Geo-spatial Predictions of Soil Organic Carbon: A Review

Abstract

Soil Organic Carbon (SOC) plays an important role in conservation of biophysical environment, i.e., biogeochemical cycles, soil fertility, water quality, atmospheric carbon control, temperature control, sustainable land management and food production. Geo-statistical techniques can be useful for precise estimations and preparation of high quality SOC maps essential for planning and monitoring the biophysical environment. Several studies have used geo-statistical techniques successfully for description of spatial patterns, quantitative modeling, spatial predictions and uncertainty assessments of SOC at local, regional and global scale. However, the results are still susceptible to: 1) sufficient number of samples representing variability of soils and land use, 2) suitable methods and techniques selected for laboratory analyses, 3) selection, availability, precision and correlations of used ancillary data used for modeling, and 4) selected geo-statistical techniques for SOC predictions. Therefore, the reported success studies are very site-, data- and situation-specific and insufficient to reach global conclusions. The field is active, attractive, challenging and interesting area of research for soil fertility and climate change management.

Keywords: Soil sampling, soil organic carbon, spatial estimations, modeling, semivariogram, kriging, hybrid kriging, adjusted distance metric, multiple linear regression, weighted index