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Special Issue SIEA2023

Early View

Innovativeness in organic farming system: The case of the Marche region

DOI
https://doi.org/10.3280/ecag2024oa17604
Submitted
marzo 30, 2024
Published
2024-12-18

Abstract

The importance of research and innovation is crucial for addressing the challenges posed by evolving climatic and environmental conditions, along with the urgent need to mitigate greenhouse gas emissions and to deal with unstable markets.
To establish Sustainable Agri-Food Systems, in environmental, social, and economic terms, it is essential to ensure access to technologies that can reduce biological and market risks.
The objective of this paper is to understand how different factors influence the innovativeness of organic farmers in the Marche region, in Italy, with a particular focus on the adoption of a digital tool, Decision Support System (DSS).
The analysis, developed through the application of the SEM model to a sample of organic farmers, highlights the significant role of support services in facilitating the implementation of innovations. Therefore, it is important for policymakers, especially at the regional level, to define specific and coherent measures that incentivize the adoption of innovations.

References

  1. Acock, A. C. (2013). Discovering structural equation modeling using Stata. College Station: Stata Press.
  2. Agarwal, R., & Prasad, J. (1998). A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology. Information Systems Research, 9(2), 204-215. doi: 10.1287/isre.9.2.204.
  3. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. doi: 10.1016/0749-5978(91)90020-T.
  4. Ara, I., Turner, L., Harrison, M. T., Monjardino, M., deVoil, P., & Rodriguez, D. (2021). Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review. Agricultural Water Management, 257, 107161. doi: 10.1016/j.agwat.2021.107161.
  5. Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers’ adoption decision of precision agriculture technology. Decision support systems, 54(1), 510-520. doi: 10.1016/j.dss.2012.07.002.
  6. Avolio, G., Blasi, E., Cicatiello, C., & Franco, S. (2014). The drivers of innovation diffusion in agriculture: Evidence from Italian census data. Journal on Chain and Network Science, 14(3), 231-245. doi: 10.3920/JCNS2014.x009.
  7. Barberi, P. (2015). Functional Biodiversity in Organic Systems: The Way Forward? Sustainable Agriculture Research, 4(3), 26. doi: 10.5539/sar.v4n3p26.
  8. Bàrberi, P., Canali, S., Ciaccia, C., Colombo, L., & Migliorini, P. (2017). Agroecologia e agricoltura biologica. BioReport 2016. 101-113. -- www.researchgate.net/publication/320710691_Agroecologia_e_agricoltura_biologica.
  9. Barnes, A. P., Soto, I., Eory, V., Beck, B., Balafoutis, A., Sánchez, B., Vangeyte, J., Fountas, S., van der Wal, T., & Gómez-Barbero, M. (2019). Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy, 80, 163-174. doi: 10.1016/j.landusepol.2018.10.004.
  10. Brunori, G. (2022). Agriculture and rural areas facing the “twin transition”: Principles for a sustainable rural digitalisation. Italian Review of Agricultural Economics, 77(3), 3-14. doi: 10.36253/rea-13983.
  11. Canavari, M., Gori, F., Righi, S., & Viganò, E. (2022). Factors fostering and hindering farmers’ intention to adopt organic agriculture in the Pesaro-Urbino province (Italy). AIMS Agriculture and Food, 7(1), 108-129. doi: 10.3934/agrfood.2022008.
  12. Colglazier, W. (2015). Sustainable development agenda: 2030. Science, 349(6252), 1048-1050. doi: 10.1126/science.aad2333.
  13. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology, Massachusetts, USA (1985).
  14. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003. doi: 10.1287/mnsc.35.8.982.
  15. Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434-449. doi: 10.1007/s11747-011-0300-3.
  16. Diederen, P., Meijl, H. V., Wolters, A., & Bijak, K. (2015). Innovation Adoption in Agriculture: Innovators, Early Adopters and Laggards. doi: 10.22004/ag.econ.205937.
  17. El Bilali, H., Hassen, T. B. E. N., Bottalico, F., Berjan, S., & Capone, R. (2021). Acceptance and adoption of technologies in agriculture. AGROFOR, 6(1). doi: 10.7251/AGRENG2101135E.
  18. European Commission (2017). Communication from the commission. The Future of Food and Farming. -- https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52017DC0713.
  19. European Commission (2020). Communication from the Commission to the european parliament, the council, the european economic and social committee and the committee of the regions – A Farm to Fork Strategy for a fair, healthy and environmentally-friendly food system. 0-20. -- https://eur-lex.europa.eu/resource.html?uri=cellar:ea0f9f73-9ab2-11ea-9d2d-01aa75ed71a1.0009.02/DOC_1&format=PDF.
