سناریونگاری کشاورزی دیجیتال در ایران؛ پیشران های اصلی، عدم قطعیت‌ها و سناریوها

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار مدیریت فنّاّوری، دانشکدگان فارابی، دانشگاه تهران،قم، ایران

2 کارشناسی ارشد مدیریت فناوری اطلاعات، دانشکدگان فارابی، دانشگاه تهران، قم، ایران

3 دانشیار مدیریت فنّاّوری، پژوهشکده مطالعات بنیادین علم و فنّاّوری، دانشگاه شهید بهشتی، تهران، ایران

چکیده

توسعه کشاورزی دیجیتال در ایران به دلیل محدودیت‌های ساختاری و منابع آبی، یک ضرورت راهبردی غیرقابل اجتناب است. با وجود اینکه مطالعات بین‌المللی بر مؤلفه‌های پذیرش اجتماعی و زیرساخت‌های فناورانه تأکید دارند، فقدان سناریوهای بومی و روش‌شناختی منسجم، بزرگترین خلا تحقیقاتی در این حوزه است. این پژوهش کاربردی و آینده‌نگر با بهره‌گیری از روش سناریونگاری شهودی، به تدوین سناریوهای گذار به کشاورزی دیجیتال در ایران پرداخته و بینش‌های سیاستی ملموسی را ارائه می‌دهد. روش پژوهش کیفی، توسعه‌ای و آینده‌نگر بوده و داده‌ها در دو فاز اسنادی و میدانی (با مشارکت ۱۶ خبره) گردآوری شد. فرآیند تحلیل در پنج مرحله شامل تعریف افق زمانی ۱۴۲۵، شناسایی ۲۰ نیروی پیشران در قالب الگوی STEEP، ارزیابی تأثیر و عدم قطعیت، ساخت ماتریس سناریو و شناسایی سناریوهای ممکن، محتمل و مطلوب انجام شد. یافته‌ها نشان داد دو عدم قطعیت کلیدی «سطح توسعه فناوری» و «سطح پذیرش اجتماعی» محورهای اصلی هستند که بر اساس آن‌ها چهار سناریو تدوین شد: «طلای سبز دیجیتال» (سناریوی مطلوب با فناوری پیشرفته و پذیرش بالا)، «نبض سبز» (سناریوی محتمل با پذیرش بالا اما فناوری محدود)، «طلوع منجمد» (سناریوی ممکن و نامطلوب با فناوری بالا اما پذیرش پایین) و «زمین خاموش» (سناریوی فاجعه‌بار با فناوری و پذیرش پایین). تحقق سناریوی مطلوب نیازمند پنج اقدام سیاستی اساسی است: توسعه زیرساخت‌های دیجیتال روستایی، ارتقای سواد دیجیتال کشاورزان از طریق آموزش‌های هدفمند، طراحی سیاست‌های حمایتی و انگیزشی شامل تسهیلات مالی و بیمه هوشمند محصولات، اجرای پروژه‌های پایلوت و شبکه‌های نوآوری، و تقویت همکاری‌های بین‌المللی برای انتقال دانش فنی و تجربیات موفق. نتیجه نهایی اینکه بدون مداخله فعال، هماهنگ و پایدار دولت و سایر بازیگران و ذینفعان در این پنج حوزه، احتمال حرکت کشور به سمت سناریوهای نامطلوب به ویژه «زمین خاموش» به طور قابل توجهی افزایش خواهد یافت.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Scenario planning of digital agriculture in Iran: Key drivers, uncertainties, and scenarios

نویسندگان [English]

  • Mostafa Safdari Ranjbar 1
  • Zahra Karimi 2
  • Kiarash Fartash 3
1 Associate Professor in Management ofTechnology, College of Farabi, University of Tehran, Qom, Iran
2 M.Sc. in Information Technology Management,College of Farabi, University of Tehran, Qom, Iran,
3 Associate Professor in Management of Technology, Institute for Science and Technology Studies,Shahid Beheshti University, Tehran, Iran
چکیده [English]

