Overview |
Crop Production |
Crop Pattern |
Crop Yield |
Irrigation |
Soil Management | Precision Farming |
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GIS based crop production model and its applications Development of Dynamic Adaptations cum Management 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 of technology (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 in the EPIC because it can not adopt the management as per the climatic and resources prevail in temporal time scale. Therefore, the “Spatial-EPIC” carries a component where all these management and technologies practices have been made dynamic.  Fig. 4: Modelling Linkage Diagram Generating “Fine” Resolution Data from “Coarse” Resolution Data As discussed before the model used for development is a field scale model hence the data requirement in terms of their resolution is a big gap. Therefore, the first question may come to readers mind is how to “spatialise” the point-based models? What data is appropriate for these models? The concept of “generators” helps to answer these questions. The weather and slope generators were used. These generators are used not to save data storage size but to provide high-resolution (temporal and spatial) data from coarse resolution data. These generators help in integration of data and knowledge to build a multi-scale GIS database. These “climate analog” models here used as a “Weather Generator” [Richardson (1981a)] serve to describe the initial domain or target area for a range of priority setting.  Fig. 5: Rough Spatial Validation of Maize Crop in 1990 - 91 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 the physical processes involved using readily available inputs. 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 3 shows physical factors considered in computing a mathematical model to find the effects of crop productivity coming from different processes. How all these different processes affects overall crop productivity is being modeled while simulation is shown in figure 4. “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 with other sub models as explained in figure 4. In brief, the each sub module are dealt with their computation procedure. Weather: daily rain, maximum and minimum temperature, solar radiation, wind and relative humidity can be based on measured and data and/or generated stochastically. Hydrology: runoff, percolation, lateral subsurface flow 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 fertilisation, transformations, crop uptake and nutrient movement. Nutrient can be applied as mineral fertilizers, in irrigation water, or as animal 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 parameters 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 other naturally occurring processes. These include different crop characteristics, plant population, dates of planting and harvest, fertilisation, irrigation, tillage and many more those are normally practiced in the field.  Fig. 6: Rough Spatial Validation of Wheat in Crop 1990 - 91 |