Progress 10/01/23 to 09/30/24
Outputs
PROGRESS REPORT Objectives (from AD-416): 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. Approach (from AD-416): 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. This research documents progress for the project 8042-21600-001-000D �Sustainable and Resilient Crop Production Systems Based on the Quantification and Modeling of Genetic, Environment, and Management Factors� under NP216, �Agricultural Systems Competitiveness and Sustainability�. Research focused on (i) experimental studies that measured climate stress responses of economically important plants, (ii) development of measurement systems for in-season crop status evaluation, (iii) improvements in the science of crop and soil modeling, (iv) development of on-farm decision support, and (v) model application studies that evaluated sensitivity of crop production to climate and farm management factors. In support of Objectives 1A and 2B, two growth chamber studies evaluated the influence of episodic drought and heat stress on cotton yield, development, and fiber quality. Data from this study will be used to test and improve model capabilities to understand changes in economic yield and cotton water requirements in response to extreme weather factors. In support of Objectives 1A, 2A, 2D, and 3C, a soil-plant-atmosphere- research experiment evaluated the influence of alternate wetting/drying cycles on rice at two different atmospheric carbon dioxide (CO2) levels. Results are being analyzed and will improve understanding relationships between rice production and soil moisture status and water availability. A study on high night-time temperature stress is on-going. In support of Objectives 2A, 2D, and 3C, a new rice model, RICESIM, was developed to more accurately simulate response to water availability and heat stress. Novel features include a coupled leaf-leaf gas exchange and energy balance methodology, nonlinear temperature functionality, and integration with the two-dimension soil module, 2DSOIL. In support of Objectives 2A, 2B, 2C, 2D, 3A, 3B, 3C, and 3D, methods to simulate tillage were added to the 2DSOIL model which mechanistically simulates root, nitrogen, water, gas, and temperature movements in vertical and lateral soil directions beneath a plant. Simulations of nitrous oxide and methane emissions are being tested and will enable end- users to comprehensively study management consequences on crop response, soil health, and greenhouse gas emissions. In support of Objectives 2B and 3A, the GOSSYM cotton model was used to determine optimum cotton planting dates throughout the U.S. cotton belt that maximize fiber quality and agronomic yield. The study provides a spatial, county-level map illustrating the best planting dates for current and future climatic conditions. The model also predicts how future climatic conditions will affect cotton fiber quality. A manuscript was submitted. In support of Objectives 2C, 2D, 3A, and 3B, a new model, RYESIM, was developed to simulate growth and development of a cereal rye cover crop. The model was integrated with the laboratory�s 2DSOIL model and a surface residue decomposition module. RYESIM can be integrated with other cash crop models to improve simulation of conservation practices on long term soil health and crop yield. A manuscript was submitted regarding the influence of the cover crop on cash crop yield and soil water and nitrogen dynamics. In support of Objective 2D, a methodology to improve representation of cultivars in the GOSSYM cotton model was developed. A revised parameter set was developed which allows the crop model to more accurately simulate older and newer U.S. cultivars. Data from field experiments on 40 of the most recent cotton cultivars grown in the cotton belt were used. A manuscript was submitted. In support of Objectives 2D and 3A, simulations of soil water content at different layers in field plots with, and without, cover crops, were compared with measured data as part of a Ph.D. student�s doctoral work at University of Maryland utilizing laboratory MAIZSIM and 2DSOIL models. In support of Objectives 2D and 3A, simulations of soil water content at different layers in field plots with, and without, cover crops, were compared with measured data as part of a Ph.D. student�s doctoral work at University of Maryland utilizing laboratory MAIZSIM and 2DSOIL models. In support of Objective 3A, the influence of cereal rye cover crop management on cash crop production was simulated at a 1-km spatial scale for three major agricultural producing counties in each of seven U.S. states using MAIZSIM and RYESIM. Simulations incorporated annual variability in weather and changes in cereal rye biomass amount at termination. Data are being analyzed to identify best management practices for cereal rye as influenced by location. In support of Objective 3B, a study to evaluate whether model predictions can reduce the need for agronomic field trials associated with identifying optimum fertilizer and irrigation applications was conducted with the MAIZSIM model. Data from a three-year agronomic trial in Nebraska with approximately 100 different combinations of cultivar, row spacing, and timing and quantities of irrigation and fertilizer applications were obtained. A similar study was used to determine capabilities of MAIZSIM to explain relationships between maize yields and weather conditions in temperate and tropical climates. This work is being carried out in collaboration with the University of Nebraska-Lincoln. A manuscript was submitted. In support of Objective 3B, laboratory crop and soil models were used to evaluate and understand relationships between no-till corn yield and yield resiliency in the USDA-ARS Beltsville, Maryland, farming systems project (FSP). Linkages between weather, soil, yield sustainability and climate variability are being established from the research. The USDA Northeast Climate Hub is collaborating on this project. In support of Objective 3C, water demand in U.S. rice production systems was shown to increase an average 51 mm under future mid-century climate projections using the RICESIM model. This response was associated with projected decreases