Location: Plant Physiology and Genetics Research
2023 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: 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 is a final report, which documents overall progress for project 2020-11000-013-000D, titled, "Strengthening the Analysis Framework of G x E x M under Climate Uncertainty", which started in October 2018.
Under Objective 1, ARS scientists and a retired collaborator from Maricopa, Arizona, worked in collaboration with numerous international researchers and other ARS scientists to improve energy and water balance-related subroutines of ecophysiological models. This work was coordinated through the Agricultural Model Inter-Comparison and Improvement Project (AgMIP). In support of Objective 1, research focused on three activities 1) Model intercomparison, 2) Model improvement, and 3) Compile data for future model intercomparison studies.
A retired ARS collaborator led the team of 21 researchers around the world, including ARS scientists from Maricopa, Arizona, Bushland, Texas, and Beltsville, Maryland, to perform the intercomparison of 41 maize crop growth models in their ability to simulate water use or evapotranspiration (ET). For this inter-comparison, datasets from the University of Nebraska at Mead, Nebraska, and ARS in Bushland, Texas, were used. A paper detailing this research was recently published. In addition, analyses of soil temperature simulations from 33 maize models using the same datasets were performed to evaluate models’ soil temperature functions. A manuscript from this work was recently submitted for publication.
Following the approaches used to compare maize ET, an AgMIP project to compare ET estimates for winter wheat was initiated under the leadership of a researcher at the Leibniz Centre for Agricultural Landscape Research in Germany. A retired ARS collaborator in Maricopa, Arizona, assisted 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. Furthermore, ARS scientists from Maricopa, Arizona, along with a retired ARS collaborator are participating in the soybean model intercomparison experiment conducted as part of AgMIP. Soybean Free Air Concentration Enrichment (SOYFACE) experiment data was used to perform the calibration of the EPIC and DSSAT crop models.
With respect to model improvement, a retired ARS collaborator, along with scientists from Brazil and Universities of Florida, Washington, and Nebraska, has implemented a revised 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. Initial results were mixed when compared against eddy covariance data on soybean from Mead, Nebraska, so improvements were made to the code. The revised version out-performs the FAO-56 method for estimating ET. This work was documented in the publication. 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).
To support future studies of model inter-comparison, 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 was published by the Open Data Journal for Agricultural Research. Similarly, several of the same researchers plus others in industry, from the ARS Laboratory at Bushland, Texas, and retired from Brookhaven National Laboratory assembled 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 provided valuable data on the response of cotton to elevated carbon dioxide (CO2), water supply, nitrogen fertilizer, and planting density. The resultant dataset and paper were published in March 2023.
Even though considerable progress was made in support of Objective 1, loss of personnel resulted in limited or no progress under Sub-objectives 1A and 1C.
Under Objective 2, ARS scientist from Maricopa, Arizona, established the Thermal Regime Agronomic Cereal Experiment (TRACE) project to create different temperature regimes by staggering sowing dates, and examine the response of four cool-season cereal crops (bread wheat, durum wheat, barley and triticale) under different temperature regimes, in terms of crop growth and development. In support of Sub-objectives 2A and 2B, a drip-tape irrigation system was installed to enable precise manipulation of soil moisture regimes.
In support of Sub-objective 2A, four cereal grain crops were planted in a 12-ha field in four replicates, spanning a total of 21 sowing dates (November to June) during 2016, 2017, and 2019, respectively. The crops were subjected to a well-watered flood irrigation (100% ET) management regime. A wide range of data characterizing phenological development, biomass partition, yield and grain quality were collected as part of this experiment. These results have enhanced our understanding of the impact of global climate uncertainty on cereal grain producing regions of the globe. This data was shared with research collaborators at the Potsdam Institute of Climate Impact Research (PIK), Potsdam, Germany for conducting “stochastic” multivariate principal component regression analysis. It was observed that time-dependent temperature effects on final biomass and grain yields were proportional to the cumulative sum of total temperature exposure during a cropping season. Furthermore, the results of this experiment are currently being formatted into an Institute for Complex Additive Systems Analysis (ICASA) version 2.0 AgMIP database. Deficit irrigation TRACE field trial was postponed for several reasons such as COVID-19 and irrigation drip tape damage. This resulted in no-progress under Sub-objective 2B.
