Research Project: Improving Crop Performance and Precision Irrigation Management in Semi-Arid Regions through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

2023 Annual Report

Objectives
Objective 1: Identify crop physiological trait networks and soil nitrogen processes that improve the performance of agricultural systems under water and nutrient stress. Sub-objective 1.A: Identify physiological trait networks that advance process-based plant growth models, artificial intelligence (AI)/statistical models, and conceptual understanding of crop stress physiology. Sub-objective 1.B: Identify plant and soil processes that determine crop nitrogen requirements under varying water availability. Sub-objective 1.C: Develop rapid and cost-effective phenotyping methods to quantify complex physiological traits across genotypes. Objective 2: Develop methods to guide precision agricultural water management using remote-sensing, climate and soil data. Sub-objective 2.A: Develop algorithms and tools that integrate in-situ sensor and remotely sensed image data with soil and weather data to inform precision variable-rate irrigation (VRI) decisions. Sub-objective 2.B: Link multi-source remote-sensing data for detection of crop abiotic and biotic stress and estimation of crop water use using machine learning and AI techniques to support precision irrigation. Objective 3: Build better field- to farm-scale decision support datasets, tools, and models for stakeholders in water-limited regions to optimize water use, profitability, and sustainability.

Approach
Urban demand for water will increase ca. 80% over the next 30 years, independent of climate change (Florke et al. 2018). Considering the combined effects of urban demand and the changing climate, we can expect an increase in the needs for agricultural water and a decrease in the supply of agricultural water over the next several decades, resulting in decreased food security world-wide (Wallace 2000, Harmel et al. 2020, Hasegawa et al. 2020, Qin et al. 2021). There is therefore an urgent need to make crop species and agricultural practices more water efficient in the face of these challenges. The research proposed herein addresses key knowledge gaps and confronts these challenges with a multifaceted approach. Specifically, we aim to improve scientific understanding of which crop traits should be targeted to increase crop water productivity (crop production per unit water) and nitrogen use efficiency under limited water (Objectives 1.A, 1.B, & 1.C). This will be achieved through a truly broad multidisciplinary approach combining plant physiology, genetics, soil biogeochemistry, and process modeling. In parallel, we will develop novel irrigation scheduling techniques that will leverage newly emerging technologies (i.e., plant stress sensing, proximal sensing, airborne remote sensing, precision agriculture, machine learning) to improve the spatial and temporal application of both water and nitrogen (Objectives 2.A & 2.B). Lastly, these plant, soil, and irrigation data streams will be woven together to build new decision support datasets, tools, and models for stakeholders in water-limited regions (Objective 3).

Progress Report
Objective 1a: The Sunflower Association Mapping (SAM) population was grown in the greenhouse to assess physiological responses to “drought” and freezing tolerance. Measured traits included gas-exchange, chlorophyll fluorescence, root traits, vulnerability to xylem embolism, and low temperature tolerance. At the same time, and in coordination with the greenhouse study, five 8-plant cohorts of a single sunflower and maize genotype were grown in an especially designed (in-house) growth chamber to assess the sequence (with respect to time and water potential) of physiological systems failures – stomatal conductance, gas exchange, photosystem II function, leaf and stem xylem function, parenchyma capacitance. These results are helping us determine the structural and physiological trait networks in crop species that confer improved performance in water limited contexts, and importantly, which traits and trait combinations fail first during drought and are therefore more likely to improve performance if modified. Our experiments seeking to determine which physiological systems are the first to fail during drought are particularly important because we are finding that the traits most often focused on in breeding programs (e.g., leaf level traits – gas exchange, stomatal conductance, water content) are not permanently damaged at most levels of drought experienced by crop plants, whereas other traits are permanently impaired by drought but are not being explicitly considered in drought breeding programs. Objective 1b: Maize was grown under two levels of water and 6 levels of nitrogen availability during the 2022 field season to test biomass and grain productivity, nitrogen accumulation in plant tissues, water productivity, nitrogen productivity, and physiological responses, including cell membrane stability and leaf hydraulic status. The literature in this area has found both positive and negative effects of nitrogen availability on crop water productivity. We did not find any indication that increases in nitrogen availability increased maize productivity or stress tolerance under limited water with our first year of data. Nitrogen mineralization rates and soil microbial and fungal communities were sampled and analyzed. More water and nitrogen availability independently increased nitrogen mineralization rates, thus providing proportionally more nitrogen availability to plants than what was available from fertilizer additions. Nitrogen and water availability shifted both microbial and fungal communities. Further analysis is underway to explore these community shifts and what they might indicate. Objective 1c: We have measured leaf, vein, vessel, and sieve tube traits across 288 sunflower genotypes (Sunflower Association Mapping population). These data are being used to identify key connections among leaf “hydraulic” traits in sunflower that could be used in crop programs to improve performance in water limited and non-limited conditions. In parallel with the collection of these traits, collaborations with researchers at Guangxi University, Colorado State University, the Swiss Federal Institute of Technology, the University of Ulm, and Leiden University, are extending this dataset to include more species and functional groups, and thus expanding our scientific understanding of leaf functioning (and underlying physiological mechanisms), so that key functional characteristics can be identified and exploited towards the improvement of crop species. Objective 2a: Field experiments of real-time irrigation scheduling were successfully conducted in the 2022 field season at the Limited Irrigation Research Farm. Treatments included both full and limited irrigation treatments, using several evapotranspiration (ET) and water balance methods, including water balance, standardized ET methods (FAO-56; Food and Agriculture Organization, Technical Paper 56), energy balance, canopy temperature and Degrees Above Non-Stressed (DANS) index, and a combination of remote sensing and the Root Zone Water Quality Model (RZWQM2). Each of these methods showed promise to characterize crop water use under various stress levels. Several important datasets are being collected for this objective, with many of them integral in water balance modeling that has quick turnaround (e.g. within a day) for near real-time irrigation decisions. To streamline this process, a related side-project has been initiated to streamline all data collection and processing into a programmable and repeatable database model, which may be the basis for subsequent projects for spatially variable irrigation. Objective 2b: Leaf-level hyperspectral, SPAD (Soil Plant Analysis Development), Leaf Area Index, and UAV (Unmanned Aerial Vehicle) and satellite remote sensing images were collected from maize grown under 6 levels of nitrogen and 2 levels of water availability in the 2022 field season. These data showed measurable differences in maize response to varying levels of nitrogen and water availability. This data will be used to determine indicators of crop growth, water, and nitrogen stress. Objective 3: Applied calibrated phenology using the Unified Plant Growth Model (UPGM) to simulate planting dates at field sites.

