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Agronomy
Special Issues
Intelligent Decision Support for Agri-Food Green Supply Chain
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Intelligent Decision Support for Agri-Food Green Supply Chain

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Farming Sustainability".

Deadline for manuscript submissions: closed (22 October 2021) | Viewed by 18509

Special Issue Editors

Dr. Giacomo Falcone

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Guest Editor
Department of Agricultural Sciences, Mediterranean University of Reggio Calabria, Feo di Vito, 89122 Reggio Calabria, Italy
Interests: life cycle assessment; life cycle costing; social life cycle assessment; life cycle sustainability assessment; agricultural economics; food production cost; agribusiness economics; organization and management of agribusinesses
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Violi

E-Mail Website
Guest Editor
Department of Law, Economics, Management and Quantitative Methods, Università degli Studi del Sannio, Via delle Puglie 82, 82100 Benevento, Italy
Interests: decision support systems; stochastic programming; decision models for energy and financial markets; optimization models for agri-food logistics; risk management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

The agri-food sector is moving towards a new evolutionary era that will characterize the economy and ecology of the future: a productive version of agriculture that, in line with the principles of ecology, puts order back into the agronomic disciplines, a perfectly agro-ecological version of agriculture: the so-called Agriculture 5.0.

Consistent with this wave of renewal of production models, the European Union, through the Green Deal, has set the goal of rewriting the future of food and agriculture within a global program that aims to achieve climate neutrality by 2050 and a 55% reduction in emissions by 2030.

The reduction of environmental impacts, the reduction of waste, and the valorisation of by-products are only some of the possible strategies that the entrepreneur can adopt for a greener and more circular supply chain.

Technologies can play a decisive role in achieving these objectives without, however, threatening the economic sustainability of the company and affecting the technical feasibility of the processes. A new impetus towards this change has been given by the COVID-19 pandemic, which is redesigning economic sectors and posing new challenges to the world population.

It is therefore necessary that agri-food entrepreneurs are supported in their choices in order to pursue the ambitious objective of re-designing the supply chains with a view to sustainability. In such a context Intelligent Decision Support Systems (IDSS), based on the new huge availability of data and on advanced methodological results, can lead to significant improvements on both efficiency and effectiveness of decision processes in the agri-food supply chain.

Methodological applications, theoretical discussions, and literature reviews are welcome to this Special Issue. Papers received will be subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications.

Dr. Giacomo Falcone
Dr. Antonio Violi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agri-food green supply chains
  • environmental sustainability
  • smart agriculture
  • operations research methods for agri-food
  • decision making under uncertainty & risk management
  • decision Support Systems
  • green logistics

Published Papers (5 papers)

