1,707 research outputs found

    Collaborative, Distributed Simulations of Agri-Food Supply Chains. Analysis on How Linking Theory and Practice by Using Multi-agent Structures

    Get PDF
    Simulations help to understand and predict the behaviour of complex phenomena’s, likewise distributed socio-technical systems or how stakeholders interacts in complex domains. Such domains are normally based on networked based interaction, where information, product and decision flows comes in to play, especially under the well-known supply chains structures. Although tools exist to simulate supply chains, they do not adequately support multiple stakeholders to collaboratively create and explore a variety of decision-making scenarios. Hence, in order to provide a preliminary understanding on how these interaction affects stakeholders decision-making, this research presents an study, analysis and proposal development of robust platform to collaboratively build and simulate communication among supply chain. Since realistic supply chain behaviours are complex, a multi-agent approach was selected in order to represent such complexities in a standardised manner. The platform provides agent behaviours for common agent patterns. It provides extension hotspots to implement more specific agent behaviour for expert users (that requires programming). Therefore, as key contribution, technical aspects of the platform are presented, and also the role of multi-level supply chain scenario simulation is discussed and analysed, especially under de context of digital supply chain transformation in the agri-food context. Finally, we discuss lessons learned from early tests with the reference implementation of the platform

    Collaborative, Distributed Simulations of Agri-Food Supply Chains : Analysis on How Linking Theory and Practice by Using Multi-agent Structures

    Get PDF
    Simulations help to understand and predict the behaviour of complex phenomena's, likewise distributed socio-technical systems or how stakeholders interacts in complex domains. Such domains are normally based on networked based interaction, where information, product and decision flows comes in to play, especially under the well-known supply chains structures. Although tools exist to simulate supply chains, they do not adequately support multiple stakeholders to collaboratively create and explore a variety of decision-making scenarios. Hence, in order to provide a preliminary understanding on how these interaction affects stakeholders decision-making, this research presents an study, analysis and proposal development of robust platform to collaboratively build and simulate communication among supply chain. Since realistic supply chain behaviours are complex, a multi-agent approach was selected in order to represent such complexities in a standardised manner. The platform provides agent behaviours for common agent patterns. It provides extension hotspots to implement more specific agent behaviour for expert users (that requires programming). Therefore, as key contribution, technical aspects of the platform are presented, and also the role of multi-level supply chain scenario simulation is discussed and analysed, especially under de context of digital supply chain transformation in the agri-food context. Finally, we discuss lessons learned from early tests with the reference implementation of the platform.Laboratorio de Investigación y Formación en Informática Avanzad

    Big data analytics tools for improving the decision-making process in agrifood supply chain

    Get PDF
    Introduzione: Nell'interesse di garantire una sicurezza alimentare a lungo termine di fronte a circostanze mutevoli, è necessario comprendere e considerare gli aspetti ambientali, sociali ed economici del processo di produzione. Inoltre, a causa della globalizzazione, sono stati sollevati i problemi delle lunghe filiere agroalimentari, l'asimmetria informativa, la contraffazione, la difficoltà di tracciare e rintracciare l'origine dei prodotti e le numerose questioni correlate quali il benessere dei consumatori e i costi sanitari. Le tecnologie emergenti guidano verso il raggiungimento di nuovi approcci socioeconomici in quanto consentono al governo e ai singoli produttori agricoli di raccogliere ed analizzare una quantità sempre crescente di dati ambientali, agronomici, logistici e danno la possibilità ai consumatori ed alle autorità di controllo della qualità di accedere a tutte le informazioni necessarie in breve tempo e facilmente. Obiettivo: L'oggetto della ricerca riguarda lo studio delle modalità di miglioramento del processo produttivo attraverso la riduzione dell'asimmetria informativa, rendendola disponibile alle parti interessate in un tempo ragionevole, analizzando i dati sui processi produttivi, considerando l'impatto ambientale della produzione in termini di ecologia, economia, sicurezza alimentare e qualità di cibo, costruendo delle opportunità per le parti interessate nel prendere decisioni informate, oltre che semplificare il controllo della qualità, della contraffazione e delle frodi. Pertanto, l'obiettivo di questo lavoro è quello di studiare le attuali catene di approvvigionamento, identificare le loro debolezze e necessità, analizzare le tecnologie emergenti, le loro caratteristiche e gli impatti sulle catene di approvvigionamento e fornire utili raccomandazioni all'industria, ai governi e ai policy maker.Introduction: In the interest of ensuring long-term food security and safety in the face of changing circumstances, it is interesting and necessary to understand and to take into consideration the environmental, social and economic aspects of food and beverage production in relation to the consumers’ demand. Besides, due to the globalization, the problems of long supply chains, information asymmetry, counterfeiting, difficulty for tracing and tracking back the origin of the products and numerous related issues have been raised such as consumers’ well-being and healthcare costs. Emerging technologies drive to achieve new socio-economic approaches as they enable government and individual agricultural producers to collect and analyze an ever-increasing amount of environmental, agronomic, logistic data, and they give the possibility to the consumers and quality control authorities to get access to all necessary information in a short notice and easily. Aim: The object of the research essentially concerns the study of the ways for improving the production process through reducing the information asymmetry, making it available for interested parties in a reasonable time, analyzing the data about production processes considering the environmental impact of production in terms of ecology, economy, food safety and food quality and build the opportunity for stakeholders to make informed decisions, as well as simplifying the control of the quality, counterfeiting and fraud. Therefore, the aim of this work is to study current supply chains, to identify their weaknesses and necessities, to investigate the emerging technologies, their characteristics and the impacts on supply chains, and to provide with the useful recommendations the industry, governments and policymakers

