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

Titolo Rivista Economia agro-alimentare
Autori/Curatori Selene Righi, Elena Viganò
Anno di pubblicazione 2025 Fascicolo 2024/3
Lingua Inglese Numero pagine 24 P. 137-160 Dimensione file 0 KB
DOI 10.3280/ecag2024oa17604
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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.

Parole chiave:; Farm innovation; Support services; Organic agriculture; Decision Support System; Italy

  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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    DOI: 10.3280/ecag2024oa18954

Selene Righi, Elena Viganò, Innovativeness in organic farming system: The case of the Marche region in "Economia agro-alimentare" 3/2024, pp 137-160, DOI: 10.3280/ecag2024oa17604