Examining the Adoption of Drones and Categorisation of Precision Elements among Hungarian Precision Farmers Using a Trans-Theoretical Model

Abstract

This article discusses the use of drones in Hungary and considers their future penetration, based on the responses to a nationally representative 2021 questionnaire among 200 large-scale farmers engaged in precision farming and in crop production. Both the applied trans-theoretical model (with ordinal logit regression model) and the questionnaire design are suitable for comparison with the results of a similar survey in Germany. In this study, similar results were found for farm size, age, main job and education, but the evidence that higher education in agriculture has the largest positive effect on the use of drones is a novelty. The frequency values obtained for adopting precision technology elements are not fully suitable for classification due to interpretational shortcomings. The use of drones within precision technologies is no longer negligible (17%), but is nevertheless expected to grow significantly due to continuous innovation and the selective application of inputs. The state could play a major role in future uptake, particularly in the areas of training and harmonisation of legislation.

Farmers’ acceptance of robotics and unmanned aerial vehicles: A systematic review

“…The TAM has been criticized mainly for its simplicity and low explanatory power, but the model is still used extensively (Shachak et al, 2019;Weng Marc, 2018). The TTMA model used by Bai et al (2022) and Michels et al (2020) is based on the transtheoretical model of behavioral change (Prochaska & Velicer, 1997), consisting of multiple stages in the adoption process (i.e., no intention to use, intention to use, intention to use with a concrete plan, and already using). The transtheoretical model seems to be rather underexplored in agricultural research.…”

“…Regarding the use of UAVs, Bai et al (2022) showed that only approximately 25% of the surveyed farmers rejected using UAVs. Moreover, Chen et al (2020) identified that Chinese farmers had a slightly positive willingness to adopt on average.…”

“…Attitude of confidence + (in some cases, indirect effects) Bai et al, 2022;Hashem et al, 2021;Michels et al, 2020Michels et al, , 2021 Perceived ease of use Therefore, further research is needed to make definitive conclusions since the number of studies on the adoption of UAVs in Germany and Missouri is limited, including the sample size in the German study. Furthermore, only the state of Missouri was considered by Skevas and Kalaitzandonakes (2020) and Skevas et al (2022), which was not representative of the whole country, further underlining the need for more extensive data collection and research.…”

“…Internal factors directly and indirectly affecting the intention to adopt or the adoption of robotics or unmanned aerial vehicles. some cases, indirect effects)Arjune & Srinivasa Kumar, 2022;Bai et al, 2022;Michels et al, 2020;Skevas & Kalaitzandonakes, 2020;Skevas et al, 2022; Gender (male)+Chen et al, 2020;Michels et al, 2020;Wachenheim et al, 2021; Zheng et al, 2019 (Household) income + (in some cases, indirect effects)/-Arjune & Srinivasa Kumar, 2022; Chen et al, 2020; Skevas & Kalaitzandonakes, 2020; Skevas et al, 2022; Zuo et al, 2021 Availability of a successor + Skevas & Kalaitzandonakes, 2020; Zuo et al, 2021 Share of agricultural income +/-Wachenheim et al, 2021; Zheng et al, 2019; Zuo et al, 2021 (Agricultural/technical) education + Arjune & Srinivasa Kumar, 2022; Bai et al, 2022; Pivoto et al, 2018*; Zaman et al, 2023*; Zuo et al., 2020; Liu et al, 2022; Wachenheim et al, 2021 Farm size + Chen et al, 2020; Liu et al, 2022; Michels et al, 2020; Wachenheim et al, 2021; Zuo et al, 2021 Current use of other technologies + Michels et al, 2020; Pivoto et al, 2018* Renting out land -(in some cases, indirect effects) Skevas & Kalaitzandonakes, 2020; Skevas et al, 2022 Cooperative membership + Chen et al, 2020; Liu et al, 2022; Skevas & Kalaitzandonakes, some cases, indirect effects) Arjune & Srinivasa Kumar, 2022; Barrett & Rose, 2020*; Caffaro et al, 2020; Carolan, 2020*; Hashem et al, 2021; Michels et al, 2021; Skevas & Kalaitzandonakes, 2020; Skevas et al, 2022; Spykman et al, 2021; von Veltheim & Heise, 2020*; Wachenheim et al, 2021; Zaman et al, 2023*; Zheng et al…”

Smart Decision-Support System for Pig Farming

“…Through the use of Artificial Intelligence (AI) [8], Internet-of-Things (IoT) technologies [9,10], and big data analysis [11], the next wave of the agricultural revolution promises to reduce the aforementioned operational risks and promote the flow of information [12,13]. However, the introduction of such technologies in developing nations in Southeast Asia and elsewhere faces a number of particular difficulties, including the predominance of small and medium-sized farms, unusual farm conditions, inadequate capital support, and a lack of skilled management.…”