Open Access Open Access  Restricted Access Subscription Access

Democratising Agricultural Knowledge: A Global Historical Analysis of Open Access

Arun Chacko, Janu S. Nair, A. M. Shahiba, Milka Susan Kollannur Biju, Amrith Raj, Amruth Hari, Mahima Tobi, N. L. Nayana, Fathimath Shamsa, Trisa Maria Joseph, S. ADARSH

Abstract


Open Access (OA) is changing agriculture by making knowledge more widely available to everyone, especially farmers who were previously left out due to historical, economic, and technological barriers. In the past, important agricultural information was often controlled by elite institutions or limited by outdated systems. Now, thanks to OA resources, global partnerships, and technology—like AI, agricultural apps, and smart devices—information is more accessible and useful at the community level.Platforms such as AGRIS and CGIAR help share research widely, and projects like Microsoft’s work with ICRISAT show how OA can improve farming practices. However, challenges remain, especially in rural areas with poor Internet access, and issues like Intellectual Property Rights still need to be addressed.Looking forward, using new technologies like AR, VR, and block chain, along with strong ethical practices and inclusive data policies, will be key to making sure OA benefits everyone fairly. Overall, OA is helping build a more connected and informed global farming community empowering farmers with the knowledge and tools to increase productivity and sustainability. This paper provides a comprehensive review of the evolution of OA to agricultural knowledge and information over time and space, exploring its impact, challenges, and future directions.


Keywords


Knowledge Management, Open Knowledge, Open Science, Agricultual Informatics, Internet, Intellectual Property Rights, Farming, Food

Full Text:

PDF

References


Abdel-Basset, M., Hawash, H., & Abdel-Fatah, L. (2024). Artificial Intelligence and Internet of Things in Smart Farming. CRC Press.

Abedi, E. A., and Ackah-Jnr, F. R. (2023). First-Order Barriers Still Matter in Teachers' Use of Technology: An Exploratory Study of Multi-Stakeholder Perspectives of Technology Integration Barriers. Int. J. Edu. Dev. Using Inf. Commun. Technol., 19(2), 148-165.

Abtew, A., &Endebu, A. (2023). The role of big data analytics in improving teacher training in developing countries: A literature Review. https://doi.org/10.21203/rs.3.rs-3111391/v1

Adarsh, S., Chacko, A., Shahiba A. M., and Jose, J. (2023a). New Generation Agriculture: Social media and Artificial Intelligence [abstract]. In: Abstracts, Abstract Volume 2nd International Conference on Biodiversity: Exploration, Exploitation and Conservation for Sustainable Development (ICB-2023) on 10-11th February 2023, Assam, p.16.https://doi.org/10.5281/zenodo.7755040

Adarsh, S., Chacko, A., Shahiba, A. M., Mathew, S., and Thomas, G. (2023b). Social Media and Artificial Intelligence for New Generation Agriculture. In: Bhuyan, S. I., Islam, M., Teronpi, V., Agarwal, H., and Barua, D.(eds), Biodiversity conservation: issues and prospects. Mahaveer Publication, Dibrugarh, Assam, pp.100-106. ISBN: 978-81-961-731-0-4. https://doi.org/10.5281/zenodo.8265123

Adarsh, S., Milka, S. K., Nayana, N. L., & Deb, A. (2024). Advancing agricultural sustainability: Harnessing crop modeling for climate change resilience and global food security. Informatics Studies. 11(3), 7-30. https://www.informaticsstudies.org/index.php/informatics/article/view/665/574

Adegbeye, M.J., Elghandour, M.M., Monroy, J.C., Abegunde, T.O., Salem, A.Z., BarbabosaPliego, A., &Faniyi, T.O. (2019). Potential influence of Yucca extract as feed additive on greenhouse gases emission for a cleaner livestock and aquaculture farming-A review. J. Clean. Prod., 239, 118074. doi: 10.1016/j.jclepro.2019.118074

Agbehadji, I. E., Schütte, S., Masinde, M., Botai, J. and Mabhaudhi, T. (2023). Climate risks resilience development: A bibliometric analysis of climate-related early warning Systems in Southern Africa. Climate, 12(1), 3.https://dx.doi.org.10.3390/cli12010003

Aguilar, E., Boqué, A., Olano, J., Gascon, T., Gray, S. and Agniga, K. (2024). Creating tools for the generation of weather-based crop calendars to support climate services (No. EGU24-15388). Copernicus Meetings.https://dx.doi.org.10.5194/ems2024-608

Ahirwar, S., Swarnkar, S. and Namwade, G. (2019). Application of Drone in Agriculture. International Journal of Current Microbiology and Applied Sciences. 8. 2500-2505.

Ahlf, M., McNeil, S., and Nguyen, P. (2024). Open Educational Resources: Time, Resources and Sustainability in an Ephemeral Digital World. In Educational Research and the Question (s) of Time (pp. 345-369). Singapore: Springer Nature Singapore.

Ajani, Y. A., Oladokun, B. D., Olarongbe, S. A., Amaechi, M. N., Rabiu, N., & Bashorun, M. T. (2024). Revitalizing Indigenous Knowledge Systems via Digital Media Technologies for Sustainability of Indigenous Languages. Preservation, Digital Technology & Culture, 53(1), 35-44.

Alant, B. P., and Bakare, O. O. (2021). A case study of the relationship between smallholder farmers' ICT literacy levels and demographic data wrt their use and adoption of ICT for weather forecasting. Heliyon, 7(3).

Albornoz, D., Huang, M., Martin, I., Mateus, M., Touré, A., & Chan, L. (2018). Framing power: Tracing key discourses in open science policies. ELectronicPUBlishing, (Long Papers).

Alok Khode, Sagar Jambhorkar, A literature review on patent information retrieval techniques, Indian J. Sci. Technol. 10 (37) (2017) 1–13, https://doi.org/ 10.17485/ijst/2017/v10i37/116435.

Amentae, T. K., Song, W., & Wang, J. (2024). Intellectual property rights in the agri-food chains: A systematic review and bibliometric analysis. World Patent Information, 77, 102279.

Ammar, A., Iftikhar, Z., Khan, U.A., Bibi, A., Tahseen, N., Haider, I., Abid, M., Khalid, M.N., Amjad, I. (2023). Global collaborations in breeding crops for climate resilienceBiological and Agricultural Sciences Research Journal, 2: 25. https://doi.org/10.54112/basrj.v2023i1.25

Ananda K. R., Saikanth, D. R. K., Chaudam, V., Sravani, S., Nayak, S. H., Dam, A., & Shukla, A. (2024). Impact of Mobile Technology on Extension Service Delivery in Remote Farming Communities: A Review. Journal of Scientific Research and Reports, 30(3), 1-13.

Anderson, C. R., & McLachlan, S. M. (2016). Transformative research as knowledge mobilization: Transmedia, bridges, and layers. Action Research, 14(3), 295-317. https://doi.org/10.1177/1476750315616684

Antwi-Agyei, P., Dougill, A.J., Stringer, L.C., 2015. Barriers to climate change adaptation: evidence from northeast Ghana in the context of a systematic literature review. Clim. Dev. 7 (4), 297–309. https://doi.org/10.1080/17565529.2014.951013.

