A final element to consider in creating smart farming innovation processes that yield more effective configuration comes to light from actions in India. Over the last few years, a team of Berkeley University technologists, economists, and development practitioners has worked with the government in Andhra Pradesh to create ‘smart villages’ . Reflecting the vision that innovation processes can deliver effective results when they are open, as argued by Chesbrough in particular, the plan was to bring the team together to produce new sociotechnical arrangements in one village, Mori, that would empower villagers, improve their material situations, and yield insights about how to ‘scale up’ the interventions across the entire state. There is no evidence to suggest that people in Mori wanted their village to be ‘smart’ prior to the intervention, but from the outset the process was designed to tap the Mori crowd for insights in a form of co-design that identified specific problems that might be addressed by new technical fixes. One such problem involved the condition of textile weavers within value chains, which the ‘smart village’ initiative tried to address by creating a new ‘virtual village mall.’ Another problem concerned the structural relationship between farmers and the suppliers of agricultural inputs. To make the village ‘smart,’ the apparent solution was to create more direct connections between farmers and retailers. A partner on the project was the Indian agricultural e-commerce startup firm BigHaat. So long as farmers could access the Internet – as was facilitated by Google, one of the project partners – they could consider purchasing inputs directly from BigHaat and for a lower price than if they had to rely on various intermediaries. In this smart village, tapping the crowd informed and then guided a tech firm to create a ‘win-win’ solution: Mori farmers paid less for inputs, while BigHaat made new sales and,gutter berries crucially, created opportunities to learn from analysing data generated by the new flows of information when farmers tapped screens on their devices and communicated with BigHaat’s servers.
Writ large over the entire state – ‘scaled up’ – this new type of ‘smart’ engagement would conceivably lay the ground for further innovations based on tapping the crowd for insights. The smart village envisioned by this project would play a new role in expanded open innovation ecosystems designed to upgrade the technical sophistication of rural life and address societal challenges. Yet, the technical dimensions of all this action deserve critical scrutiny. Initiatives such as the smart village might empower some or indeed many villagers and they could improve their material situations. However, based on what we know about digital life in general, what seems much more likely is that these initiatives will generate significant scope for tech firms to create new assets and value from data flows ; assets and value, moreover, that they will not share with the users of their technologies. Whether framed as a matter of surveillance capitalism or data colonialism , an important dynamic of digital life concerns the maldistribution of opportunity to convert data curation into profits. The asymmetries of digital life mean firms such as BigHaat stand to gain the most from smart village projects. In this context, then, it is worth remembering some pertinent lessons from the green revolution. Consider that when India embraced green revolution practices in the 1960s, the government redirected scarce resources toward importing fertilizer needed to support the planting and growth of new green revolution wheat varieties . Part of the issue was a realization in India that, although the country had “doubled its output of machinery, chemicals, and power […] ‘you can’t eat steel’” . In the contemporary context – when investment in smart cities, villages, and farming is bound up with the notion that “data is the new cash crop” – the refrain ‘you can’t eat data’ might have some purchase, especially given India’s rush toward smart technologies despite malnutrition currently affecting around one-seventh of the population . The stark difference now, though, is that some of the lead actors in the production of smart life in India do eat data, albeit by virtue of their ability to convert data into profits.
In a place such as Mori, it is not so much that villagers can’t eat data but rather that the current rush toward using digital technologies is underpinned by approaches and economies that mean Mori’s villagers are unlikely to share in the harvest. The Mori smart village project yields a unique but striking type of misconfigured innovation. Given the growing number of similar digital initiatives rolling out in the shadow of high-level belief that digital technologies can “play an increasingly important role in achieving global food security and improving livelihoods especially in rural areas” , it is necessary to ask whether an emancipatory version of smart farming could do any better. What might be the intricacies of building innovation processes that reconfigure the sociotechnical relations of smart farming within the ‘planetary cognitive ecology’ to enable all food producers, not only those in the global north heartlands of smart farming, to eat data? In the context of significant inequalities in the ability of digital pioneers and laggards to take advantage of smart life, a minimum insistence of an emancipatory version of smart farming should be that adopting digital technologies works from the ground up to create incremental adjustments via information-intensive iterative processes that target systemic or structural change. In effect, the task should be to find models of emancipatory smart farming that use algorithmic affordances to pursue ‘productive resistance’ to dominant formations, such as the corporate food regime. The point here is, plainly, that new and potentially radical arrangements of digital platforms, devices, and software are waiting to be established. Thus, as outlined in the final column of Table 1, arrangements of devices, software, and practice that lead to something akin to emancipatory smart farming are at least conceivable. Departing from the mainstream model of smart farming, emancipatory smart farming arrangements will use technology to support agroecological and regenerative food production in a food sovereignty framework. Such arrangements would need to consist of hackable devices that users can repair. Open source software would be a requirement. If digital platforms are involved, for example to pool computational resources, they would be run as platform cooperatives. Users’ privacy would be built-in by default.