    Eco‐Holonic 4.0 Circular Business Model to  Conceptualize Sustainable Value Chain Towards  Digital Transition 

    Get PDF
    The purpose of this paper is to conceptualize a circular business model based on an Eco-Holonic Architecture, through the integration of circular economy and holonic principles. A conceptual model is developed to manage the complexity of integrating circular economy principles, digital transformation, and tools and frameworks for sustainability into business models. The proposed architecture is multilevel and multiscale in order to achieve the instantiation of the sustainable value chain in any territory. The architecture promotes the incorporation of circular economy and holonic principles into new circular business models. This integrated perspective of business model can support the design and upgrade of the manufacturing companies in their respective industrial sectors. The conceptual model proposed is based on activity theory that considers the interactions between technical and social systems and allows the mitigation of the metabolic rift that exists between natural and social metabolism. This study contributes to the existing literature on circular economy, circular business models and activity theory by considering holonic paradigm concerns, which have not been explored yet. This research also offers a unique holonic architecture of circular business model by considering different levels, relationships, dynamism and contextualization (territory) aspects

    Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture

    Full text link
    [EN] The term "Agri-Food 4.0" is an analogy to the term Industry 4.0; coming from the concept "agriculture 4.0". Since the origins of the industrial revolution, where the steam engines started the concept of Industry 1.0 and later the use of electricity upgraded the concept to Industry 2.0, the use of technologies generated a milestone in the industry revolution by addressing the Industry 3.0 concept. Hence, Industry 4.0, it is about including and integrating the latest developments based on digital technologies as well as the interoperability process across them. This allows enterprises to transmit real-time information in terms behaviour and performance. Therefore, the challenge is to maintain these complex networked structures efficiently linked and organised within the use of such technologies, especially to identify and satisfy supply chain stakeholders dynamic requirements. In this context, the agriculture domain is not an exception although it possesses some specialities depending from the domain. In fact, all agricultural machinery incorporates electronic controls and has entered to the digital age, enhancing their current performance. In addition, electronics, using sensors and drones, support the data collection of several agriculture key aspects, such as weather, geographical spatialization, animals and crops behaviours, as well as the entire farm life cycle. However, the use of the right methods and methodologies for enhancing agriculture supply chains performance is still a challenge, thus the concept of Industry 4.0 has evolved and adapted to agriculture 4.0 in order analyse the behaviours and performance in this specific domain. Thus, the question mark on how agriculture 4.0 support a better supply chain decision-making process, or how can help to save time to farmer to make effective decision based on objective data, remains open. Therefore, in this survey, a review of more than hundred papers on new technologies and the new available supply chains methods are analysed and contrasted to understand the future paths of the Agri-Food domain.Authors of this publication acknowledge the contribution of the Project 691249, RUC-APS "Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems" (www.ruc-aps.eu), funded by the European Union under their funding scheme H2020-MSCARISE-2015.Lezoche, M.; Hernández, JE.; Alemany Díaz, MDM.; Panetto, H.; Kacprzyk, J. (2020). Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture. Computers in Industry. 