For example, Long et al. explored the barriers to the adoption and diffusion of technological innovation for climate-related smart agriculture in Europe and specifically in the Netherlands, France, Switzerland and Italy. Carrer et al. investigated the factors influencing the adoption of farm management information systems by Brazilian citrus farmers. Morris et al. , focusing on pastoral farmers across Wales, analysed the interconnectedness between farm diversification and technology adoption, with farm strategy as the central focus. Furthermore, Caffaro and Cavallo considered the roles of objective and subjective factors in the adoption of smart farming technologies in a sample of Italian farmers from the Piedmont region . Finally, Gittins et al. investigated the benefits and challenges associated with the adoption of new farm management technologies and software adoption in the UK livestock sector. Overall, these prior studies have suggested that the rate of adoption and perceived barriers relate to a specific innovation, to the farmer himself/herself and to the specific context . Factors depending on the specific type of innovation are related to the financial costs and economic benefits expected from the adoption of the new technology; the risks connected with this adoption; the advantages in terms of prestige, convenience and satisfaction; its consistency with the needs of adopters; the difficulties and complexity connected to the understanding and maintenance of the technology; and the observability of the innovation results . Factors related to the farmer are connected to his/her sociodemographic characteristics, hydroponic nft channel with the most important of which affecting the adoption of new technologies being farmers’ age, educational level, income, farm size, technological skills and actual use of technologies .
Finally, the adoption of new technologies is influenced by context-specific factors, such as political and social pressures, the national context and the regulatory environment . This study was conducted in the mountainous rural area of Valtellina, Valchiavenna and Alto Lario in the Lombardy region, close to the border with Switzerland . The area is characterized by a specific geographical conformation: the altitude varies from approximately 198 to 4000 m above sea level, and this area has a relevant east-west extension covering 3,212 km2 . More than 70% of the provincial territory is located 1500 m above sea level, and only a small portion is urbanized. Most of the area is composed of natural and forest areas , while the agricultural area covers only 7.4% of the total area . Nonetheless, farming activities represent an important economic source of income in this area. In 2016, 17% of the companies belonged to the agricultural sector, second only to the commercial sector, which accounted for 21% . The agricultural sector is also important for youth employment: 11% of young entrepreneurs operate a business in the agricultural sector, second only to tertiary companies.Agriculture is mainly based on traditional products of animal origin, such as the Bresaola of Valtellina GPI, Bitto PDO and Casera PDO cheeses, which are marketed in national and international markets . According to the last national census, livestock farms represented 44% of all farms, a percentage that seems in line with the average regional value .
Despite their economic importance, the number of livestock farms decreased from 1982 to 2010, on average accounting for 35%, one of the highest rates in the Lombardy region compared to the other provinces. In this case study, among livestock farms, dairy farms showed the smallest decrease , and unlike sheep, pig and poultry farms, the decrease in the number of dairy farms has been partially accompanied by a decrease in the number of animals , showing a general increase in farm dimensions and the withstanding of the dairy farming system . According to estimates, livestock products in the area account for approximately 63% of gross products sold, confirming the importance of bovine dairy farms for the local rural economy. As is typical in the alpine landscape, farming activities are performed in valleys, which are characterized by the presence of stable meadows interspersed with cultivated fields, particularly those containing corn for fodder production, and at higher altitudes, the farming system is based on pastures. The integration of valley agriculture and mountain pastures based on dairy farming systems is still of economic value and of important cultural and identity significance for the whole territory, representing a significant environmental and naturalistic heritage . Compared to other areas in the Lombardy region, only approximately 6% of farms have declared, according to the last national census, that they use computers and ICT devices for farm management, while the regional average is approximately 17%. Farms tend to use ICTs more to manage administrative services and crop systems than to manage herd production . In contrast, internet usage by farmers in the area is in line with the average regional value of 3% . With respect to farm size, more than half of the sample had less than 50 cows under lactation , 27.0% had from 50 to 100 cows, and only 8.0% of the sample had more than 100 cows. As shown in Table 1, the farmers from the cooperative seem to be younger and better educated than those farmers localized in other mountain areas. When asked, the cooperative managers confirmed that there had been important generational turnover in the last 10 years.
