Knowledge networks and their role in shaping the relations within the Agricultural Knowledge and Innovation System in the agroenergy sector. The case of biogas in Tuscany (Italy)

Gava, Oriana; Favilli, Elena; Bartolini, Fabio; Brunori, Gianluca

doi:10.1016/j.jrurstud.2017.09.009

Cited by 44 publications

(22 citation statements)

References 96 publications

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“…As regards technological innovation in agriculture, statistics show that while in the USA possibly up to 80% of farmers use some kind of SFTs, on farms in European Union Countries, the rate is no more than 25% [27]. Adoption of SFTs represents a strategic priority of the EU policies, and a Horizon 2020 thematic network has been developed, the Smart-AKIS (Agricultural Knowledge and Innovation System) Network [28], to bridge the gap between the field and research on the identification and delivery of new Smart Farming solutions to fit farmers' needs [28,29]. Italy is the second agricultural power in the EU-28, with a turnover of more than EUR 55 billion in 2015 [30].…”

Section: Context and Aims Of The Present Studymentioning

confidence: 99%

The Effects of Individual Variables, Farming System Characteristics and Perceived Barriers on Actual Use of Smart Farming Technologies: Evidence from the Piedmont Region, Northwestern Italy

Caffaro

Cavallo

2019

Agriculture

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Smart Farming Technologies (SFTs) have a real potential to deliver more productive and sustainable agricultural production. However, limited empirical research is available on the role played by objective and subjective factors in the adoption of such disruptive innovations, especially in the Italian context. This study investigated the role of education, farm size, being a sole farmer, and perceived barriers in affecting the use of SFTs in a sample of Italian farmers from the Piedmont region (North-West Italy). Three hundred and ten farming operators were questioned via a paper-and-pencil questionnaire. The analyses showed that low levels of education and working on-farm alone were positively associated with perceived economic barriers, which in turn were negatively associated with the adoption of SFTs. Farm size had a positive direct effect on SFT adoption. The results pointed out the need for targeted policies and training interventions to encourage the use of SFTs.

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Section: Context and Aims Of The Present Studymentioning

confidence: 99%

The Effects of Individual Variables, Farming System Characteristics and Perceived Barriers on Actual Use of Smart Farming Technologies: Evidence from the Piedmont Region, Northwestern Italy

Caffaro

Cavallo

2019

Agriculture

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“…It does so by combining the results of the adoption model with agglomeration analysis. In many studies, the linkages between farm choices and agglomeration are often addressed indirectly, as there are several interplays relating to the location, dominant farming systems, market opportunities, and the uneven distribution of agricultural advice and extension services, as well as different levels of efficiency among local institutions and trajectories of territorial development [7,31,32].…”

Section: Methodsmentioning

confidence: 99%

Understanding the Spatial Agglomeration of Participation in Agri-Environmental Schemes: The Case of the Tuscany Region

Bartolini

Vergamini

2019

Sustainability

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Agri-environmental schemes (AESs) constitute one of the main agricultural policy instruments that address environmental objectives in the Common Agricultural Policy. However, in spite of a 20-year application window and large budgetary shares allocated by EU member states, several studies demonstrate lower-than-expected environmental impacts. The reasons for poor environmental outcomes are the lack of targeting, low participation rates, spatial heterogeneity, and information asymmetry between farmers and public administrators. This study comprehensively analyses the determinants of AES adoption while highlighting patterns of the spatial agglomeration of participation in organic and integrated production. For this, we combine the results of farm-level adoption analysis with a spatial analysis of the participation rate. The results show that both micro- and meso-level characteristics strongly impact AES participation. In fact, farm and household structure, quality of extension services, and territorial conditions all significantly affect AES adoption.

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“…Empirical analysis of farming systems has shown that farmers are integrated in multiple kinds of network, such as family, friends, and other farmers [17], which may all have a different impact on the farmer's action and performance. At the same time, farmers relate with different public and private bodies, such as organizations, associations, institutions, and so on [6,12,19]. According to studies, the networks of peers seem to have a stronger impact on the farmers' decision-making than the technical support, the networks between family members [11,17], or organization communities [20].…”

Section: The Influence Of Social Network On Technological Adoptionmentioning

confidence: 99%

Social Networks as Drivers for Technology Adoption: A Study from a Rural Mountain Area in Italy

Filippini¹,

Marescotti²,

Demartini³

et al. 2020

Sustainability

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Innovation processes includes social and communicative elements. The role of innovative technology for the development of farming systems is investigated in literature, but only a few studies deal with the influence of networks on the adoption of technologies by farmers. The aim of this paper is to verify if the adoption of smartphones for professional reasons by farmers is influenced by the networks to which farmers belong, the socio-demographic characteristics of the farmers, and their farm’s size. The case study is in the Valtellina valley, a rural mountain area in the north of Italy, where 53 livestock farmers were interviewed. The methodology is based on two steps. First, social network analysis is applied to diagnose the relationships of farmers in terms of connectivity and closeness and to detect the centrality measures of farmers in three different social relationships: production networks, market networks, and information exchange networks. A multiple linear regression model is then applied to test whether centrality measures of the three networks, the farmers’ socio-demographic characters, and the farm’s features drive the adoption of smartphones for professional reasons. Results suggest that the centrality measures in production networks positively drive the adoption of smartphones, while the centrality measures of market and information exchange networks do not have the same effect. At the same time, the farmers’ age negatively affects the use of smartphones for professional reasons, while the size of the herd, and the education and gender of farmers have no significant influence. The study contributes to the debate around the Agricultural Knowledge Innovation System and supports local policies based on the inclusion of farmers in the technological development in rural areas.

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Knowledge networks and their role in shaping the relations within the Agricultural Knowledge and Innovation System in the agroenergy sector. The case of biogas in Tuscany (Italy)

Cited by 44 publications

References 96 publications

The Effects of Individual Variables, Farming System Characteristics and Perceived Barriers on Actual Use of Smart Farming Technologies: Evidence from the Piedmont Region, Northwestern Italy

The Effects of Individual Variables, Farming System Characteristics and Perceived Barriers on Actual Use of Smart Farming Technologies: Evidence from the Piedmont Region, Northwestern Italy

Understanding the Spatial Agglomeration of Participation in Agri-Environmental Schemes: The Case of the Tuscany Region

Social Networks as Drivers for Technology Adoption: A Study from a Rural Mountain Area in Italy

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