117:1-15. https://doi.org/10.1016/j.compind.2020.103187S115117Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1-20. doi:10.1016/j.ejor.2008.02.014Ait-Mouheb, N., Bahri, A., Thayer, B. B., Benyahia, B., Bourrié, G., Cherki, B., … Harmand, J. (2018). The reuse of reclaimed water for irrigation around the Mediterranean Rim: a step towards a more virtuous cycle? Regional Environmental Change, 18(3), 693-705. doi:10.1007/s10113-018-1292-zAli, J., & Kumar, S. (2011). Information and communication technologies (ICTs) and farmers’ decision-making across the agricultural supply chain. International Journal of Information Management, 31(2), 149-159. doi:10.1016/j.ijinfomgt.2010.07.008Alzahrani, S. M. (2018). Development of IoT mining machine for Twitter sentiment analysis: Mining in the cloud and results on the mirror. 2018 15th Learning and Technology Conference (L&T). doi:10.1109/lt.2018.8368490Amandeep, Bhattacharjee, A., Das, P., Basu, D., Roy, S., Ghosh, S., … Rana, T. K. (2017). Smart farming using IOT. 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). doi:10.1109/iemcon.2017.8117219Annosi, M. C., Brunetta, F., Monti, A., & Nati, F. (2019). Is the trend your friend? An analysis of technology 4.0 investment decisions in agricultural SMEs. Computers in Industry, 109, 59-71. doi:10.1016/j.compind.2019.04.003Baio, F. H. R. (2011). Evaluation of an auto-guidance system operating on a sugar cane harvester. Precision Agriculture, 13(1), 141-147. doi:10.1007/s11119-011-9241-6Belaud, J.-P., Prioux, N., Vialle, C., & Sablayrolles, C. (2019). Big data for agri-food 4.0: Application to sustainability management for by-products supply chain. Computers in Industry, 111, 41-50. doi:10.1016/j.compind.2019.06.006Nicolaas Bezuidenhout, C., Bodhanya, S., & Brenchley, L. (2012). An analysis of collaboration in a sugarcane production and processing supply chain. British Food Journal, 114(6), 880-895. doi:10.1108/00070701211234390Bhatt, M. R., & Buch, S. (2015). Prediction of formability for sheet metal component using artificial intelligent technique. 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). doi:10.1109/spin.2015.7095356Birkel, H. S., & Hartmann, E. (2019). Impact of IoT challenges and risks for SCM. Supply Chain Management: An International Journal, 24(1), 39-61. doi:10.1108/scm-03-2018-0142Boehlje, M. (1999). Structural Changes in the Agricultural Industries: How Do We Measure, Analyze and Understand Them? American Journal of Agricultural Economics, 81(5), 1028-1041. doi:10.2307/1244080Bonney, L., Clark, R., Collins, R., & Fearne, A. (2007). From serendipity to sustainable competitive advantage: insights from Houston’s Farm and their journey of co‐innovation. Supply Chain Management: An International Journal, 12(6), 395-399. doi:10.1108/13598540710826326Boshkoska, B. M., Liu, S., Zhao, G., Fernandez, A., Gamboa, S., del Pino, M., … Chen, H. (2019). A decision support system for evaluation of the knowledge sharing crossing boundaries in agri-food value chains. Computers in Industry, 110, 64-80. doi:10.1016/j.compind.2019.04.012Brewster, C., Roussaki, I., Kalatzis, N., Doolin, K., & Ellis, K. (2017). IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot. IEEE Communications Magazine, 55(9), 26-33. doi:10.1109/mcom.2017.1600528Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1), 205395171664817. doi:10.1177/2053951716648174Brown, K. (2013). Global environmental change I. Progress in Human Geography, 38(1), 107-117. doi:10.1177/0309132513498837Chilcanan, D., Navas, P., & Escobar, S. M. (2017). Expert system for remote process automation in multiplatform servers, through human machine conversation. 2017 12th Iberian Conference on Information Systems and Technologies (CISTI). doi:10.23919/cisti.2017.7975913Choi, J., In, Y., Park, C., Seok, S., Seo, H., & Kim, H. (2016). Secure IoT framework and 2D architecture for End-To-End security. The Journal of Supercomputing, 74(8), 3521-3535. doi:10.1007/s11227-016-1684-0Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/2393553Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems Engineering, 120, 65-80. doi:10.1016/j.biosystemseng.2013.09.006Del Borghi, A., Gallo, M., Strazza, C., & Del Borghi, M. (2014). An evaluation of environmental sustainability in the food industry through Life Cycle Assessment: the case study of tomato products supply chain. Journal of Cleaner Production, 78, 121-130. doi:10.1016/j.jclepro.2014.04.083Devarakonda, R., Shrestha, B., Palanisamy, G., Hook, L., Killeffer, T., Krassovski, M., … Lazer, K. (2014). OME: Tool for generating and managing metadata to handle BigData. 