Search Results (3)

The cowpea is a vital crop for low-resource farmers in the Sahel, but post-harvest losses due to insect pests remain a major challenge. Callosobruchus maculatus (Fabricius, 1775), is the primary pest responsible for most of the damage to stored cowpeas. Recently, Trogoderma granarium (Everts, 1898) was found infesting cowpeas in large warehouses in Niger. This study evaluated hermetic storage bags to manage both common and emerging insect pests. Treatments included (i) the Purdue Improved Crop Storage (PICS) hermetic bag; (ii) a woven polypropylene (PP) bag with a polyethylene (PE) liner and Phostoxin; and (iii) a woven PP bag without Phostoxin (control). Naturally infested cowpea grains were obtained from the Office des Produits Vivriers du Niger (OPVN) warehouse in Maradi, Niger. Infestation levels were assessed using 12 samples of 500 g each, randomly collected from each treatment at the start and end of the trial. Major pests identified were C. maculatus, T. granarium, and Tribolium sp., with initial populations of 0.83, 0.44, and 0.83 adults per 500 g of cowpea, respectively. After six months of storage, pest densities in the control increased significantly: 232-fold for C. maculatus, 7.4-fold for T. granarium, and 2.7-fold for Tribolium sp.; resulting in a 38.5% weight loss. In contrast, both the Phostoxin and the PICS hermetic bags effectively suppressed pest populations, preventing weight loss. This study confirms the efficacy of hermetic storage, such as the PICS bag, in protecting cowpeas from both common and emerging pests. Full article

Classification algorithms have shown exceptional prediction results in the supervised learning area. These classification algorithms are not always efficient when it comes to real-life datasets due to class distributions. As a result, datasets for real-life applications are generally imbalanced. Several methods have been proposed to solve the problem of class imbalance. In this paper, we propose a hybrid method combining the preprocessing techniques and those of ensemble learning. The original training set is undersampled by evaluating the samples by stochastic measurement (SM) and then training these samples selected by Multilayer Perceptron to return a balanced training set. The MLPUS (Multilayer perceptron undersampling) balanced training set is aggregated using the bagging ensemble method. We applied our method to the real-life Niger_Rice dataset and forty-four other imbalanced datasets from the KEEL repository in this study. We also compared our method with six other existing methods in the literature, such as the MLP classifier on the original imbalance dataset, MLPUS, UnderBagging (combining random under-sampling and bagging), RUSBoost, SMOTEBagging (Synthetic Minority Oversampling Technique and bagging), SMOTEBoost. The results show that our method is competitive compared to other methods. The Niger_Rice real-life dataset results are 75.6, 0.73, 0.76, and 0.86, respectively, for accuracy, F-measure, G-mean, and ROC with our proposed method. In contrast, the MLP classifier on the original imbalance Niger_Rice dataset gives results 72.44, 0.82, 0.59, and 0.76 respectively for accuracy, F-measure, G-mean, and ROC. Full article

The Niger Inland Delta (NID) wetland comprises a large flooded area that plays an important role in the ecosystem services. This study provides a comprehensive understanding of the NID’s hydro-climatological functioning using water balance approach. After a clear description of the water budget’s elements specific to the NID catchment, a spatial and temporal dynamics of the annual flood across the NID over the period 2000–2009 was performed using data from satellite QuickSCAT and its associated sensor SeaWinds. The estimated areas were used along with observed discharge and remotely-sensed climatic data to quantitatively evaluate each water balance component. The results indicate: (i) a clear spatiotemporal of the flooded areas varied between 25,000 km² in wet periods and 2000 km² in dry periods; (ii) an average evapotranspiration loss of 17.31 km³ (43% of the total inflow) was assessed in the catchment; (iii) precipitation’s contribution to the NID’s budget totals 5.16 km³ (12.8% of the total inflow); and (iv) the contribution of return flow from irrigated fields totals 1.8 km³ (4.5% of the total inflow, among which 1.2 km³ are from Office du Niger) to the flooded areas, refined the NID’s water balance estimates. Knowledge gained on NID’s water balance analysis will be used to develop and calibrate hydrological models in the Niger Inland Delta of the basin. Full article