case study of agricultural products

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• 15 Nov 2018

Can the Global Food Industry Overcome Public Distrust?

The public is losing trust in many institutions involved in putting food on our table, says Ray A. Goldberg, author of the new book Food Citizenship. Here's what needs to be done. Open for comment; 0 Comments.

• 17 Oct 2016

Business Solutions That Help Cut Food Waste

Up to 40 percent of food grown in the United States for human consumption is wasted. But solutions are starting to come together from retailers, farmers, academics, policy makers, and social service organizations, according to José Alvarez. Open for comment; 0 Comments.

• 09 Apr 2012
• Research & Ideas

Who Sways the USDA on GMO Approvals?

Government agencies can be "captured" by the very companies or industries they regulate. Looking at how genetically altered food products are approved, Assistant Professor Shon R. Hiatt finds unexpected influencers on the US Department of Agriculture. Key concepts include: "Regulatory capture" describes the phenomenon whereby regulatory agencies tasked with serving the public instead end up advancing the interests of the companies they regulate. Traditional theories of capture such as lobbying and campaign contributions had little effect on whether the US Department of Agriculture approved any particular genetically altered agriculture product. What did seem to affect the approval process was the influence of third-party groups such as associations and even related regulatory agencies. Open for comment; 0 Comments.

• 19 Jun 2009
• Research Event

Business Summit: The Evolution of Agribusiness

Agribusiness has come to be seen not just as economically important, but as a critical part of society. The future for this massive industry will be both exciting and complex. Closed for comment; 0 Comments.

• 03 Nov 2008

Economics of the Ethanol Business

What happens when a group of Missouri corn farmers gets into the energy business? What appears to be a very lucrative decision quickly turns out to be much more risky. Professor Forest Reinhardt leads a case discussion on what the protagonists should do next. From HBS Alumni Bulletin. Key concepts include: The case examines the complex political and economic underpinnings of the ethanol industry. By investing in corn-based ethanol, farmers reduce their exposure to corn prices, but at the expense of exposure to the oil market. The case promotes greater understanding of the way materials and energy flow in the modern U.S. agricultural system. Closed for comment; 0 Comments.

• 02 Jul 2001

Ray A. Goldberg

Closed for comment; 0 Comments.

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Please note you do not have access to teaching notes, marketing of agricultural products: case findings.

British Food Journal

ISSN : 0007-070X

Article publication date: 1 November 2002

This article focuses on the relationship marketing approach to marketing of agricultural products. The article provides specific insights into, and comparisons between, suppliers of two particular agricultural products sectors: in Britain, the fresh produce (fruits and vegetables) sector and, in New Zealand, the wine sector. The article examines the nature of marketing relationships from the perspective of the suppliers in these sectors and their relationships, networks, and interactions with importers and retail buyers in the food and beverage industry. The research methodology is qualitative and inductive in nature and utilises multiple cases. Interpretation is first through content analysis of each individual case in order to identify important themes, clusters, and patterns in the research data and secondly through across‐case analysis. Investigated marketing issues include the following: nature of relationship marketing, implementation of relationship marketing, and monitoring and measurement of relationship marketing.

• Agriculture
• Marketing strategy
• Relationship marketing
• Research methods

Hingley, M. and Lindgreen, A. (2002), "Marketing of agricultural products: case findings", British Food Journal , Vol. 104 No. 10, pp. 806-827. https://doi.org/10.1108/00070700210448908

Copyright © 2002, MCB UP Limited

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IoT in Agriculture: 9 Technology Use Cases for Smart Farming (and Challenges to Consider)

The article was updated on March 1, 2023.

With the growing adoption of the Internet of Things (IoT), connected devices have penetrated every aspect of our life , from health and fitness, home automation, automotive and logistics, to smart cities and industrial IoT.

Thus, it is only logical that IoT, connected devices, and automation would find its application in agriculture, and as such, tremendously improve nearly every facet of it. How could one still rely on horses and plows when self-driving cars and virtual reality are no longer a sci-fi fantasy but an everyday occurrence?

Farming has seen a number of technological transformations in the last decades, becoming more industrialized and technology-driven. By using various smart agriculture gadgets, farmers have gained better control over the process of raising livestock and growing crops, making it more predictable and improving its efficiency.

This, along with the growing consumer demand for agriculture products, has contributed to the increased proliferation of smart farming technologies worldwide. In 2022, the market share for IoT in agriculture reached $13.76 billion.

In this article, we will explore the IoT use cases in agriculture and examine their benefits. So, if you are considering investing into smart farming, or are planning to build an IoT solution for agriculture, dive right in.

• What is smart agriculture? The definition and market size

There are many ways to refer to modern agriculture. For example, AgriTech refers to the application of technology in agriculture in general.

Smart agriculture , on the other hand, is mostly used to denote the application of IoT solutions in agriculture. So what is smart agriculture using IoT? By using IoT sensors to collect environmental and machine metrics, farmers can make informed decisions, and improve just about every aspect of their work – from livestock to crop farming.

For example, by using smart agriculture sensors to monitor the state of crops, farmers can define exactly how many pesticides and fertilizers they have to use to reach optimal efficiency. The same applies to the smart farming definition.

Although smart agriculture IoT, as well as industrial IoT in general, aren’t as popular as consumer connected devices; yet the market is still very dynamic. The adoption of IoT solutions for agriculture is constantly growing.

Namely, COVID-19 has had a positive impact on IoT in the agriculture market share. Disruptions in the supply chain, and the shortage of qualified workers, has propelled its CAGR to 9,9%. In fact, as per recent reports, the smart framing market share is set to reach $28.56 billion by 2030.

At the same time, the global smart agriculture market size is expected to triple by 2025, reaching $15.3 billion (compared to being slightly over $5 billion back in 2016).

Because the market is still developing, there is still ample opportunity for businesses willing to join in. Building IoT products for agriculture within the coming years can set you apart as an early adopter, and as such, help you pave the way to success.

But why should you consider building an IoT application for agriculture in the first place?

The Benefits of smart farming: How’s IoT shaping agriculture

Technologies and IoT have the potential to transform agriculture in many aspects. Namely, there are 6 ways IoT can improve agriculture :

• Data, tons of data, collected by smart agriculture sensors, e.g. weather conditions, soil quality, crop’s growth progress or cattle’s health. This data can be used to track the state of your business in general as well as staff performance, equipment efficiency, etc.
• Better control over the internal processes and, as a result, lower production risks . The ability to foresee the output of your production allows you to plan for better product distribution. If you know exactly how much crops you are going to harvest, you can make sure your product won’t lie around unsold.
• Cost management and waste reduction thanks to the increased control over the production . Being able to see any anomalies in the crop growth or livestock health, you will be able to mitigate the risks of losing your yield.
• Increased business efficiency through process automation . By using smart devices, you can automate multiple processes across your production cycle, e.g. irrigation, fertilizing, or pest control.
• Enhanced product quality and volumes . Achieve better control over the production process and maintain higher standards of crop quality and growth capacity through automation.
• Reduced environmental footprint. Automation also carries environmental benefits. Smart farming technologies can cut down on the use of pesticides and fertilizer by offering more precise coverage, and thus, reduce greenhouse gas emissions.

As a result, all of these factors can eventually lead to higher revenue .

Now that we have outlined how IoT can be advantageously applied in the sphere of agriculture, let’s take a look at how the listed benefits can find their application in real life.

• IoT use cases in agriculture (with examples)

There are many types of IoT sensors for agriculture as well as IoT applications in agriculture in general:

1. Monitoring of climate conditions

Probably the most popular smart agriculture gadgets are weather stations, combining various smart farming sensors. Located across the field, they collect various data from the environment and send it to the cloud. The provided measurements can be used to map the climate conditions, choose the appropriate crops, and take the required measures to improve their capacity (i.e. precision farming).

