Context of Agri-Food Short Chains through Agriculture 4.0: An Approach to Food Security
DOI:
https://doi.org/10.47212/rtcAlinin.3.125.8Palabras clave:
family farming, food security, industry 4.0, self-sufficiency, short circuitsResumen
Agro-industrial production has received improvements in agricultural practices supported by Industry 4.0 (I4.0), especially focused on intensifying agricultural production and post-harvest processes. However, despite a high level of scientific and technological development, the family agri-food system is stagnant as a mechanism of self-sufficiency and a driver of socio-economic development. These unbalanced systems make it difficult to guarantee food security for the agricultural society. The Food and Agriculture Organization of the United Nations estimates today that one in nine people is undernourished. This phenomenon is accompanied by reduced incomes and increased food prices, slowing progress toward reducing poverty, food insecurity, and malnutrition. In this context, the Short Agri-Food Chains (SAFC) are presented as a strategy that contributes to food security and is, at the same time, an enhancer of socioeconomic development from Family Farming, especially of the most vulnerable rural families. In this study, we review the I4.0 technologies implemented in the agribusiness sector from 2010-2022 and find that SAFCs can adopt the Internet of Things, deep learning, and digital twins to intensify sustainable food production to ensure food security.
Descargas
Citas
Alanzi, S., & Ratten, V. (2023). The use of technology in facing the COVID-19 negative consequences and the associated opportunity for digital entrepreneurship in KSA. Journal of Trade Science, 11(2/3), 31–44. https://doi.org/10.1108/JTS-06-2023-0002
Alonso, R. S., Sittón-Candanedo, I., García, Ó., Prieto, J., & Rodríguez-González, S. (2020). An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario. Ad Hoc Networks, 98, 102047. https://doi.org/10.1016/J.ADHOC.2019.102047
Alves, T. S., Pinto, M. A., Ventura, P., Neves, C. J., Biron, D. G., Junior, A. C., de Paula Filho, P. L., & Rodrigues, P. J. (2020). Automatic detection and classification of honey bee comb cells using deep learning. Computers and Electronics in Agriculture, 170, 105244. https://doi.org/10.1016/J.COMPAG.2020.105244
Avalos, E., Cirera, X., Cruz, M., Iacovone, L., Medvedev, D., Nayyar, G., & Reyes Ortega, S. (2024). Firms’ digitalization during the COVID-19 pandemic: a tale of two stories. Science and Public Policy, scae022. https://doi.org/10.1093/scipol/scae022
Badgujar, C., Flippo, D., & Welch, S. (2022). Artificial neural network to predict traction performance of autonomous ground vehicle on a sloped soil bin and uncertainty analysis. Computers and Electronics in Agriculture, 196, 106867. https://doi.org/10.1016/J.COMPAG.2022.106867
Balabathina, V. N., Raju, R. P., Mulualem, W., & Tadele, G. (2020). Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia. Environmental Systems Research, 9(1), 35. https://doi.org/10.1186/s40068-020-00203-3
Balamurugan, S., Ayyasamy, A., & Joseph, K. S. (2021). Enhanced petri nets for traceability of food management using internet of things. Peer-to-Peer Networking and Applications, 14(1), 30–43. https://doi.org/10.1007/s12083-020-00943-0
Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., & Duong, L. (2023). Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic. International Journal of Production Research, 1–34. https://doi.org/10.1080/00207543.2023.2263102
Béné, C., Bakker, D., Chavarro, M. J., Even, B., Melo, J., & Sonneveld, A. (2021). Global assessment of the impacts of COVID-19 on food security. Global Food Security, 31, 100575. https://doi.org/10.1016/J.GFS.2021.100575
Biglia, A., Zaman, S., Gay, P., Ricauda Aimonino, D., & Comba, L. (2022). 3D point cloud density-based segmentation for vine rows detection and localisation. Computers and Electronics in Agriculture, 199, 107166. https://doi.org/10.1016/J.COMPAG.2022.107166
Blazy, J. M., Causeret, F., & Guyader, S. (2021). Immediate impacts of COVID-19 crisis on agricultural and food systems in the Caribbean. Agricultural Systems, 190, 103106. https://doi.org/10.1016/J.AGSY.2021.103106
Blondeau, S., & Korzenszky, A. (2022). Family farming, Legal Brief 8. https://doi.org/10.4060/cb8227en
Boyacι-Gündüz, C. P., Ibrahim, S. A., Wei, O. C., & Galanakis, C. M. (2021). Transformation of the food sector: Security and resilience during the covid-19 pandemic. In Foods (Vol. 10, Issue 3). MDPI AG. https://doi.org/10.3390/foods10030497
Butollo, F., Flemming, J., Gerber, C., Krzywdzinski, M., Wandjo, D., Delicat, N., & Herzog, L. (2023). COVID-19 as a Jump Start for Industry 4.0? Motivations and Core Areas of Pandemic-Related Investments in Digital Technologies at German Firms. Sci, 5(3). https://doi.org/10.3390/sci5030028
Calçada, D. B., Rezende, S. O., & Teodoro, M. S. (2019). Analysis of green manure decomposition parameters in northeast Brazil using association rule networks. Computers and Electronics in Agriculture, 159, 34–41. https://doi.org/10.1016/J.COMPAG.2019.02.013
CEPAL, FAO, & IICA. (2015). Short food supply chain as an alternative for promoting family agriculture. http://bit.ly/1pqMZMj.
