摘要
Soil quality assessment methods, based on different attributes, are available but not well calibrated/validated for subsequent operational applications. We have developed a method for soil quality index assessment by considering soil texture, organic carbon, pH, available water, cation exchange capacity, bulk density, total porosity, saturated hydraulic conductivity, salinity, aggregate stability, slope and soil depth. The scoring was done on 0 - 100 scale and the lowest score was assigned to the most limiting factor of crop growth and development. Attribute-wise rating was made by using Macros developed in MS-Excel and IDRISI3.2 was used to delineate the rating maps. About 64.6% soils scored more than 60 and the best soil group (score > 70) was only about 15%. Soil health score, as determined through our method, showed good relationship with wheat yield. Multiplicative response function was more sensitive than simple regression model. The correlation analyses with one or two attributes with most severe stress and relatively with lower rating values showed better predictability of wheat yields. The soil quality index as estimated from principal component analysis having strongly loaded (>0.75) factors showed inferior correlation with grain yields of wheat than geometric mean approach. It is concluded that geometric mean approach for soil health scoring can be utilized in similar environments around the globe with or without further improvement.
Soil quality assessment methods, based on different attributes, are available but not well calibrated/validated for subsequent operational applications. We have developed a method for soil quality index assessment by considering soil texture, organic carbon, pH, available water, cation exchange capacity, bulk density, total porosity, saturated hydraulic conductivity, salinity, aggregate stability, slope and soil depth. The scoring was done on 0 - 100 scale and the lowest score was assigned to the most limiting factor of crop growth and development. Attribute-wise rating was made by using Macros developed in MS-Excel and IDRISI3.2 was used to delineate the rating maps. About 64.6% soils scored more than 60 and the best soil group (score > 70) was only about 15%. Soil health score, as determined through our method, showed good relationship with wheat yield. Multiplicative response function was more sensitive than simple regression model. The correlation analyses with one or two attributes with most severe stress and relatively with lower rating values showed better predictability of wheat yields. The soil quality index as estimated from principal component analysis having strongly loaded (>0.75) factors showed inferior correlation with grain yields of wheat than geometric mean approach. It is concluded that geometric mean approach for soil health scoring can be utilized in similar environments around the globe with or without further improvement.
