If what you care about is "substrate induced respiration" you ultimately want to know the standard errors of the differences between the substrate treatments and the control soil+water treatment. Presumably you want to use those standard errors to determine statistical significance or confidence intervals for the differences of treatments from control and among each other. The question is how best to get those values in a way that most reliably allows you to perform those comparisons.
The way you are doing the differences (in a 96-well plate with rows A through H and columns 1 through 12, calculating the difference for a single replicate of each treatment from the single replicate of the control within each individual column) probably isn't the best way to use your data.
If it's reasonable to assume that the variance among replicates of observations is the same for each of the treatments and control about their corresponding mean values, you would be better off analyzing your data as a linear model. In that way you pool the information from all the observations to get a combined estimate of the variance, which is then used to estimate significance levels and confidence intervals. This helps protect you from having a control or treatment that just happens to have a randomly large or small sampling variance among replicates in your particular data set.
In the linear regression you would handle all of the substrate treatments and soil+water basal treatment as a multi-level categorical variable. If you use the soil+water basal treatment as the reference category and your software is instructed to use treatment coding, then the regression coefficients and corresponding standard errors for each substrate will give exactly what you are looking for: the difference of each substrate treatment from soil+water, and the standard error of that difference. Software typically also reports a p value for the difference of each substrate from the soil+water control when you set up the analysis this way.
If you suspect that the soil samples differ systematically in terms of baseline or substrate induced respiration, then you can also include the soil samples as predictors in the linear regression to handle baseline differences, and include interactions of soil samples with substrate treatments to handle differences in substrate induced responses. You would have a choice of whether to use fixed or random effects to model the differences among soil samples.
I know that this goes quite far beyond the question that you asked. But you seem to have a nicely designed and executed study that deserves a solid statistical analysis. Think of this as an opportunity to start to learn some fundamental principles of statistics that will serve you well throughout your career.