in growing season duration, soil available water, and air humidity, and increased air temperature. A manuscript is being developed for submission. In support of Objective 3C, water demand in U.S. rice production systems was shown to increase an average 51 mm under future mid-century climate projections using the RICESIM model. This response was associated with projected decreases in growing season duration, soil available water, and air humidity, and increased air temperature. A manuscript is being developed for submission. In support of Objective 3D, machine learning (ML) methods were used to simulate cotton yield in the southern U.S. cotton belt. Accumulated heat units, nitrogen fertilizer, soil, and cultivar were used as inputs to build the models along with more than 1000 datasets. The results showed the approach was highly accurate when constrained to simulate yields within the same spatial range as the training datasets. A paper was accepted on this topic. In support of Objective 3D, the graphical user interface, Crop Land and Soil SIMulator (CLASSIM) now provides options to simulate cover crop surface residue decomposition, tillage, irrigation, and soil carbon and nitrogen balances. An expert system was developed that provides in-season irrigation recommendations based on laboratory crop/soil model predictions and weather forecasts. A web-based version is being developed to facilitate access by stakeholders. Collaborators from multiple universities, two Nebraska Resource Districts, USDA-ARS laboratories in Beltsville, Maryland, and international partners in Taiwan are working with the interface. A manuscript was published, and an invited book chapter was submitted. In support of Objective 3E, an automated system to evaluate changes in remotely sensed signals from plants in response to abiotic stresses was developed. This system monitors solar induced fluorescence (SIF), hyperspectral reflectance, and photosynthesis of crops grown in outdoor Soil-Plant-Atmosphere Research chambers where temperature, water and nutrient availability and CO2 are fully controlled. This monitoring station enables unprecedented evaluation of in-season plant status and was tested under a range of future climate conditions. A manuscript on system design and user guide for applications was submitted. In support of Objective 3E, scientists from several USDA-ARS laboratories in Beltsville, Maryland, observed significant changes in microgreen and baby green stem elongation, anthocyanin production and flowering times were observed in response to supplemental far-red light in growth chambers. These changes suggest lamps with far-red components can be used to manipulate plant nutritional and quality characteristics for controlled-environment agriculture. A paper was published from this study. In support of Objective 3E, an automated high-throughput phenotyping system was developed to monitor plant performance in controlled environments. This system measures chlorophyll fluorescence, hyperspectral reflectance, and visual imagery of plant growth. It can be adjustably configured for different plant species and automatically provides a backup of digital data. This system can significantly augment USDA-ARS experimental capabilities and was conducted in cooperation with an industry partner. The first prototype is anticipated to be completed in summer 2024 and a manuscript is in preparation. Artificial Intelligence (AI)/Machine Learning (ML) Machine learning methods Random Forest and LightGBM were used to simulate cotton yield as a function of growing season temperature, soil conditions, nitrogen fertilizer, and cultivar. Over 1200 data sets from nine locations in Texas, Mississippi, and Georgia were used to train and test these methods. The Random Forest method was shown to be more accurate at replicating yields. The use of this ML method is intended to reduce the overhead (i.e. computational time and requirements) involved in simulating crop yields versus traditional process crop modeling approaches. While the ML approach is not as comprehensive as crop models, its simplicity may be preferrable in certain applications that are web- based or smart-phone application centric. Local computing hardware and SCINet were used for the model generation and testing. ACCOMPLISHMENTS 01 Optimizing cotton planting dates throughout the U.S. cotton belt for fiber quality. Cotton fiber quality and value is influenced by management, weather, and other factors. Identifying the best planting dates which result in highest possible fiber quality metrics throughout the U.S. multi-state cotton belt, particularly as growing season temperatures vary as a result of climate change, is needed by farmers. ARS scientists in Beltsville, Maryland, used ehe GOSSYM cotton model to identify a range of planting dates under historical and future climates for more than 700 counties in 17 states. A spatial map was produced that stakeholders can use to identify best management practice associated with their location and indicates how quality components for fiber length, strength, micronaire, and uniformity are likely to vary. 02 Automated system remotely monitors plant status and detects stresses using a novel approach. Extreme weather events are unpredictable and pose challenges to farm operations. Methods to effortlessly and easily monitor crop status are needed. ARS scientists in Beltsville, Maryland, developed a system that automatically measures both hyperspectral reflectance and solar induced fluorescence (SIF) from plants. The measurements relate to leaf and canopy photosynthetic rate and provide increased sensitivity to stress detection in comparison to greenness- based indices used in conventional remote sensing approaches. The system has been tested in outdoor research chambers which allow for variations in temperature, water, nutrient availability, and carbon dioxide concentration. This system and the practical measurement methods integrated within represent a valuable tool to provide non- contact assessment of crop physiological status, such as drought or temperature stress. It can be integrated with decision support tools to improve farm management for extension agents and growers.