Due to structural limitations and water resource constraints, the development of digital agriculture in Iran is an unavoidable strategic necessity. Although international studies emphasize the components of social acceptance and technological infrastructure, the lack of indigenous scenarios and a coherent methodological framework represents the most significant research gap in this field. This applied and forward-looking research, utilizing the intuitive logic scenario planning method, develops transition scenarios for digital agriculture in Iran and provides tangible policy insights. The research methodology is qualitative, developmental, and forward-looking, with data collected in two phases: documentary and field studies (with the participation of 16 experts). The analysis process was conducted in five stages: defining the time horizon to 1425 (2045 AD), identifying 20 driving forces using the STEEP framework, assessing impact and uncertainty, constructing a scenario matrix, and identifying possible, probable, and preferable scenarios. The findings revealed two key uncertainties—"level of technological development" and "level of social acceptance"—as the main axes, based on which four scenarios were developed: "Digital Green Gold" (preferable scenario with advanced technology and high acceptance), "Green Pulse" (probable scenario with high acceptance but limited technology), "Frozen Dawn" (possible and unfavorable scenario with high technology but low acceptance), and "Silent Ground" (catastrophic scenario with low technology and low acceptance). Realizing the preferable scenario requires five fundamental policy actions: developing rural digital infrastructure, enhancing farmers' digital literacy through targeted training, designing supportive and incentive policies including financial facilities and smart crop insurance, implementing pilot projects and innovation networks, and strengthening international cooperation for the transfer of technical knowledge and successful experiences. Ultimately, without active, coordinated, and sustained intervention by the government and other actors and stakeholders in these five areas, the likelihood of the country moving toward unfavorable scenarios, especially "Silent Ground," will increase significantly.

کلیدواژه‌ها [English]

  • Futures Studies
  • Scenario Planning
  • Digital Agriculture
  • Digital technologies
  • Social acceptance

