1. Successful adaptation often involves changes to the decision context to enable new ways of thinking and acting on climate change. Using 16 adaptation initiatives the authors were engaged with, we analysed how and why decision contexts changed to identify ways to improve adaptation as a process of collective deliberation and social learning. 2. We used the scope of the adaptation issue and governance arrangements to classify initiatives into four types and scored changes in the decision context using three frameworks: (1) the values, rules and knowledge (VRK) perspective to identify changes to adaptation decision-making; (2) the five dimensions of futures consciousness to identify the building of adaptation capabilities and (3) the social learning cycle to reveal evidence of reflexive learning. 3. Initiatives using novel governance arrangements for discrete problems (‘problem governance’) or complex, systemic issues (‘systems governance’) scored highest for influences of VRK, futures consciousness and the social learning cycle on the decision context. Initiatives using existing management for discrete problems (‘problem management’) scored moderately for change in the decision context, while those using existing management for systemic issues (‘systems management’) scored low because change was often impeded by existing rules. 4. All three frameworks influenced decision contexts in systems governance initiatives. Problem governance initiatives revealed interactions of VRK and futures consciousness but limited influence of VRK on the social learning cycle. Scope and governance arrangements differ with the adaptation issue and initiatives adapt over time: some small-scale ones became more systemic, developed novel governance arrangements and changed the decision context. 5. Our findings do not show that some adaptation initiatives are better or more transformative than others; just that their scope and appropriate governance arrangements are different. This questions the notion that successful adaptation requires building generic transformative adaptation approaches and capabilities. There is a diversity of arrangements that work. What is important is to align the approach to the adaptation problem. We suggest two directions for improving adaptation initiatives: first, by influencing how they can shift between problem and systems focus and between standard management and novel governance, and secondly, by using methods to diagnose and direct change in the decision context.

According to the United Nations (n.d.), climate change is the long-term shift in temperatures and weather patterns due to natural changes, such as the sun’s activity and significant volcanic eruptions, or human activities, such as burning fossil fuels like coal, oil, and gas. The effects of and challenges caused by climate change on farmers’ ability to manage mixed farming systems in sub-Saharan Africa are well documented in the literature. However, the synergies among mixed farming systems’ components and farmers’ innovation demands and responses to climate change impacts remain fragmented. Using a case of mixed crop-livestock-tree (MCLT) systems in northern Ghana, this paper examined farmers’ responses, their innovation needs, and how these innovations can be catalyzed to enable more farmers to adopt similar climate change adaptations. Our findings show that climate change impacts mixed farming systems in several domains, with these impacts being more visible in some domains. Significant productivity declines are observed in crops, livestock, and the whole mixed farming system. Productivity declines lead to decreased incomes, food availability, and household food security. Female farmers’ access to production factors, resource management, and market participation is reduced. Farmers make technical, managerial, and business changes in response to climate change impacts. Such changes are dominated by technical changes, including using highyielding, disease-resistant, and early-maturing crop varieties, crop and animal pest and disease management, agricultural water and land management, and wind and bush fire control. Interconnections between the MCLT system components include cross-component investments, additional income generation, animal feeding and healthcare improvement, nutrition exchanges, and family nutrition improvement. These interconnections generate income and cash flow and support food and nutrition security, enabling farmers’ adaptation. Climate-resilient innovation bundles to enable farmers’ adaptation include good agricultural practices, circular farming techniques, irrigation packages, information services, and value-chain linkages. Scaling climate-resilient innovations in northern Ghana and other sub-Saharan African contexts require multiple pathways, including innovation platforms, innovation bundling, multi-actor partnerships, inclusive finance, and multistakeholder dialogues to support farmers’ adaptation to climate change.

Reservoirs play a significant role in the mobilization of water resources in Burkina Faso, contributing to the management and availability of water for various purposes. Operational management of reservoirs requires accurate and timely water level information, which remote sensing can provide cost-effectively and with limited resources. In this study, the surface area of 20 reservoirs is first determined using a Random Forest classifier and Sentinel-2 images acquired between 2015 and 2022. The accuracy of the classified surface water areas is evaluated by calculating 5 accuracy assessment metrics. The classifications were validated using manually digitized water areas from high-resolution Google Earth images and compared to the Dynamic World (DW) land cover dataset. Afterward, the spatial variation in the areal extent of the reservoirs is analyzed over time. A linear relationship is established between the estimated surface area and the corresponding observed water level of the reservoirs. The results indicate that reservoir surface areas were accurately classified with Sentinel-2 images (Kappa above 90.35%) for all dates. Moreover, validation with high-resolution images provided an R2 of 0.99 and a Normalized Root Mean Square Error (NRMSE) of 3.53%. Smaller reservoirs exhibit significant variations in surface areas over time as compared to larger ones, which are more stable. The relationship between surface area and water level is satisfactory (R2 ranging from 0.76 to 0.97) for 14 of the 20 analyzed reservoirs. The remaining six reservoirs are affected by aquatic plant intrusion which leads to an underestimation of the surface area. The high accuracy and operational feasibility of the proposed approach demonstrate that Sentinel-2 imagery and machine learning techniques can be recommended for reservoir mapping within the framework of water level monitoring in Burkina Faso.