Journal of Water and Soil Vol. 26, No.5, Nov.-Dec. 2012, p SWAP :

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1 Journal of Water and Soil Vol. 26, No.5, Nov.-Dec. 22, p ( ) *2-39/2/2 : 39/4/4 : SWAP. /33 2/9 / SWAP :. (). (4).. /3 /5 /63 (4). / (2). /28 -/75 (8) ( Navabian@guilan.ac.ir : -*)

2 ' 5 48' 48 34' ' /4..() SWAP (3 ) -. (4). (3) /5 -/3 /73 -/48 /2 -/ (5). - /4 - (27). /6 -/6-25 (2). /5 -/6.. - SWAP. WOFOST (3). SWAP... (24) GANET (2). SWAP (26). - (23).

3 SWAP.. WOFOST RETC. 2 /4 ) ( 598 ( ) 2.. SWAP.(7) ) 8 5 ( ) 5 (. (9).(4 ) (6) Y model : WUE ( ) Virr p Y model 2 : WUE (2) 2 ET act Y model 3 : WUE (3) 5 D 6mm 2 D2 3mm 2 D3 4mm D 4 2mm T 8day 3 T act ( 4) P T act ET act V irr Y ( ) ( ) ) ( ) - WUE 2 WUE. ( ) ( - WUE 3 D 4 D.. T. - (4 ) SWAP. SWAP (9) - ( ) / /2 (%) (%) (%) 4 7 (%) /72 /54 ( ) ( ) /27 / ( ) ( ) ( ) ( ) /28 /3 /36 /4 /3 /33 /37 /4 ( )

4 (5) T j j eff j Tav Tb Ds Ds Ds Tsum, i Tav Tb (6) T sum, i D s j T eff ( ). -3 (7) SWAP PEST. (26) (6) IR72. 5.(3 ) 3 SWAP : - (2 ) -2 SWAP.(5 ) T ( T T ) sum, i av b ( 5) T b ( c) T av. 2.(5) 8 /2 /33 3/ 2/9 / /6 /8 2/35 / / /24 2/97 /57 / /3/2 386/4/ 386/4/25 386/5/9 386/5/25 386/3/2 386/4/ 386/4/25 386/5/9 386/5/25 386/3/2 386/4/ 386/4/25 386/5/9 386/5/25 ( ) ( )

5 m j -4 2 /9 / SWAP SWAP. - (4 ) (4-6 ) ( ) SWAP SWAP /4 454/23 / ) 8. ( ( 8 ) 5 SWAP. 2.(8 7 ) n 2.5 RMSE ( ( p o ) / n) ( 7) I i i n 2.5 RMSE N ( ( p o ) / n) / o ( 8) n I i i o i p i. o.() 3 (22) (26) ( m j ) m 5 j 2 ( b a) 2 ( 9) b a - RMSE (Root Mean square Error) 2 - RMSE-N(Root Mean square Error- Normal) 3 - Crossover

6 () () SWAP. 8 ( 8 ) SWAP.. SWAP. 8 PEST -5 8 /28 / m 2 /m 2 /646 /22 ha/kg /7964 /72 - /6373 / /9832 /68 g ha - hr - /j m 2 EFF 4/27 35/99 Kg ha - hr - /97 / Kg/kg /992 /2586 Kg/kg /4 /36 -

7 ) ( ) ( ) y =.48x R² =.985 RMSE=489 kg/ha RMSE N (%)=2.2 SWAP y =.29x R² =.984 RMSE=454.23kg/ha RMSE N (%)=.4 ( ( ) 8 SWAP -3 ) ( /6 /33 2/ ( ) 7/33 6/7 4/65 /62 /45 / ( ) 39/58 34/2 34/42 29/73 28/39 28/7 5/97 5/2 5/ /9 / /6 -

8 ( ) 8.7 ( ) ( ) ( )

