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Results Table 3.1 Results of the Monte Carlo Random generation for Texture-Temperature- Water Filled Porosity. Organic Carbon (%) Actual Rdn Generated Rdn Minimum Maximum Error % Error 109 Within Range 0.60 4.57 8.46 0.00 10.27 -3.88 85.12 Y 0.72 0.17 8.49 0.00 10.39 -8.32 4894.12 Y 0.96 0.16 8.78 0.00 10.63 -8.62 5387.50 Y 1.20 6.88 9.11 0.00 10.87 -2.23 32.41 Y 1.26 0.22 9.19 0.00 10.93 -8.97 4077.27 Y 1.32 8.50 9.18 0.00 10.99 -0.68 8.00 Y 1.44 0.20 9.40 0.00 11.11 -9.20 4600.00 Y 1.50 1.05 9.41 0.00 11.17 -8.36 796.19 Y 1.56 0.32 9.48 0.00 11.23 -9.16 2862.50 Y 1.68 0.25 9.69 0.00 11.35 -9.45 3776.00 Y 2.44 0.19 10.85 0.00 12.11 -10.66 5610.53 Y 2.64 2.80 11.25 0.00 12.31 -8.45 301.79 Y 3.10 0.10 16.60 12.77 99.83 -16.50 16500.00 N 3.15 4.67 16.95 12.82 99.83 -12.28 262.96 N 3.20 8.09 17.39 12.87 98.61 -9.30 114.96 N 3.30 3.19 17.82 12.97 99.68 -14.62 458.62 N 4.15 0.48 20.73 13.82 99.86 -20.25 4218.75 N 12.20 0.54 34.10 21.87 99.96 -33.56 6214.81 N 13.50 15.00 36.33 23.17 99.72 -21.33 142.20 N The actual denitrification values for the lower organic carbon values (OC 0.60 – 2.64 %) are within the range predicted by the Monte Carlo simulation. The actual denitrification rate for the higher organic carbon (> 3.10%) values lie outside the range predicted by the Monte Carlo analysis. The errors in many cases are over a 1000% and this makes the simulations unreliable.
3.2.2. Texture-Temperature-WFP-pH The equations from the texture-temperature-WFP-pH breakdown are selected based on a correlation coefficient value of 0.4 or greater, the intercepts and the slope for each of the equations are plotted and the relationship is linear with an extremely strong correlation (r 2 = 0.96). A Lognormal probability plot reveals that the data is log-normally distributed. The same procedure is applied to the second set of data. Hence the equations for texture- temperature and water filled porosity used in the Monte Carlo Generation are, Intercept 0 -100 -200 -300 -400 -500 0 20 Texture Temperature WFP pH Intercept = 11.17 - 3.118 Slope 40 60 80 Slope 110 100 120 S 10.2732 R-Sq 99.4% R-Sq(adj) 99.3% Regression 95% CI 95% PI Figure 3.3 Intercept Vs. slope (Texture-Temperature-WFP-pH). 140 160
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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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Texture 7 Temperature 28 : Denitrif
Page 79 and 80: Texture 8 Temperature 15 : Denitrif
Page 81 and 82: 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 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: Hence for the first set of equation
Page 133 and 134: Organic Carbon (%) Actual Rdn Gener
Page 135 and 136: 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
Page 177 and 178: 18 ⎛ ⎛ ⎜ O ⎞ 16 ⎜ ⎜ ⎟
Page 179 and 180: 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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