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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

2021 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 third year of the project (8042-11660-001-00D) "Experimentally Assessing and Modeling the Impact of Climate and Management on the Resiliency of Crop-Weed-Soil Agro-Ecosystems". This project is encompassed within National Program 216, Agricultural Systems Competitiveness and Sustainability. Research continued in crop and soil modeling, decision support development, and empirical studies on the influence of climate stress on grain yield and quality of U.S. crops, in addition to weed pressures. Emphasis is placed on improving the accuracy of models and obtaining experimental data. Two new scientists were hired to fill vacancies in this project plan, leaving one remaining vacancy due to the retirement of a Scientist in 2020 to be filled. Research progress was summarized below. Experimental Progress Corn: A greenhouse study was conducted to assess the effect of drought stress imposed at critical developmental stages on corn genotypes grown at ambient and elevated temperatures. Measurements on growth, development, and physiology were obtained along with remotely sensed data. The data will be used to build a library connecting remotely sensed signals to the timing and severity of stress responses critical to crop health, productivity, and yield. Scientists were involved in research evaluating the use of chlorophyll fluorescence and hyperspectral reflectance indices for monitoring plant growth status under stresses. This research addresses Objective 1a and supports Objective 2c. Rice: A soil-plant-atmosphere research (SPAR) chamber study was conducted to assess the impact of heat during grain-filling on an in-bred rice variety under different CO2 concentrations. Interactions between CO2 and temperature, temperature and phosphorus, and CO2 and alternate wetting drying cycles in growth chamber and mini-greenhouse facilities were conducted in collaboration with a visiting scientist from ARS-Dale Bumpers Rice Research Center, Arkansas. Results included yield assessments, grain mineral composition, and chalk (a quality measure of economic importance) for a subset of rice lines. High CO2 levels can exacerbate the negative effects of warming temperatures on chalk content; however, responses varied widely among varieties. This variation is being used to identify genetic bases for heat tolerance. Two manuscripts are being developed. These experiments address Objectives 1a,b and support Objective 2b. Rye: An experiment to assess temperature effects on rye growth and development was conducted in SPAR chambers. Physiological responses to temperatures ranging from 12 to 38 degrees Celsius were recorded, including biomass, growth rates, canopy photosynthesis, and water uptake. The results will be used to calibrate a new ARS model of rye growth and development. This effort addresses Objective 1c and supports Objective 2a. Soybean: A study on the response of early-maturing soybean varieties to heat stress, water availability, and CO2 was conducted in SPAR facilities. Indoor growth chamber experiments were also conducted to evaluate the influence of temperature and photoperiod corresponding to regional differences in the U.S. on different soybean maturity groups. Longer photoperiods did not affect the flowering time for early maturing varieties, but cooler temperatures delayed germination and prolonged vegetative stage. Flowering was induced earlier with shorter photoperiods in later maturing varieties, while cold temperatures exerted the opposite effect. This research addressed knowledge gaps on soybean and helped evaluate the accuracy of ARS modeling tools. This supports Objective 1a and assists Objective 2a. Weeds: An experiment was conducted to evaluate the response of Parthenium, an invasive species, to CO2 concentrations. The weed was observed to grow faster and produce more parthenin (which reduces the productivity of crop fields and pastures and is a cause of dermatitis in humans) with rising CO2 levels as compared to a non-invasive biotype. This suggested that the current levels of CO2 contributed to the plant's global invasiveness and toxicity. This information will allow for assessing better weed control strategies and provides ecological information on subspecies variation. A manuscript was published. This research supports Objective 1d. Model Development Rice, soybean, and cotton models (RICESIM, GLYCIM, GOSSIM, respectively) were integrated with energy balance algorithms to improve the simulation of photosynthesis and transpiration. This allowed for more accurate predictions of climate responses compared to older methods. Soybean and cotton models were integrated with the two-dimensional soil model, 2DSOIL, to improve studies associated with root processes, water and heat flow, and nutrient transport. The rice model was modified to reflect production conditions more accurately in the Mississippi Delta. This included the effect of high heat during anthesis on grain yield. Two manuscripts were published for the rice work, a paper was submitted for the soybean model, and a paper is under development for the cotton efforts. These studies address Objectives 2b,c and Objectives 3a,d. The use of cover crops to maintain and improve soil health is of significant interest. Decision support tools are needed to understand the interplay of climate, location, and cropping system on management options. A cereal model was developed to simulate rye cover crop production and is undergoing evaluation. A simple AI (Artificial Intelligence) learning algorithm was developed to predict seasonal rye biomass, canopy coverage, and nitrogen content from remotely sensed data. This research addresses Objective 2a and supports Objective 3b. New subroutines, referred to as 'surface simulation group', were integrated with the ARS model MAIZSIM and accurately simulate surface water content, temperature, and carbon/nitrogen exchanges between soil and residue mulch under a variety of agricultural management and climate conditions. This includes an approach to incorporate vapor transfer and phase changes. This improved corn model permits cropping rotation studies along with management strategies to be evaluated. This will be integrated with potato, soybean, cotton, and rice models. Numerical examples and experimental datasets were used to test the implementation of the models. Two manuscripts were submitted. This research addresses Objectives 2a and supports Objective 3b. Model Application A collaborative study on cover crop use was conducted as part of a multi-location project (Precision Sustainable Agriculture - PSA) with several partners including the University of Maryland, USDA-ARS in Beltsville, Maryland, and North Carolina State University. The HYDRUS1_D model was used to estimate evapotranspiration, water drainage, and nitrogen leaching related to cover crop species' effects. A manuscript was submitted. We also collaborated with members of the PSA team to apply our MAIZSIM, rye, and the new mulch residue decomposition models to assess the effects of cover crops on nitrogen availability and water use at multiple locations. This work is being integrated with a geospatial tool to identify regional guidelines for optimal cover crop management. A manuscript is being developed. This supports Objectives 2a and 3b. We collaborated with the University of Washington to use our MAIZSIM model to investigate a range of corn phenotypes and how they respond to different environments. A geospatial methodology was developed to assess rice and soybean production in the Mississippi Delta. For rice, simulations were conducted for several genotypes to evaluate the effects of heat stress on grain yield and determine potential adaptation strategies. Initial results showed a potential decline between 24 to 46% in rice yields by 2070, depending on proximity to the Gulf of Mexico. In collaboration with Dale Bumpers Rice Research Center, Arkansas, a manuscript is being prepared. A manuscript was also submitted for the soybean evaluation. This work supported Objective 3a. The impact of heat and water limitations on potato production in Washington State, one of the major production regions in the U.S., was evaluated using the ARS SPUDSIM model. Effects of fertilizer and irrigation methods on yield and nitrogen leaching were studied. Best management practices were identified for current and future climate conditions. This research supported Objective 3c. Collaborative efforts with the international agricultural model intercomparison and improvement project (AgMIP) group continue for corn, potato, and soybean models. We continue to lead the potato pilot, which includes participants from 18 countries. A paper was published evaluating the accuracy of 10 potato models at eight European locations. Models were more accurate in predicting relative responses to rising CO2 as opposed to absolute values. Calibration was at least as important as the structure of the models. Work for corn on a separate AgMIP project investigates evapotranspiration and yield predictions under irrigation regimes in two locations. We are working with the soybean AgMIP project to evaluate simulated varietal yield and water use differences among multiple models in the Southeast U.S. Our soybean model GLYCIM is contributing to this study. This research addressed Objective 3d. The graphical user interface, Crop Land and Soil SIMulator (CLASSIM), was developed to facilitate ARS crop, cover-crop, and soil models for on-farm studies. Work has continued on improving and expanding this desktop application. The interface is currently integrated with the MAIZSIM, SPUDSIM, and 2DSOIL models and is being linked with GLYCIM and GOSSYM models. Collaborators from Taiwan, the University of Nebraska, the University of Maryland, USDA-ARS in Beltsville, Maryland, and the University of North Carolina are working with the interface and models. This addresses all sub-objectives in Objective 2.