مراجع

 
Adeborode, D. O., & Kolawole, K. V. O. (2025). The role of digital technologies in reducing food waste and loss in agricultural supply chains. IRE Journals, 8(11), 479-487.
Afesorgbor, S. K., Kornher, L., & Santeramo, F. G. (2025). The impacts of economic sanctions on food (prices) security: Evidence from targeted countries. The World Economy, 48(8), 1942-1956. https://doi.org/10.1111/twec.13739
AgAmerica. (2023 August 23). How to cut costs with smart farming technology. Retrieved from: https://agamerica.com/blog/smart-farming-technology/
Alahmad, T., Neményi, M., & Nyéki, A. (2023). Applying IoT sensors and big data to improve precision crop production: a review. Agronomy, 13(10), 2603. https://doi.org/10.3390/agronomy13102603
Alilou, K., & Jamshidi, B. (2023). Application of artificial intelligence in climate smart agriculture. Journal of Agricultural Information Science and Technology, 5(2), 51-60. (Persian) https://doi.org/10.22092/jaist.2023.362065.1090
An, I. (2024 March). Meeting the European Union's digital agriculture requirements. An ITU-FAO compendium for preaccession countries and territories. Geneva, Switzerland.
Analytica, A. (2025). Organic food market poised to surpass USD 472.72 billion by 2033 [Market report]. Astute Analytica. Retrieved from: https://www.astuteanalytica.com/press-release/organic-food-market
Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Characterising the agriculture 4.0 landscape—Emerging trends, challenges and opportunities. Agronomy, 11(4), 667. https://doi.org/10.3390/agronomy11040667
Atik, C. (2022). Towards comprehensive European agricultural data governance: Moving beyond the "data ownership" debate. IIC-International Review of Intellectual Property and Competition Law, 53(5), 701-742. https://doi.org/10.1007/s40319-022-01191-w
Bachhav, H. S. (2025). Teach farmers how to use digital tools and data analysis to inform decision making. International Journal of Teaching, Learning and Education, 4(3), 103-105. https://doi.org/10.22161/ijtle.4.3.12
Bagheri, N., Keshavarz Tork, M., Abasi, F., & Bordbar, M. (2020). Futures studies of agricultural engineering research institute in 1404. Strategic Research Journal of Agricultural Sciences and Natural Resources, 5(1), 1-18. (Persian) https://doi.org/10.22047/srjasnr.2020.113048
Bang, J., & Han, J. W. (2025). Factors influencing farmers' motivation to adopt smart farm technology in South Korea. https://doi.org/10.48550/arXiv.2504.01795
Bélanger, J., & Pilling, D. (2019). The state of the world's biodiversity for food and agriculture [Report]. Rome: FAO. https://doi.org/10.4060/CA3129EN
Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5, 359-387. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa
Börjeson, L., Höjer, M., Dreborg, K. H., Ekvall, T., & Finnveden, G. (2006). Scenario types and techniques: Towards a user's guide. Futures, 38(7), 723-739. https://doi.org/10.1016/j.futures.2005.12.002
Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., ... & Goudos, S. K. (2022). Internet of things (IoT) and agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18, 100187. https://doi.org/10.1016/j.iot.2020.100187
Brasioli, D., Guercio, L., Landini, G. G., & de Giorgio, A. (Eds.). (2025). The Routledge handbook of artificial intelligence and international relations. New York: Routledge, Taylor & Francis Group.
Bui, H. T., Aboutorab, H., Mahboubi, A., Gao, Y., Sultan, N. H., Chauhan, A., ... & Yan, S. (2024). Agriculture 4.0 and beyond: Evaluating cyber threat intelligence sources and techniques in smart farming ecosystems. Computers & Security, 140, 103754. https://doi.org/10.1016/j.cose.2024.103754
Campi, M., Asai, M., McFadden, J., Pindado, E., & Rosburg, A. (2024 August 22). The evolving profile of new entrants in agriculture and the role of digital technologies. OECD Food, Agriculture and Fisheries Papers. Retrieved from: https://www.oecd-ilibrary.org/en/publications/the-evolving-profile-of-new-entrants-in-agriculture-and-the-role-of-digital-technologies_d15ea067-en.html
Change, I. C. (2013). The physical science basis [Report]. Retrieved from: https://cir.nii.ac.jp/crid/1371413280484207233
Châteauvieux, C., & Baddache, F. (2024 October 21). Digital tools: Empowering smallholder farmers [Policy brief]. Retrieved from: https://ksapa.org/digital-tools-empowering-smallholder-farmers/
Cheng, C., Gao, Q., Ju, K., & Ma, Y. (2024). How digital skills affect farmers' agricultural entrepreneurship? An explanation from factor availability. Journal of Innovation & Knowledge, 9(2), 100477. https://doi.org/10.1016/j.jik.2024.100477
