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

2023 Annual Report

Objectives
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.

Progress Report
This research covers the fifth and final year for the Project 8042-11660-001-000D “Experimentally Assessing and Modeling the Impact of Climate and Management on the Resiliency of Crop-Weed-Soil Agro-Ecosystems” which ended August 1, 2023. New NP216 OSQR approved project entitled “Sustainable and Resilient Crop Production Systems Based on the Quantification and Modeling of Genetic, Environmental, and Management Factors was established. Both projects are encompassed within National Program 216, Agricultural Systems Competitiveness and Sustainability. Significant contributions were made during the expired project in the areas of crop production resilience and sustainability. Corn, cotton, potato, rice, and soybean models were upgraded with the latest science. An improved soil model provides more realistic accounting of soil – plant – atmospheric relationships. Knowledge gaps regarding impacts of extreme weather events and climate change on plant growth, development, and resource use were addressed. New decision support approaches, including non-contact sensor-based plant stress measurement methods and computer software tools, were implemented. These products provide highly accurate assessments of the influence of genetics and environment on crop production and facilitate studies involving on-farm management practice for current and future climates. Final year progress of the expired project is addressed below: Experimental Progress Rice: A soil-plant-atmosphere research (SPAR) study assessed effects of alternate wetting and drying cycles on a rice hybrid. The extent to which CO2 influences grain yield and quality under varying water management strategies was identified. A manuscript was published on high temperature exposure during grainfill and grain chalk expression. This supports Objectives 1a, 1b, 2b, and 3a. Soybean: The impact of heat waves were assessed to understand climate vulnerability. Prior experiments on the interaction of temperature and daylength on different maturity groups were summarized. These data help scientists understand, assess, and identify adaptation options to climate and resource impacts. Methodology using chlorophyll a fluorescence accurately identified stress tolerance of different maturity groups. This detection method may be useful for growers and researchers for early-stress detection purposes. Two papers were submitted. This supports Objectives 1a and 2a. Cotton: The influence of high temperature during flowering was evaluated including the impact on fiber quality. Given the economic value of cotton and the scarcity of quality data, this information will be of high importance for management and improving crop model-based decision support tools. This supports Objective 1a and assists Objective 2a. Early drought warning systems: Collaborative experiments with USDA-ARS laboratories (SASL and FNPRU in Beltsville, Maryland) were conducted to evaluate use of non-invasive sensing technologies for detection of crop physiological, structural and metabolic responses. Differences in spectral signatures associated with soybean varieties exposed to drought and disease measured with hyperspectral reflectance and solar-induced chlorophyll fluorescence were identified. These non-contact measurement systems are being integrated with growth chambers to provide high throughput phenotyping. Sensing technology helps scientists and breeders rapidly screen for drought/disease tolerant varieties. These systems can be integrated with crop models to help growers anticipate and respond to in-season stresses. Two papers were published. This research supports Objectives 1a and 2c. Model Development A new approach to improve simulation of wheat was developed. Accurate estimates of leaf number are important for estimating development, plant growth, and solar radiation interception. A nonlinear approach describing the relationship between leaf number, daylength, and temperature was adapted from literature and shown to be more realistic than existing linear equations. This method incorporates effects of extreme temperatures which improves production estimates. A paper was published. This research supports Objective 2c. The GOSSYM cotton model was enhanced to expand capabilities for field management. Three modules for soil, photosynthesis, and transpiration were replaced with more advanced theories. A new module accounted for effects of management and environment on fiber characteristics. A relationship between water flooded soils and growth and development was developed. These improvements were tested with experiments at Mississippi State University. One paper was published and three submitted. This work supports Objective 2c. The soybean model GLYCIM was modified to incorporate the phenotype of later maturing soybean varieties using previously collected experimental data from our laboratory. These improvements allow users to evaluate environment and management consequences over the broad phenotypic range typical of U.S. production. This work supports Objectives 2a and 2c. The 2DSOIL model was enhanced with a carbon dioxide (CO2) module. This allows simulation of CO2 production from soil microbes and plant roots, and its transport within the soil and at the soil surface. This research will be useful to scientists, agricultural managers and policymakers interested in assessing the effects of climate and agricultural management on CO2 dynamics in the soil. Two papers were submitted. This research addresses Objectives 2a, 3a, and 3b. Model Application Effects of warming temperatures and elevated CO2 on soil organic carbon (SOC) long-term (100-year) dynamics in the U.S. Midwest were studied using soybean (GLYCIM) and corn (MAIZSIM) models. Differences in soil carbon occurred based on location and climate. Projected SOC changes will be strongly influenced by the magnitude of future temperature and CO2 increase. Research demonstrates use of models for scientists to quantify soil health metrics in response to climate change and identify optimal long-term field management methods. A manuscript was submitted on this research which addressed Objective 3a. Production risks associated with climate trends in the U.S. Midwest were evaluated using the GLYCIM soybean model. The occurrence of extreme heat and drought days were likely increase. Changes in vapor pressure deficit, a measure related to moisture in the air, were a primary yield limiting factor, although substantial variations were associated with geography. A manuscript was published on this research which addressed Objective 3a. A collaborative effort with ESRI (Redlands, California) was completed to evaluate the effects of climate change on cropland suitability. Changes in corn, soybean, and winter-wheat distribution at 1-km resolution within the U.S. were associated with bioclimatic variables. Spatial maps indicated regions of climate vulnerability, and opportunity, for each crop. Spatially referenced adaptation strategies were identified. A manuscript was published on this research which address Objective 3a. Collaborative work with North Carolina State, University of Maryland, and SASL (USDA-ARS, Beltsville, Maryland) as part of a NIFA funded project continued. The MAIZSIM model was integrated into a soil water decision support tool for growers to determine the effects of cover crops on water. The model was integrated into a geospatial framework to identify best cover crop management practices for soil water and nitrogen availability. A manuscript is being developed on the methodology. This supports Objectives 2a and 3b. A collaboration was initiated with Paulman Farms, Sutherland, Nebraska, and the University of Nebraska to develop on-farm based decision support. This multi-year effort includes validation of our corn, soybean, and potato models and development of approaches to simplify their use. Methods to integrate our models with in-season soil, weather and management data will be developed, along with weather-forecasts, to enable in-season evaluation of potential management decisions. This research addresses Objectives 2c and 3a. The graphical user interface, Crop Land and Soil SIMulator (CLASSIM), was integrated with our corn, cotton, potato and soybean models and can simulate tillage, fallow periods and multi-year rotation sequences. A sensitivity analysis feature was added to allow study of variations in weather parameters. Collaborators from Taiwan, University of Nebraska, University of Maryland, and USDA-ARS in Beltsville, Maryland, are working with the interface and models. A manuscript was accepted on this work. This addresses all sub-objectives in Objective 2. Collaborative efforts with the international agricultural model intercomparison and improvement project (AgMIP) continue for the soybean (GLYCIM) and corn (MAIZSIM) models. We contributed model simulations on soybean growth from sites in Urbana, Illinois and provided simulations of evapotranspiration and soil temperature as part of a group of forty-one models. The effort identifies strengths and weaknesses in models for food security research. A manuscript was submitted. This research addresses Objective 3c. A collaboration with modelers from Germany, Finland, China, Spain, and the United Kingdom examined effects of climatic change and adaptive technologies on future wheat and corn yields. Simulations of potential, water limited, and actual farmers’ yields were run for future climate scenarios. Site specific comparisons between current and advanced management practices were tested. Technological improvements consistently counteracted some of the negative impacts of climate change, but the effectiveness was dependent on the location. A manuscript was submitted which is associated with Objective 3a.

