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ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Soil and Water Management Research » Research » Research Project #437535

Research Project: Spatiotemporal Decision Support Systems for Recognizing Variability and Managing Precision Irrigation in Field Crops

Location: Soil and Water Management Research

Project Number: 3090-13000-016-049-T
Project Type: Trust Fund Cooperative Agreement

Start Date: Dec 1, 2019
End Date: Jun 30, 2023

Characterize the spatial variability within center-pivot irrigated fields to produce irrigation prescription maps for optimal crop water requirements using precision irrigation.

ARS scientists will estimate spatiotemporal variability in plant available water and crop evapotranspiration in potatoes and sorghum using infrared thermometry, soil water sensors and spectral reflectance from UAV-mounted sensors for potato and grain sorghum crops grown under a 3-span variable rate irrigation (VRI) center pivot system located at USDA-ARS, Bushland. The field will be divided into management zones (MZs) and irrigation treatment levels (designated Full, Mild Deficit, and Severe Deficit) will be applied to designated MZs to establish spatial and temporal variability in plant available water. Soil water sensing stations will be strategically located in non-contiguous homogeneous MZs, and canopy temperature measurements from IRTs positioned on a moving sprinkler and from stationary canopy temperature sensors located in each type of management zone will be made. Remote sensing data will be collected on a weekly basis (weather permitting) using a UAV consisting of a DJI Matrice 600 pro hexacopter, Micasense RedEdge multispectral sensor, and DJI Zenmuse XT thermal sensor. Calibration panels of known reflectance will be used to calibrate the reflectance data at the time of each flight. Agisoft PhotoScan will be used for image stitching and radiometric calibration. ARS scientists will develop high-resolution ET maps to characterize spatial variability using existing remote sensing models such as METRIC (Allen et al., 2007) and ground-truthing will be by canopy temperature, leaf area, plant height and soil water sensing. At the end of the season maps of crop water productivity will be compared with predictions by BYU modeling using soil water sensing, stationary canopy temperature measurements and climatic data. Spatial statistics will be used to assess the benefit of the existing in-situ soil water sensors and stationary IRTs. Recommendations will be made as to the optimal siting of stationary sensors and the cost-benefit ratio relative to crop water productivity.