Araújo, S. O., Peres, R. S., Ramalho, J. C., Lidon, F., & Barata, J. (2023). Machine learning applications in agriculture: current trends, challenges, and future perspectives. Agronomy, 13(12), 2976.

Araújo, S. O., Peres, R. S., Ramalho, J. C., Lidon, F., & Barata, J. (2023). Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives. Agronomy, 13(12), 2976. https://doi.org/10.3390/agronomy13122976

Arnold, D. (2005a). Agriculture and ‘improvement’ early colonial India: A pre-history of development. Journal of Agrarian Change, 5(4), 315–325.

Arnold, D. (2005b). The tropics and the travelling gaze: India, landscape and science 1800–56. Permanent Black.

Asadullah, M.N., and S. Rahman. 2009. Farm Productivity and Efficiency in Rural Bangladesh: The Role of Education Revisited. Applied Economics 41(1):17–33.

Ayaz M., Ammad-Uddin M., Sharif Z., Mansour A., Aggoune. E.H.M. (2019). Internet-of-things (IoT)-based smart agriculture: toward making the fields talk. IEEE Access 7, 129551–129583.

Ayim, C., Kassahun, A., Tekinerdogan, B., and Addison, C. (2020). Adoption of ICT innovations in the agriculture sector in Africa: A Systematic Literature Review. arXiv preprint arXiv:2006.13831.

Babu, S. C., Claire J., Glendenning, K. A., & Senthil K. G. (2012). Farmers’ information needs and search behaviours case study in Tamil Nadu, India. IFPRI Discussion Paper 01165. Retrieved from: https://pdfs.semanticscholar.org/f745/3c1285c45f1c6cad1f1ee9fa9819091af266.pdf.

Balkrishna, A., Sharma, J., Sharma, H., Mishra, S., Singh, S., Verma, S., & Arya, V. (2021). Agricultural mobile apps used in India: Current status and gap analysis. Agricultural Science Digest-A Research Journal, 41(1), 1-12.

Bampasidou, M., Goldgaber, D., Gentimis, T., &Mandalika, A. (2024). Overcoming ‘Digital Divides’: Leveraging higher education to develop next generation digital agriculture professionals. Computers and Electronics in Agriculture, 224, 109181.

Baumüller H. (2017). Towards smart farming? Mobile technology trends and their potential for developing country agriculture. In Skouby, K.E., Williams, I., Gyamfi, A. (Eds.), Handbook on ICT in developing countries. River Publishers: Delft.

Baumüller, H. (2012). Facilitating agricultural technology adoption among the poor: The role of service delivery through mobile phones.

Begna, T. (2020). Major challenging constraints to crop production farming system and possible breeding to overcome the constraints. International Journal of Research Studies in Agricultural Sciences (IJRSAS), 6(7), 27-46.

Benos, L., Tagarakis, A. C., Dolias, G., Berruto, R., Kateris, D., &Bochtis, D. (2021a). Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758

Berdegue, J., & Escobar, G. (2002). Rural Diversity, Agricultural Innovation Policies and Poverty Reduction. Paper No. 122. Agricultural Research and Extension Network. ISBN: 085003 601 1

Bhat, P. P., Prasad, R., Anil, K., Jadhav, A., Manohar, K., Rajesh, C. and Reddy, S. L. (2024). The Role of Information and Communication Technology in Enhancing the Effectiveness of Agricultural Extension Programs Worldwide: A Review. Journal of Scientific Research and Reports, 30(7), 963-976. https://dx.doi.org.10.9734/jsrr/2024/v30i72206

Bigonah, M., Jamshidi, F., & Marghitu, D. (2024). Immersive Agricultural Education: Gamifying Learning with Augmented Reality and Virtual Reality. In Cases on Collaborative Experiential Ecological Literacy for Education (pp. 26-76). IGI Global.

Bilderback, S., Movahed, M., & McCarthy, V. (2024). The role of virtual training in implementing Sustainable Development Goals globally. European Journal of Training and Development.

Bonye, S.Z., Alfred, K.B., Jasaw, G.S., 2012. Promoting community-based agricultural extension agents as an alternative approach to formal agricultural extension service delivery in Northern Ghana. Asian J Agric Dev. 2 (1), 76–95.

Boogaard, H., Pratihast, A. K., Laso Bayas, J. C., Karanam, S., Fritz, S., Van Tricht, K., and Gilliams, S. (2023). Building a community-based open harmonised reference data repository for global crop mapping. Plos one, 18(7), e0287731. https://dx.doi.org.10.1371/journal.pone.0287731

Borg, E., & Policante, A. (2024). The Gene Editing Business: Rent Extraction in the Biotech Industry. Review of Political Economy, 1-36.

Borrero, A., Ramos, M., Arsenal, A., Lopez, K., & Hettel, G. (2007). Scholarly publishing initiatives at the International Rice Research Institute: Linking users to public goods via open access. First Monday.

Boven, K., &Morohashi, J. (2002). Best practices using indigenous knowledge (pp. 12-13). The Hague: Nuffic.

Bradshaw, E., Kimmons, R., and Bondah, F. E. (2024). Understanding formal localization of OER: Remixing United Nations human rights resources in Ghana. Open Praxis, 16(3), 362-373.

Browne, T., et al. (2010). The Challenges of OER to Academic Practice.

Brush, S. B. (2007). Farmers’ rights and protection of traditional agricultural knowledge. World Development, 35(9), 1499-1514.

Bryan, E., Deressa, T.T., Gbetibouo, G.A., Ringler, C., 2009. Adaptation to climate change in Ethiopia and South Africa: Options and constraints. Environ. Sci. Policy 12 (4), 413–426. https://doi.org/10.1016/j.envsci.2008.11.002.

Butcher, N. (2015). Basic guide to open educational resources (OER).

Byerlee, D., & Dubin, H. J. (2010). Crop improvement in the CGIAR as a global success story of open access and international collaboration. International Journal of the Commons, 4(1), 452-480.

Cassab, H., & MacLachlan, D. L. (2009). A consumer‐based view of multi‐channel service. Journal of Service Management, 20(1), 52-75.

Caturano, C. (2023). Technological and biological innovations in agrifood commodities for sustainable rural development.

Celli, F., Malapela, T., Wegner, K., Subirats, I., Kokoliou, E., & Keizer, J. (2015). AGRIS: providing access to agricultural research data exploiting open data on the web. F1000Research, 4.

Chandra, S. S. V., Hareendran, A., & Albaaji, G. F. (2024). Precision farming for sustainability: An agricultural intelligence model. Computers and Electronics in Agriculture, 226, 109386.

Chatterjee, J. and Prabhakar, T.V. (2005). On to action–building a digital ecosystem for knowledge diffusion in rural India. In Knowledge Management: Nurturing Culture, Innovation, and Technology (pp. 401-416).

Cohen, J. I., Falconi, C., Henson-Apollonio, V., Komen, J., and Salazar, S. (2002). Managing intellectual property and proprietary technology in agricultural research. In Globalization and the developing countries: emerging strategies for rural development and poverty alleviation (pp.219-234). Wallingford UK: CABI Publishing.

Cullen, R. (2003). The digital divide: a global and national call to action. Electron. Libr., 21(3), 247-257.

Das, Dr. Rajib Kumar and Singha, Dr. Anuradha. (2023). Open Educational Resources (OER) Repositories: A Comprehensive Analysis of Usage Patterns and Impact on Education. Library Philosophy and Practice (e-journal).

