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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Research Project #432342

Research Project: Improving the Sustainability of Irrigated Farming Systems in Semi-Arid Regions

Location: Water Management and Systems Research

2019 Annual Report

1. Improve water use efficiency (WUE) by identifying plant traits, mechanisms, and agronomic practices that increase productivity per unit of water used by the crop. 2. Develop simple and accurate methods to quantify evapotranspiration (ET) in agricultural systems under limited water availability to improve the efficiency of irrigation scheduling. 3. Create Water Production Functions (WPF, yield per ET) for alternative crops under limited water availability.

Increased productivity of cropping systems as well as yield stability is vital to meet the challenge of expanding human populations and increased needs for food and fiber. Effective management of cropping systems and irrigation water will depend on our ability to maximize crop water productivity (yield per unit water used by the crop). This, in turn, requires a better understanding and evaluation of complex plant traits, better management of interacting agricultural inputs, and better tools to more efficiently manage agricultural water supplies, especially in the face of greater competition and less water availability. Finally, there is increased efficiency at the farm scale that can be realized with better farm-scale decision making. The overarching goal of this research is to improve the sustainability of irrigated farming systems for agronomic producers in semi-arid and arid regions. These producers vary both in control over the timing and amount of irrigation, and in methods of irrigation; thus multi-faceted solutions are required. Solutions are in three parts: 1) increasing the knowledge base of plant traits, mechanisms and agronomic practices related to crop productivity under limited water; 2) developing tools to assist with real-time decision making for irrigation management; and 3) developing information and tools for farm-scale decision-making regarding crop selection, land area partitioning among crops, and within-farm irrigation distribution. This research will lead to better understanding of crop physiology needed to improve germplasm, increased productivity of cropping systems, and improved irrigation management.

Progress Report
Objective 1. Hydraulic conductance and xylem anatomy were measured across 125 winter wheat genotypes and 100 maize genotypes (inbred lines) in the field, in collaboration with geneticists at Cornell and Colorado State University. Additionally, root pressure was measured across eight maize genotypes in the field. The traits measured in these experiments are known to confer drought resistance/tolerance. The majority of genotypes used are part of Nested Mapped Populations (NAM), such that if strong trait-performance linkages can be found, the genetic code underpinning these linkages could also be determined. Although the hydraulic conductance and root pressure measurements have been completed, the xylem anatomy is a labor-intensive measurement and is still underway (using preserved samples and electron microscopy) and is expected to be completed in September 2019. We have also been working towards developing an indirect method to accurately quantify xylem anatomy in maize stems using carbon-based nanoparticles. This new methodology is moving forward in collaboration with Colorado State University. In parallel with these projects, but also directly addressing Objective 1, we have helped to improve and implement a plant growth model that allows for the explicit manipulation of hydraulic parameters (e.g., increasing water transport efficiency) and the impact of these manipulations on crop performance under different climate and irrigation scenarios. This project represents an international collaboration between the USDA-ARS, the National Institute for Agricultural Research (France), and the University of Tasmania (Australia). Given the complexity of plant trait interactions (hydraulics, anatomy, root pressure, stomatal conductance, growth), we have also developed a quantitative method for assessing the relative importance of plant traits on growth under different climate scenarios. The results of these research efforts (all addressing Objective 1) will be presented at invited talks (contributed travel) at conferences in Padua (Italy), San Antonio, University of Colorado, and the University of Guam this year. Objective 2. A hex-copter equipped with a multispectral camera and a thermal camera was used to collect multispectral and thermal imagery one or two days before the scheduled irrigation day from each plot in 2018. Canopy cover and crop water use was calculated from multispectral imagery within 24 hours to assist in determining irrigation amount. Canopy temperature measured by thermal imagery and vegetation indices from multispectral imagery were used to calculate crop evapotranspiration. Due to hard landing of the Unmanned Aerial Vehicle (UAV) system in mid-August, imagery was not available after August 16, 2018. We have analyzed available UAV images and compared with sap flow measurements. An irrigation scheduling experiment for corn was fully established in 2019 using four irrigation control methods for three levels of water availability, one using a traditional soil water balance approach and three using canopy temperature-based crop water stress indices. Canopy temperature data was collected using UAV thermal imagery typically twice per week, and using stationary infrared thermometers (IRTs) with continuous measurement. Canopy cover was measured using multispectral images typically twice per week using UAV, and on the ground smartphone-based images using a new Canopeo phone app developed by Oklahoma State University. We will assess optimum methods for full and deficit irrigation scheduling.

1. Developed global-level understanding of crop drought response to improve maize production under deficit irrigation and dryland conditions. Through the application of field, greenhouse, and laboratory experiments, ARS scientists in Fort Collins, Colorado, improved the scientific understanding of maize (corn) response to drought and determined which plant-level traits (roots, xylem, stomata) can be expected to improve the performance of maize in dry environments. This research, conducted in close collaboration with scientists in the United States, France, Australia, and China, improved the understanding of traits that enable higher water extraction efficiency. These advances have arisen from theoretical, empirical, and modelling approaches to this problem and have resulted in many high-impact publications and invitations to numerous high-profile national and international scientific meetings. This important contribution, as part of a long-term, international effort to improve dryland and irrigated maize production, will benefit maize producers world-wide.

