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indicates that ET o will increase/decrease as the variable increases. The larger the sensitivity coefficient, the larger effect a given variable has on ET o . In graphical form, the sensitivity coefficient is the slope of the tangent at the origin of the sensitivity curve. A sensitivity coefficient of 0.1 for a variable would in this case mean that a 10% increase of that variable, while all other variables are held constant, may increase ET o by 1%. Many researchers have used this methodology to characterize sensitivity models (Saxton, 1975; Coleman and DeCoursey, 1976; Piper, 1989; Meyer et al., 1989; Rana and Katerji, 1998) using different variables and equations. In the present study, sensitivity coefficients for each station were calculated on a daily basis for air temperature, wind speed, relative humidity and solar radiation. Monthly and yearly average sensitivity coefficients were obtained by averaging daily values. 3. Results and Discussion Global warming due to greenhouse effect is expected to cause major changes in climate of some areas (Goyal, 2004). The increase in temperature affects ET o primarily by increasing the capacity of air to hold water vapor. Daily sensitivity coefficients exhibit large fluctuations during the year at all locations. The same feature has also been reported by Hupet and Vanclooster (2001) and Gong et al. (2006). In general, net radiation (R n ) had large sensitivity coefficients thought the year at all cities. At all locations, the coefficients for temperature are systematically and significantly lower very close from zero to 0.15 than all other variables, but all variables exhibited large fluctuations during the year. Daily variation patterns of sensitivity coefficients for air temperature (S T ) agree with those of air temperature. ET o is insensitive to temperature in winter and the sensitivity gradually increases and achieves its maximum value in summer (Fig. 1). The similar patterns of ST and air temperature indicate that air temperature determines the extent of the seasonal variation of ST. Fig. 1 shows that ET o is most sensitive to wind in winter season in Baft, Bam and Kerman, but, the fluctuations of the sensitivity coefficients of wind speed (S U2 ) is large in all of the day in Jiroft (Fig. 1d). In general, temperature and wind speed were less influential to ET o and their sensitivities were similar to each other in warm months. Sensitivity coefficients of vapor pressure deficit (S VPD ) shows a pronounced seasonal cycle with a maximum value in January, February, March, October, November and December, similar to the seasonal cycle of the S U2 . The effects of VPD, which increases the evaporation, reached higher levels during the winter and cold months. As it shown in Figs. 1a, b, c, S U2 and S VPD had a similar pattern in all cities. In winter, 10% change in VPD could cause approximately a 5%, 3.7%, 3.8% and 1.8% change in ET o in Kerman, Baft, Bam and Jiroft, respectively. But in summer months, 10% change in VPD could cause a 3.5%, 2.7%, 2.5% and 1.7% change in ET o in these cities. There was also a considerable difference Between Jiroft from others. The sensitivity coefficients of U 2 , VPD and T are changes between zero to 0.4 in Jiroft. The sensitivity of evapotranspiration rates to changes in temperature and wind speed is less in Jiroft than in other study areas. In general, R n was the most sensitive variable at the daily scale in all of cities. Strong positive sensitivity coefficients indicated that increases in Rn greatly induce the potential evapotranspiration. In summer, 10% change in Rn could cause approximately a 6.5%, 7.4%, 7.5% and 8.3% change in ET o in Kerman, Baft, Bam and Jiroft, respectively. In other words, The ET o estimates were very sensitive to R n , with an annual mean value of 0.58, 0.68, 0.70 and 0.83 in Kerman, Bam, Baft and Jiroft, respectively (Table 2). This confirms that the radiation term in Penman-Monteith equation is generally dominant over the aerodynamic term in the prediction equation as reported by Beven (1979). 5
(d) Baft (c) Bam (b) Kerma n (a) Jiroft FIGURE 1: Mean daily sensitivity coefficients for air temperature (S T ), wind speed (S U2 ), vapour pressure deficit (S VPD ), and net radiation (S Rn ) in (a) Baft, (b) Bam, (c) Kerman, and (d) Jiroft TABLE 2: Monthly and annual average sensitivity coefficients for individual climate variables 6
Page 1 and 2: POSTER SW: SOIL AND WATER ENGINEERI
Page 3 and 4: Presenter: Jose Euclides Paterniani
Page 5 and 6: 1 Faculdade de Engenharia Agricola
Page 7 and 8: Matsura Department of hydraulic and
Page 9 and 10: P-2064 MULTIVARIATE STATISTICAL OF
Page 11 and 12: temperature-based (e.g., Thornthwai
Page 13: two types of reference surfaces rep
Page 17 and 18: Estevez, J., Gavilan, P., & Berenge
Page 19 and 20: 2. Materials and Methods 2.1 The hy
Page 21 and 22: Ia = n × v ec Equation 3 which: Ia
Page 23 and 24: 5. References ALLEN, R.G.; PEREIRA,
Page 25 and 26: The density analysis was performed