9 ( ) ( ) ).. ( (6 )..(6 ).(7 ) (25)...(2) 7/33 (8) (7 ) 7/33 ( 34) /33. 2

10 ( ) (.... ) - ( ( /6) (3 ) 6. ( 2/9) ) ( ) SWAP

11 () : :() Aly A.H., and Peralta R.C Comparison of a genetic algorithm and mathematical programming to the design of groundwater cleanup systems Agricultural systems 35: Akhtar S., Kiyoshi H., and Yann C. 25. Input assimilation of soil water atmosphere and plant (SWAP) model whith GA using cluster computers. Global Change Biol. 8 (4): Belder P. Bouman B.A.M. Spiertz J.H.J. and Guoan L. 27. Exploring options for water savings in lowland rice using a modeling approach. Agric. Syst 92: Bouman B., and Tuong T.P. 2. Field water management to save water and increase its productivity in irrigated lowland rice. Agricultural Water Management 49: Bouman B.A.M.L., Feng T.P., Tuong, G. Lu., Wang H., and Feng Y. 27. Exploring options to grow rice under water-short conditions in northern China using a modeling approach. II: Quantifying yield, water balance components, and water productivity. Agric Water Manage. 88: Boogaard H.L., Van Diepen C.A., Rötter R.P., Cabrera J.M.C.A., and Van Laar H.H WOFOST 7. User guide for the WOFOST 7. crop growth simulation model and WOFOST Control Center 5..Techn. Doc. 52, Alterra, WUR, Wageningen, The Netherlands, 44 pp. 7- Confalonieri R., Gusberti D., and Acutis M. 26. Comparison of WOFOST, CropSyst and WARM for simulating rice growth (Japonica type short cycle varieties). Italian Journal of Agrometeorology: Facon T. 26. Water management in rice in AsiaWorld. Journal of Microbiology and Biotechnology. 3: Feddes, R.A., Kowalik P.J., and Zaradny H Simulation of field water use and crop yield.simulation Monographs. Pudoc. Wageningen. 89 pp. 2- Hwang Gi., Kim Kt., and Jeong Ju The effect of drought at the reproductive stage on the degeneration sterility, ripening and nutrient uptake of rice. Research Report of the Rural Development Administration, Rice.3(): Molden D., Murry-Rust H., Sakthivandival R., and Makin I. 27. Water Productivity in Agriculture, Limits and Opportunities for Improvement. CABI Publishing. Wallingford, UK: Praveen k., Almeda J., Bajcsy P., Folk M., and Markus M. 26. Hydro infolmatics data integrative approaches in compution, analysis and modeling. Chapter 24: Genetic Algorithm. CRC press, Taylor and France group. 529 pages. 23- Raju K and Kumar D. N., 24. Irrigation Planning using Genetic Algorithms. In: Water Resources Management, 8 (2): Savic D., and Walter G Genetic Algorithms for least cost design of water distribution networks. J. Water Resour. Plng. Mgmt. ASCE. 23(2): Yoshida Sh. 98. Fundamentals of Rice Crop Science. Theintern Rice Inst. Philippines. 26- Zhao Y., Zhang D., Yonglu T., Jiao W., and Lingyong J. 29. An optimal model of a agriculture circular system for paddy & edible fungus & dry land International Journal of Management Science and Engineering Management.69 ( 4): Zwart S.J, and Bastiaanssen W.G.M. 24. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manage. 69(2): 5-33.

12 Journal of Water and Soil Vol. 26, No.5, Nov.-Dec. 22, p ( ) Comparison of Optimized Some Water Use Efficiency in Paddy Fields of Rasht M. Aghajani - M. Navabian 2* Received: Accepted: Abstract Water for rice cultivation is one of the main inputs. The new administration of irrigated rice is increase water efficiency and water conservation in the paddy fields. In this research, for optimization of intermittent irrigation management in proportion to water requirement of different stages of rice growth was present an optimizationsimulation model to maximize irrigation water, transpiration and evapotranspiration productivity Indexes. Irrigation water depth in stages of tiller, vegetative, maturity, harvest and irrigation intervals were selected as decided values in optimization model. Simulation of plant growth stages, using the hydrological model SWAP and genetic algorithm was used to solve the optimization model to maximize agricultural productivity. Finally, the optimum amount of irrigation water productivity, transpiration and evaporation - transpiration were obtained.6, 2.9 and.33(kg/m 3 ) respectively. Results showed, irrigation water productivity index has more harmonize with Sefidroud irrigation network. Also the index is user-friendly in applying and calculating. So according to maximizing of water productivity index irrigation depth was recommended 5, 29, 39 and mm respectively in stages of tiller, vegetative, maturity, harvest and and 8 days period of irrigation intervals to improve water productivity index in Hashemi variety in Rasht. Optimization results showed optimal intermittent irrigation is successive compared with flood irrigation in rice. Keywords: Genetic Algorithm, Rice, Optimization model, SWAP model, Intermittent irrigation management,2- MSc Student and Assistant prof., Dept. of Water Eng., Faculty of Agriculture, University of Guilan, Rasht, Iran (*-Corresponding Author Navabian@guilan.ac.ir)