NATIONAL SPATIAL CROP YIELD SIMULATION USING GIS-BASED CROPPRODUCTION MODEL

Satya Priya and Ryosuke SHIBASAKI
Center for Spatial Information Science
University of Tokyo, 4-6-1 Komaba
Meguro-ku, Tokyo 153-8505, JAPAN
Phone: +81-3-5452-6412; Fax: +81-3-5452-6414
Email: satya@skl.iis.u-tokyo.ac.jp; shiba@skl.iis.u-tokyo.ac.jp

ABSTRACT
Traditional decision support systems based on crop simulation models are normally site-specific. In
policy formulation, however, spatial variability of crop production often need to be evaluated due todifferent soil conditions, weather conditions and agricultural practices within a target-region. To
address the spatial variability, a spatial model “Spatial EPIC” was developed based on a crop
simulation model EPIC (Erosion Productivity Impact Calculator). Since site-specific crop simulation
models require point-based or fine resolution data, it is necessary to feed the fine resolution data at
each grid-cell in order to “spatialize” crop simulation models. The authors proposed a method togenerate fine resolution data from coarse resolution data which are usually available at regional or
national level. In addition, since the original EPIC crop management practices are static in nature, adynamic adaptation loop is added to evaluate the impacts of agricultural practice changes overtemporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. National
scale (approx. 50km cell-size) and regional scale (approx. 10km cell-size) in India. Results showedthat at both resolutions level crop yield varied significantly as a function of seasonal climatic
variation, soil water holding characteristics and applied crop management strategies. Also, the study
successfully demonstrated model applicability in evaluating an impact of climate changes over major
cereal crops productivity at national level taking spatial variability into account.
Keywords: Crop simulation models, Geographic Information Systems (GIS), Agroecosystem,
National analysis and planning, Crop productivity.
1. INTRODUCTION
There is an emerging need to support policy formulation and decision-making in agriculture at verylarge geographic scales. Typical issues are climate change impact assessment and formulation of
mitigating measures, water resource allocation in a river basin at sub-continental scale such as Yellow
River and Ganges River, and environmental impact assessment of agriculture activities in such a large
river basin.
In order to develop a decision support system, biophysical processes and human interactions such as
adaptive changes of agricultural practice have to be modelled. The model should simulate, forexample, how the changes of environment such as climate change may influence crop yield and how
the changes in cropping pattern, cultivation intensity and management practices may affect theenvironment over time. Spatial variability in the impacts and changes of the processes need special
attention in the modeling process.
When we focus major crop production, it would be appropriate to apply process-based crop
simulation model for the purposes above. However, crop simulation models usually need site specificcharacteristics such as weather, physical and chemical parameters of soil, water management, andagronomic practices (Whistler, et., al, 1986, Penning de Vries et al., 1989) as input data. Applicability1

