64
national level wheat forecasting is described by Supit
(1997).
(f) Linking of SOYGRO model to RS inputs and
ARC/INFO for Orangeburg County, South Carolina,
USA: In this study, the multidate SPOT data was used to
prepare an agricultural mask, meteorological data was
spatially organised in form of Thiessen polygons, soil
survey map (46 soil types reduced to 8 dominant soil
types) linked to attribute table for depth and soil water
storage characteristics. The large area simulation was
carried out by 40 model runs representing 8 dominant
soil types in each of 5 Thiessen polygons (Carbone et
al., 1996).
Table 3. Summary of results ofNational Level wheat forecasts using multi-date WiFS data.
1995-96
1996-97
1997-98
Sample Approach $
20x20 km, ST
20x20 km, ST
15x15 km, ST2
Mha
CV%
Mha
CV%
Mha
CV%
Bihar
2.2744
20.46
1.9333
19.42
2.1876
10.57
Haryana
1.9675
10.56
1.8735
13.00
2.1944
5.49
MP
3.9665
11.58
4.3005
11.17
4.4670
8.23
Punjab
3.2392
5.56
3.5301
6.03
3.2865
5.03
Rajasthan
2.3542
16.62
3.0768
20.11
4.4761
10.14
UP
10.5942
4.91
8.0572
7.03
8.7402
4.11
Other#
2.7750
5.35
2.8850
13.52
2.8651
10.01
INDIA
27.171
3.68
25.6569
4.22
27.2169
3.26
$ : Segment size, Stratification, (ST; Stratified Design, ST2 = Two stage stratified approach)
Table 4. Crop simulation models for regional yield modelling and prediction.
Crop
Model
Study Area
GIS
RS
Application
Reference
Millet
CERES-Millet
Burkina Faso
IDRISI
(0.25 x 0.25 deg)
Meteosat-Rainfall
Famine Early
Warning
Thornton etal., 1997
Sorghum
QSORG
South Australia
Arc/Info
Rainfall-polys#
-
Yield Forecasting
Rosenthal et al., 1998
Soybean
SOYGRO
S. Carolina, USA
Arc/Info
Rainfall, Soil, LC
Land cover (LC)
Spatial Yield
variability
Carbone et ai, 1996
Wheat
WOFOST
Europe
Arc/Info
50 x 50 km grid
-
Yield Forecasting
Supit, 1997
Multiple
crops
CROPCAST
Global
48 x 48 km grid
VI for validation
Global yield
forecasting
-
# polys : Polygon coverage