determinants in irrigation adoption. The results also show that water availability, soil characteristics, farm size and operator demographics are important determinants of irrigation.

[1]  Michael Dalton.  The Welfare Bias from Omitting Climatic Variability in Economic Studies of Global Warming , 1997 .

[2]  Rulon D. Pope,et al.  Allocatable Fixed Inputs and Jointness in Agricultural Production: Implications for Economic Modeling , 1984 .

[3]  P. Crosson,et al.  Implications of Climate Change for U.S. Agriculture , 1995 .

[4]  Robert R. Pierce,et al.  Estimated Use of Water in the United States in 1995 , 1998 .

[5]  Nonparametric Spatial Rainfall Characterization Using Adaptive Kernel Estimator , 1998 .

[6]  Robert Chambers,et al.  Estimating Multioutput Technologies , 1989 .

[7]  C. Rosenzweig,et al.  Climate Change and Extreme Weather Events; Implications for Food Production, Plant Diseases, and Pests , 2001 .

[8]  D. Schimmelpfennig.  Uncertainty in economic models of climate-change impacts , 1996 .

[9]  J. A. Calvin.  Regression Models for Categorical and Limited Dependent Variables , 1998 .

[10]  W. Nordhaus,et al.  The Impact of Global Warming on Agriculture: A Ricardian Analysis: Reply , 1999 .

[11]  Mark T. Kanazawa.  Pricing Subsidies and Economic Efficiency: The U. S. Bureau of Reclamation , 1993, The Journal of Law and Economics.

[12]  K. Segerson,et al.  Impacts of Increased Climate Variability on the Profitability of Midwest Agriculture , 1999, Journal of Agricultural and Applied Economics.

[13]  William D. Nordhaus,et al.  The Impact of Climate Change on the United States Economy: The impact of climate variation on US agriculture , 1999 .

[14]  Linda O. Mearns,et al.  The effect of changes in daily and interannual climatic variability on CERES-Wheat: A sensitivity study , 1996 .

[15]  G. Grant,et al.  EVALUATION OF PROBABILITY DENSITY FUNCTIONS IN PRECIPITATION MODELS FOR THE PACIFIC NORTHWEST 1 , 1998 .

[16]  A. Dinar,et al.  Water and Land as Quantity-Rationed Inputs in California Agriculture: Empirical Tests and Water Policy Implications , 1995 .

[17]  D. Brooks,et al.  Determinants of Irrigation Technology Choice , 1990 .

[18]  R. Katz,et al.  Extreme events in a changing climate: Variability is more important than averages , 1992 .

[19]  E. Runge.  Effects of Rainfall and Temperature Interactions During the Growing Season on Corn Yield1 , 1968 .

[20]  N. M. Idaikkadar,et al.  CHAPTER 10 – Census of Agriculture , 1979 .