MIRRIG: A decision support system for design and evaluation of microirrigation systems

Abstract

The decision support system (DSS) MIRRIG has been developed to support the design of microirrigation systems and to advise farmers as a result of field evaluations. It is written in Visual Basic 6.0, runs in a Windows environment, and uses a database with information on emitters and pipes available in the market, as well as on crops, soils and the systems under design. MIRRIG is composed by design and simulation models and a multicriteria analysis model that ranks alternative design solutions based upon an integration of technical, economic and environmental criteria. User friendly windows are adopted for handling the databases and to manage the sub-models. The model allows creating and comparing a set of design alternatives relative to the pipe system and the emitters, either drip or microsprinkling emitters. For each alternative, the pipe system is sized and the irrigation system is simulated to produce performance, environmental and economic indicators. These include uniformity of water application, potential for contamination with agrochemicals due to water percolation, and installation and operation costs. Those indicators are used as attributes of the selected criteria. All alternatives are then compared and ranked through multicriteria analysis where the weights giving the relative importance of the adopted criteria are defined by the user. These procedures allow selecting the best design alternative and solving the complexities involved in the design of microirrigation systems. The model is available from the website www://ceer.isa.utl.pt/cms or by contacting [email protected].

Introduction

Sustainable irrigated agriculture requires irrigation practices that are environmentally friendly, economically viable and lead to high irrigation performance (Wichelns and Oster, 2006, Oster and Wichelns, 2003, Pereira et al., 2002). Microirrigation systems have the potential for achieving high irrigation performance and offer a large degree of control, enabling accurate water and fertilizer applications according to crop-water and nutrients requirements, thereby minimizing environmental impacts and providing for increased performance and water productivity. Achieving this requires that systems are designed and operated in such a way that water is applied at a rate, duration and frequency that maximize water and nutrient uptake by the crop, while minimizing the leaching of nutrients and chemicals out of the root zone (Hanson et al., 2006). Highly uniform and timely water application is therefore required (Mermoud et al., 2005, Li et al., 2007) since the uniformity of nutrient distribution within a field depends upon the uniformity of water application, both of which affect crop yields (Santos, 1996, Hanson et al., 2006). If water is applied with low uniformity, some parts of the cropped field will receive more water and nutrients than others. Under-irrigation can reduce crop yields while over-irrigation will not result in increased crop yields, but will generate higher energy and fertilizer costs, and the loss of fertilizers and other chemicals leached with the percolating water.

The basic components of a microirrigation system are: the pump/filtration station, consisting of the pump, filtration equipment, controllers, main pressure regulators, control valves, water-measuring devices and chemical injection equipment; the delivery system, that includes the main and submain pipelines that transfer water from the source to the manifolds, which also may have filters, pressure regulators, and control valves; the manifolds, which supply water to the laterals and the laterals that carry water to the emitters (Pereira and Trout, 1999, Evans et al., 2007). Design of microirrigation systems is therefore complex considering the need to select and size all system components and the need to design for a targeted uniformity of water application (Bralts et al., 1987, Keller and Bliesner, 1990, Wu and Barragan, 2000). Uniformity is determined by a combination of design parameters, mainly referring to: the pressure at emitters and the variation in pressure along the unit or system, which depend upon the pipe sizing and related head losses; the pressure-discharge relation of the emitter, which refers to the sensitivity of the emitter to variations in pressure; the emitter characteristics relative to variations in discharge, mainly representing the sensitivity to clogging and to temperature; the coefficient of manufacturing variation for the emitter and the filtering capabilities of the system, which relates to the quality of irrigation water and the characteristics of the emitters (Pereira and Trout, 1999, Pereira et al., 2002).

Main advances in design of microirrigation systems refer to pipe sizing and layout and to the selection of emitters because these system components control the potential irrigation performance and costs. The design options relative to the pump, valves, controllers, filters and fertilizer devices are generally made after pipes and emitters are selected since they depend upon related pressure and discharges at the various nodes of the system network (Keller and Bliesner, 1990). However, their appropriate selection also influences the irrigation performance, and they also produce additional head losses that must be considered when sizing the system. To support and ease design, a variety of models have been developed such as for the pump/filtration station (Haghighi et al., 1989), for assessing emitter uniformity (Barragan et al., 2006), for pipe sizing (Kang and Nishiyama, 1996, Valiantzas, 1998, Valiantzas, 2002, Demir et al., 2007) and for economic optimization of systems (Saad and Mariňo, 2002, Valiantzas, 2003, Valiantzas et al., 2007).

Decision support systems (DSS) have started recently to be used in irrigation (Thysen and Detlefsen, 2006, Gonçalves et al., 2007, Smith et al., 2007). A DSS is a computerized system for helping any decision-making process, which integrates databases, modelling tools and multicriteria analysis methodologies that are useful to analyse and rank a set of alternatives. Supporting a decision means helping decision-makers to generate alternatives, rank them and make choices (Finlay, 1994), which is particularly useful for design. Supporting the selection-making process involves the estimation of the attributes relative to selected criteria for each alternative, evaluating them, comparing the alternatives, and to identify an “ideal” compromise between several and often adversative criteria. A DSS enables decision-makers to take into consideration complex and interacting factors. The main advantages from using a DSS are: an increased number of alternatives can be examined; better understanding of the business/processes; identification of unexpected situations; improved communication; cost savings; better decisions; time savings; better use of data and resources.

Multicriteria analysis (MCA) allows the integration of different kind of attributes and a trade-off analysis between technical, economic and environmental criteria. MCA facilitates the search for satisfactory compromises among adverse objectives that a designer needs to make. In irrigated agriculture, it is more often used for economic analysis (Bazzani, 2005, Riesgo and Gómez-Limón, 2006), water and land allocation (Latinopoulos, 2007), as well as for performance assessment, irrigation planning or water demand and delivery decisions (Rao et al., 2004, Raju et al., 2006, Oad et al., 2006). MCA applications to irrigation problems are often integrated in a DSS to be used together with simulation tools. This is the case of applications for the design of farm irrigation systems where solutions are aimed at satisfying requirements of technical, economic and environmental nature (Gonçalves et al., 2007, Gonçalves and Pereira, 2009).

This paper describes the underlying science and engineering of MIRRIG, a DSS developed for design of microirrigation systems and to support the evaluation of existing systems. MIRRIG is used to develop different design alternatives for the same field and to analyse and rank them based on technical, economic and environmental criteria using MCA. An application example including a sensitivity analysis of parameters used for ranking the alternatives is presented in a companion paper (Pedras and Pereira, 2008).