2014 IEEE International Conference on Big Data (Big Data). doi:10.1109/bigdata.2014.7004476Nascimento, A. F. do, Mendonça, E. de S., Leite, L. F. C., Scholberg, J., & Neves, J. C. L. (2012). Calibration and validation of models for short-term decomposition and N mineralization of plant residues in the tropics. Scientia Agricola, 69(6), 393-401. doi:10.1590/s0103-90162012000600008Dolan, C., & Humphrey, J. (2000). Governance and Trade in Fresh Vegetables: The Impact of UK Supermarkets on the African Horticulture Industry. Journal of Development Studies, 37(2), 147-176. doi:10.1080/713600072Dragincic, J., Korac, N., & Blagojevic, B. (2015). Group multi-criteria decision making (GMCDM) approach for selecting the most suitable table grape variety intended for organic viticulture. Computers and Electronics in Agriculture, 111, 194-202. doi:10.1016/j.compag.2014.12.023Dworak, V., Selbeck, J., Dammer, K.-H., Hoffmann, M., Zarezadeh, A., & Bobda, C. (2013). Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems. Sensors, 13(2), 1523-1538. doi:10.3390/s130201523Eisele, M., Kiese, R., Krämer, A., & Leibundgut, C. (2001). Application of a catchment water quality model for assessment and prediction of nitrogen budgets. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7-8), 547-551. doi:10.1016/s1464-1909(01)00048-xElsayed, K. M. F., Ismail, T., & S. Ouf, N. (2018). A Review on the Relevant Applications of Machine Learning in Agriculture. IJIREEICE, 6(8), 1-17. doi:10.17148/ijireeice.2018.681Esteso, A., Alemany, M. M. E., & Ortiz, A. (2017). Métodos y Modelos Deterministas e Inciertos para la Gestión de Cadenas de Suministro Agroalimentarias. Dirección y Organización, 41-46. doi:10.37610/dyo.v0i0.509Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706GERHARDS, R., GUTJAHR, C., WEIS, M., KELLER, M., SÖKEFELD, M., MÖHRING, J., & PIEPHO, H. P. (2011). Using precision farming technology to quantify yield effects attributed to weed competition and herbicide application. Weed Research, 52(1), 6-15. doi:10.1111/j.1365-3180.2011.00893.xGovindan, K., Jafarian, A., Khodaverdi, R., & Devika, K. (2014). Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. International Journal of Production Economics, 152, 9-28. doi:10.1016/j.ijpe.2013.12.028Gumaste, S. S., & Kadam, A. J. (2016). Future weather prediction using genetic algorithm and FFT for smart farming. 2016 International Conference on Computing Communication Control and automation (ICCUBEA). doi:10.1109/iccubea.2016.7860028Hashem, H., & Ranc, D. (2016). A review of modeling toolbox for BigData. 2016 International Conference on Military Communications and Information Systems (ICMCIS). doi:10.1109/icmcis.2016.7496565Hefnawy, A., Elhariri, T., Cherifi, C., Robert, J., Bouras, A., Kubler, S., & Framling, K. (2017). Combined use of lifecycle management and IoT in smart cities. 2017 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). doi:10.1109/skima.2017.8294112Hosseini, S. H., Tang, C. Y., & Jiang, J. N. (2014). Calibration of a Wind Farm Wind Speed Model With Incomplete Wind Data. IEEE Transactions on Sustainable Energy, 5(1), 343-350. doi:10.1109/tste.2013.2284490Hu, Y., Zhang, L., Li, J., & Mehrotra, S. (2016). ICME 2016 Image Recognition Grand Challenge. 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). doi:10.1109/icmew.2016.7574663A. Irmak, J. W. Jones, W. D. Batchelor, S. Irmak, K. J. Boote, & J. O. Paz. (2006). Artificial Neural Network Model as a Data Analysis Tool in Precision Farming. Transactions of the ASABE, 49(6), 2027-2037. doi:10.13031/2013.22264Jeon, S., Kim, B., & Huh, J. (2017). Study on methods to determine rotor equivalent wind speed to increase prediction accuracy of wind turbine performance under wake condition. Energy for Sustainable Development, 40, 41-49. doi:10.1016/j.esd.2017.06.001Joly, P.