Precision farming is an essential element of agricultural innovation. Its most important feature is the emphasis on the role of the geographical information system (GIS) and remote sensing solutions, accurate measurement and computer control in all aspects of farming. Precision agriculture is the most important example of the emergence of information technology and artificial intelligence in agriculture. It represents a revolution in agriculture, driven by an effort to improve the profitability and efficiency of farming in harmony with the environment, in order to achieve social benefits. Precision agriculture also pursues social objectives: it helps to improve food quality and safety, while contributing to a better management of climate change and natural resources by reducing environmental pressures. The use of AI-based technology is environment- and soil-friendly, as well as a great tool to improve profitability.
The two research topics are the social conditions for the adaptation of precision farming (e.g. precision mapping of soil quality and yields) and a network analysis of its growth. It is a basic challenge for research on social conditions to identify the indicators of the characteristics of willingness by each group of farmers, i.e. innovators (inventors), early adopters, early majority, late majority and laggards, to adopt precision technologies, and the factors potentially influencing their motivations to switch to AI-based precision farming. The second research topic is an analysis of the network components of the growth in precision farming. This includes research on the diffusion of information on the method and the degree to which this is determined by the network, and a network analysis of the increase in partial or full conversion to precision farming.
Between December 2021 and the end of January 2022, we conducted a questionnaire-based survey of 200 precision farmers. The sampled farms are representative of Hungarian crop farms both regionally and in terms of scale. The survey extracted detailed information on the use of different forms of AI. In particular, the questions focused on the motivations and practical aspects of the use of drones, which is one of the most recent AI tools in agriculture, spreading more rapidly than other kits. The questionnaire survey is complemented by 30 audio interviews of precision farmers. The first analysis of the research focuses on farmers’ motivations for using drones. Due to the adoption of a trans-theoretical model (ordinal logit regression model) and the structure and questions of our questionnaire, our results are suitable for comparison with a German study conducted in the field. Many of the German questions were included in our questionnaire. Information from qualitative interviews is also used to interpret the results of our mathematical and statistical analysis. The research results are suitable for direct practical application, revealing both the nature and the combined impact of factors encouraging the use of drones.
Project participants
Attila Bai
Péter Balogh
Ibolya Czibere
Zoltán Gabnai
Imre Kovách
Noémi Loncsák
Boldizsár Megyes
Gabriella Nemes-Zámbó
Cooperating partners
University of Debrecen
Kynetec Hungary Kft
Publications
Bai Attila, Kovách Imre, Czibere Ibolya, Megyesi Boldizsár, Balogh Péter. Examining the Adoption of Drones and Categorisation of Precision Elements among Hungarian Precision Farmers Using a Trans-Theoretical Model. Drones 6 (8), 200. 2022
Kovách Imre, Megyesi Boldizsár, Bai Attila, Balogh Péter. Sustainability and agricultural regeneration in the Hungarian agriculture. Sustainability 14: 2 Paper: 969, 14 p., 2022
Balogh Péter, Bai Attila, Czibere Ibolya, Kovách Imre, Fodor László, Bujdos Ágnes, Sulyok Dénes, Gabnai Zoltán, Birkner Zoltán. Economic and Social Barriers of Precision Farming in Hungary. Agronomy. 11(6):1112, 2021
Keywords: agriculture, precision, social conditions, network