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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

2022 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 fourth 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 experimental studies on the influence of climate stress on grain yield and quality of U.S. crops. Emphasis was placed on improving accuracy and capacity of crop and soil models, obtaining experimental data needed to address climate stress knowledge gaps, and application of these and other research tools to evaluate climate and management impacts. One Lead scientist vacancy remains in the project due to retirement of a Lead scientist in 2020. Experimental Progress Corn: A new experiment to investigate effects of heat stress on pollen viability is being conducted in the summer of this year. This research addresses Objective 1a and supports Objective 2c. Rice: A soil-plant-atmosphere research (SPAR) chamber study was conducted to assess effects of alternate wetting and drying cycles under two CO2 levels on growth and development. Results will determine extent to which elevated CO2 influences grain yield and quality, as measured by nitrogen and chalk content (a quality factor of economic importance), under varying water management strategies. A manuscript was accepted into publication on the sensitivity of yield and chalk expression to high temperatures during grainfill periods along with elevated CO2 exposure. Research addresses Objectives 1a, 1b, 2b, and 3a. Rye: A database of rye growth data is being further developed to better inform crop models that interact with remote sensing data. This will enable better estimations of cover crop biomass at the point of termination, thus improving in-season decision support studies. This effort addresses Objective 1c and supports Objective 2a. Soybean: Responses of multiple maturity groups (MG) common in the U.S. to photoperiod, temperature, drought, and CO2 levels were measured in SPAR and other growth chambers. Differences in the duration of growth stages as a function of low and high temperatures were measured, and a nonlinear response in this pattern, particularly with exposure to extreme heat, was observed. A delay in flowering for later MGs in response to increasing daylength was observed, but photosynthetic traits were similar between MGs and growth stages, a property which may simplify evaluation of climate impact effects. Results are being evaluated and a manuscript is under preparation. This data helps scientists to understand, assess, and identify adaptation options to climate and resource impacts on soybean production systems. This supports Objective 1a and assists Objective 2a. Early drought warning systems: An experiment, in collaboration with SASL and Floral and Nursery Plants Research Unit (FNPRU) scientists (USDA-ARS Beltsville and Washington D.C.), was conducted to evaluate whether the use of non-invasive sensing technologies including hyperspectral reflectance and solar-induced chlorophyll fluorescence (SIF) can distinguish different physiological, structural, and metabolic plant responses associated with drought. Two soybean genotypes were evaluated along with different water availability treatments in greenhouse and SPAR chamber environments. The experiment will be replicated, and data is currently being evaluated. An additional study will be conducted in SPAR chambers this summer to evaluate the potential improvement of photosynthesis modeling by using SIF as an input. This measurement can be integrated with crop models to help growers anticipate and response to in-season stresses. Two papers were published in this research area which support Objective 1a and 2c. Model Development Additions were made to the USDA-ARS 2DSOIL model that collectively allow for new simulations of cover-crop x soil relationships, effects of tillage operations on soil water content, temperature, and carbon and nitrogen in organic matter pools (litter, humus, and organic fertilizer), and cropping-rotation studies. Specific components include improved soil organic matter dynamics that more realistically account for carbon lost or gained through both microbiological processes and root respiration, gas transport equations for the diffusion of carbon as CO2 into and out of the atmosphere, simulation of carbon (C) and nitrogen (N) dynamics of manures and buried residue, and a tillage module to allow mixing of the surface soil along with surface deformation. These changes help provide unique insights into long-term impacts of management options on crop productivity and environmental resources. These changes have been integrated with the USDA-ARS MAIZSIM model and will be linked with our cotton, soybean, and potato models. Three manuscripts were published from these results and a fourth was submitted. This research addresses Objectives 2a and supports Objectives 3a and 3b. USDA-ARS Cotton (GOSSYM) and soybean (GLYCIM) models were integrated with energy balance algorithms that improve simulation of photosynthesis, transpiration and linkages with plant and soil water status. These models were linked with the 2DSOIL model which improves research associated with studying root-soil interactions and the influence water, nutrient, and heat transport on crop growth. The improved models were verified using experimental data from field and SPAR chamber research and have been integrated with a new USDA-ARS interface, CLASSIM. A manuscript was published and three have been submitted. This research addressed Objectives 2a and 2c. Model Application USDA-ARS models for rice (RICESIM) and soybean (GLYCIM) were integrated with spatial data and used to assess effects on climate impacts. Rice yields were shown to vary substantially in response to projected temperature increases in California, the Mississippi Delta, and Gulf Coast states. In collaboration with the USDA-ARS Dale Bumpers National Rice Research Center (DBNRRC), potential