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Farming 4.0: Towards Sustainable Agriculture

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 42992

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Special Issue Editors

Dr. Charisios Achillas

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Guest Editor

Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece
Interests: data analytics; optimization techniques; circular economy
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Dionysis Bochtis

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Institute for Bio-Economy and Agri-Technology (iBO), Centre for Research and Technology-Hellas (CERTH), 38333 Volos, Greece
Interests: digital agriculture; agri-robotics; operations management; systems engineering
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Dimitrios Aidonis

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Guest Editor

Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece
Interests: operations research; supply chain management; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Undeniably, over the last few years, humanity has faced several threats, but also challenges, in relation to its security and preservation. As the world population is continuously growing, nutrition and food security are among the most critical debates in the public dialogue internationally, constituting a crucial aspect for global security and a prerequisite for democracy and freedom. In the years to come, the world will unquestionably require more (even doubled compared to current needs) food. Moreover, environmental deterioration due to agrifood production presents a major risk on global sustainability for the generations to come. In this light, the need for immediate actions is urgent. On the other hand, late rapid technological developments and advancements in ICT, ΙοΤ, robotics, automation, sensors, and farming equipment present major opportunities for humankind and provide stakeholders (governments, policy-makers, scientists, investors, agrifood companies, retailers, farmers, consumers, etc.) with revolutionary tools to boost efficiency in food production, battle against environmental degradation, and improve labor conditions and public well-being. To that end, digitization of agriculture constitutes a critical parameter of success toward sustainable development. Apart from this trend, “Farming 4.0” is widely accepted as the future of farming, influencing food security, poverty, and the overall sustainability of agricultural systems, by minimizing the required inputs in resources and maximizing agri-production. Farming 4.0 is expected to shift toward an innovation- and knowledge-based economy, ultimately resulting in safe, cost-effective, efficient, and environmentally sound agriculture.

This Special Issue seeks to contribute to the sustainable agriculture agenda through enriching scientific knowledge in an effort to proliferate performance efficiency and support decision-making in modern agri-business. In this context, we invite papers on innovative technical developments, reviews, case studies, and analytical, as well as assessment, papers from different disciplines, which are relevant to all different aspects related to the digitization of agriculture within the fields of primary agriculture, agrifood production, and agrifood supply chains. Indicatively, the following topics are welcome to be captured in the contributions to the present Special Issue: information and communication technologies (ICT), Internet of Things (IoT), machine-embedded tools, robotics, automation, human–computer interaction, artificial intelligence, remote sensing (e.g., wireless sensor networks, remote sensing and GIS applications, biosensors, physical/chemical/optical sensors), data management (e.g., big data, data mining, data visualization, image processing, knowledge management, data/metadata standards, ontologies for agriculture, knowledge repository, web of data and open data), traceability tools, social networking, etc.

Prof. Charisios Achillas
Prof. Dionysis Bochtis
Prof. Dimitrios Aidonis
Guest Editors

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Keywords

ICT
IoT
precision farming
robotics
automation
remote sensing
big data
machine-embedded tools

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Published Papers (9 papers)

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Research

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13 pages, 1656 KiB

Open AccessArticle

Preparation of an Environmentally Friendly Rice Seed Coating Agent and Study of Its Mechanism of Action in Seedlings

by Jinfu Fang, Defang Zeng and Tian Xu

Sustainability 2023, 15(1), 869; https://doi.org/10.3390/su15010869 - 3 Jan 2023

Cited by 3 | Viewed by 2284

Abstract

Traditional rice seed coating agents (TRSCA) contain toxic components that pollute the environment and threaten human health. The use of safe, high-efficiency, and environmentally friendly seed coating agents is vital for environmental protection. We studied the production of a new, environmentally friendly rice seed coating agent and its mechanism at the seedling stage. We assess the difference in mechanism of action between the new seed coating agent and the representative TRSCAs on the market through laboratory and field experiments. Following the application of the new seed coating agent, bakanae disease was controlled at a rate of over 80.5% and insect pest feeding was controlled at a rate of 81%. More importantly, the LC₅₀ value was 10 times higher than following TRSCA treatment. The coating agent can enhance the activity of plant protective enzymes (peroxidase [POD], catalase [CAT], and superoxide dismutase [SOD]) and the activity of rice seedling roots. The coating agent is antibacterial, disease preventative, deworming, safe, and environmentally protective, and results in the production of strong seedlings, suggesting it would be a good alternative to TRSCA. Our analysis found that the control effect of the seed coating on rice seedling disease was likely achieved by activating the plant protection enzymes (e.g., POD, CAT, and SOD). The effect of the coating agent on rice is likely achieved through increased root activity and the improvement of the rhizosphere micro-ecological system. Full article