Under Objective 3, a cost-effective light-weight high-throughput (HTP) proximal sensing system were designed. First developed system is a multi-model remote sensing system that includes a multispectral camera, an infrared (IR) thermometer array, and a mini light detection and ranging (LiDAR) array. The system provides various phenotypic metrics, such as canopy spectral reflectance, plant architecture and biomass, vegetation index, canopy temperature, plant height, and crop coverage, in both on-the-go and stationary modes. The second one is a motorized cart with custom hub motors and a built-in gear to adjust torque according to the soil surface. This cart allows seamless monitoring of target plants or field crops through quick and easy attachable/detachable options for multiple platforms. The third one is a row-bot that navigates between crop rows and capture light interception through the canopy using photosynthetically active radiation sensors. The fourth one is a cart to carry a light-induced fluorescence transient (LIFT) system to measure chlorophyll fluorescence.
In support of Sub-objective 3A, different plant height measuring tools including ultrasonic transducers, LiDAR sensors, and images taken from a drone were evaluated for their performance. The results indicated that certain ultrasonic transducers performed better than others in estimating canopy height and images from the drone performed as well as the best ultrasonic transducer. This work has been documented in a publication.
In support of Sub-objective 3B, various optical remote sensing methods were evaluated to retrieve crop-specific leaf area index (LAI), a key state variable used to determine potential biomass in 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 (Mead, Nebraska, and Bushland, Texas). The estimated LAI values were then compared against field observations. This work has been published in the Remote Sensing journal.
For Sub-objectives 3A, 3B, and 3C, a high-throughput phenotype (HTP) cart was utilized to remotely sense tri-metric measurements on cereal grain crops: (1) three spectral bands, (2) ultrasonic (canopy height), and (3) infrared thermometry (temperature). Additionally, two aerial drones were employed. The payload of the first drone consisted of a Red, Green, and Blue (RGB) camera, while the second drone carried four spectral bands, one red-edge band, and one infrared-thermal band. A field-based HTP system measuring chlorophyll fluorescence remotely to assess photosynthetic efficiency was employed to capture the temporal dynamics of chlorophyll fluorescence in plants grown in hot and dry environments.
In support of Sub-objective 3C, we have revised the Phenocrop model to estimate winter wheat physiological crop growth stages at a high spatial resolution. The model has been updated to incorporate the computation of the required Accumulated Photothermal Time (APTT) for different growth stages of winter wheat. The APTT calculations utilize phenology data from USDA-NASS progress reports. 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.
Accomplishments
1. Published FACE, AgIIS, and FISE cotton datasets. From 1989-2003 ARS researchers from Maricopa, Arizona, and researchers at the University of Arizona, Tucson, Arizona, along with several other collaborating scientists conducted three FACE (free-air CO2 enrichment) experiments on cotton with elevated CO2 at ample and limiting levels of water and nitrogen and different plant. From these experiments, a comprehensive database has been assembled which includes plant management, soils, weather; and plant physiology, phenology, growth, and yield. This dataset is available for anyone to download, which can used by cotton crop modelers to validate and improve their cotton growth models.
2. Maize growth models developed for simulating growth, yield, and evapotranspiration. A retired ARS collaborator and another scientist at Maricopa, Arizona, in addition to ARS scientists in Bushland, Texas, and Beltsville, Maryland, and other scientists from 21 modeling groups around the world, inter-compared 41 maize growth models in their ability to simulate evapotranspiration. Six models were identified that were best at simulating growth, yield, and evapotranspiration. Other models can be improved by reconciling concepts from these 6 best performing models and developing revised sub-routines.
3. Net soil carbon storage little affected by sorghum grown under elevated carbon dioxide (CO2). A free-air CO2 enrichment (FACE) experiment on sorghum, conducted by ARS researchers at Maricopa, Arizona, revealed that elevated CO2 had little effect on net soil carbon storage on sorghum. These results will be useful to improve the representation of carbon dynamics under elevated CO2 in crop models and improve the model simulations to understand climate change impacts.