phenotypic traits, each with a known genetic basis, were evaluated using RICESIM to develop a breeding roadmap for developing climate resilient rice varieties. Potential traits included improved heat sterility tolerance, higher photosynthesis temperature tolerance, and increased potential spikelet number. Soybean studies assessed changes in yield in the Midwest associated with rising temperature, CO2, and changes in vapor pressure deficit. A manuscript was submitted on the rice work and two papers were submitted for soybean. The soybean model was also integrated with an international model intercomparison and improvement project (AgMIP) to simulate varietal yield and water use differences in the Southeast U.S. This research addressed Objectives 3a, 3b, and 3d. Continued work with North Carolina State, University of Maryland (UMD), and SASL (USDA-ARS, Beltsville) collaborators as part of a NIFA funded Precision Sustainable Agriculture (PSA) project to incorporate the MAIZSIM model into a soil water decision support tool that growers could use to determine the effects of cover crops on water availability to a subsequent cash crop. Input files were developed for all the sites where on farm data were collected and model runs were carried out. University of Maryland graduate students are evaluating model predictions of soil water content and developed a comprehensive R package that pulls data from an on-farm database and creates input files for MAIZSIM, simplifying the operation of the model for users. MAIZSIM is also being integrated with a cover-crop model into a geospatial framework to assess differences in spatial variation on optimizing cover crop management. A manuscript is being developed using hundreds of locations in Maryland as a testbed. This supports Objectives 2a and 3b. A graduate student at the University of Washington worked with MAIZSIM to learn how corn phenology is affected by management and climate over a wide range of ecotypes and management throughout the main corn growing areas in the U.S. The goal was to identify which trait and management combinations can maximize yield. The research found that strategies that allow for higher total leaf area and delayed grain-filling initiation time during the growing season were more capable of buffering against yield losses under a warmer, future climate as compared with strategies that have an earlier reproductive stage start, and thus smaller leaf area. Two papers are being prepared from this work. This supports Objective 3a. Collaborations were initiated with an industry partner (ESRI, Redlands, California) on the application of modeling studies related to land suitability using probability spatial models, and with a university partner (Seoul National University) on methods to increase utility of crop models including parallel processing. A recent collaboration with the SASL lab (USDA-ARS, Beltsville) was initiated to use MAIZSIM to evaluate effectiveness of crop management practices using the long-term data sets with the Farm Systems Project (FSP). This research addresses Objectives 3a and 3b. The graphical user interface, Crop Land and Soil SIMulator (CLASSIM) was developed to facilitate use of ARS crop, cover-crop, and soil models for on-farm studies. Work has continued improving and expanding this desktop application which now links the 2DSOIL model with corn (MAIZSIM), cotton (GOSSYM), potato (SPUDSIM) and soybean (GLYCIM) crop models. Recent additions include the ability to simulate fallow periods and multi-year rotation sequences. Collaborators from Taiwan, University of Nebraska, University of Maryland, USDA-ARS in Beltsville, and University of North Carolina are working with the interface and models. This addresses all sub-objectives in Objective 2.


Accomplishments
1. Improved soil model expands capability to study long-term management consequences on soil health indices. Agricultural systems profoundly affect, and are affected by, soils. Models that can more accurately simulate the processes that contribute to soil health and agricultural sustainability are important to optimize crop management. Scientists at ACSL (USDA-ARS, Beltsville) added a series of new algorithms into an existing USDA-ARS model, 2DSOIL, to address this need. Specific components included improved soil organic matter dynamics, gas transport equations for the diffusion of carbon into and out of the atmosphere, simulation of carbon (C) and nitrogen (N) dynamics of manures and buried residue, and a tillage module to allow mixing of the surface soil along with surface deformation. These changes allow for better understanding of the effects of long-term crop management on soil health and resource use. The improved routines have been integrated with other crop models, including USDA-ARS MAIZSIM model, and are available for other modelers interested in exploring consequences of farm management choices on soil health.

2. A model-based rice breeding strategy roadmap for climate resilience. The U.S. is the fourth largest rice exporter. Temperatures are expected to continue to increase during the growing season which has been shown to negatively impact the industry. Scientists from ACSL (USDA-ARS Beltsville) and DBRNNC (USDA-ARS, Stuttgart) used the RICESIM model, recently developed by USDA-ARS, with spatial data to study the effectiveness of three productivity traits across three different rice growing regions in the U.S. These traits each have a genetic basis and were demonstrated to be influenced by heat stress. Increases in these traits were shown to compensate for heat stress but the extent varied with location. This suggested rice breeding targets should be regionally specific in the U.S. to provide maximum benefit to climate changes. Combining model-based spatial approaches with an understanding of the genetic bases of these, and other traits, can serve as a roadmap for rice breeders to identify climate resilient breeding strategies.


