با همکاری انجمن اقتصاد کشاورزی ایران

مطالعۀ پیامدهای پذیرش فناوری‌های کشاورزی هوشمند با نقش تعدیلگر سیاست‌ها و مقررات دولتی

نوع مقاله : مقالات پژوهشی به زبان انگلیسی

نویسندگان

1 گروه مدیریت بازرگانی، دانشکده مدیریت، دانشگاه خوارزمی، تهران، ایران

2 گروه مدیریت بازرگانی، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران

چکیده
بخش کشاورزی یکی از حوزه‌هایی است که فناوری‌های کشاورزی هوشمند در آن کاربرد زیادی دارند. فناوری‌های کشاورزی هوشمند پتانسیل تجزیه‌و‌تحلیل داده‌های کشاورزی را در مقیاسی ارائه می‌دهند که قبلاً امکان‌پذیر نبود. پذیرش فناوری‌های کشاورزی هوشمند پتانسیل بهبود بهره‌وری و سودآوری در کشاورزی را دارد و در عین حال پایداری را نیز بهبود می‌بخشد. دستیابی به این پتانسیل نه تنها به پیشرفت تکنولوژی نیاز دارد، بلکه نیازمند درک دقیق پیامدهایی است که پذیرش فناوری‌های کشاورزی هوشمند بر آنها تأثیر می‌گذارد. با توجه به توضیحات مذکور، پراکندگی پژوهش‌های قبلی و نبود نگاه جامع، هدف پژوهش حاضر، مطالعۀ پیامدهای پذیرش فناوری‌های کشاورزی هوشمند با نقش تعدیلگر سیاست‌ها و مقررات دولتی است. جامعۀ آماری پژوهش شامل مدیران، کارشناسان و خبرگان حوزۀ کشاورزی هوشمند در استان تهران و البرز است. یکی از روش‌های تعیین حداقل حجم نمونه، روش نسبت تعداد اعضای نمونه به تعداد سؤالات مدل پژوهش یا نظریۀ N:q است که حداقل نسبت پیشنهادی توسط جکسون است. بر همین اساس با توجه به تعداد گویه‌های پرسش‌نامه (27 سؤال)، حجم نمونه 270 نفر برآورد شد. ابزار گردآوری داده‌ها پرسش‌نامه است. روایی پرسشنامه به‌صورت تشخیصی، هم‌گرا و واگرا و پایایی پرسشنامه نیز با ضریب آلفای کرونباخ و پایایی ترکیبی بررسی شد. آزمون مدل پژوهش بر‌اساس روش حداقل مربعات جزئی و به کمک نرم‌افزار Smart - PLS 3 انجام شد. نتایج تجزیه‌و‌تحلیل داده‌ها نشان داد که پذیرش فناوری‌های کشاورزی هوشمند بر پیامدهای نیروی کار، اقتصادی و زیست‌محیطی تأیر مثبت و معنادار دارد. علاوه بر این، سیاست‌ها و مقررات دولتی در ارتباط بین پذیرش فناوری‌های کشاورزی هوشمند و پیامدهای نیروی کار، اقتصادی و زیست‌محیطی نقش تعدیلگر مثبت دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Studying the Consequences of Adopting Smart Agricultural Technologies with the Moderating Role of Government Policies and Regulations

نویسندگان English

H. Norouzi 1
M. Mola Ghalghachi 1
B. Asgarnezhad Nouri 2
1 Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran
2 Department of Business Management, Faculty of Economics and Management, Urmia University, Urmia, Iran
چکیده English

The agricultural sector is one of the areas where smart agricultural technologies are widely used. Smart farming technologies offer the potential to analyze agricultural data on a scale not previously possible. Adoption of smart farming technologies has the potential to improve productivity and profitability in agriculture, while also improving sustainability. Achieving this potential requires not only technological advancements, but also a thorough understanding of the consequences that the adoption of smart agricultural technologies should have. The aim of the present study is to study the consequences of adopting smart agricultural technologies with the moderating role of government policies and regulations. The statistical population of the research includes managers, experts, and specialists in the field of smart agriculture in Tehran and Alborz provinces, Iran. One of the methods for determining the minimum sample size is the ratio of the number of sample members to the number of questions in the research model or the N:q theory. Accordingly, considering the number of questionnaire items (27 questions), the sample size was estimated to be 270 people. The data collection tool is a questionnaire. The validity of the questionnaire was assessed in diagnostic, convergent and divergent terms, and the reliability of the questionnaire was also assessed using Cronbach’s alpha coefficient and composite reliability. The research model was tested using the partial least squares method and Smart-PLS 3 software. The results of data analysis showed that the adoption of smart agricultural technologies has a positive and significant impact on workforce, economic, and environmental outcomes. Furthermore, government policies and regulations play a positive moderating role in the relationship between the adoption of smart agricultural technologies and workforce, economic, and environmental outcomes.

