A framework for irrigation performance assessment using WaPOR data: the case of a sugarcane estate in Mozambique

• Published in: Hydrology and earth system sciences Vol. 26; no. 10; pp. 2759 - 2778
• Main Authors: Chukalla, Abebe D, Mul, Marloes L, van der Zaag, Pieter, van Halsema, Gerardo, Mubaya, Evaristo, Muchanga, Esperança, den Besten, Nadja, Karimi, Poolad
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 25-05-2022; Copernicus Publications
• Subjects: Adequacy; Agricultural production; Analysis; Aquatic resources; Biomass; Business metrics; Climate; Datasets; Drought; Frameworks; Furrow irrigation; Furrows; Indicators; Irrigation; Irrigation water; Methods; Performance assessment; Performance testing; Precipitation; Productivity; Rain; Remote monitoring; Remote sensing; Seasons; Spatial discrimination; Spatial resolution; Sugarcane; Water resources; Mozambique
• ISSN: 1607-7938; 1027-5606; 1607-7938
• DOI: 10.5194/hess-26-2759-2022

The growing competition for finite land and water resources and the need to feed an ever-growing population require new techniques to monitor the performance of irrigation schemes and improve land and water productivity. Datasets from FAO's portal to monitor Water Productivity through Open access Remotely sensed derived data (WaPOR) are increasingly applied as a cost-effective means to support irrigation performance assessment and identify possible pathways for improvement. This study presents a framework that applies WaPOR data to assess irrigation performance indicators, including uniformity, equity, adequacy, and land and water productivity differentiated by irrigation method (furrow, sprinkler, and centre pivot) at the Xinavane sugarcane estate, Mozambique. The WaPOR data on water, land, and climate are in near-real time and spatially distributed, with the finest spatial resolution in the area of 100 m. The WaPOR data were first validated agronomically by examining the biomass response to water, and then the data were used to systematically analyse seasonal indicators for the period 2015 to 2018 on ∼8000 ha. The WaPOR-based yield estimates were found to be comparable to the estate-measured yields with ±20 % difference, a root mean square error of 19±2.5 t ha−1 and a mean absolute error of 15±1.6 t ha−1. A climate normalization factor that enables the spatial and temporal comparison of performance indicators are applied. The assessment highlights that in Xinavane no single irrigation method performs the best across all performance indicators. Centre pivot compared to sprinkler and furrow irrigation shows higher adequacy, equity, and land productivity but lower water productivity. The three irrigation methods have excellent uniformity (∼94 %) in the four seasons and acceptable adequacy for most periods of the season except in 2016, when a drought was observed. While this study is done for sugarcane in one irrigation scheme, the approach can be broadened to compare other crops across fields or irrigation schemes across Africa with diverse management units in the different agroclimatic zones within FAO WaPOR coverage. We conclude that the framework is useful for assessing irrigation performance using the WaPOR dataset.