Some examples of such agriculture IoT devices are allMETEO , Smart Elements , and Pycno .

2. Greenhouse automation

Typically, farmers use manual intervention to control the greenhouse environment. The use of IoT sensors enables them to get accurate real-time information on greenhouse conditions such as lighting, temperature, soil condition, and humidity.

In addition to sourcing environmental data, weather stations can automatically adjust the conditions to match the given parameters. Specifically, greenhouse automation systems use a similar principle.

For instance, Farmapp and Growlink are also IoT agriculture products offering such capabilities among others.

3. Crop management

One more type of IoT product in agriculture and another element of precision farming are crop management devices. Just like weather stations, they should be placed in the field to collect data specific to crop farming; from temperature and precipitation to leaf water potential and overall crop health.

Thus, you can monitor your crop growth and any anomalies to effectively prevent any diseases or infestations that can harm your yield. Arable and Semios can serve as good representations of how this use case can be applied in real life.

4. Cattle monitoring and management

Just like crop monitoring, there are IoT agriculture sensors that can be attached to the animals on a farm to monitor their health and log performance. Livestock tracking and monitoring help collect data on stock health, well-being, and physical location.

For example, such sensors can identify sick animals so that farmers can separate them from the herd and avoid contamination. Using drones for real-time cattle tracking also helps farmers reduce staffing expenses. This works similarly to IoT devices for petcare .

For example, SCR by Allflex and Cowlar use smart agriculture sensors (collar tags) to deliver temperature, health, activity, and nutrition insights on each individual cow as well as collective information about the herd.

5. Precision farming

Also known as precision agriculture, precision farming is all about efficiency and making accurate data-driven decisions. It’s also one of the most widespread and effective applications of IoT in agriculture.

By using IoT sensors, farmers can collect a vast array of metrics on every facet of the field microclimate and ecosystem: lighting, temperature, soil condition, humidity, CO2 levels, and pest infections. This data enables farmers to estimate optimal amounts of water, fertilizers, and pesticides that their crops need, reduce expenses, and raise better and healthier crops.

For example, CropX builds IoT soil sensors that measure soil moisture, temperature, and electric conductivity enabling farmers to approach each crop’s unique needs individually. Combined with geospatial data, this technology helps create precise soil maps for each field. Mothive offers similar services, helping farmers reduce waste, improve yields, and increase farm sustainability.

6. Agricultural drones

Perhaps one of the most promising agritech advancements is the use of agricultural drones in smart farming. Also known as UAVs (unmanned aerial vehicles), drones are better equipped than airplanes and satellites to collect agricultural data. Apart from surveillance capabilities, drones can also perform a vast number of tasks that previously required human labor: planting crops, fighting pests and infections, agriculture spraying, crop monitoring, etc.

Read more: Why Use Agriculture Drones? Main Benefits and Best Practices

DroneSeed , for example, builds drones for planting trees in deforested areas. The use of such drones is 6 times more effective than human labor. A Sense Fly agriculture drone eBee SQ uses multispectral image analyses to estimate the health of crops and comes at an affordable price.

7. Predictive analytics for smart farming

Precision agriculture and predictive data analytics go hand in hand. While IoT and smart sensor technology are a goldmine for highly relevant real-time data, the use of data analytics helps farmers make sense of it and come up with important predictions: crop harvesting time, the risks of diseases and infestations, yield volume, etc. Data analytics tools help make farming, which is inherently highly dependent on weather conditions, more manageable, and predictable.

For example, the Crop Performance platform helps farmers access the volume and quality of yields in advance, as well as their vulnerability to unfavorable weather conditions, such as floods and drought. It also enables farmers to optimize the supply of water and nutrients for each crop and even select yield traits to improve quality.

Applied in agriculture, solutions like SoilScout enable farmers to save up to 50% irrigation water, reduce the loss of fertilizers caused by overwatering, and deliver actionable insights regardless of season or weather conditions.

8. End-to-end farm management systems

A more complex approach to IoT products in agriculture can be represented by the so-called farm productivity management systems. They usually include a number of agriculture IoT devices and sensors, installed on the premises as well as a powerful dashboard with analytical capabilities and in-built accounting/reporting features.

This offers remote farm monitoring capabilities and allows you to streamline most of the business operations. Similar solutions are represented by FarmLogs and Cropio .

In addition to the listed IoT agriculture use cases, some prominent opportunities include vehicle tracking (or even automation), storage management, logistics, etc.

9. Robots and autonomous machines

Robotic innovations also offer a promising future in the field of autonomous machines for agricultural purposes. Some farmers already use automated harvesters, tractors, and other machines and vehicles that can operate without a human controlling it. Such robots can complete repetitive, challenging, and labor-intensive tasks.

For instance, modern agrobots include automated tractors that can work on assigned routes, send notifications, start work at planned hours, etc. Such tractors are driverless and cut farmers’ labor costs. Bear Flag Robotics is one company that works on such technology at the moment.

In addition, smart farming also uses robots for planting seeds, weeding, and watering. The given jobs are very demanding and labor-intensive. Yet, robots, such as ones from Eco Robotics , can detect weeds or plant seeds using computer vision and AI technology. These agricultural robots work delicately, drastically reducing harm to the plants and the environment.

• Things to consider before developing your smart farming solution

As we can see, the use cases for IoT in agriculture are endless. There are many ways smart devices can help you increase your farm’s performance and revenue. However, agriculture IoT apps development is no easy task.

There are certain challenges you need to be aware of if you are considering investing into smart farming.

1. The hardware

To build an IoT solution for agriculture, you need to choose the sensors for your device (or create a custom one). Your choice will depend on the types of information you want to collect and the purpose of your solution in general.

In any case, the quality of your sensors is crucial to the success of your product: it will depend on the accuracy of the collected data and its reliability.

2. The brain

Data analytics should be at the core of every smart agriculture solution. The collected data itself will be of little help if you cannot make sense of it.

Thus, you need to have powerful data analytics capabilities and apply predictive algorithms and machine learning in order to obtain actionable insights based on the collected data.

3. The maintenance

Maintenance of your hardware is a challenge that is of primary importance for IoT products in agriculture, as the sensors are typically used in the field and can be easily damaged.

Thus, you need to make sure your hardware is durable and easy to maintain. Otherwise you will need to replace your sensors more often than you would like.

4. The mobility

Smart farming applications should be tailored for use in the field. A business owner or farm manager should be able to access the information on site or remotely via a smartphone or desktop computer.

Plus, each connected device should be autonomous and have enough wireless range to communicate with the other devices and send data to the central server.

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5. The infrastructure

To ensure that your smart farming application performs well (and to make sure it can handle the data load), you need a solid internal infrastructure.

Furthermore, your internal systems have to be secure. Failing to properly secure your system only increases the likeliness of someone breaking into it, stealing your data or even taking control of your autonomous tractors.

6. Connectivity

The need to transmit data between many agricultural facilities still poses a challenge for the adoption of smart farming. Needless to say, the connection between these facilities should be reliable enough to withstand bad weather conditions and to ensure non-disruptive operations.

Today, IoT devices still use varying connection protocols, although the efforts to develop unified standards in this area are currently underway. The advent of 5G and technologies like space-based Internet will, hopefully, help find a solution to this problem.

7. Data collection frequency

Because of the high variety of data types in the agricultural industry, ensuring the optimal data collection frequency can be problematic. The data from field-based, aerial and environmental sensors, apps, machinery, and equipment, as well as processed analytical data, can be a subject of restriction and regulations.

