THE FLORIDA STATE UNIVERSITY ARTS AND SCIENCES ...

More documents

Recommendations

Info

Rdn (kgN ha-1 d-1) Texture 8 Teperature 20 WFP 89 pH 7.1 : Denitrification Rate Vs.Nitrate Concentration 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Rdn (kgN ha-1 d-1) = 0.1588 + 0.002485 NO3- Conc( µgN g-1 soil) S 0.0651966 R-Sq 95.8% 50 100 150 NO3- Conc( µgN g-1 soil) Figure 2.107 8-20-89-7.1; Denitrification Rate Vs. Nitrate Concentration. 2.7.9. Texture 9 (Silty Clay) No further subdivision possible. 2.7.10. Texture 10 (Silty Clay Loam) The silty clay loam dataset can be divided into four subsets 10-18-100-6.8 (n=3), 10-25- 60-6.5 (n=9), 10-25-75-6.5 (n=9) and 10-25-90-6.5 (n=9). None of the subsets show a significant correlation between the denitrification rate and organic carbon or pH. 2.7.11. Texture 11 (Silt) No data 2.7.12. Texture 12 (Sandy Clay) No data 2.7.13. Texture 13 (Peat) No further subdivisions possible 103 200 250
2.8. Summary The correlation coefficients for all the linear regression equations are shown in Appendix E. The correlation coefficients increase for the majority of the subclasses as more controlling factors of the denitrification rate are fixed to constant values. While it is not as evident as demonstrated by Anderson (1998), the denitrification rate is positively correlated to the amount of organic carbon present. OC is however not the sole controlling factor in the equation (Weier et al., 1993; Laverman et al., 2001). It is only when other factors are fixed that the relationship can be clearly seen. Even with the breakdowns as conducted in this section the relationship between the denitrification rate and organic carbon is not robust enough to be useful as a predictive tool. The application of these regression equations to predict denitrification while untested can at best be applied only to the areas as specified by the code (page 41) and universal application is doubtful. This methodology while being the simplest possible technique to obtain a quick and rough estimate of the denitrification rate is nevertheless fraught with the need for a substantial amount of good quality data. Given the various issues with measuring denitrification it may be several decades before such a comprehensive database is available. The application of this method is thus limited to data available and can at best be used only in similar and already known conditions. 41 104
Page 1 and 2:
THE FLORIDA STATE UNIVERSITY ARTS A
Page 3 and 4:
To my Dad, Mum and Brother iii
Page 5 and 6:
Dr. Stephen Kish for his words of e
Page 7 and 8:
1.5. Evaluation of methods to estim
Page 9 and 10:
2.5.8. Texture 8 (Silt Loam).......
Page 11 and 12:
4.2.6. Texture 6 (Sandy Clay Loam).
Page 13 and 14:
LIST OF TABLES Table 1.1 Additional
Page 15 and 16:
LIST OF FIGURES Figure 1.1 Oxidatio
Page 17 and 18:
Figure 2.51 8-20-84; Denitrificatio
Page 19 and 20:
Figure 3.6 Probability plot (Textur
Page 21 and 22:
Three statistical methods were used
Page 23 and 24:
• Microbial biomass/ plant uptake
Page 25 and 26:
Figure 1.1 Oxidation of organic car
Page 27 and 28:
possibility of denitrification (Smi
Page 29 and 30:
50ºC (36ºF - 122ºF) (Brady & Wei
Page 31 and 32:
1.2.6. Salinity Salinity is a known
Page 33 and 34:
1.3.1. The Acetylene inhibition met
Page 35 and 36:
Among the variety of successful met
Page 37 and 38:
a first-order decay process. The ma
Page 39 and 40:
look at the data shows that it fail
Page 41 and 42:
Da is the denitrification rate (mg
Page 43 and 44:
a simple linear regression of organ
Page 45 and 46:
of the annual N2O emission and deni
Page 47 and 48:
easons for the limitation of the mo
Page 49 and 50:
Table 1.2 Continued dC / dt Organic
Page 51 and 52:
also implies that the transferabili
Page 53 and 54:
CHAPTER TWO 2. LINEAR REGRESSION An
Page 55 and 56:
an improvement on the earlier attem
Page 57 and 58:
2.3. Texture Table 2.1 Soil Textura
Page 59 and 60:
2.3.5. Texture 5 (Sand) The surfici
Page 61 and 62:
2.3.10. Texture 10 (Silty Clay Loam
Page 63 and 64:
Textural Class Table 2.2 Coefficien
Page 65 and 66:
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 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: Rdn (kgN ha-1 d-1) Texture 8 Tepera
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
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 and 200:
Conversion from −3 −1 g N m d t
Page 201 and 202:
Convert From Multiply by Convert to
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
show all

THE FLORIDA STATE UNIVERSITY ARTS AND SCIENCES ...

Create successful ePaper yourself

Delete template?

Save as template?