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Intercept 0 -200 -400 -600 -800 -1000 Texture-Temperature-WFP-Nitrate Concentration 0 50 Intercept = 9.389 - 3.210 Slope 100 150 Slope 113 200 S 40.5118 R-Sq 96.8% 250 Regression 95% CI 95% PI Figure 3.5 Intercept Vs. slope (Texture-Temperature-WFP-Nitrate Concentration). Percent 99 95 90 80 70 60 50 40 30 20 10 5 1 0.0001 Loc 1.419 Scale 3.005 N 42 AD 0.597 P-Value 0.114 0.001 0.01 Probability Plot of Slope 0.1 Lognormal - 95% CI 1 10 Slope 100 1000 10000 300 100000 Figure 3.6 Probability plot (Texture-Temperature-WFP-Nitrate Concentration). The equations for texture-temperature and water filled porosity used in the Monte Carlo Generation are,
R dn Results C = 9.389 - 3.210* M ---- Equation 3.9 = exp( M ) ∗Oc − 3. 210 ∗ exp( M ) + 9. 389 114 ---- Equation 3.10 Table 3.3 Results of the Monte Carlo Random generation for Texture-Temperature- Water Filled Porosity-Nitrate Concentration. Organic Carbon (%) Actual Rdn Generated Rdn Minimum Maximum Error 0.25 0.10 8.09 0.00 9.92 -7.99 0.38 7.92 8.23 0.00 10.05 -0.31 0.50 4.93 8.37 0.00 10.17 -3.45 0.52 5.70 8.47 0.00 10.19 -2.77 0.60 4.97 8.56 0.00 10.27 -3.59 0.65 0.99 8.51 0.00 10.32 -7.52 0.72 0.17 8.64 0.00 10.39 -8.46 0.73 3.49 8.59 0.00 10.40 -5.10 0.78 0.05 8.67 0.00 10.45 -8.61 0.80 0.04 8.74 0.00 10.47 -8.70 0.81 1.17 8.71 0.00 10.48 -7.54 0.89 1.62 8.80 0.00 10.56 -7.18 0.96 0.16 8.90 0.00 10.63 -8.74 0.99 0.16 9.06 0.00 10.66 -8.90 1.09 0.32 9.02 0.00 10.76 -8.70 1.12 5.58 9.08 0.00 10.79 -3.51 1.13 0.03 9.05 0.00 10.80 -9.02 1.14 0.03 9.09 0.00 10.81 -9.06 1.20 6.88 9.18 0.00 10.87 -2.30 1.26 0.22 9.27 0.00 10.93 -9.05 % Error Within Range 7990.00 Y 3.91 Y 69.78 Y 48.60 Y 72.23 Y 759.60 Y 4982.35 Y 146.13 Y 17240.00 Y 21750.00 Y 644.44 Y 443.21 Y 5462.50 Y 5562.50 Y 2718.75 Y 62.72 Y 30066.67 Y 30200.00 Y 33.43 Y 4113.64 Y
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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
Page 83 and 84: Rdn (kgN ha-1 d-1) 16 12 8 4 0 Text
Page 85 and 86: 2.5. Break down by Texture, Tempera
Page 87 and 88: Rdn (kgN ha-1 d-1) Texture 3 Temper
Page 89 and 90: 8-20-34 (n=3), 8-20-84 (n=4), 8-20-
Page 93 and 94: Subsets 9-7-100, 9-25-100 and 9-30-
Page 95 and 96: Texture 10 Temperature 25 WFP 100 :
Page 97 and 98: a significant linear relationship b
Page 105 and 106: Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-
Page 109 and 110: C9 Rdn (kgN ha-1 d-1) 12 10 8 6 4 2
Page 113 and 114: Rdn (kgN ha-1 d-1) Texture 10 Tempe
Page 115 and 116: 2.6.11. Texture 11 (Silt) No data a
Page 117 and 118: 2.7.6. Texture 6 (Sandy Clay Loam)
Page 121 and 122: Rdn (kgN ha-1 d-1) Texture 8 Tepera
Page 123 and 124: Rdn (kgN ha-1 d-1) Texture 8 Tepera
Page 125 and 126: 2.8. Summary The correlation coeffi
Page 127 and 128: where, M is the slope of the linear
Page 129 and 130: Hence for the first set of equation
Page 131 and 132: 3.2.2. Texture-Temperature-WFP-pH T
Page 133: Organic Carbon (%) Actual Rdn Gener
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
Page 177 and 178: 18 ⎛ ⎛ ⎜ O ⎞ 16 ⎜ ⎜ ⎟
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.
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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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