Accomplishments
1. Improved model to study management options related to soil carbon loss. Carbon dioxide (CO2) released from agricultural soils is a major source of carbon loss to the atmosphere. To understand how the influence of crop, climate, and soil management affect soil respiration, tools that mathematically capture the mechanisms among these interactions are needed. In this study, scientists at USDA ARS in Beltsville, Maryland, improved an existing crop simulation model for simulating maize growth and related soil processes to simulate CO2 production and transport from the soil. This model can be a valuable tool for estimating CO2 flux from agricultural soils as affected by soil management and climate. This research will be useful to scientists, agricultural managers and policymakers interested in assessing the effects of agricultural management and changing climate on CO2 dynamics in the soil.

2. Predicting cotton fiber quality. Cotton fiber quality is of major economic importance to U.S. growers. While both cotton yield and quality are influenced by climate, soil, and management, decision support tools for cotton have primarily focused on yield. A cotton fiber quality modeling methodology was incorporated into the existing USDA-ARS GOSSYM cotton model by scientists at USDA ARS in Beltsville, Maryland. This new module is capable of predicting effects of growth temperature and plant water and nutrient status on fiber micronaire (fiber fineness and maturity), staple length, uniformity, and strength. The newly developed model is unique to cotton modeling and decision support efforts and can be a valuable tool for determining cotton fiber quality, optimizing production/fiber quality, and decision-making under varying environmental and management conditions.