Impacts
(N/A)
Publications
- Sun, W., Fleisher, D.H., Timlin, D.J., Ray, C., Wang, Z., Beegum, S., Reddy, V. 2023. Does drought stress eliminate the benefit of elevated CO2 on soybean yield? Using an improved model to link crop and soil water relations. Agricultural and Forest Meteorology. 343(2023). Article e109747. https://doi.org/10.1016/j.agrformet.2023.109747.
- Timlin, D.J., Fleisher, D.H., Tokay, M., Paff, K.E., Sun, W., Beegum, S., Li, S., Wang, Z., Reddy, V. 2023. CLASSIM: A relational database driven crop model interface. Smart Agricultural Technology. 100281. https://doi. org/10.1016/j.atech.2023.100281.
- Wang, Z., Timlin, D.J., Liu, G., Fleisher, D.H., Sun, W., Beegum, S., Heitman, J., Ren, T., Chen, Y., Reddy, V. 2024. Coupled heat and water transfer in heterogeneous and deformable soils: Numerical model using mixed finite element method. Journal of Hydrology. 634. Article e131068. https://doi.org/10.1016/j.jhydrol.2024.131068.
- Beegum, S., Sun, W., Timlin, D.J., Wang, Z., Fleisher, D.H., Reddy, V., Ray, C. 2023. Incorporation of carbon dioxide production and transport module into a soil-plant-atmosphere continuum model. Geoderma. 437. Article e116586. https://doi.org/10.1016/j.geoderma.2023.116586.
- Ndayishimiye, E., Dushimeyesu, J., Ukwishaka, Y., Ray, C., Fleisher, D.H., Timlin, D.J., Reddy, V., Malakar, A. 2024. On-field agroecosystem research experience: an undergraduate perspective. Journal of Contemporary Water Research & Education. 179(1):40-52. https://doi.org/10.1111/j.1936-704X. 2024.3401.x.
- Timlin, D.J., Paff, K.E., Han, E. 2024. The role of crop simulation modeling in assessing potential climate change impacts. Agrosystems, Geosciences & Environment. 7(1). Article e20453. https://doi.org/10.1002/ agg2.20453.
- Hsiao, J., Kim, S., Timlin, D.J., Mueller, N., Swann, A. 2024. Model-aided climate adaptation for future maize in the US. Environmental Research: Food Systems. 1(1). Article e015004. https://doi.org/10.1088/2976-601X/ ad3085.
- Thorp, K.R., Boote, K.J., Stockle, C., Suyker, A.E., Evett, S.R., Brauer, D.K., Coyle, G.G., Copeland, K.S., Marek, G.W., Colaizzi, P.D., Acutis, M., Archontoulis, S., Babacar, F., Barcza, Z., Basso, B., Kimball, B.A., De Antoni Migliorati, M., Zhou, W., Timlin, D.J. 2024. Simulation of soil temperature under maize: an inter-comparison among 33 maize models. Agricultural and Forest Meteorology. 351. Article 110003. https://doi.org/ 10.1016/j.agrformet.2024.110003.
- Mathur, S., Seo, B., Jajoo, A., Reddy, K.R., Reddy, V. 2023. Chlorophyll fluorescence is a potential indicator to measure photochemical efficiency in early to late soybean maturity groups under changing day lengths and temperatures. Journal of Integrative Plant Biology. 14.Article e1228464. https://doi.org/10.3389/fpls.2023.1228464.
- Chang, C.Y., Unda, F., Mansfield, S.D., Ensminger, I. 2023. Rapid response of nonstructural carbohydrate allocation and photosynthesis to short photoperiod, low temperature, or elevated CO2 in Pinus strobus. Physiologia Plantarum. 175(6). Article e14095. https://doi.org/10.1111/ppl. 14095.
- Teng, Z., Luo, Y., Sun, J., Pearlstein, D.J., Oehler, M., Fitzwater, J.D., Zhou, B., Hussan, M.A., Chang, C.Y., Chen, P., Wang, Q., Fonseca, J.M. 2024. Effect of far-red light on biomass accumulation, plant morphology, and phytonutrient composition of ruby streaks mustard at microgreen, baby leaf, and flowering stages. Journal of Agricultural and Food Chemistry. 72(17):9587�9598. https://doi.org/10.1021/acs.jafc.3c06834.
- Beegum, S., Reddy, V., Reddy, K.R. 2023. Development of a cotton fiber quality simulation module and its incorporation into cotton crop and development model: GOSSYM. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.108080.
- Beegum, S., Truong, V., Bheemanahalli, R., Brand, D., Reddy, V., Reddy, K. R. 2023. Developing functional relationships between waterlogging and cotton growth and physiology- towards waterlogging modeling. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2023.1174682.
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