Das, P. K. (2024). A study on intellectual property rights–Indian context. Asian Journal of Social Science and Management Technology, 6(4), 185-196.

Debauche, O., Mahmoudi, S., Manneback, P., & Lebeau, F. (2022). Cloud and distributed architectures for data management in agriculture 4.0 : Review and future trends. Journal of

Deichmann, U., Goyal, A., & Mishra, D. (2016). Will digital technologies transform agriculture in developing countries?. Agricultural Economics, 47(S1), 21-33.

Delgado, J. A., Vandenberg, B., Kaplan, N., Neer, D., Wilson, G., D'Adamo, R., and Derner, J. D. (2018). Agricultural Collaborative Research Outcomes System (AgCROS): A network of networks connecting food security, the environment, and human health. Journal of Soil and Water Conservation, 73(6), 158A-164A.https://dx.doi.org.10.2489/JSWC.73.6.158A

Delmer, D. P. (2005). Agriculture in the developing world: connecting innovations in plant research to downstream applications. Proceedings of the National Academy of Sciences, 102(44), 15739-15746.

Devi, P. I., Antony, P. D., & Umaiban, M. M. (2024). Who are the Owners of Plant Genetic Resources? An Analysis Based on the Status and Trends of Registration under the PPVFR Act 2001. Indian Journal of Agricultural Economics, 79(2), 289-305.

Dhakal, P. (2021). Education and community development: Designing of a model school (K-12) in Nepal through a socially responsible edupreneurship (SRE).

Dhal, S. B. and Kar. D. (2024). Transforming agricultural productivity with AI-driven forecasting: Innovations in food security and supply chain optimization. MDPI Forecastin 6, https://dx.doi.org.10.3390/forecast6040046

Dhulipala, R., & Singh, K. (2023). Meghdoot mobile app: Upgrades in 2023. Nairobi, Kenya: ILRI.

Diakiv. (2024, September 10). Big Data in Agriculture: From Data Collection to Strategic Action. Https://Relevant.Software/Blog/Big-Data-in-Agriculture/.

Digel, S., Krause, T., & Biel, C. (2023). Enabling Individualized and Adaptive Learning – The Value of an AI-Based Recommender System for Users of Adult and Continuing Education Platforms (pp. 797–803). https://doi.org/10.1007/978-3-031-36336-8_121

Dominic, O., & Michele, R. (2015). Educational research and innovation open educational resources A catalyst for innovation: A catalyst for innovation. OECD Publishing.

Drayton, R. (2000). Nature’s government: Science, imperial Britain and the improvement of the world. Yale University Press.

Dubé, L., Webb, P., Arora, N. K., & Pingali, P. (2014). Agriculture, health, and wealth convergence: bridging traditional food systems and modern agribusiness solutions. Annals of the New York Academy of Sciences, 1331(1), 1-14.

Duncombe, R. (2016). Mobile phones for agricultural and rural development: A literature review and suggestions for future research. The European Journal of Development Research, 28(2), 213-235.

Eells, L., Kelly, J., & Farrell, S. (2024). Repository (R) evolution: Metadata, Interoperability, and Sustainability. Journal of Librarianship and Scholarly Communication, 12(1).

Eicher, C. K. (2006). The evolution of agricultural education and training: Global insights of relevance for Africa.

Eidt, C.M., Hickey, G.M. and Curtis, M.A. (2012). Knowledge integration and the adoption of new agricultural technologies: Kenyan perspectives. Food Security, 4, pp.355-367.

Eitzinger, J., Thaler, S., Atencia, A., Hann, P., Kubu, G., Manschadi, A.M., Lalic, B., Palka, M., Schneider, S., Sremac, A.F. and Trnka, M. (2023). Tailored AGROmeteorological FORECAST for improving resilience and sustainability of Austrian farming systems under changing climate (No. EMS2023-213). Copernicus Meetings. https://dx.doi.org.10.5194/ems2023-213

El Bilali, H., &Allahyari, M. S. (2018). Transition towards sustainability in agriculture and food systems: Role of information and communication technologies. Information Processing in Agriculture, 5(4), 456-464.

Fabregas, R., Harigaya, T., Kremer, M., & Ramrattan, R. (2022). Digital agricultural extension for development. In Introduction to Development Engineering: A Framework with Applications from the Field (pp. 187-219). Cham: Springer International Publishing.

Falode, O. C., Dome, K., Chukwuemeka, E. J., & Falode, M. E. (2022). Development of an interactive mobile application for learning undergraduate educational technology concepts. International Journal of Professional Development, Learners and Learning, 4(1), ep2204.

FAO (2009) How to Feed the World in 2050 (Food and Agriculture Organization of the United Nations, Rome)

FAO (2022). E-learning platforms for agricultural education. Retrieved: December 16, 2024 FAO e-learning Academy

FAO, 2003. World Agriculture toward 2015/2030. An FAO Perspective, Food and Agriculture Organisation, Rome.

Fleischer, G., Waibel, H., & Walter‐Echols, G. (2002). Transforming top‐down agricultural extension to a participatory system: a study of costs and prospective benefits in Egypt. Public Administration and Development: The International Journal of Management Research and Practice, 22(4), 309-322.

Food and Agriculture Organization (2015). E-Agriculture 10-year review report. Retrieved from: http://www.fao.org/3/a-i4605e.pdf.

Ganeshan, M.K. and Vethirajan, C. (2021). The impact of technology and agriculture mobile applications for farmers in India. In 3rd International Conference on Recent Advances in Management and Technology. Conference Proceeding (Souvenir). (pp 372-376). Kampala: Uganda

Ganguly, K., Gulati, A., & von Braun, J. (2017). Innovations spearheading the next transformations in India's agriculture.

Ganieva, I. A. and Koteev, S. V. (2020). Development of elements of an information and resource digital platform for intelligent management of agricultural and land use systems in terms of transferring advanced knowledge and experience in organizing highly productive agricultural enterprises of a new technological paradigm. In E3S Web of Conferences (Vol. 222, p. 01024). EDP Sciences.https://dx.doi.org.10.1051/E3SCONF/202022201024

Garg, G., Gupta, S., Mishra, P., Vidyarthi, A., Singh, A., & Ali, A. (2021). CROPCARE: an intelligent real-time sustainable IoT system for crop disease detection using mobile vision. IEEE Internet of Things Journal, 10(4), 2840-2851.

Gates, A. J., Mane, I., and Gao, J. (2024). The increasing fragmentation of global science limits the diffusion of ideas. arXiv preprint arXiv:2404.05861.

Gebrehiwot, K.G., 2015. The impact of agricultural extension on households’ welfare in Ethiopia. International Journal of Social Economics 42 (8), 733–748..

Gilbert, E., Karahalios, K., & Sandvig, C. (2010). The network in the garden: Designing social media for rural life. American Behavioral Scientist, 53(9), 1367-1388.

Government of India (2023). Digital India: Empowering Rural Connectivity. Ministry of Electronics and Information Technology. Retrieved: December 16, 2024 https://digitalindia.gov.in

Gow, G. A., Dissanayeke, U., Chowdhury, A., and Ramjattan, J. (2023). Digital literacy and agricultural extension in the Global South. In Digital literacy and inclusion: Stories, platforms, communities (pp. 129-144). Cham: Springer International Publishing.

