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

Soil Research ◽  
2009 ◽  
Vol 47 (8) ◽  
pp. 763 ◽  
Author(s):  
Ai Leon ◽  
Roberto Leon Gonzalez
Keyword(s):  
Organic Carbon ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Clay Content ◽  
Ordinary Least Squares ◽  
Soil Survey ◽  
Soil Horizon ◽  
Loss On Ignition ◽  
Organic C

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).

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

scholarly journals Identification of Mortality Risks in the Advancement of Old Age: Application of Proportional Hazard Models Based on the Stepwise Variable Selection and the Bayesian Model Averaging Approach

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1098
Author(s):  
Ewelina Łukaszyk ◽  
Katarzyna Bień-Barkowska ◽  
Barbara Bień
Keyword(s):  
Vitamin D ◽  
Mortality Rate ◽  
Plasma Level ◽  
Variable Selection ◽  
Lymphocyte Count ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Multivariable Analysis ◽  
Model Averaging ◽  
Nutritional Risk Index

Identifying factors that affect mortality requires a robust statistical approach. This study’s objective is to assess an optimal set of variables that are independently associated with the mortality risk of 433 older comorbid adults that have been discharged from the geriatric ward. We used both the stepwise backward variable selection and the iterative Bayesian model averaging (BMA) approaches to the Cox proportional hazards models. Potential predictors of the mortality rate were based on a broad range of clinical data; functional and laboratory tests, including geriatric nutritional risk index (GNRI); lymphocyte count; vitamin D, and the age-weighted Charlson comorbidity index. The results of the multivariable analysis identified seven explanatory variables that are independently associated with the length of survival. The mortality rate was higher in males than in females; it increased with the comorbidity level and C-reactive proteins plasma level but was negatively affected by a person’s mobility, GNRI and lymphocyte count, as well as the vitamin D plasma level.

Exploring the drivers of wildlife population dynamics from insufficient data by Bayesian model averaging

2015 ◽  
Vol 57 (3) ◽  
pp. 485-493 ◽  
Author(s):  
Yutaka Osada ◽  
Takeo Kuriyama ◽  
Masahiko Asada ◽  
Hiroyuki Yokomizo ◽  
Tadashi Miyashita
Keyword(s):  
Population Dynamics ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Insufficient Data ◽  
Wildlife Population
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