Non-linear Optimization Model for Furrow Irrigation System in Maize Crop
DOI:
https://doi.org/10.52151/jae2009464.1392Abstract
Nonlinear optimization design models were developed for field conditions to design and manage furrow irrigation system using Lewis-Kostiakov infiltration equation. The design criterion used in the models was the depth of irrigation and basic infiltration rate of the soil. The objective function of the non-expanded nonlinear model was constructed on the basis of a relationship between net returns and water requirement efficiency, and the expanded nonlinear model was developed in terms of net returns and costs. The design variables of the models were the inflow rate, length of run, inflow time, number of furrows per set and number of sets. The expanded nonlinear model gave a better representation of the design parameters and was more flexible because it permitted easy changes in the objective function than non-expanded nonlinear model. The model can be used to compare different types of furrow irrigation management strategies.
References
Anon. 2002-2003. Comparative economics of Rabi crops. Department of Economics and Sociology, Punjab Agricultural University, Ludhiana.
Elliot R L; Walker WR; Skogerboe G V. 1982. Infiltration parameters from furrow irrigation advance data. ASAE Paper No. 82-2103, ASAE, St. Joseph, MI.
Garg S; Gulati H S; Kaushal M P; Jain AK. 2006. Development of relationship between performance irrigation parameters and furrow irrigation design variables, yield and net returns. Journal of Agricultural Engineering, 43(2), 45-49.
Holzapfel E A; Marino M A; Chavez-Morales J. 1985. Performance of irrigation parameters and their relationship to surface-irrigation design variables and yield. Agricultural Water Management, 10, 159-174.
Holzapfel E A; Marino M A; Chavez-Morales J. 1986. Surface irrigation optimization models. Journal of Irrigation and Drainage Engineering, ASCE, 112(1), 1-19.
Kuo S F; Liu C W. 2003. Simulation and optimization model for irrigation planning and management. Hydrol. Processes, 17, 3141-3159.
Raghuwanshi N S; Wallender W W. 1999. Forecasting and optimizing furrow irrigation management decision variables. Irrig. Sci., 19,1-6.
Reddy J M; Clyma W. 1981(a). Optimal design of furrow irrigation systems. Trans. ASAE, 25(3),617-623.
Reddy J M; Clyma W. 1981(b). Optimal design of border irrigation systems. Proceedings of the ASCE, 107(IR3), 289-306.
Reddy J M; Horacio M A. 1991. Sensitivity of furrow irrigation system cost and design variables. Journal of Irrigation and Drainage Engineering, ASCE, 117 (2), 201- 219.
Valiantzas J D. 2001. Optimal furrow design II: Explicit calculation of design variables. Journal of Irrigation and Drainage Engineering, ASCE, 127 (4), 209- 215.
Vaquez F; Lopez P T; Chagoya A B. 2005. Comparison of water distribution uniformities between increased discharge and continuous flow irrigations in blocked end furrows. Journal of Irrigation and Drainage Engineering, ASCE, 131 (4), 379- 82.
Wholing T H; Frohner A; Schmitz G H; Liedl R. 2006. Efficient solution of coupled one dimensional surface and two dimensional subsurface flow during furrow irrigation advance. Journal of Irrigation and Drainage Engineering, ASCE, 132 (4),380- 88.