Choudhary, V., Guha, P., Pau, G., & Mishra, S. (2025). An overview of smart agriculture using Internet of Things (IoT) and web services. Environmental and Sustainability Indicators, 26, 100607. https://doi.org/10.1016/j.indic.2025.100607
European Commission (2026 July 2). Digitalising agriculture large-scale pilots and innovation hubs [Report]. Retrieved from: https://digital-strategy.ec.europa.eu/en/policies/digitalising-agriculture-large-scale-pilots
Davies, A. R. (2020). Toward a sustainable food system for the European Union: Insights from the social sciences. One Earth, 3(1), 27-31. https://doi.org/10.1016/j.oneear.2020.06.008
Eastwood, C., Klerkx, L., Ayre, M., & Dela Rue, B. (2019). Managing socio-ethical challenges in the development of smart farming: From a fragmented to a comprehensive approach for responsible research and innovation. Journal of Agricultural and Environmental Ethics, 32(5), 741-768. https://doi.org/10.1007/s10806-017-9704-5
Ehlers, M.-H., Huber, R., & Finger, R. (2021). Agricultural policy in the era of digitalization. Food Policy, 100, 102019. https://doi.org/10.1016/j.foodpol.2020.102019
Esri. (2025). GIS supports climate change monitoring in Azerbaijan. Retrieved from: https://www.esri.com/en-us/lg/industry/government/stories/gis-supports-climate-change-monitoring-in-azerbaijan
FAO, U. (2019). The state of food and agriculture 2019: Moving forward on food loss and waste reduction [Report]. Food and Agriculture Organization of the United Nations. Retrieved from: https://openknowledge.fao.org/items/ba08937f-4a41-4ff5-a4e7-e495e5f5f599
FAO. (2010). Price volatility in agricultural markets: Evidence, impact on food security and policy responses [Report]. Food and Agriculture Organization of the United Nations. Retrieved from: https://ideas.repec.org/p/fao/pbrief/12en.html
FAO. (2022a December 02). The future of food and agriculture- drivers and triggers for transformation [Report]. Food and Agriculture Organization of the United Nations. Retrieved from: https://www.fao.org/north-america/resources/publications/The-future-of-food-and-agriculture-Drivers-and-triggers-for-transformation/en
FAO. (2022b). The state of food and agriculture 2022: Leveraging agricultural automation for transforming agrifood systems. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cb9479en
FAO. (2022c). The state of food and agriculture 2022: Leveraging automation in agriculture for transforming agrifood systems [Report]. Food and Agriculture Organization of the United Nations. Retrieved from: https://digitallibrary.un.org/record/3993910?v=pdf
FAO. (2025a June 02). FAO and Iran to exchange advanced agricultural knowledge and practices between countries [Press release]. Food and Agriculture Organization of the United Nations. Retrieved from: https://www.fao.org/iran/news/detail/fao-and-iran-to-exchange-advanced-agricultural-knowledge-and-practices-between-countries/en
FAO. (2025b). Improve agricultural monitoring systems through satellite imagery for Iran. Food and Agriculture Organization of the United Nations. Retrieved from: https://openknowledge.fao.org/items/0b7bee9f-5413-4c39-abad-ce7410eca5eb
Far, S. T., & Rezaei-Moghaddam, K. (2018). Impacts of the precision agricultural technologies in Iran: An analysis experts' perception & their determinants. Information Processing in Agriculture, 5(1), 173-184. https://doi.org/10.1016/j.inpa.2017.09.001
Farmonaut. (2025). Status of digital agriculture in 18 countries: 2025 insights [Market report]. https://farmonaut.com/europe/status-of-digital-agriculture-in-18-countries-2025-insights
Fartash, K., Ghorbani, A., & Bagheri, M. (2023). Slow adoption of EVs under a weak policy regime: Future scenarios of EVs development and diffusion in Iran. Futures, 151, 103196. https://doi.org/10.1016/j.futures.2023.103196
Finger, R. (2023). Digital innovations for sustainable and resilient agricultural systems. European Review of Agricultural Economics, 50(4), 1277-1309. https://doi.org/10.1093/erae/jbad021
Fleming, A., Jakku, E., Fielke, S., Taylor, B. M., Lacey, J., Terhorst, A., & Stitzlein, C. (2021). Foresighting Australian digital agricultural futures: Applying responsible innovation thinking to anticipate research and development impact under different scenarios. Agricultural Systems, 190(c), 103120. https://doi.org/10.1016/j.agsy.2021.103120
Geng, W., Liu, L., Zhao, J., Kang, X., & Wang, W. (2024). Digital technologies adoption and economic benefits in agriculture: A mixed-methods approach. Sustainability, 16(11), 4431. https://doi.org/10.3390/su16114431
