Agriculture Reference
In-Depth Information
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ties require new innovations in characterizing, monitoring, analyzing, and
communicating the emergence of drought to allow pastoral communities
to cope with a rapidly changing environment. To this end, the United States
Agency for International Development (USAID) awarded the Texas A&M
University System an assessment grant to develop a Livestock Early Warn-
ing System (LEWS) as part of the Global Livestock Collaborative Research
Support Program. If a drought is imminent in an area, causing scarcity of
forage, pastoralists can benefit from LEWS and would be able to move
their livestock to other (nondrought) areas where forage is available.
Li
vestock Early Warning System
The LEWS (
http://cnrit.tamu.edu/lews)
was designed to provide an early
warning system for monitoring rangeland forage conditions, livestock nu-
trition, and health for maintaining food security of pastoralists. The pro-
gram is an integral part of the existing framework of early warning systems
for drought and famine in five countries (Tanzania, Kenya, Uganda, Ethio-
pia, and Eritrea) in pastoral areas of eastern Africa (figure 22.1). The de-
velopment and implementation of LEWS include spatial characterization,
establishment of monitoring sites, biophysical modeling, model analysis
and verification, and automation of information dissemination.
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Sp
atial Characterization
Spatial characterization first required stratification of long-term historical
weather patterns in each of the five countries. Subsequently, a map show-
ing zones of similar climate was created from a 5
[284
5-km gridded weather
surface of the entire continent of Africa developed by Corbett (1995) using
the AUSPLINE algorithm of Hutchinson (1991). To conduct the climate
clustering, the grid cells of the region were identified on the continental
climate surface, and the primary weather attributes were queried. These
attributes included maximum and minimum temperatures, annual rainfall,
potential evapotranspiration (PET), and accumulated rainfall correspond-
ing with the onset of a growing season. The attributes of the weather grid
subset were then subjected to a Ward's minimum variance clustering algo-
rithm (SAS, 1999). The resulting climatic clusters provided a mechanism
to help define the boundaries for mapping the point-model output and al-
lowed an objective mechanism to ensure that monitoring sites were located
in a manner that optimized the subsequent geostatistical analysis of the
model output.
×
Es
tablishment of Monitoring Sites
The LEWS monitoring technology toolkit was built to serve both relief
agencies and pastoral communities in the Greater Horn of Africa (Eritrea,
Ethiopia, Djibouti, Somalia, Sudan, Kenya, Uganda, Tanzania, Rwanda,
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