In this section I argue that building a contextual climate for agricultural publics in general, and for rangelands graziers in particular, involves much more than the management of translation risks around particular boundary objects. It involves the co-production of knowledges, places and identities. Where the conceptual climate furnishes primary producers with a scientific description of mechanism and statistical history, thus making some of the boundary objects of seasonal climate prediction more meaningful, the contextual climate is an integration of seasonal climate prediction with other predictive and descriptive tools. This process is usually assisted by systems modelling and social technologies, which contextualise climate risk in relation to other farm risks. This integration of risk factors at local or farm scale intimately ties the contextual climate to places, and to their history. It is through their representation of place and history that systems models appear to be deployed as valuable. This value, however, is difficult to quantify and tends to rely on primary producers’ own estimations of the validity of the model, which in turn is partly mediated by climate risk technologists’ performance.
As I demonstrate in this section, the extension of a contextual climate involves forms of boundary-work which not only reform the ways publics see scientific boundary-objects, but can modify discourses in agriculture and identities of agricultural publics. Climate risk technologists typically talk and write about the process of engagement as iterative, sometimes adaptive, and as being an ongoing conversation, often facilitated around modelling technologies, the functioning of agro-ecosystems in the context of markets, climate, aspirations, attitudes, knowledge, management skills, and risk. It is a process of renovating conceptions of management, ideally among both graziers and researchers, through a dialogue about the functioning of ecological, economic, and climatic systems together. Through this dialogue, scientists hold qualified hopes for long-term increase in economic returns alongside improved natural resource management outcomes. Thus, the rhetoric of contextual applications of climate prediction is driven by mutual benefit: the private incentive of profit and the public good of environmental management are both obligatory elements. Yet, as I detail, a less examined implicit aspiration of such programs is the replacement, or at least augmentation, of graziers’ current ways of knowing with more scientistic ways of understanding biophysical systems, principally via monitoring and modelling. Such an epistemic shift has not progressed far in the rangeland study areas where substantial cultural, geographic, and – as I will argue in Part 3 – epistemic distance separates researchers from the people who maintain and transform these ecological systems. Yet, where graziers and researchers do meet, there are distinct signs in the talk of the latter that their knowledge-making processes are broadening to be inclusive of epistemic difference. At the end of this section, I focus on how such engagement is conveyed by systems researchers at interview and in their literature.
Extending systems for managing climate risk
The ‘need’ for systems approaches in order to improve property-scale management of climate risk was a dominant narrative among participants, and a repeated theme in the literature (e.g. McKeon et al. 1990; Hammer et al. 2000;
The argument for a research focus on contextual approaches to climate variability, above and beyond the extension of conceptual climate information, is perhaps most succinctly summed up by Hammer et al. (2001:531): ‚the leap directly from a seasonal forecast to a decision is too great to be done (well) intuitively‛. In preliminary terms, the argument is that, people do not deal well with probabilities and that the integration of large amounts of complex information is better done by good systems models than by people. The impacts of climate variability are thus seen as most usefully integrated with other factors before they can be of full benefit to decision-makers. This argument props up the broader claim that climate information on its own has no (or only very limited) value, and that value to agricultural decision-makers becomes apparent largely through adding further dimensions to the climate information by integrating it with systems models (e.g. Hammer et al. 2001; Meinke and Stone 2005). In the application of such models, Meinke and Stone (2005:223) argue that ‚causes, choices and consequences must be clearly outlined and quantified‛ in order to facilitate better decision-making, though not simply by providing numbers to land managers. As I will demonstrate in this section, the extension of contextual climate prediction is as much about building trust in models and scientists as it is about the application of modelling technologies in decision-making.
