Location: Adaptive Cropping Systems Laboratory2019 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.
This research represents the first 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. This new project integrates four researchers from prior projects and includes two scientist vacancies. It continues research previously initiated in agricultural systems modeling, decision support development, and empirical studies regarding the influence of climate stress on grain yield and quality of major U.S. commodity crops, in addition to weed pressures. Particular emphasis is placed on improving accuracy of our models and the assessment of high temperature stress on crop yield and quality factors in a food security context. Experimental Progress: Experimental studies linked to Objective 1 were conducted with corn, cotton, potato, rice, rye, and soybean. Results drive modeling and decision support data needs in Objectives 2 and 3, and quantify linkages among crop physiology, genetic variability, climate, and management. Results from these experiments (1) help with selection of ideal cultivars for U.S. production systems, (2) evaluate on-farm management options in response to heat stress, declining soil quality, and resource availability, (3) identify genetic traits for breeders that can improve heat tolerance and water/nutrient use efficiencies, and (4) provide for multi-location, geospatial assessments that address these aforementioned benefits across multiple cropping systems and production areas. 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 these studies. High temperature (T) stress effects on rice growth and development was evaluated. Grain yield and quality were reduced by high T, and chalk content, which influences cooking, milling, and marketability, was also observed to increase for several varieties. Multiple rice lines were also screened with respect to chalk expression under high T and different levels of atmospheric carbon dioxide (CO2). This was a collaborative effort among ARS scientists in different locations which will also provide assessment of nutrient and arsenic uptake profiles as well as genetic profiles that can be suitable breeding targets for conferring chalk resistance. An optimal range for nitrogen fertilizer application was observed for corn grown at different air T with respect to maximizing photosynthetic rate. This study helps scientists understand physiological responses to nitrogen under T stress and can farmers determine most efficient fertilizer rates under warming climates. Cotton varieties were screened with respect to drought tolerance and water use efficiency under water limited production conditions. This study demonstrated that there are several alternative traits for enhancing soil water conservation for growth under dryland conditions. Experiments were also conducted with soybean, where multiple levels of production factors including T, CO2, and water supply (W) were held constant over the growing season. Data regarding the influence of these factors separately, and interactively, on soybean metabolism, growth, development and yield were obtained. By understanding the magnitude of the response of the soybean to each of these stresses independently and also combined will help research efforts in modeling to predict the yields in future climatic conditions. Experiments were carried out on corn to quantify the high temperature effects on pollination and grain fill. Elevated temperatures, 2 and 4C above optimum (28C) were applied to corn plants after the corn silks emerged. The tassels were cut from half the plants to prevent pollination of the developing kernels. Grain number and size, and canopy photosynthesis were reduced by high temperatures. Senescence rate was also increased thus reducing the growing. Photosynthesis rate was not affected by detasseling but it was noted that detasseled plants formed additional ears along the stem. Modeling and Decision Support: Multiple model application studies were conducted with respect to food security concerns. In collaborative work among ARS and University scientists, we showed the influence of increasing T over the past 25-year period was associated with longer aero-allergen seasons and increased pollen load. In a separate study, a global economic model of the agricultural sector was linked with empirical data regarding the effect of elevated CO2 impacts on reducing protein and nutrient content on staple crops. Nutrient availability would decline as much as 4.3% by mid-century on a per capita basis. These data indicate that the net effect of rising atmospheric CO2 concentrations will slow progress in achieving reductions in global nutrient deficiencies. We also charted an increase in the range of Kudzu, an invasive species which carries a fungus known to damage soybean and measured an increased potential for this weed to migrate northward in the U.S. as T continues to rise. This research thus linked anthropogenic warming with public health. A positive management strategy due to increasing growing season duration in the Mississippi Delta region was observed. It was demonstrated that there will be increasing opportunity for U.S. rice farmers to grow a second rice crop, or ratoon crop, as season length and associated number of frost-free days continue to increase. Manuscripts were published from each of these studies. These studies address milestones in Objectives 1 and 3. Progress regarding assessment/evaluation