poster - International Conference of Agricultural Engineering

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z 5 cm -10 cm -20 cm 5 4 3 Pressure head Volumetric water content m 0.0 -0.4 -0.8 -1.2 -1.6 0.25 0.15 -10cm -20cm -30cm -30 cm 2 1 Tensiometer Soil moisture probe Thermometer FIGURE 1: Schematic illustration of observation and computation Soil temperature 0.05 o C 20 10 0 11/26 11/27 11/28 11/29 11/30 12/1 12/2 Time in day FIGURE 2: Observed data Eq.(10), respectively. Additionally, the saturated water content θ s is defined as the value of θ in case that ψ is equal to zero while the residual water content θ r as the minimum value in observed data of θ. Thus, θ s and θ r are determined to be 0.30 and 0.05 from all observed data series including them shown in Fig.2, respectively. As the reference soil temperature, the average value of observed data is adopted which equals to 12.4 degrees Celsius. Conclusively, a best fitting curve for θ −ψ relation at the reference soil temperature is obtained with the value of α and n vg being 28.9 m -1 and 1.43, respectively. 4.3 Identification of RHC Firstly, the number of identified unknown parameters is determined. Since the optimal number of parameters is approximately from seven to nine according to Bitterlich et al. (2004) and Iden and Durner (2007), the number of unknown parameters is set to be 10 in this study. Secondly, the manner of division on the definition domain (effective saturation domain) is determined. The function shape of RHC generally has steeper gradient near the saturated zone. In the context of accuracy, the range with steeper gradient should be divided into finer subdomain. Therefore the effective saturation range from 0.8 to 1.0 where RHC function rapidly changes is partitioned into sub-range in the manner of geometric series. Eventually, RHC function is identified as shown in Fig.3 after the saturated hydraulic conductivity at the reference temperature is determined to be 5.18×10 -5 m/s through laboratory experiment. 4.4 Reproducibility of Calibrated Forward Simulation Model The validity of the inverse modeling proposed is assessed in terms of reproducibility for the time-series observed data by calibrated forward simulation model through comparing the observed and computed values. The result of reproducibility for water movement is shown in Fig.4. In the lower half of Fig.4, the absolute errors |ψ obs -ψ com | are also shown to demonstrate the time-varying difference in solution reproducibility. From the results, it is found that water movement is reproduced with high accuracy. 5. Conclusion An inverse modeling in variably saturated and non-isothermal subsurface water flow is developed. To consider the water movement depending on the soil temperature, a couple of the mixed form RE with heat conduction equation is employed as the governing equation.
Relative hydraulic conductivity r 1.0 0.8 0.6 0.4 0.2 r 0.0 0.2 0.6 1.0 Effective saturation FIGURE 3: Identified RHC e Pressure head (m) Fitting error (m) 0.0 -0.2 -0.4 -0.6 0.16 0.08 0.0 11/26 11/27 11/28 11/29 11/30 12/1 12/2 Time in day FIGURE 4: Reproducibility of forward solution for water movement RHC function is represented using a free-form approach and determined by solving IP with the simulation-optimization technique after SWRC which can be determined through experiments with relative ease is given in advance. The validity of the inverse modeling proposed is confirmed from the practical application with in-situ soil near the surface soil. obs com Reference list Bitterlich, S., Durner, W., Iden, S.C., & Knabner, P. (2004). Inverse estimation of the unsaturated soil hydraulic properties from column outflow experiments using free-form parameterizations. Vadose Zone J., 3, 971-981. Chung, S.O., & Horton, R. (1987). Soil heat and water flow with a partial surface mulch. Water Resour. Res., 23(12), 2175-2186. Hillel, D. (1998). Environmental Soil Physics. Academic Press, (Chapter 8). Huyakorn, P.S. & Pinder, G.F. (1983). Computational Methods in Subsurface Flow. Academic Press. Iden, S.C., & Durner, W. (2007). Free-form estimation of the unsaturated soil hydraulic properties by inverse modeling using global optimization. Water Resour. Res., 43, W07451, doi:10.1029/2006WR 005845. Iden, S.C., & Durner, W. (2008). Free-form estimation of soil hydraulic properties using Wind's method. Eur. J. Soil Sci., 59, 1228-1240. Izumi, T., Takeuchi, J. Kawachi, T., Unami, K., & Maeda, S. (2008). An inverse method to estimate soil hydraulic properties in saturated-unsaturated groundwater flow. J. of Rainwater Catchment Systems, 13(2), 23-28. Izumi, T., Takeuchi, J. Kawachi, T., & Fujihara, M. (2009). An inverse method to estimate unsaturated hydraulic conductivity in seepage flow in non-isothermal soil. Trans. of The Japanese Society of Irrigation, Drainage and Rural Engineering, 264, 35-42. Izumi, T., Takeuchi, J. Kawachi, T., & Fujihara, M. (2011). Inverse modeling of massconservative numerical model for variably saturated seepage flow. J. of Rainwater Catchment Systems, 17(2), 11-16. Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res., 12(3), 513-522. Seki, K. (2007). SWRC fit - a nonlinear fitting program with a water retention curve for soils having unimodal and bimodal pore structure. Hydrol. Earth Syst. Sci. Discuss., 4, 407- 437. Sun, N.Z. (1994). Inverse Problems in Groundwater Modeling. Kluwer Academic Publishers, (Chapter 4). van Genuchten, M.Th. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J., 44, 892-898.
