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Conformity Analysis of Cotton Crop using Remote Sensing and GIS Methodology The methodology used in the study consists of: (a) Preparation of soil map, (b) Generation of soil suitability map, (c) Estimation of area under different soil suitability classes, (d) Analysis of satellite data and (e) Estimation of cotton area under different suitability classes. The overall methodology followed is illustrated in Fig. 1  Fig. 1: Flow chart depicting the steps of the conformity analysis 1 Preparation of Soil Map: The soil layer was prepared from the soil maps of NBSS & LUP at 1:250,000 scale. The NBSS & LUP soil map was scanned with 300 dpi and digitized using Arc/Info software to generate a vector layer. The digitized coverage was corrected for all the digitization errors and polygon topology was built. Four attributes namely; soil depth, drainage, texture and available water capacity corresponding to the various soil parameters were added to the Polygon Attribute Table. These attributes were added to store the soil category information for all the spatial units. The appropriate attribute values were then attached to each polygon in the coverage. 2 Generation of soil suitability map: The suitability map was prepared considering the soil requirements for the cultivation of cotton crop. The important pedo-climatic requirements are presented hereunder: 2.1Pedo-climatic requirements of cotton: The physico-chemical properties of soils such as depth, texture, available water capacity (AWC), pH, cation exchange capacity etc., are the important soil characteristics in land evaluation that affect the yields under specific climatic and site conditions. Deep (>100 cm), well drained, gentle slopy soils with clay to fine loamy texture are most preferred soil environments for the growth of cotton crop. The combined effects of these parameters are to be looked into in a holistic approach rather than comparing individual characteristics with yield. In order to have realistic idea about yield contributing soil characteristics / qualities, data on level of crop management and the socio-economic data are required. Cotton is mostly concentrated (under rainfed agriculture) in semiarid to dry sub-humid areas with average annual rainfall varying from 700 to 1200mm, with growing season rainfall varying from 650 to 900mm. The mean temperature during the growing season ranges from 21 to 280 C and mean relative humidity during the growing season varies from 45 to 84 per cent. The crop is grown in the places where the moisture availability period (LGP) is 135 to 180 days (NBSS & LUP, 1994). 2.2 Approach for deriving suitability maps: Deriving soil suitability zones can be considered as a Multi Criteria Decision Making problem, wherein the criteria / parameters controlling the soil suitability are soil depth, drainage, texture and available water capacity, each of which in turn contain different categories (Jack, 1999 and Sys et al., 1991). These criteria and sub-criteria are of varying importance to the decision-maker. Consequently, information about the relative importance of the criteria is required. This is achieved by assigning a weight to each criterion. The derivation of weights is the central step to elicit the decision maker’s preferences. After the weightages are derived, these evaluation criteria have to be integrated using multicriteria decision rules. The decision rules provide the basis for ordering the decision alternatives and for choosing the most preferred alternative. 2.3 Multi Criteria based Decision Making: The GIS based method for solving Multi Criteria based Decision Making problems involves the following steps:
The soil suitability map is derived by the following steps:
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