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Precision farming in Indian context - Role of Remote Sensing 3.3 Geographical Information Systems (GIS) GIS has two different roles in precision farming vehicles (Bregt, 1997). First, a combination of GIS and simulation models is highly relevant for precision farming. There are many simulation models for different purpose like the flow of water, crop growth, soil erosion, nutrient and pesticide leaching. GIS helps in integrating geographical data on various aspects such as soil, crop, weather and field history along with simulation models. Another aspect of GIS support to precision agriculture is the engineering component, in which the research findings are translated into operational systems for use at farm level. GIS can support this engineering activity by providing a good platform for storage of base data, simple modelling, presentation of results, development of a user interface, and, in combination with a GPS, controlling the navigation of farm. On the basis of GIS, a decision support system can be developed for operationalisation of precision farming at farm level. Many farm information systems (FIS) are available, which use simple programmes to create a farm level database. One example of such FIS is LORIS (Figure 1). LORIS (Local Resources Information System) consists of several modules, which enable the data import; generation of raster files by different gridding methods; the storage of raster information in a database; the generation of digital agro-resource maps; the creation of operational maps etc. (Schroder et al., 1997)  Figure 5. Structure of LORIS - Local resource Information System (Source: Schroder, 1997) 3.4 Remote Sensing Precision farming needs information about mean characteristics of small, relatively homogeneous management zones. These mean characteristics may be obtained from soil tests for nutrient availability, yield monitors for crop yield, soil samples for organic matter content, information in soil maps, or ground conductivity meters for soil moisture. Generally, the fields are manually sampled along a regular grid and the analysed results of the samples are interpolated using geostatistical techniques. Geostatistical modelling of soil, water and crop variability requires that large number of samples at close intervals are collected throughout the agricultural landscape. Such samplings are costly and time consuming. Various workers have shown the advantages of using remote sensing technology to obtain spatially and temporally variable information for precision farming. Remote sensing imagery for PF can be obtained either through satellite-based sensors or CIR video digital cameras on board small aircraft. Moran et al. (1997) in their review paper summarized the applications of remote sensing for precision farming. There are, basically, three approaches for use of remote sensing for precision farming (Barnes et al., 1996). In the first approach, the multi-spectral images can be used for anomaly detection. These anomalies can be in the forms of disease/pest, weed growth, water stress, etc. Using the reflectance measurements in the visible part of the spectrum, it has been possible to detect diseases and identify weeds from crops. The difference between remotely sensed surface temperature and ground-based measurements of air temperature has been established as a method to detect water stress in plants. However, such type of anomaly detection needs regular observation of the crop through remote sensing sensor. This calls for use of a remote sensing system with high temporal resolution, which can provide at-least 5-6 observation per season. Hence the temporal resolution needed is of the order of a fortnight. The next approach is based on correlating variationas in spectral response to specific variables such as soil properties or crop yield. Soil physical properties such as soil water, organic matter, soil texture can be correlated to spectral reflectance. Vegetation spectral response has also been used to infer other soil conditions. Crop yields for many crops like, rice, wheat etc. have been found to be highly correlated with spectral vegetation index during maximum vegetative cover. Thus, the yield map generated from spectral images can be used to form management units. To find out within field variability, the remote sensing data should have high spatial resolution. Typically to analyse the variability one is looking for about 750 to 1,500 data points per hectare. With current satellites, one can see areas that are 30 meters x 30 meters (11.1 measurements/ha), 23 x 23 meters (18 measurements/ha), 10 x 10 meters (100 measurements/ha) and 5 x 5 meters (400 measurements/ha). With future satellites, we will be receiving data that have a variety of spatial resolutions (Table 1) that in some cases will be as detailed as 1 x 1 meter or over 10000 data points per hectare.
The third approach is to integrate biophysical parameters (such as Leaf Area Index or temperature) derived from high-resolution satellite based remote sensing data, with physical crop growth modeling towards an operational decision support system for precision farming. For example, Moran et al. (1995) utilized remotely sensed estimates of LAI and evapotranspiration as inputs to a simple alfalfa growth model. To derive biophysical parameter, the remote sensing system need to have high spectral resolution, covering the whole range of optical and thermal region. However, use of RS data for mapping has many inherent limitations, which includes, requirements for instrument calibration, atmospheric correction, normalization of off-nadir effects on optical data, cloud screening for data especially during monsoon period, processing images from airborne video and digital cameras (Moran et al, 1997). Keeping in view the agricultural scenario in developing countries, the requirement for a marketable RS technology for precision agriculture is the delivery of information with the following characteristics:
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