Duncan MacEwan’s research while affiliated with University of California, Davis and other places

Irrigated agriculture has grown rapidly over the last 50 years, helping food production keep pace with population growth, but also leading to significant habitat and biodiversity loss globally. Now, in some regions, land degradation and overtaxed water resources mean historical production levels may need to be reduced. We demonstrate how analytically supported planning for habitat restoration in stressed agricultural landscapes can recover biodiversity and create co-benefits during transitions to sustainability. We apply our approach in California's San Joaquin Valley where groundwater regulations are driving significant land use change. We link agricultural-economic and land use change models to generate plausible landscapes with different cropping patterns, including temporary fallowing and permanent retirement. We find that a large fraction of the reduced cultivation is met through temporary fallowing, but still estimate over 86,000 hectares of permanent retirement. We then apply systematic conservation planning to identify optimized restoration solutions that secure at least 10,000 hectares of high quality habitat for each of five representative endangered species, accounting for spatially varying opportunity costs specific to each plausible future landscape. The analyses identified consolidated areas common to all land use scenarios where restoration could be targeted to enhance habitat by utilizing land likely to be retired anyway, and by shifting some retirement from regions with low habitat value to regions with high habitat value. We also show potential co-benefits of retirement (derived from avoided nitrogen loadings and soil carbon sequestration), though these require careful consideration of additionality. Our approach provides a generalizable means to inform multi-benefit adaptation planning in response to agricultural stressors.

Crop rotation systems are an important part of agricultural production for managing pests, diseases, and soil fertility. Recent interest in sustainable agriculture focuses on low input-use practices which require knowledge of the underlying dynamics of production and rotation systems. Policies to limit chemical application depending on proximity to waterways and flood management require field-level data and analysis. Additionally, many supply estimates of crop production omit the dynamic effects of crop rotations. We estimate a dynamic programming model of crop rotation which incorporates yield and cost intertemporal effects in addition to field-specific factors including salinity and soil quality. Using an Optimal Matching algorithm from the Bioinformatics literature, we determine empirically observed rotations using a geo-referenced panel dataset of 14,000 fields over 13 years. We estimate the production parameters which satisfy the Euler equations of the field-level rotation problem and solve an empirically observed four-crop rotation.

Making the transition from open-access groundwater rights to sustainable groundwater management is a formidable task for newly formed groundwater sustainability agencies in California. As agencies begin to decide how to make equitable water allocations, how to monitor groundwater use and what mix of supply- and demand-side mechanisms to adopt to satisfy sustainability criteria, the groundwater management strategies in place across other basins in the western United States are worth studying. We surveyed 18 groundwater districts in California and other Western states to identify the management approaches and practices they have instituted. The conclusions we draw suggest a correlative rights framework of water allocation with phase-ins for heavy users; metered pumping; flexible arrangements for trading and carrying over allocations for multiple years; and incentivizing groundwater recharge, including recharge from deep percolation from crops. Rigid formulas for significantly reducing groundwater use in medium- and high-priority basins are likely to have significant negative effects on the regional economy.

In 2014 California passed legislation requiring the sustainable management of critically overdrafted groundwater basins, located primarily in the Central Valley agricultural region. Hydroeconomic modeling of the agricultural economy, groundwater, and surface water systems is critically important to simulate potential transition paths to sustainable management of the basins. The requirement for sustainable groundwater use by 2040 is mandated specifically for overdrafted groundwater basins that are decoupled from environmental and river flow effects. We argue that, for such cases, a modeling approach that integrates a biophysical response function from a hydrologic model into an economic model of groundwater use is preferable to embedding an economic response function in a complex hydrologic model as is more commonly done. Using this preferred approach, we develop a dynamic hydroeconomic model for the Kings and Tulare Lake sub-basins of California and evaluate three groundwater management institutions–open access, perfect foresight, and managed pumping. We quantify the costs and benefits of sustainable groundwater management, including energy pumping savings, drought reserve values, and avoided capital costs. Our analysis finds that, for basins that are severely depleted, losses in crop net revenue are offset by the benefits of energy savings, drought reserve value, and avoided capital costs. This finding provides an empirical counter-example to the Gisser and Sanchez Effect. This article is protected by copyright. All rights reserved.

Soil salinity accumulation in California’s Central Valley and other irrigated areas around the world affects agricultural productivity, regional economies, urban areas, and the environment. The direct costs of salinity to agriculture in the California’s Central Valley have been estimated to be equal to US$ 500 million per year. Reduced crop yields from salinity in the root zone account for the largest direct cost of salinity but these losses can be partially offset by regional and field-level management including blending with higher quality water, improving field drainage, or leaching. Effective salinity management must consider the behavioral adjustments by irrigation districts and growers, and importantly, must be based on data available at the regional scale required for policy analysis. In this paper, we estimate crop-specific yield-salinity functions using geo-referenced crop data and shallow groundwater salinity. We model farmers as risk-averse crop portfolio managers and estimate farmer-behavior based yield-salinity functions for six crop groups in Kern County, California. The resulting farmer-behavior based yield-salinity functions account for field-level management of salinity and use the regionally available shallow groundwater salinity to proxy for the true salinity at root zone. We calibrate a regional economic model of Kern County agriculture to evaluate the cost of salinity using the estimated functions, and compare these estimates to the standard field-experiment based yield-salinity functions.

As in many places, groundwater in California (USA) is the major alternative water source for agriculture during drought, so groundwater’s availability will drive some inevitable changes in the state’s water management. Currently, agricultural, environmental, and urban uses compete for groundwater, resulting in substantial overdraft in dry years with lowering of water tables, which in turn increases pumping costs and reduces groundwater pumping capacity. In this study, SWAP (an economic model of agricultural production and water use in California) and C2VISim (the California Department of Water Resources groundwater model for California’s Central Valley) are connected. This paper examines the economic costs of pumping replacement groundwater during drought and the potential loss of pumping capacity as groundwater levels drop. A scenario of three additional drought years continuing from 2014 show lower water tables in California’s Central Valley and loss of pumping capacity. Places without access to groundwater and with uncertain surface-water deliveries during drought are the most economically vulnerable in terms of crop revenues, employment and household income. This is particularly true for Tulare Lake Basin, which relies heavily on water imported from the Sacramento-San Joaquin Delta. Remote-sensing estimates of idle agricultural land between 2012 and 2014 confirm this finding. Results also point to the potential of a portfolio approach for agriculture, in which crop mixing and conservation practices have substantial roles.

This paper describes calibration methods for models of agricultural production and water use in which economic variables can directly interact with hydrologic network models or other biophysical system models. We also describe and demonstrate the use of systematic calibration checks at different stages for efficient debugging of models. The central model is the California Statewide Agricultural Production Model (SWAP), a Positive Mathematical Programming (PMP) model of California irrigated agriculture. We outline the six step calibration procedure and demonstrate the model with an empirical policy analysis. Two new techniques are included compared with most previous PMP-based models: exponential PMP cost functions and Constant Elasticity of Substitution (CES) regional production functions. We then demonstrate the use of this type of disaggregated production model for policy analysis by evaluating potential water transfers under drought conditions. The analysis links regional production functions with a water supply network. The results show that a more flexible water market allocation can reduce revenue losses from drought up to 30%. These results highlight the potential of self-calibrated models in policy analysis. While the empirical application is for a California agricultural and environmental water system, the approach is general and applicable to many other situations and locations.

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.