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Research

29 pages, 3760 KiB  
Article
A Network Analysis for Environmental Assessment in Wine Supply Chain
by Giulia Maesano, Mirco Milani, Elisabetta Nicolosi, Mario D’Amico and Gaetano Chinnici
Agronomy 2022, 12(1), 211; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12010211 - 16 Jan 2022
Cited by 15 | Viewed by 3495
Abstract
In the agri-food sector, the Life Cycle Assessment method (LCA) is used to evaluate the environmental impact of a product. Within agri-food products, wine is among the most analysed products, not only for its economic importance but also for the environmental impact of [...] Read more.
In the agri-food sector, the Life Cycle Assessment method (LCA) is used to evaluate the environmental impact of a product. Within agri-food products, wine is among the most analysed products, not only for its economic importance but also for the environmental impact of its activity. The paper aims to identify the main trends in the wine sector revolving around environmental evaluation using the LCA method in the academic literature. The aim is to investigate the literature on life cycle assessment analysis of grape and wine production through the systematic grouping of papers into clusters of research. So, the purpose is to discuss the gaps and insights identified by the study in order to aid in the development of a comprehensive state of the art on the topic. Scopus and Web of Science were used to search all articles following a clear and replicable protocol. The results (keywords) were subjected to co-occurrence analysis using VOSviewer, after which the articles were further analysed. Through a bibliographic coupling analysis, the research results were grouped through a network analysis that allowed identifying the research trends on the topic. Three clusters were identified containing the main lines of research on the subject. The results show that nowadays the literature is focusing on concerns related to climate change and consumer awareness on sustainability issues and certifications as well as environmental impacts generated mainly in the production phase in the vineyard. The research results are of interest for future research on LCA analysis in the wine sector in order to contribute to the discussion on the current model in the global wine sector. Full article
(This article belongs to the Special Issue Intelligent Decision Support for Agri-Food Green Supply Chain)
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21 pages, 1017 KiB  
Article
A Decision Support System for Sustainable Agriculture: The Case Study of Coconut Oil Extraction Process
by Gianfranco Gagliardi, Antonio Igor Maria Cosma and Francesco Marasco
Agronomy 2022, 12(1), 177; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12010177 - 12 Jan 2022
Cited by 4 | Viewed by 3311
Abstract
The high demand of information and communication technology (ICT) in agriculture applications has led to the introduction of the concept of smart farming. In this respect, moving from the main features of the Fourth Industrial Revolution (Industry 4.0) promoted by the European [...] Read more.
The high demand of information and communication technology (ICT) in agriculture applications has led to the introduction of the concept of smart farming. In this respect, moving from the main features of the Fourth Industrial Revolution (Industry 4.0) promoted by the European Community, new approaches have been suggested and adopted in agriculture, giving rise to the so-called Agriculture 4.0. Improvements in automation, advanced information systems and Internet technologies allow for farmers to increase the productivity and to allocate the resources reasonably. For these reasons, agricultural decision support systems (DSS) for Agriculture 4.0 have become a very interesting research topic. DSS are interactive tools that enable users to make informed decisions about unstructured problems, and can be either fully computerized, human or a combination of both. In general, a DSS analyzes and synthesizes large amounts of data to assist in decision making. This paper presents an innovative decision support system solution to address the issues faced by coconut oil producers in making strategic decisions, particularly in the comparison of different methods of oil extraction. In more detail, the adopted methodology describes how to address the problems of coconut oil extraction in order to minimize the processing time and processing cost and to obtain energy savings. To this end, the coconut oil extraction process of the Leão São Tomé and Principe Company is presented as a case study: a DSS instance that analyzes the problem of the optimal selection between two different oil coconut extraction methods (fermentation-based and standard extraction processes) is developed as a meta-heuristics with a mixed integer linear programming problem. The obtained results show that there is clearly a trade-off between the increase in cost and reliability that the decision-maker may be willing to evaluate. In this respect, the proposed model provides a tool to support the decision-maker in choosing the best combination between the two different coconut oil extraction methods. The proposed DSS has been tested in a real application context through an experimental campaign. Full article
(This article belongs to the Special Issue Intelligent Decision Support for Agri-Food Green Supply Chain)
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18 pages, 5745 KiB  
Article
An Internet of Things Solution for Smart Agriculture
by Gianfranco Gagliardi, Marco Lupia, Gianni Cario, Francesco Cicchello Gaccio, Vincenzo D’Angelo, Antonio Igor Maria Cosma and Alessandro Casavola
Agronomy 2021, 11(11), 2140; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11112140 - 26 Oct 2021
Cited by 19 | Viewed by 4011
Abstract
Over the last decade, the increased use of information and communication technology (ICT) in agriculture applications has led to the definition of the concept of precision farming or equivalently smart agriculture. In this respect, the latest progress in connectivity, automation, images analysis [...] Read more.
