Researching farmer behaviour in climate change adaptation and sustainable agriculture: Lessons learned from five case studies
Introduction
This paper examines the challenge of researching the complexity of farmer behaviour in the face of increasing and simultaneous ecological, economic, and social pressures, and in the dynamic frame of their institutional context, biophysical environment, power relations, and social networks. We are concerned with identifying what to investigate regarding farmer behaviour, and how to do it, to generate the knowledge needed to inform adaptation to global environmental change and transitions to sustainable agriculture. With this aim, we identify three areas of knowledge that are necessary to understand farmer behaviour, examine the utility of an integrated and interdisciplinary approach, and discuss related methodological challenges by applying it to five case studies.
Agriculture is exposed to multiple, simultaneous and interconnected ecological, economic and social pressures (O'Brien and Leichenko, 2000). Increased economic interconnections in a globalized world create unpredictable dynamics and conditions of price volatility, which can affect agricultural incomes and livelihoods (Fader et al., 2013). Moreover, pressures on agricultural systems include the competition between different land uses (Smith et al., 2010) and different uses for agricultural land (Cassidy et al., 2013), the global shift in consumption patterns towards a more dairy and meat-based diet (Popkin, 2001), and the diversification of rural livelihoods in the South (Reardon et al., 2007). Adaptation in agricultural systems to these multiple pressures is therefore an urgent need.
On the other hand, agricultural activities are themselves major contributors to a range of environmental issues, including greenhouse gas emissions, biodiversity loss, deforestation, water and soil pollution, and soil erosion (Foley et al., 2011, IPCC, 2013). In the face of a greater challenge of producing food while preserving the environment, a sustainable and fair global food system will require a new approach to food production, distribution, and consumption (Ingram et al., 2010; Horlings and Marsden, 2011).
Understanding farmer behaviour1 is central to enhancing adaptive capacity and promoting sustainable agriculture. Farmers are the agents undertaking adaptation and sustainability policies and programs, so their behaviour influences how and with what success these programs are realized on the ground (e.g., Home et al., 2014, Moon and Cocklin, 2011). Understanding farmer actions in their social-ecological context is essential to identify cases where intervention is needed, and the type of policies that can effectively promote socio-technical change and innovation. This can inform the design and implementation of measures such as incentives (e.g., Home et al., 2014), regulations (e.g., Bartel and Barclay, 2011), or institutional reforms (e.g., Ziervogel and Ericksen, 2010). Furthermore, a systematic understanding of farmers’ adaptive behaviour can provide a basis for drawing the boundaries of policies or external aid, that is, to identify when not to intervene. This will avoid wasting resources on planned adaptation policies where bottom-up, autonomous adaptation (i.e., adaptation undertaken “as a regular part of on-going management” and not “consciously and specifically planned in light of a climate-related risks” (Smit and Skinner, 2002, p.93)) is already imminent or effective (Mortimore and Adams, 2001).
However, while farmer behaviour is a key determinant of agricultural systems’ adaptability, too often research relies on theories and methods that do not capture the complexity of farmer behaviour. This then translates into ineffective adaptation or sustainability policies (Vanclay, 2004, Barnes et al., 2013). Furthermore, understanding farmer behaviour is plagued by the common difficulty in communicating and conducting collaborative research on sustainability and global change across disciplines and paradigms (Feola and Binder, 2010a; Podestá et al., 2013). Finally, the role of on-the-ground decision-making by individual farmers is often studied in individual cases to determine its environmental, economic, and social effects. There have been few efforts to link across studies in a way that provides opportunities to better understand empirical farmer behaviour, design effective adaptation and sustainable agriculture policies, and be able to aggregate from case studies to a broader level.
As an author team, we realized some of these shortcomings when we came together as part of a meeting of Coupled Human and Natural System (CHANS) Fellows,2 an event designed to encourage synthesis in research on coupled human and natural systems. We were encouraged by this focus on synthesis to take the case-level empirical material from our recent fieldwork on farmer behaviour and develop an integrated way of looking at it more rigorously and in a broader context.
In this paper, we first develop a framework comprising three areas of knowledge on farmer behaviour that we have identified as critical based on previous literature: decision-making model, cross-scale and cross-level pressures, and temporal dynamics. By developing this framework we do not aim to propose a new theory of farmer behaviour, but use the framework to compare five previously conducted case studies to illustrate how these areas of knowledge can be investigated in different geographical areas, agricultural systems, and from different disciplinary perspectives to understand farmer behaviour. Finally, we compare and discuss the five case studies to draw general lessons and identify avenues for future research. The framework and the lessons learned from this analysis can facilitate interdisciplinary research on farmer behaviour by opening up spaces of structured dialogue on assumptions, research questions and methods employed in investigation.
Section snippets
Conceptual framework
In this section, we briefly review the recent literature and recognize three areas of knowledge that we identify as a conceptual model to understand the complexity of farmer behaviour, namely: 1) decision-making model; 2) cross-scale and cross-level pressures; and 3) temporal dynamics (Fig. 1). While these areas overlap in practice, they are constructs that can be useful in examining farmer behaviour analytically from three complementary perspectives. They correspond to three distinct broad
Case studies
In this section we use the framework (Fig. 1) to compare and reflect upon five previously conducted case studies that span a range of agricultural systems in distinct geographical contexts across the globe (Table 1). None of the cases was originally informed by the framework, which is used here to reflect upon the studies post hoc. All cases were conducted as interdisciplinary research projects in fields ranging from Geography to Biology to Environmental Policy, and adopted a range of
Researching farmer behaviour: challenges and lessons learned
We discuss here best practices, limitations and open issues specifically involved in studying farmer behaviour in an interdisciplinary fashion, and lessons learned that may inform adaptation to climate change and sustainable agriculture across a wide variety of settings.
While individual studies should identify specific research questions and aim to select the most appropriate methods to match the research goals (Poteete et al., 2010), we suggested above that to avoid oversimplification in
Conclusions
We have developed and used an interdisciplinary framework that aims to facilitate interdisciplinary research on farmer behaviour by opening up spaces of structured dialogue on assumptions, research questions and methods employed in investigation. Indeed, the framework provided us with a common mental map of farmer behaviour that facilitated a structured conversation which resulted in the comparative analysis presented in this paper. It helped raise further questions about the research we had
Acknowledgements
This research was sparked by participation by the authors in the CHANS Fellows Program, part of the International Network of Research on Coupled Human and Natural Systems, which is sponsored by the National Science Foundation and coordinated by the Center for Systems Integration and Sustainability at Michigan State University. The authors are thankful to Agatha Herman for her useful comments on a previous version of this manuscript.
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