Post-hoc comparison procedures are commonly used to determine which group means differ after a significant analysis of variance (ANOVA). Several post-hoc tests have been proposed, but their use requires certain assumptions to be met, such as normality, equality of variance, and balanced group size. This review examined the statistical literature on post-hoc tests and their use in the environmental and biological sciences. Through this review, we found that post-hoc tests are effective but often inadequately used in these sciences. We conducted a search of reputed search engines to identify articles in which post-hoc tests were used and found ten post-hoc tests used in the environmental and biological literature. Tukey HSD (30.04%), Duncan's (25.41%) and Fisher's LSD (18.15%) were the most commonly used post-hoc tests over the past 20 years, whereas the Games-Howell (1.13%), Holm-Bonferroni (1.25%), and Scheffe's tests (2.25%) were the least used. The choice of post-hoc test depended on the statistical method used prior. In addition, the assumptions of applying post-hoc tests were not always verified. In fact, the normality condition was mostly only checked in the cases of Tukey HSD, Duncan's, and Fischer's LSD tests, and equality of variance was often met for the Tukey HSD, Duncan's, Fischer's LSD, and Bonferroni tests. This review opens a new avenue for comparing post-hoc test performance in ANOVA using linear or generalised mixed effect models.

Feeding the world in 2050 requires us to find ways to boost yields of the main local crops. Among those crops, cowpea is one of the grain legumes that is playing an important role in the livelihood of millions of people in West Africa, especially in Benin. Unfortunately, cowpea on-farm yields are very low. In order to understand the main factors explaining cowpea yield gaps, we collected and analyzed detailed survey data from 298 cowpea fields in Benin during the 2017, 2018 and 2019′s rainy seasons, respectively. Composite soil samples were collected from cowpea fields and analyzed in the laboratory. Data on farm field management practices and field conditions were recorded through interviews with 606 farmers. Average cowpea grain yields were low and seldom surpassed 700 kg ha⁻¹ on farmer’s fields. Significant differences were observed between cowpea grain yields from northern to southern Benin (p < 0.05), and the lowest yields were observed in northern Benin. These low yields are related to crop management practices, soil nutrient contents, and the interaction of both. According to the model of regression tree from northern to southern Benin, the use of mineral fertilizer, insecticide sprays to control pests, and the improvement of phosphorus, nitrogen, potassium (P, N, K) and cation sum content in the topsoil would increase cowpea grain yields. Insect pests, diseases, and soil fertility decline are the largest constraints limiting grain yield in Benin. Future research should focus on formulating site-specific fertilizer recommendations for effective cowpea cultivation in Benin, as well as the control of insect pests and diseases.

Analyse des besoins en équipements et matériels de production des spéculations des pôles de développement agricole du Bénin

Understanding direct and indirect relationships of environmental, socio-economic and climate variables and the dynamics of epidemics is key to guiding targeted public health policy and interventions. This study investigates the direct and indirect effects of environmental and socio-economic factors on the COVID-19 dynamics in Africa (54 African countries from 2019 to 2021) using SEM approach. Specifically, the study aimed to: (i) assess the performance of two SEM estimation methods (Lisrel and PLS-SEM) in relationship to sample size (100, 200, 500, and 1000) and level of model complexity (No, two, and four indirect effects) and (ii) use the most performing SEM estimation method to examine direct and indirect effects of factors influencing the number of cases and deaths of COVID-19 in Africa. The results highlight a positive spatial correlation between factors such as temperature, humidity, age, the proportion of people aged over 65, and the COVID-19 incidence. Under the control of confounding factors, Lisrel turns out to be the most performing method, identifying climate, demographic and economic factors as the main determinants of COVID-19 dynamics. These factors have a direct and significant impact on the incidence of COVID-19. An indirect relationship was also observed between economic factors and the incidence of COVID-19 through air pollutants. The results highlight the importance of considering these factors in understanding the spread of the virus to avoid further disasters.