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- Determine the distribution of all of the slopes for a given category. - Plot all the slopes and intercepts for a given category and determine S1 and M1 - Generate various values of slopes and for each slope evaluate an intercept based on the information from the previous step. - For a given OC value, determine the denitrification rate for every slope generated and corresponding intercept - Average the denitrification rates and plot a histogram of the denitrification rates. The above methodology is applied to the different datasets as outlined in the sections below. 3.2. Analysis for Organic Carbon 3.2.1. Texture-Temperature-WFP The equations from the texture-temperature and WFP breakdown are selected based on a correlation coefficient value of 0.4 or greater, the intercepts and the slopes for each of the 14 equations are plotted and the relationship appears to be linear (Figure 3.1) with a extremely strong correlation (r 2 = 0.96). A Lognormal probability plot of M reveals that the data is log-normally distributed (Figure 3.2). Intercept (C) 100 0 -100 -200 -300 -400 -500 0 Texture-Temperature-Water Filled Porosity 20 Intercept = 8.532 - 2.871 Slope 40 60 80 Slope (M) 107 100 120 Regression 95% CI 95% PI S 27.2026 R-Sq 96.4% Figure 3.1 Intercept Vs. slope (Texture-Temperature-WFP). 140 160
Hence for the first set of equations (texture-temperature and water filled porosity), R dn = M * OC + C ---- Equation 3.4 C = 8.532 - 2.81* M ---- Equation 3.5 Rdn = exp( M ) ∗Oc − 2. 81∗ exp( M ) + 8. 532 ---- Equation 3.6 Percent 99 95 90 80 70 60 50 40 30 20 10 5 1 0.0001 Loc 0.8739 Scale 2.567 N 12 AD 0.293 P-Value 0.540 0.001 0.01 Probability Plot of Slope 0.1 Lognormal - 95% CI 1 10 Slope 108 100 1000 10000 100000 Figure 3.2 Probability plot (Texture-Temperature-WFP). The equations above are used to generate 10000 different denitrification values using MATLAB, the average of the generated results was then compared to the actual measured value. The maximum and minimum generated values are also noted. For the organic carbon values with more than one denitrification rate an average measured value is used for comparison to the generated values.
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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
Page 77 and 78: 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: where, M is the slope of the linear
Page 131 and 132: 3.2.2. Texture-Temperature-WFP-pH T
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 ⎜ ⎜ ⎟
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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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