MOPECO: an economic optimization model for irrigation water management

Water is a natural, sometimes scarce, and fundamental resource for life, essential both for agriculture in many regions of the world and also to achieve sustainability in production systems. Maximizing net returns with the available resources is of the utmost importance, but doing so is a complex problem, owing to the many factors that affect this process (e.g. climatic variability, irrigation system configuration, production costs, subsidy policies). The MOPECO model is a tool for identifying optimal production plans, and water irrigation management strategies. The model estimates crop yield, production and gross margin as a function of the irrigation depth. Finally, these gross margin functions are used to determine an optimum cropping pattern and irrigation strategy to maximize the gross margin on a farm in a specific scenario. Since the relationships between the variables are generally non-linear and the number of alternative strategies is quite large, the optimum process is complex and computationally intensive. Genetic algorithms are therefore used to identify optimal strategies. This paper describes the MOPECO model, which comprises three computing modules: (1) estimation of net water requirements; (2) derivation of the relationship between gross margin and irrigation depth; and (3) identification of the crop planning and the water volumes to be applied. The results obtained by applying the MOPECO model to a specific irrigable area in a semi-arid area of Spain, with great deficits and high water costs, are also included and discussed. These results usually show that the irrigation depth for maximum benefits is lower than that necessary to obtain maximum production. In some areas of Spain, horticultural crops are nearly always part of the optimum alternative. The crops that become part of the optimum alternative are mainly horticultural crops with a high gross margin and low water requirements. The irrigation depths selected for the ideal crop rotation are included among the irrigation depth of maximum economic efficiency and the maximum gross margin irrigation depth. Both are lower than that necessary for the maximum yield. This model helps farmers, extension services, and other agents to analyse, make decisions and optimize water management.

[1]  Frederick S. Hillier,et al.  Introduction of Operations Research , 1967 .

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  W. O. Pruitt,et al.  Optimizing crop production through control of water and salinity levels in the soil , 1977 .

[4]  J. Doorenbos,et al.  Yield response to water , 1979 .

[5]  M. J. English,et al.  The Uncertainty of Crop Models in Irrigation Optimization , 1981 .

[6]  C. Romero,et al.  Multiple Objectives in Agricultural Planning: A Compromise Programming Application , 1987 .

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  J. Berbel.  TARGET RETURNS WITHIN RISK PROGRAMMING MODELS: A MULTI-OBJECTIVE APPROACH , 1988 .

[9]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[10]  R. Sebastián,et al.  Los costes en los procesos de producción agraria: metodología y aplicaciones , 1991 .

[11]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[12]  Slim Zekri,et al.  A methodology to assess the current situation in irrigated agriculture: An application to the village of Tauste (Spain) , 1992 .

[13]  Selección de planes de cultivo en contexto de riesgo mediante el modelo MEDIA-DAP , 1993 .

[14]  I. P. Wu,et al.  Normal distribution water application for drip irrigation schedules , 1994 .

[15]  Everaldo Chartuni Mantovani.  Evaluación de modelos para el manejo del riego: estimación de la evapotranspiración y efectos de la uniformidad de aplicación del riego sobre la producción de los cultivos , 1994 .

[16]  Growth and production of onion crop (Allium cepa L.) under different irrigation schedulings , 1994 .

[17]  N. Dtian,et al.  Improvement of irrigation management and enhancement of economic benefits by contract management , 1994 .

[18]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[19]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[20]  Zbigniew Michalewicz,et al.  Adaptation in evolutionary computation: a survey , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[21]  W. O. Pruitt,et al.  Crop water requirements , 1997 .

[23]  F.Martı́n de Santa Olalla Mañas,et al.  Improvement of irrigation management towards the sustainable use of groundwater in Castilla-La Mancha, Spain , 1999 .

[24]  José Maria Tarjuelo,et al.  IRRIGATION UNIFORMITY WITH MEDIUM SIZE SPRINKLERS PART II:INFLUENCE OFWIND AND OTHER FACTORS ONWATER DISTRIBUTION , 1999 .

[25]  José Fernando,et al.  Desarrollo de una Metodología para la Ayuda a la Toma de Decisiones en el Marco de una Agricultura de Regadío Sostenible. Aplicación del modelo en el Sistema Hidrogeológico 08.29. , 2000 .

[26]  S. Kuo,et al.  Decision support for irrigation project planning using a genetic algorithm , 2000 .

[27]  Gerald J. Lieberman,et al.  Introduction to operation research. , 2001 .

[28]  E. Camacho,et al.  Seasonal furrow irrigation model with genetic algorithms (OPTIMEC) , 2001 .

[29]  J. A. Juan,et al.  Yield and size of deficit irrigated potatoes , 2001 .

[30]  Sistemas de cultivo: evaluación de itinerarios técnicos , 2003 .

[31]  Alfonso Calera,et al.  Monitoring irrigation water use by combining Irrigation Advisory Service, and remotely sensed data with a geographic information system , 2003 .