(This article belongs to the Special Issue Farming 4.0: Towards Sustainable Agriculture)

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21 pages, 1241 KiB

Open AccessArticle

Stakeholders’ Preferences towards Contract Attributes: Evidence from Rice Production in Vietnam

by Mai Chiem Tuyen, Prapinwadee Sirisupluxana, Isriya Bunyasiri and Pham Xuan Hung

Sustainability 2022, 14(6), 3478; https://doi.org/10.3390/su14063478 - 16 Mar 2022

Cited by 8 | Viewed by 3190

Abstract

Contract farming is typically considered an appropriate measure for small-scale farmers to solve their constraints and problems. However, despite positive effects, low participation in and high dropout rates from contract farming schemes remain challenges. Therefore, this study objects to evaluate preferences for contract attributes and attribute levels among contracting buyers, farmers, and government officials through data triangulation from key informant interviews, focus group discussions, and participant observations. Based on Henry Garrett Ranking, Rank Based Quotient, and Rank Based Sum methods, results indicate that the most important attributes were price options, payment, delivery arrangement, input provision, input-use requirements, and product quality standards. Despite a consensus on the ranking of the contract attributes, the preferences for the attribute levels among the stakeholders were heterogeneous. It is recommended that attributes and their levels should be pertinent in contract agreements. Thus, contract design with an adjusted or premium price, 50% of estimated payment before harvesting and the rest after delivery three to five days or lump-sum immediate payment, delivery after harvesting, inputs provision by the contractors through the representative branches or stores located at the local areas or cooperatives, banning active-ingredients or flexible use of inputs from the contractors to produce Good Agricultural Practices or organic products are considerable options. Full article

(This article belongs to the Special Issue Farming 4.0: Towards Sustainable Agriculture)

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15 pages, 2543 KiB

Open AccessArticle

The Future of Agricultural Jobs in View of Robotization

by Vasso Marinoudi, Maria Lampridi, Dimitrios Kateris, Simon Pearson, Claus Grøn Sørensen and Dionysis Bochtis

Sustainability 2021, 13(21), 12109; https://doi.org/10.3390/su132112109 - 2 Nov 2021

Cited by 21 | Viewed by 3460

Abstract

Robotics and computerization have drastically changed the agricultural production sector and thus moved it into a new automation era. Robots have historically been used for carrying out routine tasks that require physical strength, accuracy, and repeatability, whereas humans are used to engage with more value-added tasks that need reasoning and decision-making skills. On the other hand, robots are also increasingly exploited in several non-routine tasks that require cognitive skills. This technological evolution will create a fundamental and an unavoidable transformation of the agricultural occupations landscape with a high social and economic impact in terms of jobs creation and jobs destruction. To that effect, the aim of the present work is two-fold: (a) to map agricultural occupations in terms of their cognitive/manual and routine/non-routine characteristics and (b) to assess the susceptibility of each agricultural occupation to robotization. Seventeen (17) agricultural occupations were reviewed in relation to the characteristics of each individual task they entail and mapped onto a two-dimensional space representing the manual versus cognitive nature and the routine versus non-routine nature of an occupation. Subsequently, the potential for robotization was investigated, again concerning each task individually, and resulted in a weighted average potential adoption rate for each one of the agricultural occupations. It can be concluded that most of the occupations entail manual tasks that need to be performed in a standardised manner. Considering also that almost 81% of the agricultural work force is involved with these activities, it turns out that there is strong evidence for possible robotization of 70% of the agricultural domain, which, in turn, could affect 56% of the total annual budget dedicated to agricultural occupations. The presented work silhouettes the expected transformation of occupational landscape in agricultural production as an effort for a subsequent identification of social threats in terms of unemployment and job and wages polarization, among others, but also of opportunities in terms of emerged skills and training requirements for a social sustainable development of agricultural domain. Full article

(This article belongs to the Special Issue Farming 4.0: Towards Sustainable Agriculture)

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13 pages, 2810 KiB

Open AccessArticle

A Farm Management Information System for Semi-Supervised Path Planning and Autonomous Vehicle Control

by Hao Wang, Yaxin Ren and Zhijun Meng

Sustainability 2021, 13(13), 7497; https://doi.org/10.3390/su13137497 - 5 Jul 2021

