Performing Department
(N/A)
Non Technical Summary
Nutrient runoff from agricultural production leads to eutrophication in the Gulf of Mexico and other locations with significant detrimental consequences for aquatic ecosystems. Cost-effective management of nutrient runoff from cropland requires a thorough understanding and quantitative modeling of the effects of fertilizer use, irrigation and Best Management Practices (BMPs) on crop production and nutrient runoff in the respective watersheds. Since crop production and fertilizer use are primarily driven by economic incentives, including input and output prices, it is essential that the quantitative assessment includes both economic and biophysical determinants of nutrient runoff. In this project, we will develop a large-scale spatially explicit integrated assessment model (IAM) to examine the interdependencies between crop production and water quality and quantity. The integrated hydro-economic agricultural land use (IHEAL) model combines a US scale multi commodity (corn, wheat, sorghum, soy) partial equilibrium market formulation with a process-based Soil and Water Assessment Tool (SWAT). The system includes spatially explicit county scale production decisions for planting, nitrogen (N) fertilizer use, and irrigation. This project will expand the current IHEAL model to include additional four crops, explicit consideration of best management practices (BMPs), and phosphorus (P) use via Hydrologia and Ware Quality System (HAWQS). The model will be used to study interdependencies between water quality outcomes across watersheds with significant nutrient runoff. We will also examine the implications of nutrient runoff abatement policies for regional production, commodity prices, regional water quality, and demand for irrigation water in areas with limited water supplies.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
Objective 1. Expand the IHEAL model to include additional elements.The proposed expansions are:1a. Add cotton, rice, barley and alfalfa to the model.1b. Add P management via prescribed BMPs or as fertilizer use depending on the location.1c. Add spatially explicit BMPsstarting with the MRB where applicable.Objective 2. Estimate the opportunity cost of reducing nutrient runoff .The IHEAL.2 will be used to estimate the opportunity costs of reducing nutrient runoff to the Gulf and other outlets.Objective 3. Quantify cross-watershed nutrient runoff leakages.The spillover effects of nutrient runoff reduction to the Gulf on nutrient runoff outside the MRB will be quantified. Nutrient runoff reduction to the Gulf will be reevaluated with explicit constraints on runoff to other regions.Objective 4. Quantify the water scarcity impacts. Cropland in the Lower MRB relies heavily on the Mississippi River Valley alluvial aquifer for irrigation water. The Ogallala aquifer, mostly located within the MRRB and ARB, is also a major irrigation water source in some of the counties.4a. The effect of reducing N and P runoff on irrigation in water-stressed locations will be examined.4b. Nutrient runoff abatement outcomes will be evaluated under constrained irrigation in water-stressed areas. Irrigation will be restricted from increasing beyond the baseline (business as usual) values when nutrient runoff is constrained and runoff abatement outcomes like opportunity costs, prices, and acreages will reevaluated. In addition, we will examine the impacts of potential future declines in irrigation capacities on nutrient runoff.Objective 5. Investigate the interdependencies between N and P runoff reductions . Starting with select sub watersheds, HAWQS (Hydrological and Water Quality System) platform will be used to obtain parameters needed to examine the interdependencies between N and P runoff.
Project Methods
Freshwater nutrient enrichment from agricultural activities has been extensively addressed in previous literature (Ribaudo et al., 2001; Rabotyagov et al., 2010; Kling et al., 2014; Rabotyagov et al., 2014a; Marshall et al., 2018). However, most of the prior studies do not explicitly include consumer and producer surplus measures and/or rely on production activities with fixed inputs, which limits the changes in production activities to the extensive margin precluding adjustment opportunities at the intensive margin.This project contributes to prior literature by developing an integrated hydro-economic model, which includes a price endogenous commodity market specification with county scale land use. Per acre production functions are expressed in terms of fertilizer use and irrigation where applicable. Hence, the IHEAL model includes production choices at the intensive (per acre yield as a function of fertilizer and irrigation use) and extensive (planted acreage) margins. The model will be used to evaluate land use, fertilizer use, irrigation and BMP adoption corresponding to water quality objectives. The principle economic and hydrologic elements of IHEAL are presented below. The economic component of the model and the integration with biophysical data will be coordinated by PI Dr. Elbakidze.The IHEAL.2 system design The conceptual structure of the proposed IHEAL.2 system is provided in Figure 5. The elements in green shapes are the proposed expansions or updates of the IHEAL model. An integrated hydro-economic assessment model consists of a price-endogenous partial equilibrium model with exogenous trade for major crops in the US, merged with the corresponding land use components and SWAT elements. The model will produce solutions for land use, fertilizer use, BMP adoption, prices, water quantity and quality impacts for various environmental policy scenarios.4.2 Price-endogenous partial equilibrium and land use modelPrice-endogenous partial equilibrium models have