Location: Adaptive Cropping Systems Laboratory2020 Annual Report
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.
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.
Experimental studies linked to Objective 1 help with: (i) identification of ideal cultivars for U.S. production systems, (ii) on-farm management options in response to heat stress and water availability, (iii) addressing knowledge gaps regarding the effects of abiotic factors such as temperature, water stress, and rising CO2 concentration on plant yield and development, and (iv) understanding the physiological factors associated with these processes. Experiments also provide for modeling and decision support data needs in Objectives 2 and 3, and quantify linkages among crop physiology, genetic variability, climate, and management. These studies are directly relevant to the USDA-ARS Grand Challenges associated with increasing food availability and lowering environmental impacts. Manuscripts are under development for all studies mentioned below. State-of-the-art Soil-Plant-Atmosphere-Research (SPAR) chambers and similar facilities were used to explore rice and corn responses to high temperature during important reproductive stages. High heat during the grain filling stages in rice was shown to significantly reduce photosynthetic rates, grain yield and quality. There was no apparent compensatory effect on the negative impacts of heat stress at higher levels of atmospheric CO2 concentration. A SPAR study with corn evaluated the impact of heat and water stress during ear development. Data on associated effects of these factors on yield, photosynthesis, water use, and grain yield were developed and used to help improve laboratory modeling efforts. Temperature effects on rye growth, development, and photosynthesis were also collected and utilized to build components of a rye cover model. In a study using electric lamp growth chambers, soybean yields were shown to be reduced as much as 60% due to high heat and drought. The yield loss was primarily associated with decreased seed weight, which appeared to be influenced positively by rising CO2 concentration. These studies addressed Objectives 1a and 1c. Modeling and Decision Support: Model development, improvement, and application efforts focused on corn, potato, rice, rye, and soybean. Underlying soil processes were also addressed. The most significant changes were made to soybean (GLYCIM) and rice (RICESIM) models to more accurately account for the effects of rising temperature and decreased water availability on the development and yield of these crops. These are linked with atmospheric CO2 concentration. The best approach to simulate interactions among these abiotic climate factors on crop response uses leaf energy balance algorithms that mimic photosynthesis on a biochemical basis. These sub-components were tested using laboratory experimental SPAR data and shown to more accurately simulate growth and transpiration than the original model versions. Rice developmental routines that describe effects of temperature, photoperiod, and phenotypic responses more representative of U.S. growing conditions were also improved using data from over 30 cultivars grown in the Mississippi Delta region. A manuscript was submitted for the rice work and is under development for soybean. These studies addressed Objectives 2b and 2c. Rye cover crop residue, in the form of mulch, can retain nitrogen and supply this fertilizer to a cash crop planted in the spring season. The rye mulch may also have a favorable impact on soil water balance. To test and incorporate this management approach in our suite of crop models, a new residue decomposition algorithm was added to the USDA-ARS 2DSOIL model. The model code is being tested with data from a large multi-state cover crop research project. Data on residue decomposition was obtained from collaborators at the University of Georgia and University of Maryland, and a database for rye cover crop growth in the Northeast, Southeast, and Midwest regions of the U.S. from collaborating partners was also completed. A novel artificial intelligence method of predicting cover crop biomass and nitrogen content at the time of termination was also developed using these data. A paper on this research was accepted for publication. Incorporation of these components in our models will facilitate studies involving cropping rotation strategies on soil health. This research addressed Objectives 2a and 3b. A new graphical user interface, (CLASSIM), was developed to facilitate use of ARS crop, cover-crop, and soil models for on-farm studies. This desktop application was integrated with modern database design approaches and simplifies user operations as well as storage of input and output data. The model is currently integrated with the MAIZSIM and 2DSOIL models and is being linked with SPUDSIM, GLYCIM, and GOSSYM models. Collaborators from Taiwan, USDA-ARS Sustainable Agricultural Systems Laboratory (SASL) in Beltsville, Maryland, and University of North Carolina are currently testing the interface. This addresses all sub-objectives in Objective 2. Database driven graphical user interface simplifies access to crop system decision support. USDA-ARS crop models are sophisticated tools that can be used for decision support related to crop management by farmers, breeders, agronomists, crop consultants and other scientists interested in agricultural systems resiliency. However, these decision support tools are complex and not easy to use by non-modelers. ARS scientists in Beltsville, Maryland, developed a new 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 that is integrated with a sophisticated database management system to facilitate input of climate, soil, crop, and management information and simulated output data. CLASSIM has been tested for use in corn producing regions by national and international collaborators and will be integrated with cotton, potato, soybean, rice, and cover-crop models to study optimal on-farm management options and other food security related questions. An ARS scientist at Adaptive Cropping Systems Laboratory (ACSL) continues to lead the agricultural model intercomparison and improvement project (AgMIP) potato pilot in which ten different models were used to estimate rising CO2 impacts on potato yields at seven locations in Europe. The study showed that models were more accurate in predicting relative yield responses to rising CO2 as opposed to absolute values. Model accuracy was also strongly influenced by calibration methods, which implies that ground-truthing is very important when using crop models to estimate current and future food security responses. The SPUDSIM potato model was used as part of this comparison and was among the most accurate models. The corn model MAIZSIM was used in a separate AgMIP study to investigate the variation of different corn models when used to predict yield in low input agriculture. Data from four sites in Africa were used. The study found that lack of sufficient nitrogen fertilizer limits the response of maize yields even when climate and water conditions are otherwise optimum for growth. A new corn AgMIP project was initiated in October, 2019, to further investigate evapotranspiration and yield predictions of maize under several irrigation regimes in two locations. The ARS soybean model GLYCIM is currently being used in a similar AgMIP soybean pilot for 2020. These studies addressed Objective 3c.