Cited by 8 | Viewed by 3454

Abstract

This paper presents a farm management information system targeting improvements in the ease of use and sustainability of robot farming systems. The system integrates the functionalities of field survey, path planning, monitoring, and controlling agricultural vehicles in real time. Firstly, a Grabcut-based semi-supervised field registration method is proposed for arable field detection from the orthoimage taken by the drone with an RGB camera. It partitions a complex field into simple geometric entities with simple user interaction. The average Mean Intersection over Union is about 0.95 when the field size ranges from 2.74 ha to 5.06 ha. In addition, a desktop software and a web application are developed as the entity of an FMIS. Compared to existing FMISs, this system provides more advanced features in robot farming, while providing simpler user interaction and better results. It allows clients to invoke web services and receive responses independent of programming language and platforms. Moreover, the system is compatible with other services, users, and devices following the open-source access protocol. We have evaluated the system by controlling 5 robot tractors with a 2 Hz communication frequency. The communication protocols will be publicly available to protentional users. Full article

(This article belongs to the Special Issue Farming 4.0: Towards Sustainable Agriculture)

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15 pages, 1266 KiB

Open AccessFeature PaperArticle

Feeding Models to Optimize Dairy Feed Rations in View of Feed Availability, Feed Prices and Milk Production Scenarios

by Othman Alqaisi and Eva Schlecht

Sustainability 2021, 13(1), 215; https://doi.org/10.3390/su13010215 - 4 Jan 2021

Cited by 6 | Viewed by 4158

Abstract

In the global dairy production sector, feed ingredient price and availability are highly volatile; they may shape the composition of the feed ration and, in consequence, impact feed cost and enteric methane (CH₄) emissions. The objective of this study is to explore the impact of changes in feed ingredients’ prices and feed ingredients’ availability on dairy ration composition, feed cost and predicted methane yield under different levels of milk production. To meet the research aim, a series of multi-period linear programming models were developed. The models were then used to simulate 14 feed rations formulations, each covering 162 months and three dairy production levels of 10, 25 and 35 kg milk/d, representing a total of 6804 feed rations altogether. Across milk production levels, the inclusion of alfalfa hay into the feed rations declined from 55% to 38% when daily milk production increased from 10 to 35 kg, reflecting the cows’ increased energy requirements. At a daily milk production level of 35 kg, CH₄ production (per kg milk) was 21% and 53% lower than in average and low milk producing cows, respectively, whereas at 10 kg of milk production the potential to reduce CH₄ production varied between 0.6% and 5.5% (average = 3.9%). At all production levels, a reduction in CH₄ output was associated with an increase in feed costs. Overall, and considering feeding scenarios in low milk producing cows, feed cost per kg milk was 30% and 37% higher compared to that of average and high milk production, respectively. The feed ration modeling approach allows us to account for the interaction between feed ingredients over time, taking into consideration volatile global feed prices. Overall, the model provides a decision-making tool to improve the use of feed resources in the dairy sector. Full article

(This article belongs to the Special Issue Farming 4.0: Towards Sustainable Agriculture)

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17 pages, 983 KiB

Open AccessArticle

Can the Adoption of Protected Cultivation Facilities Affect Farm Sustainability?

by Pei-An Liao, Jhih-Yun Liu, Lih-Chyun Sun and Hung-Hao Chang

Sustainability 2020, 12(23), 9970; https://doi.org/10.3390/su12239970 - 28 Nov 2020

Cited by 5 | Viewed by 2993

Abstract

Given the increasing threat of climate change to agriculture, determining how to achieve farm sustainability is important for researchers and policy makers. Among others, protected cultivation has been proposed as a possible adaptive solution at the farm level. This study contributes to this research topic by quantifying the effects of the use of protected cultivation facilities on farm sustainability. In contrast to previous studies that relied on small-scale random surveys, a population-based sample of fruit, flower and vegetable farms was drawn from the Agricultural Census Survey in Taiwan. Propensity score matching, inverse probability weighting and inverse probability weighting regression adjustment methods were applied. Empirical results show that the use of protected cultivation facilities increases farm profit by 68–73%, other things being equal. This finding is persistent when farms suffer from disaster shocks. Moreover, the changes in farm labor use can be seen as a mechanism behind the positive effect of the protected cultivation facility use on farm profit. Our findings suggest that agricultural authority can consider subsidizing farms to increase the adoption of protected cultivation facilities to mitigate the risks resulting from natural disaster shocks. Full article

(This article belongs to the Special Issue Farming 4.0: Towards Sustainable Agriculture)

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Review

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18 pages, 942 KiB

Open AccessReview

Benefits and Challenges of Making Data More Agile: A Review of Recent Key Approaches in Agriculture

by Elena Serfilippi, Daniele Giovannucci, David Ameyaw, Ankur Bansal, Thomas Asafua Nketsia Wobill, Roberta Blankson and Rashi Mishra

Sustainability 2022, 14(24), 16480; https://doi.org/10.3390/su142416480 - 9 Dec 2022