Chanchaichujit, J., Balasubramanian, S., & Shukla, V. (2024). Barriers to Industry 4.0 technology adoption in agricultural supply chains: a Fuzzy Delphi-ISM approach. International Journal of Quality & Reliability Management, 41(7), 1942–1978. https://doi.org/10.1108/IJQRM-07-2023-0222
Chehri, A., Chaibi, H., Saadane, R., Hakem, N., & Wahbi, M. (2020). A Framework of Optimizing the Deployment of IoT for Precision Agriculture Industry. Procedia Computer Science, 176, 2414–2422. https://doi.org/10.1016/J.PROCS.2020.09.312
Cong, L. W., Yang, X., & Zhang, X. (2024). Small and Medium Enterprises Amidst the Pandemic and Reopening: Digital Edge and Transformation. Management Science, 70(7), 4564–4582. https://doi.org/10.1287/mnsc.2023.02424
Costa, F., Frecassetti, S., Rossini, M., & Portioli-Staudacher, A. (2023). Industry 4.0 digital technologies enhancing sustainability: Applications and barriers from the agricultural industry in an emerging economy. Journal of Cleaner Production, 408, 137208. https://doi.org/10.1016/J.JCLEPRO.2023.137208
Dahane, A., Benameur, R., & Kechar, B. (2022). An IoT Low-Cost Smart Farming for Enhancing Irrigation Efficiency of Smallholders Farmers. Wireless Personal Communications. https://doi.org/10.1007/s11277-022-09915-4
Daum, T., Ravichandran, T., Kariuki, J., Chagunda, M., & Birner, R. (2022). Connected cows and cyber chickens? Stocktaking and case studies of digital livestock tools in Kenya and India. Agricultural Systems, 196, 103353. https://doi.org/10.1016/J.AGSY.2021.103353
de Souza Rocha Junior, C., Moreira, M. Â. L., dos Santos, M., & Gomes, C. F. S. (2022). Creation and implementation of an IoT-based thermometer prototype for a food organization: case study. Procedia Computer Science, 199, 710–717. https://doi.org/10.1016/J.PROCS.2022.01.088
Food and Agriculture Organization of the United Nations (FAO, 2013). El huerto orgánico familiar. Centro de Conocimientos Sobre Agroecología. https://www.fao.org/agroecology/database/detail/es/c/443032/
Food and Agriculture Organization of the United Nations. (FAO, 2014). Agricultura Familiar en América Latina y el Caribe. Recomendaciones de Política (S. Salcedo & L. Guzmán, Eds.). Organización de las Naciones Unidas para la Alimentación y la Agricultura. www.fao.org/publications
Food and Agriculture Organization of the United Nations. (FAO, 2017). The future of food and agriculture – Trends and challenges.
Food and Agriculture Organization of the United Nations. (FAO, 2018). El trabajo de la FAO en la agricultura familiar: Prepararse para el Decenio Internacional de Agricultura Familiar (2019-2028) para alcanzar los ODS.
