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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

2021 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
Under Sub-objective 1B, analyses were completed, and a publication documented an intercomparison among 29 maize growth models in their ability to simulate evapotranspiration (ET). Work continues with 21 groups from around the world (led by a retired ARS collaborator from Maricopa, Arizona, and including additional ARS scientists from Maricopa, Arizona, 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 now the analysis of the results from all the models and phases is underway. A retired ARS collaborator, and scientists from Brazil and Universities of Florida, Washington, and Nebraska, have implemented an energy balance routine into the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM)-CROPGRO model. This model is theoretically sound with energy and water balances conducted on sunlit leaves, shaded leaves and the soil surface. Model simulations were compared against eddy covariance data on soybean from Mead, Nebraska, and some improvements were made. The resultant model performed well, and a publication was written based on the comparisons with the Mead, Nebraska, data. Now additional datasets are being identified for further tests at other locations, and the model is one of several participating in an intercomparison led by University of Kentucky scientists of many soybean models in their ability to simulate ET. While working on the DSSAT energy balance routine, a retired ARS collaborator realized that the default soil temperature routine in DSSAT simulated soil temperatures that were too warm. He modified a particular equation in the routine, which greatly improved the prediction, and it is expected the modified routine will become the default. A retired ARS collaborator, and other ARS and University of Arizona scientists have assembled data observed during the 1998 and 1999 free air carbon dioxide (CO2) enrichment (FACE) sorghum experiments at Maricopa, Arizona. The dataset has been accepted for publication by the Open Data Journal for Agricultural Research. Several of the same researchers plus others in industry, from the ARS Laboratory at Bushland, Texas, and retired from Brookhaven National Laboratory are now similarly assembling the data from the 1989-1991 FACE Cotton experiments, the 1999 Agricultural Irrigation Imaging System (AgIIS, pronounced Ag Eyes) experiment, and the 2002-2003 FAO-56 Irrigation Scheduling Experiments (FISE). The three projects provide valuable data on the response of cotton to elevated CO2, water supply, nitrogen fertilizer, and planting density. Once published, these data will be available to sorghum and cotton modelers to validate their models. For Sub-objective 1C, progress on better understanding how improved responses to energy and water balances affect cropping system responses was made indirectly. via progress on Sub-objective 1B. Under Objective 2, numerous infrastructure components of the Thermal Regime Agronomic Cereal Experiment (TRACE) were completed and interfaced for the first time in the project including: (1) test and utilization of a wet-well pumping station; (2) test and utilization of a 36-zone drip-tape irrigation manifold distribution system; (3) test and utilization of a field-based drip-tape irrigation system; (4) use of autonomous control of 36-irrigation zones with irrigation controller; (5) installation of a second replication of a real-time soil moisture monitoring system; and, (6) service and maintenance of a dedicated on-site meteorological station. In support of Sub-objective 2A, an ongoing collaboration with scientists from the Potsdam Institute of Climate Impact Research (PIK), Potsdam, Germany, are using a TRACE database for a “stochastic” multivariate principal component regression analysis that formulates the time-dependent temperature effect on cereal grain crops (support for Objective 1). Time-dependent temperature effects on final biomass and grain yields were proportional to the cumulative sum of total temperature exposure during a cropping season. Also, a proof-of-concept manuscript was completed. Now that the proof of concept is confirmed, the three-year TRACE field trial results for the well-watered flood irrigation M regime is being assessed in a similar manner. In support of Sub-objective 2B, the three-way analysis of variance of Genotype (G) by Environment (E) by Management (M) (GxExM) interaction was investigated with the addition of a drought treatment [M component (100% and 50% ET)]. Four sowing dates were staggered, starting from the normal December cropping season and proceeding with closer intervals of subsequent sowings dates during March-May. Use of day-neutral cultivars negated any vernalization requirement or photoperiod effect. The cereal grain crops included: Spring and Durum wheats; Barley; and Triticale (WheatxRye). During the GxExM TRACE project, databases were collected that characterized and quantified phenological development, biomass distribution by plant organ, marketable yield, and grain quality for four cereal grain crops grown under a wide range of ambient air temperatures – including near-lethal and lethal temperatures. In yet another component of the TRACE project relationships between biophysical characteristics of grain crop canopies and remotely sensed high throughput phenotyping (HTP) databases were derived using cart and/or drone technologies [tri-metric: spectral indices; sonic (canopy height); infrared thermometry (temperature)] (support Objective 3). In support of Objective 3, a cost-effective solution was developed for a leaf temperature monitoring system that delivers vertical temperature profile of plants. The system is mounted on an on-the-go ground or aerial high throughput phenotyping (HTP) platform or can be stationary next to a target plant. Wireless protocol allows the user to remotely monitor the plant temperature and download data. The system is self-powered by a solar rechargeable battery and interfaced with an open-source hardware and software. The system was tested and validated in the lab and can be easily replicated or integrated with other sensors to expand metrics and the field coverage as needed. Software design for graphical user inerface (GUI)-based user-friendly image analytics was developed to process images and point data and deliver plot-level metrics (in support of Objective 2). To support Sub-objectives 3A, B, and C, a high throughput phenotype (HTP) cart was used to remotely sense tri-metric measurements: (1) three spectral bands; (2) ultrasonic (canopy height); and (3) infrared thermometry (temperature), on cereal grain crops. In addition, two aerial drones were employed. The first drone’s payload contained a Red, Green and Blue (RGB) camera, and the second drone’s payload contained four spectral-, one red-edge-, and one infrared-thermal-band. An HTP software pipeline under development has enabled image analytics to process images and point data and deliver plot-level metrics (in support of Objective 2). The use of a HTP cart and drone technology with similar sensor arrays has enabled comparison and contrast assessment, which is underway. The next steps will be to apply proximal information with different crop growth models to evaluate their capability to characterize and quantify biophysical characteristics, canopy architecture, light interception, biomass partitioning, and marketable yield. In addition, a field-based HTP for chlorophyll fluorescence was developed to remotely detect photosynthetic efficiency - an important phenotype for selecting to improve crop yields in a hot dry environment. The chlorophyll fluorescence imaging system that rapidly measures important components of photosynthetic efficiency and the TERRA REF LEMNA-TEC field PSII scanner were used to develop experiments to validate the Photo System II (PSII) system for a field setting. Furthermore, a data processing pipeline to extract florescence parameters and other measurements needed to determine photosynthetic efficiency is under development. The validation of this system and development of the processing pipeline has enabled field trials that capture the temporal dynamics of chlorophyll fluorescence for plants grown in a hot dry environment.

