GIS-Based Regional Spatial Crop Yield Modeling
Study Area and Databases
The chosen study area is India, lies to the north of equator, between 8° 4’ and 37° 6’ North and 68° 7’
and 97° 25’ East. It is bounded in the south by the Indian Ocean, in the west by the Arabian Sea, in
the east by Bay of Bengal, in the north-east, north and a part of the north-west by Himalayan ranges,
and the rest of the north-west by the Great Indian Desert. The soil characteristics of Indian nation
were obtained after digitization of survey of India soil map with many properties like soil
texture, soil pH and soil depth. Slope information of the country was derived from GTOPO30
(NGDC, 1997). Weather data were obtained and their surfaces were generated using World
Meteorological Organization station falling around 230 in number scattered throughout India.
Agricultural management data were obtained at state level where there numbers are more than
30 in total of entire India at 5 year interval which was used for coarse level whole country
simulation of 50 km cell size. On the other hand we succeed in procuring time-series data from
1974-1994 for one of the Indian State Bihar for detailed study at finer resolution simulation of
10-km cell size.
Results and Discussion
The model developed described in the earlier part of paper was found capable for simulating an
unlimited number of crop management strategies, based on the selection and data provided by
the user. In contrast to a stand-alone original EPIC crop simulation model, where the
management information given in the beginning continues for the total no. of simulations year,
the trend of output used to be more or less static and doesn't correspond to the actual farm
practice. With the development of dynamic loop under "Spatial-EPIC" it got rectified. Now with
this, during computation the model runs for each and every pixel following the rows and
columns sequence with various multiple soil, climate, and management information provided in
the form of layers. Two-year crop rotation was found appropriate for long term simulation. The
crops selected in a row were maize-wheat-rice. Crop management options provided by user are
applied to run the model. Besides these there are many other information which need to be fed
like start of simulation date, planting date, harvesting date, tillage time, irrigation timing its
amount, fertilization time and so on. Amount of fertilizer applied used was the reported state and
district level time-series data procured during the study. The crop selected in sequence for
modeling was rainfed maize (without irrigation), irrigated wheat and monsoon rice with one
user specified assured irrigation. All possible measures explained above were taken into account
to mimic the more realistic field practice. Yield simulation of the rainfed maize varied from 0.4
to 3.5 t/ha as described validation. Yield distribution of irrigated wheat crop varied between 0.5
to 3.5 t/ha also shown in figure described below under validation section clears that only the
northern part of India is the wheat belt. Because of the fact that the Indo-Gangetic plains form
the most important wheat area. The cool winters and the hot summers are very conducive to a
good crop of wheat, whereas the rice is being grown throughout India and the southern part was
found more favorable from agro-climatic conditions. Similarly yield variation of monsoon rice
was found to be fluctuating from 0. 3 to 3.0 t/ha. . As a reference and to serve a clear
understanding rather image of major and minor growing areas of these three crops in India with
their planting and harvesting calendar, the maps will be shown during the presentation of the
paper in conference. The distribution of crop productivity output is shown in figure as detailed
in validation section with its spatial validation to show their correspondence.
Validation
The first approach used to evaluate "Spatial-EPIC" yield simulation was to compare the output
at state level average reported data values. Closeness between measured and predicted yield at
state level is first and coarse level validation to see whether the simulated output is following the
trend is of reported aggregate average. For doing this the simulated 0.5 degree pixel resolution
falling under the state were averaged and their mean were compared with the reported state level
average for wheat and rice crop respectively. Also, all the pixels following under particular
district were averaged and their computed means were compared with the average reported
statistical district value for these three crops. To see the same output spatially distributed over
the country (as a spatial rough validation) where the main growing zone of these crops can also
be identified could be seen in figure 1 to 3 as validation process. Simulated vs. reported yield of
maize, wheat and rice as a rough cum spatial validation is rather important to have more explicit
understanding of the area and their correspondence between productivity. Although there were
some places where model has simulated high or low yields in case of maize and rice but in
general it gives a very nice comparison hence one can easily identify the model performance by
seeing these three maps as shown in the above said figure. Other validation results will be
shown during the presentation in terms of several regression analyses and their r squared values.
Figure 1. Rough Spatial Validation of Maize Crop in 1990-91
Figure 2. Rough Spatial Validation of Wheat Crop in 1990-91
Figure 3. Rough Spatial Validation of Rice Crop in 1990-91
Conclusions
The methodology presented found to be encouraging that provides an opportunity to plant
physiologist, a modeler and GIS user a common ground to discuss simulation results and further
potential research directions. Simulated crop yield and other maps generated under different
scale dependencies within India can be used to better communicate model predictions. Hence,
using this methodology a region/nation can be modeled for any crop productivity, which help
researchers and decision-makers understand the status and extent of climate, soils and crop cum
field management effects on global processes such as rice, wheat and maize production.
Acknowledgements
This research is funded by "Research for the future" (in Japanese, Mirai Kaitaku ) under the
program of Japan Society for Promotion of Science.
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