Assessing soil quality and their indicators for long-term rice-based cropping systems in hot sub-humid eco-region of India
Uttam Kumar
A * , V. N. Mishra A , Nirmal Kumar B , L. K. Srivastava A , K. Tedia A , R. K. Bajpai A , Muneshwar Singh C and M. Mohanty C
+ Author Affiliations
- Author Affiliations
A Department of Soil Science and Agricultural Chemistry, Indira Gandhi Krishi Vishwavidyalaya, Raipur 492 012, Chhattisgarh, India.
B Division of Remote Sensing Application, ICAR – National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, Maharashtra, India.
C ICAR-Indian Institute of Soil Science, Berasia road, Nabibagh, Bhopal 462 038, M.P., India.
Soil Research 60(6) 610-623 https://doi.org/10.1071/SR21122
Submitted: 7 May 2021 Accepted: 4 May 2022 Published: 14 June 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Rice-based cropping system is a key for food security in the Indian subcontinent. However, the productivity of such systems has declined continuously due to deterioration in soil quality. The present study was carried out to identify key soil quality indicators and to develop indices of soil quality under long-term rice-based cropping systems in the hot sub-humid eco-region (HSE) of India.
Methods: Surface soil samples were collected from farmers’ field from two soil orders (Vertisols and Inceptisols) and with four long-term rice-based cropping systems: (1) rice-wheat (RW); (2) rice-chickpea (RC); (3) rice-lathyrus (RL); and (4) rice-fallow (RF). A minimum data set (MDS) of 24 soils attributes were screened to identify soil quality indicators using principal components analysis (PCA), and soil quality index (SQI) was determined.
Key results: The MDS indicators (in descending order of sensitivity) are field capacity (FC) (35.4%) followed by pH (30.5%) > porosity (13.6%) > potentially mineralisable carbon (PMC) (11.8%) > available boron (AB) (8.7%). Soil moisture content under FC was found as the most sensitive indicator affecting soil quality and rice equivalent yields (REY). The SQI of Vertisols (0.83) was significantly higher (P < 0.05) than Inceptisols (0.73). SQI under RC (0.86) cropping systems was significantly higher than RL (0.81), RW (0.76) and RF (0.78) (P < 0.05).
Conclusions: The inclusion of legumes (chickpea and lathyrus) into cropping systems sustained better soil quality and productivity.
Implications: SQI is not only useful for assessing soil health but also provides indicators of best-bet management practices for sustaining intensive cultivation.
Keywords: field capacity, Inceptisols, legumes, MDS, PCA, rice-based cropping system, rice equivalent yield, soil quality index, soil health, Vertisols.
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