Food and Agriculture Organization of the United Nations. (FAO, 2021). Introducción al Decenio de las Naciones Unidas de la Agricultura Familiar. Decenio de Las Naciones Unidas de La Agricultura Familiar (2019-2028). https://www.fao.org/family-farming-decade/home/es/
Food and Agriculture Organization of the United Nations, International Fund for Agricultural Development, United Nations Children's Fund, World Food Programme & World Health Organization. (FAO, IFAD, UNICEF, WFP, & WHO, 2020). The State of Food Security and Nutrition in the World 2020: Transforming food systems for affordable healthy diets. In The State of Food Security and Nutrition in the World 2020. FAO, IFAD, UNICEF, WFP and WHO. https://doi.org/10.4060/ca9692en
Food and Agriculture Organization of the United Nations, International Fund for Agricultural Development, United Nations Children's Fund, World Food Programme & World Health Organization. (FAO, IFAD, UNICEF, WFP, & WHO, 2021). The State of Food Security and Nutrition in the World 2021. Transforming food systems for food security, improved nutrition and affordable healthy diets for all. In The State of Food Security and Nutrition in the World 2021. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cb4474en
García, M., Saltijeral, J. L., & Sosa, S. M. (2017). Guía para el desarrollo de mercados de productores Proyecto “Creación de Cadenas Cortas Agroalimentarias en la Ciudad de México.”
Garre, A., Ruiz, M. C., & Hontoria, E. (2020). Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty. Operations Research Perspectives, 7, 100147. https://doi.org/10.1016/J.ORP.2020.100147
Grijalba, M. A., Hernández, Y. B., Perez-Encinas, A., & Urda, B. S. (2024). Does the use of digital tools improve a firm’s performance? Review of Managerial Science. https://doi.org/10.1007/s11846-024-00750-4
Heiß, A., Paraforos, D. S., Sharipov, G. M., & Griepentrog, H. W. (2020). Modelling and Simulation of a Fuzzy System for Site-Specific Nitrogen Fertilization. IFAC-PapersOnLine, 53(2), 15790–15795. https://doi.org/10.1016/J.IFACOL.2020.12.208
Holl, A., & Rama, R. (2024). SME digital transformation and the COVID-19 pandemic: a case study of a hard-hit metropolitan area. Science and Public Policy, scae023. https://doi.org/10.1093/scipol/scae023
Hu, Y., Tian, G., Mayer, A. L., & He, R. (2015). Risk assessment of soil erosion by application of remote sensing and GIS in Yanshan Reservoir catchment, China. Natural Hazards, 79(1), 277–289. https://doi.org/10.1007/s11069-015-1841-4
Hussain, T., Wang, D., & Li, B. (2024). The influence of the COVID-19 pandemic on the adoption and impact of AI ChatGPT: Challenges, applications, and ethical considerations. Acta Psychologica, 246, 104264. https://doi.org/10.1016/J.ACTPSY.2024.104264
Karmaker, C. L., Aziz, R. Al, Ahmed, T., Misbauddin, S. M., & Moktadir, M. A. (2023). Impact of industry 4.0 technologies on sustainable supply chain performance: The mediating role of green supply chain management practices and circular economy. Journal of Cleaner Production, 419, 138249. https://doi.org/10.1016/J.JCLEPRO.2023.138249
Kneip, J., Fleischmann, P., & Berns, K. (2020). Crop edge detection based on stereo vision. Robotics and Autonomous Systems, 123, 103323. https://doi.org/10.1016/J.ROBOT.2019.103323
Konfo, T. R. C., Djouhou, F. M. C., Hounhouigan, M. H., Dahouenon-Ahoussi, E., Avlessi, F., & Sohounhloue, C. K. D. (2023). Recent advances in the use of digital technologies in agri-food processing: A short review. Applied Food Research, 3(2), 100329. https://doi.org/10.1016/J.AFRES.2023.100329
Kumar, R., Kumar, P., Tripathi, R., Gupta, G. P., Gadekallu, T. R., & Srivastava, G. (2021). SP2F: A secured privacy-preserving framework for smart agricultural Unmanned Aerial Vehicles. Computer Networks, 187, 107819. https://doi.org/10.1016/J.COMNET.2021.107819
Luo, Y., Mobley, C., Hossfeld, L., Koob, C., Hossfeld, C., Baxter, S. L. K., & Griffin, S. F. (2022). The Association between Food Insecurity and Making Hunger-Coping Trade-Offs during the COVID-19 Pandemic: The Role of Sources of Food and Easiness in Food Access. Nutrients, 14(21), 4616. https://doi.org/10.3390/nu14214616