کلیدواژه‌ها English

Economic consequences
Environmental consequences
Smart farming
Technology adoption
Workforce consequences

Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

  1. Alumfareh, M.F., Humayun, M., Ahmad, Z., & Khan, A. (2024). An Intelligent LoRaWAN-based IoT Device for Monitoring and Control Solutions in Smart Farming through anomaly detection integrated with unsupervised machine learning. IEEE Access, 99, 1-1. https://doi.org/10.1109/ACCESS.2024.3450587
  2. Amondo, E., & Simtowe, F. (2018). Technology Innovations, Productivity and Production Risk Effects of Adopting Drought Tolerant Maize varieties in Rural Zambia.‏ International Association of Agricultural Economists, 28(2), 276049. https://doi.org/10.1108/ijccsm-03-2018-0024
  3. Aryal, J. P., Jat, M. L., Sapkota, T. B., Khatri-Chhetri, A., Kassie, M., Rahut, D. B., & Maharjan, S. (2018). Adoption of multiple climate-smart agricultural practices in the Gangetic plains of Bihar, India. International Journal of Climate Change Strategies and Management, 10(3), 407-427.‏ https://doi.org/10.1108/IJCCSM-02-2017-0025
  4. Asgarnezhad Nouri, B., Ebrahimpour, H., Nami, B., & Hamidzadeh Arbabi, A. (2022). The Impact of Knowledge Management on Employee Professional Development: The Mediating Role of Entrepreneurial Capabilities. Journal of Innovation Ecosystem, 2(1), 25-45. (In Persian). https://doi.org/10.22111/innoeco.2022.41876.1031
  5. Aslan, M. F., Durdu, A., Sabanci, K., Ropelewska, E., & Gültekin, S. S. (2022). A comprehensive survey of the recent studies with UAV for precision agriculture in open fields and greenhouses. Applied Sciences, 12(3), 1047.‏ https://doi.org/10.3390/app12031047
  6. Azadi, H., Moghaddam, S. M., Burkart, S., Mahmoudi, H., Van Passel, S., Kurban, A., & Lopez-Carr, D. (2021). Rethinking resilient agriculture: From climate-smart agriculture to vulnerable-smart agriculture. Journal of Cleaner Production, 319, 128602.‏ https://doi.org/10.1016/j.jclepro.2021.128602
  7. Azam, M. S., & Shaheen, M. (2019). Decisional factors driving farmers to adopt organic farming in India: a cross-sectional study. International Journal of Social Economics, 46(4), 562-580.‏ https://doi.org/10.1108/IJSE-05-2018-0282
  8. Bahari, M., Arpaci, I., Der, O., Akkoyun, F., & Ercetin, A. (2024). Driving Agricultural Transformation: Unraveling Key Factors Shaping IoT Adoption in Smart Farming with Empirical Insights. Sustainability, 16(5), 2129.‏ https://doi.org/10.3390/su16052129
  9. Balafoutis, A. T., Evert, F. K. V., & Fountas, S. (2020). Smart farming technology trends: economic and environmental effects, labor impact, and adoption readiness. Agronomy, 10(5), 743.‏ https://doi.org/10.3390/agronomy10050743
  10. Basir, A., Sutawi, S., Ariadi, B. Y., Sapar, S., Tonda, R., Marhani, M., & Rosa, I. (2024). Influence of agriculture counseling agent’s performance and smart farming technology usage on farmers’ behavior. International Journal of Agriculture and Environmental Research, 10(4), 493-512.‏ https://doi.org/10.51193/IJAER.2024.10402
  11. Chen, R., Meng, Q., & Yu, J. J. (2023). Optimal government incentives to improve the new technology adoption: Subsidizing infrastructure investment or usage?. Omega, 114, 102740.‏ https://doi.org/10.1016/j.omega.2022.102740
  12. Cihan, P. (2023). IoT Technology in Smart Agriculture. In International Conference on Recent Academic Studies, 185-192.‏ https://doi.org/10.59287/icras.693
  13. De Pinto, A., Cenacchi, N., Kwon, H. Y., Koo, J., & Dunston, S. (2020). Climate smart agriculture and global food-crop production. PLoS One, 15(4), e0231764.‏ https://doi.org/10.1371/journal.pone.0231764