Today, the safe and timely delivery, and sharing of this data is one of the current smart farming challenges.

8. Data security in the agriculture industry

Precision agriculture and IoT technology imply working with large sets of data, which increases the number of potential security loopholes that perpetrators can use for data theft and hacking attacks. Unfortunately, data security in agriculture is still, to a large extent, an unfamiliar concept.

Many farms, for example, use drones that transmit data to farm machinery. This machinery connects to the Internet but has little to zero security protection, such as user passwords or remote access authentications.

Some of the basic IoT security recommendations include monitoring data traffic, using encryption methods to protect sensitive data, leveraging AI-based security tools to detect traces of suspicious activity in real-time, and storing data in the blockchain to ensure its integrity.

To fully benefit from IoT, farmers will have to get familiar with the data security concept, set up internal security policies, and adhere to them.

• Our work case of IoT solutions for agriculture

Our team at Eastern Peak has also contributed to the progress of IoT applications in agriculture. The IoT-powered irrigation application, GreenIQ, helps gardeners reduce water usage by 50%, monitor humidity levels, and predict the best timing for irrigation. GreenIQ uses smart sensors to analyze meteorological conditions and soil types, creating the perfect irrigation strategy and adapting to new environments.

The GreenIQ application also integrates with the most well-known home automation platforms. This app is another valuable contribution to eco-friendly gardening and one of many examples of how smart farming products can change the future of agriculture.

• Grow your agriculture business with smart IoT solutions from Eastern Peak

According to the UN Food and Agriculture Organization (FAO) , the global population is expected to surpass 9 billion people by 2050. To produce enough food for the given population, agriculture production volumes have to increase by 50%.

As the resources for agricultural operations are limited (most of the lands suitable for farming are already in use), the only way to increase volume is to improve production efficiency. There is no doubt as to the extent with which smart farming can help tackle this challenge; in fact, it seems that it is not possible without it. Here at Eastern Peak we develop custom IoT solutions for agriculture, tailored to your particular needs.

How to get started?

From cattle tracking to advanced field mapping, IoT applications in smart agriculture vary from farm to farm depending on your market segment, climate, and region. In many instances, out-of-the-box tools won’t be relevant, and you may need a tailored smart farming IoT solution. At Eastern Peak we approach each customer individually to meet their unique needs.

The product discovery phase is the best first step you can take to lay a solid foundation for the development of your app. It includes a functional specification, UX/UI design, and a visual prototype that will give you a clear vision of the end product. On average, this phase takes 4-6 weeks.

The product discovery phase can help you:

• define a full scope of work and develop a roadmap for the project
• set a realistic budget for your MVP and plan your resources
• test the waters with your audience using a visual prototype
• craft a convincing investment pitch
• get to know your team

We at Eastern Peak have already helped many startups and Fortune 500 companies digitize and streamline their operations with the help of technologies. We provide end-to-end services building IoT solutions across a number of business domains, from hardware design to software development, testing, and integration.

To receive professional consultation from our experts, get in touch with us using our contact form .

• Smart Farming: How Automation Is Transforming Agriculture
• 3 Edge Computing Use Cases for Smart Farming

Smart Agriculture Monitoring Solutions to Optimize Farming Productivity

• 6 Cool Examples of Internet of Things Applications and How to Develop One

About the author:

Alexey Shalimov, CEO at Eastern Peak

As CEO at Eastern Peak, a professional software consulting and development company, Alexey ensures top quality and cost-effective services to clients from all over the world. Alexey is also a founder and technology evangelist at several technology companies. Previously, as a CEO of the Gett (GetTaxi) technology company, Alexey was in charge of developing the revolutionary Gett service from ground up and deploying the operation across the globe from New York to London and Tel Aviv.

• The Benefits of smart farming: How’s IoT shaping agriculture

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Farm to school is taking place in all 50 states, D.C. and U.S. Territories! Select a location from the list below to learn more or contact a Core Partner.

Bringing the Farm to School: Case Studies

Throughout the Bringing the Farm to School: Agricultural Producers' Toolkit training you will find case studies (in written, video, and podcast format) that highlight how farmers have applied key concepts on their own farms or business operations. These case studies offer real-world examples of the concepts taught in the Local Producer Training lesson plans. Bringing the Farm to School was developed in partnership by USDA Food and Nutrition Services, the National Center for Appropriate Technology, and the National Farm to School Network.

Case Studies

These case studies were specifically developed for the Bringing the Farm to School program.

Anthony Youth Farm

Anthony, new mexico.

Alma Maquitico

Arizona Microgreens

Phoenix, arizona.

Joseph Martinez

Bear Paw Meats

Chinook, montana.

Camas Country Mill & Umi Organics

Tom & Sue Hunton and Lola Milholland

Cattail Organics

Athens, wisconsin.

Katrina Becker

Common Ground Farm

Wappingers falls, new york.

Sember Weinman and Erika Rincon

Farm to School of Park County

Park county, montana.

Rachel Jones

Fayetteville Public Schools

Fayetteville, arkansas.

Ally Mrachek, Director of Child Nutrition

Fiery Ginger Farm

West sacramento, california.

Hope Sippola

Food Connects and Windham Northeast Supervisory Union

Grasmick Produce

Boise, idaho.

Chris Gaskell

Holmes County Food Hub and New North Florida Cooperative

Marianna, fl.

Glyen Holmes

Kansas City Food Hub and KC Farm School

Kansas city, missouri.

Alicia Ellingsworth

L&R Poultry and Produce

Goodhue county, minnesota.

Living Root Farm

Hardin, montana.

Teri and Evan Van Order

Sweat’s Produce

Wrightsville, georgia.

Kenneth Sweat

Additional Case Study Examples : We have compiled a playlist of additional videos that offer helpful case studies to learn from as you consider ways to launch or grow your efforts to market to schools.

Have Questions?

Contact Tomas Delgado, NFSN Program Manager, [email protected] , or Tammy Howard, NCAT Agricultural Specialist, [email protected] .

This project has been funded in part by federal funds from the U.S. Department of Agriculture, Food and Nutrition Service through an agreement with the National Center for Appropriate Technology in partnership with the National Farm to School Network. The contents of this publication do not necessarily reflect the views or policies of the U.S. Department of Agriculture, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. government.

Farmer-to-Farmer Case Study Series

Farmers adapt to challenges in unique ways. Some of these strategies are unique to a specific location, while others are universal to agriculture. By adopting farming practices such as tillage, residue management, crop rotations, soil organic amendments and resource-use efficiency farmers have been able to overcome barriers, often in unexpected ways. Innovative approaches used by Pacific Northwest farmers to improve on-farm sustainability and longevity are being featured in a series of case studies.

The REACCH Producer Survey showed that other farmers are the most trusted source of information for producers. The goal of these case studies is to inspire others to take management risks on their farms that can improve their overall sustainability and resiliency into the future, by showcasing producers who have done so successfully. Case studies are in progress and will be added to this page as they are completed.

Profiled Farmers

Dryland case studies.

Ron and Andy Juris: Stripper Header (low rainfall)    Video     Full Case Study Highlights the experiences of a father-son team who use the stripper header and direct seeding to maximize water retention and residue retention in a low-rainfall area of WA.

Ron Jirava: Conservation Tillage in a Winter Wheat-Fallow System (low rainfall)    Video    Full case study in progress. Explores tillage strategies used by an innovative farmer in an area that receives 11.5 inches of precipitation annually. These strategies include use of an undercutter sweep, and experimentation with a no-till winter wheat-fallow rotation.

Bill Jepson: Flex Cropping (low rainfall)    Video     Full Case Study Features a direct-seed, OR grain farmer who produces cash crops annually in a traditional wheat-fallow system using a flexible approach based on weather and markets. In addition to increasing the overall farm profit, this system has improved weed control and increased soil organic matter on the farm.