of these models can be extended to much boarder spatial scales by combining them with a Geographic
Information System (GIS). Several researchers have demonstrated or discussed the strength of thisconcept for agricultural management decision and planning at various spatial scales (Dent andThorton, 1988; Hoongenboom et al., 1990; Hoogenboom and Thorton 1990; Thorton and Dent 1987;
Throrton and Dent 1990). Thornton (1991) discussed the possibility of linking GIS with crop models
on a farm and regional level. Curry et. al. (1990) studied the crop model to find possible effects ofclimate change on agricultural productivity at regional level. Hoongenboom and Thornton (1990)
applied GIS to bean, maize and sorghum crop models to test potential of linked systems foragro-technology transfer in Guatemala. The crop model CERES-SORGHUM, using IBSNAT
standard input/output data formats, was linked to GIS to assist decision making in estimatingsorghum production in Indian semiarid tropics. Calixtte et al. (1992) developed an Agricultural andEnvironmental Geographic Information System (AEGIS), which combined DSSAT crop models with
GIS to assess the impact of different agricultural practices of Puerto Rico.
However, there is no attempt to apply process-based crop simulation models at scale of a very large
country or sub-continental scale. Since many of models applied to climate impact assessment at
continental to global scale estimate potential productivity, not actual productivity, using climate data,
effects of mitigation measures such as adaptation of agricultural practices can not be evaluated.
The objective of this paper is to develop a GIS-based crop model, which can predict crop yield, plant
growth and nutrient/moisture dynamics under different agricultural practices at national tosub-continental scale. At first, we have to evaluate how much accuracy can be achieved by“extending” the idea of GIS/crop model integration to large geographical scale using coarse
resolution data, which are usually available at such large scale. Technically, we have to devise amechanism to “generate” fine resolution data from coarse resolution data to run the models, whichwere originally developed as a site-specific model. Moreover, since some of process-based cropmodels are static in the sense that they can not take dynamic changes of practices into account, wehave to modify them to simulate the impacts of dynamically changing practices.
Therefore, the paper introduces the “Spatial-EPIC” (an extension of original EPIC), a dynamic
adaptive crop growth model on GIS platform. A two-tier, country and regional level simulations wereconducted to evaluate the model performances in India as a case study area. Finally to demonstratepotential of the model application, national scale climate change impact over rice crop is estimated inthis paper.
2. DEVELOPMENT OF “SPATIAL-EPIC”
In process of the development of “Spatial-EPIC”, the authors went for an extensive literature review
pertaining crop environment management field scale models because of the fact that such cropmodels are the outcome of decades not years. The aim of this research in terms of model development
was to select the most appropriate field scale/point based model rather than to start from scratch, andto develop them on a GIS platform with proper modifications to give a new approach through spatialmodelling.
2.1 Model Selection
Proper model selection is one of the most important step in any modelling exercise, if the model is not
being developed from very beginning. It was kept in mind that the selected model should provide
agricultural managers with a powerful tool to assess simultaneously the affect of farm practices oncrop production as well as on soil and water resources. Other model selection criteria included
minimum data requirements to run the model, wide usage, and a reasonable accuracy in predictions.
The following models were reviewed: (1) CREAMS and GLEAMS; (2) AGNPS; (3) ANSWERS; (4)SWRRB; (5) DSSAT and (6) EPIC. CREAMS and GLEAMS are field scale continuous models, they
do not possess a robust crop growth model (Ramanarayam 1994). ANSWERS and AGNPS were2

