Location: Plant Physiology and Genetics Research2012 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.
3. Progress Report:
The project seeks to improve prediction of impacts of increased CO2 and climate uncertainty on crop production. Our focus is on application of field data through simulation models that encapsulate knowledge of ecophysiology, agroclimatology and allied fields. These models are recognized as among the best options for examining complex interactions among environment and crop management. Progress in model improvement under Objectives 1 (“Assess the relative utility of experimental approaches…”) has slowed due to unexpected findings by ARS Researchers at Beltsville, MD, that short-term (e.g., 30 second) fluctuations in CO2 strongly inhibit crop growth. The previous assumption was that differences among methods for measuring responses to CO2 likely involved artifacts such as shading. Thus, such differences likely also include responses to fluctuating CO2, and previous estimated CO2 effects on growth and yield may be too low. CO2 fluctuations occur with all experimental CO2 enrichment methods, but few experimenters have documented CO2 fluctuations or included side-by-side comparisons among enrichment methods. The proposed mechanism relates to asymmetric stomatal responses. These can be modeled, but this requires a better, more quantitative understanding. Our project is assisting in compiling historic datasets on CO2 fluctuations and in designing future experiments. Emphasis has also shifted in Sub-objective 2.B (“Refine and apply approaches for gene-based modeling of ecotypic adaptations to factors relevant to climate change research”). Knowing genotypes for key traits is insufficient to predict useful plant traits (“phenotypes”). This genotype-to-phenotype (G2P) problem has led researchers to scale up field measurements from perhaps two to ten cultivars to measurements on hundreds to thousands of breeding lines. Such “high throughout phenotyping” (HTP) dictates use of vehicles carrying multiple electronic sensors as well as novel approaches to data analysis through simulation modeling. Our project members are contributing expertise in ecophysiology, modeling and data management in field research that targets cotton, wheat and biofuel crops but seeks flexible systems applicable to other crops. We propose to modify our research activities to accommodate HTP for heat and drought stress. Improving the physiological hypotheses represented in the models requires detailed information on crop management, soils and daily weather. The project has continued locating, converting and reformatting data for use with crop models, emphasizing wheat and sorghum. A major effort has gone toward revising data standards which are being used not only in our own work but in the global Agricultural Model Intercomparison and Improvement Project (AgMIP). We continue to test the standards for the Decision Support System for Agrotechnology Transfer (DSSAT) models, and through AgMIP, the data standards are being tested with other models.
Ottman, M.J., Kimball, B.A., White, J.W., Wall, G.W. 2011, Wheat growth response to increased temperature from varied planting dates and supplemental infrared heating. Agronomy Journal, 104(1):7-16.