Gul, D., and Banday, R. U. Z. (2024). Transforming crop management through advanced AI and machine learning: Insights into innovative strategies for sustainable agriculture. AI, Computer Science and Robotics Technology. https://dx.doi.org.10.5772/acrt.20240030

Guttikonda, A., &Gutam, S. (2009). Prospects of open access to Indian agricultural research: A case study of ICAR. First Monday.

Hafeez, A., Husain, M. A., Singh, S. P., Chauhan, A., Khan, M. T., Kumar, N., ... & Soni, S. K. (2023). Implementation of drone technology for farm monitoring & pesticide spraying: A review. Information processing in Agriculture, 10(2), 192-203.

Hajjaj, S. S. H., Moktar, M. H., & Weng, L. Y. (2024). Review of Implementing the Internet of Things (IoT) for Robotic Drones (IoT Drones). E3S Web of Conferences, 477, 00016. https://doi.org/10.1051/e3sconf/202447700016

Hashimy, S. Q., & Benjamin, D. (2024). Intellectual Property and Biotechnology: A Dual Driver of Agricultural Transformation. Available at SSRN 5015935.

Hassan, M., Kowalska, A., & Ashraf, H. (2023). Advances in deep learning algorithms for agricultural monitoring and management. Applied Research in Artificial Intelligence and Cloud Computing, 6(1), 68-88.

Himesh, S., Rao, E. P., Gouda, K. C., Ramesh, K. V., Rakesh, V., Mohapatra, G. N., ... &Ajilesh, P. (2018). Digital revolution and Big Data: a new revolution in agriculture. CABI Reviews, (2018), 1-7.

Holger Ernst, Patent information for strategic technology management, World Patent Inf. 25 (3) (2003) 233–242, https://doi.org/10.1016/S0172-2190(03) 00077-2.

Hu, X., Xiao, B., & Tong, Z. (2024). Technological Integration and Obstacles in China’s Agricultural Extension Systems: A Study on Disembeddedness and Adaptation. Sustainability, 16(2), 859.

Huyer, S., Simelton, E., Chanana, N., Mulema, A. A., & Marty, E. (2021). Expanding opportunities: A framework for gender and socially-inclusive climate resilient agriculture. Frontiers in Climate, 3, 718240.

IPCC, 2007. Impact, Adaptation and Vulnerability. Contribution of Working Group I of the Intergovernmental Panel on Climate Change to the Third Assessment Report of IPCC. Cambridge University Press, London.

Ishamuddin Mustapha, VichayananRattanawiboonsom, &RuankwanIntanon. (2023). Data-Driven Insights in Higher Education: Exploring the Synergy of Big Data Analytics and Mobile Applications. International Journal of Interactive Mobile Technologies (IJIM), 17(20), 21–37. https://doi.org/10.3991/ijim.v17i20.45037

Ison, R. L., & Russell, D. (Eds.). (2000). Agricultural extension and rural development: breaking out of knowledge transfer traditions. Cambridge University Press.

ITU (2021). Measuring digital development: Facts and figures 2021. Retrieved: December 16, 2024 https://www.itu.int

Izuchukwu, A., Erezi, E., & David Emeka, E. (2023). Assessing the Impact of Farmer-to-Farmer Communication Networks on Knowledge Sharing and Adoption of Sustainable Agricultural Practices in Africa. Int. J. Agric. Earth Sci, 9, 58-76.

Jaiswal, A. (2024). Open Data Kit. In Open Electronic Data Capture Tools for Medical and Biomedical Research and Medical Allied Professionals (pp. 131-239). Academic Press.

Janssen, S. J., Porter, C. H., Moore, A. D., Athanasiadis, I. N., Foster, I., Jones, J. W., & Antle, J. M. (2017). Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology. Agricultural systems, 155, 200-212.

Janssen, S. J., Porter, C. H., Moore, A. D., Athanasiadis, I. N., Foster, I., Jones, J. W., & Antle, J. M. (2017). Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology. Agricultural systems, 155, 200-212.

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.

Jeyalakshmi, J., Vijay., K., Eugene, I., Berna, K. and Sowmia, R. (2024). AI in Optimisation of Agricultural Output in Precision Agriculture.221-237.https://dx.doi.org.10.1201/9781003504900-11

Jha, K., Yadav, P., & Gupta, S. (2022). Role of Machine Learning and Internet of Thing in Agriculture- A Survey. 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET), 1–4. https://doi.org/10.1109/CCET56606.2022.10080222

Jones, A. T. (2023). Identifying systemic barriers to co-developing Indigenous food systems research within colonial institutions: a case study of Agriculture and Agri-Food Canada (Doctoral dissertation, University of British Columbia).

Jones, E. O. (2024). Indigenous knowledge management practices in subsistence farming: A comprehensive evaluation. Sustainable Technology and Entrepreneurship, 3(2), 100058.

Jones, K., Glenna, L. L., & Weltzien, E. (2014). Assessing participatory processes and outcomes in agricultural research for development from participants’ perspectives. Journal of Rural Studies, 35, 91–100. https://doi.org/10.1016/j.jrurstud.2014.04.010

Jones, L., & Boyd, E. (2011). Exploring social barriers to adaptation: insights from Western Nepal. Global environmental change, 21(4), 1262-1274.

Judd-Murray, R., Warnick, B. K., Coster, D. C., & Longhurst, M. L. (2024). Development and validation of a high school agricultural literacy assessment. Advancements in Agricultural Development, 5(3), 91–104. https://doi.org/10.37433/aad.v5i3.407

Judijanto, L. (2023). Analysis of Weather Prediction, Resource Management, and Land Optimization on the Application of Big Data Analytics in Agricultural Land Utilization in Agrarian Areas of West Java. West Science Nature and Technology, 1(02), 81–90. https://doi.org/10.58812/wsnt.v1i02.489

Kamakaula, Y. (2024). Ethnoecology and Climate Change Adaptation in Agriculture. Global International Journal of Innovative Research, 2(2), 473-485.

Kambale, P., Dharmaraj, B, M., Patil, D., Ganavi, N. R. (2024). Mobile technology for farmers: An overview of agricultural apps. Asian Journal of Agricultural Extension, Economics and Sociology, 42(9),75-81. https://dx.doi.org/10.9734/ajaees/2024/v42i92543

Kanwar, A., Balasubramanian, K., and Balaji, V. (2015). Agricultural Higher Education in the 21st Century: Non-traditional educational models. In: Agricultural Higher Education in the 21st Century: A global challenge in knowledge transfer to meet world demands for food security and sustainability. (pp 1-8). Zaragoza:Spain

Kassem, B., Rossini, M., Frecassetti, S., Costa, F., and Portioli Staudacher, A. (2024). An implementation model for socio-technical digital tools. J. Manuf. Technol. Manag. 35(5), 941-961.

Khaki, S., & Wang, L. (2019). Crop Yield Prediction Using Deep Neural Networks. Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.00621

Khan, M., Nawab, K., Ullah, J., Khatam, A., Qasim, M., Ayub, G., Nawaz, N., 2012. Communication gap and training needs of Pakistan’s agricultural extension agents in horticulture. Sarhad J. Agric. 28 (1), 129–135.