Ghooshchi, F. (2014). Futures studies in Iranian agriculture. Journal of Agriculture and Sustainable Development, 53-54, 1-2. (Persian)
Green, A. G., Abdulai, A. R., Duncan, E., Glaros, A., Campbell, M., Newell, R., ... & Fraser, E. D. (2021). A scoping review of the digital agricultural revolution and ecosystem services: Implications for Canadian policy and research agendas. Facets, 6(1), 1955-1985. https://doi.org/10.1139/facets-2021-0017
Hakkim, V. A., Joseph, E. A., Gokul, A. A., & Mufeedha, K. (2016). Precision farming: The future of Indian agriculture. Journal of Applied Biology and Biotechnology, 4(6), 068-072. https://doi.org/10.7324/JABB.2016.40609
https://doi.org/10.48550/arXiv.2504.20215
Jouanjean, M. A., Casalini, F., Wiseman, L., & Gray, E. (2020). Issues around data governance in the digital transformation of agriculture: The farmers' perspective. Paris: OECD Publishing.
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. https://doi.org/10.1016/j.compag.2017.09.037
Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways? Global Food Security, 24, 100347. https://doi.org/10.1016/j.gfs.2019.100347
Klumpp, M., Clausen, U., & Ten Hompel, M. (2013). Logistics research and the logistics world of 2050. In Efficiency and logistics (pp. 1-6). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-32838-1_1
Kumar, V., Sharma, K. V., Kedam, N., Patel, A., Kate, T. R., & Rathnayake, U. (2024). A comprehensive review on smart and sustainable agriculture using IoT technologies. Smart Agricultural Technology, 100487. https://doi.org/10.1016/j.atech.2024.100487
La Rocca, P. (2023). Towards a methodology to consider the environmental impacts of digital agriculture. arXiv preprint arXiv:2305.09250. https://doi.org/10.48550/arXiv.2305.09250
Larch, M., Luckstead, J., & Yotov, Y. V. (2024). Economic sanctions and agricultural trade. American Journal of Agricultural Economics, 106(4), 1477-1517. https://doi.org/10.1111/ajae.12473
Lasdun, V. (2021). Peer learning in a digital farmer-to-farmer network: Effects on technology adoption and self-efficacy beliefs [Master's thesis]. McGill University, Montreal.
Latino, M. E., Corallo, A., Menegoli, M., & Nuzzo, B. (2021). Agriculture 4.0 as enabler of sustainable agri-food: A proposed taxonomy. IEEE Transactions on Engineering Management, 70(10), 3678-3696. https://doi.org/10.1109/TEM.2021.3101548
Li, C., & Freeborn, D. P. W. (2025). Exploring AI-powered digital innovations from a transnational governance perspective: Implications for market acceptance and digital accountability. arXiv preprint arXiv:2504.20215.
, L., & Mutegi, L. (2019). Leveraging low-power wide area networks for precision farming: Limabora—A smart farming case using LoRa modules, gateway, TTN and Firebase in Kenya. In: Mobile Technologies and Applications for the Internet of Things: Proceedings of the 12th IMCL Conference.
MacPherson, J., Rosman, A., Helming, K., & Burkhard, B. (2025). A participatory impact assessment of digital agriculture: A Bayesian network-based case study in Germany. Agricultural Systems, 224, 104222. https://doi.org/10.1016/j.agsy.2024.104222
MacPherson, J., Voglhuber-Slavinsky, A., Olbrisch, M., Schöbel, P., Dönitz, E., Mouratiadou, I., & Helming, K. (2022). Future agricultural systems and the role of digitalization for achieving sustainability goals. A review: Future agricultural systems and the role of digitalization for achieving sustainability goals. A review. Agronomy for Sustainable Development, 42(4), 70. https://doi.org/10.1007/s13593-022-00792-6
Magesa, M., Jonathan, J., & Urassa, J. (2023). Digital literacy of smallholder farmers in Tanzania. Sustainability, 15(17), 13149. https://doi.org/10.3390/su151713149
Maroofi, A., Hasanpour, S., Omidi Shahabadi, O., Hoseini, A., & Azizpour, F. (2022). Foresight of the Iran's agricultural sector in 2024 using structural analysis and system dynamics. Quarterly Journal of The Macro and Strategic Policies, 10(39), 610-642. (Persian) https://doi.org/10.30507/jmsp.2022.331083.2386
Martelli, A. (2001). Scenario building and scenario planning: State of the art and prospects of evolution. Futures Research Quarterly, 17(2), 57-74.
McFadden, J., Njuki, E., & Griffin, T. (2023). Precision agriculture in the digital era: Recent adoption on U.S. farms [Research report]. Economic Research Service, U.S. Department of Agriculture. Retrieved from: https://www.ers.usda.gov/publications/105893
