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where, N is the nitrate concentration, S is the Soil Saturation and T is the temperature. The model developed is unable to predict the denitrification rate (Rdn) (Figure 1.4); this is perhaps because organic carbon is not considered as a factor in the development of the equation. In addition as the model was empirically developed it is feasible only for areas which are similar to the study area. The model is based on only five different sets of data and is hence limited in its use. Actual 50 40 30 20 10 0 0 Colbourn (1992) - Denitrificaiton Rate : Predicted Vs. Actual 10 20 Predicted Figure 1.4 Predicted denitrification rates based on Equation 1.10. 1.5.4. Anderson (1998) This is perhaps the least complicated method to determine the denitrification rate. Many authors consider organic carbon to be the primary controlling factor of the denitrification rate (Burford and Bermner, 1975; Knowles, 1982; Smith and Duff, 1988; Bradley et al., 1992; Korom, 1992,Cosandey et al., 2003; Kamewada, 2007). In addition several authors believe that the denitrification rate is directly proportional to the amount of organic carbon (Burford and Bermner, 1975; Smith and Duff, 1988; Bradley et al., 1992). Hence, 21 30 40 50
a simple linear regression of organic carbon versus the denitrification rate should yield a linear relationship with a significant statistical relationship. Anderson (1998) demonstrated that this linear regression could be used to determine the denitrification rate. A plot of the data used by Anderson (1998) yields a straight line with a significant correlation (R 2 0.85) between the denitrification rate and the percentage of organic carbon (Figure 1.5). This indicates that the regression could be used to predict denitrification. Rdn (Kg N ha-1 d-1) 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Denitrification Rate (Kg N ha-1 d-1) Vs. Organic Carbon (%) 0.0 Rdn (Kg N ha-1 d-1) = - 0.03942 + 0.4774 OC (%) S 0.143173 R-Sq 84.5% 0.5 1.0 OC (%) Figure 1.5 Denitrification Rate Vs. Organic Carbon (adapted from Anderson 1998). Based on the regression above the denitrification rate may be determined by the equation below Rdn (Kg N ha-1 d-1) = - 0.03942 + 0.4774 OC (%) ---- Equation 1.11 Using the same dataset as in section 1.5.1 the denitrification rate is estimated based on Equation 1.11 and the results are shown in Figure 1.6. This method while not initially successful may still be useful in predicting denitrification rates, if the equations are 22 1.5 2.0 2.5
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: Da is the denitrification rate (mg
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-
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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) Texture 5 Temper
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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
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The two equations are applied to th
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R R dn dn = - 0.05 ∗Temperature (
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Table 4.19 Texture 8, Linear multi-
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Table 4.20 Continued Actual Rdn Pre
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Once again the equations developed
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4.2.11. Texture 11 (Silt) No data a
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CHAPTER FIVE 5. ANALYSIS USING NEUR
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Figure 5.2 McCulloch-Pitts (Meyer-B
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developed. In order to control over
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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,
Page 229 and 230:
Zhu, J., Liu, G., Han, Y., Zhang, Y
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