Khan, N. A., and Ahangar, H. (2017). Emerging trends in open research data. In 2017 9th International Conference on Information and Knowledge Technology (IKT) (pp. 141-146). IEEE.https://dx.doi.org.10.1109/IKT.2017.8258631

Kix, G. (2024). Teacher Professional Development A research synthesis.

Kock, M. A. (2023). Intellectual Property Protection for Plant Related Innovation. Springer Cham.

Koohafkan, P., & Altieri, M. A. (2011). Globally important agricultural heritage systems: a legacy for the future (pp. 1-47). Rome: Food and Agriculture Organization of the United Nations.

Kotrlik, J. W., Redmann, D. H., & Douglas, B. B. (2003). Technology integration by agriscience teachers in the teaching/learning process. Journal of Agricultural Education, 44(3), 78-90.

Kulkarni, S. A., & Rani, S. (2021). Impact analysis of meghdoot App through farmersfeedback in weather forecasting and disseminationof agromet advisory services information on realtime basis. Journal of Farm Sciences, 34(Spl 5), 576-578.

Kumar, A., &Veeranjaneyulu, K. (2018). Digital Initiatives for Agricultural Research and Education under ICAR in India. BS Publications, 1.

Kumar, R. (2023). Farmers’ use of the mobile phone for accessing agricultural information in Haryana:An analytical study. Open Information Science, 7(1), 20220145.

Kumari, N. K., Kumar, G. N., and Husain, A. S. (2017). Constraints Perceived by Agricultural Extension Personnel in Using M-Tools. J. Krishi Vigyan, 6(1), 37-39.

Lachgar, M., Hrimech, H., &Kartit, A. (2023). Unmanned aerial vehicle-based applications in smart farming: A systematic review. International Journal of Advanced Computer Science and Applications, 14(6).

Laskar, S. I. (2016). Mausam App: Enhancing Weather Communication for Indian Farmers..

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Edinburgh, UK: Cambridge University Press..

Leeuwis, C., Cieslik, K. J., Aarts, M. N. C., Dewulf, A. R. P. J., Ludwig, F., Werners, S. E., &Struik, P. C. (2018). Reflections on the potential of virtual citizen science platforms to address collective action challenges: Lessons and implications for future research. NJAS-Wageningen Journal of Life Sciences, 86, 146-157.

Lei, Xiankai, and Dongmei Yang (2024). An analysis of the impact of digital technology adoption on the income of high quality farmers in production and operating. PloS one 19(9): e0309675.

Liakos, K., Busato, P., Moshou, D., Pearson, S., &Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674

Liang, C., & Shah, T. (2023). IoT in agriculture: The future of precision monitoring and data-driven farming. Eigenpub Review of Science and Technology, 7(1), 85-104.

Lilian, I. U. (2024). Digital Divide in Agricultural Extension: Exploring ICT Accessibility and Its Influence on Rural Farming Communities. Int. J. Agric. EarthSci., 10(6), 121-135.

Liu, T., and Liao, L. (2024). Can farmers’ digital literacy improve income? Empirical evidence from China. PloS one, 19(12), e0314804.

Liu, W., Shao, X. F., Wu, C. H., & Qiao, P. (2021). A systematic literature review on applications of information and communication technologies and blockchain technologies for precision agriculture development. Journal of Cleaner Production, 298, 126763.

Livingston, G., Schonberger, S., & Delaney, S. (2011). Sub-Saharan Africa: The state of smallholders in agriculture. In IFAD Conference on New Directions for Smallholder Agriculture (Vol. 24, p. 25). Rome: IFAD HQ.

Lukasiewicz, J. M., van de Wiel, C. C., Lotz, L. A., & Smulders, M. J. (2024). Intellectual property rights and plants made by new genomic techniques: Access to technology and gene-edited traits in plant breeding. Outlook on Agriculture, 53(3), 205-215.

Lupu, V., Sobetchi, V., Razmadze, M., & Costin, L. (2024). Awareness, impact, and usage of AGRIS in the Republic of Moldova and Georgia.

Lynam, J., Byerlee, D., & Moock, J. L. (2024). The organizational challenge of international agricultural research: the fifty-year odyssey of the CGIAR. Food Policy, 124, 102617.

Madaswamy, M. (2023). Spices Informatics Network and Value Chain for Open Innovation and Value Creation Network. In Handbook of Spices in India: 75 Years of Research and Development (pp. 1147-1191). Singapore: Springer Nature Singapore.

Mandal, S. D., and Singhal, M. (2024). Enhancing collaboration quotient in crop protection research and development – multi‐disciplinary cross‐learning to promote sustainability. Pest Management Science, 6(4),52-63 https://dx.doi.org/10.1002/ps.8540

Mangal, H. (2009). Best practices for youth in agriculture: The barbados, grenada and Saint Lucia Experience. Final report.

Manobharathi, K., & Anandaraja, N. (2021). Mobile based agricultural Apps and portals for farmers’ welfare in India. Journal of Farm Sciences, 34(Spl 5), 562-566.

Mantaw, C. S., & M, P. (2024). Leveraging Machine Learning for Accurate Groundnut Price Forecasting in Tamil Nadu: An XGboost Approach. https://doi.org/10.21203/rs.3.rs-4293571/v1

Mapiye, O., Makombe, G., Molotsi, A., Dzama, K., & Mapiye, C. (2023). Information and communication technologies (ICTs): The potential for enhancing the dissemination of agricultural information and services to smallholder farmers in sub-Saharan Africa. Information Development, 39(3), 638-658.

Marinov, M. (2022). Using HBase to Implement Speed Layer in Time Series Data Storage Systems. International Journal of Advanced Computer Science and Applications, 13(2). https://doi.org/10.14569/IJACSA.2022.0130245

Maru, A., Berne, D., Beer, J. D., Ballantyne, P. G., Pesce, V., Kalyesubula, S., ... & Chavez, J. (2018). Digital and data-driven agriculture: Harnessing the power of data for smallholders. Global Forum on Agricultural Research and Innovation.

Masi, M., De Rosa, M., Vecchio, Y., Bartoli, L., and Adinolfi, F. (2022). The long way to innovation adoption: insights from precision agriculture. Agric. Food Econ., 10(1), 27.

Mauthner, N. S., & Parry, O. (2013). Open access digital data sharing: Principles, policies and practices. Social epistemology, 27(1), 47-67.

Medhi-Thies, I., Ferrera, P., Gupta, N., O’Neill, J., & Cutrell, E. (2015). KrishiPustak: A social networking system for low – literate farmers. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Works & Social Computing, Vancouver, Canada, 1670 - 1681.

Meirmanova, A. (2019). Mobile applications and youth involvement in farming. Oradea Journal of Business and Economics, 4(1), 56-64.

Mishra, S., Roopa, H. S., Sarkar, U. G., & Rai, A. (2024). Digital Innovations for Agro-Advisory Services for Soil and Land Management. In Key Drivers and Indicators of Soil Health Management: Transitioning from Conventional to Regenerative Agriculture (pp. 95-113). Singapore: Springer Nature Singapore.

Mishrif, A., and Khan, A. (2023). Technology adoption as survival strategy for small and medium enterprises during COVID-19. J. Innov. Entrepreneurship, 12(1), 53.

Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of things Journal, 9(9), 6305-6324.