Steve and Becky Camp: Enhancing Crop Diversity (intermediate rainfall)   Video     Full Case Study Features a husband and wife team who have been able to improve soil health and moisture retention by diversifying to a 4-5 year crop rotation utilizing unconventional crops in a low-rainfall area in eastern WA.

Eric Odberg: Precision Nitrogen Application (high rainfall)    Video     Full Case Study Highlights the experiences of a fourth generation, no-till grain farmer for incorporating variable rate nitrogen technology into his farm management strategy in a high-rainfall dryland production region in ID.

Drew Leitch (high rainfall)    Video    Full Case Study in progress Highlights a third-generation farmer who has successfully produced both spring seeded and fall seeded cover crops on his farm in Nez Perce county. Cover crops improve soil health and provide needed grazing for his cow-calf herd.

Steve and Nate Riggers (high rainfall)     Video   Full case study in progress

Irrigated Case Studies

Dale Gies: Biofumigant Cover Cropping in Potatoes    Video     Full case study in progress.  Demonstrates how a wheat-potato farmer has incorporated a mustard cover crop to act as a soil fumigant without destroying soil structure in an irrigated agriculture system in WA.

Jake Madison: Deficit Irrigation    Video    Full Case Study in progress Relates unique strategies used by an Oregon farmer to cope with water limitations. By providing wheat, corn, and alfalfa with less water than they would need to achieve maximum yields, but still enough to be profitable, this farmer saves water for the farm's most valuable crops, primarily potatoes and onions.

Lorin Grigg: Strip-Tillage for Onions and Sweet Corn    Video     Full Case Study in progress Discusses Grigg’s cover cropping and strip tillage strategy to protect onion and sweet corn seedlings from windblown sand near Quincy, WA.

Eric Williamson: Strip-Tillage of Vegetables with Livestock Integration    Video    Full Case Study in progress Williamson's vegetable farm in the Columbia Basin has transitioned to strip-tillage and direct seeding over the past 15 years in order to reduce soil loss and crop damage caused by high winds. The farm also incorporates cover cropping, soil amendments, and integrated livestock.

Rangelands and Dairy Case Studies

Maurice and Beth Robinette: Holistic Management (ranching) Near Cheney WA, Maurice Robinette and his daughter Beth use holistic management practices to run their ranch. See videos on Maximizing Water and Summer Calving

Jay Gordon: A Community-Based Response to Flooding (dairy)    Video     Full Case Study in progress Gordon, a sixth-generation dairy farmer and member of the Washington State Dairy Federation, is part of a group of community partners and researchers who are developing proposals to respond to flooding in the Chehalis Valley.

Ron and Andy Juris: Stripper Header (low rainfall)

Highlights the experiences of a father-son team who use the stripper header and direct seeding to maximize water retention and residue retention in a low-rainfall area of WA.

Left: The Jurises' stripper header, mounted on their combine. Photo by Hilary Davis.

Ron Jirava: Conservation Tillage in a Winter Wheat-Fallow System (low rainfall)

Left: Undercutter. Photo by Bill Schillinger

Bill Jepsen: Flex Cropping (low rainfall)

Features a direct-seed, OR grain farmer who produces cash crops annually in a traditional wheat-fallow system using a flexible approach based on weather and markets. In addition to increasing the overall farm profit, this system has improved weed control and increased soil organic matter on the farm.

Left: Spring wheat grows in winter wheat stubble. When sufficient water is stored in the soil profile over the winter, Jepsen plants spring wheat or spring barley. Photo by Bill Jepsen. 

Steve and Becky Camp: Enhancing Crop Diversity (intermediate rainfall)

Features a husband and wife team who have been able to improve soil health and moisture retention by diversifying to a 4-5 year crop rotation utilizing unconventional crops in a low-rainfall area in eastern WA.

Left: Austrian winter peas farm contrast with wheat and fallow in the background—a more common pattern for the Camps’ area. Photo by Karen Sowers.

Eric Odberg: Precision Nitrogen Application (high rainfall)

Photo by Guy Swanson

Highlights the experiences of a fourth generation, no-till grain farmer for incorporating variable rate nitrogen technology into his farm management strategy in a high-rainfall dryland production region in ID.

Drew Leitch: Grazed Cover Cropping (high rainfall)

Left: Cows grazing in cover crop. Photo by Doug Finkelnburg.

Steve and Nate Riggers: Enhancing Cropping Diversity (high rainfall)

Steve and Nate Riggers grow winter and spring wheat on the Camas Prairie in Idaho, but have incorporated spring broadleaf crops such as peas, lentils, and canola. They also grow less-common crops like buckwheat, turf grass seed, crested wheatgrass seed, and alfalfa in an area that receives about 22 inches of rain annually.

Dryland alfalfa by Darrell Kilgore

Dale Gies: Biofumigant Cover Cropping in Potatoes

Trials of biofumigant efficacy at the Gies farm. Photo: Andy McGuire.

Demonstrates how a wheat-potato farmer has incorporated a mustard cover crop to act as a soil fumigant without destroying soil structure in an irrigated agriculture system in WA.

Jake Madison: Deficit Irrigation

Because Madison’s water sources are limited, Madison deficit irrigates wheat, corn, alfalfa and other hay crops, while high-profit vegetable crops, including potatoes and onions, receive full water. Photo: Darrell Kilgore

Relates unique strategies used by an Oregon farmer to cope with water limitations. By providing wheat, corn, and alfalfa with less water than they would need to achieve maximum yields, but still enough to be profitable, this farmer saves water for the farm's most valuable crops, primarily potatoes and onions.

Lorin Grigg: Strip-Tillage for Onions and Sweet Corn

Grigg plants onions into tilled strips between spring wheat residues. The residues reduce wind erosion, protecting emerging onion seedlings. Photo: Darrell Kilgore

Eric Williamson: Strip-Tillage of Vegetables with Livestock Integration

Planting wheat in strips facilitates planting of the following corn crop using strip tillage. Photo by Darrell Kilgore

Livestock-related case studies are also available at the website of Washington State University’s Center for Sustaining Agriculture and Natural Resources .

The Camp, Gies, Grigg (video), Jepsen, Jirava, Juris, Leitch, Madison, Odberg, Riggers and Williamson case studies are material that is based upon work that is supported by the National Institute of Food and Agriculture, US Department of Agriculture, under award number 2011-68002-30191 (Regional Approaches to Climate Change for Pacific Northwest Agriculture). The Grigg case study (written and video) relied on support from Western Sustainable Agriculture Research and Education Program (Western SARE). The Gies case study was completed with the support of the Laird Norton Family Foundation.

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Optimizing the Agricultural Supply Chain through E-Commerce: A Case Study of Tudouec in Inner Mongolia, China

Associated data.

The data presented in this study are available within the article.

E-commerce has the potential to address problems in the agricultural supply chain and support the implementation of rural revitalization strategies. Previous research has largely focused on the business models of rural e-commerce platforms, but has not examined the mechanisms by which they can optimize and reconfigure the agricultural supply chain. This study aims to fill this gap through a case study of Tudouec, a potato e-commerce platform in Inner Mongolia, China. The study employs a single-case study method and utilizes data from interviews, fieldwork, and secondary sources. The findings show that Tudouec is a multi-functional platform offering technical support, warehousing, logistics, supply chain finance, and insurance, among other services. It not only serves as a multi-channel information management platform, but also enhances supply chain capabilities through the interaction of information flow with capital and material flows. This rural e-commerce model addresses the limitations of traditional agricultural models and promotes poverty reduction and rural revitalization. The study’s main contribution is in demonstrating the potential for the Tudouec model to be applied to other agricultural products and in other developing countries.

