poster - International Conference of Agricultural Engineering

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Example of the manufactured pans of galvanized steel with 57 cm diameter and 30 cm height Cover using wire mesh fixed on steel frames and pieces of wood base its height is 10 cm The water level measured by ruler scale and a pointer mounted on a steel rod ended by a float on water. Class A pan Defferent types of pan: very small pan (VS= Ф 16), small pan (S= Ф 32), medium pan (M= Ф 40), larger pan (L= Ф 57) FIGURE 1: Examples of the different pan sizes and class A pan used in the Pan development and field experimental setup. Measuring the Pan Water Levels Water levels in an evaporation pan can be most accurately measured using a stilling well (as illustrated in Figure 1) with a hook gauge and micrometer. The stilling well provides a fixed reference point for accurate measurements even if the pan is not perfectly level. The hook gauge and micrometer permit depths to be measured to the nearest 0.001 inches, which is much greater accuracy than required for irrigation scheduling. Accuracies sufficient for irrigation scheduling can be obtained using a finely-graduated ruler or meter stick. However, the water depth measurement must always be made at the same location, and care must be taken to assure that the ruler is held vertically when measurements are made. For ease of interpretation, evaporation measurements should be made at about the same time each day. Normally, early morning or late evening measurements are most convenient. Early morning measurements permit yesterday's ET to be estimated and today's irrigations to be scheduled. Late evening measurements can be used to estimate today's ET for irrigations to be scheduled tonight or early tomorrow morning. Determination of K pan (Pan Coefficients) When using the evaporation pan to estimate the ET o , in fact, a comparison is made between the evaporation from the water surface in the pan and the evapotranspiration of the standard grass. Of course the water in the pan and the grass do not react in exactly the same way to the
climate. Therefore a special coefficient is used (K pan ) to relate one to the other. ET o and E pan are required to calculate (K pan ) coefficients. K pan values depend upon the location of the evaporation pan, the average daily relative humidity, the average daily wind speed, and the type of area surrounding the pan. The pan evaporation rate (and thus the value of K pan ) is different if the pan is surrounded by bare soil as opposed to a vegetated surface. ET o = K pan × E pan ET o : reference crop evapotranspiration, K pan : pan coefficient, E pan : pan evaporation If the water depth in the pan drops too much (due to lack of rain), water is added and the water depth is measured before and after the water is added. If the water level rises too much (due to rain) water is taken out of the pan and the water depth before and after is measured. 3. Results and Discussion 3.1 Overall Daily Reading of Atmosphere Data Daily averaged meteorological data recorded in the study field (Ismailia city) from (15/4) to (5/5) are shown in Table 1. The rate of evaporation is the amount of water evaporated over a given period of time. On open bodies of water, the rate is affected by several ground and atmospheric factors of which the main ones are: Solar radiation, Relative humidity, Winds, temperature. It is important to note that the effects of ground and atmospheric conditions on large water bodies can be different to the effects on an evaporation pan. TABLE 1: Meteorological data recorded during the experiment: air temperature, relative humidity (RH), and average wind speed. Day Julian Day Temperature Hummidity MAX TEMP MIN TEMP MAX RH MIN RH AVG Wind SPEED 15 April 106 28.6 12.0 83 35 9.5 16 April 107 29.4 14.4 85 38 7.9 17 April 108 30.7 12.5 84 28 6.3 18 April 109 34.0 14.4 71 23 7.3 19 April 110 36.5 16.8 69 22 8.9 20 April 111 35.3 17.0 65 26 6.0 21 April 112 29.1 15.9 80 38 8.0 22 April 113 22.6 13.9 81 48 9.2 23 April 114 26.0 13.0 88 35 7.0 24 April 115 28.1 12.9 80 35 9.3 25 April 116 30.9 14.0 80 32 9.2 26 April 117 31.0 14.8 82 35 7.2 27 April 118 34.3 14.8 80 25 6.1 28 April 119 26.8 14.7 85 44 9.1 29 April 120 28.1 14.9 87 39 6.4 30 April 121 28.8 15.2 81 36 7.2 1 May 122 27.4 13.5 83 35 6.6 2 May 123 26.0 12.4 88 37 6.4 3 May 124 28.5 12.9 83 32 6.3 4 May 125 29.9 13.9 84 31 6.2 5 May 126 30.8 14.4 84 30 6.2 7 June 159 34.9 14.7 86 33 8.3 8 June 160 29.3 14.6 87 46 6.9 9 June 161 30.5 12.5 85 38 6.5 10 June 162 33.8 13.1 78 30 8.4 11 June 163 35.3 14.9 79 28 6.8 12 June 164 31.3 15.8 86 43 7.4 13 June 165 32.5 16.1 86 40 6.3 14 June 166 32.7 14.9 85 35 6.3 15 June 167 31.5 14.4 84 37 7.1 16 June 168 34.8 18.0 81 40 7.4 17 June 169 36.8 19.6 83 37 8.0 18 June 170 36.5 17.2 82 39 7.9 19 June 171 36.1 15.0 86 37 9.1 20 June 172 40.1 15.0 73 27 8.9 21 June 173 42.4 14.5 70 22 6.2
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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
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TABLE 1 Stream discharge for each m
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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 and 312: with L 1 2 com obs J ( k ) = ∑{ f
Page 313 and 314: Relative hydraulic conductivity r 1
Page 315: surfaces requires information on th
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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