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7.4. Summary The information derived in this work is applied to the three study areas in Jacksonville. Each of the methods is an independent statistical analysis all be it on the same database. The results of the four methods are, within reason, in agreement. For the Lake Shore area based on the hierarchal linear regression the denitrification rate is estimated to be 7.59 Kg N ha -1 d −1 be 8.46 Kg N ha -1 d . Using the Monte Carlo method the denitrification rate is estimated to −1 , with minimum possible value of 0.00 maximum possible value of 11.74 Kg N ha value of 4.588 Kg N ha -1 d −1 -1 d 167 −1 Kg N ha -1 d −1 and a . The multi-regression analysis yields a ; this is the only anomaly in the results that have been obtained so far. Based on results from the ANN the denitrification rate is estimated at 6.69 Kg N ha -1 d −1 . The results for all the areas are given below. 7.5. Summary of denitrification rates for the three study areas. Table 7.2 Linear Regression Results. Study Area Linear Regression A B C Eggleston Heights (April 2010) 2.45 2.14 - Eggleston Heights (June 2010) 2.45 2.14 - Eggleston Heights (Sept 2010) 2.45 2.14 - Julington Creek (April 2010) 2.70 2.45 - Julington Creek (Dec 2010) 2.70 2.45 - Lakeshore 2.61 2.38 7.58 Table 7.3 Monte Carlo Results Monte Carlo A Range B Range C Range Eggleston Heights (April 2010) 6.53 0.00 - 7.64 7.69 0.00 - 9.96 8.92 0.00 - 10.25 Eggleston Heights (June 2010) 6.53 0.00 - 7.64 7.69 0.00 - 9.96 8.92 0.00 - 10.25 Eggleston Heights (Sept 2010) 6.53 0.00 - 7.64 7.69 0.00 - 9.96 8.92 0.00 - 10.25 Julington Creek (April 2010) 6.92 0.00 - 7.93 8.08 0.00 - 10.25 8.54 0.00 - 9.97 Julington Creek (Dec 2010) 6.92 0.00 - 7.93 8.08 0.00 - 10.25 8.54 0.00 - 9.97 Lakeshore 6.81 0.00 - 7.82 7.87 0.00 - 10.14 8.75 0.00 - 10.15
Table 7.4 Multi-Regression and Neural Network Results Multi-regression Neural Network All Texture 5 Texture 5 (L) 5-7-20 Eggleston Heights (April 2010) - - 5.83 6.68 Eggleston Heights (June 2010) - - 13.53 6.03 Eggleston Heights (Sept 2010) - - 11.67 7.89 Julington Creek (April 2010) 2.70 - 19.14 2.22 Julington Creek (Dec 2010) - - 13.88 5.53 Lakeshore - - 1.38 8.34 A Texture-Temp-WFP B Texture-Temp-WFP-pH C - Texture-Temp-WFP-NO3 Concentration 168
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THE FLORIDA STATE UNIVERSITY ARTS A
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To my Dad, Mum and Brother iii
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Dr. Stephen Kish for his words of e
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1.5. Evaluation of methods to estim
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2.5.8. Texture 8 (Silt Loam).......
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4.2.6. Texture 6 (Sandy Clay Loam).
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LIST OF TABLES Table 1.1 Additional
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LIST OF FIGURES Figure 1.1 Oxidatio
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Figure 2.51 8-20-84; Denitrificatio
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Figure 3.6 Probability plot (Textur
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Three statistical methods were used
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• Microbial biomass/ plant uptake
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Figure 1.1 Oxidation of organic car
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possibility of denitrification (Smi
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50ºC (36ºF - 122ºF) (Brady & Wei
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1.2.6. Salinity Salinity is a known
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1.3.1. The Acetylene inhibition met
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Among the variety of successful met
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a first-order decay process. The ma
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look at the data shows that it fail
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Da is the denitrification rate (mg
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a simple linear regression of organ
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of the annual N2O emission and deni
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easons for the limitation of the mo
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Table 1.2 Continued dC / dt Organic
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also implies that the transferabili
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CHAPTER TWO 2. LINEAR REGRESSION An
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an improvement on the earlier attem
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2.3. Texture Table 2.1 Soil Textura
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2.3.5. Texture 5 (Sand) The surfici
