A New Toolkit for Monitoring and Forecasting Forage Supply in the Grazing Lands of Eastern Africa
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Abstract Pastoral communities in East Africa have faced unprecedented variation in weather, desertification and degradation of forage resources in recent times, leading to large-scale losses of livestock and reducing both marketing and management options, which renders pastoralists dependent on food aid. The increased uncertainty due to drought, coupled with the breakdown of options to use traditional risk management strategies, has led to a need for new approaches for early warning to make proactive decisions before drought sets in. One such possibility is the development of new technologies capable of providing information on emerging forage conditions to assist in improving livestock movement and sales options of pastoralists. The Livestock Early Warning System project of the Global Livestock Collaborative Research Support Program led by Texas A&M University has developed an automated modeling package to assist these mobile dry-rangelands livestock keepers in East Africa to cope with shocks of climate and make informed decisions about current and future forage conditions and thereby guide their mobility and decision-making patterns. The approach makes use of modeling techniques, Geographic Information System (GIS) and information and communication technology (ICT) and takes real-time, satellite weather data to drive a biophysical model called PHYGROW to simulate daily forage conditions and near-term forecasts of these conditions. Using geo-statistics, these point-based model simulations are linked with Normalized Difference Vegetation Index (NDVI) satellite images to create maps of forage supply and its deviation from normal. This information is updated every ten days with situation reports and maps distributed via WorldSpace radios, email, internet, and newsletters in the region. This technology suite has been developed in collaboration with national government agencies in Ethiopia, Tanzania, Kenya, and Uganda and NGOs working in pastoral areas.
KeywordsEarly warning East Africa monitoring PHYGROW co-kriging
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- Box, G. P., Jenkins, G. M. and Reinsel, G. C. 1994. Time Series Analysis: Forecasting and Control. Third edition. Prentice Hall, Upper Saddle River, NJGoogle Scholar
- Jama, A., Kingamkono, M., Mnene, W., Ndungu, J., Mwilawa, A., Sawe, J., Byenkya, S., Muthiani, E., Goromela, E., Kaitho, R., Stuth, J. and Angerer, J. 2003. Field Verification of Simulated Grazed Forage Standing Crop Using the PHYGROW Model and Satellite-Based Weather Data. USAID Global Livestock CRSP, Research Brief 03-03-LEWS AprilGoogle Scholar
- Kaitho, R., Stuth, J., Angerer, J., Jama, A., Mnene, W., Kingamkono, M., Ndungu, J., Mwilawa, A., Sawe, J., Byenkya, S., Muthiani, E. and Goromela, E. 2003. Forecasting Near-Term Forage Conditions For Early Warning Systems in Pastoral Regions of East Africa. USAID Global Livestock CRSP, Research Brief 03-02-LEWS AprilGoogle Scholar
- Stuth, J. W., Angerer, J., Kaitho, R., Jama, A. and Marambii, R. 2003. Livestock Early Warning System for Africa rangelands V. Boken (ed.), Agricultural Drought Monitoring Strategies in the World. Oxford University Press, Oxford UKGoogle Scholar