IRRIGATION MANAGEMENT AND AUTOMATION FOR INCREASED WATER USE EFFICIENCY
Location: Soil and Water Management Research
Project Number: 6209-13000-012-00
Start Date: Jan 10, 2007
End Date: May 19, 2010
Determine methods for improved quantification of evapotranspiration (ET) and crop coefficients under all constraints in order to improve irrigation scheduling and water use efficiency. Develop remote sensing technologies and tools designed for improved prediction of crop water use and water stress at field and watershed spatial scales. Develop, test, and implement feedback systems for spatially and temporally variable irrigation application of water and nutrients, and develop, test and implement improved sensors for soil water content and plant stress. Develop and validate remote sensing technologies and procedures to enhance spatially and temporally variable crop water status feedback systems for use in variable rate irrigation systems. Quantify and improve crop water use efficiency in dryland/irrigated cropping systems in relation to tillage, irrigation, and crop management practices.
Research approaches include determinations of crop water use by soil water balance techniques (weighing lysimeters and neutron scattering methods) in practically all experiments, which include variations in irrigation method (subsurface drip at several depths and spacings, sprinkler, and low energy precision application or LEPA), irrigation amount (full and two to three levels of deficit), tillage (no-tillage, conventional, strip till, etc.), and/or crop and crop rotation, including rotation between irrigated and dryland cropping. Automatic irrigation systems based on sensing of crop status are designed/engineered and tested for ability to control crop water use efficiency and yield, thus reducing management expense (time and effort) while allowing management to control irrigation for best profitability and optimum water use. Key in this effort is evaluation and design of new crop and soil water status sensors. Remote sensing approaches to water use prediction are expected to improve energy balance modeling methods to make them useful for managers at farm, irrigation project, and watershed scales, and for policy makers.