Meshram, K., Mishra, U., & Rathnayake, U. (2025). Application of artificial intelligence in agri-tech, environmental and biodiversity conservation. Array, 26, 100412. https://doi.org/10.1016/j.array.2025.100412
Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., Łobodzińska, A., & Śnieg, M. (2025). The IoT and AI in agriculture: The time is now- A systematic review of smart sensing technologies. Sensors (Basel), 25(12), 3583. https://doi.org/10.3390/s25123583
Mirpanahi, S., & Javadi, A. (2022 May 18). Investigating the importance and necessity of transition from traditional agriculture to digital agriculture in Iran's food security [Paper presentation]. In The 7th International congress on agricultural development and environment with an emphasis on the UN development program, Tehran, Iran.
Niftiyev, I. (2023). Third-country economic consequences of Western sanctions on Russia: a thematic analysis of expert opinions in Azerbaijan. SocioEconomic Challenges, 7(3), 113-140. https://doi.org/10.61093/sec.7(3).113-140.2023
OECD. (2022b). Digital opportunities for better agricultural policies [Report]. OECD Publishing. Retrieved from: https://www.oecd.org/en/publications/digital-opportunities-for-better-agricultural-policies_571a0812-en.html
Osorio, C. P., Leucci, F., & Porrini, D. (2024). Analyzing the relationship between agricultural AI adoption and government-subsidized insurance. Agriculture, 14(10), 1804. https://doi.org/10.3390/agriculture14101804
Ozdogan, B., Gacar, A., & Aktas, H. (2017). Digital agriculture practices in the context of agriculture 4.0. Journal of Economics Finance and Accounting, 4(2), 186-193. https://doi.org/10.17261/Pressacademia.2017.448
Pakzat, M., & Rezvani, M. (2025). Assessment of the relationships between the agricultural sector and the macroeconomy in Iran with emphasis on structural breaks. Scientific Quarterly Journal of Majlis and Economy, 1(2), 265-277. (Persian) https://doi.org/10.22034/mec.2025.17746.1104
Panahian, P., Borimnejad, V., Samari, D., & Nozari, N. (2025). Shaping farmers' purchase intentions: The role of agricultural input subsidies and marketing strategies in Iran. Discover Sustainability, 6(1), 567. https://doi.org/10.1007/s43621-025-01307-5
Papadopoulos, G., Papantonatou, M.-Z., Uyar, H., Kriezi, O., Mavrommatis, A., Psiroukis, V., Kasimati, A., Tsiplakou, E., & Fountas, S. (2025). Economic and environmental benefits of digital agricultural technological solutions in livestock farming: A review. Smart Agricultural Technology, 100783. https://doi.org/10.1016/j.atech.2025.100783
Parson, E. A., Burkett, V., Fisher-Vanden, K., Keith, D., Mearns, L., Pitcher, H., ... & Webster, M. (2007). Global-change scenarios: their development and use [Report]. University of Michigan Law School. Retrieved from: https://repository.law.umich.edu/cgi/viewcontent.cgi?article=1034&context=other
Petrović, B., Bumbálek, R., Zoubek, T., Kuneš, R., Smutný, L., & Bartoš, P. (2024). Application of precision agriculture technologies in Central Europe- review. Journal of Agriculture and Food Research, 15, 101048. https://doi.org/10.1016/j.jafr.2024.101048
Pigford, A.-A. E., Hickey, G. M., & Klerkx, L. (2018). Beyond agricultural innovation systems? Exploring an agricultural innovation ecosystems approach for niche design and development in sustainability transitions. Agricultural Systems, 164, 116-121. https://doi.org/10.1016/j.agsy.2018.04.007
Prause, L., Hackfort, S., & Lindgren, M. (2021). Digitalization and the third food regime. Agriculture and Human Values, 38(3), 641-655. https://doi.org/10.1007/s10460-020-10161-2
Rezvani, M., Shamshiri, R., Moghaddam, J., Balasundram, S., & Hameed, I. (2022). Digital agriculture in Iran: Use cases, opportunities, and challenges. In R. R. Shamshiri, & S. Shafian (Eds.), Digital Agriculture, Methods and Applications. London: IntechOpen Publishing. https://doi.org/10.5772/intechopen.103967
Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems, 2, 87. https://doi.org/10.3389/fsufs.2018.00087
Ryan, M., Atik, C., Rijswijk, K., Bogaardt, M. J., Maes, E., & Deroo, E. (2024). The future of agricultural data-sharing policy in Europe: Stakeholder insights on the EU Code of Conduct. Humanities and Social Sciences Communications, 11(1), 1-15. https://doi.org/10.1057/s41599-024-03710-1
Saini, A. K., & Yadav, A. K. (2025). A comprehensive review on technological breakthroughs in precision agriculture: IoT and emerging data analytics. European Journal of Agronomy, 163, 127440. https://doi.org/10.1016/j.eja.2024.127440
