Diagnostic analysis of the spatial and temporal performance variation of large-scale agricultural projects : A case study of Gezira Irrigation Scheme, Sudan

Water scarcity is mostly driven by global climate change and population expansion. Accordingly, water scarcity is undoubtedly one of the main challenges to achieving the sustainable development goals of zero hunger (SDG 2) and clean water and sanitation (SDG 6). At the same time, water and food security are intimately linked. As a result, the "produce more with less" approach is significantly risen to aid in expanding the agricultural areas while considering the land and water resources limitations. Hence, improving the land and water productivity of current agricultural lands is the optimal opportunity for boosting agricultural production to meet the world's rising demand. This research aimed at assessing the performance of irrigation indicators by detecting spatial and temporal variation. The Food and Agriculture Organization (FAO) portal to monitor Water Productivity through Open access of Remotely sensed derived data (WaPOR) was utilized as a cost-effective Remote Sensing (RS) data coupled with ground-based data to compute the performance indicators. The Gezira irrigation scheme was selected as a case study to benefit the scheme, Sudan, the Nile basin, and any similar irrigation scheme globally. Moreover, the scheme has an area covered by level 3 of the WaPOR. As a result, the research was expanded to include two scheme areas; the WaPOR layers were utilized for level 3, covering 2009 to 2020. At the same time, the local crop maps were provided by the Hydraulic Research Center with high ground resolution and covered the winter seasons of 2018-2019, 2019-2020, and 2020-2021 and combined with WaPOR level 2 data to assess the entire scheme. The performance indicators were selected to fulfill the study's main objectives using the WaPOR layers and ground-based data. Hence, the list of the indicators included adequacy, uniformity, beneficial fraction, and land and crop water productivity. The performance analysis was implemented for irrigated wheat as a major winter crop. The WaPOR land cover classification (LCC) was compared to the local crop map; the LCC map is inaccurately classified and requires more enhancement. After that, the performance indicators were computed and presented in temporal and spatial form for level 3 and level 2, respectively. The performance indicators for the scheme were associated with the irrigation and agriculture divisions. In general, season 2019-2020 witnessed improvement for all performance indicators but unfortunately followed by a deterioration in season 2020-2021. For seasons 2018-2019, 2019-2020, and 2020-2021, the average wheat yield computed at the scheme level was 2.21, 3.00, and 2.17 ton/ha, respectively, while the water productivity (WP) ranged from 0.3 to 0.54 kg/m3. Both computed yield and WP are below the optimum ranges stated by FAO; 6–9 ton/ha for yield and 0.8–1.6 kg/m3 for WP. The observed yield measurements were also used to validate the agriculture divisions' calculated yield. The results indicated that season 2020-2021 had the strongest correlation (0.71) and was associated with the lowest yield, while season 2019-2020 had the highest yield with a correlation value of 0.6 over the three seasons. These outcomes were linked with the temperature degrees, which found that season 2020 had the lowest temperature degrees over the study period. The bright and hot spots revealed that the Southern Gezira sector had the lowest land and water productivity performance over the three seasons. In contrast, divisions located in the Managil sectors (Western and Eastern) have the highest performance, followed by the upper parts of the Northern Gezira sector. The canalization system was shown to have a head-to-tail impact on water distribution