  14. Deljo, S. M., Hosseini, S. S., Karmi, A., Sanobar, N., & Nikkhah, Y. (2021). The effect of green human resource management on green innovation with the moderating role of green intellectual capital. Development of human resource management and support, 61(16), 1-28. (In Persian). https://doi.org/10.4236/jhrss.2026.142011
  15. Dibbern, T., Romani, L. A. S., & Massruhá, S. M. F. S. (2024). Main drivers and barriers to the adoption of Digital Agriculture technologies. Smart Agricultural Technology, 8, 100459.‏ https://doi.org/10.1016/j.atech.2024.100459
  16. Emmi, L., Fernández, R., Gonzalez-de-Santos, P., Francia, M., Golfarelli, M., Vitali, G., ... & Wollweber, M. (2023). Exploiting the internet resources for autonomous robots in agriculture. Agriculture, 13(5), 1005.‏ https://doi.org/10.3390/agriculture13051005
  17. Ena, G. W. W., & Huib, I. T. S. Development of Smart Farming Technologies in Malaysia-Insights from Bibliometric Analysis. Journal of Agribusiness, 10(1), 30-48.‏ https://doi.org/10.56527/fama.jabm.10.1.3
  18. Everest, B. (2021). Farmers’ adaptation to climate-smart agriculture (CSA) in NW Turkey. Environment, Development and Sustainability, 23(3), 4215-4235.‏ https://doi.org/10.1007/s10668-020-00767-1
  19. Fiawoo, H. D., Tham-Agyekum, E. K., Ankuyi, F., Osei, C., & Bakang, J. A. (2024). Rice farmers’ adoption of climate-smart agricultural technologies and its effects on yield and income: empirical insights from Ghana. SVU-International Journal of Agricultural Sciences, 6(1), 120-137.‏ https://doi.org/10.21608/svuijas.2024.268924.1342
  20. Giua, C., Materia, V. C., & Camanzi, L. (2022). Smart farming technologies adoption: Which factors play a role in the digital transition?. Technology in Society, 68, 101869.‏ https://doi.org/10.1016/j.techsoc.2022.101869
  21. Hashim, N., Ali, M. M., Mahadi, M. R., Abdullah, A. F., Wayayok, A., Kassim, M. S. M., & Jamaluddin, A. (2024). Smart Farming for Sustainable Rice Production: An Insight into Application, Challenge, and Future Prospect. Rice Science, 31(1), 47-61.‏ https://doi.org/10.1016/j.rsci.2023.08.004
  22. He, G., Li, C., Song, M., Shu, Y., Lu, C., & Luo, Y. (2023). A hierarchical federated learning incentive mechanism in UAV-assisted edge computing environment. Ad Hoc Networks, 149, 103249.‏ https://doi.org/10.1016/j.adhoc.2023.103249
  23. Hosseininia, G., Moghaddas Farimani, S., & Marefat Gharehbaba, N. (2022). Constructs Affecting the Willingness to Adopt Internet of Things (IoT) by Early Adopter Farmers in Tehran Province. Iranian Agricultural Extension and Education Journal, 17(2), 235-249. (In Persian). https://dor.isc.ac/dor/20.1001.1.20081758.1400.17.2.15.3
  24. Hussien, Z. A., Abdulmalik, H. A., Hussain, M. A., Nyangaresi, V. O., Ma, J., Abduljabbar, Z. A., & Abduljaleel, I. Q. (2023). Lightweight integrity preserving scheme for secure data exchange in cloud-based IoT systems. Applied Sciences, 13(2), 691.‏ https://doi.org/10.3390/app13020691
  25. Islam, M. H., Anam, M. Z., Hoque, M. R., Nishat, M., & Bari, A. M. (2024). Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and sustainability. Journal of Economy and Technology, 2, 278-295.‏ https://doi.org/10.1016/j.ject.2024.09.002
  26. Jabbari, A., Humayed, A., Reegu, F. A., Uddin, M., Gulzar, Y., & Majid, M. (2023). Smart farming revolution: Farmer’s perception and adoption of smart iot technologies for crop health monitoring and yield prediction in jizan, Saudi Arabia. Sustainability, 15(19), 14541.‏ https://doi.org/10.3390/su151914541
