Soil erosion is a complex process that results in soil and fertility losses from agricultural land and, ultimately, leads to river sedimentation. This study aimed to assess various influential factors and processes affecting soil erosion and to recommend suitable site-based Soil Erosion Control Measures (SECM) for sustainable agriculture while minimizing the downstream rivers and reservoir sedimentation in the Sebeya watershed of Rwanda. The present research used a literature review, site visits, and focus groups to assess various SECM within the Sebeya watershed. As a result, various site-based SECM were evaluated, recommended, and simulated to alleviate high soil loss rates in the Sebeya watershed using the Universal Soil Erosion Equation (USLE) model. Simulating existing and proposed SECM, soil loss was reduced significantly from 73 t/ha/y to 29 t/ha/y. To highlight the implication of the site-based recommended SECM in improving agricultural productivity, this study suggests field investigations in soil erosion plots and prediction of crop yields from an established linear correlation model between soil loss and crop yields in the Sebeya watershed. For effective action in reducing high soil erosion rates to tolerable rates in the Sebeya watershed, the present research recommends implementing the site-based recommended SECM with mulching and drainage channels on the same farmland. However, lack of money and knowledge are the main limitations in implementing SECM in the Sebeya watershed. Therefore, governmental and non-governmental organizations should technically and financially help farmers in the Sebeya watershed.

Soil erosion is a worldwide environmental problem leading to low agricultural productivity and water quality degradation. Improving soil erosion control measures is essential. This study reports the results of a survey of 75 farmers, using structured interviews, field observations, and focus groups to analyze farmers’ perceptions concerning current and future efforts to adopt Soil Erosion Control (SEC) measures in the Sebeya catchment located in the Western Province of Rwanda. Various factors influencing farmers’ perceptions of soil erosion causes, effects, and willingness to adopt SEC measures were analyzed using descriptive statistics and SPSS (Version 20), including t-tests, chi-square tests, and a binary logistic regression model. Chi-square test results indicate that gender, farmer age, land ownership, farmland size, social media access, and credit access were strongly associated (p < 0.05) with the adoption of SEC measures, while marital status and education were not. A binary logistic regression model showed that among farmers’ socioeconomic characteristics, farming experience (B = 0.749; p = 0.020) and access to socio media (B = 2.107; p = 0.027) were positively correlated, while age (B = −0.642; p = 0.035) and gender (B = −2.034; p = 0.032) were negatively correlated (p < 0.05) with the adoption of SEC measures. In order to mitigate high soil erosion rates and increase food production, there is a need for the government to support farmers, and train them. A highly skilled technical team should be mobilized to assist in implementing SEC measures in the Sebeya catchment.

Flood susceptibility mapping is crucial for understanding flood-prone areas and mitigating the associated risks, particularly in vulnerable regions like the Sebeya Catchment. This study has adopted a GIS-AHP approach integrated with local community knowledge over flood susceptibility factors such as Topographic Wetness Index (WTI), Digital Elevation Model (DEM), Precipitation, Slope, Land Use/ Land Cover (LULC), Normalized Vegetative Index (NDVI), Distance to Roads, Distance to River, and Drainage Density. The pairwise comparison matrix was used to determine each factor's weight according to its influence in inducing flood. The findings revealed that 33.1% of the total area has a very and high susceptibility to floods, whereas the rest part of the catchment is moderately susceptible to floods. Most social economic activities in this study are located in high-risk zones, which significantly to appearance of flooding impacts. Current study indicates that, damage to infrastructure, loss of livelihoods, displacement of communities, and increased costs of disaster response are key consequences observed in affected regions. A confusion matrix approach was employed to validate the flood susceptibility map, and the results indicate an overall accuracy of 0.92, confirming strong model performance and reliability. The study further proposes adaptive strategies and provides recommendations for enhancing flood resilience, including improvement in land-use planning, use of early warning systems, and sustainable catchment management. Further studies should develop an economic-loss prediction model based on flood-susceptibility mapping.