THE FLORIDA STATE UNIVERSITY ARTS AND SCIENCES ...

CHAPTER EIGHT
8. CONCLUSIONS
This work was conducted in order to develop an inexpensive method that will enable
environmental agencies to have a tool that is able to quickly assess the denitrification
rate. As such it is acknowledged that this is not a precise tool or a panacea to the
complexity of determining denitrification rates but a method to roughly estimate the loss
of nitrated due to denitrification. If detailed denitrification rates are required traditional
methods and field experiments are unavoidable.
8.1. Data Collection
In order to develop statistical relationships a database containing 1198 records of
information is collated from literature and directly obtained from research scientists. Each
record has eight variables and a denitrification rate. While there are some records that
contain missing data, overall this accounts for less that three percent of the dataset. The
denitrification rates are reported in several different units (Heinen, 2006) and are hence
converted to a common unit of Kg N ha
8.2. Statistical Analysis
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Three different statistical methods are used to develop a relationship between the factors
that control denitrification and the denitrification rate. Hierarchical Linear Regression,
Monte Carlo simulations, Multiple-Regression analysis and Neural Networks are used to
develop the relationships between the eight available parameters in the database and the
denitrification rate. Each method developed has limitations but when used as described in
this work, it is possible to have a semi-quantitative estimate of the denitrification rate.
Besides statistical analysis, isotope data is also to estimate the loss of nitrates due to
denitrification.
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