Comparison of soil quality indexing methods for salt-affected soils of Indian coastal region

Abstract

The coastal ecosystem is one of the most fragile ecosystems to climate change. Soil salinization in these ecosystems due to climate change-induced sea-level rise could be a major threat and constraint to agricultural production. Thus, assessing the soil quality of these soils using a suitable indexing method can help to decide the countermeasures for their sustainable utilization. The present study aimed to evaluate the soil quality of the salt-affected soils in the coastal region of India using different soil quality indexing methods. The soil quality indices (SQIs) were developed using two scoring methods: linear and non-linear of the minimum dataset and weighted approach. Based on electrical conductivity (EC1:2.5, EC in 1:2.5 soil to water ratio), the soils were categorized into five classes as non-saline, slightly-saline, moderately-saline, strongly-saline, and very strongly-saline. The soil salinity impacted the soil's physical, chemical, and biological properties significantly. Using principal component analysis and correlation, a minimum dataset comprising of eight soil properties namely basal soil respiration, urease enzyme activity, EC, soil available copper, zinc, boron, iron and soil pH was identified. The overall performance of the weighted SQIs developed using non-linear scoring was better than linear scoring. The weighted SQI developed using non-linear scoring (SQINLW) revealed that the class non-saline had the highest soil quality values and the very strongly saline the lowest. The SQINLW correlated strongly with the EC1:2.5 (r = 0.96; p < 0.05). The SQINLW for salinity classes was in order as non-saline > moderately saline = slightly saline > strongly saline > very strongly saline and thus, the SQINLW could be used as an effective tool to assess the soil quality of salt-affected soils of the coastal region. The correlation analysis between the SQIs and grain yield for different salinity classes revealed significant (p < 0.01) and the highest values of correlation coefficient in the SQINLW (r = 0.67?0.74, p < 0.01). The urease enzyme activity (35.1?66.6%) and EC (10.1?40.6%) contributed the most to the SQINLW and thus emphasizes the importance of these properties while assessing the soil quality of salt-affected soils. The soil quality indexing approach (non-linear scoring and weighting of a minimum dataset) identified in the study could reduce cost and save time and be a good guide for growers, land managers, extension specialists and policy or decision-makers for its utilization.