Accomplishments
1. Use of models for on-farm management decisions. Tools to help evaluate on-farm management options are of high value for crop production given available, resource use, climate change, and other pressures. USDA-ARS has developed crop and soil models that can provide decision support for farmers, breeders, agronomists, crop consultants and other scientists interested in agricultural systems resiliency. Scientists in Beltsville, Maryland, improved a graphical user interface to reduce the learning curve and simplify the tasks needed to operate these models. The interface, termed Crop Land and Soil SIMulator (CLASSIM), is a Windows-based desktop application integrated with a database management system to facilitate input of climate, soil, crop, and management information and simulated output data. National and international collaborators are evaluating CLASSIM at USDA-ARS, University of Nebraska, University of Maryland, North Carolina State University, and Taiwan Agricultural Research Institute for evaluating G x E x M strategies at various field locations. It will be available for download via the laboratory website and the source code-sharing site GitHub.

2. Translating complex crop and soil science into tools for cover crop management. Cover crops can help maintain soil health and promote cash crop yield resiliency. However, there is little information available for farmers to evaluate best management practices. Scientists at USDA-ARS in Beltsville, Maryland, developed two tools, an artificial intelligence algorithm and an improved corn model, MAIZSIM, that more accurately predicts effects of decaying cover-crop mulch residue and effects of tillage, on soil characteristics. These tools can predict crop biomass and soil nitrogen content changes through cover and cash crop growing seasons. Soil, environment, plant genetics, and climate change effects on cover crop performance can be evaluated for specific locations. This work is being used collaboratively with scientists at USDA-ARS and North Carolina State University to develop a set of best cover crop management guidelines for different regions in the U.S.