Mahmood, H., Furqan, M., Meraj, G., & Hassan, M. S. (2024). The effects of COVID-19 on agriculture supply chain, food security, and environment: a review. In PeerJ (Vol. 12, Issue 4). PeerJ Inc. https://doi.org/10.7717/peerj.17281
Marchant-Forde, J. N., & Boyle, L. A. (2020). COVID-19 Effects on Livestock Production: A One Welfare Issue. In Frontiers in Veterinary Science (Vol. 7). Frontiers Media S.A. https://doi.org/10.3389/fvets.2020.585787
Marrero, A., & Mattei, J. (2022). Reclaiming traditional, plant-based, climate-resilient food systems in small islands. The Lancet Planetary Health, 6(2), e171–e179. https://doi.org/10.1016/S2542-5196(21)00322-3
Mat Aripin, A., & Brougham, D. (2024). COVID-19 and disruptive technology in New Zealand. Journal of Asian Business and Economic Studies, 31(1), 15–26. https://doi.org/10.1108/JABES-12-2022-0311
Matei, A. P., Swagemakers, P., Dominguez Garcia, M. D., da Silva, L. X., Ventura, F., & Milone, P. (2017). State Support in Brazil for a Local Turn to Food. Agriculture, 7(1). https://doi.org/10.3390/agriculture7010005
Melesse, T. Y., Bollo, M., Pasquale, V. di, Centro, F., & Riemma, S. (2022). Machine Learning-Based Digital Twin for Monitoring Fruit Quality Evolution. Procedia Computer Science, 200, 13–20. https://doi.org/10.1016/J.PROCS.2022.01.200
Middendorf, B. J., Faye, A., Middendorf, G., Stewart, Z. P., Jha, P. K., & Prasad, P. V. V. (2021). Smallholder farmer perceptions about the impact of COVID-19 on agriculture and livelihoods in Senegal. Agricultural Systems, 190, 103108. https://doi.org/10.1016/J.AGSY.2021.103108
Miranda, J., Ponce, P., Molina, A., & Wright, P. (2019). Sensing, smart and sustainable technologies for Agri-Food 4.0. Computers in Industry, 108, 21–36. https://doi.org/10.1016/J.COMPIND.2019.02.002
Mishrif, A., & Khan, A. (2023). Technology adoption as survival strategy for small and medium enterprises during COVID-19. Journal of Innovation and Entrepreneurship, 12(1), 53. https://doi.org/10.1186/s13731-023-00317-9
Molleví Bortoló, G., Álvarez Valdés, J., & Nicolas-Sans, R. (2023). Sustainable, technological, and innovative challenges post Covid-19 in health, economy, and education sectors. Technological Forecasting and Social Change, 190, 122424. https://doi.org/10.1016/J.TECHFORE.2023.122424
Monteiro, J., Barata, J., Veloso, M., Veloso, L., & Nunes, J. (2022). A scalable digital twin for vertical farming. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-04106-2
Nakkella, D. K., Maurya, S., Khan, N. A., Saurabh, P., Kumar, V., Khan, R. A., Stefen, Karthigeyan, P., Choudhary, K., Saha, S., Islam, S., Sharma, A., Panda, R. C., Patel, A., Singh, E. J., & el Morabet, R. (2020). A novel wind powered iot based sustainable organic compost machine. World Intellectual Property Organization.
Okunlola, J. O., Naicker, S. R., & Uleanya, C. (2024). Digital leadership in the fourth industrial revolution enacted during the COVID-19 pandemic: a systematic review. Cogent Education, 11(1), 2317258. https://doi.org/10.1080/2331186X.2024.2317258
Oliveira, E. de, Barros, S. E. dos S., Sugai, M. O. da S., Vieira, D. D., Freitas, H. R., & Neto, J. R. C. (2021). Short Commercialization Circuits: Contributions and challenges for the strengthening of Family Farming. International Journal of Advanced Engineering Research and Science, 8(11), 209–220. https://doi.org/10.22161/ijaers.811.23
Paranitharan, K. P., Ebenezer, G., Balaji, V., Adham Khan, M., & Ramesh Babu, T. (2022). Application of industry 4.0 technology in containing Covid-19 spread and its challenges. Materials Today: Proceedings, 68, 1225–1232. https://doi.org/10.1016/J.MATPR.2022.06.009
Pascual, L., Gras, M., Vidal-Brotóns, D., Alcañiz, M., Martínez-Máñez, R., & Ros-Lis, J. v. (2018). A voltammetric e-tongue tool for the emulation of the sensorial analysis and the discrimination of vegetal milks. Sensors and Actuators B: Chemical, 270, 231–238. https://doi.org/10.1016/J.SNB.2018.04.151
Patel, T., Pal, A., Pal, G., Pandey, V., Pandey, P. K., Debnath, S., Saravanan, S., Khan, H., E. S., A., Sathian, B., Singh, D., & Mishra, M. (2022). Iot and blockchain based smart agriculture system to increase production with minimum environmental footprint (Patent 202231006761). World Intellectual Property Organization.