Morrone, V. (2017). Outreach to support rural innovation. In Agricultural Systems (pp. 407-439). Academic Press.https://dx.doi.org.10.1016/B978-0-12-802070-8.00012-8

Mourtzinis, S., Esker, P. D., Specht, J. E., & Conley, S. P. (2021). Advancing agricultural research using machine learning algorithms. Scientific Reports, 11(1), 17879. https://doi.org/10.1038/s41598-021-97380-7

Murumba, J., & Micheni, E. (2017). Big Data Analytics in Higher Education: A Review. The International Journal of Engineering and Science, 06(06), 14–21. https://doi.org/10.9790/1813-0606021421

Mustapha, S.B., Undiandeye, U.C., Gwary, M.M., 2012. The role of extension in agricultural adaptation to climate change in the Sahelian zone of Nigeria. J. Environ. Earth Sci. 2 (6), 48–58.

Naika, M. B., Kudari, M., Devi, M. S., Sadhu, D. S., &Sunagar, S. (2021). Digital extension service: quick way to deliver agricultural information to the farmers. In Food technology disruptions (pp. 285-323). Academic Press.

Nandeesha, M. C., Halwart, M., Gómez, R. G., Alvarez, C. A., Atanda, T., Bhujel, R., ... & Yuan18, D. (2010). Supporting farmer innovations, recognizing indigenous knowledge and disseminating success stories. Farming the waters for people and food, 823.

Narwaria, Y. P. S., Rathod, M. K., Roy, A. D., & Agrawal, T. (2024). Introduction to Intellectual Property Rights (IPR). Academic Guru Publishing House.

Nemade, S., Ninama, J., Kumar, S., Pandarinathan, S., Azam, K., Singh, B., and Ratnam, K. M. (2023). Advancements in agronomic practices for sustainable crop production: A review. International Journal of Plant & Soil Science, 35(22), 679-689. https://dx.doi.org/10.9734/ijpss/2023/v35i224178

Noura, M., Atiquzzaman, M., and Gaedke, M. (2019). Interoperability in internet of things: Taxonomies and open challenges. Mobile networks and applications, 24, 796-809.

Odongo, H. J., Opio, A., Mwesigye, A., &Bariyo, R. (2023). Contribution of Pluralistic Agriculture Extension Service Provision to Smallholder Farmer Resilience. Journal of Sustainable Development, 16(6), 79. https://doi.org/10.5539/jsd.v16n6p79

Okoronkwo, D. J., Ozioko, R. I., Ugwoke, R. U., Nwagbo, U. V., Nwobodo, C., Ugwu, C. H., Okoro, G. G., and Mbah, E. C. (2024). Climate smart agriculture? Adaptation strategies of traditional agriculture to climate change in sub-Saharan Africa. Frontiers in Climate, 6, 1272320. doi: 10.3389/fclim.2024.1272320

Olumola, S. S. (2015). Using participatory video materials for learning and disseminating push-pull technology among smallholder farmers in selected districts in Western Kenya (Doctoral dissertation, Moi University).

Panda, S., Das, T.K., Devi, Y.L., Das, L. and Pal, S.M.P. (2019). Role of mobile phone in agriculture and allied activities of rural household. International Journal of Inclusive Development, 5(1): 25-29.

Pandey, N., de Coninck, H., & Sagar, A. D. (2022). Beyond technology transfer: Innovation cooperation to advance sustainable development in developing countries. Wiley Interdisciplinary Reviews: Energy and Environment, 11(2), e422.

Pant, L. P., and Hambly-Odame, H. (2010). Creative commons: non-proprietary innovation triangles in international agricultural and rural development partnerships. The Innovation Journal: The Public Sector Innovation Journal, 15(2), 1-22.

Parsa, S., Morse, S., Bonifacio, A., Chancellor, T. C., Condori, B., Crespo-Pérez, V., ... & Dangles, O. (2014). Obstacles to integrated pest management adoption in developing countries. Proceedings of the National Academy of Sciences, 111(10), 3889-3894.

Parween, S., Hameed, R. S., & Sinha, K. (2021). Iot and its real-time application in agriculture. In Handbook of Research on Knowledge and Organization Systems in Library and Information Science (pp. 103-123). IGI Global.

Pasupuleti, M. K. (2024). AI and Big Data for Climate Resilience: Predictive Analytics in Environmental Management. In book: AI and Big Data in Climate Change: Predictive Analytics for Environmental Management (pp.255-280) Publisher: National Education Services. https://dx.doi.org.62311/nesx/46601

Patil, H., & Naik, N. (2024). Analysis of Interactive Media Usage for Improving the Outcome of the Agricultural Sector in India. In Interactive Media with Next-Gen Technologies and Their Usability Evaluation (pp. 22-46). Chapman and Hall/CRC.

Perryman, L.-A., Hemmings-Buckler, A., and Seal, T. (2014). Learning from TESS-India’s Approach to OER Localisation Across Multiple Indian States. J. Interact. Media Educ., 2014(2), 7-7.

Pimbert, M. P. (2017). Democratizing knowledge and ways of knowing for food sovereignty, agroecology, and biocultural diversity. In Food sovereignty, agroecology and biocultural diversity. Taylor & Francis.

Pingali, P. L. (2012). Green Revolution: Impacts, limits, and the path ahead. Proceedings of the National Academy of Sciences, 109(31), 12302–12308. doi:10.1073/pnas.0912953109

Pingali, P. L., & Traxler, G. (2002). Changing locus of agricultural research: will the poor benefit from biotechnology and privatization trends?. Food policy, 27(3), 223-238.

Prangya, S., Jena, A., Behera, A. and Sahoo. S. (2024). Digital extension system: scaling up digital technologies for extension and advisory services.Futuristic Trends in Social Sciences 9 (3), 28-40. https://dx.doi.org.10.58532/v3bjso19p1ch5

Prasad M, D., & Menon C, S. (2020). The Personal Data Protection Bill, 2018: India’s regulatory journey towards a comprehensive data protection law. International Journal of Law and Information Technology, 28(1), 1-19.

Pretty, J., & Bharucha, Z. P. (2014). Sustainable intensification in agricultural systems. Annals of botany, 114(8), 1571-1596.

Quradaa, F. H., Shahzad, S., &Almoqbily, R. S. (2024). A systematic literature review on the applications of recurrent neural networks in code clone research. PLOS ONE, 19(2), e0296858. https://doi.org/10.1371/journal.pone.0296858

Rachman L.M., Muhammad B. D., Purwakusuma P.W., Rasyid A.R, (2019). The role of drones for supporting precision agricultural management, Proc. SPIE 11372, Sixth International Symposium on LAPANIPB Satellite, 1137202

Rahaman, M., Lin, C. Y., Pappachan, P., Gupta, B. B., & Hsu, C. H. (2024). Privacy-centric AI and IoT solutions for smart rural farm monitoring and control. Sensors, 24(13), 4157.

Read, E. (2024). The ethics of cybersecurity, data use and AI in agriculture.

