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APEX considers denitrification as a function of temperature, organic carbon and water content and uses the following equations to estimate the denitrification rate DN=WNO3*(1.-exp (-1.4*TFN*WOC)); SWF>0.95 ---- Equation 1.3 DN=0.0; SWFWP ---- Equation 1.7 Where ST is the soil water content in the root zone, WP is the wilting point soil water content in millimeters and FC is the field capacity of soil water content in millimeters. Assuming that SWF > 0.95 and using data which have a WFP of 100%, Equation 1.3 and Equation 1.5 are used to compute denitrification rates. A total of 884 records of data had a WFP below 0.95; this leaves 245 records of records of which 25 sets do not have a nitrate concentration value. Thus the totals of 220 records of data are used. As can be seen from Figure 1.2 and Figure 1.3 the APEX model does not yield any significant result. It fails to predict the denitrification rates for the entire range of values. A close
look at the data shows that it fails to predict the denitrification rate for data at both the upper end of the scale as well as the lower end of the scale. Predicted 50 40 30 20 10 0 0 APEX - Denitrification Rate : Predicted Vs. Actual 10 20 Actual Figure 1.2 Predicted denitrification rates based on equations Equation 1.3 and Equation 1.5 (All Data, n=220). In all fairness to the APEX model it must be noted that it is designed as an agro- ecosystem model that simulates crop production as a function of weather, soil conditions, and production practices employed (e.g., tillage types, tillage frequency and crop rotations). EPIC was developed and designed originally to explore the impacts of soil erosion on crop productivity (Williams et al. 1983). The model EPIC, and the models that have evolved from it, such as APEX, have been applied extensively to cropping systems worldwide on a variety of soils and cropping systems. 18 30 40 50
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: a first-order decay process. The ma
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
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8-20-34 (n=3), 8-20-84 (n=4), 8-20-
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Rdn (kgN ha-1 d-1) Texture 8 Temper
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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
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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,
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Zhu, J., Liu, G., Han, Y., Zhang, Y
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