Estimating spatially distributed SOC sequestration potentials of sustainable land management practices in Ethiopia.

The sustainable land management program (SLMP) of Ethiopia aims to improve livelihoods and create resilient communities and landscape to climate change. Soil organic carbon (SOC) sequestration is one of the key co-benefits of the SLMP. The objective of this study was to estimate the spatial dynamics of SOC in 2010 and 2018 (before and after SLMP) and identify the SOC sequestration hotspots at landscape scale in four selected SLMP watersheds in the Ethiopian highlands. The specific objectives were to: 1) comparatively evaluate SOC sequestration estimation model building strategies using either a single watershed, a combined dataset from all watersheds, and leave-one-watershed-out using Random Forest (RF) model; 2) map SOC stock of 2010 and 2018 to estimate amount of SOC sequestration and potential; 3) evaluate the impacts of SLM practices on SOC in four SLMP watersheds. A total of 397 auger composite samples from the topsoil (0-20 cm depth) were collected in 2010, and the same number of samples were collected from the same locations in 2018. We used simple statistics to assess the SOC change between the two periods, and machine learning models to predict SOC stock spatially. The study showed that statistically significant variation (P < 0.05) of SOC was observed between the two years in two watersheds (Gafera and Adi Tsegora) whereas the differences were not significant in the other two watersheds (Yesir and Azugashuba). Comparative analysis of model-setups shows that a combined dataset from all the four watersheds to train and test RF outperform the other two strategies (a single watershed alone and a leave-one-watershed-out to train and test RF) during the testing dataset. Thus, this approach was used to predict SOC stock before (2010) and after (2018) land management interventions and to derive the SOC sequestration maps. We estimated the sequestrated, achievable and target level of SOC stock spatially in the four watersheds. We assessed the impact of SLM practices, specifically bunds, terraces, biological and various forms of tillage practices on SOC using partial dependency algorithms of prediction models. No tillage (NT) increased SOC in all watersheds. The combination of physical and biological interventions ("bunds + vegetations" or "terraces + vegetations") resulted in the highest SOC stock, followed by the biological intervention. The achievable SOC stock analysis showed that further SOC stock sequestration of up to 13.7 Mg C ha--1 may be possible in the Adi Tsegora, 15.8 Mg C ha-1 in Gafera, 33.2 Mg C ha-1 in Azuga suba and 34.7 Mg C ha-1 in Yesir watersheds.

Evaluating rural household well-being and empowerment among women and young farmers in Senegal.

This article provides a description of baseline survey data that was collected in Senegal in the regions of Sedhiou and Tambacounda in 2020, respectively, and as part of an agricultural development project aimed at improving the well-being and resilience of farming households. The survey was implemented using a structured questionnaire administered among 1503 households, 70% of whom are women and 30% are young people, in the two regions. This paper contains data that can helps in understanding the socioeconomic well-being and resilience of smallholder farming households, especially among women and youth. This data helps to associate information on: (i) the socioeconomic project area variables, (ii) the extent of use of irrigated and climate change-adapted crops; (iii) the level of soil and water resource management in the study regions; and (iv) the food security and dietary diversity with the well-being and empowerment of women and young smallholder farming households. In addition, the dataset can be used as a baseline or reference point to track the economic empowerment and climate resilience building achieved in the study regions.

A scalable approach to improve CSA targeting practices among smallholder farmers.

With climate change, population growth, and land degradation exerting mounting pressures on agricultural systems in developing countries, climate-smart agriculture (CSA) strategies have been prioritized as a means to strengthen smallholder farmers' resilience. However, precise targeting methodologies remain a challenge. This study employs a comprehensive approach, integrating Socio-economic, and Biophysical (SEBP), and the Five Capitals Model analyses encompassing human, social, physical, natural, and financial capital. The study employs factor analysis for mixed data (FAMD), cluster analysis using partitioning around the medoids (PAM) and univariate and bivariate techniques to identify and classify distinct typologies of smallholder farming systems in Senegal's Tambacounda and Sedhiou regions in 2020. A probit regression model gauges CSA adoption probability, to better focus CSA efforts. Results underscore the pivotal role of SEBP factors in shaping distinct farmer typologies, enabling precise CSA targeting. Geographical distribution patterns of these typologies reveal non-random clustering, particularly in specific regions. Four farmer typologies emerge: Cluster 1 (Sedhiou, low-income, high climate challenges), Cluster 2 (Sedhiou and Tambacounda, low-to middle-income, moderate climatic challenges), Cluster 3 (Tambacounda, high income, favorable climate), and Cluster 4 (Tambacounda, low income, severe climate challenges). Technology mismatches emerge between farmers' SEBP profiles and capital assets, prompting the identification of relevant technologies for soil fertility restoration and increased output. These findings highlight the importance of implementing CSAs in accordance with specific requirements, such as enhancing soil fertility, yield, and nutritional quality. A contextual understanding of local agricultural dynamics is likewise necessary for optimizing intervention strategies, according to the study.