1. The effect of climate stress on soybean yields. Changing climate conditions of heat and drought will negatively impact soybean growth and also with changes in atmospheric carbon dioxide (CO2) concentration. ARS scientists at Beltsville, Maryland, using a large growth chamber determined the extent to which these climate stresses interact to reduce soybean production. High temperatures and moderate drought combined reduced growth and marketable seed yield weight between 10 to 60%. Elevated CO2 reduced grain protein content, but also benefited yields by reducing water loss deficit. Breeders can now use this knowledge to develop heat resistant and drought tolerant soybean varieties.
2. Improved soil water sensor device saves money. Soil health is measured by Electrical Conductivity (EC). Time-domain reflectometry (TDR) probes are among the most accurate sensors commonly used to measure water content in the soil. ARS Beltsville, Maryland, and the University of Maryland scientists collaborated to develop a new mathematical model that improves EC estimation from the raw data obtained using TDR probes. This new methodology uses a software program on a personal computer to read TDR data acquired from the field. This new technique reduces the need for a farmer to purchase multiple soil sensors and saves them money. Also, scientists and crop consultants focused on soil health, and resiliency will save cost and operation expenses associated with obtaining electronic field sensors.
Fleisher, D.H., Haynes, K.G., Timlin, D.J. 2020. Cultivar coefficient stability and effects on yield projections in the SPUDSIM model. Agronomy Journal. 2020:1-16.
Kim, J., Park, J., Hyun, S., Fleisher, D.H., Kim, K. 2020. Development of an automated gridded crop growth simulation support system for distributed computing with virtual machines. Environmental Modelling & Software. 169:1-8.
Bunce 2019. Variation in responses of photosynthesis and apparent rubisco kinetics to temperature in three soybean cultivars. Photosynthesis Research. 8:443.
Zhuangji, W., Timlin, D.J., Kouznetsov, M., Fleisher, D.H., Tully, K., Li, S., Reddy, V. 2019. Coupled model of surface runoff and surface-subsurface water movement. Advances in Water Resources. https://doi.org/10.1016/j.advwatres.2019.103499.
Jeffries, G., Griffin, T., Fleisher, D.H., Naumova, E.N., Koch, M., Wardlow, B.D. 2019. Mapping sub-field maize yields in Nebraska, USA by combining remote sensing imagery, canopy simulation models, and machine learning. Precision Agriculture. https://doi.org/10.1007/s11119-019-09689-z.
Vega, F.E., Ziska, L.H., Simpkins, A., Infante, F., Davis, A., Rivera, J., Barnaby, J.Y., Wolf, J.E. 2020. Early growth phase and caffeine content response to recent and projected increases in atmospheric carbon dioxide in coffee (Coffea arabica and C. canephora). Scientific Reports. 10:5875.
Bunce, J.A. 2020. Normal cyclic variation in CO2 concentration in indoor chambers decreases leaf gas exchange and plant growth. Plants. 9(5):663. https://doi.org/10.3390/plants9050663.
Ziska, L.H., Blumenthal, D.M., Franks, S.J. 2019. Understanding the nexus of rising CO2, climate change and evolution in weed biology. Invasive Plant Science and Management. 12:79-88. https://doi.org/10.1017/inp.2019.12.
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