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6.3. Use of isotopes to estimate the loss of Nitrogen due to denitrification. .......... 159 7. Application to Jacksonville, Fl...................................................................... 164 7.1. Linear Regression and Monte Carlo analysis .................................................. 165 7.2. Multiple Regression......................................................................................... 166 7.3. Neural Network................................................................................................ 166 7.4. Summary.......................................................................................................... 167 7.5. Summary of denitrification rates for the three study areas. ............................. 167 8. Conclusions..................................................................................................... 169 8.1. Data Collection ................................................................................................ 169 8.2. Statistical Analysis........................................................................................... 169 8.3. Main Results .................................................................................................... 170 8.4. Recommendations............................................................................................ 171 Appendix A..................................................................................................................... 173 Appendix B ..................................................................................................................... 174 Appendix C ..................................................................................................................... 181 Appendix D..................................................................................................................... 185 Appendix E ..................................................................................................................... 189 Appendix F...................................................................................................................... 197 References....................................................................................................................... 198 Biographical Sketch........................................................................................................ 209 xii
LIST OF TABLES Table 1.1 Additional Models Considered for the Rate of Denitrification. (Adapted from Heinen 2006)....................................................................................... 27 Table 1.2 Terminology used in Section 1.5 ................................................................... 27 Table 2.1 Soil Textural Classes ..................................................................................... 36 Table 2.2 Coefficient of determination for all Texture categories................................. 42 Table 3.1 Results of the Monte Carlo Random generation for Texture-Temperature- Water Filled Porosity. .................................................................................. 109 Table 3.2 Results of the Monte Carlo Random generation for Texture-Temperature- Water Filled Porosity-pH............................................................................. 111 Table 3.3 Results of the Monte Carlo Random generation for Texture-Temperature- Water Filled Porosity-Nitrate Concentration............................................... 114 Table 4.1 PCA Results................................................................................................. 118 Table 4.2 Stepwise multi-regression analysis for complete dataset. ........................... 119 Table 4.3 Texture 1, Linear multi-regression with all variables.................................. 121 Table 4.4 Texture 1, Linear multi-regression with selected variables (S) ................... 121 Table 4.5 Comparison of actual and predicted denitrification rate values. ................. 122 Table 4.6 Texture 2, Linear multi-regression all variables.......................................... 123 Table 4.7 Texture 2, Linear multi-regression with selected variables (S) ................... 123 Table 4.8 Texture 2, Comparison of actual and predicted denitrification rate values. 124 Table 4.9 Texture 3, Linear multi-regression with all variables.................................. 125 Table 4.10 Texture 3, linear multi-regression with selected variables (S) .................. 125 Table 4.11 Comparison of actual and predicted denitrification rate values. ............... 126 Table 4.12 Texture 5, Linear multi-regression with all variables................................ 127 Table 4.13 Texture 5, Linear multi-regression with selected variables (S)................. 127 Table 4.14 Comparison of actual and predicted denitrification rate values. ............... 128 Table 4.15 Texture 7, Linear multi-regression using all variables. ............................. 129 Table 4.16 Texture 7, Linear multi-regression using selected variables (S) ............... 129 Table 4.17 Comparison of actual and predicted denitrification rate values ................ 130 xiii
Page 1 and 2: THE FLORIDA STATE UNIVERSITY ARTS A
Page 3 and 4: To my Dad, Mum and Brother iii
Page 5 and 6: Dr. Stephen Kish for his words of e
Page 7 and 8: 1.5. Evaluation of methods to estim
Page 9 and 10: 2.5.8. Texture 8 (Silt Loam).......
Page 11: 4.2.6. Texture 6 (Sandy Clay Loam).