  27. Jackson, D. L. (2003). Revisiting sample size and number of parameter estimates: Some support for the N: q hypothesis. Structural equation modeling, 10(1), 128-141.‏ https://doi.org/10.1207/S15328007SEM1001_6
  28. Jaroenwanit, P., Phuensane, P., Sekhari, A., & Gay, C. (2023). Risk management in the adoption of smart farming technologies by rural farmers. Uncertain Supply Chain Management, 11(2), 533-546.‏ https://doi.org/10.5267/j.uscm.2023.2.011
  29. Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15-30.‏ https://doi.org/10.1016/j.aac.2022.10.001
  30. Jiang, Z., & Xu, C. (2023). Policy incentives, government subsidies, and technological innovation in new energy vehicle enterprises: Evidence from China. Energy Policy, 177, 113527.‏ https://doi.org/10.1016/j.enpol.2023.113527
  31. Kasimati, A., Papadopoulos, G., Manstretta, V., Giannakopoulou, M., Adamides, G., Neocleous, D., ... & Stylianou, A. (2024). Case Studies on Sustainability-Oriented Innovations and Smart Farming Technologies in the Wine Industry: A Comparative Analysis of Pilots in Cyprus and Italy. Agronomy, 14(4), 736.‏ https://doi.org/10.3390/agronomy14040736
  32. Kernecker, M., Knierim, A., Wurbs, A., Kraus, T., & Borges, F. (2020). Experience versus expectation: Farmers’ perceptions of smart farming technologies for cropping systems across Europe. Precision Agriculture, 21, 34-50.‏ https://doi.org/10.1007/s11119-019-09651-z
  33. Khaspuria, G., Aarushi, K., Mahima, A., Mayank, B., Rupesh, Y., & Abhishek, Y. (2024). Adoption of Precision Agriculture Technologies Among Farmers: A Comprehensive Review. Journal of Scientific Research and Reports, 30 (7), 86-671. https://doi.org/10.9734/jsrr/2024/v30i72180
  34. Li, J., Liu, G., Chen, Y., & Li, R. (2023). Study on the influence mechanism of adoption of smart agriculture technology behavior. Scientific Reports, 13(1), 8554.‏ https://doi.org/10.1038/s41598-023-35091-x
  35. Lieder, S., & Schröter-Schlaack, C. (2021). Smart farming technologies in arable farming: Towards a holistic assessment of opportunities and risks. Sustainability, 13(12), 6783.‏ https://doi.org/10.3390/su13126783.
  36. Loures, L., Chamizo, A., Ferreira, P., Loures, A., Castanho, R., & Panagopoulos, T. (2020). Assessing the effectiveness of precision agriculture management systems in mediterranean small farms. Sustainability, 12(9), 3765.‏ https://doi.org/10.3390/su12093765
  37. Ma, R., Zou, J., Han, Z., Yu, K., Wu, S., Li, Z., ... & Zhu‐Barker, X. (2021). Global soil‐derived ammonia emissions from agricultural nitrogen fertilizer application: A refinement based on regional and crop‐specific emission factors. Global Change Biology, 27(4), 855-867.‏ http://dx.doi.org/10.1111/gcb.15437.
  38. Madushanki, A. R., Halgamuge, M. N., Wirasagoda, W. S., & Syed, A. (2019). Adoption of the Internet of Things (IoT) in agriculture and smart farming towards urban greening: A review. International Journal of Advanced Computer Science and Applications, 10(4), 11-28. http://dx.doi.org/10.14569/IJACSA.2019.0100402.
  39. Makam, S., Komatineni, B. K., Meena, S. S., & Meena, U. (2024). Unmanned aerial vehicles (UAVs): an adoptable technology for precise and smart farming. Discover Internet of Things, 4(1), 12.‏ http://dx.doi.org/10.1007/s43926-024-00066-5.
  40. Mamilianti, W. (2020). Persepsi petani terhadap teknologi informasi dan pengaruhnya terhadap perilaku petani pada risiko harga kentang. Jurnal Ilmu-Ilmu Pertanian, 14(2), 125-139.‏ http://dx.doi.org/10.31328/ja.v14i2.1390.
  41. Mana, A. A., Allouhi, A., Hamrani, A., Rahman, S., el Jamaoui, I., & Jayachandran, K. (2024). Sustainable AI-Based Production Agriculture: Exploring AI Applications and Implications in Agricultural Practices. Smart Agricultural Technology, 100416.‏ http://dx.doi.org/10.1016/j.atech.2024.100416.