To the extent that data emerging from emancipatory smart farming arrangements will have value, it will be shared and held according to principles of data sovereignty. In all of these respects, therefore, emancipatory smart farming would depart significantly from mainstream practices. Further, striking differences pertain to innovation processes. An emancipatory smart farming arrangement would need to be constructed from the bottom-up in a participatory approach that empowers food producers to remain independent of ATPs. Ultimately, its aim would be to undermine, resist and overcome systemic challenges facing food producers. The point here is that, with novel innovation processes, it should be possible for even the most oppressed food producers to participate in the creation of emancipatory smart farming practices that engage digital technology in transformative ways. A key concept introduced by the European Commission’s “The future of food and farming” communication is that the next common agricultural policy post-2020 reform must foster a smart agricultural sector. As pointed out by the EC, “smart farming” or “smart agriculture” represents the application of modern information and communication technologies to agriculture, leading to what can be called a “Third Green Revolution” . ICTs include products and services that allow entrepreneurs to store, process, transmit, convert, duplicate, or receive electronic information. Among the ICTs for smart agriculture, farmers can adopt software and hardware solutions, such as professional applications and operating systems, mobile phones, remote sensors, and multimedia products . These technologies provide farmers with updated information, such as farms’ input and yields and agricultural markets, promoting an increase in the efficiency of the farm production process through evidence-based managerial decisions . Moreover, as reported by the FAO , ICTs can promote learning and therefore facilitate technology adoption among farmers. Despite the advantages provided by these technologies, in the last decade in the European Union ,strawberry gutter system only one out of four farmers adopted ICTs . Furthermore, despite Italy being the first-ranked European country in terms of agricultural value added and the second-ranked in terms of production value , grow strawberry in containers the last agricultural census showed that only 76,000 out of 1.6 million farms adopted organizational innovations such as ICTs . According to the European Innovation Scoreboard analysis , Italy has moderate innovation performance compared to the other EU member states.
In the agricultural sector, structural and cultural factors surely affect the innovation process, which is not uniform within Italy . Although the fostering of smart farming appears to be even more important for increasing the competitiveness of mountain farming, these mountainous areas show the highest aversion towards innovation .For instance, climatic conditions limit the length of the growing season and lead to the scarce accessibility of lands, the presence of slopes impedes the use of machinery , and poor mobile network coverage can hamper the use of ICTs . Such limitations imply some difficulties in the development of economies of scale and thus have a great impact in terms of increased costs and lower productivity compared to lowland agriculture. Despite these constraints, mountain farming’s persistence and prevention of land abandonment are essential for protecting landscapes and ecosystems, reducing erosion and natural hazards , supporting the local economy and preserving local traditions . Considering the crucial role of mountain farming in the provision of public goods to society, special support programmes for mountain farmers have been developed in the CAP since the early 2000s, and the current public policies increasingly support innovative practices in these areas, encouraging farmers to adopt ICTs to ensure agricultural sustainability . Despite all these efforts to promote ICT application in mountain farming, these technologies remain scarcely used in these areas .
Scholars and institutions have widely recognized the importance of fostering smart farming for improving mountain farming competitiveness ; nonetheless, to the best of our knowledge, no previous studies on ICT adoption have been developed focusing on a sample of mountain farmers. By means of the clustering analysis method, the present study examines how attitudes and the characteristics of farmers and farms influence the use of ICT devices . To the best of our knowledge, this is the first study clustering mountain dairy farmers based on their attitudes towards technologies. The results from this study are especially important considering the limited adoption and diffusion of ICTs among mountainous farmers. In fact, understanding the factors that affect the adoption of these technologies is fundamental for the development of tailored policies in support of different types of mountain farmers. Our results can also help service providers indicate future directions for the design of their products. The remainder of this article is structured as follows. Section 2 presents a literature review focused on farmers’ adoption of technologies and their attitudes towards innovation. Section 3 describes the methods and procedures that were implemented in the analysis, including the conceptual framework , the case study , sample and data collection and the data analysis . Section 4 describes the results, while Section 5 provides a related discussion. Finally, Section 6 provides a summary of the research and some conclusions. Most of the literature on the factors affecting farmers’ adoption of technologies and innovation in developed countries seems to be related to specific types of technologies . For instance, Wheeler focused his study on the adoption of organic farming and genetic engineering practices in Australia. Additionally, in the Australian context, Sneddon et al. investigated farmers’ adoption of new agricultural technologies in the wool sector. A number of studies have investigated the adoption of specific sustainable and pro-environmental agricultural innovations within the wine industry and more generally in land management . Tey and Brindal and Pierpaoli et al. investigated the factors influencing the adoption of precision agricultural technologies by summarizing the findings of past studies. In Italy, Cavallo et al. analysed the innovative attitudes of farmers towards the technological innovation of agricultural tractors.