Page 27 and 28: Figure1 - Relationship between the
Page 29 and 30: 4 Conclusions • The density obtai
Page 31 and 32: characteristics resulting from of g
Page 33 and 34: Table1. Morphological characteristi
Page 35 and 36: Transpiration of Eucalyptus spp see
Page 37 and 38: The fertilization growth and harden
Page 39 and 40: Cool, J. B., Rodrigo, G. N., Garcí
Page 41 and 42: Abstract Agriculture and water sour
Page 43 and 44: In 1985 and 1986 hygienic protectio
Page 45 and 46: spring area. It is also prohibited
Page 47 and 48: Biological Nitrogen Fixation In Gen
Page 49 and 50: We used a completely randomized in
Page 51 and 52: 5. References AYERS, R.S.; WESTCOT,
Page 53 and 54: 2 However, the cultures are not alw
Page 55 and 56: 4 TABEL 2: Mean values of radiation
Page 57 and 58: accumulated ETo (mm dia -1 ) 6 900
Page 59 and 60: only the expansion of agricultural
Page 61 and 62: FIGURE 2: Content of chlorophyll a,
Page 63 and 64: 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
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TABLE 1 Stream discharge for each m
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TABLE 5 Correlation analysis of wat
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turbulent flow energy produced by w
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The numerical model was validated a
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4. Conclusion FIGURE 6: The shape o
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2.1 Case study The Zayandeh-Rud bas
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economic factors. In this is a very
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FIGURE 1. Location of the Wuliangsu
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WT( o C) (a) 30 25 20 15 10 5 0 -5
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(a) (b) (c) (d) (e) (f) (g) (h) (i)
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• The effectiveness of the crop c
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As evidenced by Rana et al. (2005),
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1.20 1.20 2010 2011 0.90 0.90 K c 0
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elationships. There is forest area,
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B = ( c − a) A − ( c − d) c
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References Choi, W.-J., Lee, S.-M.,
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of faecal bacteria (Kummerer, 2004;
Page 295 and 296:
FIGURE 1 - Project tasks and links
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Oliveira, A.B. & Henriques, M. (201
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1 Introduction To irrigate is to su
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the inverter that provides a refere
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OLIVEIRA FILHO, D. ; SAMPAIO, R. P.
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1.1 Description of the Study Area T
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Refrences: Bruce J.P. (1994). Natur
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RE because it has the advantages ov
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with L 1 2 com obs J ( k ) = ∑{ f
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Relative hydraulic conductivity r 1
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surfaces requires information on th
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climate. Therefore a special coeffi
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FIGURE 2: The relation between K pa
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Coagulation using Moringa oleifera
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After the assembly of the experimen
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For the positive control test, the
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MULTIVARIATE STATISTICAL OF PRINCIP
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The multiple regression equations w
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Table 3 - Regression models that be
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Is Imaging Analysis Quantifying the
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of the computer. All additional ima
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The final enhanced images were segm
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image analysis is well suited and f
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EVALUATION OF CROP CANOPY EFFECT ON
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∂e ∂e ∂e + u + v ∂t ∂x
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4. Results and discussion 4.1. Spat
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Benchmarking of Irrigated Agricultu
Page 349 and 350:
Indicators can be thought of as sta
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total area is about 13,700 km2. The
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