Location: Plant Physiology and Genetics Research2019 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.
This report documents progress for project 2020-11000-013-00D, which started October 2018 and continues research from project 2020-11000-012-00D, Strengthening the Analysis Framework of G x E x M under Climate Uncertainty." Under Objective 1, research continued on multiple fronts to improve the accuracy with which genotype (G) x environment (E) x management (M) (G x E x M) processes are simulated. In support of Sub-objective 1A, work continued to compile large datasets of common bean and soybean field trials that are suitable for testing gene-based models of crop phenology. Through collaboration with the University of Florida and a retired scientist from the University of Guelph, we are also preparing reviews that combine evidence from field experiments with recent findings on the molecular control of daylength and vernalization (low temperature) responses. This work seeks to provide a robust, generic conceptual model of control of flowering that can then be used to guide improved model software code that easily and more accurately allows for testing of genetic effects. Under Sub-objective 1B, we continued work to improve modeled crop energy and water balances. Through the Agricultural Modeling Intercomparison and Improvement Project (AgMIP), analyses continued on comparisons of five wheat models that simulate a partial or complete energy balance, which importantly allows estimating the true crop surface temperature. We used the detailed and well-documented Free-Air carbon dioxide (CO2) Enrichment (FACE) studies conducted at Maricopa (1993-1997) as the reference dataset. Relating measured canopy temperature to hourly air temperatures, rather than daily maximum temperature, reduced apparent simulation error by approximately 10%, a substantial improvement. Current work seeks to understand how temporal or spatial scales and crop processes such as leaf area development affect accuracy of the simulations in order to suggest directions for further model improvement. Parallel to these efforts, the energy balance submodel in the widely used Cropping Systems Model (CSM) was further revised in collaboration with the University of Florida and the Brazilian Agricultural Research Corporation (EMBRAPA). Also, through AgMIP, a retired ARS collaborator and colleagues from other locations continued coordinating an inter-comparison of 29 maize models in their ability to simulate water use or evapotranspiration (ET). An initial manuscript was completed and published using data from Ames, Iowa. However, there were issues with that dataset, such as a water table, and the lack of deep soil moisture measurements and within-season growth measurements. Therefore, a second round of such inter-comparisons is being initiated using datasets from the University of Nebraska at Mead, Nebraska, and ARS in Bushland, Texas. Both sites feature accurate ET measurements, as well as soil moisture measurements, at several depths and frequent within-season growth measurements. Following the approaches used to compare maize ET, an AgMIP project to compare ET estimates for winter wheat is being initiated under the leadership of a researcher at the Leibniz Centre for Agricultural Landscape Research in Germany. A retired ARS collaborator in Maricopa, Arizona, is assisting in designing the model intercomparisons, compiling datasets of measured crop ET from Institute of Agricultural Research (INRA) Avignon in France and the Bushland, Texas, ARS location, and analyzing initial simulation results. For Sub-objective 2A, the Thermal Regime Agronomic Cereal Experiment (TRACE) project compares how the genetic components (G) of four cool-season cereal crops (bread wheat, durum wheat, barley and triticale) respond to naturally occurring environment (E) conditions by taking advantage of the large annual variations in ambient air temperature in Maricopa, Arizona. The four cereal crops are being sown yearly on as many as eight planting dates chosen to emphasize near-lethal to lethal growth temperatures. To include a management (M) component, which completes the G x E x M interaction, a drip-tape irrigation system was installed that, when combined with the existing flood irrigation system, enables a more accurate manipulation of soil moisture regimes. This will be particularly relevant later in the project plan for Sub-objective 2B, which includes a drought treatment. Building on the prior two-year TRACE field trials - five and eight plantings, respectively - a third year of eight plantings is nearing completion, for a total of 21 planting dates under a flood irrigation management regime. Preliminary results from 2-year field trials (March-June) plantings, where air temperatures ranged from 15 to 44 Celsius (C), indicated that yields decreased as temperature increased. However, the response varied with crop and year, as did the lethal air temperature. Data collected over the course of this 3-year study includes inter-season and final biomass, grain yield, weather and high-throughput phenotyping (HTP) data such as canopy spectral reflectance, canopy temperature, and crop height. A database and data analysis pipeline are being developed and standardized. In part, this database has been distributed to colleagues at the Potsdam Institute of Climate