ABSTRACT
388In the present study, an attempt was made to disaggregate the soil suitability classes for soybean in to spatially finite grid cells (as opposed to map units) with some indicator of land suitability such as multi-temporal Normalized Difference Vegetation Index (NDVI) data over 4 years (2008–11) and terrain parameters (viz., slope and topographic wetness index) derived from Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) digital elevation models of 30 m in combination with the ancillary soil resource inventory datasets. Based on suitability criteria for soybean, four main factors, i.e., texture, drainage, depth and soil reaction (pH) available in soil survey report of Wardha district and terrain parameters have been selected. The soil productivity factors have been replaced with principal component (PC) of Moderate Resolution Imaging Spectroradiometer (MODIS) 16 days composite time series NDVI data (250 m) for 4 years (from January 2008 to December, 2011). Multi-year time series NDVI reflects growth of vegetation for different seasons and thus indirectly reflects soil productivity. Instead of using multi-year average monthly NDVI images over the growing seasons, a few standardized principal components of the original NDVI images are used to screen out anomalies related to inter-annual climate variability and different farming practices and images of undesirable dates (e.g., peak NDVI images) effectively. The PC 3 was found to be strongly associated with the NDVI of the period of peak growth of soybean crop in the district. Based on these parameters, the culturable areas of the district were categorized as very good, good, moderate, poor, and very poor by adopting the logical criteria. These categories were arrived at by integrating the various layers with corresponding weights in Geographic Information System (GIS) using multi-criteria analysis. The analysis shows that majority area of the district is suitable for soybean. Majority of culturable area (84%) comes under moderate, good and very good categories. Tehsil wise analysis shows that Arvi tehsil is least suitable with 44% of culturable areas under poor and very poor categories. Less than 25% of culturable area – least among the teh- sils-comes under good and very good categories for Arvi tehsil. Hinganghat, Samudrapur and Seloo are the most favorable tehsils with more than 90% of culturable area above poor category. The significantly different seasonal NDVI for all the classes validates the findings. The model provides a better insight to the suitability of land parcels for specific crop production as the suitability classes are quantified and evaluated on pixel basis.