1. Introduction

Since the implementation of the reforming and opening up policies in China, there has been a marked disparity in the rate of development between rural and urban regions [ 1 ]. Imbalanced urban–rural development, and a lack of progress in rural areas, have become major issues in contemporary Chinese society [ 2 ]. In response to these pressing rural challenges, China introduced the rural revitalization strategy in 2017, which places a particular emphasis on the growth of agriculture, rural regions, and farmers [ 3 , 4 ].

The Agricultural Supply Chain (ASC) is considered to play a critical role in farmers’ income, rural economic growth, social development, and environmental sustainability, as demonstrated by numerous studies [ 5 , 6 ]. Despite its importance, ASC management is considered to be challenging when compared to other industries. This can be attributed to a number of factors, one of which is the sensitivity of agriculture to natural influences, such as weather conditions, regional climatic differences, soil quality, and seasonal variations [ 7 ]. Additionally, agricultural markets tend to be unstable and highly susceptible to economic and financial fluctuations, including changes in market supply and demand conditions, distribution channels, and harvest periods [ 8 ]. This high level of uncertainty in agriculture poses significant risks to ASC management [ 9 ], including difficulties in Supply Chain Management (SCM), due to environmental variability [ 10 ], long chains affecting quality consistency levels of agricultural products [ 11 ], and the dependence of agricultural production on the environment, as well as high distribution costs, due to the perishable nature of agricultural products [ 12 ]. These factors can significantly impact agricultural development and farmers’ income [ 13 ].

The proliferation of information and communication technology, particularly the growing prevalence of e-commerce in developing nations, such as China, and nations in Southeast Asia and Africa, has resulted in significant improvement in rural economies [ 12 ]. Rural e-commerce is regarded as a key factor in reducing the disparity between urban and rural areas, and in boosting the income of rural dwellers [ 14 ]. Adoption of e-commerce enables small-scale farmers to overcome barriers to market access and engage in online transactions, thereby accessing both national and global markets [ 15 ]. The elimination of price squeeze and information asymmetry by intermediaries has enabled the farmers to sell their products at higher prices compared to before [ 9 ]. E-commerce has been shown to enhance farm performance and decrease transaction costs [ 16 ]. Agricultural e-commerce in China has seen tremendous growth, owing to continued high-level support and promotion from the central government [ 9 , 17 ]. According to data from the Ministry of Commerce (2020), rural online retail sales in China reached 1.7 trillion yuan in 2019, accounting for 16.1% of total retail sales, with a growth rate of 19.1% which was 2.6% higher than the growth rate of total retail sales. Online retail sales of agricultural products reached 397.5 billion yuan in the same year. Therefore, exploring the implementation of e-commerce in ASC could play a role in advancing rural revitalization.

An ASC refers to the series of production-to-market activities involved in the transportation of agricultural products from the farm to the consumer’s table [ 18 ]. This encompasses all processes, from production on the farm to processing, distribution, and retailing, ultimately reaching the end-user [ 8 ]. In the academic sphere, numerous studies have compared ASCs with other supply chains so as to identify the defining characteristics of ASCs. Like other supply chains, ASCs are an intricate network of organizational entities that bring products and services to the market with the aim of satisfying the needs of customers [ 19 ]. Despite being closely akin to fast-moving consumer goods supply chains in many aspects, the primary difference is that ASCs source raw materials directly from fields, with the final products intended for either human or animal consumption [ 20 ]. The objective of ASC management is to swiftly and efficiently transport agricultural products from farmers to end-users, while minimizing damage to the products [ 21 ]. This not only contributes to the financial stability of farmers by ensuring fair returns, but also ensures that consumers receive high-quality products. However, the unique characteristics of agricultural products, such as limited shelf life, variability in demand and price, and consumer requirements for product traceability, make ASCs more complex and challenging to manage, compared to other supply chains [ 22 ]. Consequently, it can be challenging to apply the practical experience of SCM from other industries to the management of ASCs [ 23 ]. Given the critical role that product traceability, quality specifications, and food safety play in ASCs, effective ASC management necessitates the incorporation of activities and decision-making processes at the strategic, tactical, and operational levels [ 5 , 24 ]. Consequently, this research aimed to examine the potential for e-commerce to optimize the agricultural supply chain by restructuring three flows (material flow, information flow, and financial flow [ 25 ]) of the supply chain, in accordance with the unique characteristics of the agricultural supply chain.

Despite the challenges posed by the inherent characteristics of agricultural products to the implementation of e-commerce in agriculture, there remains a positive outlook towards its future application, as demonstrated by several studies [ 26 , 27 , 28 , 29 , 30 ]. The application of rural e-commerce has been touted as a means of transforming the configuration and relationships between various segments of the ASC [ 31 , 32 ]. The potential benefits of rural e-commerce include reduced production and transaction costs, improved logistics and distribution efficiency, decreased information asymmetries, enhanced links between agricultural production supply and demand, and facilitated connections between users globally [ 33 , 34 , 35 ]. Li et al. [ 36 ], Zhu et al. [ 37 ] and Juan et al. [ 38 ] also found that rural e-commerce offers significant advantages in enhancing the marketing and trading of agricultural products. Chiang et al. [ 39 ] were the first to propose the concept of rural e-commerce for supply chain optimization and found that its adoption into the traditional agricultural channel could result in improved ASC efficiency. Fritz et al. [ 31 ] posited that rural e-commerce involves the utilization of electronic strategies in interaction and transaction between participants in the agricultural industry, leading to new relationships and reconfigured relationships between various stages and segments of ASCs. Research into the optimization of the supply chain by e-commerce mainly focuses on its intermediary and information-sharing roles [ 30 ]. Li et al. [ 32 ] found that e-commerce enables the creation of effective information sharing mechanisms within an ASC. Zeng et al. [ 9 ] believed that rural e-commerce could mitigate the negative effects of information asymmetry caused by physical distance and increase the selling prices for small farmers. GuoHua et al. [ 40 ] used an evolutionary game model to compare traditional and modern ASCs and found that e-commerce could address the problem of information asymmetry. However, there are also opposing viewpoints among scholars. Some limitations and challenges in the implementation of e-commerce in ASCs have been brought to light. Mueller [ 14 ] analyzed the impact of e-commerce on the agricultural market and concluded that existing e-commerce for agricultural products suffers from low logistics efficiency, high costs, and low service levels, which cannot meet consumers’ requirements for high quality and efficiency. Zhao et al. [ 41 ] also identified some problems with national rural e-commerce platforms and suggested that local e-commerce platforms could be more efficient in ASC management. Bao et al. [ 42 ] and Montealegre et al. [ 43 ] pointed out that the deep integration of e-commerce and ASC still requires further analysis of the supply chain process. However, few studies have focused on how rural e-commerce platforms optimize and reconstruct ASCs, and few studies have analyzed their operational mechanisms by examining the three flows of the supply chain [ 9 ].

Numerous scholars have investigated the mechanism of supply chain optimization by examining the interplay between the three flows [ 44 , 45 ]. Kim et al. [ 46 ] posited that the management of these flows constitutes the foundational components of e-commerce supply chain management activities. Studies have been conducted on the interdependence between the three flows, exploring how they can be integrated to deliver more efficient supply chain services. Sahin et al. [ 47 ] and Lee et al. [ 48 ] emphasized that, without information flow, material and financial flows are unable to function optimally. Rai et al. [ 49 ] suggested that information technology could significantly improve supply chain performance by integrating information and material flow. Costa et al. [ 50 ] found that the integration of information and material flows through RFID technology contributed to quality management of agricultural products. The integration of capital flow and logistics could enhance inventory management and financial planning [ 51 ], as demonstrated by studies, such as those by Pfohl et al. [ 52 ] and Wang et al. [ 53 ], showing that the segmentation and integration of the three flows could reflect the underlying operational mechanisms of the supply chain and improve its service capability through effective integration.