eliminated as they are watershed scale models simulating the effect of single rainfall events and are
not suited for simulating the continuous nature of plant growth (Binger 1990). The data requirements
for these models are extensive (where the use of GIS data are helpful) because of the distributed
nature of modelling. Furthermore distributed models provide tools to advance one`s understanding ofphysical processes, but for management purposes, their usefulness is limited (Grayson 1992).
SWRRB and EPIC are almost synonymous, except for the fact that SWRRB is a basin scale
simulation model. EPIC (Williams, J. R. and Sharpley, A.N.,1989), has improved residue-handlingcapabilities over SWRRB, and better nutrient cycling. Also EPIC has the added advantage ofaccounting for wind erosion (Binger 1990). DSSAT, crop simulation models and similar computer
decision have been developed for each crop management and other related processes (IBSNAT, 1989).
The limitation with DSSAT is that it doesn’t provide one model to simulate many crops instead it hasmodels for all specific crops. Also, earlier it was not public domain software (recently they havestarted to distribute their code to the licensed user only) where the other researcher may take up for its
further development as we did in case of EPIC model after having its source code.
Therefore, based on the literature reviewed and expert opinion gathered from model developers,
EPIC was selected for further development under the defined framework of this study. Some
additional model features that favored the selection of EPIC are (Dumesnil 1993):➀. EPIC is a continuous, field scale agricultural management/water quality model.
➁. EPIC is broad-based in terms of its components to model major biophysical processes which
include weather, hydrology, erosion, nutrients (nitrogen and phosphorus) cycling, pesticide fate, soil
temperature, crop growth, tillage, plant environmental controls and economics.
➂. The data required by EPIC are relatively minimal and was made available after deriving theconcept of generators (weather generator and slope generator).
➃ The model provides parameter data files for major crops, soils, and tillage practices. ➄ EPIC is
also equipped with a stochastic weather generator, and ➅. EPIC is capable of simulating thelong-term effects of cropping systems on soil erosion and productivity in specific environments.
2.2 Biophysical Computation
The model is composed of physically based components for simulating plant growth, nutrient,erosion, and related process for assessing crop productivity, determining optimal management
strategies, erosion and so on. Simultaneously and realistically, model simulates all these physicalprocesses. Commonly used input data are weather, crop, tillage, soil-attributes and management
parameters. The model runs on defined rather derived cell size data layers provided by the user
depending on their availability. Figure 1 shows physical factors considered in computing amathematical model to find the effects of crop productivity coming from different processes.
“Spatial-EPIC” is composed of physically based submodels for simulating weather, hydrology,
erosion, plant nutrients, plant growth, soil tillage and management, and plant environment control.
The model runs on daily time-step therefore, each model is linked subsequently and interactively withother sub models as demonstrated in figure 2. In brief, the computational procedures of all thesubmodels are described here under. Weather: daily rain, maximum and minimum temperature, solar
radiation, wind and relative humidity can be given directly or else they can be generated
stochastically. Hydrology: runoff, percolation, lateral subsurface flows are simulated. Erosion: it
simulates soil erosion by wind and water (for this paper the erosion part has not been included).
Nutrient Cycling: the model simulates, nitrogen and phosphorus fertilization, transformations, crop
uptake and nutrient movement. Nutrient can be applied as mineral fertilizers, in irrigation water, or asanimal manures. Soil: soil temperature responds to weather, soil water content and bulk density. It is
computed daily in each soil layer. Tillage: the equipment used affects soil hydrology and nutrient
cycling. The user can change the characteristics of simulated tillage equipment, if needed. Crop
Growth: A single crop model capable of simulating major agronomic crops. Crop-specific parameters3are available for most crops. The model also simulates crop grown in complete rotations. Plant
Environment: It is capable of variety of cropping variables, management practices, and othernaturally occurring processes. These include different crop characteristics, plant population, dates ofplanting and harvest, fertilization, irrigation, tillage and many more those are normally practiced in
the field.
Figure 3 shows a brief schematic presentation GIS data layers required for crop modelling andintegrated model run process respectively under “Spatial-EPIC”. This serves as an example of listed
natural resources and climatic data obtained from different sources and formats for creating GIS
layers to feed the model inputs
2.3 Development of Dynamic Adaptations Loop
The original EPIC is static with respect to management and technology. A single crop or rotation,
tillage practice, conservation measure, crop planting and harvesting date, and machine sequence is
specified prior to an EPIC simulation and cannot be varied during a simulation. The level oftechnology (such as plant genetic material and efficiency, plant varieties or cultivar, irrigation
efficiencies, and so on) is also fixed. This was one of the main bottlenecks to apply EPIC for cropmodelling over temporal scales because it can not adopt the management as per the climatic andresources prevailed. Therefore, the “Spatial-EPIC” carries a component where all these management
and technologies practices have been made dynamic. With this, one can easily change the practices,
crop rotation, other crop management like time-series fertilizer application, irrigation scheduling,tillage practice and so on as per the crop and area depending upon the resources and adaptations
required. The management practices applied under this simulation were crop based as well as theirrespective growing seasons. Hence, the development of “Spatial-EPIC” with an addition of dynamic
adaptations model is able to evaluate the impacts of agricultural practices more realistically over a
temporal run which were lacking in the original EPIC model.
2.3 Generating “Fine” Resolution Data from “Coarse” Resolution Data
As discussed before the “EPIC” model used for development is a field scale model. Hence the fineresolution data requirements was a big gap for applying it on a regional or national level scale, where
only relatively coarse resolution data are usually available. The concept of “generators” helps fill thegap. The generators are used not to save data storage size but to provide high-resolution (temporaland spatial) data from coarse resolution data. The generators helped to integrate or establish thelinkage of data to build a multi-scale GIS database. A weather generator model developed by
Richardson (1981a) was used for generating the daily weather data from statistical characteristics ofthe actual recorded weather data. The model is designed to preserve the dependence in time, theinternal correlation, and the seasonal characteristics that exist in actual daily weather data to compute
their monthly coefficients. These coefficients were used to generate back the daily data for model
input by stochastic simulation method for the said number of years as shown briefly in figure 4. The
authors compared the estimated yield using recorded daily weather condition data vs. generated daily
weather condition data, to see the differences, whereas after comparison they were negligibly small.
By extending the idea underlying the weather generator to spatial dimension, the authors develop aslope generator, which generates fine resolution slope gradient from coarse resolution ones. Fine
resolution slope gradient data is essential in estimating soil loss and resulting productivity
degradation. Figure 5 illustrates the procedure of slope generation from 10 km resolution DEM dataand how to apply the slope generator to soil loss estimation. The right part of the figure is ascatterogram of fine resolution slope gradient values (i.e. 1km grid size) against coarse resolution
slope gradient values (i.e. 10km grid size). When a coarse resolution slope gradient value is given at a
specific location, it can be inferred from the scatterogram to how much extent fine resolution slope
gradient value can fluctuate at the same location. The range of fluctuation or the distribution of fine4

