African basil (Ocimum gratissimum L.) is a perennial herb of the Lamiaceae family, commonly called Tchiayo in the local Fongbe language. It is a traditional leafy vegetable that is widely consumed in Benin because of its high nutritional, aromatic and medicinal properties (Kpètèhoto et al., 2017). In 2014, many of these plants were found to be wilting in the INRAB experimental field crop plots. In 2018, wilting of African basil crops was documented in farmer’s fields in the districts of Sèmè-Podji, Ouinhi, Tori-Bossito, Ouidah and Abomey-Calavi in Benin, with a 20–90% disease incidence in 173 African basil crop plots (7.2 m²) out of a total of 1,450 surveyed plots. In January 2025, wilted African basil plants were observed near bacterial wilt-infected tomato field crops in the Ouidah district. Stem sections of wilted African basil plants showed brown xylem with the release of whitish bacterial ooze in water. Plating the bacterial solution on SMSA medium (Engelbrecht, M. C. 1994) revealed bacterial colonies morphologically typical of the Ralstonia solanacearum species complex (RSSC). Koch’s postulates were applied by inoculating a single African basil accession growing in sterilized field soil with two separate isolates from the 2025 collection. Ten plants per isolate were inoculated by drenching the soil around the plant crown with 20 ml of bacterial suspension (108 CFU/ml). Susceptible cv. Akikonkoun tomato and cv. Kpinman gboma plants were also inoculated with the same bacterial suspensions. African basil, gboma and tomato plants drenched with sterile distilled water served as the negative control. The inoculated plants were kept in a greenhouse at 32°C (day) and 28°C (night). The African basil plants started wilting 15 days after inoculation (DAI), with 80% of the plants wilted at 28 DAI, while the gboma and tomato plants started wilting 7 and 5 DAI, respectively, and 90% and 100% of these plants had wilted at 28 DAI. The negative control plants remained asymptomatic. No typical RSSC colonies were recovered from control plants, whereas typical RSSC colonies were consistently re-isolated from all inoculated plants, including both symptomatic and latently infected individuals. ImmunoStrip assays (Agdia Inc., Elkhart, IN, USA) and diagnostic PCR using 759/760 primers (Opina et al, 1997) confirmed the RSSC identity of the two selected isolates. A phylotype-specific multiplex PCR (Fegan and Prior 2005) classified the African basil strains in R. pseudosolanacearum phylotype I. The RUN8084 and RUN8085 strains were both typed as sequevar I-31 through phylogenetic inference according to Cellier et al. (2025) (endoglucanase GenBank accession nos. PX273783 and PX273784). This sequevar has been reported to be epidemiologically widespread in southwest Indian Ocean and African regions (Cellier et al. 2023), while showing high geographical and host adaptiveness. This is the first report of R. pseudosolanacearum causing bacterial wilt on O. gratissimum anywhere in the world, especially in Benin. Further surveys are needed to assess the distribution of bacterial wilt on African basil in Benin and to reduce epidemic risks by designing a disease management plan to avoid African basil rotations with solanaceous crops hosting R. pseudosolanacearum.
Several studies have examined intercropping in cashew-based agroforestry systems, but few have focused on its effects on soil biophysical parameters and cashew yields. This study aims to evaluate the influence of major intercropping practices on cashew productivity and key soil fertility indicators in the main producing regions of Benin. The experiment consisted of 48 plots arranged in a Randomized Complete Block Design (RCBD) with four blocks (sites) and four treatments, each with three replicates (farmers) per site. The treatments included the main cashew-based intercropping: cashew-soybeans, cashew-maize, cashew-peppers, and a control (cashew without intercropping). The effects of intercrops on soil cover, soil moisture, and major soil fertility indicators (C, OM, pH, CEC, N, P2O5, K2O, Ca, and Mg), as well as cashew nut yields, were analyzed using appropriate models in R 4.5.1. Results showed that soybeans provided significantly higher soil cover (22–26
Finally, BME remains the benchmark for spatially explicit predictions in limited and asymmetric datasets. However, the integration of ML’s non-linear predictive capacity with BME’s spatial reasoning offers a promising hybrid BME–ML framework, combining statistical robustness with spatial adaptability to advance digital soil mapping and guide more reliable soil fertility management strategies. Accurate prediction of soil fertility parameters is essential for sustainable agricultural management and environmental conservation. However, the performance of predictive models often depends on data characteristics such as spatial dependence, skewness, and sample size. This study compared the robustness of three Machine Learning (ML) algorithms Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) with the geostatistical Bayesian Maximum Entropy (BME) method in predicting key soil fertility