Rangelands dominate the arid and semi-arid lands of the Greater Horn of Africa (GHA) region, where pastoralism is the primary livelihood source.

The seasonal variability of pasture and water resources has an impact on pastoral livelihoods.

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 the March-May and October-December seasons depicting a similar pattern but with a different magnitude in tons per hectare.

The output is critical for actionable early warning over the GHA region’s rangeland areas.

This mode is expected to be used operationally for forage monitoring and prediction over the eastern African region and further guide regional, national, and subnational actors and policymakers on issuing advisories before the season.