Accomplishments
1. Created an open-source standardized evapotranspiration and water balance model. ARS scientists in Fort Collins, Colorado, and Maricopa, Arizona, created an updated Python-based software package called “pyfao56” Version 1.1.0 to calculate standardized crop and reference evapotranspiration (ET) and water balance. This state-of-the-art tool more accurately represents the influence of soil variability on ET, which is a critical indicator of irrigation water needs. This tool will benefit ET researchers, hydrologists, agronomists, irrigation manufacturers, and software developers by creating an open-source platform to estimate crop water use and inform irrigation requirements and is scalable to be used as a basis for variable rate irrigation management. We envision this tool being integral in model scaling for use in real-time variable rate irrigation management, which will apply the right amount of water exactly where it is needed based on real-time feedback.

Review Publications
Katimbo, A., Rudnick, D.R., Zhang, J., Ge, Y., DeJonge, K.C., Franz, T.E., Shi, Y., Liang, W., Qiao, X., Heeren, D.M., Kabenge, I., Nakabuye, H.N., Duan, J. 2023. Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for precision irrigation water management. Smart Agricultural Technology. 4. Article e100176. https://doi.org/10.1016/j.atech.2023.100176.
Zhang, Y., Han, W., Zhang, H., Niu, X., Shao, G. 2023. Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms. Journal of Hydrology. 617. Article e129086. https://doi.org/10.1016/j.jhydrol.2023.129086.
Shao, G., Han, W., Zhang, H., Zhang, L., Wang, Y., Zhang, Y. 2022. Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods. Agricultural Water Management. 276. Article e108064. https://doi.org/10.1016/j.agwat.2022.108064.
Bushey, J.A., Hoffman, A.M., Gleason, S.M., Smith, M.D., Ocheltree, T.W. 2023. Water limitation reveals local adaptation and plasticity in the drought tolerance strategies of Bouteloua gracilis. Ecosphere. 14(1). Article e4335. https://doi.org/10.1002/ecs2.4335.
Cui, X., Han, W., Zhang, H., Cui, J., Ma, W., Zhang, L., Li, G. 2023. Estimating soil salinity under sunflower cover in the Hetao Irrigation District based on UAV remote sensing. Land Degradation and Development. 34(1):84-97. https://doi.org/10.1002/ldr.4445.
Delfin, E.F., Drobnitch, S.T., Comas, L.H. 2021. Plant strategies for maximizing growth during water stress and subsequent recovery in Solanum melongena L. (eggplant). PLoS ONE. 16(9). Article e0256342. https://doi.org/10.1371/journal.pone.0256342.
Dong, N., Prentice, I.C., Wright, I.J., Wang, H., Atkin, O.K., Bloomfield, K.J., Domingues, T., Gleason, S.M., Maire, V., Onoda, Y., Poorter, H., Smith, N.G. 2022. Leaf nitrogen from the perspective of optimal plant function. Journal of Ecology. 110(11):2585-2602. https://doi.org/10.1111/1365-2745.13967.
Flynn, N.E., Stewart, C.E., Comas, L.H., Del Grosso, S.J., Schnarr, C., Schipanski, M., Von Fischer, J.C., Stuchiner, E.R., Fonte, S.J. 2022. Deficit irrigation impacts on greenhouse gas emissions under drip-fertigated maize in the Great Plains of Colorado. Journal of Environmental Quality. 51(5):877-889. https://doi.org/10.1002/jeq2.20353.
