Las Respuestas de las Personas al Conflicto

Since the 1960s, crop modelling approaches have played an important role in examining the impact of management options under a range of environmental conditions, as defined by the combination of weather and soil-type at a specific location’s ‘representative farm’. The outputs from this theoretical research have been used to develop recommendations for crop production (McCown, 2001). However, the majority of these ‘best bet’ practices have failed to address long-term problems in real-world situations (Keating and McCown, 2001;

McCown and Parton, 2006; Woodward et al., 2008; Le Gal et al., 2011). Such modelling approaches have been criticised for failing to address the objectives, preferences and

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expectations of farmers (Carberry et al., 2004; Woodward et al., 2008; McCown et al., 2009; Martin et al., 2011).

An extensive review of system modelling research of the past 30 years showed that the majority of the simulation modelling practices had been developed without the direct

participation of farmers (Woodward et al. 2008). Farmer participation could be increased by directly consulting them in simulation scenarios, allowing the system modelling approach to be used as a tool for facilitating experiential learning rather than for designing ‘best practices’ (McCown, 2001). However, there is recent evidence that clearly shows how simulation-aided discussions about crop management have facilitated management intervention (Keating and McCown, 2001). This calls for the relevance of participatory approaches using simulations to drive relevant and significant intervention (Robertson et al., 2000; Keating and McCown, 2001; Meinke et al., 2001).

Participatory modelling is a general term used to describe a number of specific methodologies and processes associated with the integration of system modelling and participation from stakeholders (Gaddis and Voinov, 2008). These methods and processes involve stakeholder involvement at different stages of the overall modelling exercise spanning from involving stakeholders (not necessarily model users) in the construction and use of models as well as their involvement only in the use of models (Dreyer and Renn, 2011). There is value in client participation in problem definition, model design, testing, and evaluation phases of model-based research projects (Woodward et al., 2008). The theory is that simulation modelling enables participants to learn by ‘virtual’ experience with the unique advantages that any mistakes and losses are not actual (McCown et al., 2009). Models may be effective tools to facilitate dialogue, share learning, and potentially enhance uptake of new practices such as improving food security, within participatory research approaches (Meinke et al., 2001; McCown et al., 2002; Carberry et al., 2004; Ncube et al., 2007; Carberry et al., 2009; Rodriguez et al., 2014). For effective application, Carberry et al. (2002) and McCown et al. (2009) asserted that a model should be flexible and comprehensive in its capability to address relevant issues in farm management decision-making. As a result, a simulation model can be used to jointly create a ‘virtual world’ wherein simulation experiments may be

conducted to facilitate learning. The innovation is that this process may change the way an actual system is managed. Connectivity among key players, i.e., researchers, farmers and extension persons, through simulation-aided discussions about crop management is essential

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to facilitate dialogue about management options that are relevant and significant to decision makers (Keating and McCown, 2001; McCown, 2001; Hammer et al., 2002). Virtual experiments and discussions of ‘what if?’ analyses could be a good example of computer- supported thought experiments in which the information can be used for strategic learning and for supporting farmers in situation planning and decision-making (McCown et al., 2012). For instance, simulation-aided discussions that engage farmers and their advisors are essential

to facilitate the dialogue about management options and significant to changes to

management practices (Keating and McCown, 2001; McCown, 2001; Hammer et al., 2002; Hochman et al., 2009, 2017b).

For a simulation model to be taken seriously by farmers and potentially influence

management decisions, the model must be seen as credible (Carberry et al., 2002). Models are judged as being credible or ‘good’ if simulation outputs correspond adequately to empirical measurements. As criteria of model evaluation, Carberry et al. (2009) suggested that the needs of farmers and consultants for model assessment should be included for a model to be useful in practice. Rodriguez et al. (2014) demonstrated farmers’ evaluation as a practical, albeit unconventional, form of model validation. They evaluated a simulation model by asking participating farmers whether they agreed with model outputs in showing the expected crop yields, gross margins, business profits and their variability. Thus, farmers judged the model’s ‘goodness’ based on their practical experience. Such model evaluations by farmers are important to create mutual understanding and credibility among farmers and scientists (Carberry et al., 2009). Carberry et al. (2009) noticed that farmers who developed trust in, and gained appreciation of, the model’s abilities were motivated to participate in a participatory modelling project. As a result of the research process, farmers were able to learn which factors make a difference in their planning and decision-making. On the other side, computer-aided discussions with farmers and advisors influences the way researchers understand farmers’ reality and subsequently identify knowledge gaps (Le Gal et al., 2011).

