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Carr, M. K. V.; Knox, J. W. (2011). The water relations and irrigation requirements of sugarcane (Saccharum officinarum): a review. Experimental Agriculture, 47,1–25. CONSECANA - Council of the producers of cane sugar, sugar alcohol in the State of Sao Paulo. (2006). Manual Instruction. (5 th ed.). Piracicaba: CONSECANA. (In Portuguese) Dalri, A. B.; Cruz, R. L. (2008). Productivity of sugarcane fertigation with NK by subsurface drip Engenharia Agrícola, 28, 516-524. (In Portuguese, with abstract in English). Doorenbos, J.; Pruitt, W.O. (1984). Crop water requirements. Rome: FAO. (FAO, Irrigation and Drainage Paper, 24). Du, Y. C.; Nose, A.; Wasano, K.; Uchida y. (1998). Responses to water stress of enzyme activities and metabolite levels in relation to sucrose and starch synthesis, the Calvin cycle and the C 4 pathway in sugarcane (Saccharum sp.) leaves. Australian Journal of Plant Physiology, 25, 253 – 260. Gil, M.; Rodriguez-Sinobas, L.; Juana, L.; Sanchez, R.; Losada, A. (2008). Emitter discharge variability of subsurface drip irrigation in uniform soils: effect on water-application uniformity. Irrigation Science, 26, 451-458. INMAN-BAMBER, N.G. (2004). Sugarcane water stress criteria for irrigation and drying off. Field Crops Research, 89, 107-122. IPCC - Intergovernmental Panel on Climate Change. (2007). Climate change 2007 - The Physical Science Basis. Contribution of working group I to the fourth assessment report of the IPCC. Cambridge: Cambridge University Press. Lamm, F.R.; Camp, C. C. (2007). Subsurface drip irrigation. In: F. R, Lamm; J. E, Ayars; F. S, Nakayama. Microirrigation for crop production: design, operation, and management. (pp. 473-551). Amesterdam: Elsevier. Lamm, F. R.; Manges, H. L.; Stone, L. R.; Khan, A. H.; Rogers, D.H. (1995). Water requirement of subsurface drip-irrigated corn in northwest Kansas. ASAE, 38, 441-448. Lamm, F. R.; Trooien, T. P. (2007). Subsurface drip irrigation for corn production: a review of 10 years of research in Kansas. Irrigation Science, 22, 195-200. Roberts, T. L. (2008). Improving nutrients use efficiency. Turkish Journal of Agriculture and Forestry, 32,177-182. Oliveira, E. L.; Andrade, L. A. B.; Faria, M. A.; Custódio, T. N.(2009). Alembic vinasse and nitrogen in sugar cane in irrigated and non-irrigated áreas. Revista Brasileira de Engenharia Agrícola e Ambiental, 23, 694-699. (In Portuguese, with abstract in English). Prado, R. M.; Pancelli, M. A. (2006). Nitrogen Nutrition in ratoon and technological quality of sugarcane. STAB, v.25, 60-63. (In Portuguese, with abstract in English). Raij, B. van; Cantarella, H.; Quaggio. J.A.; FurlanI, A.M.C. (1996). Liming and fertilization recommendations for the state of Sao Paulo. (2nd ed). Campinas: IAC. (In Portuguese, with abstract in English). Tasso Junior, L. C.; Marques, M. O.; Franco, A.; Nogueira, G. A.; Nobile, F. O.; Camilotti, F.; Silva, A. R. (2007). Yield and quality of sugar cane cultivated in sewage sludge, vinasse and mineral fertilization supplied soil. Engenharia Agrícola, 27, 276-283. (In Portuguese, with abstract in English) Robaina, A. A.; Vieira, J. R.; Azeredo, D. F.; Bolsanello, J.; Manhães, M. S. (1983). Doses and mineral supplementation of vinasse in clogs of cane sugar. Brasil Açucareiro, 102, 26- 33. (In Portuguese). Simambuco, S. M.; Nascimento Filho, V. F. (1994). Study on vinasse dynamics in soil using energy dispensive x-ray fluorescence with radioisotopic excitation. Scientia Agricola, 5, 207- 215.
Optimal Reservoir Operation Model with a Streamflow Network Model and a Global Optimization Method Mingoo Kang 1* , Seungwoo Park 2 , and Jooheon Lee 3 1 Research Fellow, Future Resources Institute, Woolim Lions Valley C-405, 371-28, Gasan-dong, Keumcheon-gu, Seoul, South Korea, 153-786 2 Professor, Department of Rural Systems Engineering, Research Institute for Agriculture and Life Sciences, College of Agriculture and Life Sciences, Seoul National University, 599 Gwanak-ro, Gwanak-gu, Seoul, South Korea, 151-742 3 Professor, Department of Civil Engineering, Joongbu University, 101 Daehak-ro, Chubu-myeon, Geumsan-gun, Chungnam, South Korea, 312-702 * Corresponding author. E-mail: kmg1218@gmail.com Abstract In this study, an optimal reservoir operation model is presented, featuring a nonlinear, multiple-objective function and a global optimization method. In order to verify the model’s applicability, the model is applied to optimal water allocations of both Balan Reservoir and Seomjin Dam in South Korea that are an irrigation reservoir and a multi-purpose, multi-outlet dam, respectively. For Balan Reservoir’s optimal operations, a streamflow network model is used to simulate daily inflows, and a water demand computation sub-model is developed, taking into account the amounts of water required by irrigated rice paddy fields. The operation results show that optimal release patterns are similar to those of the observed. For the optimal operations of Seomjin Dam, a hydro-electric power outputs computation sub-model is developed and calibrated. The results of the optimal operations for Seomjin Dam show that, during a wet term, the hydro-electric power outputs increase by -7.52 to 10.04 %, varying with target water stages; during a dry term, they increase by -5.94 to 3.98 %; and during a normal term, they increase by 4.53 to 10.37 %. To solve an operation problem at Seomjin Dam that is the result of the increase of water demand downstream of the dam, the model is applied to various cases, considering the amounts of additional water supply. The results show that in these cases that the amounts of additional water supply are less than 0.70 m 3 /sec with target water stages lower than 194.0 m, the hydro-electric power outputs are more than the historical average, and the total amounts of water supply are less influenced by the amounts of additional water supply. Consequently, it is concluded that the model is useful for evaluating the water supply capacities of water resources facilities and to suggest amounts of additional water supply and new operation rules appropriate to the changes in the circumstances related to water management of systems. Key words: Optimal water allocation; Global optimization method; Streamflow network model; Non-linear, multiple-objective function; Water supply capacity evaluation. 1. Introduction In South Korea, on average, the annual rainfall amounts to 1,245 mm, that is 1.4 times larger than the average in the world. However, the amount of the annual rainfall per capita is 13.2 % of the average in the world. Since there are large gaps in rainfalls among regions and seasons, water resources have been developed inequitably. Particularly, because, during summer seasons, about two-thirds of the annual rainfalls have been concentrated, non-effective runoffs have been generated, and floods have occurred frequently. In order to resolve these problems that result from geographic and
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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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Page 109 and 110: 3. Results and Discussion The value
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Page 153 and 154: Urmia Salt Lake Urmia FIGURE 1: Gha
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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
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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
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Indicators can be thought of as sta
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total area is about 13,700 km2. The
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