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Table 4.18 Texture 8, Linear multi-regression all variables........................................ 131 Table 4.19 Texture 8, Linear multi-regression Limited variables (L). ........................ 132 Table 4.20 Comparison of actual and predicted denitrification rate values ................ 133 Table 4.21 Texture 9, Linear multi-regression using all variables. ............................. 134 Table 4.22 Texture 9, Linear multi-regression using selected variables (S) ............... 135 Table 4.23 Comparison of actual and predicted denitrification rate values ................ 135 Table 4.24 Texture 10, Linear multi-regression using all variables. ........................... 136 Table 4.25 Texture 10, Linear multi-regression using selected variables (S) ............. 137 Table 4.26 Comparison of actual and predicted denitrification rate values ................ 137 Table 6.1 Eggleston Heights (April 2010)................................................................... 160 Table 6.2 Eggleston Heights (June, 2010)................................................................... 160 Table 6.3 Eggleston Heights (September, 2010). ........................................................ 161 Table 6.4 Julington. Creek (April 2010)...................................................................... 162 Table 6.5 Julington Creek (December, 2010)............................................................. 163 Table 7.1 Linear Regression Results. ......................................................................... 167 Table 7.2 Monte Carlo Results ................................................................................... 167 Table 7.3 Multi-Regression and Neural Network Results.......................................... 168 xiv
LIST OF FIGURES Figure 1.1 Oxidation of organic carbon in the saturated zone with the sequence of electron acceptors and the resulting inorganic compounds (Korom, 1992). . 4 Figure 1.2 Predicted denitrification rates based on equations Equation 1.3 and Equation 1.5 (All Data, n=220).................................................................................... 18 Figure 1.3 Predicted denitrification rates based on equations Equation 1.3 and Equation 1.5. (Limited to 10). ...................................................................................... 19 Figure 1.4 Predicted denitrification rates based on Equation 1.10. ............................... 21 Figure 1.5 Denitrification Rate Vs. Organic Carbon (adapted from Anderson 1998)... 22 Figure 1.6 Anderson (1998), Predicted denitrification rates based on Equation 1.11... 23 Figure 1.7 Measured and SimDen-modeled denitrification rates in the entire range (left) and the lower range of values (right), (Vinther and Hansen, 2004). ............ 26 Figure 2.1 Relationship of denitrification rate and OC based on the literature data. .... 33 Figure 2.2 Denitrification Vs. Rdn using data from similar studies. .............................. 34 Figure 2.3 USDA Soil Textural Classification scheme ................................................. 35 Figure 2.4 Texture 4; Denitrification rate Vs. Organic Carbon..................................... 37 Figure 2.5 Texture 6; Denitrification Rate Vs. pH. ....................................................... 38 Figure 2.6 Texture 6; Denitrification Rate Vs. Water Filled Porosity........................... 39 Figure 2.7 Peat: Denitrification rate Vs. Organic Carbon. ............................................ 40 Figure 2.8 1-20; Denitrification rate Vs. pH.................................................................. 43 Figure 2.9 2-10; Denitrification Rate Vs. Organic Carbon............................................ 44 Figure 2.10 2-10; Denitrification Rate Vs. pH .............................................................. 44 Figure 2.11 2-22; Denitrification Rate Vs. Water Filled Porosity................................. 45 Figure 2.12 2-25; Denitrification Rate Vs. Nitrate Concentration................................. 45 Figure 2.13 3-12; Denitrification Rate Vs. Water Filled Porosity................................. 46 Figure 2.14 3-12; Denitrification Rate Vs. Nitrate Concentration................................. 47 Figure 2.15 3-14; Denitrification Rate Vs. Nitrate Concentration................................. 47 Figure 2.16 3-16; Denitrification Rate Vs. Nitrate Concentration................................. 48 Figure 2.17 3-17; Denitrification Rate Vs. Water Filled Porosity................................. 48 Figure 2.18 3-18; Denitrification Rate Vs. Water Filled Porosity................................. 49 Figure 2.19 4-25; Denitrification Rate Vs. Organic Carbon.......................................... 49 xv
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 and 12: 4.2.6. Texture 6 (Sandy Clay Loam).
Page 13: LIST OF TABLES Table 1.1 Additional
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,
Page 229 and 230:
Zhu, J., Liu, G., Han, Y., Zhang, Y
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