Opportunities and Limitations for Achieving Land Degradation-Neutrality through the Current Land-Use Policy Framework in Kenya

Water Governance in Mediterranean Farming Systems through the Social-Ecological Systems Framework—An Empirical Case in Southern Portugal

Water governance is a major challenge in the Mediterranean context. Any action to drive water governance towards sustainability needs to be grounded in a holistic understanding of such challenges. Therefore, a first step towards the improvement of water governance is a grounded understanding of what is at stake, who are the actors involved, and how they interact. To achieve this level of understanding, we propose the use of the social–ecological Systems (SES) framework. This framework was developed to grasp the complexity of issues related to the sustainable use of public goods such as water. This study looks at water governance in the farming sector of three municipalities in the Alentejo and Algarve, in the south of Portugal. Data were collected using a literature review and 22 semi-structured interviews with territorial actors (i.e., public administration, non-governmental associations, private sector, decision-makers, and farmers). By using the SES framework, we provide an integrated characterization of water governance in the case study and identify the implicated factors. Between these factors, and focusing on the overlap between literature and actors’ perspectives, are (1) the lack of integrated and supported strategies for development, and (2) lack of communication between the actors that need to congregate efforts towards sustainable use of water resources. The study found few examples of collective efforts and long-lasting networks of collaboration, especially between science and practice. We conclude by arguing that place-based tailored policies are needed. Such policies should promote communication and collective actions between researchers, local organizations, public administration, and farmers.

Soil Erosion Susceptibility Mapping in Kozetopraghi Catchment, Iran: A Mixed Approach Using Rainfall Simulator and Data Mining Techniques

Soil erosion determines landforms, soil formation and distribution, soil fertility, and land degradation processes. In arid and semiarid ecosystems, soil erosion is a key process to understand, foresee, and prevent desertification. Addressing soil erosion throughout watersheds scales requires basic information to develop soil erosion control strategies and to reduce land degradation. To assess and remediate the non-sustainable soil erosion rates, restoration programs benefit from the knowledge of the spatial distribution of the soil losses to develop maps of soil erosion. This study presents Support Vector Machine (SVM), Random Forest (RF), and adaptive boosting (AdaBoost) data mining models to map soil erosion susceptibility in Kozetopraghi watershed, Iran. A soil erosion inventory map was prepared from field rainfall simulation experiments on 174 randomly selected points along the Kozetopraghi watershed. In previous studies, this map has been prepared using indirect methods such as the Universal Soil Loss Equation to assess soil erosion. Direct field measurements for mapping soil erosion susceptibility have so far not been carried out in our study site in the past. The soil erosion rate data generated by simulated rainfall in 1 m2 plots at rainfall rate of 40 mmh−1 was used to develop the soil erosion map. Of the available data, 70% and 30% were randomly classified to calibrate and validate the models, respectively. As a result, the RF model with the highest area under the curve (AUC) value in a receiver operating characteristics (ROC) curve (0.91), and the lowest mean square error (MSE) value (0.09), has the most concordance and spatial differentiation. Sensitivity analysis by Jackknife and IncNodePurity methods indicates that the slope angle is the most important factor within the soil erosion susceptibility map. The RF susceptibility map showed that the areas located in the center and near the watershed outlet have the most susceptibility to soil erosion. This information can be used to support the development of sustainable restoration plans with more accuracy. Our methodology has been evaluated and can be also applied in other regions.

A Climate-Smart Approach to the Implementation of Land Degradation Neutrality within a Water Catchment Area in Kenya

At the sub-national level, the United Nations Convention to Combat Desertification (UNCCD) proposes the analysis and contextualization of land degradation-neutrality (LDN) at a water catchment scale to provide decision support for the formulation of policies and programmes towards transformative LDN interventions. Building on a number of national LDN studies in Kenya, an approach for the implementation of LDN that is based on the spatial and temporal characterization of key land degradation and climate change variables was defined. For a selected water catchment area, the LDN baseline was computed, the drivers that affect land degradation and regeneration trends within the main land cover types were identified and described, the trends of key climate change variables were described, and appropriate sustainable land management interventions for the main land cover types were identified. A climate-smart landscape approach that delineated the catchment area into zones focused on adaptation, and both adaptation and mitigation objectives was then proposed. The operationalization of a climate-smart landscape will require significant investment to not only provide an understanding of the bio-physical processes and interactions occurring at the catchment level but also to develop the institutional and technical capacities of relevant actors. The landscape approach proposed for the catchment area has the potential to improve livelihoods and the productivity of ecosystems while concurrently facilitating synergies between land degradation, climate change, and other development objectives.