AgMIP USDA-ARS Research
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AgMIP United States Department of Agriculture, Agricultural Research Service

USDA’s objectives for its international research collaborations align with the U.S. Global Food Security Strategy (GFSS) goal to “Sustainably reduce global hunger, malnutrition, and poverty.” The AgMIP USDA-ARS research group directly supports Objective 1, support inclusive and sustainable agricultural-led economic growth and Objective 2, strengthened resilience among people and systems. To achieve these objectives USDA-ARS AgMIP research contributes to the Intermediate Result of “Increased sustainable productivity, particularly through climate-smart approaches.”

AgMIP USDA-ARS research is being carried out in four research locations:

Beltsville Agricultural Research Center, Beltsville, Maryland
In collaboration with ARS scientists at Beltsville, MD, researchers from the University of Washington have been improving the maize simulation model, MAIZSIM. They are working on MAIZSIM’s accuracy in predicting grain maturity, planting density effects on canopy development, carbon and nitrogen stress effects on leaf area and thickness, and leaf senescence. The maize model MAIZSIM has become one of the crop simulation models participating in a modeling inter comparison pilot study organized by AgMIP (Agricultural Model Inter comparison and Improvement Project) in 2012. As part of the AgMip activities, ARS scientists at Beltsville, MD and researchers from the University of Washington collaborated to produce maize yield simulations for multiple locations in Iowa, France, Brazil, and Tanzania sites under current and future climate conditions. In related activities, they also continued to participate in the Maize Model Improvement Expert Panel on-line meetings to represent MAIZSIM.

ARS researchers at Beltsville are leading the AgMIP potato model inter-comparison and the AgMIP maize leaf expansion pilots. As part of the potato initiative, they coordinated model simulation runs and experimental datasets from over twenty-four international collaborators. Results of the model-intercomparisons were analyzed, including sensitivity to climate change at different potato production locations around the world and results were recently published in Global Change Biology. Similar efforts are taking place with the maize leaf expansion project.

More information about the Beltsville Agricultural Research Center can be found at their website.

National Laboratory for Agriculture and the Environment, Ames, Iowa
Studies being conducted at the National Laboratory for Agriculture and the Environment rhizotron facility include efforts to quantify the genetics x environment x management (GxExM) interactions on crops, and to provide information on carbon dioxide-temperature-water interactions.

Data from our long-term studies of crop response to different management conditions was used as part of the intercomparison effort on maize models and data from field scale studies of water use is being used for ET model intercomparison effort. The experimental databases collected from these projects provide direct support to the model evaluation efforts in AgMIP.

More information about the National Laboratory for Agriculture and the Environment can be found at their website.

Center for Agricultural Resources Research, Fort Collins, Colorado
The longer term goal of this cooperative research project between USDA-ARS and Colorado State University (CSU) is to inter-compare agricultural system models and foster improvements in these models to increase their capability to utilize data from climate scenarios in evaluating the effects of climate change on agriculture and natural resources, as part of AgMIP (the Agricultural Model Inter-comparison Project). A major current objective of this international AgMIP project is to compare multiple crop system models with common datasets to identify the best scientific approaches (and knowledge gaps) for simulating the effects of climate change variables of temperature, CO2, water, and their interactions on natural resources processes and plant growth. The use of these approaches will improve the assessment of climate change effects on natural resources and crop production, and identify adaptations to climate change.

The focus in this cooperative research will be on comparing different approaches in major current models for simulating climatic and water effects on production and soil carbon storage processes in different cropping systems and management practices, and identify and improve the science of these natural resource processes for improved assessments and enhancing production and soil carbon. Specific longer-term objectives are to: 1) identify soil water, soil carbon, and plant growth modules across models that contribute to the best fit with the experimental data and identify the best approaches to simulate the process and its interaction with temperature and management regimes; 2) learn from the comparisons to further improve science for the processes in the current models; 3) improve simulation of water-temperature-CO2-N interactions and assist in exploring management adaptations to limited water and climate change to maintain production and soil carbon in the Central Great Plains.

More information about the Center for Agricultural Resources Research can be found at their website.

Arid Lands Agricultural Research Center (ALARC), Maricopa, AZ
Research supporting AgMIP activities at the Arid Lands Agricultural Research Center (ALARC, Maricopa, AZ) includes studying crop responses to high temperatures, water deficits and elevated atmospheric CO2, taking advantage of the unique desert environment, which allows year-round field experimentation.

As a member of the AgMIP IT team, ALARC also supports strategies to improve management of data from field experiments, with dual goals of promoting access to research results and of facilitating use of such data in simulation model testing and improvement. Collaboratively with the University of Florida, ALARC maintains the ICASA data dictionary, which harmonizes terminology and units of measurements for data used in AgMIP modeling exercises.

Work with the AgMIP Wheat Team also led to research into how to improve the Decision Support System for Agrotechnology Transfer (DSSAT) and the associated Cropping Systems Model (CSM), which have been widely used by AgMIP researchers.

In addition Jeff White is contributing to the Rapid Assessment of Agriculture in a +1.5 – 2.0°C World.

More information about ALARC can be found at their website.

AgMIP-USDA/ARS ACTIVITY AT A GLANCE

CROP WATER EVAPOTRANSPIRATION

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CO-LEADERS:
Bruce Kimball (ARS-collaborator),
Jerry Hatfield (USDA-ARS),
Ken Boote (U Florida),

GOALS:
• Intercompare, evaluate, and improve multiple crop models for more accurate predictions of ET under weather variability.
• Assess soil water balance & evaporation; crop transpiration, evapotranspiration, crop dry matter accumulation.

CO2-TEMPERATURE WATER

Corn-in-Iowa

CO-LEADERS:
Jerry Hatfield (USDA-ARS),
Ken Boote (U Florida)

GOALS:
• Establish baseline climate data time-series sources and gap-filling methods to link with crop models for North America.
• Evaluate sensitivity of models to changes in CO2, temperature, and water for current and future scenarios.

POTATO MODEL INTERCOMPARISON AND IMPROVEMENT

potatoes mixed

CO-LEADERS:
David Fleisher (USDA-ARS),
Roberto Quiroz (International Potato Center, Peru)

GOALS:
• Quantify variation or uncertainty among potato crop models and use multi-model methodologoy to assess predicted climate change responses at multiple production locations.
• Evaluate model calibration and sensitivity and identify areas for model improvement.

AgMIP DATA INTEROPERABILITY GROUP

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CO-LEADERS:
Cheryl Porter (U Florida),
Jeff White (USDA-ARS),
Medha Devare (CGIAR)

GOALS:
• Establish and convene an international team of agricultural production modelers and data and IT experts regarding linked data and model requirements.
• Make data findable, accessible, interoperable and re-useable for modeling activities.
• Foster the consistent use of accepted standards and approaches for annotating and archiving data, including metadata, ontologies, and data dictionaries.