Behavioral Salinity Response: Estimating Salinity Policies from Remote Sensed Micro-Data

In arid regions, including Australia's Murray-Darling basin and California's Central Valley, increasing salinity is a problem affecting agriculture, regional economies, urban areas, and the environment. The direct costs of salinity to agriculture in the Murray-Darling basin and California’s Central Valley are on the order of $500 million per year. Policymakers want to design policies to effectively manage salinity and, as such, need to understand how farmers respond to changing salinity levels. Reduced crop yields account for the largest direct cost of salinity to agriculture however farmers are able to mitigate effects through field management. Consequently, there is a difference between experimentally estimated yield-salinity functions and those which result from farmer behavioral response to salinity. The latter are relevant for salinity policy analysis and, to our knowledge, have not previously been estimated in the literature. We model farmers as profit-maximizing crop portfolio managers and estimate the behavioral yield-salinity functions for 6 crop groups using geo-referenced field data. We find behavioral yield-salinity functions are close to those generated in experimental settings but costs using experimental functions understate the costs of salinity.