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Top1. Introduction
Agricultural management has become an increasingly complex endeavor, since it must integrate typical production factors with an increasing need for sustainable use of the biological, physical and social capital, while trying to mitigate effects of climate change (Walker, 2002). This increased complexity has been recognized and been integrated in the goals of the European Union's rural policies, which are aimed at the socio-economic development of rural areas and the protection of the environment, through a sustainable management of agricultural production. Consequently, farmers are expected to address a wider range of issues (and their consequences) when making a decision.
As a result, there is the need to generate knowledge from information that can guide practice (Oquist, 1978). Furthermore, this information should be integrated under different criteria (Dutta et al., 2014) in order to produce multi-dimensional knowledge and actionable recommendations (Arnold, 2013; Nino-Ruiz et al., 2013).
In that aspect, Decision Support Systems (DSS) can contribute to these goals. A DSS can be defined as a computer-based information system that can be used to support complex decision-making (Shim et al., 2002). They normally integrate databases and mathematical models to support the decision maker (Adelman, 1991) and in general should have the following attributes:
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They should reflect the process of thinking and decision-making of the decision-maker.
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They should be interactive.
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They should act as complementary tools to other decision-making techniques and processes (Turban, 1993).
As a result, DSSs were first applied in an industrial context (Bennett, 1983; Cox, 1996), where they were regarded as a mean to understand complex processes and were used to structure a problem and apply a mathematical algorithm to solve it.
They were grouped into two categories:
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Operational: Focused on everyday decisions that may be repetitive.
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Strategic: Focused on long-term decisions and planning actions that may involve group decision-making and/or negotiations (Walker, 2002).
Irrespective of their purpose, DSSs in agriculture are necessary because agricultural management has become increasingly complex and demanding in terms of time and expertise. Their advantages are that they can summarize information allowing a more holistic view and they can mimic an expert that constantly assists the decision maker (Plant & Stone, 1991). In conclusion, agricultural DSSs are necessary in order to survive economic and environmental crises (Mackrell et al., 2009), while ensuring sufficient production with less land, less labor and less water (De la Rosa et al., 2004).
The importance of DSSs in the agricultural sector has seen a surge of new applications. However, several key issues still need to be addressed:
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Real case studies could greatly increase the relevance and value of the research efforts on agricultural DSSs.
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More social factors need to be incorporated in the underlying mathematical models, shifting the focus from a purely technical to socio-economic (Arnold, 2013; Arnott & Pervan, 2016).