attributes, including Organic Matter (OM), Potassium (K), Phosphorus (P), Potential of Hydrogen (pH), Copper (Cu), Zinc (Zn), Iron (Fe), Magnesium (Mg), Nitrogen (N), Boron (B), Electrical Conductivity (EC), Calcium Carbonate (CaCO 3 ), Cation Exchange Capacity (CEC), and Organic Carbon (OC). A comprehensive simulation experiment was conducted based on twelve scenarios that systematically combined variations in sample size (200, 500, 1,000, and 10,000), degrees of skewness (symmetric, moderately skewed, and highly skewed), and spatial dependence levels (weak, moderate, and strong) under Spherical, Exponential, and Gaussian semivariogram models. Model performance was evaluated using multiple accuracy and spatial consistency metrics, including the Coefficient of Determination (R 2 ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Spatial Efficiency (SE), and Moran’s I of residuals, while uncertainty quantification employed Prediction Interval Coverage Probability (PICP), Mean Prediction Interval Width (MPIW), and Continuous Ranked Probability Score (CRPS). The results indicate that BME consistently outperformed all ML models under conditions of strong spatial dependence and small sample sizes (R 2 > 0.75, RMSE < 0.18, SE ≈ 0.82, and Moran’s I < 0.05). In contrast, ML algorithms, particularly ANN and SVM, showed significant performance degradation in highly skewed datasets (R 2 < 0.60; RMSE > 0.25), while RF demonstrated relative resilience (R 2 ≈ 0.70). Increasing the sample size from 200 to 10,000 markedly improved the performance of all ML models, thereby narrowing their performance gap with BME (average ∆R 2 = 0.10). BME also exhibited superior uncertainty quantification, with higher PICP (0.93) and lower CRPS (0.12) compared to ML models.
In Africa, insects have long been used as human food and animal feed. This practice is presently being promoted to reduce malnutrition and provide new opportunities for the economic development of the rural poor, in particular women. However, gender issues need to be considered at an early stage and, to set up a baseline for such considerations, it is essential to understand the role of women in all present aspects of the sector. This paper reviews the role of women in the collection, production, processing, marketing and consumption of insects used as food and feed in Africa. Women tend to dominate the value chain although their role varies with insects and regions. Most insects used as human food are still field collected, usually by women, who also lead marketing activities, especially when the market is of low value. Women also prepare and cook the insects. Consumption is usually shared among men, women and children, but gender balance may vary with insects and regions and may be influenced by traditions and taboos. There is less information available on gender roles in the sector of insects as feed, largely because, until recently, only a few insect species were used to feed livestock, and these insects were not traditionally traded. Nowadays, systems for producing, processing and marketing fly larvae as feed are being developed but gender issues have not yet been really taken into consideration. Efforts should be made to ensure that this emergent activity will also provide business opportunities for women entrepreneurs.
Food security remains a persistent challenge in sub-Saharan Africa, and sustainable agricultural intensification has been proposed as a promising strategy to improve both agricultural productivity and household welfare. However, there is limited empirical evidence on the heterogeneous welfare effects of the intensity of adopting multiple sustainable agricultural practices, particularly when these technologies are implemented individually or in combination. This study addresses this gap by examining the welfare impacts of certified seeds (CS), improved technical itineraries (ITK), and irrigation systems on annual per capita expenditure and the Food Consumption Score (FCS) among vegetable farm households in southern Benin. The analysis uses an unbalanced three-round panel dataset collected in 2018, 2020, and 2022. It estimates heterogeneous effects of multiple treatments in the presence of the endogeneity with respect to both time-constant and time-varying unobserved heterogeneity and selection on idiosyncratic gains. Accordingly, a panel-data fixed-effects endogeneity-corrected correlated random coefficients model combined with a latent factor approach is used. The main findings are that the largest effects on per capita expenditure (an average partial effect of 82%) were obtained with an increase in the area covered by the adoption of the three technologies combined, especially in the presence of an irrigation system. Similarly, an increase of area under the three combined technologies increases the probability of achieving an acceptable FCS by 0.45 points, specifically among male-headed farm households. The findings have important policy implications for designing targeted agricultural programs that promote complementary technology packages and account for household heterogeneity to maximize welfare.