2. Developed new technology to apply multispectral unmanned aerial systems for irrigation management. In arid areas where agricultural production relies on irrigation, there is a need to accurately map crop water status and actual crop water use (evapotranspiration) to manage irrigation efficiently. Unmanned Aerial Systems (UAS) can fly frequently and acquire very high spatial resolution images to monitor crop water stress and make near real-time decisions. ARS scientists in Fort Collins, Colorado, in collaboration with scientists at Colorado State University and the Northwest Agriculture and Forestry University (China), tested multispectral UAS over irrigated corn fields under different levels of irrigation at different growth stages and evaluated the capabilities of the UAS on irrigation management. We developed models between crop water stress index and several vegetation indices and suggested that crop water stress index values retrieved from coupled regression models better assessed field variability of crop and soil conditions. Importantly, this research demonstrated that using reflectance-based crop coefficients from very high-resolution remote sensing imagery from a UAS platform can accurately estimate actual crop transpiration. These findings benefit producers by improving irrigation water management in systems with variable rate irrigation capabilities.

Review Publications
Wang, J., Lan, Y., Zhang, H., Wen, S., Yao, W., Yue, C. 2018. Drift and deposition of pesticide applied by UAV on pineapple plants under different meteorological conditions. International Journal of Agricultural and Biological Engineering. 11(6):5–12.
Huasheng, H., Lan, Y., Deng, J., Yang, A., Zhang, H., Zhang, L. 2019. Detection of Helminthosporium Leaf Blotch disease based on UAV RGB imagery. Applied Sciences. 9(3):558.
DeJonge, K.C., Zhang, H., Gleason, S.M. 2019. Simple background subtraction of thermal imagery for crop water stress detection in greenhouse. Applied Engineering in Agriculture. 35(3):339-344.
Varzi, M.M., Trout, T.J., DeJonge, K.C., Oad, R. 2019. Optimal water allocation under deficit irrigation in the context of Colorado water law. Journal of Irrigation and Drainage Engineering. 145(5).
Thorp, K.R., Marek, G.W., DeJonge, K.C., Evett, S.R., Lascano, R.J. 2019. Novel methodology to evaluate and compare evapotranspiration algorithms in an agroecosystem model. Journal of Environmental Modeling and Software. 199:214-227.
Comas, L.H., Trout, T.J., DeJonge, K.C., Zhang, H., Gleason, S.M. 2018. Water productivity under strategic growth stage-based deficit irrigation in maize. Agricultural Water Management. 212:433-440.
Gleason, S.M., Cooper, M.S., Wiggans, D.R., Bliss, C.A., Romay, C.M., Gore, M.A., Mickelbart, M.V., Topp, C., Zhang, H., DeJonge, K.C., Comas, L.H. 2019. Stomatal conductance, xylem water transport, and root traits underpin improved performance under drought and well-watered conditions across a diverse panel of maize inbred lines. Field Crops Research. 234:119-128.
Liu, H., Gleason, S.M., Hao, G., Hua, L., He, P., Goldstein, G., Ye, Q. 2019. Hydraulic traits are coordinated with maximum plant height at the global scale. Science Advances. 5(2):1-14.
Zhang, L., Zhang, H., Niu, Y., Han, W. 2019. Mapping maize water stress based on UAV multispectral remote sensing. Remote Sensing. 11(6):605.
Niu, Y., Zhang, L., Zhang, H., Han, W., Peng, X. 2019. Estimating above-ground biomass of maize using features derived from UAV-based RGB imagery. Remote Sensing. 11(11):1261.
Zhang, Y., Zhang, L., Zhang, H., Song, C., Lin, G., Han, W. 2019. Crop coefficient estimation method of maize by UAV remote sensing and soil moisture monitoring. Transactions of the Chinese Society of Agricultural Engineering. 35(1):83-89. https://doi: 10.11975/j.issn.1002-6819.2019.01.010.
Wang, L., Lan, Y., Zhang, Y., Zhang, H., Tahir, M.N., Ou, S., Liu, X., Chen, P. 2019. Application and prospect of agricultural UAV’s obstacle avoidance technology in China. Sensors. 19(3):642.
Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., Wen, S., Zhang, H., Zhang, Y. 2018. Accurate weed mapping and prescription map generation based on fully convolutional networks using UAV imagery. Sensors. 18(10):3299.
Zhang, H., Malone, R.W., Ma, L., Ahuja, L.R., Anapalli, S.S., Marek, G.W., Gowda, P.H., Evett, S.R., Howell, T.A. 2018. Modeling evapotranspiration and crop growth of irrigated and non-irrigated corn in the Texas high plains using RZWQM. Transactions of the ASABE. 61(5):1653-1666.
Malone, R.W., Herbstritt, S., Ma, L., Richard, T., Cibin, R., Gassman, P., Zhang, H., Karlen, D.L., Hatfield, J.L., Obrycki, J., Helmers, M., Jaynes, D.B., Kaspar, T.C., Parkin, T.B. 2019. Corn stover harvest and N losses in central Iowa. Science of the Total Environment. 663:776-792.
Comas, L.H., Trout, T.J., Banks, G.T., Zhang, H., DeJonge, K.C., Gleason, S.M. 2018. USDA-ARS Colorado maize growth and development, yield and water-use under strategic timing of irrigation, 2012-2013. Data in Brief. 21:1227-1231.