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179 5. d ha N kg m kg Density Bulk d kg N g = ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∗ 3 6. d ha N kg cm g Density Bulk d kg N g = ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∗ 3 3 10 * Bulk density 1. 3 3 3 10 cm kg cm gm = ∗ − 2. 3 6 3 3 10 * 10 * m kg cm gm = − − 3. 3 3 3 10 * m kg cm gm =
Convert From Multiply by Convert to Remarks mg mg N N kg kg −1 −1 d −1 −1 d 10 -3 10 -6 −1 −1 −3 mg N kg d Bulk Density ( kg m )*10 -3 −1 −1 −3 mg N kg d Bulk Density ( g cm ) −1 −1 −3 g N Kg d Bulk Density ( kg m ) −1 −1 −3 g N Kg d Bulk Density ( g cm )*10 3 −1 −1 −3 kg N kg d Bulk Density ( kg m ) −1 −1 −3 kg N kg d Bulk Density ( g cm )*10 3 mg N mg N mg N g N L L L L −1 −1 d −1 −1 d −1 −1 d −1 −1 d 10 -3 Porosity 10 3 −1 −1 -3 g N kg d 10 −1 −1 −3 g N kg d Bulk Density ( kg m ) −1 −1 −3 g N kg d Bulk Density ( kg m ) *10 3 −1 −1 −3 g N kg d Bulk Density ( kg m ) −3 −1 -1 g N m d Porosity −3 −1 −3 g N m d Bulk Density ( kg m ) -1 −3 −1 3 kg N m d 10 −3 g cm 10 3 180 g kg N N kg −1 −1 d −1 −1 kg d −1 −1 kg N ha d −1 −1 kg N ha d 1 g = 1000 mg −1 −1 kg N ha d See Above −1 −1 kg N kg d 1 Kg = 1000 g −3 −1 kg N m d −3 −1 kg N m d g N L −1 −1 d 1 g = 1000 mg;1 L = 10 -3 m 3 −3 −1 g N m d * Do not use in calculation Soil solution = Volume of −3 −1 g N m d soil * Porosity −3 −1 g N m d * Do not use in calculation −1 −1 kg N kg d g g N N −3 −1 m d −1 −1 ha d −1 −1 kg N ha d mg N g N L −1 −1 d −1 −1 kg d −1 −1 kg N ha d −3 kg m Soil solution = Volume of soil * Porosity 100 cm = 1 m 1 m 2 = 10 -4 ha
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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
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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
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.
Page 185 and 186: CHAPTER SEVEN 7. APPLICATION TO JAC
Page 187 and 188: 7.2. Multiple Regression Three equa
Page 189 and 190: Table 7.4 Multi-Regression and Neur
Page 191 and 192: 8.3. Main Results The linear equati
Page 193 and 194: isotopes can be particularly useful
Page 195 and 196: APPENDIX B CONVERSION SHEET FOR DEN
Page 197 and 198: iv. v. vi. Given in the dataset: 1.
Page 199: Conversion from −3 −1 g N m d t
Page 203 and 204: Texture 4 Variable Count N* Mean St
Page 205 and 206: Texture 10 Variable Count N* Mean S
Page 207 and 208: Texture-Temperature-WFP-pH Code Equ
Page 209 and 210: 09-15-25-09 Rdn = 0.0106688*OC + 0.
Page 211 and 212: Code Rdn - OC Code Rdn-WFP Code Rdn
Page 213 and 214: - Code Rdn - OC Code Rdn - pH Code
Page 215 and 216: - Code Rdn - OC Code Rdn - NO3 Conc
Page 217 and 218: Code Rdn - OC Code Rdn - pH 08-25-9
Page 219 and 220: REFERENCES Almasri, M. N., & Kaluar
Page 221 and 222: Fellows, C., Hunter, H., Eccleston,
Page 223 and 224: King, D., & Nedwell, D. B. (1985).
Page 225 and 226: Ozden, T., & Muhammetoglu, H. (2008
Page 227 and 228: Smith, R., Bohlke, J., Garabedian,
Page 229 and 230: Zhu, J., Liu, G., Han, Y., Zhang, Y
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