-B. (2005). Resilient farming systems in a complex world — new issues for the governance of science and innovation. Australian Journal of Experimental Agriculture, 45(6), 617. doi:10.1071/ea03252Joshi, R., Banwet, D. K., & Shankar, R. (2009). Indian cold chain: modeling the inhibitors. British Food Journal, 111(11), 1260-1283. doi:10.1108/00070700911001077Kamata, T., Roshanianfard, A., & Noguchi, N. (2018). Heavy-weight Crop Harvesting Robot - Controlling Algorithm. IFAC-PapersOnLine, 51(17), 244-249. doi:10.1016/j.ifacol.2018.08.165Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194. doi:10.1016/j.ijpe.2019.05.022Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture, 143, 23-37. doi:10.1016/j.compag.2017.09.037Kelepouris, T., Pramatari, K., & Doukidis, G. (2007). RFID‐enabled traceability in the food supply chain. Industrial Management & Data Systems, 107(2), 183-200. doi:10.1108/02635570710723804Khan, S. F., & Ismail, M. Y. (2018). An Investigation into the Challenges and Opportunities Associated with the Application of Internet of Things (IoT) in the Agricultural Sector-A Review. Journal of Computer Science, 14(2), 132-143. doi:10.3844/jcssp.2018.132.143Kladivko, E. J., Helmers, M. J., Abendroth, L. J., Herzmann, D., Lal, R., Castellano, M. J., … Villamil, M. B. (2014). Standardized research protocols enable transdisciplinary research of climate variation impacts in corn production systems. Journal of Soil and Water Conservation, 69(6), 532-542. doi:10.2489/jswc.69.6.532Ko, T., Lee, J., & Ryu, D. (2018). Blockchain Technology and Manufacturing Industry: Real-Time Transparency and Cost Savings. Sustainability, 10(11), 4274. doi:10.3390/su10114274KÖK, M. S. (2009). Application of Food Safety Management Systems (ISO 22000/HACCP) in the Turkish Poultry Industry: A Comparison Based on Enterprise Size. Journal of Food Protection, 72(10), 2221-2225. doi:10.4315/0362-028x-72.10.2221Kvíz, Z., Kroulik, M., & Chyba, J. (2014). Machinery guidance systems analysis concerning pass-to-pass accuracy as a tool for efficient plant production in fields and for soil damage reduction. Plant, Soil and Environment, 60(No. 1), 36-42. doi:10.17221/622/2012-pseLamsal, K., Jones, P. C., & Thomas, B. W. (2016). Harvest logistics in agricultural systems with multiple, independent producers and no on-farm storage. Computers & Industrial Engineering, 91, 129-138. doi:10.1016/j.cie.2015.10.018Laube, P., Duckham, M., & Palaniswami, M. (2011). Deferred decentralized movement pattern mining for geosensor networks. International Journal of Geographical Information Science, 25(2), 273-292. doi:10.1080/13658810903296630Li, F.-R., Gao, C.-Y., Zhao, H.-L., & Li, X.-Y. (2002). Soil conservation effectiveness and energy efficiency of alternative rotations and continuous wheat cropping in the Loess Plateau of northwest China. Agriculture, Ecosystems & Environment, 91(1-3), 101-111. doi:10.1016/s0167-8809(01)00265-1Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18(8), 2674. doi:10.3390/s18082674Meichen, L., Jun, C., Xiang, Z., Lu, W., & Yongpeng, T. (2018). Dynamic obstacle detection based on multi-sensor information fusion. IFAC-PapersOnLine, 51(17), 861-865. doi:10.1016/j.ifacol.2018.08.086Louwagie, G., Northey, G., Finn, J. A., & Purvis, G. (2012). Development of indicators for assessment of the environmental impact of livestock farming in Ireland using the Agri-environmental Footprint Index. Ecological Indicators, 18, 149-162. doi:10.1016/j.ecolind.2011.11.003Luque, A., Peralta, M. E., de las Heras, A., & Córdoba, A. (2017). State of the Industry 4.0 in the Andalusian food sector. Procedia Manufacturing, 13, 1199-1205. doi:10.1016/j.promfg.2017.09.195Malhotra, S., Doja, M. ., Alam, B., & Alam, M. (2017). Bigdata analysis and comparison of bigdata analytic approches. 2017 International Conference on Computing, Communication and Automation (ICCCA). doi:10.1109/ccaa.2017.8229821Mayer, J., Gunst, L., Mäder, P., Samson, M.-F., Carcea, M., Narducci, V., … Dubois, D. (2015). «Productivity, quality and sustainability of winter wheat under long-term conventional and organic management in Switzerland». European Journal of Agronomy, 65, 27-39. doi:10.1016/j.eja.2015.01.002McGuire, S., & Sperling, L. (2013). Making seed systems more resilient to stress. Global Environmental Change, 23(3), 644-653. doi:10.1016/j.gloenvcha.2013.02.001Mekala, M. S., & Viswanathan, P. (2017). A Survey: Smart agriculture IoT with cloud computing. 