Review Publications
Wang, Z., Thapa, R., Timlin, D.J., Li, S., Sun, W., Beegum, S., Fleisher, D.H., Mirsky, S.B., Cabrera, M., Sauer, T.J., Reddy, V., Horton, R., Tully, K. 2021. Simulations of water and thermal dynamics for soil surface with residue mulch and surface runoff. Water Resources Research. 57. https://doi.org/10.1029/2021WR030431.
 Han, J., Chang, C.Y., Gu, L., Zhang, Y., Meeker, E.W., Magney, T.S., Walker, A.P., Wen, J., Kira, O., Mcnaull, S., Sun, Y. 2022. The physiological basis for estimating photosynthesis from Chl a fluorescence. New Phytologist. 234; 1206-1219. https://doi.org/10.1111/nph.18045.
 Alsajri Firas, A., Wijewardana, C., Bheemanahalli, R., Irby, T.J., Krutz, J., Golden, B., Reddy, V., Reddy, R.K. 2022. Morpho-physiological, yield, and transgenerational seed germination responses of soybean to temperature. Frontiers in Plant Science. 13:839270. https://doi.org/10.3389/fpls.2022.839270.
 Hyun, S., Park, J., Kim, J., Fleisher, D.H., Kim, K. 2022. GLUEOS: A high performance computing system based on the orchestration of containers for the GLUE parameter calibration of a crop growth model. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.106906.
 Liu, Y., Kim, J., Fleisher, D.H., Kim, K. 2021. An analogy-based crop yield forecast scheme assessing similarity of the time series of leaf area index. Remote Sensing. 13(6):3069. https://doi.org/10.3390/rs13163069.
 Kim, J., Park, J., Hyun, S., Yoo, B., Fleisher, D.H., Kim, K. 2021. Development of an automated gridded crop growth simulation support system using Kubernetes. Computers and Electronics in Agriculture. 186. https://doi.org/10.1016/j.compag.2021.106187.
 Mathur, S., Agnihotri, R., Sharma, M.P., Reddy, V., Jajoo, A. 2021. Impact of high temperature stress on plant physiological traits and mycorrhizal symbiosis in maize plants accessed under microcosm conditions. The Journal of Fungi. 7:867. https://doi.org/10.3390/jof7100867.
 Paff, K.E., Fleisher, D.H., Timlin, D.J. 2022. Changes in the effects of water and nitrogen management for potato under current and future climate conditions in the U.S. Computers and Electronics in Agriculture. 197:106980. https://doi.org/10.1016/j.compag.2022.106980.
 Rice, C., Wolf, J.E., Fleisher, D.H., Acosta, S.M., Bajwa, A.A., Adkins, S.W., Ziska, L.H. 2021. Recent CO2 levels promote increased production of the toxin parthenin in an invasive Parthenium hysterophorus biotype. Nature Plants. 1-5. https://doi.org/10.1038/s41477-021-00938-6.
 Sun, W., Fleisher, D.H., Timlin, D.J., Li, S., Wang, Z., Reddy, V. 2021. Effects of elevated CO2 and temperature on soybean growth and gas exchange rates: a modified GLYCIM model. Agricultural and Forest Meteorology. 197:106980. https://doi.org/10.1016/j.agrformet.2021.108700.
 Thapa, R., Tully, K., Schomberg, H.H., Reberg-Horton, C., Davis, B., Poncet, A., Hitchcock, R., Gaskin, J.W., Cabrera, M., Mirsky, S.B., Seehaver, S., Timlin, D.J., Fleisher, D.H. 2021. Cover crop residue decomposition in no-till cropping systems: Insights from multi-state on-farm litter bag studies. Agriculture Ecosystems and the Environment. https://doi.org/10.1016/j.agee.2021.107823.
 Wang, Z., Timlin, D.J., Li, S., Fleisher, D.H., Dathe, A., Luo, C., Dong, L., Reddy, V., Tully, K. 2021. A diffusive model of maize root growth in MAIZSIM and its applications in Ridge-Furrow Rainfall Harvesting. Agricultural Water Management. 254:106966. https://doi.org/10.1016/j.agwat.2021.106966.
 Wang, Z., Timlin, D.J., Fleisher, D.H., Sun, W., Beegum, S., Li, S., Chen, Y., Reddy, V., Tully, K., Horton, R. 2022. Modeling vapor transfer in soil water and heat simulations – a modularized, partially-coupled approach. Journal of Hydrology. 608:127541. https://doi.org/10.1016/j.jhydrol.2022.127541.