Impact of Agricultural Water Allocation on the Ecosystems in the Inner Niger River Delta

Abstract

ABSTRACT:The Niger River is a lifeline for humans and biodiversity in Africa's Sahelian region. Owing to climate change and population increase over the past three decades, the inhabitants of the Sahel have witnessed that their ecosystem is under threat, and a significant reduction of its resources has occurred, particularly in the Inner Niger Delta. In the recent years, because of the Malian government's policy of developing large-scale irrigation, a sustained rate of expansion of around 5,000 ha of new reclamation area has been seen each year in Office du Niger located upstream of the Inner Delta. The expansion of annual rice double cropping and sugarcane cultivation constitute the main factors for the reduction of the water resources downstream in this Delta. This paper analyzes the hydrological impacts of this large-scale irrigation and crop pattern on the floodplain of the Inner Delta during high-flow and low-flow periods. The study results indicated that despite the rainfall and recent increases in upstream flows of the Niger River, the flooded area size has reduced. We could confirm that the annual average water-withdrawal proportion barely changed during the high-flow period but changed significantly during the low-flow period when the size of the paddy rice and sugarcane area increased. Therefore, changing the crop pattern toward vegetables instead of paddy rice and sugarcane during the low-flow period might be a solution.

Vehicular Livestock Mobility in West Africa: Seasonal Traffic Flows of Cattle, Sheep, and Goats across Bamako

“…The major areas were southwards of Bamako along the Niger river, namely Farada, Samaya, and Tourela. These areas encompass the flooded plains along the Niger river, which have better access to water and are therefore favourable for crop farming and pasture [44] in comparison with the northern downstream section of the Niger river which is narrower, rockier, and drier with less possibility of crop farming and lower availability of pasture [45].…”

Leveraging Machine Learning for Size and Shape Analysis of Nanoparticles: A Shortcut to Electron Microscopy

“…One way to measure this predictive power is through the use of the R 2 score, which indicates the level of agreement between the regression model and the target variable. The R 2 score is a coefficient of determination, and a value of 1 signifies a perfect match between the predictions and the actual measured results . As the R 2 score approaches 1, the model’s performance improves, reflecting its effectiveness in making accurate predictions.…”

“…The R 2 score is a coefficient of determination, and a value of 1 signifies a perfect match between the predictions and the actual measured results. 56 As the R 2 score approaches 1, the model’s performance improves, reflecting its effectiveness in making accurate predictions. The predictive performance during testing of these models can be observed in Figure 3 for all parameters, and exemplary parity plots are shown in the Supporting Information .…”

Development and Validation of a Clinical Prediction Tool for Seasonal Influenza Vaccination in England

“…For the LASSO and ridge models, an optimal value of λ, or the penalty coefficient for the loss functions, was determined via 10-fold cross-validations using the 2013 training data, in which the optimal λ minimized model deviance (eFigure 2, eFigure 3, eFigure 4, and eFigure 5 in the Supplement). Both the backward stepwise algorithm and LASSO autonomously performed feature or variable selection, which resulted in a reduced model, whereas ridge maintained the full model.…”