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通过小农户农业系统灌溉区的准确空间信息为公平的水和粮食政策提供信息。

Informing Equitable Water and Food Policies through Accurate Spatial Information on Irrigated Areas in Smallholder Farming Systems.

作者信息

Magidi James, van Koppen Barbara, Nhamo Luxon, Mpandeli Sylvester, Slotow Rob, Mabhaudhi Tafadzwanashe

机构信息

Geomatics Department, Tshwane University of Technology, Pretoria 0001, South Africa.

International Water Management Institute (IWMI), Southern Africa Office, Pretoria 0184, South Africa.

出版信息

Water (Basel). 2021 Dec 16;13(24):3627. doi: 10.3390/w13243627.

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.

摘要

准确掌握灌溉区域的空间分布和范围,对于提高农业用水效率、水资源管理以及制定增强水和粮食安全及生态可持续发展的战略政策至关重要。然而,小农户灌溉区域的数据通常有限,而这对于实现社会公平和财政资源的公平分配至关重要。本研究通过随机森林算法(一种非参数机器学习分类器)划分小农户和商业灌溉区域,填补了这一空白。以前种族隔离“家园”内外的位置被用作小农户灌溉和商业灌溉的代理指标。因处于中等降雨地区,因科马蒂 - 乌苏图水管理区(UWMA)在以前家园以外的商业作物生产方面,其巨大的灌溉潜力已得到充分开发。然而,以前家园内灌溉区域的空间分布和范围信息(大多是非正式的)却缺失。因此,我们首先将2019年和2020年的耕地作为基线进行分类,在此基础上利用归一化植被指数(NDVI)区分灌溉农田和雨养农田,重点关注作物主要依靠灌溉的干燥冬季。84.9%的制图精度提高了在小农户和商业空间尺度上界定当前灌溉区域实际空间范围的效能。商业灌溉(92.5%)和小农户灌溉(96.2%)的灌溉区域比例都很高。此外,2019年至2020年期间,小农户灌溉面积增加了19%以上,而商业部门仅略高于7%。这些信息对于制定关于小农户农业系统中公平和包容性水资源分配、灌溉扩展、土地改革以及粮食和水安全的政策至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c54/7615039/64495d324c4c/EMS187260-f001.jpg

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