The long way to innovation adoption: insights from precision farming

1. Introduction Precision farming offers a fundamental contribution to the transition towards a sustainable agriculture, through the sustainable management of the soil and the improvement of product quality.The work is set against this background and aims to analyze the rate of introduction of precision farming tools and the variables preventing / facilitating this adoption. If, on the one hand, the adoptionrates in Italy are still relatively modest, on the other hand, it is necessary to highlight those factors that prevent a wider diffusion of precision agricultural tools within the farms. To this end, the literature has highlighted various elements of complexity (structural, socio-economic, psychological),which can hinder or generate perceived complexity and, therefore, greatly reduce the potential for technology adoption. In this context emerges the increasing importance of activities, public and private, related to knowledge transfer and the introduction of innovations. The paper focuses on agricultural knowledge and innovation systems (AKIS), which are also relevant in the light of the recent proposal for the new regulation on rural development. In order to appreciate, on the one hand, the process of adoption of innovations and the related barriers,on the other hand, the role played by knowledge systems, the paper proposes a relatively original methodology of analysis. 2. Description of the work The process of innovation adoption related to precision farming is analyzed through the AKAP (awareness- knowledge-adoption-productivity) sequence, theorized by Evenson (1997). This sequence specifies how the mechanism of adoption of innovation is not "epidemic" as theorized by some theoretical approaches, but rather follows a series of steps starting from the awareness of the existence of an innovation, which must be associated with an adequate knowledge of the same (knowledge) before proceeding to the adoption, from which the company should derive certain expected benefits (product). Evenson says: Awareness is not knowledge. Knowledge requires awareness, experience, observation, and the critical ability to evaluate data and evidence. Knowledgeleads to adoption, but adoption is not productivity. Productivity depends notonly on the adoption of technically efficient practices, but of allocatively efficient practices as well. Productivity also depends on the infrastructure ofthe community and on market institutions. Of course, interdependecies between the sequence and the system of knowledge transfer and adoptionprovided by AKIS are evident and must be explored. To this end, unlike Evenson's theories, however,the outcome of the adoption is not translated into the increase in productivity, as in Evenson's study,applied to the agricultural extensions services (AES), but into the benefits expected from theintroduction of precision farming techniques. In the AKIS method, farmers are the main beneficiariesof a networked knowledge production process. However, in our model knowledge production is not the key variable but becomes an essential condition. In our case study, the importance of the AKIS approach is included as a determinant variable in the adoption of innovations. Furthermore, what we want to verify is the possible impact of AKIS on the adoption of these innovations. Our analysis is grounded on primary sources. A questionnaire was administered to a sample of companies registered in Coldiretti registers, divided into 4 parts: • Socio-economic and structural news • Sources of information - related to ordinary business activity 180 - related to the introduction of innovations • Adoption of precision farming tools • Role of AKIS in the adoption of precision agriculture Data are processed through descriptive statistical techniques allowing to analyze farm composition in each step of the sequence, assuming that the share of farms in each step tends to decrease. In a second step we try to estimate the influence of the AKIS in adopting new innovation through the useof a probabilistic model in which the adoption of the technologies represents our dependent variable,while the independent variables express their degree of knowledge and involvement in the AKIS. 3. Discussion of theoretical and/or empirical results Application of the AKAP model reveals the goodness of sequence in defining the mechanisms for adoption of innovations. The gaps between each link in the sequence underscore the farmer's perceived complexity in adopting innovation, and particularly radical innovations such as smart farming technologies. The introduction of explanatory variables, in particular socio-demographic and structural aspects, also makes it possible to better specify the results of the analysis, thus articulating them on the basisof the aforementioned dimensions, such as age, sex of the conductor, physical size of the company. In the following lines, each phase is detailed, reporting the main results. The first phase of the sequence concerns awareness, that is the farmer’s consciousness about the existence of the technique to be evaluated, in the perspective of a potential adoption. In this preliminary phase there is already an important feedback, related to the selected variables: a) age: as the age of the subject increases, this awareness about precision farming toolsdecreases (awareness is inversely correlated with the age of the entrepreneur): in fact, very high values are found in young people (84.2), while in the mature and elderly age groups theshare tends to decrease; b) level of education, in particular: specialized education is associated with high percentages ofawareness (such as diploma or degree in agriculture: 90.6% and 95.4%). Moving from technical diplomas (78.4) to specialized degrees (80.5), an increasing value emerges, even though the gap with the previous ones remains clearly visible (by at least 10 points). Therefore, the greater and more sectoral the education, the greater the degree of awareness; c) farm size. Also in this case, the hypothesis that size is positively correlated with awareness is confirmed, so this tends to decrease in small companies. The second step in the sequence is knowledge of the technology; at this stage farmer has to provideevidence on knowledge of precision agriculture tools, specifically: • Monitoring (GPS, GIS, data processing, GSM.; • Internetof Things (Wireless sensor network, RFID, Bluetooth, Zigbee, Wi-fi, Microcontroller, Arduino); • Automation (Autonomous Vehicle, Assisted Driving, Mobile Robot, Unmanned AerialVehicle, Agricultural Robot, Computer Vision, Data Management); • Decision Support (Artificial Intelligence, Data mining, Forecasting, Machine Learning) • Hardware (Embedded Systems, Cybernetic Systems, CMOS, FPGA) • Laser (Sensors) • Other. The results of our analysis show a remarkable contraction with respect to the previous phase. As a matter of fact, despite the interpretative hypothesis is confirmed (inverse correlation with age and company size and direct correlation with educational qualification), a gap of 20 percentage points emerges. This trend becomes even more evident in the phase of adoption of precision farming technologies. In this case, there is an even greater decrease than that seen between the first and second 181 phases. Most respondents did not respond or did not adopt any techniques so we have a total of 17.2%users. From a demographic standpoint, young and mature farmers get similar results (just over 20%).As far as level of education is concerned, the professional diploma and degree are confirmed, with astrong gap compared to all other items, but the gap between the two has narrowed considerably. Finally, the stage of the effect of adoption, the product, shows similar percentages to adoption i.e. most users show good levels of satisfaction. In the second step of the analysis, a logit model has been carried out, with the aim to test the hypothesis that AKIS act as facilitator for the introduction of innovations. Awareness of AKIS Willingness to use counselling Constant B S.E. 0,502 0,107 0,357 0,048 -2,127 0,181 Wald Sig. Exp(B) 21,99 0,0000 1,652 54,708 0,0000 1,429 137,577 0,0000 0,119 The results show that awareness of AKIS is highly significant in determining the adoption of new technologies. In addition, a second variable, willingness to use consulting services, was added in a second step, and again both were significant and positively influenced the likelihood of adopting newtechnologies. Conclusion The analysis carried out confirms the validity of the AKAP model in defining the complex process of maturation of the decision to adopt/not to adopt an innovation, highlighting structural constraints widely underlined in the literature. Moreover, the empirical results evidence the mediating role of innovation brokering in boosting innovation adoption. As far as the AKAP model is concerned, the articulation of the sequence, allows to appreciate possiblepolicy actions, on the eve of the important programming period of the CAP. In particular, the 2023- 2027 CAP will strengthen agricultural knowledge and innovation systems (AKIS) that are propaedeutic to change, stimulating processes of wider diffusion especially in marginal rural areas. Therefore, strengthening knowledge systems, acting on the different phases of the sequence, would allow, on the one hand, a greater knowledge of the techniques of precision agriculture and, on the other hand, to break down the constraints of adoption often linked to aspects of a perceptive nature (complexity and familiarity).