The importance of the manner in which extension proceeds is as important, perhaps even more important, than the content provided and generated. As Stone (1995:31) notes, in seasonal climate prediction workshops many producers had ’So what?’ questions after they had initially been introduced to the concepts of ENSO and climate variability. These sorts of questions related to how forecasts can be applied and, for Stone, ‚highlighted the need for participative, follow-up processes if an effective research, development and education system involving climate forecasts for rural producers is to be initiated‛ (Stone 1995:31). This approach of following up initial conceptual work is seen as highly contextual work of developing a ‚common language‛ (p.34) among researchers and particular primary producers with whom they are engaged. Stone’s assertion that people delivering forecasts need to have a ‚foot in both camps‛ (p.34) extends
the role of the forecast provider to someone with ‚a thorough working knowledge of the whole integrated modelling system (including the climate system) together with an ability to understand the producers’ pre-existing farming management models‛ (p.34). The person doing contextual extension, then, must work many boundaries at once, the most important of them being the epistemic boundaries among disciplines (e.g. agricultural systems science and climate science) and cultures (e.g. scientific and agricultural).
This sort of approach to engaging primary producers with integrated systems models is most often applied to the intensive cropping regions where the signal from ENSO is strongest, particularly in the northern grains belt of Queensland. In the sparsely populated and varied rangelands, the approach is confounded by a variety of factors. There is substantially less investment in R,D&E than in more productive agricultural systems. Also, the pastoral industry of semi-arid areas is mostly oriented to maintaining self-replacing flocks of sheep or herds of cattle, and so there is a great deal of ‘memory in the system’ which makes the future resource condition highly contingent on the decisions made in the present and the past; more so than cropping operations, which can ‘reset’ themselves on shorter timescales. Along with low levels of interaction between pastoralists and technical advisers and the tendency of rangeland graziers not to maintain detailed records (e.g. Ison 2000), the issues above are a major challenge to meaningful contextual engagement between scientists and rangeland managers. Nonetheless, a handful of researchers have been attempting to develop linkages between systems approaches and rangeland management practices. The substantive thrust of this endeavour is reflected in the more abundant participatory research in cropping systems. Thus, before pursuing the ways systems researchers have attempted to contextualise climate prediction for rangelands graziers, it is informative to consider the recent application of systems approaches to agronomic decision-making. In particular, in examining boundary-ordering between scientists and publics, a large scale Australian participative systems modelling project, FARMSCAPE (Carberry et al. 2002) presents an interesting example of how researchers reconfigured simulation
models into a form of virtual experiential learning for farmers, and how this type of engagement may have served to transform the knowledges and perhaps cultures of the farmers and the researchers.
Making experiments into experience
FARMSCAPE was a long term study conducted by the Agricultural Production Systems Research Unit (APSRU) through the 1990s and early 2000s35. The goal was to assess the means by which simulation modelling of crop production in the northern grains belt of Queensland might be an applicable tool to help farmers manage their systems and, if so, how such simulation-based technology might be delivered cost effectively. The evaluation of the project in the mid to late 1990s recorded that the biggest benefit reported by farmers in evaluations of the FARMSCAPE cropping projects was the replacement of gut feel and general concepts with locally relevant data and simulations which allowed for farmers to experiment without taking risks (Carberry et al. 2002). Thus it would appear, at least from the evaluation, that these particular farmers were pleased to move beyond their former knowledge of their cropping operation. Quoting a local farmer, Carberry et al. (2002:152) point to how the dialogue paradigm of engagement brings systems science to the fore of a risk perspective: ‚A few years ago I may have taken a gamble, but today, no way. We’re getting a better understanding of the science behind these computer models and it’s giving us confidence to make decisions using the information. We’re getting a handle on the situation rather than functioning on gut feeling.‛
The indication in the previous statement is that the farmer is ‘getting a handle on the situation’, something more real than a ‘gut feeling’, which points to credibility building in process. Such a process is something Carberry et al. (2002)
35 APSRU is a joint venture of the CSIRO, the Queensland Government and the University of Queensland. FARMSCAPE is also an acronym: Farmers’, Advisors’, Researchers’, Monitoring, Simulation, Communication And Performance Evaluation.