Priya, Satya
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, hence 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 option
provided by user the model could be briefly seen from figure 1 on its right hand side given management table. 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
ofthe rainfed maize varied from 0.4 to 3.5 t/ha as shown in figure described below under validation section for its
spatial distribution of productivity throughout India. The maize yield shows quite high potentiality but being a third
cereal it is not grown so widely like rice and wheat. 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 but the
southern part of India is found favorable from agro-climatic conditions. Similarly yield variation of monsoon rice was
found to be fluctuating from 0. 3 to 3.0 t/ha.
5.1 Validation
The first approach used to evaluate “Spatial-EPIC” yield simulation was to compare the output at state level average
reported data for the year 1995 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 maize, wheat and rice crop respectively. Again to go further ahead at same
resolution validation for whole India, the output for maize, rice and wheat for the year 1990 of these growing belts were
compared by overlaying the district coverage. To extract the mean value of a district simulated yield; all pixels were
overlaid with all India district boundaries, which are roughly 450 in number. All the pixels following under particular
district were averaged and their computed means were compared with the average reported statistical value for these
three crops. All of these results can not be presented in this paper due to limited allowed volume in terms of total page
no. Therefore, to see the same output spatially distributed over the country simulated vs. reported yield of maize, wheat
and rice, a rough cum spatial validation map are given in figure 5 to 7 to have more explicit understanding of the area
and their correspondence between productivity. Although there were some places where model has simulated more or
less 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. The reason for getting less and more
yields especially in rice crop is due to the limitation of not having district wise time series data of entire nation instead
we applied state level procured management data like fertilizer and others. But, with the above validation figures it is
self evident that the model was quite successful for simulating any piece of land as India could be one of the best
example of showing the diversity from one place to other in terms of climate, natural, economical as well as social
conditions.
Under the scope of the paper presented here country level (low-resolution) results have been explained whereas detailed
state level (high resolution) could not be illustrated due to space limitation. But to give a feeling on how high-resolution
results differs and gives more accurate output can be sensed seeing figure 8 comparing impact of two different
resolution input data over wheat yield.
5.2 Limitations
Validation of models with a high spatial resolution is difficult and in some cases impossible, as it is impossible to
validate each pixel output to a field data unless it is really being conducted under the same project. However, historic
analysis gives possibilities to validate the model assuming the reported input applied in a area and validating it with
simulated results. Usually in developing world all the data reported which could be fetched are not lower than the
district boundaries and size of those district also varies to a greater extent. But the multi-scale approach helps in
simulating the developing world where data are always a limitation. If certain grid cells, at the coarse allocation scale,
have more information then accordingly a cell size can be estimated and could be applied to model the area/region more
realistically.
1194 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000.
