Benchmarking farm-level cotton water productivity using on-farm irrigation measurements and remotely sensed yields

Abstract

Benchmarking farm-level irrigation water productivity (WPI) and water productivity (WP) can assist in understanding the irrigation effectiveness of individual farms and in developing strategies to improve their irrigation management. This study introduces a method to integrate on-farm irrigation measurements, remotely sensed yields and publicly available rainfall data for multi-year farm-level WPI and WP benchmarking. The method was tested over cotton farms located in south-eastern Australia during the 2011–19 cropping seasons. We trained remote sensing (RS)-based machine learning (ML) models – Random Forest Regression (RFR), Gradient Boosting Regression (GBR) and Support Vector Regression (..