There is a standing argument in the world of precision irrigation: are management zones (MZs) inherently static, influenced mainly by soil and elevation dictated properties, or are they dynamic, with possible changes to the nature of spatially variable crop features dictating locally different irrigation strategies or amounts occurring over time, even within growing seasons?
We addressed this question using a corn-cotton rotation in a variable rate center pivot sprinkler system at the Gilat Research Center of Israel’s Agricultural Research Organization – Volcani Institute. Drone –acquired multispectral and thermal images were used to generate normalized difference vegetation index (NDVI) and crop water stress index (CWSI) maps weekly over three irrigation seasons. The maps were used to make weekly decisions regarding number and location of MZs in three large replicate areas of the 12.5 hectare field. Each MZ was irrigated according to best practice protocol depending on crop and growth stage.
The dynamic MZs were compared to static MZs determined using multi-parameter statistics via machine learning based on elevation data (topographic wetness index), soil data (apparent electrical conductivity), past year’s representative NDVI and past year’s yield and using crop water status (as stem water potential and/or CWSI) as a predicted factor. The MZ schemes were also compared to a control treatment irrigated uniformly.
Results showed the advantage of MZ-based precision irrigation over uniform irrigation in reducing field-scale variability and increasing yield and a slight advantage of dynamic over static approaches, which may not be justified economically.