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Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase II. The GGCMI Phase II experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological mean yield response without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.

Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase II experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase II experimental protocol and its simulation data archive. Twelve crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (``CTWN') for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase II archive. For example, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that indicates yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions, but is largest in high-latitude regions where crops may be grown in the future.

This paper introduces a spatial bioeconomic model for study of potential cellulosic biomass supply at regional scale. By modeling the profitability of alternative crop production practices, it captures the opportunity cost of replacing current crops by cellulosic biomass crops. The model draws upon biophysical crop input-output coefficients, price and cost data, and spatial transportation costs in the context of profit maximization theory. Yields are simulated using temperature, precipitation and soil quality data with various commercial crops and potential new cellulosic biomass crops. Three types of alternative crop management scenarios are simulated by varying crop rotation, fertilization and tillage. The cost of transporting biomass to a specific demand location is obtained using road distances and bulk shipping costs from geographic information systems. The spatial mathematical programming model predicts the supply of biomass and implied environmental consequences for a landscape managed by representative, profit maximizing farmers. The model was applied and validated for simulation of cellulosic biomass supply in a 9-county region of southern Michigan. Results for 74 cropping systems simulated across 39 sub-watersheds show that crop residues are the first types of biomass to be supplied. Corn stover and wheat straw supply start at $21/Mg and $27/Mg delivered prices. Perennial bioenergy crops become profitable to produce when the delivered biomass price reaches $46/Mg for switchgrass, $118/Mg for grass mixes and $154/Mg for Miscanthus giganteus. The predicted effect of the USDA Biomass Conversion Assistance Program is to sharply reduce the minimum biomass price at which miscanthus would become profitable to supply. Compared to conventional crop production practices in the area, the EPIC-simulated environmental outcomes with crop residue removal include increased greenhouse gas emissions and reduced water quality through increased nutrient loss. By contrast, perennial cellulosic biomass crops reduced greenhouse gas emissions and improved water quality compared to current commercial cropping systems.

The Agricultural Policy Environmental eXtender (APEX) model was developed by the Blacklands Research and Extension Center in Temple, Texas. APEX is a flexible and dynamic tool that is capable of simulating a wide array of management practices, cropping systems, and other land use across a broad range of agricultural landscapes, including whole farms and small watersheds. The model can be configured for novel land management strategies, such as filter strip impacts on pollutant losses from upslope cropfields, intensive rotational grazing scenarios depicting movement of cows between paddocks, vegetated grassed waterways in combination with filter strip impacts, and land application of manure removal from livestock feedlots or waste storage ponds. A description of the APEX model is provided, including an overview of all the major components in the model. Applications of the model are then reviewed, starting with livestock manure and other management scenarios performed for Livestock and the Environment: A National Pilot Project (NPP), and then continuing with feedlot, pesticide, forestry, buffer strip, conservation practice, and other management or land use scenarios performed at the plot, field, watershed, or regional scale. The application descriptions include a summary of calibration and/or validation results obtained for the different NPP assessments as well as for other APEX simulation studies. Available APEX Geographic Information System–based or Windows-based interfaces are also described, as are forthcoming future improvements and additional research needs for the model.

Computer models are important tools for assessing regional carbon sequestration and other environmental impacts of agricultural management practices. The Environmental Policy Integrated Climate (EPIC) model is a very flexible model that has been used to make a wide range of field- and regional-scale environmental assessments. Large regional-scale applications of EPIC and similar models can require thousands of runs, resulting in a huge data management task. To address this problem, the Center for Agricultural and Rural Development (CARD) has developed an interactive EPIC (i_EPIC) software package that provides an automated approach to executing large sets of EPIC simulations. Overviews of both the latest EPIC version and the i_EPIC software package are presented. We also present examples of regional applications using both EPIC and i_EPIC conducted by the Resource and Environmental Policy Division of CARD, by the Joint Global Change Research Institute of the University of Maryland and the Pacific Northwest National Laboratory, and by the Resource Assessment Division of the Natural Resources Conservation Service, U.S. Department of Agriculture.

This report describes an integrated agro-ecological modeling system that was developed to assess the potential economic and soil erosion impacts of different agricultural policies for the Canadian prairie provinces of Alberta, Saskatchewan, and Manitoba. The system was constructed by linking erosion metamodels (response functions), based on multiple simulations of the USDA Erosion Productivity Impact Calculator (EPIIC), with a modified version of Agriculture Canada's Canadian Regional Agriculture Model (CRAM) denoted as RS-CRAM (resource sensitive CRAM). A summary of both the environmental and agricultural decision (RS-CRAM) components are presented, including a description of the modifications and enhancements that were made to CRAM. Results of policy analyses are discussed for the following scenarios: (1) Gross Revenue Insurance Program (GRIIP), (2) sensitivity of GRIP results to different risk levels, (3) tillage practice sensitivity, and (4) industrial crops sensitivity. Future recommendations are also presented, emphasizing how the current system can be improved and the potential to include additional regions, environmental indicators, and other environmental models within the system.