Changing systems for supporting farmers' decisions: problems, paradigms, and prospects

By R. Mccown. 2002

482 citations recorded by the source corpus.

Subfields: General Agricultural and Biological Sciences

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In General Agricultural and Biological Sciences

Citations in the corpus

Establishing the precision and robustness of farmers’ crop experiments (2019, background): “Farmers seldom recognise this benchmark (McCown, 2002; Whelan et al., 2012) and are likely to be primarily concerned about the expected profitability of applying a particular management intervention and the uncertainty associated with this estimate.”
Conceptual model of a future farm management information system (2010, unknown): “TheapplicationofSSMinagri-culture is covering a large number of agricultural domains such as the grain, sugar or beef industry (e.g. Macadam et al., 1990; Reid et al., 2000; Walker et al., 2001; Carberry et al., 2002; McCown 2002; Fountas et al., 2009).”
Description and evaluation of the Farmax Dairy Pro decision support model (2010, background): “…of uptake include overly complex models that are not easy to use, insufficient evaluation of model predictions, lack of involvement of users in the design and refinement of the models, lack of demonstration of their value to business and lack of training (Cox 1996; Borenstein 1998; McCown 2002).”
Agent-based land-use models: a review of applications (2007, background): “specifically designed as Decision Support Systems for direct use by end-users, but experience in other more mature areas of simulation modelling suggest that at some stage they may be promoted as such tools (McCown 2002b). However, it should be noted that such DSS tools have had a chequered history ”
Towards the development of a decision support system for water resource management (2005, background): “Emphasis on the multi-perspective exploration of problems (Shim et al., 2002) and shifting the target of DSS from semi-structured or unstructured to ‘‘wicked’’ problems are occasionally seen as a paradigm change (Beynon et al., 2002; Courtney, 2001; McCown, 2002; Rauscher, 1999).”
Locating agricultural decision support systems in the troubled past and socio-technical complexity of 'models for management' (2002, unknown): “It is ironical that ‘Expert Systems’, the school with the best-developed theory, experienced the greatest reversal of prospects of any of Stabell’s DSS ‘schools of thought’ as a result of the agricultural DSS learning experience in combination with a paradigm shift in theory (McCown, 2002).”
Decision support tools for agriculture: Towards effective design and delivery (2016, background): “However, despite their apparent value the uptake of DST by farmers and advisers in the UK, and elsewhere, has been limited (Alvarez and Nuthall, 2006; Gent et al.,2013;McCown,2002;Parkeretal., 1997).”
Constraints and opportunities in applying seasonal climate forecasts in agriculture (2007, background): “However, while the IPO index can be used to explain historical interactions with ENSO on rainfall variability there is still debate as to whether it can be used in a predictive capacity (Meinke et al. 2005). For example, White et al. (2003) demonstrated that quasidecadal and bi-decadal signals could”
Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies (2016, background): “Hence, one major identiﬁed reason for the ‘‘problem of implementation’’ is the normative way of developing new technology without considerations of the actual needs of the end-users (e.g., Leeuwis 2004; McCown 2002, 2005; McCown et al. 2009).”
Critical Success Factors for the Adoption of Decision Tools in IPM (2019, background): “Indeed, the aim of a DT should be not to replace humans, but to provide a support to the end-users in making a rational decision based on the available information (and possibly the potential uncertainties) [22,37].”
Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate. (2009, unknown): “As with parallel efforts around the world, the effect on farm decision making has been disappointing (McCown 2002a, 2002b; McCown et al. 2002, 2006; Hayman 2004; Stone and Hochman 2004).”
Re-inventing model-based decision support with Australian dryland farmers. 1. Changing intervention concepts during 17 years of action research (2009, methodology): “Study of the rise and demise of the DSS and of the histories of significant precursors as a source of lessons became an important part of our research on a new approach (McCown 2001b, 2002a, 2002b, 2005; McCown et al. 2002, 2006; McCown and Parton 2006).”
Brief history of agricultural systems modeling (2017, unknown): “Participatorymodeling,where the development of amodel is accomplished by model developers and stakeholders working together and discussing model results to refine simulations and better represent stakeholders' objectives, has also been successfully used (Thornton and Herrero, 2001; McCown, 2002).”
Environmental decision support systems (EDSS) development - Challenges and best practices (2011, methodology): “The analysis of agricultural DSS use by McCown (2002b) showed the same situation to be true e farmers tended to adopt DSS to learn about and understand how to adapt to changing economic, environmental or other conditions, and then discard the DSS once learning was complete.”
Helping farmers face the increasing complexity of decision-making for crop protection (2012, background): “Rather than making decisions on behalf of the users by prescribing an action as the optimal solution, DSSs should attempt to help crop managers satisfying their needs in working real - ity which is characterized by high uncertainty and complexity (McCown, 2002b; Hochman and Carber - ry, 2011).”
Evaluating agricultural trade-offs in the age of sustainable development (2016, background): “Several terms have been used to characterize what is missing from the relationship between the producer of information and its intended user: ‘mutual understanding’ (McCown, 2002), ‘receptivity’ (McIntosh et al.”