Impact Research (PIK), Potsdam, Germany. Firstly, these colleagues have assessed a “stochastic” empirical nonlinear regression model formulation of the time-dependent temperature effect on cereal grain crops. Time-dependent temperature effects on final biomass and grain yields were proportional to the cumulative sum of total temperature exposure during a cropping season. Secondly, as a proof of concept, these results have been compared with those derived from prior “deterministic point-source” and “gridded-modeling” AgMIP wheat-team modeled results for a model inter-comparison and improvement. Hence, the three-year TRACE field trial results for well-watered cereal grains serve as a trifecta test bed for a comparison of stochastic, deterministic point-source and gridded modeling approaches in AgMIP. In support of Objective 3, a cost-effective light-weight proximal sensing system was designed to provide a portable module that is equipped with a multispectral camera, an infrared (IR) thermometer array, and a Mini light detection and ranging (LiDAR) array. This multi-modal remote sensing system delivers phenotypic metrics including canopy spectral reflectance, plant architecture and biomass, vegetation index, canopy temperature, plant height, and crop coverage, in both on-the-go and stationary modes. All cameras and sensors were interfaced to a microcontroller that features low power consumption and easy programming. An algorithm was developed to record data and provide camera control. Furthermore, the system was designed for user-friendly operation and to have a low-weight payload for easier mobility. Additionally, a new HTP system was designed to include a motorized cart using custom hub motors with a built-in gear to adjust torque according to the soil surface. The expected outcome of this work is to augment proximal sensing tools to seamlessly monitor the target plants or field crops using quick and easy attachable/detachable options for multiple platforms (e.g., carts, drones, and tractors). This adaptive multi-modal proximal sensing system is of great interest for ecophysiological modeling for quantitative prediction and crop assessment. Software design for a graphical user interface (GUI)-based, user-friendly system was initiated to develop a pipeline for processing the point and image data and correlating them to plot-level metrics. Furthermore, a robotic-based cart was constructed, equipped with instrumentation for tri-metric measurements of spectral indices, canopy height and canopy temperature, and was used in support of the TRACE project as part of Sub-objective 2A. In support of Sub-objective 3A, plant height was assessed in cotton using ultrasonic transducers LiDAR sensors, and images taken from a drone to determine which method is most effective for measuring crop height. The results showed that some ultrasonic transducers performed better than others in estimating canopy height when compared to manual measurements and that images from the drone performed as well as the best ultrasonic transducer. In support of Sub-objective 3B, a new row-bot was developed to go between crop rows and capture light interception through the canopy with photosynthetically active radiation sensors. The row-bot is also equipped with a small camera pointing 45 degrees upward into the canopy to measure canopy architecture. The row-bot was field tested last year and will collect data in sorghum and cotton trials this season. Also, in support of Sub-objective 3B, a new terrestrial field cart was designed and built to carry a light induced fluorescence transient (LIFT) system. The LIFT system captures chlorophyll fluorescence and estimates plant photosynthesis. To quantify conversion of light into biomass, cotton and grain sorghum trials were planted this spring. The row-bot and LIFT field cart will collect data bi-weekly along with manual measurements, including biomass, to estimate radiant use efficiency. In support of Sub-objective 3C, 24 grain sorghum plants in pots were placed under the TERRA REF instrument and treated with an herbicide. The plants were scanned with the photosystem II fluorescence camera and visible-near infrared hyperspectral camera four times over seven days. Manual leaf punches to extract leaf chlorophyll content were also collected. Data analysis is ongoing; however, initial results show a decrease in photosynthesis with the herbicide treatment.
Thompson, A.L., Thorp, K.R., Conley, M.M., French, A.N., Andrade-Sanchez, P., Pauli, D. 2019. Comparing nadir and multi-angle view sensor technologies for measuring in-field plant height of upland cotton. Remote Sensing of Environment. 11:700-719. https://doi.org/10.3390/rs11060700.
Moni, C., Silvennoinen, H., Kimball, B.A., Fjelldal, E., Brenden, M., Barud, I., Flo, A., Rasse, D. 2019. Controlled infrared-heating of an arctic meadow: challenge in the vegetation establishment phase. Plant Methods. 15:3. https://doi.org/10.1186/s13007-019-0387-y.
Oladzad, A., Porch, T.G., Rosas, J.C., Moghaddam, S., Beaver, J., Beebe, S.E., Burridge, J., Jochua, C.N., Magalhaes, A.M., Miklas, P.N., Ratz, B., White, J.W., Lynch, J., McClean, P.E. 2019. Single and multi-trait GWAS identify genetic factors associated with production traits in common bean under abiotic stress environments. G3, Genes/Genomes/Genetics. 9(6):1881-1892. https://doi.org/10.1534/g3.119.400072.