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

Abstract

Abstract. 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.

Evapotranspiration estimation using Surface Energy Balance Model and medium resolution satellite data: An operational approach for continuous monitoring

“…7 ), undetected cloud and cloud shadow pixels, coarse spatial resolution and the difference in interpolation techniques used in WaPOR database. WaPOR level 1 data is reported to be underestimating the ET a values due to the coarse resolution of input land surface temperature data (1 km) from MODIS sensor which is used to derive moisture stress and thus affecting the spatial variation 60 . A recent study evaluating the consistency between different levels of WaPOR data found higher correlation between level 1 AETI and the field observations over Zankalon irrigated area in Egypt 61 .…”

Satellite ET-based irrigation performance: Strategies to increase rainfed crops production in the lower Baro watershed, Ethiopia

“…Additionally, a linear association between sugarcane crop production and ET was discovered, with a slope of around 10 kg•m -3 and a corresponding value of 1.3 kg•m -3 (Karimi et al, 2019). The typical annual ET for a different producer cluster of crops was 1.5 Mg, compared to the typical annual value of 1.3 Mg for a crop, such as wheat or maize (Chukalla et al, 2022). A fixed value of 2.7 was applied to cropland in WaPOR and, through this interface, the value was multiplied by 1.8 for C4 crops, which had a higher LUE as shown in Table 3.…”

“…The WaPOR for 10-year real data shows that for each subcatchment of Baro Gambella, Gog, Gilo Fugnido, Birbir Yubdo, Baro Gambella, Gumero Gore, Sor Metu, and Baro Itang was 2.54, 1.3, 1.21, 0.96, 0.96, 0.96, and 0.95 kg•m -3 . The 4-year seasonal average WaPOR-based yield was 89 Mg•ha -1 (86 Mg•ha -1 for regions with furrow irrigation, 88 Mg•ha -1 for areas with sprinkler irrigation, and 93 Mg•ha -1 for areas with center pivot irrigation) (Chukalla et al, 2022). The average CV for metrics was determined as the performance of various clusters.…”

Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review

“…Ref. [108] presented complementary limitations regarding the use of RS: spatial resolution; land cover noise of non-sugarcane land use, such as farm roads and irrigation and drainage infrastructures within a pixel; the number of cloud-free images on which the analysis and numerical interpolation are based; the time of day when images are taken; and the angle of image capture and its correction function. Ref.…”