In the tropics, extreme weather associated with global climate teleconnections can have an outsized impact on food security. In Ethiopia, the El Niño Southern Oscillation (ENSO) is frequently linked to drought-induced food insecurity. Many projections hold that El Niño events will become more frequent or more intense under climate change, suggesting that El Niño associated droughts may become more destructive. Agricultural vulnerability to extremes under climate change, however, is a function of exposure, sensitivity, and adaptive capacity. Sensitivity, in this context, can depend on subseasonal distribution of rainfall. This paper investigates crop sensitivity to sub-seasonal rainfall variability under climate change in a food insecure area of the Ethiopian highlands through analysis of process-oriented crop model results for the years 1981-2100 driven by 14 GCMs chosen for the ability to represent ENSO and rainfall characteristics of the study area. Further, adaptive capacity in the region is investigated with in-depth interviews and focus groups concerning the 2015 strong El Niño event. Crop model results for sorghum highlight that exposure and sensitivity to sub-seasonal extremes of low rainfall can diverge significantly from sorghum's response to seasonal drought. Even though climate change will bring generally warmer and wetter seasons to the study area, there is an increased occurrence of sub-seasonal failure of rains early in the rainy season which will likely have negative impacts on sorghum yield. In-depth interviews show that biophysical constraints significantly reduce farmer adaptive capacity to this type of sub-seasonal extreme. This work highlights the need to consider sub-seasonal weather when assessing climate change threats to agriculture, particularly for subsistence farmers in the developing world.

Ethiopia is a largely agrarian country with nearly 85% of its employment coming from agriculture. Nevertheless, it is not known how much land is under cultivation. Mapping land cover at finer resolution and global scales has been particularly difficult in Ethiopia. The study area falls in a region of high mapping complexity with environmental challenges which require higher quality maps. Here, remote sensing is used to classify a large area of the central and northwestern highlands into eight broad land cover classes that comprise agriculture, grassland, woodland/shrub, forest, bare ground, urban/impervious surfaces, water, and seasonal water/marsh areas. We use data from Landsat spectral bands from 2000 to 2011, the Normalized Difference Vegetation Index (NDVI) and its temporal mean and variance, together with a digital elevation model, all at 30-m spatial resolution, as inputs to a supervised classifier. A Support Vector Machines algorithm (SVM) was chosen to deal with the size, variability and non-parametric nature of these data stacks. In post-processing, an image segmentation algorithm with a minimum mapping unit of about 0.5 hectares was used to convert per pixel classification results into an object based final map. Although the reliability of the map is modest, its overall accuracy is 55%-encouraging results for the accuracy of agricultural uses at 85% suggest that these methods do offer great utility. Confusion among grassland, woodland and barren categories reflects the difficulty of classifying savannah landscapes, especially in east central Africa with monsoonal-driven rainfall patterns where the ground is obstructed by clouds for significant periods of time. Our analysis also points out the need for high quality reference data. Further, topographic analysis of the agriculture class suggests there is a significant amount of sloping land under cultivation. These results are important for future research and environmental monitoring in agricultural land use, soil erosion, and crop modeling of the Abay basin.

No abstract

No abstract

No abstract