Review Publications
Mura, J.D., Reddy, V., Timlin, D.J. 2022. Drought-induced responses in maize under different vapor pressure deficit conditions. Plant, Cell & Environment. 11:2771. https://doi.org/10.3390/plants11202771.
Kimball, B.A., 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., Alimagham, S., Archontoulis, S., Babacar, F., Barcza, Z., Basso, B., Bertuzzi, P., Constantin, J., De Antoni Migliorati, M., Dumont, B., Durand, J., Fodor, N., Gaiser, T., Garofalo, P., Gayler, S., Giglio, L., Grant, R., Guan, K., Hoogenboom, G., Jiang, Q., Kim, S., Kisekka, I., Lizaso, J., Masia, S., Meng, H., Mereu, V., Mukhtar, A., Perego, A., Peng, B., Priesack, E., Qi, Z., Shelia, V., Snyder, R., Soltani, A., Spano, D., Srivastava, A., Thomson, A., Timlin, D.J., Trabucco, A., Webber, H., Weber, T., Willaume, M., Williams, K., van der Laan, M., Ventrella, D., Viswanathan, M., Xu, X., Zhou, W. 2023. Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models. Agricultural and Forest Meteorology. 333. Article 109396. https://doi.org/10.1016/j.agrformet.2023.109396.
Timlin, D.J., Anapalli, S.S. 2022. Introduction: System models integrated with experiments can be useful tools to develop improved management practices for subsistence farming to address increased intensification and climate change. Advances in Modeling Agricultural Systems. 9:1-9. https://doi.org/10.1002/9780891183891.
Fitzgibbon, A., Pisut, D., Fleisher, D.H. 2022. Evaluation of maximum entropy (Maxent) machine learning model to assess relationships between climate and corn suitability. Land. 11. https://doi.org/10.3390/land11091382.
Thapa, R., Cabrera, M., Reberg-Horton, C., Dann, C., Balkcom, K.S., Fleisher, D.H., Gaskin, J., Hitchcock, R., Poncet, A., Mirsky, S.B., Schomberg, H.H., Timlin, D.J. 2023. Modeling surface residue decomposition and N release using the Cover Crop Nitrogen Calculator (CC-NCALC) . Nutrient Cycling in Agroecosystems. https://doi.org/10.1007/s10705-022-10223-3.
Fleisher, D.H., Barnaby, J.Y., Li, S., Timlin, D.J. 2022. Response of a U.S. rice hybrid variety to extreme heat and varying CO2 concentration during grain filling. Agricultural and Forest Meteorology. 323. https://doi.org/10.1016/j.agrformet.2022.109058.
Luo, C., Shi, Y., Timlin, D.J., Ewing, R., Fleisher, D.H., Horton, R., Tully, K., Wang, Z. 2022. A multiscale finite element method for coupled heat and water transfer in heterogeneous soils. Journal of Hydrology. 612:10828. https://doi.org/10.1016/j.jhydrol.2022.128028.
Sun, W., Fleisher, D.H., Timlin, D.J., Li, S., Wang, Z., Beegum, S., Reddy, V. 2022. Simulating the effects of global warming on soybean growth and yield in the U.S. Mississippi Delta. European Journal of Agronomy. 140. https://doi.org/10.1016/j.eja.2022.126610.
Sun, Y., Wen, J., Gu, J., Van Der Tol, C., Porcar-Castell, A., Joiner, J., Chang, C.Y., Magney, T.S., Wang, L., Hu, L., Rascher, U., Zarco-Tejada, P., Barrett, C.B., Lai, J., Han, J. 2023. Transforming remotely-sensed SIF to ecosystem structure, functions, and service: Part II - Harnessing data. Global Change Biology. 29(11):2893-2925. https://doi.org/10.1111/gcb.16646.
Sun, Y., Wen, J., Gu, L., Van Der Tol, C., Porcar-Castell, A., Joiner, J., Chang, C.Y., Magney, T.D., Wang, L., Hu, L., Rascher, U., Zarco-Tejada, P., Barrett, C.B., Lai, J., Han, J. 2023. Transforming ecosystem structure, function, and service: Part I—Harnessing theory. Global Change Biology. 29(11):2926-2952. https://doi.org/10.1111/gcb.16634.
Wang, Z., Hua, S., Timlin, D.J., Kojima, Y., Lu, S., Sun, W., Fleisher, D.H., Horton, R., Reddy, V., Tully, K. 2023. Time domain reflectometry waveform interpretation with convolutional neural networks. Water Resources Research. 59(2). Article e2022WR033895. https://doi.org/10.1029/2022wr033895.
Sun, W., Fleisher, D.H., Timlin, D.J., Ray, C., Wang, Z., Beegum, S., Reddy, V. 2023. Projected long-term climate trends reveal the critical role of vapor pressure deficit for soybean production in the US Midwest. Science of the Total Environment. 878. Article e162960. https://doi.org/10.1016/j.scitotenv.2023.162960.
Paff, K.E., Timlin, D.J., Fleisher, D.H. 2023. A comparison of wheat leaf appearance rate submodules for DSSAT CROPSIM-CERES (CSCER). Ecological Modelling. 482:110406. https://doi.org/10.1016/j.ecolmodel.2023.110406.
Beegum, S., Timlin, D.J., Reddy, R., Reddy, V., Sun, W., Wang, Z., Fleisher, D.H., Ray, C. 2023. Improving cotton simulation model, GOSSYM, for soil, photosynthesis, and transpiration processes. Scientific Reports. 13:7314. https://doi.org/10.1038/s41598-023-34378-3.