The study of land use and land cover (LULC) change and the evolution of ecosystem service values (ESV) is important for optimizing the allocation of regional land resources and achieving sustainable development, but traditional land use analysis methods cannot dig deeper into the information on the internal transitions between different land types. Therefore, in this paper, we use the component analysis and Intensity Analysis methods to systematically analyze the land use landscape changes at multiple levels.

One of the world’s major issues is climate change, which has a significant impact on ecosystems, human beings, agricultural productivity, water resources, and environmental management. The General Circulation Models (GCMs), specially the recently released (coupled model intercomparison project six) CMIP6 are very indispensable to understand and support decision makers to identify adaptation strategies in response to future climate change in a basin. However, proper selection of skillful GCMs and future climate assessment is a prior task to climate impact studies.

Compelling evidence in Sub-Saharan Africa (SSA) shows that Climate-Smart Agriculture (CSA) has a positive impact on agricultural productivity. However, the uptake of CSA remains low, which is related to anthropogenic, or human-related, decisions about CSA and agricultural land use. This paper assesses households’ decisions to allocate agricultural land to CSA technologies across space and over time. We use the state-contingent theory, mixed methods, and mixed data sources. While agricultural land is increasing, forest land is decreasing across countries in SSA.