  20. European Commission (2022a). Agricultural Knowledge and Innovation Systems (AKIS). -- https://ec.europa.eu/eip/agriculture/sites/default/files/eip-agri_agricultural_knowledge_and_innovation_systems_akis_2021_en_web.pdf.
  21. European Commission (2022b). Commission staff working document. Executive summary of the evaluation of the CAP’s impact on knowledge exchange and advisory activities. -- https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52022SC0138.
  22. Fenu, G., & Malloci, F. M. (2020). DSS LANDS: A decision support system for agriculture in Sardinia. High Tech and Innovation Journal, 1(3), 129-135. doi: 10.28991/HIJ-2020-01-03-05.
  23. Fieldsend, A. F., Cronin, E., Varga, E., Biró, S., & Rogge, E. (2020). Organisational Innovation Systems for multi-actor co-innovation in European agriculture, forestry and related sectors: Diversity and common attributes. NJAS: Wageningen Journal of Life Sciences, 92(1), 1-11. doi: 10.1016/j.njas.2020.100335.
  24. Firsova, A., & Derunov, V. (2018). Monitoring of innovative activities effectiveness in agriculture. 18(3), 89-100. -- https://tapipedia.org/content/monitoringinnovative-activities-effectiveness-agriculture.
  25. Frantzeskaki, N., Loorbach, D., & Meadowcroft, J. (2012). Governing societal transitions to sustainability. International Journal of Sustainable Development, 15(1-2), 19-36. doi: 10.1504/IJSD.2012.044032.
  26. Hoek, A. C., Malekpour, S., Raven, R., Court, E., & Byrne, E. (2021). Towards environmentally sustainable food systems: Decision-making factors in sustainable food production and consumption. Sustainable Production and Consumption, 26, 610-626. doi: 10.1016/j.spc.2020.12.009.
  27. Ibragimov, G. A. (2014). Consulting Services in Uzbekistan Agriculture – ReCCAConference, n. 212557, Institute of Agricultural Development in Transition Economies (IAMO). doi: 10.22004/ag.econ.212557.
  28. Kline, R., & St, C. (2022). Principles and Practice of Structural Equation Modeling. Guilford publications.
  29. Läpple, D., & Kelley, H. (2013). Understanding the uptake of organic farming: Accounting for heterogeneities among Irish farmers. Ecological Economics, 88, 11-19. doi: 10.1016/j.ecolecon.2012.12.025.
  30. Liu, X., Pattanaik, N., Nelson, M., & Ibrahim, M. (2019). The Choice to Go Organic: Evidence from Small US Farms. Agricultural Sciences, 10(12), 1566-1580. doi: 10.4236/as.2019.1012115.
  31. Maydeu-Olivares, A. (2017). Assessing the size of model misfit in structural equation models. Psychometrika, 82(3), 533-558. doi: 10.1007/s11336-016-9552-7.
  32. Maydeu-Olivares, A., & Shi, D. (2017). Effect sizes of model misfit in structural equation models. Methodology. doi: 10.1027/1614-2241/a000129.
  33. Mencarelli, E., & Mereu, M. G. (2021). Anticipazione dei fabbisogni professionali nel settore dell’agricoltura e silvicultura. Report tecnico. -- https://oa.inapp.org/xmlui/handle/20.500.12916/833.
  34. Mir, S. A., & Padma, T. (2020). Integrated Technology Acceptance Model for the Evaluation of Agricultural Decision Support Systems. Journal of Global Information Technology Management, 23(2), 138-164. doi: 10.1080/1097198X.2020.1752083.
  35. Momani, A. (2020). The Unified Theory of Acceptance and Use of Technology: A New Approach in Technology Acceptance. International Journal of Sociotechnology and Knowledge Development, 12, 79-98. doi: 10.4018/IJSKD.2020070105.
  36. Mouratiadou, I., Wezel, A., Kamilia, K., Marchetti, A., Paracchini, M. L., & Bàrberi, P. (2024). The socio-economic performance of agroecology. A review. Agronomy for Sustainable Development, 44(2), 19. doi: 10.1007/s13593-024-00945-9.
  37. Olim, M., Ablaqulovich, I. G., & Ugli, K. A. M. (2020). Service Provision and Development In Agriculture. International Journal of Innovations in Engineering Research and Technology, 7(07), 84-88. -- www.neliti.com/publications/337216/service-provision-and-development-in-agriculture.
  38. Pino, G., Toma, P., Rizzo, C., Miglietta, P. P., Peluso, A. M., & Guido, G. (2017). Determinants of farmers’ intention to adopt water saving measures: Evidence from Italy. Sustainability, 9(1), 77. doi: 10.3390/su9010077.
  39. Pivoto, D., Barham, B., Waquil, P. D., Foguesatto, C. R., Corte, V. F. D., Zhang, D., & Talamini, E. (2019). Factors influencing the adoption of smart farming by Brazilian grain farmers. International Food and Agribusiness Management Review, 22(4), 571-588. doi: 10.22434/IFAMR2018.0086.
  40. Righi, S., Russo, C., & Viganò, E. (2022). Il ruolo dei contratti di filiera nei mercati «turbolenti» di oggi. Informatore Agrario, (30), 32-34. -- https://hdl.handle.net/11576/2712592.
