Regional Centre for Energy and Environmental Sustainability (RCEES), University of Energy and Natural Resources (UENR), Sunyani, Ghana.
Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, UK.
Sci Total Environ. 2022 Jan 10;803:149959. doi: 10.1016/j.scitotenv.2021.149959. Epub 2021 Aug 27.
Small-scale irrigation has gained momentum in recent years as one of the development priorities in Sub-Saharan Africa. However, farmer-led irrigation is often informal with little support from extension services and a paucity of data on land suitability for irrigation. To map the spatial explicit suitability for dry season small-scale irrigation, we developed a method using an ensemble of boosted regression trees, random forest, and maximum entropy machine learning models for the Upper East Region of Ghana. Both biophysical predictors including surface and groundwater availability, climate, topography and soil properties, and socio-economic predictors which represent demography and infrastructure development such as accessibility to cities and proximity to roads were considered. We assessed that 179,584 ± 49,853 ha is suitable for dry-season small-scale irrigation development when only biophysical variables are considered, and 158,470 ± 27,222 ha when socio-economic variables are included alongside the biophysical predictors, representing 77-89% of the current rainfed-croplands. Travel time to cities, accessibility to small reservoirs, exchangeable sodium percentage, surface runoff that can be potentially stored in reservoirs, population density, proximity to roads, and elevation percentile were the top predictors of small-scale irrigation suitability. These results suggested that the availability of water alone is not a sufficient indicator for area suitability for small-scale irrigation. This calls for strategic road infrastructure development and an improvement in the support to farmers for market accessibility. The suitability for small-scale irrigation should be put in the local context of market availability, demographic indicators, and infrastructure development.
近年来，小规模灌溉作为撒哈拉以南非洲地区的发展重点之一，得到了迅速发展。然而，农民主导的灌溉往往是非正式的，几乎没有来自推广服务的支持，而且对灌溉土地适宜性的数据也很少。为了绘制旱季小规模灌溉的空间适宜性图，我们为加纳上东部地区开发了一种使用集成提升回归树、随机森林和最大熵机器学习模型的方法。考虑了生物物理预测因子，包括地表水和地下水的可用性、气候、地形和土壤特性，以及代表人口和基础设施发展的社会经济预测因子，如城市可达性和道路接近度。我们评估仅考虑生物物理变量时，适合旱季小规模灌溉发展的面积为 179584 ± 49853 公顷，而当将社会经济变量与生物物理预测因子一起考虑时，适合旱季小规模灌溉发展的面积为 158470 ± 27222 公顷，分别占当前雨养农田的 77-89%。到城市的旅行时间、小水库的可达性、可交换钠百分比、可潜在储存在水库中的地表径流、人口密度、道路接近度和海拔百分位数是小规模灌溉适宜性的最重要预测因子。这些结果表明，水的可用性本身并不是确定小规模灌溉适宜性的充分指标。这需要进行战略道路基础设施开发，并改善对农民的支持以提高市场可达性。小规模灌溉的适宜性应根据市场可用性、人口指标和基础设施发展情况，放在当地背景下考虑。