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS). doi:10.1109/icmdcs.2017.8211551Mishra, S., Mishra, D., & Santra, G. H. (2016). Applications of Machine Learning Techniques in Agricultural Crop Production: A Review Paper. Indian Journal of Science and Technology, 9(38). doi:10.17485/ijst/2016/v9i38/95032Mocnej, J., Seah, W. K. G., Pekar, A., & Zolotova, I. (2018). Decentralised IoT Architecture for Efficient Resources Utilisation. IFAC-PapersOnLine, 51(6), 168-173. doi:10.1016/j.ifacol.2018.07.148Mohanraj, I., Gokul, V., Ezhilarasie, R., & Umamakeswari, A. (2017). Intelligent drip irrigation and fertigation using wireless sensor networks. 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR). doi:10.1109/tiar.2017.8273682Montecinos, J., Ouhimmou, M., Chauhan, S., & Paquet, M. (2018). Forecasting multiple waste collecting sites for the agro-food industry. Journal of Cleaner Production, 187, 932-939. doi:10.1016/j.jclepro.2018.03.127Yandun Narváez, F., Gregorio, E., Escolà, A., Rosell-Polo, J. R., Torres-Torriti, M., & Auat Cheein, F. (2018). Terrain classification using ToF sensors for the enhancement of agricultural machinery traversability. Journal of Terramechanics, 76, 1-13. doi:10.1016/j.jterra.2017.10.005Nguyen, T., ZHOU, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, 98, 254-264. doi:10.1016/j.cor.2017.07.004Nilsson, E., Hochrainer-Stigler, S., Mochizuki, J., & Uvo, C. B. (2016). Hydro-climatic variability and agricultural production on the shores of Lake Chad. Environmental Development, 20, 15-30. doi:10.1016/j.envdev.2016.09.001Nolan, P., Paley, D. A., & Kroeger, K. (2017). Multi-UAS path planning for non-uniform data collection in precision agriculture. 2017 IEEE Aerospace Conference. doi:10.1109/aero.2017.7943794Oberholster, C., Adendorff, C., & Jonker, K. (2015). Financing Agricultural Production from a Value Chain Perspective. Outlook on Agriculture, 44(1), 49-60. doi:10.5367/oa.2015.0197Opara, L. U., & Mazaud, F. (2001). Food Traceability from Field to Plate. Outlook on Agriculture, 30(4), 239-247. doi:10.5367/000000001101293724Ott, K.-H., Aranı́bar, N., Singh, B., & Stockton, G. W. (2003). Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts. Phytochemistry, 62(6), 971-985. doi:10.1016/s0031-9422(02)00717-3Panetto, H. (2007). Towards a classification framework for interoperability of enterprise applications. International Journal of Computer Integrated Manufacturing, 20(8), 727-740. doi:10.1080/09511920600996419Paulraj, G. J. L., Francis, S. A. J., Peter, J. D., & Jebadurai, I. J. (2018). Resource-aware virtual machine migration in IoT cloud. Future Generation Computer Systems, 85, 173-183. doi:10.1016/j.future.2018.03.024Pilli, S. K., Nallathambi, B., George, S. J., & Diwanji, V. (2015). eAGROBOT — A robot for early crop disease detection using image processing. 2015 2nd International Conference on Electronics and Communication Systems (ICECS). doi:10.1109/ecs.2015.7124873Pinho, P., Dias, T., Cruz, C., Sim Tang, Y., Sutton, M. A., Martins-Loução, M.-A., … Branquinho, C. (2011). Using lichen functional diversity to assess the effects of atmospheric ammonia in Mediterranean woodlands. Journal of Applied Ecology, 48(5), 1107-1116. doi:10.1111/j.1365-2664.2011.02033.xPrathibha, S. R., Hongal, A., & Jyothi, M. P. (2017). IOT Based Monitoring System in Smart Agriculture. 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). doi:10.1109/icraect.2017.52Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D., & Zilberman, D. (2019). Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research & innovations. Agricultural Systems, 172, 47-59. doi:10.1016/j.agsy.2018.01.022Ribarics, P. (2016). Big Data and its impact on agriculture. Ecocycles, 2(1), 33-34. doi:10.19040/ecocycles.v2i1.54Rosell, J. R., & Sanz, R. (2012). A review of methods and applications of the geometric characterization of tree crops in agricultural activities. Computers and Electronics in Agriculture, 81, 124-141. doi:10.1016/j.compag.2011.09.007Roshanianfard, A., Kamata, T., & Noguchi, N. (2018). Performance evaluation of harvesting robot for heavy-weight crops. IFAC-PapersOnLine, 51(17), 332-338. doi:10.1016/j.ifacol.2018.08.200Routroy, S., & Behera, A. (2017). Agriculture supply chain. Journal of Agribusiness in Developing and Emerging Economies, 7(3), 275-302. doi:10.1108/jadee-06-2016-0039Ruiz-Garcia, L., Steinberger, G., & Rothmund, M. (2010). A model and prototype implementation for tracking and tracing agricultural batch products along the food chain. Food Control, 21(2), 112-121. doi:10.1016/j.foodcont.2008.12.003Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big

