I undertook a sensitivity analysis to study the contribution of explanatory variables to

**soil organic carbon**variations. Here, the explanatory variables were the

**soil, climate, fertilization, tillage, stubble and rotation**managements.

Since we have a linear model, a combination of R2 (coefficient of determination) of the explanatory variable alone with semi-partial R2 was an efficient way to summarize the influence of the variables on the Soil Organic Carbon. R2 of the explanatory variable i and the semi-partial R2 represent the contribution of the variable alone and the contribution of the variable with its interaction with other variables to the Soil Organic Carbon variance respectively.

General model:

E (Y| X_1,…,X_p) =α+β_1 X_1+•••+β_p X_p+ Ɛ Where Y is the response measurement Soil Organic Carbon, Xi is the explanatory variable i (soil, climate and fertilization, tillage, stubble and rotation managements), α is the intercept, the βj are the slopes or coefficients and Ɛ the errors.

**My question is:**the influence of the explanatory variable ‘soil’ on soil organic carbon is the biggest and very obvious so I don’t want to include it in my analyses anymore and just want to look at the influence of climate, fertilization, tillage, stubble and rotation managements. Is it correct to just simply delete it from the General model although it is a significant variable? If not, I guess another option would be to fix the value of the soil variable to a certain soil type.

Thank you for your help!