3. Effects of a warming climate on rice production in the U.S. vary by location. The U.S. is the fifth largest rice exporter in the world. Growers are already experiencing negative effects on grain yield and quality due to warming temperatures, and future projections indicate temperatures are likely to increase by several degrees. A new rice model was developed and validated for U.S. production conditions and linked with relevant spatial data from the six largest rice-producing rice states. Simulations showed declines in yield up to 20% based on 2040 climate predictions, but these varied spatially throughout the region. These yield variations were correlated with rising temperatures, and negative impacts on grain were slightly offset by elevated CO2. Location-specific adaptation strategies can be developed by growers, including adjusting planting dates to avoid the heat during anthesis and cultivar selection. These simulations are of interest to the ARS-Dale Bumpers Rice Research Center, Arkansas, breeders, and farmers and can be used for identifying phenotypic traits ideal for location-specific cultivar breeding.


Review Publications
Li, S., Fleisher, D.H., Timlin, D.J., Reddy, V., Wang, Z., McClung, A.M. 2020. Evaluation of different crop models for simulating rice development and yield in the U.S. Mississippi Delta. Agronomy. https://doi.org/10.3390/agronomy10121905.
 Mathur, S., Sunoj, V., Elsheery, N.I., Reddy, V., Jajoo, A., Cao, K. 2021. Regulation of Photosystem II heterogeneity and photochemistry in two cultivars of C4 crop sugarcane under chilling stress. Frontiers in Plant Science. 12:627012. https://doi.org/10.3389/fpls.2021.627012.
 Liu, Y., Kim, K.S., Beresford, R.M., Fleisher, D.H. 2020. A generic composite measure of similarity between geospatial variables. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101169.
 Barnaby, J.Y., Kim, J., Mura, J.D., Fleisher, D.H., Tucker, M.L., Reddy, V., Sicher Jr, R.C. 2020. Varying atmospheric CO2 mediates the cold-induced CBF-dependent signaling pathway and freezing tolerance in "Arabidopsis". International Journal of Molecular Sciences. 21:7616. https://doi.org/10.3390/ijms21207616.
 Hyun, S., Yang, S.M., Junhwan, K., Kim, K.S., Shin, J.H., Lee, S.M., Lee, B.W., Beresford, R.M., Fleisher, D.H. 2021. Development of a mobile computing framework to aid decision-making on organic fertilizer management using a crop model. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2020.105936.
 Fernandez-Baca, C.P., McClung, A.M., Edwards, J., Codling, E.E., Reddy, V., Barnaby, J.Y. 2021. Grain inorganic arsenic content in rice managed through targeted introgressions and irrigation management. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2020.612054.
 Fernandez-Baca, C.P., Rivers, A.R., Maul, J.E., Kim, W., McClung, A.M., Roberts, D.P., Reddy, V., Barnaby, J.Y. 2021. Rice plant-soil microbiome interactions driven by root and shoot biomass. Diversity. https://doi.org/10.3390/d13030125.
 Fernandez-Baca, C.P., Rivers, A.R., Kim, W., McClung, A.M., Roberts, D.P., Reddy, V., Barnaby, J.Y. 2021. Changes in rhizosphere soil microbial communities across plant developmental stages of high and low methane emitting rice genotypes. Soil Biology and Biochemistry. http://doi.org/10.1016/j.soilbio.2021.108233.
 Fleisher, D.H., Condori, B., Barreda, C., Berguiis, H., Bindi, M., Boote, K., Craigon, J., Van Evert, F., Fangmeier, A., Ferrise, R., Gayler, S., Hoogenboom, G., Kremer, P., Merante, P., Nendel, C., Ninanya, J., Pleijel, H., Raes, D., Ramirez, D., Reidsma, P., Silva, J.V., Stockle, C.O., Supit, I., Stella, T., Vandermeiren, K., Van Oort, P., Vanuytrecht, E., Vorne, V., Wolf, J. 2021. Yield response of an ensemble of potato crop models to rising CO2 concentration in continental Europe. European Journal of Agronomy. https://doi.org/10.1016/j.eja.2021.126265.
 Marcillo, G.S., Mirsky, S.B., Aurelie, P., Reberg-Horton, S., Timlin, D.J., Schomberg, H.H., Ramos, P. 2020. Using statistical learning algorithms to predict cover crop biomass and nitrogen content. Agronomy Journal. 112(6):4898-4913. https://doi.org/10.1002/agj2.20429.
 Wang, Z., Timlin, D.J., Kojima, Y., Luo, C., Chen, Y., Li, S., Fleisher, D.H., Tully, K., Reddy, V., Horton, R. 2021. A piecewise analysis model for electrical conductivity calculation from time domain reflectometry waveforms. Computers and Electronics in Agriculture. 182:106012. https://doi.org/10.1016/j.compag.2021.106012.