THE FLORIDA STATE UNIVERSITY ARTS AND SCIENCES ...

More documents

Recommendations

Info

(2) Soil structural models, and (3) Simplified process models. The microbial growth models consider the dynamics of microbial organisms responsible for the N cycling processes; examples can be found in the DENLEFWAT model (Leffelaar, 1988; Leffelaar and Wessel, 1988); model in the DNDC model (Li et al., 1992, 2000), in the NLOSS model (Riley and Matson, 2000), in the ECOSYS model (Grant, 2001), and in the RZWQM model. The soil structural models consider diffusion of gases into and out of aggregates. The distribution of aggregates is considered and denitrification occurs only in the anoxic parts of aggregates; examples of such models can be found in Arah and Smith (1989), Grant (1991), and Vinten et al. (1996). Simplified process models are easier to use and do not consider microbial processes or gaseous diffusion. Denitrification is assumed to be determined by easily measurable parameters such as degree of saturation, soil temperature and nitrate content of the soil. Such models are of practical use in studies where denitrification at field scale is to be determined. Since the eventual aim of the project is to determine the denitrification rate at a field or catchment scale, microbial models and soil structural models are not reviewed. While the first two types of models are extremely useful to the study of denitrification they do not provide the frame work to promote a simplified field scale model. Within the variety of simplified models reviewed, there seems to be general consensus about the mathematical fromulation of the model. The froms and values of the reduction functions however, differ largely between the models, especially for the water content reduction function. There are several simplified models to predict denitrification. The majority of these models are based on potential denitrification (Heinen, 2006) (which is measured either as a soil property or computed from organic carbon dynamics) or consider denitrification as 15
a first-order decay process. The majority of the existing models accept that environmental soil conditions affect the denitrification process and hence use reduction functions to achieve the actual denitrification rate from the potential rate (Heinen, 2006). Among the many models that are reviewed in Heinen (2006), a selected set was evaluated against the dataset developed in this work. This allows us to compare the established models against a widely collated database, allowing us to test the effectiveness and accuracy of the models at predicting denitrification on a field or water shed scale. The majority of models reviewed almost always need a potential denitrification rate and this rate is usually determined by lab and field measurements or by using existing models which considered denitrification as a function of a set of controlling parameters. In many cases the attempt to establish a potential denitrification rate in itself is a cost intensive exercise and this defeats the purpose of a quick, accurate and cost effective method to determine the rate of denitrification. The following section describes some of the models in detail with a focus on their effectiveness in predicting denitrification at a field scale. 1.5. Evaluation of methods to estimate the rate of denitrification. 1.5.1. Agricultural Policy/Environmental eXtender (APEX) The Agricultural Policy/Environmental eXtender (APEX) model (Williams and Izaurralde, 2006) is developed based on the Environmental Policy Integrated Climate (EPIC) model. APEX is developed for use in whole farm/small watershed management. The model is constructed to evaluate various land management strategies considering sustainability, erosion (wind, sheet, and channel), economics, water supply and quality, soil quality, plant competition, weather and pests (Williams and Izaurralde, 2006). While the specific focus of the model was not denitrification, it is however one of the few models in use that accounts for denitrification. 16
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: Among the variety of successful met
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 87 and 88:
Rdn (kgN ha-1 d-1) Texture 3 Temper
Page 89 and 90:
8-20-34 (n=3), 8-20-84 (n=4), 8-20-
Page 91 and 92:
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 99 and 100:
Page 101 and 102:
Page 103 and 104:
Page 105 and 106:
Rdn (kgN ha-1 d-1) Rdn (kgN ha-1 d-
Page 107 and 108:
Page 109 and 110:
C9 Rdn (kgN ha-1 d-1) 12 10 8 6 4 2
Page 111 and 112:
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 119 and 120:
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
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 ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?