4. Reliable optical Leaf Area Index (LAI) method improves regional LAI estimates. Regional LAI estimates are required to develop and improve modeling tools which 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 the regional differences. An ARS scientist from Maricopa, Arizona, and researchers from the University of Maryland evaluated existing optical methods under contrasting growing conditions. This research found reliable LAI method, physically based PRO-SAIL radiative transfer model inversion approach that can perform reasonably well irrespective of differences in growing conditions. The use of this method is expected to produce reasonable regional scale LAI estimates. These estimates help to develop reliable model-based crop monitoring and forecasting tools that can help farmers to make informed decisions. This work was published in Remote Sensing Journal.
5. A remote sensing-based data assimilation pipeline improves soil water balance simulations. Crop models often simulate soil-water balance incorrectly due to errors in model structure and biases in inputs used in the crop models. Remote sensing offers spatial observations of surface soil moisture which can be integrated into crop models to improve soil water balance simulations. An ARS scientist from Maricopa, Arizona, in collaboration with other ARS LTAR scientists, an ORISE ARS post-doc, and a graduate student at the University of Maryland, developed a pipeline to assimilate Soil Moisture Active Passive (SMAP) data products into the environmental policy integrated climate (EPIC) crop model using an ensemble Kalman-filter recursive algorithm. This pipeline was found to improve EPIC soil water balance simulations which further enhance simulations of crop yield and carbon and nitrogen stocks and fluxes.
6. 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 production.
7. Decision Support System for Agrotechnology Transfer (DSSAT) improved leading to release of Version 4.8. A retired collaborator and ARS researcher in Maricopa, Arizona, contributed improvements in the simulation of evapotranspiration, soil temperature, and energy balance by DSSAT, a framework for crop growth models, leading to the release of updated Version 4.8. This new version improves the simulations of evapotranspiration which will help improve irrigation scheduling tools.
Review Publications
Nandan, R., Bandaru, V., He, J., Daughtry, C.S., Gowda, P.H., Suyker, A. 2022. Evaluating optical remote sensing methods for estimating leaf area index for corn and soybean. Remote Sensing. 14(21). Article 5301. https://doi.org/10.3390/rs14215301.
Li, K., Kirkland, S., Yeo, B., Tubbesing, C., Bandaru, V., Song, L., Holstege, L., Hartsough, B., Kendall, A., Jenkins, B. 2023. Integrated economic and environmental modeling of forest biomass for renewable energy in California: Part I - Model development. Biomass and Bioenergy. 173. Article 106774. https://doi.org/10.1016/j.biombioe.2023.106774.
Kimball, B.A., Thorp, K.R., Boote, K.J., Stockle, C., Suyker, A.E., Evett, S.R., Brauer, D.K., Coyle, G.G., Copeland, K.S., Marek, G.W., Colaizzi, P.D., Acutis, M., Alimagham, S., Archontoulis, S., Babacar, F., Barcza, Z., Basso, B., Bertuzzi, P., Constantin, J., De Antoni Migliorati, M., Dumont, B., Durand, J., Fodor, N., Gaiser, T., Garofalo, P., Gayler, S., Giglio, L., Grant, R., Guan, K., Hoogenboom, G., Jiang, Q., Kim, S., Kisekka, I., Lizaso, J., Masia, S., Meng, H., Mereu, V., Mukhtar, A., Perego, A., Peng, B., Priesack, E., Qi, Z., Shelia, V., Snyder, R., Soltani, A., Spano, D., Srivastava, A., Thomson, A., Timlin, D.J., Trabucco, A., Webber, H., Weber, T., Willaume, M., Williams, K., van der Laan, M., Ventrella, D., Viswanathan, M., Xu, X., Zhou, W. 2023. Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models. Agricultural and Forest Meteorology. 333. Article 109396. https://doi.org/10.1016/j.agrformet.2023.109396.
Morris, C.F., Luna, J., Caffe-Treml, M. 2021. The Vromindolines of cv. Hayden oat (Avena sativa L.) – A review of the Poeae and Triticeae Indolines and a suggested system for harmonization of nomenclature. Journal of Cereal Science. 97. Article 103135. https://doi.org/10.1016/j.jcs.2020.103135.
