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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 #435645

Research Project: Analysis and Quantification of G x E x M Interactions for Sustainable Crop Production

Location: Plant Physiology and Genetics Research

2022 Annual Report

Objective 1: Improve ecophysiological models for quantitative prediction of G x E x M. Sub-objective 1A: Models improved with respect to their ability to simulate phenology and canopy architecture from genetic information. Sub-objective 1B: Models improved with respect to simulating crop responses to energy and water balances. Sub-objective 1C: Models assessed to understand how improved responses to energy and water balances affect cropping system responses. Objective 2: Characterize the temperature response of agronomic crops using an exceptionally wide range of natural air temperatures. Sub-objective 2A: Responses of cereal crops to temperature characterized using an exceptionally wide range of natural air temperatures emphasizing near-lethal high temperatures under well-watered conditions. Sub-objective 2B: Responses of cereal crops to temperature characterized by combining deficit irrigation and an exceptionally wide range of natural air temperatures. Objective 3: Develop tools for proximal sensing and remote sensing for improved quantification of crop growth. Sub-objective 3A: Tools developed for assessing crop architecture and light interception through proximal and remote sensing. Sub-objective 3B: Tools developed for quantifying the conversion of intercepted radiation to biomass via proximal and remote sensing. Sub-objective 3C: Tools developed for assessing partitioning of vegetative and reproductive growth via proximal and remote sensing.

Objective 1: To improve ecophysiological models that quantify crop responses to G x E x M, the project will strengthen simulation of phenology, canopy architecture, and crop energy balances (CEB) and water balances (CWB). Targeting common bean, soybean and sorghum, research on phenology and architecture focuses on improving how genetic differences within a crop species are represented in existing models such as the Cropping Systems Model (CSM). The work exploits large phenotypic datasets from multiple environment trials, linked to data on daily weather conditions, crop management and crop genetics. Improved simulation of crop energy and water balances should benefit overall simulation of cropping systems. To improve calculation of the three-source (sunlit and shaded leaves, soil surface) CEB as implemented in the CSM, planned work will ensure that crop and soil temperatures estimated are correctly transferred to routines for other temperature-sensitive processes and calculation of the CEB is numerically stable. Improved calculation of the CWB builds on comparisons of over 30 maize models (including CSM), which we lead as part of the global Agricultural Model Intercomparison and Improvement Project. This work will identify approaches providing the best estimates of crop water use and indicate how model calibration affects CWB estimates. Objective 2: Through detailed monitoring of crop growth and development, field trials will be used to compare responses of cereal crops to thermal stress at near-lethal and lethal temperatures. This will provide a unique dataset to analyze how temperature affects multiple processes of crop growth and development. Four spring cereals (bread wheat, durum wheat, barley and triticale) will be sown on sequential dates that expose the crops to the exceptionally high mid-day air temperatures. In a second phase, a water deficit treatment will augment the range of temperatures experienced by the four crops as well as allow characterizing how temperature and water deficits interact to affect cereals at near-lethal temperatures. Objective 3: Crop models require high quality data on growth, novel sensor systems will be used to monitor growth at lower cost, higher accuracy and higher throughput than previously possible. This builds on advances in high throughput phenotyping, which is usually associated with genetic research but is applicable to many aspects of crop research. The focus is to analyze data from the Transportation Energy Resources from Renewable Agriculture Phenotyping Reference Platform (TERRA REF) field scanner to quantify growth of sorghum and wheat using a conceptual framework of light interception and radiation use efficiency. The first year, four seasons each of sorghum and durum diversity panels will have been variously scanned with stereo cameras, a thermal camera, a 3-D laser scanner and two hyperspectral cameras (covering 400 to 2,500 nanometers) and imaged with unmanned aerial vehicles. In collaboration with image analysts of TERRA REF, we will quantify crop growth and architecture, cross-validating results with light interception, crop height and biomass data from our manual assessments.