1. Effects of elevated carbon dioxide (CO2) on global agriculture food production. Essential information is required to accurately assess the impacts of climate uncertainty on global agricultural food production. An ARS scientist and a retired ARS collaborator, from Maricopa, Arizona, in cooperation with scientists from 23 other domestic and international research centers determined that it is possible to narrow the uncertainties in CO2-induced crop responses so that climate change impact simulations omitting CO2 effects – known as the ‘without CO2-fertilization effects’ scenario – can now be conducted. This proposed approach will improve and streamline future investigations on climate uncertainty effects on global food security.

2. Improved temperature-yield response of irrigated United States wheat by normalizing for phenology and growing season length. High temperature and drought have detrimental effects on growth and phenology that result in yield reductions in agricultural crops. Nevertheless, homeostatic ranges of tolerance exist. An ARS scientist from Maricopa, Arizona, and two German scientists applied a binned temperature exposure statistical yield model on experimental and simulated data. They determined that accounting for phenological development (phenological effect), and rescaling (normalizing) the absolute seasonal length in the temperature counts to a maximum season length, resulted in more realistic yield predictions. This improved statistical modeling approach will enable more accurate assessment of climate uncertainty on global food security.

3. Energy balance routine improves popular Decision Support System for Agrotechnology Transfer (DSSAT) - Cropping System Model (CSM) - Crop Growth (CROPGRO) model. Crop growth models that simulate effects of weather, soils, and management practices on crop growth and yield are valuable tools for assisting today’s farmers in their management decisions, as well as for developing strategies to cope with future global change. However, most “grow” their crops at air temperature rather than at the crop’s vegetation temperature, which can differ from air temperature by several degrees, especially for irrigated agriculture. A retired ARS collaborator from Maricopa, Arizona, along with other researchers from Brazil and Universities of Florida, Nebraska, and Washington, improved the energy balance code in the popular DSSAT-CSM-CROPGRO model. The improved model performed well as compared against data collected for soybean at Mead, Nebraska. The improved CROPGRO model shows promise for helping to improve present and future crop management practices.

4. Late sowing date is a global warming adaptive strategy for bean production on the Mexican high plateau. On the Mexican high plateau, major crops of beans and corn are currently limited by late spring hailstorms and October frosts. A potential adaption under current climate uncertainty would be to delay planting to later in the season to avoid the spring hailstorms, while taking advantage of warmer fall temperatures. A retired ARS researcher, in collaboration with scientists from Mexico, grew beans at current and delayed sowing dates and with a rainout shelter apparatus to restrict rainfall. Also, an infrared heater system was used to simulate future global warming. Results demonstrated that the reduced water supply had little effect, whereas the warming with later planting produced substantial increases in bean yield. The delayed planting strategy shows promise for increasing bean yields in this high plateau region of Mexico.

Review Publications
Toreti, A., Deryng, D., Muller, C., Kimball, B.A., Moser, G., Boote, K., Asseng, S., Pugh, T., Vanuytrecht, E., Pleijel, H., Webber, H., Durand, J.L., Dentener, F., Ceglar, A., Wang, X., Badeck, F., Lecerf, R., Wall, G.W., Van Den Berg, M., Hoegy, P., Lopez-Lozano, R., Zampieri, M., Galmarini, S., Rosenzweig, C. 2020. Narrowing uncertainties in the effects of elevated CO2 on crops. Nature Food. 1:775-782.
Wechsung, F., Ritter, M., Wall, G.W. 2021. The upper homeostatic range for the temperature-yield response of irrigated US wheat down revised from a theoretical and experimental perspective. Agricultural and Forest Meteorology. 307. Article 108478.
Arrendondo, T., Delgado, B.J., Kimball, B., Luna, L.M., Yepez, G.E., Huber, S.E., Garcia, M.E., Garatuza, P.J. 2020. Late sowing date as an adaptive strategy for rainfed bean production under warming and reduced precipitation in the Mexican Altiplano?. Field Crops Research. 255. Article 107903.
MacQueen, A., White, J.W., Lee, R., Osorno, J., Schmutz, J., Miklas, P., Myers, J.R., McClean, P., Juenger, T. 2020. Genetic associations in four decades of multienvironment trials reveal agronomic trait evolution in common bean. Genetics. 215(1):267-284.
Cuadra, S.V., Kimball, B.A., Boote, K.J., Suyker, A.E., Pickering, N. 2021. Energy balance in the dssat-csm-cropgro model. Agricultural and Forest Meteorology. 297. Article 108241.