Over the last decade, the increased use of information and communication technology (ICT) in agriculture applications has led to the definition of the concept of precision farming or equivalently smart agriculture. In this respect, the latest progress in connectivity, automation, images analysis and artificial intelligence allow farmers to monitor all production phases and, due to the help of automatic procedures, determine better treatments for their farms. One of the main objectives of a smart agriculture system is to improve the yield of the field. From this point of view, the Internet of Things (IoT) paradigm plays a key role in precision farming applications due to the fact that the use of IoT sensors provides precise information about the health of the production. In this paper, the results of the recently concluded R&D project ENOTRIA TELLUS are reported. The project aimed at the development of all hardware/software components for implementing a precision farming architecture allowing the farmers to manage and monitor the vineyards’ health status. The smart architecture combines various sub-systems (web application, local controllers, unmanned aerial vehicles, multi-spectral cameras, weather sensors etc.) and electronic devices, each of them in charge of performing specific operations: remote data analysis, video processing for vegetation analysis, wireless data exchanges and weather and monitoring data evaluation. Two pilot sites were built where the smart architecture was tested and validated in real scenarios. Experimental activities show that the designed smart agriculture architecture allowed the farmers to properly schedule the various phases of cultivation and harvesting. Full article
(This article belongs to the Special Issue Intelligent Decision Support for Agri-Food Green Supply Chain)
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20 pages, 3597 KiB  
Article
Life Cycle Assessment to Highlight the Environmental Burdens of Early Potato Production
by Giuseppe Timpanaro, Ferdinando Branca, Mariarita Cammarata, Giacomo Falcone and Alessandro Scuderi
Agronomy 2021, 11(5), 879; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11050879 - 29 Apr 2021
Cited by 14 | Viewed by 3044
Abstract
Climate change, food security, and the protection of the planet’s resources require the adoption of sustainable production models. Achieving sustainable development in the agri-food sector enables the creation of new opportunities for operators, guiding farmers towards more environmentally friendly practices and offering cost-effective [...] Read more.
Climate change, food security, and the protection of the planet’s resources require the adoption of sustainable production models. Achieving sustainable development in the agri-food sector enables the creation of new opportunities for operators, guiding farmers towards more environmentally friendly practices and offering cost-effective results. Organic farming paradigms are promoted by the transformation of some harmful practices of conventional agriculture, such as the wide use of chemical products of synthesis, the deep workings that favor the erosive processes, the excessive use of nitrogenous fertilizers. There are still gaps in the knowledge of the real performance of some products that strongly support the local economic system of Sicily (Italy). The research aims to highlight the differences in environmental impact caused by the cultivation of organic early potatoes compared to the conventional regime and the same per kg of product obtained. To this end, the widely used methodology for comparing the environmental impacts of agricultural production systems is the Life Cycle Assessment, which allows us to highlight the phases in which environmental criticalities are most concentrated. An interesting agroecological picture of knowledge emerges, since organic farming is by definition an ecological model that supports the principles of the Green Deal, it often requires interventions to improve the yields obtained in order to achieve a positive result both in terms of cultivated surface and kg of product obtained. Full article
(This article belongs to the Special Issue Intelligent Decision Support for Agri-Food Green Supply Chain)
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14 pages, 2419 KiB  
Article
Optimized Supply Chain Management of Rice in South Korea: Location–Allocation Model of Rice Production
by Kyunam An, Sumin Kim, Seoho Shin, Hyunkyoung Min and Sojung Kim
Agronomy 2021, 11(2), 270; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy11020270 - 31 Jan 2021
Cited by 2 | Viewed by 3676
Abstract
Planning for optimized farming with the aim of providing ideal site and cultivar selection is critical for a stable and sustainable supply of rice with sufficient quantity and quality to customers. In this study, a range of morphological characteristics and yield of eight [...] Read more.
Planning for optimized farming with the aim of providing ideal site and cultivar selection is critical for a stable and sustainable supply of rice with sufficient quantity and quality to customers. In this study, a range of morphological characteristics and yield of eight rice cultivars that are commonly cultivated in Korea were investigated from 2005 to 2020. All morphological characteristics were significantly different among the eight rice cultivars. The dataset of morphological characteristics and yield was used to isolate groups of similar rice cultivars. The k-means clustering method was used to group the rice cultivars. Three groups (Group 1, Group 2, and Group 3) were created. Most cultivars were in Group 1. High-yielding rice cultivars were in Group 2, while the rice cultivars in Group 3 had the lowest rice grain yield. After grouping these rice cultivars, ideal farming locations for all three rice cultivar groups were identified to reduce transportation cost using an optimized location–allocation model. Simulation results suggested the following: (1) Group 1 should be produced in Jellanam-do (south west region), (2) Group 2 should be produced in Chungcheongnam-do (central west region), and (3) Group 3 should be mainly produced in the central west region of South Korea. Simulation results showed the potential to reduce transportation cost by around 0.014%. This can also reduce 21.04 tons of CO2 emission from a freight truck. Because these eight cultivars only make up 19.76% of the total rice production in South Korea, the cost reduction proportion was only 0.014% of total revenue. In future studies, more rice cultivars should be investigated to increase the efficiency of the model performance. Full article
(This article belongs to the Special Issue Intelligent Decision Support for Agri-Food Green Supply Chain)
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