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Agriculture is still by far the greatest consumer of water in the region, and is well above the world average. More than 80% of water resources are allocated to irrigation, with relatively high losses that exceed 50%. With the intensification of water stress and the limited potential for additional water supply, in recent years great emphasis has been given to improving water use efficiency. In the agricultural sector, this has been expressed as “more crop and higher value per drop” (FAO, 2000). In the last decade the EU Commission produced several documents addressing the integration of environmental concerns into the agricultural policy. Particularly was highlighted the needs of reliable indicators to monitor, measure and evaluate the real impact of proposed innovation on the environmental sustainability. Although the need is evident, still are missing practical indicators that farmers and water authorities can use to achieve their goals by taking note of and controlling changes occurring on-farm and on-irrigation district. Benchmarking tools and performance indicators can be efficient used for this purpose. Benchmarking may be defined as the identification and application of organisation specific best practices with the goal of improving competitiveness, performance and efficiency. It is a continuous process that involves (a) internal assessment of the organisation, (b) comparing it with the best practices of more successful similar businesses in the market, (c) determining performance gap between current practice and best practice, and (d) selecting best practices, tailoring them to fit the organisation and implementing them. Guidelines for benchmarking in the irrigation sector were proposed recently (Malano et al., 2004; Farmani et al., 2003). Performance assessment is based on performance indicators that are specifically identified to enable the comparison and to monitor progress towards closing the identified performance gap. Comparison between performance indicators is widely used in irrigation systems, very much as a tool for water management policies. Previous applications and checks have shown that performance indicators and benchmarking can be successfully applied using the common general guidelines, taking into account the special features of every zone, because not all irrigation zones of the world are similar. The core of any benchmarking exercise is data collection. In order to enable comparison between irrigation districts, data used for benchmarking need to be consistent and comparable. In the present paper a first set of data including both historic and pilot (experimental) data is presented and relative performance indicators were calculated. 2. Materials and Methods 2.1. Data collection The core of any benchmarking exercise is data collection. In order to enable comparison between irrigation districts, data used for benchmarking need to be consistent and comparable. There are three types of data collection: • data collected for day-to-day management, operation and maintenance of the irrigation systems, such as pumping costs, reservoir and canal level, flow rate, etc. • data collected for benchmarking and comparison with other systems. • data collected as part of the diagnostic process within the benchmarking exercise to identify causes of performance. To enable irrigation districts with different levels of data available to participate in the benchmarking process, a range of benchmarking indicators will be proposed. 2.2. Performance indicators
Indicators can be thought of as statistical constructs which support decision-making, through benchmarking analysis, by revealing trends in data and subsequently, they can be used to analyse the results of policy actions. Indicators of sustainability seek to describe and measure key relationships between economic, social and environmental factors with sustainable development being seen as a better balance between all three dimensions. Successful indicators must be readily understandable, representative of key environmental policies and concerns, and capable of illustrating trends over time. In addition, indicators could provide an early warning of potential economic, social or environmental damage A further type of indicator is a derived indicator, which is used when it is either impossible or impractical to directly measure an impact. For example, soil productivity can be described by a large list of physical, chemical or biological soil characteristics that would be expensive and time-consuming to measure. Water productivity performance measures are at the very basis of most of the indicators utilised in benchmarking exercises. The water productivity concept grounds on the performance ratio between the amount of resource entering into a process and the process output. There are several definitions of water productivity, so we have to precise which crop and which drop we are referring to (Table 1). TABLE 1 Some examples of stakeholders and definitions in the water productivity framework. Stakeholder Definition Scale Target Plant physiologist Dry matter / transpiration Agronomist Yield / evapotranspiration Plant Field Utilize light and water resources Sufficient food Farmer Yield / supply Field Maximize income Irrigation engineer Yield / irrigation Irrigation Proper water supply scheme allocation Groundwater policy € / groundwater Aquifer Sustainable extraction maker extraction Basin policy maker € / evapotranspiration River Basin Maximize profits Water Authorities Diverted Water/ River Basin Safeguard or restore Existing water water quality and/or consents biodiversity into river and wetlands Measurements of production from irrigated agriculture can be used in several ways to compare across the irrigation systems the effectiveness of the actual governance or to assess the impact of recently implemented technologies, strategies, infrastructures. However, compare different crops, in different regions, cultures and markets is not an easy task. The difficulties arises when comparing measurement which have the effect of more than one variable embedded in itself, as e.g irrigation methods and strategies, climate, intermittent water availability, etc. A large number of authors attempted at standardize the comparisons through performances indicators, that are now available in literature and in some cases also tested in specific
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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
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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;
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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
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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 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: Benchmarking of Irrigated Agricultu
Page 351 and 352: total area is about 13,700 km2. The
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