2013 Annual Report
1a.Objectives (from AD-416):
Objective 1. Assess the relative utility of experimental approaches such as FACE, SPAR, OTC and T-FACE for estimating impacts of climate change factors on plant responses.
Objective 2. Strengthen physiological and genetic assumptions of ecophysiological models used for climate change research.
Sub-objective 2.A: Compare and refine ecophysiological models that differ in the level of complexity used to represent key processes.
Sub-objective 2.B: Refine and apply approaches for gene-based modeling of ecotypic adaptations to factors relevant to climate change research.
Objective 3. Predict likely impacts of climate change and potential for adaptation of cropping systems.
1b.Approach (from AD-416):
To achieve the first objective, we will capitalize on the extensive wheat datasets from research at Maricopa over the past 20 years as well as recent advances in statistical analysis of simulation outputs. The second objective builds on progress in plant physiology and genomics that provide avenues for improving how processes are modeled, especially in relation to cultivar differences. In the third objective, the advances in modeling and understanding will be applied to irrigated production systems of the Southwest, both to assess potential impacts of climate change and to identify options for adaptation, including potentially complex interactions of crop calendars, cultivar types and irrigation and fertilizer management. By addressing strategic methodological constraints, the research will provide invaluable information for stakeholders in regional, national and international venues, helping to ensure that agriculture can adapt efficiently and effectively to climate change. Replacing 5347-1100-008-00D (4/10).
The project seeks to improve prediction of impacts of increased carbon dioxide (CO2) and climate uncertainty on crop production. We focus on application of field data through simulation models that integrate ecophysiology, agroclimatology, genetics and other sciences. Simulation models are among the best options for examining complex interactions among environment and crop management. Model improvement under Objective 1, has deemphasized CO2 responses due to findings by Agricultural Research Service Researchers at Beltsville, Maryland, that short-term (e.g., 30 second) fluctuations in CO2 inhibit growth. Previously, scientists believed that conflicts in measured responses to CO2 reflected experimental artifacts such as shading or impediments to root growth. CO2 fluctuations occur with all experimental CO2 enrichment methods, but few experiments have documented CO2 fluctuations, much less compared enrichment methods. Facing this impasse, we now emphasize improved modeling of crop responses to temperature, largely through collaboration with the international Agricultural Modeling Intercomparison and Improvement Project (AgMIP.org). Besides participating directly in wheat model tests and development of data management tools, a key activity has been to organize and distribute (via AgMIP) data from our Hot Serial Cereal (HSC) wheat experiment, which involved 15 planting dates from 2007 to 2009. Six dates included warming treatments using our innovative Temperature Free-Air Controlled Enhancement method, so the dataset spans an unusually wide range of temperature regimes. The dataset is now being used to assess and improve over 25 wheat models that are represented in exercises coordinated by AgMIP.
Emphasis in Sub-objective 2.B is divided between two topics. The first is to support efforts to solve the genotype-to-phenotype (G2P) problem with emphasis on scaling up field measurements characterize hundreds to thousands of breeding lines. Our high throughout phenotyping (HTP) approach emphasizes use of vehicles carrying multiple electronic sensors as well as novel approaches to data analysis through simulation modeling. The second area concerns gene-based modeling. We continue to assemble data required for a gene-based wheat model and through an iPlant/AgMIP collaboration exploring methods to improve modeling of phenology in 5000 maize lines prior to Quantitative Trait Locus (QTL) analysis.
FY2013 also saw the completion of the International Consortium for Agricultural Systems Analysis data standards for documenting field experiments. These standards provide the foundation for the AgMIP Crop Experiment Database, which is used to support their diverse model intercomparison efforts. We use the standards in organizing datasets for experiments on wheat, sorghum, and cotton, which will be used in simulating response of these crops to climate change in the southwestern United States. Plans to create queryable crop databases were suspended due to hiring constraints.
Review of potential impacts of climate change on US wheat production. The National Climate Assessment (NCA) is conducted every four years under the auspices of the Global Change Research Act of 1990, which requires a report to the President and the Congress and acts as a status report on climate change science and impacts. Scientists at the Arid Land Agricultural Research Center (ALARC) in Maricopa, Arizona, in collaboration with an expert at Washington State University, prepared the section dealing with wheat for the NCA chapter “Climate Change and Agriculture: Effects and Adaptation". A key theme for wheat is the large uncertainties over responses to elevated [CO2] and potential changes in wheat disease and insect pest complexes. These reports are an important source of information for a wide sector of stakeholders and help guide Federal policy actions relating to adaptation and mitigation. They also inform the broader public, so they can make more informed decisions relating to climate change.
Development and deployment of a low-cost, flexible cart for proximal sensing of field crops. Perhaps the easiest, lowest cost interventions that producers can pursue to adapt to climatic uncertainty and change are through adoption of more stress tolerant cultivars. Breeding for drought and heat tolerance has proven slow and difficult, and researchers increasingly seek ways to increase the speed and accuracy of their evaluations under relevant field stress. To alleviate this evaluation bottleneck, scientists at the Arid Land Agricultural Research Center (ALARC) in Maricopa, Arizona, developed a low cost, easily fabricated cart capable of carrying a wide range of sensors including instruments that measure canopy temperature, height and reflectance. The design has been shared with other institutes and has already been copied (with improvements) by another ARS laboratory. Low-cost approaches for proximal sensing can enable much higher throughput in measuring plant traits in the field. Ultimately, this will enable more efficient selection for stress tolerant cultivars that can provide stable, higher yields under drought or heat stress.
