Offered Papers Theme C: Delivering the Benefits from Grassland
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Publication Date
2005
Location
Dublin Ireland
Description
Predicting forage supply is an age old quest for pastoralists, particularly in fragile and drought- prone areas of Africa. Traditional methods of forecasting forage used by many communities have become less effective due to climate change, frequent droughts and decline of grazing areas. Conflicts relating to available forage and water resources are increasing, because more marginal lands are put to crop production. A new forage forecasting technology has been developed that provides a comprehensive view of current forage condition (Stuth et al., 2004). A multiple species grazing land plant growth hydrology based model (PHYGROW) was parameterised with site-specific soil, plant community, grazer data that was spatially linked with satellite weather and predicted daily available forage (Rowan, 1995). The objective of this study was to explore use of the Auto-Regressive Integrated Moving-Average (ARIMA) procedure in forecasting a 30, 60 and 90-day available forage.
Citation
Kaitho, Robert J.; Stuth, J. W.; Angerer, Jay; and Jama, A. A., "Forecasting Forage Yields Using the ARIMA Model in Pastoral Areas of East Africa" (2005). IGC Proceedings (1985-2023). 37.
(URL: https://uknowledge.uky.edu/igc/20/themeC/37)
Included in
Agricultural Science Commons, Agronomy and Crop Sciences Commons, Plant Biology Commons, Plant Pathology Commons, Soil Science Commons, Weed Science Commons
Forecasting Forage Yields Using the ARIMA Model in Pastoral Areas of East Africa
Dublin Ireland
Predicting forage supply is an age old quest for pastoralists, particularly in fragile and drought- prone areas of Africa. Traditional methods of forecasting forage used by many communities have become less effective due to climate change, frequent droughts and decline of grazing areas. Conflicts relating to available forage and water resources are increasing, because more marginal lands are put to crop production. A new forage forecasting technology has been developed that provides a comprehensive view of current forage condition (Stuth et al., 2004). A multiple species grazing land plant growth hydrology based model (PHYGROW) was parameterised with site-specific soil, plant community, grazer data that was spatially linked with satellite weather and predicted daily available forage (Rowan, 1995). The objective of this study was to explore use of the Auto-Regressive Integrated Moving-Average (ARIMA) procedure in forecasting a 30, 60 and 90-day available forage.
