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Background Insufficient knowledge of the genetic and phenotypic diversity in the local Beninese goat population combined with the lack of understanding of its adaptive capacity to ongoing environmental and societal changes hampers the development of strategies for better management and genetic improvement. The objective of this study was to establish the current geographical distribution of goats in Benin based on their morphology and model the potential habitat suitability of the three known main goat phenotypes (i.e., Djallonké goat or Type I, Sahelian goat or Type II, and their Crossbreeds or Type III) under climate change scenarios. Ten qualitative and 26 linear body measurements were taken on 2114 adult female goats sampled across the three vegetation zones of the country. Fifteen ratios were generated from the quantitative variables. The data were analyzed using generalized linear model procedures followed by multiple comparisons of least-squares means and multivariate analytical methods, including canonical discrimination analysis and hierarchical ascendant classification. Each goat was then assigned to one of the three aforementioned main goat phenotypes following its morphological characteristics and according to the a priori cluster membership defined in the previous step. The Maximum Entropy algorithm was used to model the current and future distribution of the three goat phenotypes under climate change scenario using the Representative Conservation Pathways 4.5 and 8.5. Results All linear body measurements varied among vegetation zones. In the discriminant function analysis, 71% of the measured individuals were correctly classified in their vegetation zone of origin by seven measured variables and three ratios. The cluster procedure analysis revealed two groups of goats subdivided into the three main phenotypes. The modeling results showed that the currently highly favorable habitats were distributed in the South for Type I, in the North for Type II, and both South and North for Type III. However, under climate change scenarios, the favorable habitats for Type I decreased while those of Types II and III increased. Conclusions The results of this study confirm the spatial variation of the goat population in Benin. The habitat suitability model can be used to support decision-making toward better management of goat genetic diversity in Benin.

The paper explores the degree of pollution of organochlorine pesticides in fish and water in the dam at Kpassa on a tributary of Okpara River that is pumped by Benin National Water agency (SONEB) in supplying drinking water to supply the city of Parakou. Doing so, fourteen parameters of organochlorine are analyzed. Most of obtain organoclorine concentration in water and fish below is indicated critical values. However, DDT and endrine concentration in water is slightly above legal tolerable values. Hexachlorobenen and dieldrine concentrations are three times higher than legal limit value while aldrine is ten times higher. However, heptachlore is double concentrated in fishery while aldrine (endrine, dieldrine aldrine, lindane, hexachloro-benzene, DDT) is found in R. Fofana et al. 1269 the dam fish and surface water three times concentrated than tolerated value. These levels of concentrations result from the intensification of organoclorine pesticide used in agriculture especially in cotton production. These are caused by the chemical application accumulated in soil and through the food value chain system. Therefore, it is very important to extract sediment from the reservoir by dredging in oder to renew the ecosystem of the dam. To persistently manage the basin water resources, it is imperatively important to observe a significant behaviour changed from all stakeholders.

The variation and non-control of the overflow of the Mono River adversely affects the performance of the Nangbetohydropower plant to the point thatitcan no longermeet the increasinglyincreaseddemand for electricity. This studypresents the development of an operational model for forecastingdaily river flows for the plants water retention. The overflow of the Mono River at the upstreamhydroelectric dam from 1991 to 2019 wasanalyzed and modeled by the deterministicprocesswith R software in order to makepredictions. First, the flow serieswasanalyzed by the ARIMA model (18, 1, 2) then by a multiplicative model afterremoving the seasonal trends fromtheseseries by the movingaveragemethod. The calculatederror of the results of said model revealsthat the deterministic model integrates the input generationprocesseswith an error of the order of . Finally, an annual flow forecasting program has been developed as a planning tool for the operation of the dam, in order to meet production needs and to plan water releases.