    Sustainable Freight Village Concepts for Agricultural Products Logistics - A Knowledge Management-oriented Study

    Get PDF
    Freight villages (FVs) are logistics phenomenon with broad economic, environmental, and social consequences. This research extends sustainability in the context of FVs to twofold meanings. This includes the sustainable development of FVs, and the sustainability contributions they created. China has showed a more recent but quicker story in developing FVs. Nevertheless, a number of problems and limits exist. Taking China as research background, this research consists of theoretical explorations, status quo analysis, and practical applications in agricultural products logistics (APL). This research proposes that knowledge management (KM) is the most desirable option to reach sustainable values of FVs. The status quo analysis examines the adaption of FVs to China s current logistics situation. It identifies several orientations for the next development step of FVs. To cope with the pervasive problems during FV planning stage, this research adopts sense-making KM approach to guide a systematic planning process including sense making, knowledge creation, and decision making. In order to achieve a high sustainability rating, this research uses KM cycle model to generate synergies among major stakeholders of FVs. Moreover, it proposes a roadmap for organizing KM process so as to build sustainability capability. Three key elements for the roadmap are identified: human ability, stakeholder engagement, and information system. This research focuses on the application of sustainable FV concepts for agricultural products logistics (APL). It deals with issues affecting city logistics and agricultural products (agri-products) supply in China. This can make best use of FV functions from a sustainability perspective. Agri-FV is proposed to bring professional logistics facilities and services, efficient distribution of agri-products, and information-based operations. An integrated APL system is structured consisting of farmers cooperatives, chain stores, and agri-FVs. In this system, agri-FV plays an important role in levering sustainability outcomes. Furthermore, KM approaches are applied to agri-FV project planning and operations, meanwhile considering the characteristics of agri-products and APL

    Urban food strategies in Central and Eastern Europe: what's specific and what's at stake?

    Get PDF
    Integrating a larger set of instruments into Rural Development Programmes implied an increasing focus on monitoring and evaluation. Against the highly diversified experience with regard to implementation of policy instruments the Common Monitoring and Evaluation Framework has been set up by the EU Commission as a strategic and streamlined method of evaluating programmes’ impacts. Its indicator-based approach mainly reflects the concept of a linear, measure-based intervention logic that falls short of the true nature of RDP operation and impact capacity on rural changes. Besides the different phases of the policy process, i.e. policy design, delivery and evaluation, the regional context with its specific set of challenges and opportunities seems critical to the understanding and improvement of programme performance. In particular the role of local actors can hardly be grasped by quantitative indicators alone, but has to be addressed by assessing processes of social innovation. This shift in the evaluation focus underpins the need to take account of regional implementation specificities and processes of social innovation as decisive elements for programme performance.
    corecore