resolution slope gradient can be converted to the distribution of soil loss through soil loss vs. absoluteslope gradient curve in the left part of Figure 5. By integrating the distribution of soil loss, total soil
loss can be estimated within a coarse grid cell from coarse resolution slope gradient. Unfortunately,
due to the lack of appropriate validation data, soil loss estimation using the slope generator was not
validated. The same kind of the idea, however, can be applied to generate a variety of spatial datasuch as soil data. Thus, dynamic linkage between short-term, fine scale data with relatively long-termand coarser resolution data can be established by devising nesting of observations at multiple
spatio-temporal scales.
3. STUDY AREA AND DATASETThe chosen study area is India, lies to the north of equator, between 8°4’ and 37°6’ North and 68°7’ and97°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 obtained fromdifferent sources (like Survey of India and National Atlas Thematic Mapping Organization, NATMO)
at national level in the form of maps (scale; 1:6 million) were used after digitization which has theproperties like soil texture, soil pH and soil depth. All these soil properties were used in the form oflayers for simulation. Slope information of the country was derived from 1km GTOPO (NGDC,
1997). Weather data were obtained and their surfaces were generated using World MeteorologicalOrganization (WMO) station falling around 230 in number scattered throughout India. With the helpof weather generator several layers were generated to feed the model, for example, minimum
temperature, maximum temperature, rainfall, minimum and maximum standard deviation oftemperatures and many more. Agricultural management data were obtained at state level where
numbers of the states are more than 30 in total of entire India at 5 year interval which was used forcoarse 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. Figure 6 shows an example illustrating how the base data layers wereused into simulation after unified scale of analysis. Also to show further characterization of some data
layers figure 7 shows the amount of fertilizer used for the year 1990 in India at state and district level.
Whereas, figure 8 shows the simulated root zone soil moisture as an intermediate output for the year
1970 and 1990 of entire country. With the help of above three figures (from 6 to 8) characterizationsof different data layers derived/used after different analysis under simulation can be easily
understood.
4. Results and Discussion
The model developed in section 2 was found capable for simulating a number of crop management
strategies, based on the selection and data provided by the user. It is a good contrast with site-specificoriginal EPIC, where the management information given in the beginning continues for the total
period of simulations year, hence the trend of output used to be more or less static and doesn’t
correspond to the actual farm practice. An addition of dynamic adaptations had overcome this issue.
Now with this, during computation, the model runs for each and every pixel following the rows andcolumns sequence with various multiple soil, climate, and management information provided in theform of layers. Two-year crop rotation was found appropriate for long term simulation. Two-year
crop rotations were used. This proved to be better for the farmers to decide their productivity-based
profit from the resources applied to continue the rotation or not. Hence, it was applied to impersonate
more realistic simulation. The crops selected were maize-wheat-rice. These three crops were selected,
as they are the first three main cereal crops from acrage and consumption in India. Crop management
5option supplied by user in the model can briefly be seen from figure 3 on its right hand side given
management table. Besides these there are many other information which need to be fed like start ofsimulation date, planting date, harvesting date, tillage time, irrigation timing and its amount,fertilization time and so on. Amount of fertilizer applied was the reported state and district level datafrom 1975 to 1992 procured during the study. The crop selected in sequence for modelling was
rainfed maize (without irrigation), irrigated wheat and monsoon rice. Possible measures explained
above were taken into account to mimic the more realistic field practice. For better understanding ofmajor and minor growing areas of these three crops in India, figure 9 can be referred with theirplanting and harvesting calendar. Yield simulation of the rainfed maize varied from 0.4 to 3.5 t/ha asshown in figure 10 described below under validation section for its spatial distribution of productivity
throughout India. The maize yield shows 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 11 described below under validation section clears that mostly the northern partof India is the wheat belt. This is because the Indo-Gangetic plains form the most important wheatarea. The cool winters and the hot summers are very conducive to a good crop of wheat, whereas therice is being grown throughout India but the southern part of India forms most favorable season fromagro-climatic conditions. The yield variation of monsoon rice was found to be fluctuating from 0. 3 to
3.0 t/ha. The spatial distribution of crop productivity output has been dealt in following validation
section with its mapping/graphical representation to demonstrate the correspondence between
simulated and reported crop yields.
4.1 Validation
The first approach used to evaluate “Spatial-EPIC” yield simulation was to compare the output atstate level average reported data for the year 1995 values. Closeness between measured and predicted
yield at state level is the first and coarse level validation to see whether the simulated output isfollowing the trend of reported aggregate average. For doing this, simulated 0.5 degree pixel
resolution falling under the state were averaged and their mean were compared with the reported statelevel average shown in the form of bar chart from figure 10 to figure 12 for maize, wheat and rice crop
respectively. Again to go further ahead at same resolution validation for whole India, the output formaize, 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 underparticular district were averaged and their computed means were compared with the average reported
statistical value for these three crops. Spatial simulated vs. reported yield of maize, wheat and ricecomparisons were helpful to find correspondence between growing region productivity. Although
there were some places where model has simulated more or less yields in case of maize and ricewhereas in general their simulated vs. reported values were close to each other (figure 13 to 15). The
reason for getting less and more yields especially in rice crop is due to the limitation of not having
district level time series data of entire nation instead we applied state level average management data
(esp. fertilizer, figure 7). The above comparison demonstrates rough spatial cum average state levelvalidation because of the fact that the applied management data for whole country simulations werestate level average.
The other approach used to evaluate “Spatial-EPIC” yield simulations was to compare the mean andstandard deviation (SD) of the simulated yields of a crop with the same statistics for measured yields.
Closeness between measured and predicted yields for these two statistics is important when making
long-term decisions. This exercise was done for Bihar (one of the slightly north eastern Indian state)
where time-series management data were applied as well as reported district level crop yield startingfrom year 1975 to 1994 made available. “Spatial-EPIC’s” mean simulated yields were found close to
the reported mean yields (table 1). The mean differed by 12% or less. The difference between the6reported and simulated means was smallest for rice than the wheat and greatest for maize. The
