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Mapping Soil Health over Large Agriculturally Important Areas
Author(s) -
Svoray Tal,
Hassid Inbar,
Atkinson Peter M.,
Moebius-Clune Bianca N.,
Es Harold M.
Publication year - 2015
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/sssaj2014.09.0371
Subject(s) - environmental science , soil water , kriging , cation exchange capacity , soil health , soil science , geostatistics , sampling (signal processing) , soil map , spatial analysis , hydrology (agriculture) , variogram , spatial variability , soil organic matter , mathematics , statistics , geology , geotechnical engineering , filter (signal processing) , computer science , computer vision
Soil health deterioration due to intensive agricultural activity is a worldwide problem. To better understand this process, there is a prime need to map soil health over wide areas. This paper aims to quantify soil health in a spatially explicit manner over a large area using soil health indicators. The methodology includes sampling design, autocorrelation analysis and Kriging interpolation. The following variables were measured from vertisol clayey soils: aggregate stability (AS); available water capacity (AWC); surface and subsurface penetration resistance (PR15 and PR45 respectively); root health (RH); organic matter (OM); pH; electrical conductivity (EC); cation‐exchange capacity (CEC); exchangeable K; nitrification potential (Np); and P. Stratified random sampling was found to be a more efficient method than random sampling for representing a large area with a limited number of sampling locations. The variogram envelope method was found to be more conservative in determining the significance of autocorrelation than the classical Moran's I approach. Phosphorus, CEC, PR15, EC, and K exhibited strong autocorrelation in space; other variables showed no autocorrelation. Land management factors were found to control the spatial variability of most soil variables. Kriging with an external drift (KED) was found to be the most useful approach for spatial prediction of soil health. A positive correlation was found between the interpolated soil health index and NDVI (Normalized Difference Vegetation Index). These results suggest that soil health maps can be used to explore how cultivation activities limit crop yields at the catchment scale, and to determine whether these activities create distinctive soil characteristics.