Agriculture Reference
In-Depth Information
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A
utomation of Information Dissemination
Th
e LEWS toolkit has focused on developing fully automated computing
en
vironments that capture geo-referenced weather data, link the weather
da
ta with pre-parameterized PHYGROW files (soils, plant community, and
st
ocking rules), and generate the necessary data files and graphics files
fo
r each monitoring site. The automation system constructs the graphs
an
d texts and updates a Web site where the information is fully acces-
sib
le to the outside world. Additional files are generated for distribution
to
the Arid Lands Information Network (ALIN)
(http://www.alin.or.ke/),
w
hich places the data on the FTP site of the African Leaning Channel.
Th
ese data are then uploaded and broadcasted as HTML files (contain-
er
s) to laptop/desktop computers linked to WorldSpace satellite radios
(h
ttp://www.worldspace.com/;
chapter 21).
[288
Line
——
10.
——
Shor
PgEn
G
eostatistics and Spatial Extrapolation Using
Ve
getation Indices
The combination of the automated modeling and mapping system based
on sparse point analysis coupled with robust satellite imagery provides an
excellent opportunity to interpolate results to areas not actively monitored.
The geostatistical methods of ordinary kriging and co-kriging (Rossi et al.,
1994) were explored for this interpolation analysis as a mechanism to make
projections across large landscapes without intensive sampling.
In our case, the secondary variable is the NASA 10-day normalized
difference vegetation index (NDVI) for continental Africa that provides a
spatially rich data set of vegetation greenness across the landscape that has
been correlated with plant biomass production (Tucker et al., 1985). A de-
scription of the index is provided at the U.S. Geological Survey Africa Data
Dissemination Service Web site (
http://edcsnw4.cr.usgs.gov/adds/NDVI
Forage production estimates from the PHYGROW biophysical simula-
tion model for each of the monitoring sites served as the primary variable
in
both the kriging and co-kriging analysis. Gridded (8
[288
8 km) dekadal
(1
0-day) NDVI was used as the covariate in the co-kriging analysis. The
m
ajority of dekads analyzed have exhibited moderate to high correlations
be
tween forage production and NDVI (
r
×
.60-.86; Angerer et al., 2002).
C
ross-validation indicated that the co-kriging analysis generally does a
go
od job of estimating forage production (
r
2
=
=
.59-.80; standard error
=
292-495 kg/ha). Mapped surfaces of the co-kriging output allow us to
pinpoint areas of drought vulnerability (figure 22.2). During the periods of
high rainfall or extended drought, we have found that the correspondence
between forage production and NDVI can be low (
r
<
.30), requiring that
ordinary kriging be used for mapping forage production.
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