Benin

To increase rice production in Africa, both intensification and area expansion are needed. Inland valley (IV) agroecosystems are important for rice production due to their relatively high water availability and soil fertility. However, the spatial distribution of suitable IVs remains uncertain. The objective of this study was to model spatial distribution patterns of suitable IV areas for rice production. Biophysical, socioeconomic and management practice data were collected from 242 IVs in two contrasting regions in Benin, namely, the Departments of Mono and Couffo (Mono-Couffo) and the Upper Oum River catchment (Upper Oum). Geographically weighted regression (GWR) and ordinary least squares (OLS) models were used to predict the spatial distribution of suitable IV areas for rice production. The results showed that the GWR model performed better than the OLS model in assessing the IV suitability spatial distribution. There were 177,714 ha (46%) and 431,954 ha (31%) of highly suitable IVs for rice production in Mono-Couffo and Upper Oum, respectively. The most significant variables for predicting suitable IV areas for rice production were irrigation water resources, soil fertility management and total nitrogen in topsoil in Mono-Couffo and the number of male farmers in the IV, shallow water table duration at the IV bottom, and elevation in Upper Oum. These results demonstrated the effectiveness of the GWR model in assessing the distribution of highly suitable IVs.

Based on primary data from fecal sludge (FS) treatment plants in three West African urban regions (Ouagadougou in Burkina Faso, Greater Accra in Ghana, and Grand Nokou in Benin), FS collection and treatment patterns were analyzed to identify possible scenarios for resource recovery (RR) through FS co-composting. FS collection was analyzed for up to 7 years, in part per day, month and season, as well as FS characteristics to understand peak flows, FS qualities and related variations to plan for appropriate RR technology and capacities. Overall, the FS volumes collected by vacuum trucks were not significantly affected by the calendar days, months or seasons. Commonly assumed increases during rainy months were, for example, only recorded in Ouagadougou. FS composition appeared highly variable with a pronounced difference in total solids between FS collected from households versus institutional sources, likely indicating that institutions are served more frequently. The analyzed treatment plants appear to be exploited beyond their capacity. RR for reuse can turn sludge disposal from a cost into a source of revenue with co-benefits for farmers and the environment, thereby reducing the pressure on tipping fees. The probability of the added co-compost production being financially viable on its own was estimated for all the study sites, indicating an earliest breakeven point after 5 to 8 years.

Inland valleys (IVs) in Africa are important landscapes for rice cultivation and are targeted by national governments to attain self-sufficiency. Yet, there is limited information on the spatial distribution of IVs suitability at the national scale. In the present study, we developed an ensemble model approach to characterize the IVs suitability for rainfed lowland rice using 4 machine learning algorithms based on environmental niche modeling (ENM) with presence-only data and background sample, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Maximum Entropy (MAXNT) and Random Forest (RF). We used a set of predictors that were grouped under climatic variables, agricultural water productivity and soil water content, soil chemical properties, soil physical properties, vegetation cover, and socio-economic variables. The Area Under the Curves (AUC) evaluation metrics for both training and testing were respectively 0.999 and 0.873 for BRT, 0.866 and 0.816 for GLM, 0.948 and 0.861 for MAXENT and 0.911 and 0.878 for RF. Results showed that proximity of inland valleys to roads and urban centers, elevation, soil water holding capacity, bulk density, vegetation index, gross biomass water productivity, precipitation of the wettest quarter, isothermality, annual precipitation, and total phosphorus among others were major predictors of IVs suitability for rainfed lowland rice. Suitable IVs areas were estimated at 155,000–225,000 Ha in Togo and 351,000–406,000 Ha in Benin. We estimated that 53.8% of the suitable IVs area is needed in Togo to attain self-sufficiency in rice while 60.1% of the suitable IVs area is needed in Benin to attain self-sufficiency in rice. These results demonstrated the effectiveness of an ensemble environmental niche modeling approach that combines the strengths of several models.

The dataset described in this data article represents four agricultural zones in West-Africa that are located in three countries: Benin, Mali and Sierra Leone. The dataset was created through a research collaboration between the Africa Rice Center (AfricaRice), Sierra Leone Agricultural Research Institute (SLARI) and the Institute for Rural Economy (IER). The dataset was compiled to investigate the potential for rice production in inland valleys of the three countries. The results of the investigation were published in Dossou-Yovo et al. (2017) and Djagba et al. (2018). The dataset describes the biophysical and socioeconomic conditions of 499 inland valleys in the four agricultural zones. In each inland valley data were collected through a focus group interview with a minimum of three farmers. In 499 interviews a total of 7496 farmers participated. The location of each inland valley was determined with handheld GPS devices. The geographic locations were used to extract additional parameters from digital maps on soils, elevation, population density, rainfall, flow accumulation, and distances to roads, market places, rice mills, chemical input stores, and settlements. The dataset contains 65 parameters in four themes (location, biophysical characteristics, socioeconomic characteristics, and inland valley land development and use). The GPS coordinates indicate the location of an inland valley, but they do not lead to the location of individual fields of farmers that were interviewed. The dataset is publicly shared as Supplementary data to this data article.