At the beginning of the interview, a brief explanation of the purpose of the study was provided to participants. The questionnaire was structured into two sections. The first section included questions on farmers’ characteristics, such as their gender, age, educational level, farm size in terms of the number of cows, professional use of a smartphone and expectations for the future of their farm. To explore farmers’ use of smartphones for professional duties, participants were first asked whether they owned a smartphone and for how long. Then, we provided a list of potential reasons for using a smartphone for farm management purposes and asked participants to indicate for which reason they were using their smartphones. For each reason, respondents were asked to express their frequency of use using a scale ranging from 1 = never to 5 = very often. To avoid any bias, there was also the possibility for them to mention any other reason not included in the list. Furthermore, to assess farmers’ expectations, participants were asked to rate how they see the future of their farms on a 5-point interval scale ranging from “very pessimistically” to “very optimistically”. The second section of the questionnaire sought to highlight the psychological measures that are expected to delineate farmers’ attitudes towards the use of technological devices, such as technophobia and technophilia, perceived obstacles and motivations for use. Farmers’ technophobia and technophilia were assessed by developing a specific scale considering the extant literature , which combines several statements of the “Technophobia and Technophilia Questionnaire – TTQ” proposed by Martínez-C´ orcoles et al. . The scale used in the current study was based on six items concerning farmers’ technophobia and technophilia towards new technologies. A five-point Likert scale with responses ranging from 1 to 5 was used to record participants’ responses. To investigate the perceived obstacles to the use of technological devices,based on the previous literature , we developed a ten-item scale including the constraints that hamper the adoption of new equipment and technologies on farms. Participants were asked to express their agreement using a scale ranging from 1 = strongly disagree to 5 = strongly agree for each item. Finally, following the extant literature , farmers’ motivations to use technological devices were investigated by developing a scale with ten items concerning the most important benefits that farmers perceive from the use of technologies in their daily work and that may drive the farmers’ adoption of new equipment and technologies.
The results showed that attitudes towards new technologies are affected by age, educational level, farm size, actual smartphone usage for professional duties and optimistic expectations for the future of the farm. Educational level, farm size, smartphone usage and expectations for the future increased significantly across clusters from the first cluster of technophobes to the third cluster of technophiles. Moreover, age significantly decreased from the first cluster to the other clusters. Therefore, our findings suggest that older farmers with lower educational levels, smaller farms, less frequent smartphone usage for professional duties, nft growing system and more pessimistic feelings regarding the future of their farm are less willing to adopt new technologies. These farmers do not have the knowledge and confidence to understand the benefits related to the use of technologies for breeding. To overcome these issues, considering that “information is the key to the diffusion of innovations” , new forms of presentation and learning may be developed by service providers and policy makers to address the needs of these adverse technophobe farmers, who constitute a relevant part of the population. This innovation process is of great importance since technophilia, or the propensity to use new technologies, plays a fundamental role in the sustainable development of mountain farming and breeding. Although our results cannot be generalized to all mountain areas and to the whole mountain area itself, we found several elements that are in line with the previous literature . More specifically, in terms of age, our findings corroborate previous evidence showing the existence of a negative relationship between age and the adoption of new technologies, probably because older farmers have shorter career horizons than do younger farmers and, therefore, are less motivated to innovate.With reference to educational level, the results are consistent with the previous literature reporting that less educated farmers are less confident and less inclined to use new technologies . Considering farm size, we found that technophobe farmers have smaller herds, as reported in the literature, probably because smaller farms do not create adequate economies of scale and incentives for the adoption of new technologies for farm management . The owners of larger farms are more able to absorb the associated costs and risks. With respect to smartphone usage, technophobe farmers had the lowest frequency of use of smartphones for professional duties, meaning that farmers who are less confident in using technological devices have fewer technological skills and are more likely to have a negative attitude towards new technologies and be more reluctant to innovate .
Although our findings related to age, educational level and farm size have been analysed in previous papers , some novelties of our study are worth emphasizing. First, we focus on a sample of mountain dairy farmers. Second, we propose a clustering analysis of the farmers based on three attitudinal determinants: technophobia and technophilia, perceived obstacles, and motivations to use. To date, this approach has not been applied to farming system analysis, even if understanding the underlying factors that affect the adoption of technologies is imperative to allow policy makers to develop more effective and targeted policies. Farming systems in Europe are experiencing multiple adverse shocks and stresses, such as weather extremes, price fluctuations and changes in policies and regulations. Under these multiple shocks and stresses, improving or even maintaining generally mediocre levels of sustainability of farming systems is increasingly challenged . The presence of critical thresholds adds dynamic complexity for farming system actors and policy makers. This is because beyond such thresholds, drastic system transformations may occur that are difficult to anticipate and to manage. For instance, the speed and scale of system processes after exceeding a critical threshold may be incompatible with the adaptation capacities of current institutions . Exceeding a critical threshold is most often undesirable as it generally leads to lower sustainability levels, e.g. a decline in biodiversity and human well-being .Moreover, this state with lower sustainability levels may be more persistent resulting in reduced options to improve sustainability. Timely knowledge on critical thresholds is therefore needed to prevent exceeding them , but it is often difficult to anticipate the exceedance of a critical threshold . In absence of clear knowledge on thresholds, Walker and Salt propose to work with thresholds of potential concern that inform management goals that aim to avoid those thresholds, without knowing exactly where they lie. In either case, the threshold level being known exactly or being a TPC, Monitoring is needed in order to detect the closing in on a critical threshold.