In light of these findings, this study aimed to shed light on how rural e-commerce platforms can construct a comprehensive and integrated agricultural supply chain model. The study examines the relationship between rural e-commerce platforms and the supply chain by investigating the enhancement, interaction, and integration of information flow, capital flow, and logistics. Through a case study approach, the study aimed to distil a model of rural e-commerce platforms and the agricultural supply chain that can be extrapolated and applied to other regions and agricultural products to advance agricultural development.

2. Materials and Methods

2.1. methodology.

The utilization of a qualitative case study approach was deemed appropriate for this research in order to explore the unique phenomena and questions present within the complex background. The case study method, as discussed by Yin et al. [ 54 ], is suitable for studying the formation of rare and significant emerging phenomena that involve diverse background conditions, multiple sources of evidence, and dynamic changes. The selection of Tudouec as the subject for a single case study was based on two factors. Firstly, as highlighted by Zhao et al. [ 41 ], local e-commerce platforms exhibit greater capability in mobilizing resources and promoting agriculture compared to national agricultural e-commerce platforms. Hence, it was deemed more appropriate to choose a representative local e-commerce platform for a single case study. Secondly, there is limited research on the integration of the three flows in rural e-commerce supply chains, making Tudouec an ideal candidate, as it represents a noteworthy example of this integration and is, therefore, deserving of an in-depth study.

2.2. Case Selection

Inner Mongolia is China’s largest potato production base, where production accounts for about 1/7 of the national production [ 55 ]. The region is characterized by abundant light, large diurnal temperature differences, and light soil texture, which are favorable for potato production [ 56 , 57 ]. Tudouec E-commerce Platfrom (Tudouec) was founded in 2012 in Hohhot, Inner Mongolia, China. Unlike other rural e-commerce platforms, Tudouec not only has the function of collecting, processing, and releasing information, but also breaks the information asymmetry and takes the initiative to optimize the supply chain to build a virtuous agricultural benefit ecosystem. The platform effectively integrates supply, production, processing, sales, storage, and logistics. Table 1 and Figure 1 show the composition of the Tudouec Platform and organizational chart. The platform consists of three subsidiaries, each with different functions. Specifically, Jinke is responsible for the operation of the online trading platform and the construction of the information platform. Golden Bean is stationed with account managers and technicians to provide offline agricultural planting technological guidance and supervision for farmers. Tengzhou handles the procurement of agricultural materials, such as seeds and chemical fertilizers. It also provides mechanized equipment rental services and offline distribution sales services for farmers.

Organizational chart and departmental mission chart of Tudouec.

Composition of the Tudouec Platform.

Compared with Alibaba and JD.com, the Tudouec platform is only a small platform for regional and niche agricultural products categories. However, the continuous growth of the Tudouec platform’s trading volume for several years in a row proves its superiority to some extent. Moreover, it is a rare comprehensive rural e-commerce platform that combines the platform business model, SCM model, and supply chain finance model. Therefore, it is a representative case study that deserves to be studied in depth.

2.3. Data Collection and Analysis

The data used in this study were obtained from both primary and secondary sources and were subjected to triangulation to ensure the reliability and validity of the results. Triangulation is recognized as an essential method in case study research [ 54 ]. This study mainly collected data from five sources: interviews, participatory observation, official websites, cooperative financial institutions, and media reports. The utilization of multiple data sources allowed for triangulation [ 58 ], thereby enhancing the credibility and accuracy of the study’s conclusions. The primary data were mainly obtained by the following means:

Interviews: To obtain the primary data needed, semi-structured interviews and focus group interviews were conducted in this research. Semi-structured interviews are not only highly maneuverable but also allow the interviewers to engage in in-depth communication with the interviewees. As shown in Table 2 , a total of 34 interviews were conducted for relevant individuals in this study. The interviews were led by one lead interviewer, who was a native speaker of the local language, and the average length was about one hour. Focus group interviews were also conducted in this research to ensure that all the participants expressed their views and to avoid the dominance of particular individuals and ensure group conformity [ 59 ]. Most respondents were interviewed more than once for this study in order to obtain comprehensive information and to ensure the authenticity of the information conveyed. The initial interviews and investigation were conducted on 17–24 May, 2020, and the supplemental interviews were carried out on 13–14 July, 2020. To reveal the changes in the villagers’ income structures, we also interviewed five rural villagers who were not involved in Tudouec. The question designed for this group only focused on their planting and marketing model.

List of interviews.

The data collection for this study involved a combination of qualitative research methods, including in-depth interviews, participatory observation, and secondary information collection. The interviews were conducted with key stakeholders of the case company and covered six essential aspects of the company’s operation: business model, distribution model, information management model, supply chain finance model, and SCM model. The interviews were recorded and transcribed, resulting in 100 pages of transcripts.

The interviews were composed of the following six parts to gain necessary information about the company: overall operation and business model, distribution model, information management model, supply chain finance model, and SCM model. The interviews were recorded and transcribed, resulting in 108 pages of transcripts. Participatory observation: field visits to the sample enterprises focused on understanding the specific mode of operation of Tudouec and the SCM methods. This further enriched the study’s research data.

Secondary information was obtained mainly through online articles, news, reports, and videos. An example was Tudouec platform information, which included the following: (1) the official website and WeChat official account; (2) cooperative financial institutions; (3) tracking media reports.

The collected data underwent coding and analysis through the utilization of Microsoft Excel for data reduction and coding. Participatory research and interviews with the principals of the three companies under the Tudou platform were conducted to examine their historical background, development, and scope of business. The framework of the companies was constructed (referred to in Figure 1 ), taking into account their respective roles and positions within the platform. This served to better illustrate the organizational structure of the companies. Building on this initial step, the network structure of the platform was drawn, based on the companies’ framework, in order to categorize the functional aspects of the platform. The supply chain services provided by Tudouec were vertically divided into e-commerce trade, offline services, and finance-related services. To further study the underlying mechanisms of its supply chain management, the supply chain model of Tudouec was mapped, based on the information flow, financial flow, and material flow, and the findings from the previous steps integrated with, and incorporating, data obtained from the semi-structured interviews. Finally, the accuracy of the design was verified through mutual validation with the participants of the platform, confirming the validity of the results of the case study.

3. Case Description

3.1. traditional asc model and existing problems.

As depicted in Figure 2 , a traditional ASC is characterized by its simplicity and fragility. The distribution channels available to farmers, including local retail, broker purchase, and starch factory purchase, are limited in scope [ 40 , 60 ]. While local retail offers higher unit prices, it is characterized by lower sales volume and stringent product quality requirements. Starch factories possess significant bargaining power and are selective in the varieties they purchase. Unlike the traditional ASC, brokers exploit information asymmetry and transportation bargaining power, leading to a substantial reduction in farmers’ profits. The dotted line in the figure linking only the middlemen and starch factories with downstream consumers highlights the difficulty that farmers face in directly connecting with their customers.

Traditional ASC model.

In sum, the problems of traditional ASCs are mainly focused on market channels, information access, cultivation, finance, insurance, logistics, and storage ( Table 3 ). First, the large number of farmers, the small scale of single-family farming, and the low degree of organization lead to farmer groups failing to establish a scale effect in the market. The unit transaction cost of almost all transactions is high [ 13 ]. Second, market imperfections are prevalent in developing countries, such as lack of technology and price opacity, weak linkages to downstream markets, and credit constraints [ 61 ]. Third, the poor efficiency of information flow, the lack of financial and insurance services, the incomplete and expensive logistics system, and the inferior storage conditions all constrain the development of the rural economy.