Paul, J., Ueno, A., Dennis, C., Alamanos, E., Curtis, L., Foroudi, P., Kacprzak, A., Kunz, W. H., Liu, J., Marvi, R., Nair, S. L. S., Ozdemir, O., Pantano, E., Papadopoulos, T., Petit, O., Tyagi, S., & Wirtz, J. (2024). Digital transformation: A multidisciplinary perspective and future research agenda. International Journal of Consumer Studies, 48(2), e13015. https://doi.org/https://doi.org/10.1111/ijcs.13015
Paul, K., Chatterjee, S. S., Pai, P., Varshney, A., Juikar, S., Prasad, V., Bhadra, B., & Dasgupta, S. (2022). Viable smart sensors and their application in data driven agriculture. Computers and Electronics in Agriculture, 198, 107096. https://doi.org/10.1016/J.COMPAG.2022.107096
Place, F., Stoian, D., & Minot, N. (2022). COVID-19, agricultural production, and food value chains. In J. S. J. McDermott (Ed.), COVID-19 and global food security: Two years later (1st ed., Vol. 1, pp. 73–81). International Food Policy Research Institute. https://doi.org/10.2499/9780896294226
Pradel, M., de Fays, M., & Seguineau, C. (2022). Comparative Life Cycle Assessment of intra-row and inter-row weeding practices using autonomous robot systems in French vineyards. Science of The Total Environment, 838, 156441. https://doi.org/10.1016/J.SCITOTENV.2022.156441
Pu, M., & Zhong, Y. (2020). Rising concerns over agricultural production as COVID-19 spreads: Lessons from China. Global Food Security, 26, 100409. https://doi.org/10.1016/J.GFS.2020.100409
Raja, S. (2020). Planning and pandemics COVID 19 illuminates why urban planners should have listened to food advocates all along. Agriculture and Human Values, 37(3), 553–554. https://doi.org/10.1007/s10460-020-10090-0
Román, C., Peris, M., Esteve, J., Tejerina, M., Cambray, J., Vilardell, P., & Planas, S. (2022). Pesticide dose adjustment in fruit and grapevine orchards by DOSA3D: Fundamentals of the system and on-farm validation. Science of The Total Environment, 808, 152158. https://doi.org/10.1016/J.SCITOTENV.2021.152158
Roubík, H., Lošťák, M., Ketuama, C. T., Procházka, P., Soukupová, J., Hakl, J., Karlík, P., & Hejcman, M. (2022). Current coronavirus crisis and past pandemics - What can happen in post-COVID-19 agriculture? Sustainable Production and Consumption, 30, 752–760. https://doi.org/10.1016/J.SPC.2022.01.007
S., L., Sundara, N. B., Ganesan, R., Suban, R., Sankarram, N., Omkumar, S., Srinivasan, J., Partheeban, N., Sriramulu, S., & Daniel, A. (2022). Design system of optimized provenance system for the food industry using iot and blockchain (Patent 202241021982). World Intellectual Property Organization.
Senturk, S., Senturk, F., & Karaca, H. (2023). Industry 4.0 technologies in agri-food sector and their integration in the global value chain: A review. Journal of Cleaner Production, 408, 137096. https://doi.org/10.1016/J.JCLEPRO.2023.137096
Shen, Y., & Zhang, X. (2024). The impact of artificial intelligence on employment: the role of virtual agglomeration. Humanities and Social Sciences Communications, 11(1), 122. https://doi.org/10.1057/s41599-024-02647-9
Shyni, E., & Sundar, A. (2022). Anomaly detection system for prevention of false data injection in agriculture 4.0 (Patent 202241013098). World Intellectual Property Organization.
