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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Plant Physiology and Genetics Research » Research » Research Project #430105

Research Project: Strengthening the Analysis Framework of G x E x M under Climate Uncertainty

Location: Plant Physiology and Genetics Research

Project Number: 2020-11000-012-000-D
Project Type: In-House Appropriated

Start Date: Dec 22, 2015
End Date: Oct 23, 2018

Objective 1: Improve the representation of key responses in crop simulation models with emphasis on temperature responses and specific genetic controls (e.g., for phenology and growth habit). Objective 2: Characterize temperature responses of four cereal grain crops using an exceptionally wide range of natural air temperatures that emphasize near-lethal high temperature regimes, and assess quantitative responses simulated by crop models. Objective 3: Develop proximal sensing approaches for field-based, high-throughput phenotyping for drought and heat tolerance, including sensor testing, vehicle design, and software for work flows and data analysis.

Process-based ecophysiological models are among the best tools for quantifying how crop genetics (G), growing environments (E) and management (M) interact to determine productivity as well as ancillary properties of crop production including net greenhouse gas sequestration or releases, nutrient runoff and leaching, and water use. Crop models are especially useful where climate uncertainty or geospatial variability are of prime concerns and where information is needed for scenarios that are not readily adduced from historical data or field experiments. Modeling analyses are constrained by the accuracy of the responses quantified in the models, and recent modeling intercomparisons have identified numerous weaknesses and areas of high uncertainty in widely-used crop models. Our research seeks to improve model accuracy via three interrelated activities. Insights from genomics and molecular biology will be used to strengthen how processes are represented in crop models and how parameters are estimated for individual cultivars. We propose to use existing large crop performance datasets and recently available genetic data (e.g., the soybean “SoySNP50K” initiative) to improve models. The two target topics are temperature interactions with water deficits and with nitrogen and representation of genetically controlled differences in phenology. Because semi-arid desert regions experience wide temperature ranges, intra- and inter-annual variations in ambient temperature provide a cost-effective means to obtain robust data across multiple cereal grain crops simultaneously. To refine modeled thermal responses of crops at higher temperatures, we will conduct a Thermal Regime Agronomic Cereal Experiment (TRACE) using sequential sowing dates that range from the normal, commercial December plantings to very late dates in April and May, with closer intervals between plantings. The genotypes will include hard red and durum wheat, barley and triticale. Sowing the four crop types (with four replicates) over 8 planting dates and 2 years will provide data from 256 genotype x air temperature conditions, with air temperatures ranging from -2 to 42°C. These data will be assembled and formatted in accordance with ICASA Version 2.0 standards and distributed to the AgMIP-wheat team for model intercomparisons and improvement. Novel proximal sensing methods will be developed, assessed and applied in characterizing crop responses (phenotypes) to effects of G x E x M both in the context of crop genetics and crop management research. These activities include testing promising new sensors, refining data logging procedures, improving cart- and tractor-based vehicles, and promoting their application in field-based phenotyping via collaborations with other research programs.