Gleason, S.M., Barnard, D.M., Green, T.R., Mackay, D.S., Wang, D.R., Ainsworth, E.A., Altenhofen, J., Banks, G.T., Brodribb, T.J., Cochard, H., Comas, L.H., Cooper, M., Creek, D., DeJonge, K.C., Delzon, S., Fritschi, F.B., Hammer, G., Hunter, C., Lombardozzi, D., Messina, C.D., Ocheltree, T., Stevens, B.M., Stewart, J.J., Vadez, V., Wenz, J.A., Wright, I.J., Zhang, H. 2022. Physiological trait networks enhance understanding of crop growth and water use in contrasting environments. Plant, Cell & Environment. 45(9):2554-2572. https://doi.org/10.1111/pce.14382.
Hunter, C., Stewart, J.J., Gleason, S.M., Pilon, M. 2022. Age dependent partitioning patterns of essential nutrients induced by copper feeding status in leaves and stems of poplar. Frontiers in Plant Science. 13. Article e930344. https://doi.org/10.3389/fpls.2022.930344.
Katimbo, A., Rudnick, D.R., DeJonge, K.C., Lo, T.H., Qiao, X., Franz, T., Nakabuye, H.N., Duan, J. 2022. Crop water stress index computation approaches and their sensitivity to soil water dynamics. Agricultural Water Management. 266. Article e107575. https://doi.org/10.1016/j.agwat.2022.107575.
Lens, F., Gleason, S.M., Bortolami, G., Brodersen, C., Delzon, S., Jansen, S. 2022. Functional xylem characteristics associated with drought-induced embolism in angiosperms. New Phytologist. 236(6):2019-2036. https://doi.org/10.1111/nph.18447.
Li, G., Cui, J., Han, W., Zhang, H., Huang, S., Chen, P., Ao, J. 2022. Crop type mapping using time-series Sentinel-2 imagery and U-Net in early growth periods in the Hetao irrigation district in China. Computers and Electronics in Agriculture. 203. Article e107478. https://doi.org/10.1016/j.compag.2022.107478.
Nakabuye, H.N., Rudnick, D., DeJonge, K.C., Lo, T.H., Heeren, D., Qiao, X., Franz, T.E., Katimbo, A., Duan, J. 2022. Real-time irrigation scheduling of maize using Degrees Above Non-Stressed (DANS) index in semi-arid environment. Agricultural Water Management. 279. Article e107957. https://doi.org/10.1016/j.agwat.2022.107957.
Nakabuye, H., Rudnick, D., DeJonge, K.C., Ascough, K.A., Liang, W., Lo, T., Franz, T., Qiao, X., Katimbo, A., Duan, J. 2023. Weather data-centric prediction of maize non-stressed canopy temperature in semi-arid climates for irrigation management. Irrigation Science. https://doi.org/10.1007/s00271-023-00863-w.
Shao, G., Han, W., Zhang, H., Wang, Y., Zhang, L., Niu, Y., Zhang, Y., Cao, P. 2022. Estimation of transpiration coefficient and aboveground biomass in maize using time-series UAV multispectral imagery. The Crop Journal. 10(5):1376-1385. https://doi.org/10.1016/j.cj.2022.08.001.
Westerband, A.C., Wright, I.J., Maire, V., Paillassa, J., Prentice, I.C., Atkin, O., Bloomfield, K.J., Cernusak, L., Dong, N., Gleason, S.M., Pereira, C.G., Lambers, H., Leishman, M.R., Malhi, Y., Nolan, R.H. 2022. Coordination of photosynthetic traits across soil and climate gradients. Global Change Biology. 29(3):856-873. https://doi.org/10.1111/gcb.16501.
Zhang, L., Zhang, H., Zhu, Q., Niu, Y. 2023. Further investigating the performance of crop water stress index for maize from baseline fluctuation, effects of environmental factors, and variation of critical value. Agricultural Water Management. 285. Article e108349. https://doi.org/10.1016/j.agwat.2023.108349.
Zhang, Y., Han, W., Zhang, H., Niu, X., Shao, G. 2022. Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach. Agricultural Water Management. 275. Article e108004. https://doi.org/10.1016/j.agwat.2022.108004.
Cai, G., Carminati, A., Gleason, S.M., Javaux, M., Ahmed, M. 2023. Soil-plant hydraulics explain the stomatal efficiency-safety tradeoff. Plant, Cell & Environment. https://doi.org/10.1111/pce.14536.