Technology adoption in small-scale agriculture

This study sets out to explore one of the most important questions for alleviating poverty in sub-Saharan Africa namely: why are advancements in agricultural technology not taking root in this region? Using data from deep interviews of 42 smallscale farmers in Ghana and Cameroon, a conceptual analysis of drivers and factors of agricultural technology adoption in this region is made and represented as causal loop diagrams. Interviews also provide a basis for weighting factors that farmers consider before adopting a new technology. These weights are then used to run a systems dynamics model with a hypothetical population of 10.000 farmers to see the effects of different drivers of technology adoption on the adoption rate and number of adopters over a 25 year period. Results show that most farmers have a bethedging strategy as they try to minimize risks of production failures. While certain factors like scale of production, long-term considerations, the history of success of past technologies, and the endorsement of technologies by opinion leaders may be important, many other factors do influence decisions to adopt new technologies. This limits any silver bullet strategy towards solving the problem of limited diffusion of agricultural technologies in this region. Addressing such a problem therefore calls for a much more holistic approach

A longer, closer, look at land degradation

Arresting land degradation, not to mention remediation, requires long-term investment. Budgetary constraints mean that we have to prioritise, so decision makers need know exactly where and how severe is the degradation, and they need early warning to act in good time. The first global assessment using actual measurements was based on 23 years of Advanced Very High Resolution Radiometer (AVHRR) Normalised Difference Vegetation Index (NDVI) data at 8km resolution. Its aim was to identify black spots that should be investigated in the field – but hardly anybody did. The dataset now extends to 33 years, revealing both long-term trends and many reversals of trend. The areas hardest hit are sub-equatorial Africa, with outliers in the Ethiopian highlands and the Sahel; the Gran Chaco, Pampas and Patagonia; southeast Asia; the steppes from Moldova eastwards into Central Asia; the Russian far east and northeast China; and swaths of high-latitude forest. Since 2000, it has been possible to seamlessly scale up the coarse-resolution picture to 250m resolution using data from the Moderate- Resolution Imaging Spectroradiometer (MODIS) and to 30m resolution with Landsat. Now, thanks to commercial satellite data, we can zoom in, anywhere in the world, with 5m-resolution

Landscape‐scale spatial modelling of deforestation, land degradation and regeneration using machine learning tools

International audienceLand degradation and regeneration are complex processes that greatly impact climate regulation, ecosystem service provision and population wellbeing and require an urgent and appropriate response through land use planning and interventions. Spatially explicit land change models can greatly help decision makers, but traditional regression approaches fail to capture the nonlinearity and complex interactions of the underlying drivers. Our objective was to use a machine learning algorithm combined with high-resolution datasets to provide simultaneous and spatial forecasts of deforestation, land degradation and regeneration for the next two decades. A 17000 km2 region in the south of Madagascar was taken as the study area. First, an empirical analysis of drivers of change was conducted, and then, an ensemble model was calibrated to predict and map potential changes based on twelve potential explanatory variables. These potential change maps were used to draw three scenarios of land change while considering past trends in intensity of change and expert knowledge. Historical observations displayed clear patterns of land degradation and relatively low regeneration. Amongst the twelve potential explanatory variables, distance to forest edge and elevation were the most important for the three land transitions studied. Random Forest showed slightly better prediction ability compared to MaxEnt and GLM. Business-as-usual scenarios highlighted the large areas under deforestation and degradation threat, and an alternative scenario enabled the location of suitable areas for regeneration. The approach developed herein and the spatial outputs provided can help stakeholders target their interventions or develop large-scale sustainable land management strategies

Impact of prolonged rainy seasons on food crop production in Cameroon

Rainfall, Seasons, Effects, Agro-ecological zones, Agriculture, Small-scale farming,