standard deviations of the simulated yields were also similar to the SD’s of measured, but thedifferences in percentage between simulated and measured were greater in general for the SD’s thanfor the means. The greatest difference, 32%, was for rice. The other two crops maize and wheat had
differences of 15 and 2%. Regression analysis for simulated yield on reported yield is anothervaluable technique of describing model performance. The first aspect of this approach was to check asignificant relationship between simulated and predicted yields. This consisted of testing whether theslope of regression was significantly different than zero. If it was not, the model would have failed toshow any superiority over simply using the mean reported yield for prediction. Finally, the r squared
value provided the final means of describing how well the simulated and reported yields agreed.
Simulated yields of maize, wheat and rice were also significantly correlated to the reported yields.
The r2 were 0.73, 0.68 and 0.59 for maize, wheat and rice respectively. Figure 16 to 18 shows the
temporal correlation against 1:1 slope line with their best-fit line for the said time series data of maize,
wheat and rice. These figures showed that the temporal changes of crop yield from 1974 to 1990
could impersonate the reality hence it can be applied to relatively long-term crop yield simulation as
well. Therefore, from the evaluation results described above, it can be concluded that the crop yield
simulation using “Spatial-EPIC” appeared to be adequate for the purpose for which it was designed.
4.2 Sensitivity Analysis
A sensitivity analysis of “Spatial-EPIC” was undertaken to identify those inputs which, whenmodified, produce important changes in the value of outputs. The aim of sensitivity analysis here wasto identify an impact of particular sensitive physical inputs. While carrying these processes thephysical changes of soil properties in terms of soil texture i.e. the content of sand, silt and clay was
given with two different data source as well as of two scales. One of them scales were 1 degreeroughly 100km on ground and was derived from global ecosystems dataset where other was bought indigital form (Source: SOI, 1988 paper map, scale 1:2million) from India. The advantages with the 1
degree dataset was that it had four layer, where the first layer was fixed to 10mm with sand, silt andclay content, the other layers information was provided based on global ecosystem dataset which
varies from 0.1 to 0.5 cm. Output with such a coarse resolution used to be good due to the different
layer information whereas the other digitized data had much better information due to high resolution
but it didn't had the information in terms of different layer. Hence, while applying the relatively highresolution data to the model it was assumed that all layer has same values in terms of information to
feed the model but in reality it was not the case. This serves a good example of showing the sensitivityof the model as data were changed and used to run the model. Its impact was found and could be seenfrom figure 19 which shows impact over yield fluctuations in terms of pixel response of these twodataset. Another interesting example of sensitivity analysis was, using soil pH map obtained from
FAO derived data source. As the FAO derived pH showed most of the Indian soils acidic hence theoutput as a crop yield response just followed the pH trend with a very low productivity to moderateone (see figure 20). Applying this we found the model is quite sensitive to data and to correct this wewent in procuring toposheet of 1:2M scale from Survey of India, digitized and used which gave asatisfactory and the desired result. Figure 20 serves an example of the sensitivity in its spatial map
form to have a clear understanding. Besides these sensitivity tests the impact of two applied cell size
10 km and 50 km respectively for Bihar State and whole India were also studied. It is very clear that
depending upon the cell size, the level of details becomes available at different resolutions. It is thus
important to realize the impact of different resolution dataset while generating it for model
application from its accuracy and better performance viewpoint. To visualize an impact of these two
scales figure 21 serve a very good example to show how an output of a wheat crop of Bihar Statevaried significantly due to the two high (~10km) and low (~50km) resolutions dataset.
74.3 Application of “Spatial-EPIC” to the climate change assessment at national scale
The following example will demonstrate the potential uses of the model application to assess climatechange impact (e.g. global warming) over Indian agriculture. As we know the large-scale distributionof crops is usually determined by climate, the results of rice crop in a changing climate with it impacts
are presented here. The whole exercises were carried out using several regional and global
agricultural-environmental databases. For this simulation, all the management inputs given at thebase year 1995 are assumed to be same except the changes in climate based on the scenarios for the
next twenty-year. Simulation with assumed weather conditions generated from climate change
scenarios resulted in spatial agricultural potential changes are shown in figure 22. Result shows that
there will be large regional differences in the response of crops across the country. The north-southand east-west regions responded differently with projected climate changes with increased anddecreased productivity (refer figure 22) depending upon the scenarios separately. As an application
the model succeeded in quantifying the spatial crop productivity and hence it might become a goodtool for planners to formulate a country or regional level policies and mitigation measures.
4 CONCLUSIONS
The methodology presented here was found to be encouraging because it provides an opportunity forplant physiologist, a modeler and GIS users to have a common ground in order to discuss simulation
results and further potential research directions. Simulated crop yield and other maps generated under
different scales within India and Bihar can be used to better communication over the 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 andcrop cum field management effects on global processes such as rice, wheat and maize production.
To evaluate “Spatial-EPIC” yield simulation validation were carried out in different pockets of India
based on the major growing reasons. Two tier validations were done at two different cell resolutions,coarse and fine for whole India (0.5 degree cell size) as well as one of the Indian states Bihar (0.1
degree cell size) respectively. Coarse resolution validation results for the entire country found to be
successful for wheat and maize productivity whereas in case of rice it was a bit under estimated insouthern most part of India whereas the other places gave better correlation between the simulated
and observed values. It is believed that the model can be used in simulating any piece of land since
India is 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 from model applicability viewpoint.
Usually in developing world all the data reported could be fetched at not lower than the district levels.
And size of those districts also may vary to a greater extent. But the multi-scale approach helps insimulating the developing world where data are always a limitation.
Hence, the “Spatial-EPIC” possesses immense potential as a decision tool especially for regional tonational policy formulations. However, further research should be focused on improving the model
prediction, and the field level interactions within the system. Also, availability of new agriculturalland-use maps with seasonal crop delineation, and other information of the management practices
will help in bettering the model results.

