Pressure on agricultural systems in developing countries has increased due to land degradation, population growth, and climate change (CC). To increase smallholder farmers’ resilience, various best-practice climate-smart agriculture (CSA) strategies and interventions have been given top priority based on widespread positive effects. However, there are no specific targeting techniques available. To analyze farming households’ orientations, it is crucial to capture variations within farming systems through socio-economic or biophysical approaches. To better target CSA initiatives, this article assesses rural livelihoods using socio-economic, biophysical, and the Five Capitals Model.Data was collected in the Tambacounda and Sedhiou areas of Senegal in 2020. Using factor analysis for mixed data (FAMD) and correlation analysis, heterogeneous smallholder farming systems were condensed into a few farm typologies (or clusters) based on SEBP factors, including the Five Capitals: human, social, physical, natural, and financial capital. A probit regression model was used to estimate farmers’ likelihood of adoption.The results show that social, economic, and biophysical (SEBP) factors contribute to unique farmer typology formations, which makes for tailored CSA targeting. The distribution of farm clusters was non-random, and certain clusters are more prevalent in certain geographic locations. The four farmer typologies (or clusters) identified are as follows: (i) over 70% of Cluster 1 smallholder farmers are from the Sedhiou region, which has low-income and high climate-related agricultural challenges, (ii) 55% of Cluster 2 farmers are from the Sedhiou region, and 45% are from the Tambacounda region, both of which have low- to middle-incomes, and moderate climate-related agricultural challenges, (iii) 75% of Cluster 3 farmers are from the Tambacounda region and have the highest income and experience good climatic conditions for agriculture, and (iv) 92% of farmers in Cluster 4 are from the Tambacounda region, have the lowest income, and experience the highest climate-related agricultural challenges. Our findings show that the technology used by farmers is not always appropriate given their SEBP and Capital Assets profile. However, by tying CSA adoption likelihood to current agricultural issues, we’ve identified relevant technologies that smallholder farmers of various clusters might apply to restore soil fertility, leading to higher output and improving nutrition indicators.Based on these results, we argue that a hypothesis should guide the characterization of local agricultural features, drivers and mechanisms of differentiation among farming systems, such as crop rotations and different crop varieties and conservation methods.