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Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-1) 4.0 3.5 3.0 2.5 2.0 Texture 7 Teperature 28 WFP 28 pH 6.5 : Denitrification Rate Vs. Organic Carbon. 0.6 S 0.470860 R-Sq 86.1% 0.7 Rdn (kgN ha-1 d-1) = 0.6783 + 2.490 OC (%) 0.8 0.9 1.0 OC (%) Figure 2.96 7-28-28-6.5; Denitrification Rate Vs. Organic Carbon. 8 7 6 5 4 3 0.6 Texture 7 Teperature 28 WFP 50 pH 6.5 : Denitrification Rate Vs. Organic Carbon. S 0.920519 R-Sq 85.2% 0.7 0.8 0.9 1.0 OC (%) 97 1.1 Rdn (kgN ha-1 d-1) = 0.657 + 5.261 OC (%) Figure 2.97 7-28-50-6.5; Denitrification Rate Vs. Organic Carbon. 1.1 1.2 1.2 1.3 1.3 1.4 1.4
Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-1) 10 9 8 7 6 5 4 Texture 7 Teperature 28 WFP 50 pH 8 : Denitrification Rate Vs. Organic Carbon. S 1.52371 R-Sq 82.6% 0.6 0.7 Rdn (kgN ha-1 d-1) = 0.277 + 6.085 OC (%) 0.8 0.9 1.0 OC (%) Figure 2.98 7-28-50-8; Denitrification Rate Vs. Organic Carbon. 16 15 14 13 12 Texture 7 Teperature 28 WFP 133 pH 8 : Denitrification Rate Vs. Organic Carbon. 0.6 S 0.543180 R-Sq 95.1% 0.7 0.8 0.9 1.0 OC (%) 98 1.1 1.1 1.2 Rdn (kgN ha-1 d-1) = 9.344 + 4.385 OC (%) Figure 2.99 7-28-133-8; Denitrification Rate Vs. Organic Carbon. 1.2 1.3 1.3 1.4 1.4
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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-
Page 67 and 68: 2.4.3. Texture 3 (Loam) Texture 3 c
Page 69 and 70: R_d_n (kgN ha-1 d-1) Texture 3 Temp
Page 71 and 72: R_d_n (kgN ha-1 d-1) 6 5 4 3 2 1 0
Page 73 and 74: 2.4.7. Texture 7 (Sandy Loam) The S
Page 75 and 76: Texture 7 Temperature 12 : 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 111 and 112: Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-
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: 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 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
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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,
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Zhu, J., Liu, G., Han, Y., Zhang, Y
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