The current situation of the traditional agricultural model and the problems faced by farmers.

3.2. Description of the Tudouec Business Model

Traditional ASCs needs to be integrated and redesigned to make them more efficient and competitive [ 8 ]. As shown in Figure 3 , Tudouec generally weaves a dense supply chain network that connects farmers, starch factories, insurance companies, financial institutions, warehouse companies, logistic companies, and downstream customers together. Compared with the traditional ASC model, this model looks complex and bloated. However, complexity does not mean inefficiency. Rather, Tudouec creates a full-function supply chain system. Tudouec acts as an integrator of information to guide the development of agricultural production plans by analyzing the overall supply and demand levels of the industry in real time. In addition, Tudouec acts as an online trade exchange platform, which improves agricultural products’ distribution efficiency and increases the profit margin. Tudouec also has the function of providing quality agricultural material supplements, including seedlings, pesticides, fertilizers, machinery, accessories, crop health care, and guidance in planting techniques. Logistics, warehousing, and cold storage services are also included. Financing and insurance are also available for members who join the platform. Therefore, we provide an in-depth analysis of the emergence mechanism of this business model.

Supply chain model of Tudouec.

We established a research road map, depicted in Figure 4 , to shed light on the functioning of the Tudouec platform and the integration of the three flows that drive the generation of SCM tools. Our initial focus was on the significance of the information flow. By considering the information flow as the core of Tudouec, we aimed to examine how the platform integrates data from all participants to establish a well-functioning agricultural supply chain network, thereby overcoming the challenges of information silos commonly encountered in traditional supply chains. Next, we analyzed the integration of the information flow with the capital flow and its potential to generate supply chain insurance and financial instruments. Furthermore, we studied the integration of the information and material flows and its impact on the development of new sales models, logistics, and warehousing strategies. Our aim was to provide a complete understanding of the various techniques and strategies employed to achieve this integration and the benefits yielded. Finally, we delved into the interplay of the three flows, leading to the optimization of the supply chain. Our analysis shed light on the capabilities generated by the combined efforts of the information, capital, and material flows. The ultimate objective of this discussion was to provide a comprehensive understanding of the operational mechanism of the Tudouec platform and how it was designed to optimize and reconfigure the agricultural supply chain.

Research Roadmap.

4. Discussion

4.1. capabilities generated from information flow.

Information sharing is a crucial component of successful SCM [ 62 , 63 ]. This sharing helps coordinate business processes and improve services provided to customers [ 53 ]. Studies have consistently demonstrated that information flow is essential to a functioning supply chain and takes precedence over other types of flows [ 62 , 64 ]. Information and Communication Technology (ICT) play a vital role in the agribusiness sector, as they can enhance the efficiency, sustainability, flexibility, and resilience of the entire supply chain from farmer to end customer [ 65 ]. Despite rapid advancements in ICT, rural areas continue to lag behind urban areas, especially in developing countries, where information asymmetry is prevalent across all stages of the Agricultural Supply Chain (ASC), resulting in reduced supply chain efficiency.

The Tudouec platform aims to collect, analyze, publish, and exchange information. As depicted in Figure 5 , the platform creates a diverse and complex information network that allows each participant to access and aggregate information, thereby establishing an efficient and transparent information exchange channel. Firstly, the platform facilitates an efficient and transparent information exchange channel to collect and summarize supply and demand information for the industry as a whole. Through big data analysis, the platform matches buyers and sellers with accuracy. As illustrated in Figure 6 , analysis of price change trends can help farmers make informed decisions about the best-selling price for their products. The platform also predicts market demand, enabling farmers to make informed decisions about the variety and scale of crops to plant the following year. Secondly, the platform provides farmers with access to advanced farming techniques through regular visits from technicians who teach and monitor the farming process. The platform leverages its information and scale advantages to negotiate competitive prices for high-quality seeds, fertilizers, and agricultural machinery with suppliers. Lastly, the platform implements cameras and internet of things monitoring equipment in plantation areas to monitor crop growth in real-time. Information about planting conditions is uploaded to the platform, making it accessible to all participants. In the future, the platform intends to utilize blockchain technology to provide traceability functions [ 66 ].

Information integration model of Tudouec.

Monthly K chart of the national wholesale potato market prices ( www.tudouec.com , accessded on 25 October 2022.).

4.2. Capabilities Generated from Information Flow + Financial Flow

4.2.1. insurance.

The provision of insurance services by insurance companies to eligible farmers in the Tudouec platform is depicted in Figure 7 . In this partnership, farmers are able to access insurance coverage at a reduced premium rate of 3%, which is lower that of traditional insurance products offered outside of the platform. The insurance coverage compensates for up to 90% of losses incurred due to natural disasters or low market prices. The effective management of information flow is a key aspect of this arrangement as Tudouec shares relevant data, including planting information, order information, and market information with the insurance companies. This helps to bridge the information gap that often exists in the traditional insurance industry. Additionally, the platform’s account managers, who provide guidance and supervision of the planting process, serve as a crucial reference for the insurance companies in reducing risks and promoting effective risk management practices [ 67 ].

Platform insurance model.

4.2.2. Agricultural Order Financing

The lack of a stable repayment source, collateral, and low credit rating have long posed challenges for farmers in securing adequate financing for their operations. Despite the need for capital to expand and reproduce, traditional financing options for farmers are often difficult and expensive to obtain [ 68 ]. Supply chain finance, which leverages the combination of information flow and capital flow resources, has emerged as a solution to this problem. Reliable information can be used to mitigate investment risk within the supply chain and reduce the cost of capital [ 52 ]. Tudouec’s platform, which is built upon information and logistics, serves as the cornerstone of the bank’s “platform + insurance + order financing” model. This model is designed to provide low-cost credit funds to farmers, while also addressing the risk management challenges faced by the bank. In the context of agricultural supply chains (ASCs), order farming is an upstream intervention mode that can be considered a supply chain finance scheme [ 69 ]. The order financing model, as illustrated in Figure 8 , enables farmers to secure financing through the information and logistics infrastructure provided by Tudouec, thereby improving access to capital and promoting sustainable agricultural development.

“Platform + Insurance + Order Financing” Model.

The borrowing individuals in this financial model are farmers who have been active participants on the Tudouec platform for a minimum of five years and have cultivated a minimum of 33 acres in the current planting season. The financial institution, in this case a bank, leverages data sharing with the Tudouec platform to obtain information related to the scale of planting, production, and market orders. As a result, the bank is able to offer unsecured loans to potato farmers with interest rates lower than 8%. During the planting period, Tudouec provides technical support and on-site supervision to farmers, who are required to adhere strictly to the platform’s established planting standards for planting, harvesting, and disease/pest control. This information is recorded in real-time and uploaded onto the platform, providing valuable data for post-credit management and insurance supervision by financial institutions and insurance companies. Moreover, the intermediary role played by the Tudouec platform ensures that farmers receive payment for their sales, while also ensuring that downstream customers receive high-quality products. In the event of market fluctuations or product stagnation, the Tudouec-assisted starch factory promises to purchase any unsold potatoes. If the crops suffer losses due to natural disasters or low market prices, the insurance company is responsible for compensating farmers and the bank for 90% of the losses, thereby minimizing potential losses and reducing risks for all involved parties.

4.3. Capabilities Generated from Information Flow + Material Flow

4.3.1. complete trading, logistics, and warehousing systems.

