Abstract
Accurate information on irrigated areas' spatial distribution and extent are crucial in enhancing agricultural water productivity, water resources management, and formulating strategic policies that enhance water and food security and ecologically sustainable development. However, data are typically limited for smallholder irrigated areas, which is key to achieving social equity and equal distribution of financial resources. This study addressed this gap by delineating disaggregated smallholder and commercial irrigated areas through the random forest algorithm, a non-parametric machine learning classifier. Location within or outside former apartheid "homelands" was taken as a proxy for smallholder, and commercial irrigation. Being in a medium rainfall area, the huge irrigation potential of the Inkomati-Usuthu Water Management Area (UWMA) is already well developed for commercial crop production outside former homelands. However, information about the spatial distribution and extent of irrigated areas within former homelands, which is largely informal, was missing. Therefore, we first classified cultivated lands in 2019 and 2020 as a baseline, from where the Normalised Difference Vegetation Index (NDVI) was used to distinguish irrigated from rainfed, focusing on the dry winter period when crops are predominately irrigated. The mapping accuracy of 84.9% improved the efficacy in defining the actual spatial extent of current irrigated areas at both smallholder and commercial spatial scales. The proportion of irrigated areas was high for both commercial (92.5%) and smallholder (96.2%) irrigation. Moreover, smallholder irrigation increased by over 19% between 2019 and 2020, compared to slightly over 7% in the commercial sector. Such information is critical for policy formulation regarding equitable and inclusive water allocation, irrigation expansion, land reform, and food and water security in smallholder farming systems.
Highlights
The impacts of climate change, such as increasing temperatures, frequency, and intensity of drought, and flooding, coupled with population growth, urbanisation, land degradation, and improper agricultural practices, are compounding the existing food and water insecurity challenges [1,2]
This study developed a more accurate irrigated area dataset for the Inkomati-Usuthu Water Management Area (IUWMA), South Africa, using a combination of the random forest classifier, Google Earth Engine (GEE), and the R-programming language
As a strategy, irrigated agriculture could play a role in poverty alleviation, helping the Southern African Development Community (SADC) region’s member states move closer to achieving food security and Sustainable Development Goals (SDGs) 2, in particular, creating employment opportunities, and encouraging economic growth in the SADC region
Summary
The impacts of climate change, such as increasing temperatures, frequency, and intensity of drought, and flooding, coupled with population growth, urbanisation, land degradation, and improper agricultural practices, are compounding the existing food and water insecurity challenges [1,2]. Knowledge of the current spatial extent and dynamic changes in irrigated land is important to inform policy and decision making in formulating coherent strategies on water allocation, agricultural water management, regulating land and water use, and directing irrigation infrastructure investment and development [17] This information is scant, compromising the sustained and transformational change needed in the agriculture sector to enhance water and food security, and socio-ecological sustainability [11,18]. There are downstream obligations of water flow and water quality to Mozambique (Sabi and Komati) and Swaziland (Usuthu Rivers); all the available freshwater resources are almost all allocated [38] We used this increased understanding to inform policy and guide decision making on informed strategies on sustainable irrigation expansion for commercial and smallholder sectors and accounting for the possible impacts of the stochastic events, such as those represented by the COVID-19 pandemic, on agriculture and agricultural water use
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