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 ofAfrica (GHA) region, whereby pastoralism being the primary source oflivelihood. The pastoral livelihood is affected by the seasonal variabilityof pasture and water resources. This research sought to design a grid-basedforage monitoring and prediction model for the cross-border areas of theGHA region. A technique known as Geographically Weighted Regressionwas 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 thusformed the model development parameters. The model performed wellin predicting the available forage biomass at each grid-cell with March-May and October-December seasons depicting a similar pattern but witha different magnitude in ton/ha. The output is critical for actionable earlywarning over the GHA region’s rangeland areas. It is expected that thismode can be used operationally for forage monitoring and prediction overthe eastern Africa region and further guide the regional, national, sub-national actors and policymakers on issuing advisories before the season.