Location: Plant Physiology and Genetics Research2011 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).
3. Progress Report
This project represents a transition from field experiments to application of field data through simulation models that can predict impacts of increased atmospheric CO2 and climate change on crop production associated variables such as crop residue amounts, water use and greenhouse gas emissions. The ecophysiological models we use encapsulate the available understanding from ecophysiology, agroclimatology, soil science and allied fields, and the models are widely recognized as among the best options for examining the complex interactions among environmental factors and crop management. Improving the physiology represented in the models requires detailed information on crop management, soils and daily weather. Furthermore, for model testing and improvement, information on crop growth and yield are indispensible. In this first year, the project has focused on locating datasets, converting data to digital formats, and reformatting the data for efficient use with crop models. Dataset assembly has benefitted from our previous efforts to develop guidelines for data management and from collaboration with other data organization efforts including the USDA ARS GRACEnet research and the global Agricultural Model Intercomparison and Improvement Project (AgMIP). The first output format being tested is for the Decision Support System for Agrotechnology Transfer (DSSAT) modeling software, which we predominantly use in our simulation studies and which is among the most widely used crop modeling systems. Model improvement continues to be constrained by incomplete quantitative understanding of the diverse physiological processes that contribute to crop growth and development. Research progress has been slower than was anticipated when we planned our research and set milestones under Sub-objective 2.B (“Refine and apply approaches for gene-based modeling of ecotypic adaptations to factors relevant to climate change research”). The plant science community has found that simply knowing a plant’s genotype sequence is insufficient to predict plant traits (“phenotypes”) including yield. This problem, termed the genotype-to-phenotype (“G-to-P” or “G2P”) problem, is fundamental to addressing the anticipated needs to breed crops with better adaptation to heat, drought and other stresses. Solving G2P requires unprecedented changes in the scale of field research on how cultivars respond to the environment. Rather than examining perhaps two to ten breeding lines or cultivars with extensive use of hand-held instruments, as was done in the past, measurements on hundreds to thousands of lines are required. The scale and intensity of measurement dictates that digital sensors or imaging technology be used. In efforts at the Arid Land Agricultural Research Center to develop the requisite “high throughput phenotyping” capabilities, our project members contribute expertise in ecophysiology, modeling and data management. The work initially targets cotton, wheat and biofuel crops but seeks to develop flexible systems applicable to other crops. While still exploratory, this work might lead to modification of our planned research activities and milestones.
Luo, Y., Melillo, J., Niu, S., Beier, C., Clark, J., Classen, A., Davidson, E., Dukes, J.S., Evans, R.D., Field, C., Czimczik, C.I., Kimball, B.A., Kueppers, L.M., Norby, R., Pelini, S.L., Pendall, E., Rastetter, E., Six, J., Smith, M., Tjoelker, M., Torn, M., 2011. Coordinated approaches to quantify long-term ecosystem dynamics in response to global change. Global Change Biology. 17:843-854.