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RESEARCH ARTICLE
Contents Vol 47(8)

Predicting soil organic carbon percentage from loss-on-ignition using Bayesian Model Averaging

Ai Leon A C and Roberto Leon Gonzalez B
+ Author Affiliations
- Author Affiliations

A National Institute for Agro-Environmental Sciences, 3-1-3 Kannondai, Tsukuba 305-8604, Japan.

B National Graduate Institute for Policy Studies (GRIPS), 7-22-1 Roppongi, Minato-Ku, Tokyo 106-8677, Japan.

C Corresponding author. Email: leon@affrc.go.jp

Australian Journal of Soil Research 47(8) 763-769 https://doi.org/10.1071/SR08119
Submitted: 10 May 2008  Accepted: 28 August 2009   Published: 11 December 2009

Abstract

A shortage of data for percentage of organic carbon (C%) makes calculation of soil profile carbon storage difficult. Loss on ignition (LOI) data, which are cheap to obtain and often readily available, can be used to estimate organic C%. This paper simultaneously considers several predictors of organic C%: LOI, parent material, drainage status, type of soil horizon, clay content, and pH. In order to model appropriately the existence of multiple hypotheses and the consequent model uncertainty, a Bayesian Model Averaging (BMA) approach was used. BMA considers all models that result from all possible combinations of explanatory variables. Based on a BMA approach and Scottish Soil Survey data, it was found that the most important factors to predict organic C% were LOI, clay content, a dummy for Countesswells Association (till derived from granite), and a dummy for B horizon soils. The validation analysis showed that prediction accuracy for organic C% was better with the BMA approach than with an ordinary least-squares approach that includes no other predictors apart from LOI (i.e. 22% reduction in horizons A, Ap, and C).

Additional keywords: soil organic C%–loss-on-ignition relationship, soil parent material, drainage status, type of soil horizon, clay content, pH.


Acknowledgement

The authors are indebted to Professor Malcolm Cresser for his constructive advice as PhD advisor, and to Dr Jimmy Gauld at The Macaulay Institute for provision of data and helpful advice.


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