ACKNOWLEDGMENTS
Authors take this opportunity to thank the Global Frontier Project (in Japanese, Mirai-Kaitaku)
sponsored by Japan Society of Promotional Science for providing generous financial support in orderto accomplish this study.
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Running, S.W. and Coughlan, J.C. 1988. A General Model of Forest Ecosystem Processes for
Regional Applications I-Hydrolgical Balance, Canopy Gas Exchange and Primary Production
Processes: Ecological Modelling, Vol. 42 pp. 125-154.
Running, S.W., and Nemani, R.R., 1991. Regional Hydrological and Carbon Balance Response ofForests Resulting from Potential Climate Change: Climate Change, Vol. 19, pp.349-368.
Satya Priya Shibasaki, Ryosuke and Shiro Ochi, 1998. Modelling Spatial Crop Production: A GIS
approach, Proceedings of the 19th Asian Conference on Remote Sensing, 16-20 Nov, 1998 held at
Manila. pp A-9-1 to A-9-6.
Schaller, N., 1990. Maintaining low input agriculture. J. Soil and Water Conservation 45:9-12.
Schimel, D.S., and Burke, I.C., 1991. Spatial Interactive Models of Atmospheric Ecosystem
9Coupling in Pre-Conference Proceedings: First International Conference/Workshop on Integrating
GIS and Environmental Models, Boulder, Coloardo, September 15-18, 1991.
SOI. 1988. Survey of India paper map used for digitization of soil and soil acidity map.
Williams, J. R. and . Sharpley, A.N., (eds.), 1989. EPIC --Erosion/Productivity Impact Calculator: 1.
Model Documentation, USDA Technical Bulletin No. 1768.
Table 1. Means and standard deviations of reported and simulated yields

