This study aimed at modeling scenarios of future land use and land cover (LULC) change and estimating ecosystem service (ES) values for the year 2051 compared to 2021 in Central Ethiopia.
The presentation elaborated on the various scientific data used to develop Climate-Smart Mapping and Adaptation Planning (CS-MAP) for Cambodia. These include spatial data for flood risk and drought risk, land cover/land use type, and other spatial data (i.e., administrative boundary, natural resources, satellite images, and topography).
Land degradation and discontinuation of ecosystem services (ES) are a common phe nomenon that causes socio-economic and environmental problems in Ethiopia. However, a dearth of information is known about how ES are changing from the past to the future with regard to land use land cover (LULC) changes.
Forest conversion caused by subsistence or “basic needs populations†is difficult to track and measure. As the dynamics of these populations change over time, their carbon footprint impacts on natural resources also change. To reduce their potential negative impacts on forest resources, it is critical to understand what underlying causes influence their livelihoods practices.
In Cameroon, the pressure on wetlands, which cover nearly 70% of the national territory, appears to be increasing, whether for subsistence needs, firewood, grazing, logging or expansion of development projects. Currently, in terms of land use, forest has decreased by 619 km² and cultivated land has increased by 321 km².
Banana (and plantain, Musa spp.), in sub-Saharan Africa (SSA), is predominantly grown as a mixed crop by smallholder farmers in backyards and small farmlands, typically ranging from 0.2 ha to 3 ha. The crop is affected by several pests and diseases, including the invasive banana bunchy top virus (BBTV, genus Babuvirus), which is emerging as a major threat to banana production in SSA.
This study maps out Ghana’s multi-hazard risk of flood and drought by using machine learning (ML) models for susceptibility analysis, socioeconomic survey for vulnerability analysis and population density for exposure analysis. The ML models used were Logistic Regression (LR), Random Forest (RF) and Support Vector Machine (SVM) with inputs of location and features of natural hazards.
The dry lowlands of Ethiopia are seasonally affected by long periods of low rainfall and, coinciding with rainfall in the Amhara highlands, flood waters which flow onto the lowlands resulting in damage to landscapes and settlements.
context and backgroundDespite the robustness of various land instruments and legal and institutional regulations on various sectors, Tanzania’s land distribution-related policy implementation and practice is facing numerous challenges. Tanzania is not only experiencing poor land conditions, but also poor land management and ineffective policy implementation.
Tropical deforestation is estimated to cause about one-quarter of anthropogenic carbon emissions, the second largest source of greenhouse gas emissions after fossil fuel combustion. Deforestation is also catastrophic for the communities that depend on tree harvesting for the livelihoods and who suffer the consequences of natural disasters linked to deforestation (e.g., mudslides).
