Location: Water Management and Systems Research2017 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.
This report is for a new project which began February 2017, and continues research from 3012-13000-007-00D, "Management strategies to sustain irrigated agriculture with limited water supplies". Please see the report for more information. As this new project just began, there is limited progress to report in FY17. Objective 1: Six maize genotypes varying in drought tolerance were grown in the greenhouse under either full water or drought conditions, and assessed for growth, photosynthesis, respiration, stomatal conductance, electron transport, root pressure, and capacity for hydraulic conductance. Results suggest that differences in grain production under drought conditions previously measured in the field among the genotypes were best associated with plant hydraulics. Eight maize genotypes varying in drought tolerance and yield stability under drought were planted in the field across three irrigation treatments, with each genotype by irrigation treatment replicated four times. With an absence of significant rainfall since planting, desired stress on the planted maize is being achieved. Several traits thought to be associated with drought tolerance or improved water use efficiency have been measured. These measured traits include leaf conductance, decline of leaf conductance with water stress, leaf water potential, sap-flow, root pressure, and the change in stomatal conductance with time and atmospheric vapor pressure deficit. Objective 2: In this initial season of the project, we are testing six irrigation scheduling methods at two levels of water stress. The irrigation scheduling experiment was planted successfully, and continuous field cameras and infrared thermometers have been installed to monitor canopy cover and temperature, respectively. We are currently awaiting delivery of additional new infrared thermometers that can interface with a real-time smartphone application to process and display canopy temperature data, including algorithms developed by Water Management and Systems Research Unit staff in Fort Collins. Testing of Unmanned Aircraft Systems (UAS) is currently underway. A quadcopter with a 4K Red-Green-Blue (RGB) camera is used to collect RGB imagery one or two days before scheduled irrigation day from each plot. We processed RGB images to collect canopy cover within 24 hr. A hex-copter integrated with a multispectral camera and a thermal camera is used to collect normalized difference vegetation index (NDVI) and surface temperatures, with data available after 36 hrs. Data will be used to explain the response of different maize genotypes to water treatment. Despite a variety of issues that can prevent the collection of good images, we are able to collect good images weekly so far.
1. Assessed linkages among plant physiological systems and plant performance under water stress. Scientists at ARS in Fort Collins, Colorado discovered that the decline of hydraulic, photosynthetic, and stomatal systems in maize is closely aligned during water stress. This study provides a contextual framework to guide plant geneticists and breeders in improving crop productivity under water stress. This study demonstrates the coordination of broad physiological systems in maize during stress and suggests that improvements to one trait in isolation of other linked traits (e.g., cold shock proteins, photosynthetic efficiency, membrane integrity) may result in small improvements to plant performance under drought, but that an approach that embraces the “whole-plant” perspective will be needed before marked improvements in drought tolerance are achieved.
Fang, Q.X., Ma, L., Ahuja, L.R., Trout, T.J., Malone, R.W., Zhang, H. 2017. Long-term simulation of growth stage-based irrigation scheduling in maize under various water constraints in Colorado, USA. Frontiers of Agricultural Science and Engineering. doi:10.15302/J-FASE-2017139.
Trout, T.J., DeJonge, K.C. 2017. Water productivity of maize in the U.S. high plains. Irrigation Science. 35(3):251-266.
Zhang, H., Wang, D., Gartung, J.L. 2017. Influence of irrigation scheduling using thermometry on peach tree water status and yield under different irrigation systems. Agronomy Journal. 7(1):12. doi: 10.3390/agronomy7010012.
Zhang, H., Han, M., Chavez, J.L., Lan, Y. 2017. Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance modelents into a soil water. International Journal of Agricultural and Biological Engineering. 10:37-46.
Kullberg, E.G., DeJonge, K.C., Chavez, J.L. 2016. Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients. Agricultural Water Management. doi: 10.1016/j.agwat.2016.07.007.
Han, M., Zhang, H., DeJonge, K.C., Comas, L.H., Trout, T. 2016. Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agricultural Water Management. 177:400-409. doi:10.1016/j.agwat.2016.08.031.
Stewart, C.E., Roosendaal, D.L., Denef, K., Pruessner, E.G., Comas, L.H., Sarath, G., Jin, V.L., Schmer, M.R., Soundararajan, M. 2017. Seasonal switchgrass ecotype contributions to soil organic carbon, deep soil microbial community composition and rhizodeposit uptake during an extreme drought. Soil Biology and Biochemistry. 112:191-203. doi:10.1016/j.soilbio.2017.04.021.