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POSTER SW: SOIL AND WATER ENGINEERI
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Presenter: Jose Euclides Paterniani
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1 Faculdade de Engenharia Agricola
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Matsura Department of hydraulic and
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P-2064 MULTIVARIATE STATISTICAL OF
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temperature-based (e.g., Thornthwai
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two types of reference surfaces rep
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(d) Baft (c) Bam (b) Kerma n (a) Ji
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Estevez, J., Gavilan, P., & Berenge
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2. Materials and Methods 2.1 The hy
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Ia = n × v ec Equation 3 which: Ia
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5. References ALLEN, R.G.; PEREIRA,
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The density analysis was performed
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Figure1 - Relationship between the
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4 Conclusions • The density obtai
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characteristics resulting from of g
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Table1. Morphological characteristi
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Transpiration of Eucalyptus spp see
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The fertilization growth and harden
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Cool, J. B., Rodrigo, G. N., Garcí
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Abstract Agriculture and water sour
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In 1985 and 1986 hygienic protectio
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spring area. It is also prohibited
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Biological Nitrogen Fixation In Gen
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We used a completely randomized in
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5. References AYERS, R.S.; WESTCOT,
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2 However, the cultures are not alw
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4 TABEL 2: Mean values of radiation
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accumulated ETo (mm dia -1 ) 6 900
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only the expansion of agricultural
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FIGURE 2: Content of chlorophyll a,
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Evapotranspiration and Crop Coeffic
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were respectively applied in the fi
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TABLE 1: Irrigation depth and actua
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NUTRIENT RETENTION IN WETLANDS USIN
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Table 2. Daily affluent concentrati
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IMPORTANCE OF DRY GEAR MASS CULTURE
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mobilizing assimilated exerted by c
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This method consists of covering th
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uncovered ones, that mixed the wate
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coliforms and E-coli that might hav
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WATER TREATMENT BY COAGULATION WITH
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in a grinder and passed through a 0
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3.2. Determination of the required
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ANALYSIS OF LEVELS OF LAND DEGRADAT
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This methodology consists of a sequ
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FIGURE 5. A - Area of exploitation
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Water technology improvements and t
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The Multiattribute Utility Theory (
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higher demand than those of scenari
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YIELD AND BEAN SIZE OF COFFEA ARABI
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uniformity of flowering. The irriga
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Table 3 - Analysis of variance for
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SUGARCANE FERTIRRIGATED WITH MINERA
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3. Results and Discussion The value
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espectively, compared to that obser
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Optimal Reservoir Operation Model w
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all periods are computed using Eq.
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(a) Calibration (b) Verification Fi
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Characteristics of Heavy Metal Cont
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2. Materials and Method 2.1. Study
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TABLE 2: Devices for collecting of
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Calibration of Hargreaves Equation
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Relative error (RE): Index of agree
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Stochastic modelling of Contaminant
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widely used for various fields such
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show that Extvalue and Logistic dis
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Efficiency of water and energy use
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Pressure: it was obtained by means
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them cover similar percentages. Dur
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Relationship among compaction, mois
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Cylindrical containers (191mm diame
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Figure 9 High compaction. Bulk dens
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Simulation of water flow with root
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water contents were almost greater
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Operation and Energy Optimization M
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Urmia Salt Lake Urmia FIGURE 1: Gha
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changes have been done in system. F
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Application of Surface Cover and So