Page 15 and 16: LIST OF FIGURES Figure 1.1 Oxidatio
Page 17 and 18: Figure 2.51 8-20-84; Denitrificatio
Page 19 and 20: Figure 3.6 Probability plot (Textur
Page 21 and 22: Three statistical methods were used
Page 23 and 24: • Microbial biomass/ plant uptake
Page 25 and 26: Figure 1.1 Oxidation of organic car
Page 27 and 28: possibility of denitrification (Smi
Page 29 and 30: 50ºC (36ºF - 122ºF) (Brady & Wei
Page 31 and 32: 1.2.6. Salinity Salinity is a known
Page 33 and 34: 1.3.1. The Acetylene inhibition met
Page 35 and 36: Among the variety of successful met
Page 37 and 38: a first-order decay process. The ma
Page 39 and 40: look at the data shows that it fail
Page 41 and 42: Da is the denitrification rate (mg
Page 43 and 44: a simple linear regression of organ
Page 45 and 46: of the annual N2O emission and deni
Page 47 and 48: easons for the limitation of the mo
Page 49 and 50: Table 1.2 Continued dC / dt Organic
Page 51 and 52: also implies that the transferabili
Page 53 and 54: CHAPTER TWO 2. LINEAR REGRESSION An
Page 55 and 56: an improvement on the earlier attem
Page 57 and 58: 2.3. Texture Table 2.1 Soil Textura
Page 59 and 60: 2.3.5. Texture 5 (Sand) The surfici
Page 61 and 62: 2.3.10. Texture 10 (Silty Clay Loam
Page 63 and 64:
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 -
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The actual denitrification values f
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Eigenvalue 1.5 1.4 1.3 1.2 1.1 1.0
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R indicated by the significant code
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R Table 4.5 Comparison of actual an
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Table 4.8 Texture 2, Comparison of
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Table 4.11 Comparison of actual and
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The two equations are applied to th
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R R dn dn = - 0.05 ∗Temperature (
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Table 4.19 Texture 8, Linear multi-
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Table 4.20 Continued Actual Rdn Pre
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Once again the equations developed
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4.2.11. Texture 11 (Silt) No data a
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CHAPTER FIVE 5. ANALYSIS USING NEUR
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Figure 5.2 McCulloch-Pitts (Meyer-B
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developed. In order to control over
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Eventually the final set of network
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5.4.4. Texture 5 (Sand) Figure 5.5
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5.4.6. Texture 8 (Silt Loam) Figure
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5.4.8. Texture 10 (Silty Clay Loam)
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CHAPTER SIX 6. USE OF ISOTOPES TO E
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18 ⎛ ⎛ ⎜ O ⎞ 16 ⎜ ⎜ ⎟
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Figure 6.1 Estimation of Nitrate Lo
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δ 18 O 12 10 8 6 4 2 Table 6.1 Egg
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δ 18 O 20.00 18.00 16.00 14.00 12.
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CHAPTER SEVEN 7. APPLICATION TO JAC
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7.2. Multiple Regression Three equa
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Table 7.4 Multi-Regression and Neur
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8.3. Main Results The linear equati
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isotopes can be particularly useful
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APPENDIX B CONVERSION SHEET FOR DEN
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iv. v. vi. Given in the dataset: 1.
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Conversion from −3 −1 g N m d t
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Convert From Multiply by Convert to
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Texture 4 Variable Count N* Mean St
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Texture 10 Variable Count N* Mean S
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Texture-Temperature-WFP-pH Code Equ
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09-15-25-09 Rdn = 0.0106688*OC + 0.
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Code Rdn - OC Code Rdn-WFP Code Rdn
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- Code Rdn - OC Code Rdn - pH Code
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- Code Rdn - OC Code Rdn - NO3 Conc
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Code Rdn - OC Code Rdn - pH 08-25-9
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REFERENCES Almasri, M. N., & Kaluar
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Fellows, C., Hunter, H., Eccleston,
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King, D., & Nedwell, D. B. (1985).
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Ozden, T., & Muhammetoglu, H. (2008
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Smith, R., Bohlke, J., Garabedian,
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Zhu, J., Liu, G., Han, Y., Zhang, Y
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