Involvement of farmers may take place at different stages of the process from model design to scenario evaluation (e.g., Castelan-Ortega et al., 2003; Lisson et al., 2010). For example, in PAR (Meinke et al., 2001; McCown, 2001; Ncube et al., 2007), farmers actively engage in discussions about building realistic scenarios for the computer simulations, which will then be run for the farmers to get their reactions and suggestions for possible improvements of the simulation scenarios (Meinke et al., 2001; McCown, 2001; Ncube et al., 2007).

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Over the past 30 years, computer-based modelling has made major advancements, but its ability to influence management decisions remains limited (Woodward et al., 2008; McCown et al., 2009). One participatory modelling approach combines crop modelling with PAR: The Farmers’, Advisers’, Researchers’, Monitoring, Simulation, Communication And

Performance Evaluation approach (FARMSCAPE) is an example of one successful

participatory modelling approach that has influenced management practices of farmers using science-based research (McCown and Parton, 2006). Developed in Australia, FARMSCAPE has been used nationally to successfully manage large commercial farms (Carberry et al., 2002; McCown and Parton, 2006; McCown et al., 2009). Farmers have come to value the FARMSCAPE approach because of its contribution in addressing specific questions regarding management in benchmarking, tactical planning, yield forecasting, and scenario exploration (Hochman et al., 2000; Carberry et al., 2002). The process involves a series of facilitated discussions with farmers about specific questions using ‘what if’ scenarios.

Farmers were able to appreciate the outputs produced by the simulations, which were credible and meaningful, while the researchers were surprised that the simulation was relevant to farmers and could be further applied within an action research framework (Carberry et al., 2004). Following the Australian experience of using FARMSCAPE, this approach has also been adapted for small-scale farmers in Indonesia, South Africa and Zimbabwe (Carberry et al., 2004). This approach has successfully been used to increase the adoption of best-bet technologies, (e.g., novel forages and animal feeding practices) in Indonesia (Lisson et al., 2010). Another preliminary study in South Africa and Zimbabwe has also shown the potential of adapting FARMSCAPE to facilitate discussions with farmers and subsequently help in identifying alternative management options being tested using on-farm experiments (Carberry et al., 2004).

An evaluation of the long-term use of computer-based models found that benchmarking contributed to the sustained adoption of technologies by farmers (Lisson et al., 2010) and was a key activity in a ‘thought experiment’ for diagnosing what had, and had not, been achieved, and the possible opportunities for enhancing yield that may be attainable (McCown et al., 2012). In rain-fed cropping systems, the gap between attainable and actual yield can be confounded by bio-physical variation such as availability of water between sites and seasons (Sadras and Angus, 2006). In this case, evaluation of water use by comparing the attainable and the actual yield provided a sound basis for yield benchmarking (Sadras and Angus, 2006; Carberry et al., 2009). Realistic simulations of crop yields over many seasons and situations

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created a strong validation case for crop models such as APSIM (Carberry et al., 2009). Furthermore, benchmarking is good for enhancing learning by both formulating expectations and understanding of casual processes (Hochman, 2000; McCown et al., 2001).

Benchmarking is an important first step in farming systems design developed in close consultation between farmers and researchers through the PAR (Lisson et al., 2010). Benchmarking has also been used for subsequent design and model-based evaluation of alternative management scenarios (Martin et al., 2011) and provides thought-provoking feedback to farmers, i.e., feedback indicating the existence of a problem or the possibilities for improvement.

In a research project carried out by McCown (2012), it was shown that scenario analysis could support thought experiments in shaping expectations by providing a historical perspective of the scenario results – history of the future – as well as bringing profound changes in strategic learning and system design. It could also assist tactical decision-making where agronomic management options for the current season are evaluated based on the known status of the system early in the season. In this case, the dialogue around the simulation analyses is more important than the underpinning models, although it is clearly reliant on their existence and reliability (Keating and McCown, 2001; McCown, 2001).

In summary, the participatory approach can facilitate the design and implementation of innovations by taking into account the needs, constraints, and knowledge of farmers and this could assist with evaluating the feasibility of a proposed innovation (Vayssières et al., 2009). Furthermore, interventions developed from models, in consultation with farmers, have led to tangible changes in management practices (Carberry et al., 2002; McCown and Parton, 2006; Lisson et al., 2010). Application of modelling tools within the framework of participatory research has proven to be effective in: (i) gaining insights into the functioning of complex farming systems; (ii) generating awareness of the potential impacts of different management options; (iii) identifying opportunities for incremental or transformational changes in farming systems; (iv) assessing the climatic risk of alternative technologies; (v) analysis of economic trade-offs of alternative resource allocation; and (vi) contributing to learning about farm management practices via computer-aided discussions with farmers (Carberry et al., 2004; Ncube et al., 2007; Carberry et al., 2009; Lisson et al., 2010).

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