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Assessing Soil Quality in a Semiarid Tropical Watershed Using a Geographic Information System
Author(s) -
Mandal Uttam Kumar,
Ramachandran Kausalya,
Sharma K. L.,
Satyam B.,
Venkanna K.,
Udaya Bhanu M.,
Mandal Moumita,
Masane Rahul N.,
Narsimlu B.,
Rao K. V.,
Srinivasarao Ch.,
Korwar G. R.,
Venkateswarlu B.
Publication year - 2011
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2009.0361
Subject(s) - environmental science , watershed , soil quality , soil water , hydrology (agriculture) , soil map , soil retrogression and degradation , soil management , soil science , geology , geotechnical engineering , machine learning , computer science
Subsistence agriculture under rainfed conditions and declining or stagnant yields on irrigated farmland has raised concerns about resource management and long‐term sustainability in the subtropical, semiarid region of India. Soil quality assessment has been recognized as an important step toward understanding the effects of land management practices within an agricultural watershed. This study addressed the spatial variability of soil properties and their quality at the watershed level using geostatistical methods. Soil samples from the 0‐ to 20‐cm depth were collected from 118 locations on a 100‐ by 100‐m grid across an 88‐ha watershed at Sakaliseripalli village in the Nalgonda District in Andhra Pradesh State, India. Geostatistical analysis showed that most of the soil parameters were moderately spatially dependent. An assessment framework, including a minimum data set, linear scoring technique, and additive indices, was used to evaluate the soil quality index (SQI). Principal component analysis identified cation exchange capacity, exchangeable Na percentage, DTPA‐extractable Zn, available P, available water, and dehydrogenase activity as the most important indicators for evaluating soil quality. A kriged map of SQI was prepared for the watershed. The SQI was higher in irrigated systems (3.01) than under rainfed conditions (2.53), and it was 2.61 and 2.53 in fallow and permanent fallow fields, respectively. In this study, potential soil loss calculated using the Universal Soil Loss Equation and crop yield were identified as the quantifiable management goals; the results indicated that good soils having higher soil quality indices were also productive and less erosion prone.