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.

由于沿海动力和土地利用的变化，沿海农业生态系统中的土壤质量迅速变化。土壤形态特性作为土壤质量指标的潜力尚未得到充分挖掘。我们提出了土壤形态特性的最小数据集（MDS），以量化沿海土壤质量。我们编辑了一个数据集，包括来自468个土壤剖面的18种土壤形态特性，代表了印度西北沿海地区的五种土地利用类型（种植作物，甘蔗，草原，水稻和棉花+木豆）。使用最佳缩放方法将分类变量转换为数值变量，并使用分类主成分分析（CATPCA）来识别MDS。CATPCA产生了五个主要成分，解释了60％的变异性。MDS包含毛孔丰度，结构尺寸，排水量，孔径和颜色（值）分别对土壤质量的贡献为32、22、21、14和12％。表层土壤的形态土壤质量指数（MSQI）从0.26到0.99，地下土壤的形态土壤质量指数从0.11到0.94。在土地利用类型中，由于结构退化，水稻生长土壤的形态质量较低。土地利用类型显着影响地表和地下土壤的MSQI，因此，我们建议将地下土壤纳入土壤质量评估。MSQI与饱和导水率（R 94用于地下土壤。在土地利用类型中，由于结构退化，水稻生长土壤的形态质量较低。土地利用类型显着影响地表和地下土壤的MSQI，因此，我们建议将地下土壤纳入土壤质量评估。MSQI与饱和导水率（R 94用于地下土壤。在土地利用类型中，由于结构退化，水稻生长土壤的形态质量较低。土地利用类型显着影响地表和地下土壤的MSQI，因此，我们建议将地下土壤纳入土壤质量评估。MSQI与饱和导水率（R²  = 0.56）验证了MDS适合评估农民和沿海地区非专家的土壤质量。此外，土壤形态数据解释的更高的变异性表明，这项研究中确定的MDS可有效地用于评估数据有限地区的土壤质量。