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2.3.10. Texture 10 (Silty Clay Loam
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Textural Class Table 2.2 Coefficien
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R_d_n (kgN ha-1 d-1) R_d_n (kgN ha-
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2.4.3. Texture 3 (Loam) Texture 3 c
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R_d_n (kgN ha-1 d-1) Texture 3 Temp
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R_d_n (kgN ha-1 d-1) 6 5 4 3 2 1 0
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2.4.7. Texture 7 (Sandy Loam) The S
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Texture 7 Temperature 12 : Denitrif
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Rdn (kgN ha-1 d-1) Texture 9 Temper
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Rdn (kgN ha-1 d-1) 16 12 8 4 0 Text
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2.5. Break down by Texture, Tempera
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8-20-34 (n=3), 8-20-84 (n=4), 8-20-
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Subsets 9-7-100, 9-25-100 and 9-30-
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Texture 10 Temperature 25 WFP 100 :
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a significant linear relationship b
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Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-
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C9 Rdn (kgN ha-1 d-1) 12 10 8 6 4 2
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Rdn (kgN ha-1 d-1) Texture 10 Tempe
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2.6.11. Texture 11 (Silt) No data a
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2.7.6. Texture 6 (Sandy Clay Loam)
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Rdn (kgN ha-1 d-1) Texture 8 Tepera
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Rdn (kgN ha-1 d-1) Texture 8 Tepera
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2.8. Summary The correlation coeffi
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where, M is the slope of the linear
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Hence for the first set of equation
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3.2.2. Texture-Temperature-WFP-pH T
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Organic Carbon (%) Actual Rdn Gener
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R dn Results C = 9.389 - 3.210* M -
Page 137 and 138: The actual denitrification values f
Page 139 and 140: Eigenvalue 1.5 1.4 1.3 1.2 1.1 1.0
Page 141 and 142: R indicated by the significant code
Page 143 and 144: R Table 4.5 Comparison of actual an
Page 145 and 146: Table 4.8 Texture 2, Comparison of
Page 147 and 148: Table 4.11 Comparison of actual and
Page 149 and 150: The two equations are applied to th
Page 151 and 152: R R dn dn = - 0.05 ∗Temperature (
Page 153 and 154: Table 4.19 Texture 8, Linear multi-
Page 155 and 156: Table 4.20 Continued Actual Rdn Pre
Page 157 and 158: Once again the equations developed
Page 159 and 160: 4.2.11. Texture 11 (Silt) No data a
Page 161 and 162: CHAPTER FIVE 5. ANALYSIS USING NEUR
Page 163 and 164: Figure 5.2 McCulloch-Pitts (Meyer-B
Page 165 and 166: developed. In order to control over
Page 167 and 168: Eventually the final set of network
Page 169 and 170: 5.4.4. Texture 5 (Sand) Figure 5.5
Page 171 and 172: 5.4.6. Texture 8 (Silt Loam) Figure
Page 173 and 174: 5.4.8. Texture 10 (Silty Clay Loam)
Page 175 and 176: CHAPTER SIX 6. USE OF ISOTOPES TO E
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Page 179 and 180: Figure 6.1 Estimation of Nitrate Lo
Page 181 and 182: δ 18 O 12 10 8 6 4 2 Table 6.1 Egg
Page 183 and 184: δ 18 O 20.00 18.00 16.00 14.00 12.
Page 185 and 186: CHAPTER SEVEN 7. APPLICATION TO JAC
Page 187: 7.2. Multiple Regression Three equa
Page 191 and 192: 8.3. Main Results The linear equati
Page 193 and 194: isotopes can be particularly useful
Page 195 and 196: APPENDIX B CONVERSION SHEET FOR DEN
Page 197 and 198: iv. v. vi. Given in the dataset: 1.
Page 199 and 200: Conversion from −3 −1 g N m d t
Page 201 and 202: Convert From Multiply by Convert to
Page 203 and 204: Texture 4 Variable Count N* Mean St
Page 205 and 206: Texture 10 Variable Count N* Mean S
Page 207 and 208: Texture-Temperature-WFP-pH Code Equ
Page 209 and 210: 09-15-25-09 Rdn = 0.0106688*OC + 0.
Page 211 and 212: Code Rdn - OC Code Rdn-WFP Code Rdn
Page 213 and 214: - Code Rdn - OC Code Rdn - pH Code
Page 215 and 216: - Code Rdn - OC Code Rdn - NO3 Conc
Page 217 and 218: Code Rdn - OC Code Rdn - pH 08-25-9
Page 219 and 220: REFERENCES Almasri, M. N., & Kaluar
Page 221 and 222: Fellows, C., Hunter, H., Eccleston,
Page 223 and 224: King, D., & Nedwell, D. B. (1985).
Page 225 and 226: Ozden, T., & Muhammetoglu, H. (2008
Page 227 and 228: Smith, R., Bohlke, J., Garabedian,
Page 229 and 230: Zhu, J., Liu, G., Han, Y., Zhang, Y
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