Agriculture Reference
In-Depth Information
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daily water balance (comprising runoff, drainage, transpiration, evapora-
tion, interception, and soil moisture). At each monitoring site, PHYGROW
simulates differences in forage production resulting from the varying plant
communities, soils, grazers, and weather parameters. Each plant commu-
nity is composed of its major plant species, with each plant characterized
by physiological response to weather, soil conditions, and grazing pressure.
Biomass production and water balance are calculated daily for each site
using loops to depict natural feedback mechanisms throughout the ecosys-
tem, as a function of the intercepted radiation, precipitation, and temper-
ature. Plant community dynamics (growth, turnover, consumption, decay,
and competition) progress with each simulated day, influencing forage pro-
duction and water balance for the site.
Given the dearth of information on growth characteristics of native
species, considerable effort has been made to catalog plant growth at-
tributes (e.g., leaf area index, base/ceiling temperatures for plant growth,
turnover rates for leaves, stems, litter, day length sensitivity). The Food and
Agriculture Organization ECOCROP (FAO, 1998; http://pppis.fao.org)
database was an excellent starting point, and our team has cataloged
growth attributes of several hundred species in East Africa. A great need
now exists for the ecological scientific community to design algorithms to
estimate growth parameters from the known growth habits, taxonomy,
and morphology of herbaceous and woody plants to accelerate the param-
eterization process.
Because the PHYGROW model is used in grazed environments, it was
critical to have proper information on temporal changes in the animal pop-
ulation densities and their dietary preferences for plant species. We have
collected, by interviewing experienced pastoralists, sufficient information
to classify major species into the preference categories (i.e., preferred, de-
sirable, undesirable, toxic, nonconsumed, or only used as an emergency
forage by the grazing animals).
Excellent soil parameter estimators are available, which use basic infor-
mation on texture and soil family class to estimate parameters such as wet
bulk density, saturated hydraulic conductivity, and water holding capac-
ity. The most useful estimators for this exercise were the Map Unit Use File
(MUUF) soil attribute estimator (Soil Conservation Service, 1997) and the
Washington State University hydraulic properties calculator (Saxton et al.,
1986; http://www.bsyse.wsu.edu/saxton/soilwater/).
Other biophysical models that would be suitable for capturing range-
land response include the SPUR model (Wight and Skiles, 2000), the SA-
VANNA landscape and regional ecosystem model (Coughenour, 1992),
the Erosion Productivity Impact Calculator (EPIC; Williams et al., 1984;
1997), the USDA Water Erosion Prediction Project model (WEPP; Flana-
gan and Nearing, 1995), and the GRASP rangeland model (Littleboy and
McKeon, 1997). GRASP has been used as the basis of a prototype drought
alert information system for Australia (Brook and Carter, 1996). These
models have not been applied in an early warning context.
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