Progress Report
This report documents progress for project 2020-21000-013-000D, titled “Molecular Genetic and Proximal Sensing Analyses of Abiotic Stress Response and Oil Production Pathways in Cotton, Oilseeds, and Other Industrial and Biofuel Crops” which was certified in February 2018 and continues research from project 2020-21000-012-000D titled, “Molecular Genetic Analysis of Abiotic Stress Tolerance and Oil Production Pathways in Cotton, Bioenergy and Other Industrial Crops.” The following documents the research progress made in fiscal year 2022. Under Sub-objective 1B, work continues with 21 groups from around the world, led by a retired ARS collaborator at Maricopa, Arizona, and including additional ARS scientists from Maricopa, Arizona, Bushland, Texas, and Beltsville, Maryland, participating with a total of 41 maize growth models. The study is being done in four phases, starting with “blind” and then giving additional information about the data with each successive phase. The fourth phase (all data supplied) was completed as of mid-June 2021, and analyses of results are continuing to date. A first draft of a major manuscript has been completed. Also, work has progressed on assembling and formatting Agricultural Model Intercomparison and Improvement Project (AgMIP) datasets from past cotton experiments that will be very useful for validating cotton growth models. The experiments included Free-air Carbon Dioxide Enrichment (FACE) with varying carbon dioxide (CO2) concentration and water supply, Ag Industry Identification System (AgIIS) with varying water supply, and FISE with varying water supply and plant density. A manuscript/dataset has been submitted to a journal for publication. The work was done by present and retired ARS scientists from Maricopa, Arizona, as well as scientists and graduate students from the University of Arizona and elsewhere. In support of Sub-objective 2A, results from the Thermal Regime Agronomic Cereal Experiment (TRACE) project, conducted under flood irrigation management (M) (well-watered) regime during 2016, 2017 and 2019, are being formatted into an Institute for Complex Additive Systems Analysis (ICASA)version 2.0 AgMIP database. This database is nearing completion but needs addition processing and final validation for adequate quality control assessment. The work was conducted by a scientist and technical support at Maricopa, Arizona. The TRACE project has proved to be a novel and cost-effective means to elucidate thermal response for four cool-season cereal crops (bread wheat, durum wheat, barley, triticale) simultaneously. The project uses intra- and inter-annual variations in ambient air temperature in a semiarid desert region such as Maricopa, Arizona, to emphasize near-lethal to lethal growth temperatures. Nevertheless, through years of experience it was discovered it would be better to conduct the first year of a two-year study and process all the numerous samples the following year and prepare the AgMIP database. The second-year experiment would then follow and again a year would be used to process and prepare the AgMIP database. This strategy benefits alleviating the overburden on personnel, resolves sample storage issues, and enhances sample quality control. Rather than conduct a TRACE project under less-than-ideal conditions, the second year of the deficit irrigation TRACE field trial was postponed for several reasons. First, because of the on-going global COVID-19 pandemic. Second, the drip tape irrigation system had become compromised. Over time the drip-tape has undergone natural degradation resulting in numerous leaks as well as rodent damage. Thus, in its present condition it is unreliable for establishing sufficiently precise irrigation control treatments. It is common in research applications that the drip tape requires periodic replacement, and the time has come to replace it in the TRACE project. Third, conducting all field operations as many as eight times a year requires a large effort and only unreliable results would have been obtained without replacing the drip tape. Hence, the drip tape needs to be replaced prior to another TRACE project. Despite several requests to get the drip tape replaced, its replacement has yet to be approved, funded, and contracted. A continuing resolution that lasted until, on or about, April 2022, the Agency held back funds beyond the continuing resolution date for at least another month, and working out the logistical cost of the new hire’s modeling position have contributed to delays in replacing the drip tape. Without replacement of the drip tape in a timelier manner the upcoming TRACE project will be placed in jeopardy. For Objective 3, loss of personnel resulted in no progress under Sub-objective 3A. In support of Sub-objective 3B, various optical remote sensing methods were evaluated to retrieve crop specific leaf area index (LAI), which is a key state variable used to determine potential biomass by most crop models. Existing statistical and physical methods, developed based on parametric, non-parametric and radiative transfer model (RTM) look-up-table based inversion, were implemented for corn and soybeans cultivated at two geographically distant locations in the United States (i.e., Mead, Nebraska, and Bushland, Texas). Further, estimated LAI values were compared against field observations. The results from this evaluation provided valuable insights on the strengths and weaknesses of existing methods. A manuscript was prepared and is currently under review for publication in the Remote Sensing journal. In support of Sub-objective 3C, we revised the Phenocrop model to estimate winter wheat physiological crop growth stages at high spatial resolution. To extend its capability to estimate growth stages of winter wheat, the model was updated through computing required Accumulated Photothermal Time (APTT) for different winter wheat crop growth stages. The phenology data from USDA-NASS progress reports were used in the APTT calculations. Currently, the work is under progress in collaboration with a faculty member from Kansas State University to validate the revised Phenocrop model using extensive field observations.

