Translating open-source remote sensing data to crop water productivity improvement actions

Abstract

A widely promoted approach to tackle food insecurity and water shortage challenges simultaneously is to enhance crop water productivity (WP). Therefore, multiple international organizations have featured WP improvements as their major policy goal, and substantial public and private investments have been made in this domain. Advances in remote sensing allow accurate, rapid, and cost-effective WP analysis for agricultural monitoring. However, translating the data to actionable information seems fraught with difficulties, as it only provides spatial and temporal variability in WP and no information on the causes of the variability. This paper introduces a standard approach using open-source remote sensing data for diagnosing reasons behind WP variations, comparing high performing fields (bright spots) with low performing fields (hotspots). The framework is applied to a case study on the Bekaa Valley in Lebanon considering wheat, potato and table grapes. Six factors (crop water stress, irrigation uniformity, soil salinity, nitrogen application, crop rotation and soil type) were analysed to identify their influence on WP and yield. This paper reveals that the growth of wheat and potatoes is negatively affected by water stress in the critical crop growth stages, non-uniform irrigation and nitrogen stress. Also, it was found that potatoes grown on clay-loam soil has better WP and yield than potatoes grown loam soil. Such information with regard to WP factors assists practitioners to identify priority areas and actions aiming at cropfield level WP improvement. While acknowledging errors, uncertainties and caveats inherent to the use of remote sensing data, this paper shows the feasibility and practical usefulness of the diagnostic framework.