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Spatial implementation of WT grows crop simulation model for regional wheat yield mapping
V. K. Sehgal, H. S. Rajpurohit, V. E. N. Mariappan, D. R. Rajak, A. Rao and V. K. Dadhwal
Crop Modelling Division, Agricultural Resources Group,
Space Applications Centre (ISRO), SAC P.O., Ahmedabad – 380 015
Dynamic crop simulations models that
incorporate a detailed understanding of mechanisms involved in plant growth as influenced
by biotic and abiotic environment, produce qualitatively better estimates of growth and
yield of crop plants in relation to changes in weather, soil properties and management practices.
However, these models have very high input data requirements and have generally been applied
at point scales (research farm plots). A study aimed at regionalizing of wheat simulation
model WTGROWS by using the GIS techniques and remote sensing data is reported. The WTGROWS
is a level three model, which simulate the potential production, phenology, soil water
balance, soil and plant nitrogen balance and water and nitrogen stress on plant growth and
development and has been well calibrated for Indian wheat cultivars. The study was carried
out for the state of Haryana for two consecutive years 1996-97 and 1997-98 representing a
good and bad year, respectively.
Vector coverages generated using Arc/Info included district boundaries (1:250,000 scale
maps) and soil map of Haryana (Source NBSS and LUP). The soil polygons were assigned mapping
unit code, which linked the coverage to the soil properties table. A soil texture map was
derived from the soil map and converted into raster layer. To account for soil fertility,
soil organic carbon map was produced by interpolating from more than 70 points collected
from literature using inverse square interpolation. Daily weather surfaces were generated
for the required meteorological parameters (Rainfall, Temperature maximum and minimum,
Sunshine hours, Wind speed, and Relative Humidity) by using inverse square interpolation of
point data of surface weather observatories. For each crop season, multi-date IRS-WiFS data
was acquired, registered and geo-referenced. Using hierarchical decision rule technique,
multi-date WiFS images were classified and wheat pixel mask was generated. All the raster
layers were projected into UTM coordinates with a pixel size of 180 meter and converted to
IDRISI raster GIS format. A linking shell was written in C language, which reads the input
raster layers and generate the required input parameters for the model for each pixel.
Pedo-transfer functions were incorporated into the shell to generate soil-water constants
from the textural class. The genetic coefficients of semi-dwarf wheat cultivar were taken
and an average scenario was used for management practices of date-of-sowing, irrigation and
nitrogen fertilizer application. The shell ran the model for each of the wheat pixel and
model outputs were written back into a raster layer to generate yield and biomass map. The
model could capture the spatial variability in average yields and also indicated lower
yields in 1997-98 in comparison to 1996-97, which is also observed in official estimates
released by State Bureau of Economics and Statistics.
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