Series of thematic maps on biophysical and socioeconomic status of Malawi to guide targeting of agricultural technologies
Status and trends of mixed farming systems, targeting, data visualization.
Status and trends of mixed farming systems, targeting, data visualization.
Mangroves are highly productive ecosystems that provide important ecosystem services, are strategic allies in carbon capture and storage, conserve different plant and wildlife species, are producers of aquatic species such as crabs and shrimp, and local communities have developed strong economic, cultural and identity ties. Despite their great ecological, economic, and social importance, mangroves are threatened by natural and anthropogenic factors, hence the importance of their constant monitoring.
La elección en Honduras para implementar esta iniciativa no es casualidad, desde hace varios años el paÃs enfrenta eventos climáticos extremos y permanece expuesto a condiciones de variabilidad que retan la estabilidad de su producción agrÃcola. Las MAPs han surgido como una acción de respuesta valiosa para abordar la problemática, conectando a eslabones de la cadena de información y traduciendo los mensajes brindados por CENAOS-COPECO.
A new report by Small Foundation and Palladium looks at the viability of geomapping as a tool to close the smallholder farmers’ financing gap and improve their livelihoods.
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. The surface area of degraded forests and land is estimated at around 12 million hectares, with a general trend towards an increase in the phenomenon due to both natural and anthropogenic factors.
River Ona Discharge Modeling Using Gis And Logarithmic Transformation Model
At the start of the UN Decade of Ecosystem Restoration (2021–2030), the restoration of degraded ecosystems is more than ever a global priority. Tree planting will make up a large share of the ambitious restoration commitments made by countries around the world, but careful planning is needed to select species and seed sources that are suitably adapted to present and future restoration site conditions and that meet the restoration objectives.
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. Topographic, precipitation, temperature, hydrology, land cover and soil cover raster images were also used in these models.
Location-specific information is required to support decision making in crop vari-ety management, especially under increasingly challenging climate conditions. Datasynthesis can aggregate data from individual trials to produce information that sup-ports decision making in plant breeding programs, extension services, and of farmers.Data from on-farm trials using the novel approach of triadic comparison of technolo-gies (tricot) are increasingly available, from which more insights could be gainedusing a data synthesis approach.
This brief presents how a participatory approach in livelihood mapping was applied to define the need for climate services in key cropping systems within major livelihoods. This process was built upon the existing product developed by the World Food Programme (WFP) in Cambodia called Consolidated Livelihoods Exercise for Analyzing Resilience (CLEAR) maps.
East African highland banana (Musa acuminata genome group AAA-EA; hereafter referred to as banana) is critical for Uganda’s food supply, hence our aim to map current distribution and to understand changes in banana production areas over the past five decades. We collected banana presence/absence data through an online survey based on high-resolution satellite images and coupled this data with independent covariates as inputs for ensemble machine learning prediction of current banana distribution.
The presentation discussed the various steps in Climate-Smart Mapping and Adaptation Planning (CS-MAP), such as: defining climate-risks and agriculture products, mapping climate-risks, proposing adaptation plans, revising climate-smart maps and adaptation plans, and map integration at province level.