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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Adaptive Cropping Systems Laboratory » Research » Research Project #445297

Research Project: Sustainable and Resilient Crop Production Systems Based on the Quantification and Modeling of Genetic, Environment, and Management Factors

Location: Adaptive Cropping Systems Laboratory

Project Number: 8042-21600-001-000-D
Project Type: In-House Appropriated

Start Date: Aug 2, 2023
End Date: Aug 1, 2028

OBJECTIVE 1: Assess interactive effects of extreme weather events and resource limitations (including nutrients and water) on physiology, yield, and quality of U.S. commodity crops and selected cover crop species. Sub-objective 1.A: Use SPAR and other growth chambers to study the effects of C, T, and W interactions on major U.S. commodity and cover crops, including maize, rice, sorghum, soybean, and cereal rye. Sub-objective 1.B: Identify and evaluate the sensitivity of remotely sensed signatures of drought response in soybean for crop monitoring and phenotyping. Sub-objective 1.C: Quantify effects of T and W flood on photosynthesis, root growth, and N dynamics in maize and rye. OBJECTIVE 2: Enhance process-level crop and soil model capabilities to simulate response to extreme climate events accurately and comprehensively and identify sustainable resource management options. Sub-objective 2.A: Improve the rice model with respect to extreme T events and depiction of diurnal weather patterns and water management options. Sub-objective 2.B. Develop a fiber quality algorithm to improve the cotton simulation model for field and management applications and to help assist policy decisions. Sub-objective 2.C: Develop process-level wheat and sorghum models to account for the effects of current and future extreme events on crop growth and development. Sub-objective 2.D: Integrate tillage, mulch residue, organic fertilizer, phosphorus dynamics, soil respiration, and gas transport components with existing and proposed maize, cotton, potato, rice, soybean, and wheat crop models to improve the simulation of sustainable soil-plant-atmospheric systems including response to soil flooding, carbon sequestration, and greenhouse gas emissions. OBJECTIVE 3: Develop and apply decision support tools to assess genetic adaptation (G), environmental resilience (E), and management options (M) that maintain crop productivity and improve land stewardship in response to climate uncertainty and dwindling natural resources. Sub-objective 3.A: Assess the effects of cover crops (CC) on cash crop production, water availability, and soil resiliency via the identification of best management practices. Sub-objective 3.B: Identify, evaluate, and design management strategies associated with cropping rotations that promote soil health and cropping system resiliency and carbon sequestration in response to climate change and limited resources. Sub-objective 3.C: Evaluate E x G and E x M adaptation strategies for U.S. rice production under a warmer climate and water-limited conditions. Sub-objective 3.D: Develop model-based decision support to enable growers to improve in-season resource use decisions with respect to maize, soybean, and potato. Sub-objective 3.E: Develop contactless phenotyping and plant stress detection systems for real-time decision support to optimize resource use and plant health.

Experiments, primarily using controlled environment facilities, will assess the influence of abiotic stressors on growth, development, yield, and resource use efficiencies of cereal rye, corn, cotton, rice, sorghum, and soybean. Hypotheses related to effects of high temperature (T) and water (W) stress, including drought and flood, and the interaction of CO2 imposed during important plant developmental stages will be tested. Quantitative relationships among plant, soil, atmospheric components will be developed. Sensing technologies, including the use of hyper- and multi-spectral sensors, will be used to link in-season crop physiological status with non-contact detection metrics. Process-level crop and soil models will be improved to accurately simulate interactions of genetic, environment, and management components. Mathematical relationships that address knowledge gaps associated with simulation of plant gas exchange, carbon allocation, development, growth rates, and water/nitrogen uptake and utilization will be (i) improved in existing corn, cotton, rice, potato and soybean models, and (ii) incorporated in new models for cereal rye, sorghum and wheat. Representation of extreme events will be included along with a cotton fiber quality module. Capabilities to simulate tillage, mulch residue, organic fertilizer, phosphorus dynamics, soil respiration, and gas transport will be developed. Existing software development platforms, USDA-ARS models, and literature sources will be used to build/test/evaluate new model source code. Model predictions will be validated using data from our experiments, cooperator field data, and literature. These models will be used to study and improve crop productivity and sustainability as influenced by climate and resource uncertainty. These efforts include multiple collaborators and stakeholders at federal, state, university, and farm levels. We will integrate our crop and soil models with geospatial data within multi-state regions to identify best management practices and climate stress adaptation strategies associated with cover crop management, soil health, cropping rotations, and rice sustainability. Current and future climates will be included as well as long-term experimental datasets from Beltsville, the Midwest, and Mississippi Delta regions. A web-based application program interface tool integrating on-farm data with our crop models to enable near real-time decision support for growers regarding water and fertilizer management questions will be developed and tested on stakeholder farms. Finally, a contactless phenotyping and plant stress detection system for real-time decision support will be developed to optimize plant health and resource use based on the sensing technologies developed in our experimental work.