Global warming affects on carbon dynamics in an agricultural soil. The amount of carbon in topsoil is an important indicator of soil health, but soil carbon is a potential source of carbon dioxide (CO2) and global warming might increase conversion of soil carbon to CO2. To determine how global warming affects soil carbon content, Agricultural Research Service scientists from the U.S. Arid-Land Agricultural Research Center (ALARC) and collaborators from the University of Arizona, Tucson, analyzed results of a two-year experiment using a temperature free-air controlled enhancement (T-FACE) system that employed infrared heating to expose wheat crops to a wide range of temperatures. Under ample soil water supply, midday carbon loss from the soil because of warming was 10% greater compared with un-warmed soil; warming also dried out the soil more quickly, which reduced loss of soil carbon by 10%. Overall, soil moisture had a larger impact on soil carbon, whereas carbon was responsive to soil temperature only under high soil moisture. Accurate prediction of warmer climatic conditions on soil carbon requires that effects of soil temperature and moisture be considered together. This information should ultimately result in more accurate predictions of potential impacts of climate change and hence better options for diverse stakeholders to deal with the global warming.
Comparison of temperature response functions used in crop models. A recurring question in crop physiology is how best to describe the effects of temperature on specific processes such as the formation of leaves. Scientists at the Arid Land Agricultural Research Center (ALARC) in Maricopa, Arizona, compared five widely used models for temperature using data from the Hot Serial Cereal field experiment, which included 15 planting dates, six of which had temperature free-air controlled enhancement (T-FACE) treatments to further extend the temperature regimes. Our results showed that for the cultivar tested, leaf appearance reaches a maximum rate at about 22°C, and that even our extensive dataset was insufficient to determine whether higher temperatures might slow leaf appearance, as is often assumed. These results emphasize the need for caution in interpreting modeled responses of wheat to elevated temperatures. They should contribute to more accurate assessments of impacts of climate change, ultimately allowing for more robust decisions on climate change policy.
White, J.W., Kimball, B.A., Wall, G.W., Ottman, M.J., 2012. Cardinal temperatures for wheat leaf appearance as assessed from varied sowing dates and infrared warming. Field Crops Research, 137:213-220.
Asseng, S., Ewert, F., Rosenzweig, C., Jones, J., Hatfield, J.L., Ruane, A., Boote, K., Thorburn, P., Rotter, R., Cammarano, D., Brisson, N., Basso, B., Martre, P., Ripoche, D., Bertuzzi, P., Steduto, P., Heng, L., Semenov, M.A., Stratonovitch, P., Stockle, C., O'Leary, G., Aggarwal, P.K., Kumar, S.N., Izaurralde, R.C., White, J.W., Hunt, L.A., Grant, R., Kersebaum, K.C., Palosuo, T., Hooker, J., Osborne, T., Wolf, J., Supit, I., Olesen, J.E., Doltra, J., Nendel, C., Gayler, S., Ingwersen, J., Priesack, E., Streck, T., Tao, F., Muller, C., Waha, K., Goldberg, R., Angulo, C., Shcherbak, I., Biernath, C., Wallach, D., Travasso, M., Williams, J.R., Challinor, A.J. 2013. Uncertainty in simulating wheat yields under climate change. Nature Climate Change. 3:827-832. DOI: 10.1038/NCLIMATE1916.
Kimball, B.A., Conley, M.M., Lewin, K.F. 2011. Performance and energy costs associated with scaling infrared heater arrays for warming field plots from 1 to 100 m. Journal of Theoretical and Applied Climatology. 108:247-265.
Ottman, M.J., Hunt, L.A., White, J.W. 2013. Photoperiod and vernalization effect on anthesis date in winter-sown spring wheat regions. Agronomy Journal. 105(4):1017-1025.
Wall, G.W., Mclain, J.E., Kimball, B.A., White, J.W., Ottman, M.J., Garcia, R.L. 2013. Infrared warming affects intrarow soil carbon dioxide efflux during early vegetative growth of spring wheat. Agronomy Journal. 105(3):607-618.
Wang, H., He, Y., Qian, B., Mcconkey, B., Cutforth, H., Mccaig, T., Mcleod, G., Zentner, R., Depaw, R., Lemke, R., Kelsey, B., Liu, T., Qin, X., White, J.W., Hunt, L.A., Hoogenboom, G. 2012. Climate change and biofuel wheat: A case study of Southern Saskatchewan. Canadian Journal of Plant Science. 92(3):421-425.
Boote, K.J., Jones, J.W., White, J.W., Asseng, S., Lizaso, J. 2013. Putting mechanisms into crop production models. Plant Cell and Environment. 36(9):1658-1672.
White, J.W., Conley, M.M. 2013. A flexible, low-cost cart for proximal sensing. Crop Science. 53(4):1646-1649.