Crop
Maize
Wheat
Rice

District_Average_Yield
Measured
Simulated
Mean
SD
Mean
SD
----------------tons/ha----------------------1.39
0.39
1.57
0.34
1.47
0.29
1.34
0.46
1.24
0.19
1.14
0.26

r

2

0.74
0.68
0.59

Radiation
Precipitation

Temperature

Evapotransipration

TillageWind

Layered Soil
Pedon

RunoffSediment

Chemicalsa. Dissolvedb. Adsorbed

Percolation
Figure 1. Physical Components of the Model

10Figure 2. Modelling Linkage Diagram

Natural Resources

Climatic Data

Elevation
Temp.
Slope
Rainfall
Soils
Crops
Management Practice
Crop Fert. Irrig. HRU
HRU Coverage
curve no. (CN)Spatial-EPIC
Crop YieldRZSWFigure 3. Brief schematic Presentation of modelling under“Spatial-EPIC”

11

Statistical analysis
Stochastic generation of“fine” resolution data
Monthly data(coarse resolution)
Daily weather conditions

50
45
40
35
30
Temp 25
20
15
10
5
0

daily_temp
monthly_temp

1

26

51

76

10

12

15

17

20

22

25

27

30

32

35

Days

Figure 4. Concept of ‘weather’ generator (generating high resolution temporal daily data from coarse
resolution monthly data.)

0.2

Variability of fineresolution slope gradient

0.4

0.2

0
20

15

10

5

Soil Loss (mm)

0

-0.4

-0.2

0.0

0

0.2

0.4

0

Slope gradient from fine resolution DEM(e.g. 1km)

-0.2

Slope gradient from coarse resolution DEM (e.g.10km)

0.4

Absolute slope gradient in degrees

0.6

-0.4

Variability of soil loss caused usingcoarse resolution DEM
Slope gradient from coarse
resolution DEM

Figure 5. Concept of ‘slope’ generator (deriving the range and impact of two resolution DEM
computed slope over soil loss)
12

Spatial Grid basedlayer information
Interpolated
map

Unified scaleof analysis

RainfallTemp.

ClimateSoil texture

Soil pH

Irrigated

Topography

Statistical data

Boundary map

Figure 6. Example of data applied in the model

Figure 7. Characterization of fertilizer layer applied (a) State level (b) district level in the year 1990
13

Figure 8. Characterization of simulated root zone soil moisture district level in the year 1990

Figure 9. Main Growing Region of Corn (Maize), Wheat and Rice Crop in India

14

dh

ra

h

m

es

sa

ad

As

Pr

Bi
ha
r
G
uj
Ja
ra
t
H
m
ar
m
ya
u
&
na
Ka
sh
Ka mi
rn r
at
ak
a
M
ad
Ke
hy
r
a
a
Pr la
ad
M
es
ah
h
ar
as
tra
O
ris
sa
Pu
n
Ta
j
m ab
il
U
N
tta
a
r P du
ra
W
de
es
s
tB h
en
ga
l

An

Average Reported Yield (t/ha)
ha
r

uj
ra
t
H
H
im
ar
ac
ya
ha
na
Ja
lP
m
r
a
m
de
u
sh
&
Ka
M
ad
sh
hy
m
ir
a
Pr
ad
es
M
h
ah
ar
as
tra
Pu
nj
ab
R
aj
as
U
th
tta
r P an
ra
de
W
sh
es
tB
en
ga
l

