Location: Plant Physiology and Genetics Research
2024 Annual Report
Objectives
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.
Approach
Objective 1: 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 nearlethal 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 is a bridging project, that was established on October 24, 2023, to replace project 2020-11000-013-000D which expired on October 23, 2023. This bridge project is now expected to be reviewed by an Ad Hoc OSQR Panel review in December 2024. For more information about the expired project, please see the final project report which was submitted in FY 2023. The following provides a summary of additional research progress that has been accomplished on this bridge project in FY 2024.
In support of Objective 1, ARS researchers in Maricopa, Arizona, have developed a prototype CO2 exchange measuring system based on the LICOR 850 infrared analyzer to take continuous canopy-level CO2 exchange observations. The important feature of this system is a new enclosure set up to control incoming air and avoid noise from CO2 mixing from external sources other than crop. This system has been used to measure nighttime CO2 exchange in two distant cropping systems under different stress treatments. ARS researchers continue to improve the existing system by comparing with observations with the LICOR 6800 Photosynthesis System. Thess observations will be used to improve the crop growth sub-routine to accurately simulate the stress impacts on crop growth and yields.
Also in support of Objective 1, ARS researchers recently developed a spatial optimization framework to parameterize the crop model at spatial scale and improve the simulations of CO2 fluxes under croplands. This framework uses remote sensing-based leaf area index (LAI) as an objective function and adjust the model parameters spatially using particle swarm optimization algorithm. As a result, the model-simulated LAI closely matches the remote sensing LAI. This framework was implemented over central Nebraska. Currently, ARS researchers are extending its implementation to other regions across croplands in the United States.
Additionally, in support of Objective 1, ARS researchers in Maricopa, Arizona, developed a moisture correction algorithm to adjust satellite-based tillage index. Spatial input data products are essential for simulating the impact of crop management practices on carbon fluxes. One such product is tillage intensity map. Satellite based tillage indices such as Normalized Difference Tillage Index have been used to map tillage practices over large regions. However, these indices are often criticized for their sensitivity to moisture, as typical tillage practices occur during the planting season, which has a high probability of rain. ARS researchers acquired harmonized Landsat Sentinel-2 satellite data on dry and wet days during the planting season of main crops (i.e., corn, soybeans, cotton, winter wheat and spring wheat) across the U.S. This satellite data was used to establish relationships between the water index and the tillage index of different crops. Based on these relationships, a moisture correction algorithm was developed and tested in different regions of the U.S. A manuscript documenting this work is currently in progress.
Finally, in support of Objective 1, ARS researchers in Maricopa, Arizona, produced a crop rotation product for multiple states in the U.S based on a Markov chain approach and historical cropping practices. Crop rotation information is needed for spin-up runs of the crop model to set initial conditions accurately. ARS researchers used historical cropland data layer product to extract historical cropping practices. Using this information, the most likely crop rotation practices for individual fields and their corresponding probabilities were estimated using the Markov chain approach. ARS researchers are extending its implementation to all other states in the United States to produce national crop rotation product.
The scope of Objective 2 is planned to be revised in the new project plan, currently planned for December 2023 Ad-hoc OSQR review. Objective 2 will focus agrivoltaics (AVS) going forward. In support of the planned revised Objective 2, ARS researchers in Maricopa, Arizona, conducted a comprehensive review of literature relevant to AVS. Approximately 70 peer-reviewed research manuscripts were collected, read and summarized to meet the objectives of the AVS research. The goal is to initiate a research program to elucidate the impacts of alterations in microclimate regime from an AVS infrastructure and the physiological response of vegetation growing underneath. Initially, ARS researchers in Maricopa, Arizona, have collaborated on research partnerships with existing AVS research programs in the semi-arid desert, while establishing an AVS program.
To jumpstart the new AVS research initiative, the main existing AVS programs-projects on private, state, and federal lands that already have solar panels installed have been identified. A collaborative research partnership will be established with an existing AVS research program. A non-profit 503-C organization known as “Spaces of Opportunity” (https://www.spacesofopportunity.org/) has recently constructed an AVS located in Phoenix, Arizona. The current crop is Jalapeno Peppers (Capsicum annuum cv. Sriracha) transplanted, from greenhouse-propagated plants at approximately 15 cm height seedlings. Crop rotation to onion and garlic is scheduled for September 2024 to January 2025. To date we have measured plant biophysical (phenology, biomass, yield) and physiological (stomatal conductance, energy transduction of photosystem II, and total plant water, osmotic and turgor potentials) parameters of uppermost canopy leaves. All inputs and outputs are being assessed including the cost of planting, cost of maintenance, net primary productivity of vegetation as well as renewable energy generation.
Another partnership will be established with the University of Arizona, Geography Department, Tucson, Arizona, and a solar power company in Casa Grande, Arizona, to perform a variety of AVS-based experiments.
Also in support of Objective 2, a retired ARS collaborator from Maricopa, Arizona, as well as ARS researchers from Temple, Texas, Beltsville, Maryland, and others from universities and institutions around the world completed an inter-comparison among 33 maize growth models in their ability to simulate soil temperature. Results show that accurate simulation of soil temperature can help improve the accuracy of the models by improving the predicted time from planting to germination, as well as improving the predictions of other soil processes like nitrification, carbon sequestration, and soil respiration. The inter-comparison was conducted under the umbrella of Agricultural Model Inter-Comparison and Improvement Project (AgMIP). The study used comprehensive datasets from two sites located in Mead, Nebraska, and Bushland, Texas, where soil temperature was measured. The average simulation errors ranged about 1.5 to 5.1°C (3 to 9°F). The six best models were identified, five of which used a “numeric” method to simulate soil temperature. Model improvement can come by wider adoption of the numeric method as well as adoption of newer routines to calculate soil thermal conductivity.
Accomplishments