Modeling Spatial Crop Production: A GIS Approach
Spatial-EPIC
Spatial-SPIC is a crop simulation model being developed to estimate the relationship between soil erosion and crop productivity, which has been implemented in GIS environment to have spatial distribution of crop output. In addition to the plant growth routine, the model includes components for weather simulation, hydrology and crop management. It operates on a daily time-step. Among factors simulated by EPIC are evapotranspiration (based on Penmen Monetih model), soil temperature, crop potential growth, constraints and yields. EPIC uses a ssingle model for simulating all crops, although, of course, each crop has unique values for model parameters. For this study, CO2 concentration held at current ambient level (350ppm) has been used. Crop yields are estimated by multiplying the aboveground biomass at maturity (determined by accumulation of heat units of specific field harvest data) by a harvest index for the particular crop.
Preparation of spatial-EPIC Dataset
Model requires data on soil properties, which has been assumed same for all the area as Spatial-EPIC is under progress and data required for these parameters are under development. The detailed profiles of relevant production characteristics (for example, fertilization, planting, harvesting, irrigation and heat units for maturity) were complied for the crops simulated across the region. The range of values for those production characteristics is shown on table 1. the weather variables necessary for those production characteristics is shown on table 1. The weather variables necessary for driving the EPIC, a main impetus of this paper, are daily values of precipitation, minimum/maximum are temperature, solar radiation and humidity. A detailed discussion of climatic data generation is described in the next section.
| Maize |
Planted | 7thJuly |
| Total Fertilizer(urea+elem.-Phosphorus) | 40+10 kg |
| Total no. of irrigation/amount, (500/800) | 2/1300 mm |
| Wheat | Sown | 10th November |
| Total ertilizer(urea+elem.-Phosphorus) | 75+15 kg |
| Total no. of irrigation/amount, in 3 splits | 3/3000 mm |
| RiceWithout Irrigation | Planted | 7th July |
| Total Fertilizer (urea+elem.-Phosphorus) | 40 +10 mg |
Table 1. Management practices used in the model
Data Generation: interpolation and interpretatio
Most simulation model assumes homogeneous conditions over the space they are representing. In general, these conditions do not exceed a few square kilometers in extent. To input weather data across India, for example, we were faced with the problem of extending required model input over several hundred square kilometers between weather stations. Several interpolation procedures are available, ranging from simple linear interpolation techniques and triangulated networks, to more sophisticated distance weighing or kriging techniques. It must be remembered that the only information used by any interpolation procedure is the location of known values. For this study, the climatic information used to make interpolation is based on inverse distance weighted method of more than 200 stations in India from which Bihar has been delineated for the purpose. While interpolation of a value at reach cell in the study area using 200 meteorological stations is technically easy, but some important questions can be raised : are the stations representative of the areas around them? How large or small an area do they represent? How does the spatial feature in question change over space? Is it continuos or discontinuous and abrupt?
We used same climate dataset for the different maps resolution across study area. Depending on its cell size, the level of detail becomes available at different resolution. It is thus important to realize the size of the dataset used to create GIS maps (as model input) and is important for model application from its accuracy and better performance view point. On the other hand a total of one or two stations falling in each administrative boundary may not be enough to give great confidence in the resulting interpolated map.