Reddy, A. M. (2022). A Review On Iot Enabling Technologies And Back- End Data-Sharing Model. Journal of Electronics, Computer Networking and Applied Mathematics, 1 (1), 41-47. https://dx.doi.org.10.55529/jecnam.11.41.47

Reddy, T. V., Reddy, A. R., Prasanna, K. S., Shiva, S. S., Meghana, S. and Reddy T. S. S. (2024). Design and Developing AI-Driven Agro-sage for Optimal Precision Agriculture. In 2024 5th International Conference on Smart Electronics and Communication (ICOSEC) 1538-1542. https://dx.doi.org.10.1109/icosec61587.2024.10722046

Reichstein, M., Benson, V., Camps-Valls, G., Boran, H., Fearnley, C., Kornhuber, K., Rahaman, N., Schöllkopf, B., Tárraga, J.M., Vinuesa, R. and Blunk, J. (2024). Early warning of complex climate risk with integrated artificial intelligence.https://dx.doi.org.10.21203/rs.3.rs-4248340/v1

Reuters. (2024). China should further ease data export rules, Neuberger Berman, Citi say. Retrieved: December 16, 2024. https://www.reuters.com/world/china/china-should-further-ease-data-export-rules-neuberger-berman-citi-say-2024-06-20/

Rivera, W. M., Qamar, M. K., &Mwandemere, H. K. (2005). Enhancing coordination among AKIS/RD actors: An analytical and comparative review of country studies on agricultural knowledge and information systems for rural development (AKIS/RD).

Rotz, S., Duncan, E., Small, M., Botschner, J., Dara, R., Mosby, I., ... & Fraser, E. D. (2019). The politics of digital agricultural technologies: a preliminary review. Sociologiaruralis, 59(2), 203-229.

Rotz, S., Gravely, E., Mosby, I., Duncan, E., Finnis, E., Horgan, M., LeBlanc, J., Martin, R., Neufeld, H.T., Nixon, A. and Pant, L. (2019). Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities. Journal of Rural Studies, 68, pp.112-122.

Roy, Suman, Maji, S. and Rakshit, S. (2023). Non-traditional Models of Agricultural Education with special reference to Open Educational Resources. Scientific Reports, 4(8),38-50.

Ryu, J. H., Clements, J., & Neufeld, J. (2022). Low-Cost Live Insect Scouting Drone: iDrone Bee. Journal of Insect Science, 22(4). https://doi.org/10.1093/jisesa/ieac036

Salemink, K., Strijker, D., & Bosworth, G. (2017). Rural development in the digital age: a systematic literature review on unequal ICT availability, adoption, and use in rural areas. Journal of Rural Studies, 54, 360-371. https://doi.org/10.1016/j.jrurstud.2015.09.001..

Samal, I., Bhoi, T. K., Pradhan, A. K., & Mahanta, D. K. (2023). Plantix app: A success story of artificial intelligence in plant protection. Van Sangyan, 24.

Sarma, H. H., Das, B. C., Deka, T., Rahman, S., Medhi, M., & Kakoti, M. (2024). Data-driven agriculture: Software innovations for enhanced soil health, crop nutrients, disease detection, weather forecasting, and fertilizer optimization in agriculture. Journal of Advances in Biology and Biotechnology, 27(8), 878-896.

Saxena, M. K., et al. (2023). Vidhya-Daan: Transforming Rural Education Access.

Schwab K. (2016). The fourth industrial revolution: what it means and how to respond. World Economic Forum. Retrieved: February 3, 2016. http://www.weforum.org/agenda/2016/01/the-fourth-industrialrevolution-what-it-means-and-how-to-respond

Šestak, M., &Copot, D. (2023). Towards trusted data sharing and exchange in agro-food supply chains: Design principles for agricultural data spaces. Sustainability, 15(18), 13746.

Sharma, A., & Kaur, P. (2023). Tamper-proof multitenant data storage using blockchain. Peer-to-peer Networking and Applications, 16(1), 431-449.

Sharma, B., Gill, K. K., & Sandhu, S. S. (2021). Comparative analysis of weather forecast from different weather websites for Ludhiana, Punjab.

Sharma, N. R., et al. (2020). The Role of Kisan Suvidha in Farmer Empowerment.

Sharma, N. R., Sharma, S., & Sharma, D. (2020). Towards a mobile app technology-enabled sustainable agriculture in India. Plant Archives, 20(2), 3065-3071.

Sharma, P., Dadheech, P., Aneja, N., & Aneja, S. (2023). Predicting Agriculture Yields Based on Machine Learning Using Regression and Deep Learning. IEEE Access, 11, 111255–111264. https://doi.org/10.1109/ACCESS.2023.3321861

Sillitoe, P. (1998). The development of indigenous knowledge: a new applied anthropology. Current anthropology, 39(2), 223-252.

Singh, N. K., Sunitha, N. H., Tripathi, G., Saikanth, D. R. K., Sharma, A., Jose, A. E. and Mary, M. V. (2023). Impact of Digital Technologies in Agricultural Extension. Asian Journal of Agricultural Extension, Economics & Sociology, 41(9), 963-970.. https://dx.doi.org.10.9734/ajaees/2023/v41i92127

Singh, P., & Absar, S. (2021). Comparative Privacy Legislation: Indian and European Personal Data Protection Legislation in the Digital Age. Journal of Legal Studies, 3, 78-92.

Singh, R. B., Paroda, R. S., & Dadlani, M. (2022). Science, technology and innovation. Indian agriculture towards, 2030(821), 51.

Singha, C., Gulzar, S., Swain, K. C., & Pradhan, D. (2023). Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir

Smith, H. E., Blackburn, J. J., Stair, K. S., & Burnett, M. F. (2018). Assessing the Effects of the Smartphone as a Learning Tool on the Academic Achievement of School-Based Agricultural Education Students in Louisiana. Journal of Agricultural Education, 59(4), 270-285.

Smith, I. J. M. (2022). Digital Agriculture in Africa: An Ethnographic Study of Global Online Networking and Resource Mobilization on Mfangano Island.

Soussi, A., Zero, E., Sacile, R., Trinchero, D., & Fossa, M. (2024). Smart Sensors and Smart Data for Precision Agriculture: A Review. Sensors, 24(8), 2647.

Sprung, R. C., &Jaroniec, S. (Eds.). (2000). Translating into Success: Cutting-edge strategies for going multilingual in a global age (Vol. 11). John Benjamins Publishing.

Šūmane, S., Kunda, I., Knickel, K., Strauss, A., Tisenkopfs, T., des Ios Rios, I., Rivera, M, and Ashkenazy, A. (2018). Local and farmers' knowledge matters! How integrating informal and formal knowledge enhances sustainable and resilient agriculture. Journal of Rural Studies, 59, 232-241.

Sun, H., Liu, J., Chen, B., and Yang, L. (2024). Exploring Intellectual Property in the Digital Realm: A Bibliometric Study on Research on the Management and Protection of Data-Based Intellectual Property. Information, 15(12), 780.

Swanson, B.E., 2008. Global review of good agricultural extension and advisory service practices. Food and Agriculture Organization of the United Nations, Research and Extension Division, Rome, Italy.