  42. Mbanasor, J. A., Kalu, C. A., Okpokiri, C. I., Onwusiribe, C. N., Nto, P. O., Agwu, N. M., & Ndukwu, M. C. (2024). Climate smart agriculture practices by crop farmers: evidence from south east Nigeria. Smart Agricultural Technology, 8, 100494.‏ http://dx.doi.org/10.1016/j.atech.2024.100494.
  43. Meng, Y. U. E., Li, W. J., Shan, J. I. N., Jing, C. H. E. N., Chang, Q., Glyn, J. O. N. E. S., ... & Frewer, L. J. (2023). Farmers’ precision pesticide technology adoption and its influencing factors: Evidence from apple production areas in China. Journal of Integrative Agriculture, 22(1), 292-305.‏ http://dx.doi.org/10.1016/j.jia.2022.11.002.
  44. Mesías-Ruiz, G. A., Pérez-Ortiz, M., Dorado, J., De Castro, A. I., & Peña, J. M. (2023). Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. Frontiers in Plant Science, 14, 1143326.‏ https://doi.org/10.3389/fpls.2023.1143326
  45. Mhlanga, D., & Ndhlovu, E. (2023). Digital technology adoption in the agriculture sector: Challenges and complexities in Africa. Human Behavior and Emerging Technologies, 2023(1), 6951879.‏ https://doi.org/10.1155/2023/6951879
  46. Mola Ghalghachi, M., & Bashir Khodaparasti, R. (2023). Investigating the Effect of Greenwashing on Green Trust and Green Purchase Intention with the Mediation of Green Confusion and Perceived Risk. New Marketing Research Journal, 12(4), 177-194. (In Persian). https://doi.org/10.22108/nmrj.2023.136059.2819
  47. Munnisunker, S., Nel, L., & Diederichs, D. (2022). The Impact of Artificial Intelligence on Agricultural Labour in Europe. Journal of Agricultural Informatics, 13(1).‏ https://doi.org/10.17700/jai.2022.13.1.638
  48. Mutlaq, K. A. A., Nyangaresi, V. O., Omar, M. A., & Abduljabbar, Z. A. (2022). Symmetric Key Based Scheme for Verification Token Generation in Internet of Things Communication Environment. In EAI International Conference on Applied Cryptography in Computer and Communications, 46-64. https://doi.org/10.1007/978-3-031-17081-2_4
  49. Nandini, H. M., & Venkataramana, M. N. (2024). Breaking the Mold: A Constraint Analysis in Adoption of Climate Smart Agricultural Technologies. Mysore Journal of Agricultural Sciences, 58(1).‏ https://doi.org/10.1016/j.agsy.2021.103284
  50. Nanseki, T., Li, D., & Chomei, Y. (2023). Impacts and policy implication of smart farming technologies on rice production in Japan. In Agricultural Innovation in Asia: Efficiency, Welfare, and Technology, 21-205. https://doi.org/10.1007/978-981-19-9086-1_13
  51. Navarro, E., Costa, N., & Pereira, A. (2020). A systematic review of IoT solutions for smart farming. Sensors, 20(15), 4231.‏ https://doi.org/10.3390/s20154231
  52. Norouzi, H., Osanlou, B., & Saleh Gohari, A. (2024). Examining the impact of cognitive and emotional factors on consumer behavioral responses in online behavioral advertising. Journal of Business Management, 16(1), 1-33.‏ (In Persian). https://doi.org/10.22059/jibm.2023.352929.4511
  53. Nugroho, B.D.A., & Aliwarga, H.K. (2019, October). RiTx; Integrating among field monitoring system (FMS), internet of things (IOT) and agriculture for precision agriculture. In IOP Conference Series: Earth and Environmental Science, 1(335), 012022. https://doi.org/10.1088/1755-1315/335/1/012022
  54. Otieno, M. (2023). An extensive survey of smart agriculture technologies: Current security posture. World Journal Advanced Research Revition, 18(3), 1207-1231.‏ https://doi.org/10.30574/wjarr.2023.18.3.1241
