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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Adaptive Cropping Systems Laboratory » Research » Research Project #434970

Research Project: Experimentally Assessing and Modeling the Impact of Climate and Management on the Resiliency of Crop-Weed-Soil Agro-Ecosystems

Location: Adaptive Cropping Systems Laboratory

Project Number: 8042-11660-001-00-D
Project Type: In-House Appropriated

Start Date: Aug 2, 2018
End Date: Aug 1, 2023

Objective:
Objective 1: Characterize quantitative production system effects of temperature (T), carbon dioxide (C) and water (W) interactions on: (a) corn, rice, soybean, and wheat varieties, and (b) crop-weed competition and potential yield loss. (1a) Quantify effects of extreme T x W fluctuations and C enrichment during critical developmental stages on growth and developmental processes of corn, rice, soybean, and wheat using soil-plant-atmosphere-research (SPAR) growth chambers and field based open top chambers (OTCs). (1b) Evaluate within-species variability in qualitative characteristics for grain nutritional components in response to C and concurrent changes in T for rice. (1c) Gather C x T responses on rye cover crop germination, growth, and other developmental processes as needed for a rye cover-crop model. (1d) Assess potential demographic changes in Kudzu, an invasive weed, in response to changing winter minimal temperatures. Objective 2: Advance the capability of USDA-ARS crop and soil models to simulate crop-system resiliency to abiotic and biotic factors. (2a) Expand current production models for corn and soybean by including a cover-crop growth model. (2b) Develop a mechanistic rice crop model for production resilience studies in the context of climate uncertainty. (2c) Improve existing crop and soil models with experimental data from multiple sources including SPAR, free-air C enrichment (FACE), open-top chamber, and long-term agricultural research (LTAR) site locations. Objective 3: Using results from objectives 1 and 2, integrate and assess genetic variables (G), and management options (M) within environmental ranges (E) that can be used to maintain, adapt and/or improve crop productivity in response to climate uncertainty (E). (3a) Using database mining and crop models, evaluate and identify management practices and/or genetic resources that can reduce or compensate climate-induced risks to corn and rice production while improving production resilience in the U.S. (3b) Apply corn and cover-crop models to evaluate soil nitrogen, water, and organic matter dynamics in Maryland based on assessment of multi-year cover-crop and cropping rotation studies. (3c) Contribute to the AgMIP initiative through multi-model inter-comparison studies including those involving evapotranspiration and potato. (3d) Utilize crop and soil models to evaluate efficacy of long-term precision agricultural management practices in the north-central Missouri area.

Approach:
Research to quantify the influence of abiotic stresses of temperature (T) and water (W) and their interaction with elevated CO2 (C) on cropping systems and resource use efficiencies will be conducted along with development of decision support tools. Experiments will focus on corn, rice, rye, soybean, and wheat and use controlled environment technologies (soil-plant-atmosphere research chambers, growth chambers, greenhouses, open-top chambers, and free-air C enrichment systems). Hypotheses related to high T and/or low W stress on agronomic responses during critical developmental stages of these crops under elevated C conditions will be tested using proven experimental protocols. Datasets to be generated will include biomass, gas exchange (photosynthesis and transpiration), developmental rates, nitrogen and water use, and grain yield processing and nutritional quality. Relationships with climate, management, and genetic (e.g. phenotypic traits) will be studied and quantified using statistical approaches. Process-level crop models of corn, potato, rye rice, and soybean, and forecasts for weed growth will be developed, tested, and validated using these and other datasets. Mathematical relationships between environment, soil, and plant processes, such as crop gas exchange, growth, carbon allocation, development, and water/nitrogen uptake will be developed and incorporated into computer source code for each of the crop models. Knowledge gaps have been identified for each crop. These will be addressed with this new data science and will include quantifying effects of extreme climate events, such as high temperature stress, on yield. Cover crop models will be integrated with corn and soybean models to facilitate cropping rotation studies. Existing software development platforms, USDA-ARS model source code, and available knowledge from literature sources will be used wherever possible. Model predictions will be tested and validated using appropriate statistical metrics. These models will be utilized as strategic decision support tools to study ways to improve crop productivity as influenced by climate and resource uncertainty. Phenotypic and management options will be evaluated. Rice and corn models will be combined with geospatial soil, management, and climate data to evaluate heat stress impacts and identify adaptation measures involving phenotype selection and water management strategies in major production centers in the U.S.. Future climate data using the most recent peer-reviewed modeling tools will be utilized. Cropping rotation studies will be conducted to evaluate water, nitrogen, and soil organic matter dynamics in Maryland using the rye, corn, soybean, and soil models. Models will also be rigorously tested using independent datasets as part of the international AgMIP initiative to improve food security decision support tools. Finally, corn, soybean, and soil models, along with empirical approaches, will be used to identify causative relationships between climate, soil, and variable rate management effects using 20 years of precision agriculture data from collaborators.