Environmental regulation of agriculture is becoming increasingly important, and growers are increasingly concerned about the effects of regulations on their profitability. Regulations governing the use of a pesticide affect its economic value. Further, growers often face a choice among pesticide alternatives, each with its own set of regulatory restrictions. In this environment, the introduction of a new regulation can have complex effects on growers' profit-maximizing pesticide choices. Buffer zones and regional emissions caps mean that pesticide choices can have important spatial components. Our paper presents an optimization model that incorporates spatial considerations at the field and regional level. We apply our model to fumigant choice by California strawberry growers. The industry is facing an impending ban on the use of methyl bromide, which in conjunction with chloropicrin was the standard fumigant for over forty years. In addition to the forthcoming ban, the state government has imposed regulations governing methyl bromide application, including buffer zones, etc. These extreme use restrictions provide us with an interesting environment for modeling the effects of pesticide regulations. There are currently two legally available fumigants that may substitute for methyl bromide in strawberries: 1,3-D and chloropicrin. 1, 3-D is subject to township caps and other restrictions. Township caps limit total application in an area. The California Department of Pesticide Regulation is currently undertaking air monitoring and other activities to determine whether or not buffer zones and other restrictions should be applied to chloropicrin. We evaluate the effects of current and proposed regulations on field-level decisions and industry costs and returns. Methodology To the best of our knowledge, no study has examined the role of pesticide use regulations in determining growers' profit-maximizing pesticide choices at the field level. We do so by combining three datasets with a field-level spatial model of the profit-maximizing fumigation decision. The first dataset includes detailed field-level information regarding the costs and yields associated with alternative fumigants obtained from a multi-disciplinary research project. The second includes chemical-specific California use regulations regarding treatment rates, buffer zones, and other restrictions. The third includes information on the shapes and sizes of strawberry fields in California. Using these data, the optimization model computes the profit-maximizing treatment for each field including pattern of treatment and number of acres treated per day, etc. Field-level results are aggregated to evaluate the impact of regional pesticide regulations, and then to estimate the industry-level effects of current and proposed pesticide use regulations. We model the effects of the entire regulatory system on the fumigation decisions made by farmers. The restrictions on fumigants are integrated into a field-level programming model of a grower's fumigant decision choice. The program calculates the optimal fumigation plan for a field, given the field's size and shape, and use regulations, and per-acre costs and returns associated with each fumigant. The resulting field-level choices are aggregated in order to check for consistency with township caps. If caps are exceeded, the model is rerun using a number of allocation rules. All choices for all fields are aggregated in order to obtain industry-level results. We perform this procedure for the current set of restrictions and for several alternative sets, assessing the profitability of each alternative. For example, we remove the existing township caps on 1,3-D and evaluate how much the results change. We include varying buffer zone restrictions on chloropicrin, and evaluate whether growers' fumigant choices are sensitive to the size of the buffer zone. Relevance Environmental regulation of agriculture is becoming increasingly important. By explicitly analyzing the effect of regulations affecting methyl bromide alternatives in a model that includes both the spatial dimensions of some regulations and the costs and yields associated with each alternative, we will obtain a more detailed and accurate assessment of the costs of these regulations than is currently available. Our results will provide a greater understanding of the effects of these regulations on industry profitability, and how these regulations interact. Our model can be applied to other cases of pesticide regulations. Given the increasing importance of environmental regulation in agriculture, it is important to aid policymakers in understanding how regulations interact with each other, possibly in unexpected ways.