Cameroon Climate Predictions Using the SARIMA-LSTM Machine Learning Model: Adjustment of a Climate Model for the Sudano-Sahelian Zone of Cameroon

It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.

Composition and Diversity of Soil Bacterial Communities along an Environmental Gradient in the Sudano-Sahelian Region of Senegal

The soils of the semi-arid Sudano-Sahelian region of West Africa have been identified as being highly vulnerable to soil degradation with impacts on their capacity to provide goods and services in which soil microorganisms participate. Unfortunately, soil microbial diversity from this semi-arid region with high rainfall variability remains largely unexplored. The aim of the present study was to characterize the diversity and composition of the soil bacterial communities and to identify factors involved in their spatial distribution along an environmental gradient in Senegal. Samples were collected from non-anthropogenic sites across four pedoclimatic zones. Bacterial communities were characterized using next-generation sequencing and soil physico-chemical parameters were determined. Our results showed that Firmicutes, Actinobacteria, Proteobacteria, Chloroflexi, Gemmatimonadetes, Acidobacteria, and Verrucomicrobia phyla were predominant in the soils of the region. Bacterial α-diversity was stable along the environmental gradient whereas β-diversity highlighted significant changes in the composition of the soil bacterial community. Changes were driven by shifts in the relative abundance of OTUs belonging mainly to the genus Bacillus, Conexibacter, Kaistobacter, Solirubrobacter, Ktedonobacter, Sphingomonas, Microvirga, Rubrobacter and Pelobacter. Soil properties like pH, soil moisture and clay content were the environmental parameters identified as drivers of the composition of the bacterial communities in the semi-arid Sudano-Sahelian region of Senegal (West Africa).

Morphological and Biometric Diversity of Colletotrichum capsici Isolates, Causal Agent of Cowpea Brown Blotch Disease (Vigna unguiculata (L.) Walp) in the Sudano-Sahelian Zone of Cameroon

Cowpea [Vigna unguiculata (L.) Walp] is an important legume in the midst of about 170 species of its genus because it is an important source of protein and other essential nutrients for humans and animals. Its production faces many constraints such as the cowpea brown blotch disease caused by Colletotrichum capsici which contributes in wet conditions of the field to losses ranging from 42% to 100%. This study was conducted to identify Colletotrichum capsici isolates responsible for cowpea brown blotch disease and to determine their diversity in the Sudano-Sahelian zone of Cameroon. Identification and isolation were made from cowpea organ samples on the Potato Dextrose Agar (PDA) medium and, morphological and biometric characteristics such as: the colony color, the mycelium shape, the abundance of acervules, the presence or absence of saltations, the mycelial growth rate, the conidia length and width were used to assess the diversity. The results obtained indicate that 55 Colletotrichum capsici isolates have been identified in the Sudano-Sahelian zone of Cameroon. Statistical analysis showed that there is a significant difference between isolates. Isolates showed multiple colony colours and were brown coloured as presented by 36.36% of isolates, compact mycelium is found in 56.36% of isolates, 56.36% of isolates have abundant acervulis, and saltations were absent in 45.45% of C. capsici isolates. The mycelial growth rate is between 6.69 mm/d and 12.33 mm/d. The principal component analysis (PCA) made indicated that there are differences between the observed and measured characteristics. The Hierarchical Ascending Classification (HAC) was done and 10 morphotypes of C. capsici in the Sudano-Sahelian zone were identified.