  55. Otitoju, M.A., Fidelis, E.S., Otene, E.O., & Anigoro, D.O. (2023). Review of climate smart agricultural technologies adoption and use in Nigeria. Ecosystem Services, 13, 14.‏ https://doi.org/10.47772/IJRISS.2023.7860
  56. Pal, B.D., Kapoor, S., Saroj, S., Jat, M.L., Kumar, Y., & Anantha, K.H. (2022). Adoption of climate-smart agriculture technology in drought-prone area of India–implications on farmers' livelihoods. Journal of Agribusiness in Developing and Emerging Economies, 12(5), 824-848.‏ https://doi.org/10.1108/JADEE-01-2021-0033
  57. Paul, J., Lim, W.., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR‐4‐SLR). International Journal of Consumer Studies, 45(4), O1-O16.‏ https://doi.org/10.1111/ijcs.12695
  58. Putri, R.D., & Zainuddin, I. (2024). Penggunaan smart farming dalam industri terpadu komoditas kambing di kabupaten karawang. Scientica: Jurnal Ilmiah Sains dan Teknologi, 2(4), 392-403.‏ https://doi.org/10.572349/scientica.v2i4.1284
  59. Reshadatnia, P., Asgarnezhad Nouri, B., Hazeri, H., & Zare, G. (2020). The Role of Consumers’ TV Personality and Interaction with Audience in Teleshopping Behavior (Case Study: Ardabil City). Journal of Business Management, 12(2), 502-519.‏ (In Persian). https://doi.org/10.22059/jibm.2019.275772.3429.
  60. Sambas, A., Mujiarto, M., Gundara, G., Refiadi, G., Mulyati, N.S., & Sulaiman, I. M. (2023). Development of smart farming technology on ginger plants in Padamulya Ciamis Village, West Java, Indonesia. International Journal of Research in Community Services, 4(3), 93-99.‏ https://doi.org/10.46336/ijrcs.v4i3.483
  61. Serote, B., Mokgehle, S., Senyolo, G., du Plooy, C., Hlophe-Ginindza, S., Mpandeli, S., ... & Araya, H. (2023). Exploring the barriers to the adoption of climate-smart irrigation technologies for sustainable crop productivity by smallholder farmers: Evidence from South Africa. Agriculture, 13(2), 246.‏ https://doi.org/10.3390/agriculture13020246
  62. Shani, F.K., Joshua, M., & Ngongondo, C. (2024). Determinants of smallholder farmers’ adoption of climate-smart agricultural practices in Zomba, Eastern Malawi. Sustainability, 16(9), 3782.‏ https://doi.org/10.3390/su16093782
  63. Singh, H., Halder, N., Singh, B., Singh, J., Sharma, S., & Shacham-Diamand, Y. (2023). Smart farming revolution: portable and real-time soil nitrogen and phosphorus monitoring for sustainable agriculture. Sensors, 23(13), 5914.‏ https://doi.org/10.3390/s23135914
  64. Sun, R., Zhang, S., Wang, T., Hu, J., Ruan, J., & Ruan, J. (2021). Willingness and influencing factors of pig farmers to adopt Internet of Things technology in food traceability. Sustainability, 13(16), 8861.‏ https://doi.org/10.3390/su13168861
  65. Virk, A.L., Noor, M.A., Fiaz, S., Hussain, S., Hussain, H.A., Rehman, M., ... & Ma, W. (2020). Smart farming: an overview. Smart Village Technology: Concepts and Developments, 191-201.‏ https://doi.org/10.1007/978-3-030-37794-6_10
  66. Yamoah, F.A., & Kaba, J.S. (2024). Integrating climate-smart agri-innovative technology adoption and agribusiness management skills to improve the livelihoods of smallholder female cocoa farmers in Ghana. Climate and Development, 16(3), 169-175.‏ https://doi.org/10.1080/17565529.2021.2024125
  67. Zulfikhar, R., Alaydrus, A.Z.A., Sutiharni, S., Nanjar, A., & Hartati, H. (2024). Utilization of smart agricultural technology to improve resource efficiency in agro-industry. West Science Agro, 2(01), 28-34.‏ https://doi.org/10.58812/wsa.v2i01.656
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دوره 40، شماره 2 - شماره پیاپی 71
تابستان 1405
صفحه 150-129

  • تاریخ دریافت 26 تیر 1404
  • تاریخ بازنگری 28 بهمن 1404
  • تاریخ پذیرش 30 فروردین 1405
  • تاریخ اولین انتشار 30 فروردین 1405