been widely used in conjunction with biophysical models for large-scale land use policy analysis (McCarl and Schneider, 2001; Ribaudo et al., 2001; Havlik et al., 2011; Chen et al., 2014; Marshall et al., Yi et al., 2018; Xu et al., 2022; Elbakidze et al., 2023). McCarl and Schneider (2001) used a price endogenous sectoral partial equilibrium model to study greenhouse gas emissions in the U.S. agriculture and forestry sectors. Havlik et al. (2011) presented a model for evaluating bioenergy policies, global land use and greenhouse gas emissions in forest, agriculture and biomass sectors. Chen et al. (2014) used a dynamic partial equilibrium model for fuel and agricultural sectors in the U.S. to study greenhouse gas emissions in scenarios with the Renewable Fuel Standard, the Low Carbon Fuel Standard, and carbon prices. Yi et al. (2018) developed a partial equilibrium model to measure the effects of surface ozone pollution on the agricultural sector in China. Following Havlik et al. (2011) and Xu et al. (2022), the objective function (equation 1) maximizes the sum of consumer and producer surplus, as the difference between aggregate consumer benefit and production cost across the commodities included in the model. The choice variables are aggregate commodity consumption and county-scale crop production with corresponding per acre N and P use and irrigation schedules where applicable.4.2.1 Cost of compliance with nutrient runoff restrictionThe model will be used to quantify the opportunity costs of achieving various N and P runoff reduction targets in the watersheds of interest. The opportunity costs include foregone producer and consumer surplus from reducing nutrient runoff relative to the baseline conditions. The changes in consumer and producer surplus are differences in objective function values between baseline and individual restricted runoff scenarios. Alternatively, marginal value product (MVP) curves of nutrient runoff can be obtained using constraint shadow prices and Box-Cox estimation to obtain a range of opportunity cost estimates following Xu et al. (2022) and Elbakidze et al. (2017). The opportunity costs will be estimated for various combinations of nutrient runoff constraint stringencies across watersheds of interest to assess interdependencies of runoff reduction targets and account for cross-watershed nutrient leakage. 4.3 SWAT/HAWQS integrationIn collaboration with the United States Environmental Protection Agency (USEPA), the proposal team members (CO-PIs Drs. Srinivasan and Bawa) have developed a web-based agroecosystem modeling tool called HAWQS- Hydrologic and Water Quality System (https://hawqs.tamu.edu/#/). They will lead the SWAT/HAWQS modeling efforts. HAWQS employs the internationally recognized public domain SWAT model (Arnold et al., 1998, 2012a,b) as its core modeling engine and offers pre-loaded high quality input data, interactive web interfaces and maps, online development, execution, and storage for user's modeling projects, output data processing capabilities (including tables, charts, graphs, and raw data), and data sharing. HAWQS has been in operation since 2012 and is widely used by USEPA and many water management agencies and water districts for SWAT assessment of environmental outcomes including water quantity and quality (HAWQS, 2020; USEPA, 2021a). The HAWQS platform contains climate, land use, management, soil, topographic and other data that can be used to construct SWAT models based on HUC8, HUC10 or HUC12 hydrologic units (USGS, 2013). Co-PI Drs. Srinivasan and Bawa have been leading the hydrological calibration of the SWAT model at HUC12 scale across the country and currently feature built-in initial calibration for ~40,000 HUC12 watershed systems in HAWQS. The calibrated HUC12 watersheds cover most of the MARB and entire Maumee River and Chesapeake Bay watersheds. Additional efforts on water quality calibration of HAWQS-based SWAT models by the team members are being performed to provide further refinement of modeling accuracy. The calibrated watersheds for water quantity and quality will assist the project with obtaining reliable yield and nutrient runoff parameters to be used in the economic partial equilibrium model.SWAT has been widely used in conjunction with economic models for land use and water quality policy analysis in watershed-scale nonpoint source pollution contexts (CARD, 2023). Xu et al. (2021) integrated SWAT-Variable Source Area (SWAT-VSA) with an economic agricultural production model, to study regional BMPs for N abatement in the Susquehanna watershed. Xu et al. (2022) introduced the IHEAL.1 with SWAT parameters for MARB, including edge-of-field N runoff at the eight-digit watershed scale (HUC8). IHEAL.1 also includes downstream delivery ratios, which indicate the proportion of the edge-of-field runoff that propagates from each HUC8 to the Gulf of Mexico. This model was also used in Elbakidze et al. (2023). We propose to extend the integration of SWAT/HAWQS and IHEAL.1 models to include P runoff, additional crops, watersheds, and BMPs.HUC8 scale simulations of per ha yield and nutrient (N and P) edge-of-field runoff for various per ha fertilizer, irrigation (where applicable), and BMP adoption scenarios will be obtained from HAWQS calibrated watershed projects at different spatial and temporal resolutions. These simulated parameter estimates are used as input data in the IHEAL model (Figure 5). IHEAL.1 uses five N use grid points for HAWQS simulations. This approach will be extended to include various combinations of N and P use grid points and regionally appropriate candidate BMPs. County scale production parameters in the partial equilibrium economic model are obtained using acreage weighted transformation from HUC8 to county scale values.