Swetha, K. R., Monisha, D., Thejaswini, H. B., Nikhil, K. P. and Rahul, N. U. (2024, April). IoT and Wireless Sensor Network Based Autonomous Farming Robot. In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS) (Vol. 1, pp. 1-6). IEEE.https://dx.doi.org.10.1109/ickecs61492.2024.10616854

Teklu, A., Simane, B., &Bezabih, M. (2023). Effect of Climate Smart Agriculture Innovations on Climate Resilience among Smallholder Farmers: Empirical Evidence from the Choke Mountain Watershed of the Blue Nile Highlands of Ethiopia. Sustainability, 15(5), 4331. https://doi.org/10.3390/su15054331

Thomas, G., Adarsh, S., and Mathew, S. (2021). Future Agriculture [abstract]. In: Abstracts, 2nd International Web-Conference on Smart Agriculture for Resource Conservation and Ecological Stability,29-31st October, 2021, Lucknow, p.22. Available: https://www.researchgate.net/publication/357746005_Future_Agriculture

Toke, T., Chakre, S., Jagtap, S., Tekale, V. and Gosavi, V. M. Precision Agriculture using ML for Soil and Weather Prediction. International Journal of Advanced Research in Science, Communication and Technology.https://dx.doi.org.10.48175/ijarsct-19945

Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. van der, Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., Wever, W. de, … Clopath, C. (2020). AI for social good: unlocking the opportunity for positive impact. Nature Communications, 11(1), 2468. https://doi.org/10.1038/s41467-020-15871-z

Tomy, B., Adarsh, S., and Mathew, S. (2021). Artificial Intelligence and Internet of Things in Agriculture [abstract]. In: Abstracts, 2nd International Web-Conference on Smart Agriculture for Resource Conservation and Ecological Stability,29-31st October, 2021, Lucknow, p.8. Available: https://www.researchgate.net/publication/357742034_Artificial_Intelligence_and_Internet_of_Things_in_Agriculture

Tonle, F. B., Niassy, S., Ndadji, M. M., Tchendji, M. T., Nzeukou, A., Mudereri, B. T., ... &Tonnang, H. E. (2024). A road map for developing novel decision support system (DSS) for disseminating integrated pest management (IPM) technologies. Computers and Electronics in Agriculture, 217, 108526.

TRAI (Telecom Regulatory Authority of India). (2024). ‘Highlights of Telecom Subscription Data as on 31st October, 2024’ [Press release]. https://trai.gov.in/sites/default/files/2024-12/PR_No.94of2024.pdf

Ullah, Z., Iqbal, J., Abbasi, B. A., Ijaz, S., Yaseen, T., Waqar, R., Kanwal, S., Sher, H., Ullah, Z., Ali, A., & Mahmood, T. (2024). The Green Revolution: Promoting Environmental Stewardship and Plant Growth. In Environment, Climate, Plant and Vegetation Growth (pp. 425-469). Cham: Springer Nature Switzerland.

UNESCO. (2002). Forum on the Impact of Open Courseware for Higher Education in Developing Countries.

UNICEF, 2024a. Child Food Poverty: Nutrition Deprivation in Early Childhood. Available at: 〈https://www.unicef.org/child-health-and-survival/child-food-poverty〉 (Accessed September 3, 2024).

UNICEF, 2024b. 2 in 3 children under five in Bangladesh face child food poverty. Available at: 〈https://www.unicef.org/child-health-and-survival/child-food- poverty〉 (Accessed September 3, 2024).

United Nations, 2023. The Sustainable Development Goals Report Special edition. Available at: 〈https://unstats.un.org/sdgs/report/2023/The-Sustainable- Development-Goals-Report-2023.pdf〉 (Accessed September 3, 2024).

van Ewijk, E., & Ros-Tonen, M. A. (2021). The fruits of knowledge co-creation in agriculture and food-related multi-stakeholder platforms in sub-Saharan Africa–A systematic literature review. Agricultural systems, 186, 102949.

Verma M. (2023). Artificial intelligence role in modern science: aims, merits, risks and its applications, Vol. 7. 335–342.

Vishnevsky, V. (2024). A Machine-Learning Approach to Queue Length Estimation Using Tagged Customers Emission. In Distributed Computer and Communication Networks: Control, Computation, Communications: 26th International Conference, DCCN 2023, Moscow, Russia, September 25-29, 2023, Revised Selected Papers (Vol. 14123, p. 265). Springer Nature.

Wang, C., Ma, F., Yan, J., De, D., & Das, S. K. (2015). Efficient Aerial Data Collection with UAV in Large-Scale Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 11(11), 286080. https://doi.org/10.1155/2015/286080

Wanise Barroso, Luc Quoniam, Eduardo Pacheco, Patents as technological information in Latin America, World Patent Inf. 31 (3) (2009) 207–215, https:// doi.org/10.1016/j.wpi.2008.11.006.

WIPO, IP Facts and Figures 2023, World Intellectual Property Org. (2023), https://doi.org/10.34667/tind.48648.

Wiseman, L., Sanderson, J., Zhang, A. and Jakku, E. (2019). Farmers and their data: An examination of farmers’ reluctance to share their data through the lens of the laws impacting smart farming. NJAS-Wageningen Journal of Life Sciences, 90, p.100301.

Wohlfart, O., and Wagner, I. (2024). Longitudinal perspectives on technology acceptance: Teachers' integration of digital tools through the COVID-19 transition. Educ. Inf. Technol., (pp. 1-25).

World Bank (2007) World Development Report 2008: Agriculture for Development (World Bank, Washington, DC).

Yadav, K., et al. (2023). Agricultural Extension Services: A Critical Analysis of Modern Approaches.

Yang, P., Wang, X., Ou, Y., Kim, J., & Lee, S. (2023). Handbook on establishing and operating multi-actors agricultural innovation platforms. Food & Agriculture Org..

Yao, M. H., Hsu, Y. H., Li, T. Y., Chen, Y. M., Lu, C. T., Chen, C. L. and Shih, P. Y. (2024). Agricultural Disaster Prevention System: Insights from Taiwan’s Adaptation Strategies. Atmosphere, 15(5), 526.https://dx.doi.org.10.3390/atmos15050526

Ye, L., & Yang, H. (2020). From digital divide to social inclusion: A tale of mobile platform empowerment in rural areas. Sustainability, 12(6), 2424.

Zahra, S., Adnan Shahid, M., Maqbool, Z., Mahmood Sabir, R., Safdar, M., Danish Majeed, M., & Sarwar, A. (2024). Application of Geospatial Techniques in Agricultural Resource Management. https://doi.org/10.5772/intechopen.112222

Zampati, F. (2023). Ethical and legal considerations in smart farming: A farmer’s perspective. Towards Responsible Plant Data Linkage: Data Challenges for Agricultural Research and Development. (pp.257-272). Springer Nature Switzerland AG.

Zhumataeva, M. (2024). Legal Frameworks for the Assessment of Agricultural Lands. Eurasian Science Review An International Peer-Reviewed Multidisciplinary Journal, 2(5), 20–29. https://doi.org/10.63034/esr-91

Zossou, E., Arouna, A., Diagne, A., & Agboh-Noameshie, R. A. (2019). Learning agriculture in rural areas: the drivers of knowledge acquisition and farming practices by rice farmers in West Africa*. The Journal of Agricultural Education and Extension, 26(3), 291–306. https://doi.org/10.1080/1389224X.2019.1702066

Zualkernan, I., Abuhani, D. A., Hussain, M. H., Khan, J., &ElMohandes, M. (2023). Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey. Drones, 7(6), 382. https://doi.org/10.3390/drones7060382


Refbacks

  • There are currently no refbacks.


Informatics Studies:  ISSN: 2583-8994 (Online), 2320-530X (Print)