1. Reliable optical leaf area index (LAI) method improves regional LAI estimates. Regional LAI estimates are required to develop and improve modeling tools that aid in monitoring crop conditions and yields at various scales ranging from small regional to global scales. Many methods have been developed to estimate regional LAI using optical remote sensing data; however, it is not clear which methods perform well irrespective of regional differences. An ARS scientist from Maricopa, Arizona, and researchers from the University of Maryland, evaluated existing optical methods under contrasting growing conditions. A physically based PRO-Scattering by Arbitrary Inclined Leaves (PRO-SAIL) inversion approach was found to consistently perform reasonably well irrespective of differences in growing conditions. The results demonstrate the PRO-SAIL inversion approach can be used to produce reliable regional LAI estimates at various spatial scales.

2. PhenoCrop-wheat model provides fine scale winter wheat phenological estimates. Crop growth stages are key factors in determining changes in assimilate partitioning, so it is essential that reliable algorithms are available to characterize crop phenology. An ARS scientist at Maricopa, Arizona, in collaboration with researchers from the University of Maryland tailored the Phenocrop algorithm to estimate crop growth stages of winter wheat. This algorithm allows agronomists and crop modelers to estimate regional crop growth stages at high spatial resolution using satellite imagery. These regional phenology estimates can be used for in-season crop management decisions and for the spatial optimization of crop model parameters.

3. Free-Air C02 Enrichment (FACE) sorghum dataset published. From 1998-1999, ARS researchers from Phoenix, Arizona, (now moved to Maricopa, Arizona,) and the University of Arizona, Tucson, Arizona, along with several other collaborating scientists conducted two FACE experiments on sorghum at ample and limiting levels of water and nitrogen. From these experiments, a comprehensive dataset has been assembled and formatted, which includes management, soils, weather, physiology, phenology, growth, yield, and cyanide data. Using carbon isotopic tracing, carbon flows were measured from the air to the plants to sequestration in the soil. This dataset has now been published, and the data are available for anyone to download, which should be very useful for sorghum crop modelers to validate and improve their sorghum growth models.

4. Net soil carbon storage little affected by sorghum grown under elevated carbon dioxide (CO2). As a part of free-air CO2 enrichment (FACE) experiments on sorghum conducted by ARS researchers at Maricopa, Arizona, measurements of the carbon isotope ratios of the high-CO2-grown plants, the control plants, and soil carbon were determined by cooperating scientists from the University of Arizona. The net new soil carbon enhancement resulting from FACE was 5.8% under ample water and 7.7% under limited water. However, at same time there was a loss of old pre-experiment carbon of about 6%, so the net soil carbon gain was very small. Thus, soil carbon storage under sorghum likely will be little affected by the increasing atmospheric CO2 concentration.

Review Publications
Leavitt, S., Cheng, L., Williams, D., Brooks, T., Kimball, B.A., Pinter Jr., P., Wall, G.W., Ottman, M., Matthias, A., Paul, E., Thompson, T., Adam, N. 2022. Soil organic carbon isotope tracing in sorghum under ambient CO2 and Free-Air CO2 Enrichment (FACE). Land. 11(2). Article 309.
Kimball, B.A., Ottman, M.J., Pinter Jr., P.J., Wall, G.W., Leavitt, S.W., Cheng, L., Conley, M.M., La Morte, R.L., Triggs, J.M., Gleadow, R. 2021. Data from the Arizona FACE (Free-Air CO2 Enrichment) experiments on sorghum at ample and limiting levels of water supply. Open Data Journal for Agricultural Research. 7:1-10.