of ARS crop and soil models was also made. The USDA-Natural Resources Conservation Service (NRCS) has been using a computer simulation model (APEX) to assess the impact of crop lands on water quality. We tested the ability of APEX to simulate multi-year corn yields in the Chesapeake Bay Watershed and found that predicted yields were obtained with acceptable accuracy. This result provides more confidence in those scientists using the tool to understand water quality in this region. A manuscript was submitted regarding the capability of our SPUDSIM model to accurately replicate results for different potato producing regions in the U.S. eastern seaboard region with minimum calibration. The model performed within desired tolerances, suggesting it can be used for accurate predictions of potato responses given just a minimum amount of experimental data for calibration. These tasks all addressed Objective 3. Experimental data regarding the influence of long-term effects on crop yield stability was obtained from cooperating ARS scientists and will be used along with our models to assess the causative relationships among soil quality, water table depth, and crop biology that contribute to the effectiveness of precision agriculture applications in the U.S. (Objective 3). Our group is also involved with Agricultural Model Intercomparison and Improvement Project (AgMIP), a multi-national and locational endeavor among crop and soil model developers and users to identify knowledge gaps and improve model components. We collaborated on two projects. For corn, we compared evapotranspiration measurements among different models and showed that predictive ability was limited by years with significant water stress and conditions like wet soils and water tables. A second corn project is investigating the ability of different models to accurately simulate yields in low-input conditions seen in subsistence farming. We are leading the potato crop model pilot where we assembled an international modeling team and have begun testing the models for ability to simulate yield under ambient and elevated CO2 conditions at multiple locations. A manuscript was published from the corn work (Objective 3). Substantial progress was made in addressing knowledge gaps and user interface development in Objective 2. Existing rice models were evaluated for their suitability to simulate U.S. varieties and production conditions using 20 years of data from the Mississippi Delta. We re-parameterized and replaced temperature functions in the ORYZA model to compensate for observed deficiencies in simulating T stress, and are now focusing on improving growth rate responses. We modified our 2DSOIL model to simulate runoff and ponding in field depressions and successfully tested the results against published data. This will provide more accurate water balance estimations in rainfed environments and better simulation for chemical and energy movement on the soil surface. Work was initiated on a water/energy dynamic model to better simulate T and moisture distribution within straw mulch to facilitate cover crop simulations. Experimental data was assembled in preparation for development and testing of a rye cover crop model that will be linked with ARS crop and soil models to permit more realistic simulations of multi-year cover-crop/main-crop rotations on soil quality traits as influenced by management. A prototype of a data-base driven graphic user interface (OSGUI) that incorporates our crop and soil simulations models was completed. This will facilitate the use of our models by stakeholders, including crop consultants, academic partners, breeders and farms, because it simplifies the process of assembling input files and managing output. User testing of the interface was initiated.
1. Kudzu will continue to invade the North. A changing climate, with warmer temperatures may alter the range and presence of invasive species that are known to cause significant economic or environmental harm. One such plant species, kudzu, is recognized as a typical invasive species, and is also a carrier of Asian soybean rust, a fungus that can damage soybeans. To determine the role of rising temperatures on kudzu distribution, ARS scientists in conjunction with University partners looked at different populations of kudzu in relation to minimal freezing temperatures. These data indicate that kudzu has increased potential to migrate northward as temperatures rise, and that it has not reached its biological northward limit.
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Singh, S.K., Reddy, V., Fleisher, D.H., Timlin, D.J. 2018. Interactive effects of temperature and phosphorus nutrition on soybean physiological traits: leaf photosynthesis, chlorophyll fluorescence, and nutrient efficiency. Photosynthetica. 57:248-257.
Barnaby, J.Y., Fleisher, D.H., Sicher Jr, R.C., Reddy, V. 2018. Combined effects of drought and CO2 enrichment on foliar metabolites of potato (Solanum tuberosum L.) cultivars. Journal of Plant Interactions. https://doi.org/10.1080/17429145.2018.1562110.
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Ma, Y., Reddy, V., Mura, J.D., Song, L., Cao, B. 2019. De novo characterization of the goji berry ( Lycium barbarium L.) fruit transcriptome and analysis of candidate genes involved in sugar metabolism under different CO2 concentrations. Tree Physiology. https://doi.org/10.1093/treephys/tpz014.
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