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4.2.11. Texture 11 (Silt)
No data available
4.2.12. Texture 12 (Sandy Clay)
No data available
4.2.13. Texture 13 (Peat)
Of the 16 available records seven are missing a nitrate concentration value, in addition
the pH for the majority of the data is the same and there is not much variation in the
temperature and density. As a result it is decided not to conduct a multi-regression
analysis on the pear dataset as any regression obtained is possibly unreliable.
4.3. Summary
The results from the linear multi-regression while displaying considerable improvements
in the coefficient of determination across all classes are however not capable of
accurately predicting the denitrification rate.
In addition to using all the factors, multi-regression analysis is carried out on each
textural set again using only the significant factors as determined by step wise multi
regression. This set of equations while not shown in this work does not yield any new
significant information to the task at hand.
The error between the actual measured denitrification values and the predicted values is
in many cases in excess of a 1000 percent. While this is certainly indicative of the
complexity of the problem what is equally confusing is the negative correlation between
the denitrification rate and organic carbon in some of the textures. While a great deal of
information about denitrification and the rate at which it occurs is yet to be conclusively
established , the one known condition is that as the amount of organic carbon increases
the denitrification rate increases. There are several experiments that support this relation,
chief of which is Anderson’s (1998) linear regression; this is further supported by the
results in chapter two.
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