Viewed by 3695

Abstract

Having reliable and timely or ongoing field data from development projects or supply chains is a perennial challenge for decision makers. This is especially true for those operating in rural areas where traditional data gathering and analysis approaches are costly and difficult to operate while typically requiring so much time that their findings are useful mostly as learning after the fact. A series of innovations that we refer to as Agile Data are opening new frontiers of timeliness, cost, and accuracy. They are leveraging a range of technological advances to do so. This paper explores the differences between traditional and agile approaches and offers insights into costs and benefits by drawing on recent field research in agriculture conducted by diverse institutions such as the World Bank (WB), World Food Program (WFP), United States Agency for International Development (USAID), and the Committee on Sustainability Assessment (COSA). The evidence collected in this paper about agile approaches—including those relying on internet and mobile-based data collection—contributes to define a contemporary dimension of data and analytics that can contribute to more optimal decision-making. Providing a theoretical, applied, and empirical foundation for the collection and use of Agile Data can offer a means to improve the management of development initiatives and deliver new value, as participants or beneficiaries are better informed and can better respond to a fast-changing world. Full article

(This article belongs to the Special Issue Farming 4.0: Towards Sustainable Agriculture)

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37 pages, 11935 KiB

Open AccessReview

Data Type and Data Sources for Agricultural Big Data and Machine Learning

by Ania Cravero, Sebastián Pardo, Patricio Galeas, Julio López Fenner and Mónica Caniupán

Sustainability 2022, 14(23), 16131; https://doi.org/10.3390/su142316131 - 2 Dec 2022

Cited by 10 | Viewed by 6999

Abstract

Sustainable agriculture is currently being challenged under climate change scenarios since extreme environmental processes disrupt and diminish global food production. For example, drought-induced increases in plant diseases and rainfall caused a decrease in food production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow analyzing a large amount of data to understand agricultural production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow the processing and analysis of large amounts of heterogeneous data for which intelligent IT and high-resolution remote sensing techniques are required. However, the selection of ML algorithms depends on the types of data to be used. Therefore, agricultural scientists need to understand the data and the sources from which they are derived. These data can be structured, such as temperature and humidity data, which are usually numerical (e.g., float); semi-structured, such as those from spreadsheets and information repositories, since these data types are not previously defined and are stored in No-SQL databases; and unstructured, such as those from files such as PDF, TIFF, and satellite images, since they have not been processed and therefore are not stored in any database but in repositories (e.g., Hadoop). This study provides insight into the data types used in Agricultural Big Data along with their main challenges and trends. It analyzes 43 papers selected through the protocol proposed by Kitchenham and Charters and validated with the PRISMA criteria. It was found that the primary data sources are Databases, Sensors, Cameras, GPS, and Remote Sensing, which capture data stored in Platforms such as Hadoop, Cloud Computing, and Google Earth Engine. In the future, Data Lakes will allow for data integration across different platforms, as they provide representation models of other data types and the relationships between them, improving the quality of the data to be integrated. Full article

(This article belongs to the Special Issue Farming 4.0: Towards Sustainable Agriculture)

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15 pages, 318 KiB

Open AccessReview

Applications of Smart Technology as a Sustainable Strategy in Modern Swine Farming

by Shad Mahfuz, Hong-Seok Mun, Muhammad Ammar Dilawar and Chul-Ju Yang

Sustainability 2022, 14(5), 2607; https://doi.org/10.3390/su14052607 - 23 Feb 2022

Cited by 38 | Viewed by 10528

Abstract

The size of the pork market is increasing globally to meet the demand for animal protein, resulting in greater farm size for swine and creating a great challenge to swine farmers and industry owners in monitoring the farm activities and the health and behavior of the herd of swine. In addition, the growth of swine production is resulting in a changing climate pattern along with the environment, animal welfare, and human health issues, such as antimicrobial resistance, zoonosis, etc. The profit of swine farms depends on the optimum growth and good health of swine, while modern farming practices can ensure healthy swine production. To solve these issues, a future strategy should be considered with information and communication technology (ICT)-based smart swine farming, considering auto-identification, remote monitoring, feeding behavior, animal rights/welfare, zoonotic diseases, nutrition and food quality, labor management, farm operations, etc., with a view to improving meat production from the swine industry. Presently, swine farming is not only focused on the development of infrastructure but is also occupied with the application of technological knowledge for designing feeding programs, monitoring health and welfare, and the reproduction of the herd. ICT-based smart technologies, including smart ear tags, smart sensors, the Internet of Things (IoT), deep learning, big data, and robotics systems, can take part directly in the operation of farm activities, and have been proven to be effective tools for collecting, processing, and analyzing data from farms. In this review, which considers the beneficial role of smart technologies in swine farming, we suggest that smart technologies should be applied in the swine industry. Thus, the future swine industry should be automated, considering sustainability and productivity. Full article

(This article belongs to the Special Issue Farming 4.0: Towards Sustainable Agriculture)

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