  41. Righi, S., & Viganò, E. (2023). How to ensure the sustainability of organic food system farms? Environmental protection and fair price/Come garantire la sostenibilità delle aziende agricole del sistema alimentare biologico? Protezione dell’ambiente e prezzo equo. IL CAPITALE CULTURALE. Studies on the Value of Cultural Heritage, 27, 377-400. doi: 10.13138/2039-2362/3185.
  42. Rijswijk, K., Klerkx, L., Bacco, M., Bartolini, F., Bulten, E., Debruyne, L., Dessein, J., Scotti, I., & Brunori, G. (2021). Digital transformation of agriculture and rural areas: A socio-cyber-physical system framework to support responsibilisation. Journal of Rural Studies, 85, 79-90. doi: 10.1016/j.jrurstud.2021.05.003.
  43. Rogers, E. M., Singhal, A., & Quinlan, M. M. (2014). Diffusion of innovations. In An integrated approach to communication theory and research (pp. 432-448). Routledge.
  44. Rommel, J., Sagebiel, J., Baaken, M. C., Barreiro-Hurlé, J., Bougherara, D., Cembalo, L., Cerjak, M., Čop, T., Czajkowski, M., & Espinosa-Goded, M. (2022). Farmers’ risk preferences in eleven European farming systems: A multi-country replication of Bocquého et al. 2014). doi: 10.1002/aepp.13330.
  45. Ronaghi, M. H., & Forouharfar, A. (2020). A contextualized study of the usage of the Internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of Unified Theory of Acceptance and Use of Technology model (UTAUT). Technology in Society, 63, 101415. doi: 10.1016/j.techsoc.2020.101415.
  46. Rose, D. C., Wheeler, R., Winter, M., Lobley, M., & Chivers, C.-A. (2021). Agriculture 4.0: Making it work for people, production, and the planet. Land Use Policy, 100, 104933. doi: 10.1016/j.landusepol.2020.104933.
  47. Santeramo, F. G., Lamonaca, E., Contò, F., Nardone, G., & Stasi, A. (2018). Drivers of grain price volatility: A cursory critical review. Agricultural Economics (Czech Republic), 64(8), 347-356. doi: 10.17221/55/2017-AGRICECON.
  48. Sezgin, E., Özkan-Yildirim, S., & Yildirim, S. (2017). Investigation of physicians’ awareness and use of mHealth apps: A mixed method study. Health Policy and Technology, 6(3), 251-267. doi: 10.1016/j.hlpt.2017.07.007.
  49. Shang, L., Heckelei, T., Gerullis, M. K., Börner, J., & Rasch, S. (2021). Adoption and diffusion of digital farming technologies – Integrating farm-level evidence and system interaction. Agricultural Systems, 190, 103074. doi: 10.1016/j.agsy.2021.103074.
  50. Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of consumer research, 15(3), 325-343. doi: 10.1086/209170.
  51. Takácsné György, K., Lámfalusi, I., Molnár, A., Sulyok, D., Gaál, M., Domán, C., Illés, I., Kiss, A., Péter, K., & Kemény, G. (2018). Precision agriculture in Hungary: Assessment of perceptions and accounting records of FADN arable farms. Studies in Agricultural Economics, 120(1), 47-54. doi: 10.22004/ag.econ.273117.
  52. Tamirat, T. W., Pedersen, S. M., & Lind, K. M. (2018). Farm and operator characteristics affecting adoption of precision agriculture in Denmark and Germany. Acta Agriculturae Scandinavica, Section B – Soil & Plant Science, 68(4), 349-357. doi: 10.1080/09064710.2017.1402949.
  53. Vecchio, Y., Agnusdei, G. P., Miglietta, P. P., & Capitanio, F. (2020). Adoption of Precision Farming Tools: The Case of Italian Farmers. International Journal of Environmental Research and Public Health, 17(3). doi: 10.3390/ijerph17030869.
  54. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. doi: 10.2307/30036540.
  55. Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological forecasting and social change, 126, 207-216. doi: 10.1016/j.techfore.2017.08.013.
  56. Viganò, E., Maccaroni, M., & Righi, S. (2022). Finding the right price: Supply chain contracts as a tool to guarantee sustainable economic viability of organic farms. International Food and Agribusiness Management Review, 1-16. doi: 10.22434/ifamr2021.0103.
  57. Wang, Y., Jin, L., & Mao, H. (2019). Farmer Cooperatives’ Intention to Adopt Agricultural Information Technology – Mediating Effects of Attitude. Information Systems Frontiers, 21(3), 565-580. doi: 10.1007/s10796-019-09909-x.
  58. Xu, Q., Huet, S., Perret, E., & Deffuant, G. (2020). Do farm characteristics or social dynamics explain the conversion of dairy farmers to organic farming? An agentbased model of dairy farming in 27 French cantons. Journal of Artificial Societies and Social Simulation, 23(2). doi: 10.18564/jasss.4204.
  59. Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350-363. doi: 10.1016/j.im.2005.08.006.
  60. Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. doi: 10.1016/j.compag.2020.105256.

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