Samuels, J. (2024). Digital agriculture: Revolutionizing farming practices through technology integration. https://doi.org/10.13140/RG.2.2.17600.51204
Sharifi Tehrani, A., & Mahdavi Damghani, A. (2023). Evaluation of modern eco-based technologies application in the agriculture and natural resource management of Iran. Journal of Strategic Research in Agricultural Sciences and Natural Resources, 8(1), 1-16. (Persian)
Sharma, K., & Shivandu, S. K. (2024). Integrating artificial intelligence and internet of things (IoT) for enhanced crop monitoring and management in precision agriculture. Sensors International, 5(1), 100292. https://doi.org/10.1016/j.sintl.2024.100292
Simion, G., & Simion, P.-S. (2024). Transforming agriculture: The impact of digital technologies on biodiversity conservation. Digitization & sustainability in the financial sector - regulations and best practices. Brașov, Romania.
Sloat, S. (2025). Farmers are using IoT to take the guesswork out of growing. Business Insider. Retrieved from: https://www.businessinsider.com/iot-technology-precision-agriculture-transforming-farming-2025-5
Stoian, M., Dobre, I., Popescu, C. G., Vasile, M. C., Dimitriu, A. T., & Ion, A. (2022). Increasing sustainability of food production and ensuring human health through agriculture digitalization. Economic of Agriculture, 69(4), 1209-1223.
Sumihiro, G. (2025). Precision agriculture is an investment in food security. My Journal Courier. Retrieved from: https://www.myjournalcourier.com/opinion/article/precision-agriculture-investment-food-security-20260610.php
Taghibaygi, M., & Alibaygi, A. (2024). The impact of ethical commitments on the intention to adopt digital agricultural technologies. Scientific Reports, 14(1), 23035. https://doi.org/10.1038/s41598-024-71723-6
Trendov, N. M., Varas, S., & Zeng, M. (2019). Digital technologies in agriculture and rural areas. Rome: Food and Agriculture Organization of the United Nations.
U.S. Government Accountability Office. (2024 January 31). Precision Agriculture: Benefits and challenges for technology adoption and use [Report]. Retrieved from: https://www.gao.gov/products/gao-24-105962
Vahdanjoo, M., Sørensen, C. G., & Nørremark, M. (2025). Digital transformation of the agri-food system. Current Opinion in Food Science, 63, 101287. https://doi.org/10.1016/j.cofs.2025.101287
Van der Burg, S., Bogaardt, M. J., & Wolfert, S. (2019). Ethics of smart farming: Current questions and directions for responsible innovation towards the future. NJAS-Wageningen Journal of Life Sciences, 90, 100289. https://doi.org/10.1016/j.njas.2019.01.001
Van der Heijden, K. (2005). Scenarios: The art of strategic conversation. Hoboken: John Wiley & Sons.
Velten, S., Leventon, J., Jager, N., & Newig, J. (2015). What is sustainable agriculture? A systematic review. Sustainability, 7(6), 7833-7865. https://doi.org/10.3390/su7067833
Von Schomberg, R. (2013). A vision of responsible research and innovation. In R. Owen, J. Bessant, M. Heintz (Eds.), Responsible innovation: Managing the responsible emergence of science and innovation in society (pp. 51-74). Hoboken: John Wiley & Sons. https://doi.org/10.1002/9781118551424.ch3
Wang, Z., Jang, W., Ruan, B., Wang, J., & Xiao, S. (2026). Developing and integrating trust modeling into multi-objective reinforcement learning for intelligent agricultural management. Smart agricultural technology, 102145. https://doi.org/10.48550/arXiv.2505.10803
Wilkinson, A., & Eidinow, E. (2008). Evolving practices in environmental scenarios: A new scenario typology. Environmental Research Letters, 3(4), 045017. https://doi.org/10.1088/1748-9326/3/4/045017
Wolfert, S., & Isakhanyan, G. (2022). Sustainable agriculture by the internet of things- A practitioner's approach to monitor sustainability progress. Computers and Electronics in Agriculture, 200, 107226. https://doi.org/10.1016/j.compag.2022.107226
Yane, D. (2011 October 22-25). Research and analysis about system of digital agriculture based on a network platform. In Computer and computing technologies in agriculture (pp. 274-282). Berlin, Heidelberg: Springer Berlin Heidelberg.
Yeo, M. L., & Keske, C. M. (2024). From profitability to trust: Factors shaping digital agriculture adoption. Frontiers in Sustainable Food Systems, 8, 1456991. https://doi.org/10.3389/fsufs.2024.1456991
Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture- A worldwide overview. Computers and Electronics in Agriculture, 36(2-3), 113-132. https://doi.org/10.1016/S0168-1699(02)00096-0
 
 

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