A minimum data set of soil morphological properties for quantifying soil quality in coastal agroecosystems

• Published in: Catena (Giessen) Vol. 198; p. 105042
• Main Authors: Vasu, Duraisamy, Tiwari, Gopal, Sahoo, Sonalika, Dash, Benukantha, Jangir, Abhishek, Sharma, Ram Prasad, Naitam, Ravindra, Tiwary, Pramod, Karthikeyan, Karunakaran, Chandran, Padikkal
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2021
• Subjects: Categorical principal component analysis; Coastal soils; Morphological soil quality index; Optimal scaling; Soil quality indicators; Optimal scaling; Coastal soils; Morphological soil quality index; Categorical principal component analysis; Soil quality indicators
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2020.105042

•Morphology data of 468 coastal soil profiles was explored to assess soil quality.•A minimum data set (MDS) of soil morphological quality indicators was identified.•Morphological soil quality index (MSQI) was derived from categorical variables.•Land-use type significantly affected MDS and MSQI.•Coastal rice-growing soils were poor in quality due to structural degradation. Soil quality in coastal agroecosystems changes rapidly owing to coastal dynamics and land-use change. The potential of soil morphological properties as soil quality indicators is mostly unexplored. We present a minimum data set (MDS) of soil morphological properties to quantify the coastal soil quality. We compiled a dataset including 18 soil morphological properties from 468 soil profiles, representing five land-use types (plantation crops, sugarcane, grassland, rice, and cotton + pigeon pea) of the north-western coastal region of India. The categorical variables were transformed into numerical variables using the optimal scaling method, and categorical principal component analysis (CATPCA) was used to identify the MDS. The CATPCA produced five principal components explaining 60% of the variability. The MDS comprises pore abundance, structure size, drainage, pore size, and colour (value) with 32, 22, 21, 14, and 12% contribution to soil quality, respectively. The morphological soil quality index (MSQI) varied from 0.26 to 0.99 for surface soils, and from 0.11 to 0.94 for the subsurface soils. Among the land-use types, the rice-growing soils were low in their morphological quality due to structural degradation. Land-use types significantly influenced the MSQI in both surface and subsurface soils, and hence, we recommend the inclusion of subsurface soils for soil quality evaluation. The strong relationship of MSQI with saturated hydraulic conductivity (R2 = 0.56) validated the suitability of the MDS for assessment of soil quality by the farmers, and non-experts in the coastal regions. Further, the higher variability explained by the soil morphology data indicates that the MDS identified in this study could be effectively used to evaluate soil quality in areas with limited data.