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y denitrification because of lower air temperature during winter. The NO 3 concentration of the animal manure origin tended to be always higher in the upper stream, no. 2, than in the mid stream, no. 9 (Fig. 3A). During flow from the upper stream to the mid stream, denitrification and/or dilution might attribute to lower NO 3 concentration at no. 9 than that at no. 2. At no. 9, the NO 3 concentration of the chemical fertilizer origin tended to higher than that of the animal manure origin whereas at no. 2 that of either origin was similar. During summer, the NO 3 concentration of the chemical fertilizer origin tended to be similar at no. 2 and 9. This might be reflected by the fact that a large amount of chemical fertilizer was used from the upper stream to the mid stream. NO 3 -N concentration (mg/L) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 (A) Animal manure origin No 2 No 9 NO 3 -N concentration (mg/L) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 (B) Chemical fertilizer origin No 2 No 9 0.1 0.1 0 Dec-03 Jun-04 Dec-04 Jun-05 Dec-05 Jun-06 Dec-06 Measuring date 0 Dec-03 Jun-04 Dec-04 Jun-05 Dec-05 Jun-06 Dec-06 Measuring date FIGURE 3: Temporal changes in estimated NO 3 concentrations at no. 2 and 9: (A) for the animal manure origin with Eq. 7, and (B) for the chemical fertilizer origin with Eq. 6 4. Conclusions The mixing model described by Eqs. 1-3 successfully estimated NO 3 concentration or δ 15 N values of the main stream based on those values obtained at tributaries only when there was no major denitrification. Discrepancies between modeled and measured values implied that N of either the chemical fertilizer or the animal manure origin merged the main stream. We examined to distinguish the origin of N using the mixing model. Because of large temporal variations in NO 3 concentrations and δ 15 N values, it was quite difficult to specify the NO 3 of the animal manure origin along the river. To monitor NO 3 concentration originated by animal manure or chemical fertilizer, more monitoring sites and more frequent sampling periods will be needed. It would be possible to improve the accuracy of the mixing model with the information on the quantities of flow in each sub-watershed. Acknowledgments This research was partly supported by the River Environment Management Foundation (project #1211). We are grateful to Ms. A. Nakano, and Mr. M. Yoshida of Iwate Agricultural Research Center for their technical assistance.
References Choi, W.-J., Lee, S.-M., & Ro, H.-M. (2003). Evaluation of contamination sources of groundwater NO 3 - using nitrogen isotope data. A review. Geosci. J., 7, 81-87. Hatano, R. (2005). Evaluating river water quality through land use analysis and N budget approach. J. Jpn. Soc. Soil Phys., 99, 21-28 (in Japanese with English abstract). Kuroda, H., Tabuchi, T., Kikuchi, H., & Suzuki, M. (1991). Nutirent outflow for a forest area. Trans. JSIDRE, 154, 25-35 (in Japanese with English abstract). Mariotti, A., Landreau, A., & Simon, B. (1988). 15 N istope biogeochemistry and natural denitirification process in groundwater: Application to the chalk aquifer of northern France. Geochemca et Cossmochimica Acta, 52,1868-1878. Mitsch, W.J., Day, J.W., Zhang, L., & Lane, R.R. (2005). Nitrate-nitrogen retention in wetlands in the Mississippi River Basin. Ecologic. Eng., 24, 267-278. Morita, A. (2002). Searching the origin of N outcome using δ 15 N. In S. Hasegawa (ed.) Predicting environmental loads (pp. 75-94). Tokyo: Hakuyusha Inc. (in Japanese). Rabalais, N.N., Turner, R.E., & Scavia, D. (2002). Beyond science into policy: Gulf of Mexico hypoxia and the Mississippi River. BioSci., 52,129-142. Shlesinger, W.H. (1997). Biogeochemistry. An analysis of global change. (2nd ed.). San Diego: Academic Press. Tabuchi, T., Yoshino, K., Shimura, M., Kuroda, S., Ishikawa, M., & Yamaji, E. (1995). Relationship between land use and nitrate concentration of outflow water from watersheds of agricultural and forest areas. Trans. JSIDRE, 178, 129-135 (in Japanese with English abstract). Tabuchi, T., Kuroda, H., & Shinoda, Y. (2005). On the decrease of NO 3 -N concentration of flow water in paddy field plots. J. Jpn. Soc. Soil Phys., 99, 65-72 (in Japanese with English abstract). Takamura, Y., Tabuchi, T., Harikae, Y., Otsuki, H., Suzuki, S., & Kubota, H. (1977). Study on mass balance in a rice paddy field (II). Jpn. J. Soc. Soil Sci. Plant Nutr., 48, 431-436 (in Japanese).
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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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Page 241 and 242: extractable and non-extractable bou
Page 243 and 244: MOD-E and MOD-B, and 65.99% and 80.
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Page 247 and 248: measures water content and electric
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Page 255 and 256: 3. Results and discussions Table 1
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Page 261 and 262: TABLE 1 Stream discharge for each m
Page 263 and 264: TABLE 5 Correlation analysis of wat
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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: B = ( c − a) A − ( c − d) c
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
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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
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Page 329 and 330: The multiple regression equations w
Page 331 and 332: Table 3 - Regression models that be
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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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