Forage Monitoring and Prediction Model for Early Warning Application over the East of Africa Region

Abstract

Rangelands dominate arid and semi-arid lands of the Greater Horn of Africa (GHA) region, whereby pastoralism being the primary source of livelihood. The pastoral livelihood is affected by the seasonal variability of pasture and water resources. This research sought to design a grid-based forage monitoring and prediction model for the cross-border areas of the GHA region. A technique known as Geographically Weighted Regression was used in developing the model with monthly rainfall, temperature, soil moisture, and the Normalized Difference Vegetation Index (NDVI). Rainfall and soil moisture had a high correlation with NDVI, and thus formed the model development parameters. The model performed well in predicting the available forage biomass at each grid-cell with March- May and October-December seasons depicting a similar pattern but with a different magnitude in ton/ha. The output is critical for actionable early warning over the GHA region’s rangeland areas. It is expected that this mode can be used operationally for forage monitoring and prediction over the eastern Africa region and further guide the regional, national, sub- national actors and policymakers on issuing advisories before the season.