Your search found 3 records
1 Akpoti, K.; Higginbottom, T. P.; Foster, T.; Adhikari, R.; Zwart, Sander J. 2022. Mapping land suitability for informal, small-scale irrigation development using spatial modelling and machine learning in the Upper East Region, Ghana. Science of the Total Environment, 803:149959. [doi: https://doi.org/10.1016/j.scitotenv.2021.149959]
Farmer-led irrigation ; Small scale systems ; Land suitability ; Modelling ; Machine learning ; Food security ; Semiarid zones ; Groundwater ; Water availability ; Land use ; Land cover ; Soil properties ; Dry season ; Forecasting ; Reservoirs ; Population density ; Socioeconomic aspects / Ghana
(Location: IWMI HQ Call no: e-copy only Record No: H050670)
(7.61 MB)
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.
2 Higginbottom, T. P.; Adhikari, R.; Dimova, R.; Redicker, S.; Foster, T. 2021. Performance of large-scale irrigation projects in Sub-Saharan Africa. Nature Sustainability, 4:501-508. [doi: https://doi.org/10.1038/s41893-020-00670-7]
Irrigation programs ; Irrigation schemes ; Large scale systems ; Performance assessment ; Infrastructure ; Water balance ; Policies ; Political aspects ; Food security / Africa South of Sahara
(Location: IWMI HQ Call no: e-copy only Record No: H050695)
(1.83 MB)
After a 30-year hiatus, large-scale irrigation projects have returned to the development agenda in sub-Saharan Africa (SSA). However, the magnitude and drivers of past schemes’ performance remains poorly understood. We quantify the performance, measured as the proportion of proposed irrigated area delivered, of 79 irrigation schemes from across SSA by comparing planning documents with estimates of current scheme size from satellite-derived land-cover maps. We find overwhelming evidence that investments have failed to deliver promised benefits, with schemes supporting a median 16% of proposed area, only 20 (25%) delivering >80% and 16 (20%) completely inactive. Performance has not improved over six decades and we find limited relationships with commonly stated causes of failure such as scheme size and climate. We attribute these findings to political and management frameworks underpinning irrigation development in SSA. First, an emphasis on national food security promotes low-value crops, reducing economic viability. Second, proposals are unrealistically large, driven by optimism bias and political incentives. Finally, centralized bureaucracies lack the technical expertise, local knowledge and financial resources to ensure long-term maintenance. Our findings highlight the need for greater learning from past investments’ outcomes if improvements in agricultural productivity and water security across SSA are to be realized.
3 Ghansah, B.; Foster, T.; Higginbottom, T. P.; Adhikari, R.; Zwart, Sander J. 2022. Monitoring spatial-temporal variations of surface areas of small reservoirs in Ghana’s Upper East Region using Sentinel-2 satellite imagery and machine learning. Physics and Chemistry of the Earth, 125:103082. [doi: https://doi.org/10.1016/j.pce.2021.103082]
(Location: IWMI HQ Call no: e-copy only Record No: H050847)
https://www.sciencedirect.com/science/article/pii/S147470652100125X/pdfft?md5=59bd7a98182c33a44b62aaf447495217&pid=1-s2.0-S147470652100125X-main.pdf
https://vlibrary.iwmi.org/pdf/H050847.pdf
https://vlibrary.iwmi.org/pdf/H050847.pdf
(9.19 MB) (9.19 MB)
Small reservoirs are one of the most important sources of water for irrigation, domestic and livestock uses in the Upper East Region (UER) of Ghana. Despite various studies on small reservoirs in the region, information on their spatial-temporal variations is minimal. Therefore, this study performed a binary Random Forest classification on Sentinel-2 images for five consecutive dry seasons between 2015 and 2020. The small reservoirs were then categorized according to landscape positions (upstream, midstream, and downstream) using a flow accumulation process. The classification produced an average overall accuracy of 98% and a root mean square error of 0.087 ha. It also indicated that there are currently 384 small reservoirs in the UER (of surface area between 0.09 and 37 ha), with 20% of them newly constructed between the 2016-17 and 2019-20 seasons. The study revealed that upstream reservoirs have smaller sizes and are likely to dry out during the dry season while downstream reservoirs have larger sizes and retain substantial amounts of water even at the end of the dry season. The results further indicated that about 78% of small reservoirs will maintain an average of 54% of their water surface area by the end of the dry season. This indicates significant water availability which can be effectively utilized to expand dry season irrigation. Overall, we demonstrate that landscape positions have significant impact on the spatial-temporal variations of small reservoirs in the UER. The study also showed the effectiveness of remote sensing and machine learning algorithms as tools for monitoring small reservoirs.
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