Skrzypczak, D., Gil, F., Izydorczyk, G., Mikula, K., Gersz, A., Hoppe, V., Chojnacka, K., & Witek-Krowiak, A. (2022). Innovative bio-waste-based multilayer hydrogel fertilizers as a new solution for precision agriculture. Journal of Environmental Management, 321, 116002. https://doi.org/10.1016/J.JENVMAN.2022.116002
Stoian, D., Marenya, P., Donovan, J., & Pamerneckyte, G. (2021). Fractures and resilience of agri-food value chains in the context of COVID-19: A review of recent evidence. In Fractures and resilience of agri-food value chains in the context of COVID-19: A review of recent evidence (Working Paper 2, Vol. 1). Center for International Forestry Research (CIFOR). https://doi.org/10.17528/cifor/008391
Terra, F. P., Nascimento, G. H. do, Duarte, G. A., & Drews-Jr, P. L. J. (2021). Autonomous Agricultural Sprayer using Machine Vision and Nozzle Control. Journal of Intelligent & Robotic Systems, 102(2), 38. https://doi.org/10.1007/s10846-021-01361-x
Tittonell, P., Fernandez, M., el Mujtar, V. E., Preiss, P. v., Sarapura, S., Laborda, L., Mendonça, M. A., Alvarez, V. E., Fernandes, G. B., Petersen, P., & Cardoso, I. M. (2021). Emerging responses to the COVID-19 crisis from family farming and the agroecology movement in Latin America – A rediscovery of food, farmers and collective action. Agricultural Systems, 190, 103098. https://doi.org/10.1016/J.AGSY.2021.103098
van Berkum, S. (2022). IFAD RESEARCH SERIES 77 The role of trade and policies in improving food security Papers of the 2021 Rural Development Report (Vol. 1). International Fund for Agricultural Development (IFAD). www.ifad.org/en/rural-development-report
Van, J. C. F., Tham, P. E., Lim, H. R., Khoo, K. S., Chang, J. S., & Show, P. L. (2022). Integration of Internet-of-Things as sustainable smart farming technology for the rearing of black soldier fly to mitigate food waste. Journal of the Taiwan Institute of Chemical Engineers, 104235. https://doi.org/10.1016/J.JTICE.2022.104235
Verderame, L., Merelli, I., Morganti, L., Corni, E., Cesini, D., D’Agostino, D., & Merlo, A. (2020). A secure cloud-edges computing architecture for metagenomics analysis. Future Generation Computer Systems, 111, 919–930. https://doi.org/10.1016/J.FUTURE.2019.09.013
Vij, A., Vijendra, S., Jain, A., Bajaj, S., Bassi, A., & Sharma, A. (2020). IoT and Machine Learning Approaches for Automation of Farm Irrigation System. Procedia Computer Science, 167, 1250–1257. https://doi.org/10.1016/J.PROCS.2020.03.440
Xu, Z. (2019). Food Dependency and Global Food Crisis. International Critical Thought, 9(2), 269–281. https://doi.org/10.1080/21598282.2019.1613920
Yao, X., Xu, Z., Škare, M., & Wang, X. (2024). Aftermath on COVID-19 technological and socioeconomic changes: A meta-analytic review. Technological Forecasting and Social Change, 202, 123322. https://doi.org/10.1016/J.TECHFORE.2024.123322
Yap, C. K., & Al-Mutairi, K. A. (2024). A Conceptual Model Relationship between Industry 4.0—Food-Agriculture Nexus and Agroecosystem: A Literature Review and Knowledge Gaps. Foods, 13(1). https://doi.org/10.3390/foods13010150
Zambrano, G., Tennhardt, L. M., Egger, M., Ramírez, K., Santos, A., Moyano, B., & Curran, M. (2024). Differing impacts of the COVID-19 pandemic on farmers and intermediaries: insights into the Ecuadorian cocoa value chain. Agricultural and Food Economics, 12(1), 9. https://doi.org/10.1186/s40100-024-00302-0
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2025 Nayeli Montalvo-Romero, Aarón Montiel-Rosales, Gregorio Fernández-Lambert, Alberto A. Aguilar-Lasserre

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.


