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significantly lower than those from
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Choi, J. D., (1997). Effect of Rura
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1.1. Scope and aim The growth of th
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Therefore, the remaining works are
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network makes such volumes unaccept
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Reclaimed wastewater reuse has been
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Fig. 2 shows the monitoring results
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3. Conclusions Reclaimed wastewater
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Q P Ia 2 P Ia S for P≥Ia Q 0
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data P (mm), gauged in 130 pluviogr
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TABLE 2: CN emp values obtained for
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References Chapman, T. G. & Maxwell
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This work, after applying Kennessey
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TABLE 2 - Partial runoff coefficien
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Figure 3 also reports a comparison
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2. Material and Methods The experim
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TABLE 2: Summary of variance analys
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BEZERRA, I. L.; GHEYI, H. R.; FERNA
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2. Materials and Methods The study
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Figure 3. Hourly values of ET estim
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Ortega-Farias, S.O., Cuenca, R.H.,
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2 Material end methods The wastewat
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Queiroz et al. (2004) and (Fonseca
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Reference list CEREDA, M.P. (2001)
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2. Data and Methods 2.1. Methods Ir
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3. Results The water balance model
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Acknowledgments This work was carri
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and the need to reduce costs, it be
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40 mm 65.30 59.00 8.70 8.10 60 mm 6
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REFERENCES BERTRAND, J. P. et al. L
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The mathematical modeling in the wa
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OD (mg L -1 ) OD obs (mg L -1 ) TAB
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OD (mg L -1 ) DBO (mg L -1 ) 8,00 7
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Effect of Rice Straw Mulch on Runof
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mg/L, 14.6 mg/L, and 1.2 mg/L, resp
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1 PAPAYA SEEDLINGS PRODUCTION FROM
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3 into an oven with circulating air
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5 14 12 Stem diameter (mm) 10 8 6 4
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7 Root dry matter (g plant -1 ) 8 7
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CAVALCANTE, L. F.; CORDEIRO, J. C.;
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This document was created with Win2
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extractable and non-extractable bou
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MOD-E and MOD-B, and 65.99% and 80.
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Houot, S., Barriuso, E., Bergheaud,
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measures water content and electric
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As observed, increasing the dischar
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irrigation: a comparison of point a
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field operations (STRECK et al., 20
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3. Results and discussions Table 1
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4. Conclusions It can be concluded
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water maintenance. At the same time
Page 261 and 262: TABLE 1 Stream discharge for each m
Page 263 and 264: TABLE 5 Correlation analysis of wat
Page 265 and 266: turbulent flow energy produced by w
Page 267 and 268: The numerical model was validated a
Page 269 and 270: 4. Conclusion FIGURE 6: The shape o
Page 271 and 272: 2.1 Case study The Zayandeh-Rud bas
Page 273 and 274: economic factors. In this is a very
Page 275 and 276: FIGURE 1. Location of the Wuliangsu
Page 277 and 278: WT( o C) (a) 30 25 20 15 10 5 0 -5
Page 279 and 280: (a) (b) (c) (d) (e) (f) (g) (h) (i)
Page 281 and 282: • The effectiveness of the crop c
Page 283 and 284: As evidenced by Rana et al. (2005),
Page 285 and 286: 1.20 1.20 2010 2011 0.90 0.90 K c 0
Page 287 and 288: elationships. There is forest area,
Page 289 and 290: B = ( c − a) A − ( c − d) c
Page 291 and 292: References Choi, W.-J., Lee, S.-M.,
Page 293 and 294: of faecal bacteria (Kummerer, 2004;
Page 295 and 296: FIGURE 1 - Project tasks and links
Page 297 and 298: Oliveira, A.B. & Henriques, M. (201
Page 299 and 300: 1 Introduction To irrigate is to su
Page 301 and 302: the inverter that provides a refere
Page 303 and 304: OLIVEIRA FILHO, D. ; SAMPAIO, R. P.
Page 305 and 306: 1.1 Description of the Study Area T
Page 307 and 308: Refrences: Bruce J.P. (1994). Natur
Page 309 and 310: RE because it has the advantages ov
Page 311: with L 1 2 com obs J ( k ) = ∑{ f
Page 315 and 316: surfaces requires information on th
Page 317 and 318: climate. Therefore a special coeffi
Page 319 and 320: FIGURE 2: The relation between K pa
Page 321 and 322: Coagulation using Moringa oleifera
Page 323 and 324: After the assembly of the experimen
Page 325 and 326: For the positive control test, the
Page 327 and 328: MULTIVARIATE STATISTICAL OF PRINCIP
Page 329 and 330: The multiple regression equations w
Page 331 and 332: Table 3 - Regression models that be
Page 333 and 334: Is Imaging Analysis Quantifying the
Page 335 and 336: of the computer. All additional ima
Page 337 and 338: The final enhanced images were segm
Page 339 and 340: image analysis is well suited and f
Page 341 and 342: EVALUATION OF CROP CANOPY EFFECT ON
Page 343 and 344: ∂e ∂e ∂e + u + v ∂t ∂x
Page 345 and 346: 4. Results and discussion 4.1. Spat
Page 347 and 348: Benchmarking of Irrigated Agricultu
Page 349 and 350: Indicators can be thought of as sta
Page 351 and 352: total area is about 13,700 km2. The
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