G

Bi

Average Yield (t/ha)
Pr
ad
es
h

R
aj
as
th
U
an
tta
rP
ra
de
W
sh
es
tB
en
ga
l

Pu
nj
ab

O
ris
sa

Bi
ha
r
Ja
m
G
m
uj
u
ra
&
Ka t
sh
m
ir
Ka
rn
M
ad
at
ak
hy
a
a
Pr
ad
es
h
M
ah
ar
as
tra

An
dh
ra

Average Yield (t/ha)
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Actu_M aize96
Sim u_M aize96

Figure 10. State Wise Comparison of Maize Crop Yield (t/ha)

4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Actu_Wheat91
Sim_Wheat91

Fig. 11. State Wise Comparison of Wheat Crop Yield (t/ha)

3.5

3.0

2.5

2.0

1.5

1.0

Actu_Rice95
Sim_Rice95

0.5

0.0

Figure 12. State Wise Comparison of Rice Crop Yield (t/ha)

15

Ma

0.5 degree cell resolution, roughly 50km
Figure 13. Rough Spatial Validation of Maize

Fig. 11

0.5 degree cell resolution, roughly
Figure 14. Rough Spatial Validation of

16

0.5 degree cell resolution, roughly 50km on

Simulated District Average (t/ha)

Figure 15. Rough Spatial Validation of Rice

3.0
Maize_75

2.5

Maize_77
Maize_81

2.0

Maize_87

1.5

Maize_91

R2 = 0.7356

1.0
0.5
0.0
0.0

1.0

2.0

3.0

Reported District Average (t/ha)

Fig. 16 Time-Series Validation of Maize Crop in Bihar

17

Simulated District Average (t/ha)

Wheat_72

3.0

Wheat_74

2.5

Wheat_76
Wheat_80

2.0

Wheat_82
Wheat_84

1.5
2Wheat_86

R = 0.686

1.0

Wheat_87

0.5

Wheat_90

0.0
0.0

1.0

2.0

3.0

Reported District Average (t/ha)

Simualted District Average (t/ha)

Fig. 17 Time-Series Validation of Rice Crop in Bihar

Rice_76
Rice_77
Rice_79
Rice_80
Rice_81
Rice_84
Rice_88
Rice_90
Rice_92
Rice_94

3.0
2.5
2.0
1.5
1.0

R2 = 0.593

0.5
0.0
0.0

1.0

2.0

3.0

Reported District Avergae (t/ha)

Fig. 18 Time-Series Validation of Wheat Crop in Bihar

Rice Yeild (t/ha)

2.5
1 degree
0.2 degree

2
1.5
1
0.5
4053

3862

3736

3668

3547

3482

3418

3353

3288

3224

3160

3099

3087

0

PixelsFigure 19. Sensitivity analysis of model response with two different resolutions of soil data over ricecrop
18

Figure 5.29 Direct Impact of Input Data on Yield a Sensitivity Example of the Model
0

N
W

E
S

Bihar_pH
3.4 - 3.622
3.622 - 3.844
3.844 - 4.067
4.067 - 4.289
4.289 - 4.511
4.511 - 4.733
4.733 - 4.956
4.956 - 5.178
5.178 - 5.4

Maize_1991 (t/ha)
0 - 0.259
0.259 - 0.517
0.517 - 0.776
0.776 - 1.035
1.035 - 1.293
1.293 - 1.552
1.552 - 1.811
1.811 - 2.07
2.07 - 2.328

Figure 20. Direct Impact of Input data (soil pH) on crop yield; an example of model sensitivity

19

Map 5.4 Comparision of Two Different Resolutions Impact Over Wheat Yield

N
W

E
S

27

25

23

21

83

85

Spatial Resolution
10 x 10 degree(~ 10 x 10 km)

87
10 km Cell Size (t/ha)
0 - 0.5
0.5 - 1
1 - 1.25
1.25 - 1.5
1.5 - 22 and above

89

Spatial Resolution
50 x 50 degree(~ 50 x 50 km)

50 km Cell Size (t/ha)
0 - 0.5
0.5 - 1
1 - 1.25
1.25 - 1.5
1.5 - 22 and above

20

Figure 21. Comparison of two different resolutions data impact over wheat yieldFig. 22 Model application as a climate impact assessment over rice crop21