The coordination of material flow in the agricultural industry is greatly improved through the facilitation of information flow. Historically, farmers have faced challenges in obtaining reasonable prices for the rental of logistics and warehousing services due to their limited bargaining power [ 41 ]. However, as the platform continues to grow and attract a larger number of stakeholders, including farmers, service providers, and downstream customers, the emergence of economies of scale has led to an increase in the number of logistics and warehousing enterprises settling in, and, thus, enhancing the overall capacity of the supply chain. Specifically, the improvement of logistics capacity offers farmers the ability to overcome the limitations imposed by distance, reducing the loss associated with transportation and enabling long-distance and high-priced orders to be fulfilled. The enhancement of storage capacity, on the other hand, reduces storage loss and extends the sales period, contributing to higher income for farmers.

The integration of information flow and material flow is demonstrated in two distinct areas. Firstly, the application of information technology in logistics and warehousing allows for real-time monitoring of cargo status, offering greater visibility and control over the supply chain. Secondly, the platform facilitates the sharing of information regarding storage and logistics capacities, enabling individual farmers to benefit from shared resources and cost savings, effectively reducing costs and minimizing resource waste.

4.3.2. Smart Plantation

Tudouec is currently exploring the potential of a technologically advanced smart plantation system. This innovative approach integrates several cutting-edge technologies, including 5G, drones, video surveillance, temperature sensors, humidity sensors, and automatic sprinkler irrigation. The deployment of these technologies has several significant benefits, including reduced labor costs, increased efficiency and quality of the planting process, and it addresses critical issues in food safety and traceability. By leveraging these advanced technologies, Tudouec aims to create a smarter, more efficient, and more sustainable agricultural ecosystem that meets the evolving needs of farmers and consumers alike.

4.3.3. Reshape Agriculture Marketing

As illustrated in Figure 9 , the platform challenges the conventional sales model by establishing three new models of online trading, order sale, and sales guarantee. The online trading model enables both the supply and demand sides to publish trading information and take advantage of the platform’s intermediary function for secure transactions. The order sale model, in turn, ensures steady supply and stabilizes prices through a three-step process. Firstly, the platform collects information on the production capacity of farmers, the demands of downstream customers, and starch factories. Secondly, the platform coordinates the supply and demand sides to reach a cooperative agreement in the form of orders [ 70 ]. Finally, farmers are only required to plant high-quality products in accordance with the variety and quantity specified in the order, eliminating the need for them to worry about sales. The platform’s sales guarantee model, represented by the dashed box in Figure 9 , provides a viable solution to the issue of overproduction. In the event of overproduction or late sales, the platform collaborates with starch factories to purchase the surplus produce at cost price.

Sales model of the Tudouec platform from the perspective of farmers.

4.4. Capabilities Generated from the Integration of the Three Flows

4.4.1. inventory financing.

Inventory financing is a subset of supply chain financing [ 71 ]. Agricultural supply chains (ASCs) are often characterized by seasonal harvesting, resulting in a large capital demand during the raw material procurement period, but a long payback period for product sales, leading to a funding gap. To address this issue, Tudouec integrated the advantages of information flow, bank capital flow, and material flow to create a potato warehouse receipt pledge financing model.

The operation process of this model is illustrated in Figure 10 . The first step involves a joint pre-lending investigation, conducted by the bank and the platform, followed by the provision of loans to the starch factory, with the inventory as collateral. Secondly, the starch factory and the platform share transaction data related to the raw material warehouse, the starch warehouse, and the starch. The pledged goods are stored in an intelligent warehouse that is supervised by the starch factory, the bank, and the platform, with real-time inbound and outbound information being synchronized to the platform. Lastly, the starch factory sales are settled through the platform, enabling monitoring of accounts receivable. In the event of loan delinquency or other repayment difficulties, the platform can repay the loan using the accounts receivable, or liquidate the starch in stock at the current price to repay the loan.

Inventory financing model.

4.4.2. Risk Control and Management

Compared to other supply chains, the Agricultural Supply Chain (ASC) is subject to a higher number of sources of uncertainty and risk, particularly when compared to a traditional model where the risks are primarily borne by farmers [ 65 ]. The integration of three streams of information, capital, and material has led to the development of a systematic supply chain risk management system, which effectively minimizes the risks faced by each participant. For farmers, the platform’s implementation of order-based sales addresses the concern of marketing, while supply chain financing addresses the risk of insufficient capital. Insurance further protects farmers from financial loss due to natural disasters. The platform also facilitates quality control and price negotiation through bulk purchasing of high-quality and cost-effective agricultural products and mechanized equipment, thereby reducing the price of fertilizers and equipment and ensuring the quality of agricultural materials. Insurance institutions can benefit from the platform’s sharing of planting data, which enables real-time tracking and monitoring and reduces regulatory costs, thereby minimizing moral hazards. Financial institutions can benefit from the platform’s provision of transaction information, such as orders, warehousing, logistics, and more, ensuring the authenticity of trade. Risk mitigation measures, including pledges of accounts receivable, help guarantee the source of repayment, and third-party intelligent warehousing supervision ensures the validity of inventory pledges. The forced liquidation mechanism further protects funds in the event of a market downside risk. For starch factories, the platform reduces the risks associated with raw material sourcing and helps to control the quality and variety of raw materials, while also addressing the risk of insufficient capital and sales. However, it is important to note that the platform, as an intermediary and information manager, can cause significant harm to all participants if moral risks or information security breaches occur. As a result, constraints and security mechanisms are necessary to monitor and ensure the stable operation of the platform.

4.5. Effect of the Integration of the Three Flows

The integration of the three flows has led to the development of a new supply chain model, which optimizes the SCM level and enhances the support for real agriculture. The platform model effectively addresses the issues faced by traditional agriculture in regards to marketing, access to information, financing, storage, and transportation (as outlined in Table 4 ). By combining capital flow, information flow, and material flow, the platform exhibits a robust supply chain service capability. From an SCM perspective, the platform ensures the seamless transportation of products, efficient dissemination of information, and secure financial transactions. The e-commerce platform serves as the central component of the supply chain, connecting all participants in the model, and can be viewed as a centralized information platform, due to the collection, integration, and sharing of information within the system. The efficient operation of the information flow removes the obstacles previously faced by capital flow and logistics operations. By establishing a comprehensive network of material flow, capital flow, and information flow, barriers from supply to demand are lifted, and new ASC management tools, such as supply chain financial services, insurance services, logistics, and warehousing services, are created, greatly improving the efficiency of the overall supply chain operations.

Comparison of models based on the farmers’ perspective.

The traditional intermediary modes of brokers take advantage of information asymmetry, but the platform-oriented SCM mode promotes information interaction among the participants and weaves the whole supply chain network using the information as the hub. The interaction of information flow with material and capital flows enables supply chain services widely used in industry, such as supply chain finance, intelligent storage, and intelligent logistics, to be applied in the agricultural field. Therefore, rather than calling the e-commerce platform model an “internet+agriculture” business model, it is better to call it an information-based SCM model. On one hand, the e-commerce platform provides high-quality supply chain services through the interconnection of information technology. On the other hand, the optimization of the supply chain by the e-commerce platform enhances SCM capability.

5. Conclusions

5.1. theoretical contributions.

This paper contributes to the field of SCM literature by making three theoretical contributions. First, it seeks to define the relationship between information flow, material flow, and capital flow by using the case study of Tudouec. The paper finds that Tudouec’s efficient information flow system improves the efficiency of both material and capital flow. Second, this study is the first to examine the resources embedded in the three processes, and to identify the resulting supply chain capabilities. It proposes that the e-commerce platform’s enhanced supply chain service capability comes from the interaction and combination of these processes. Third, the paper introduces the concept of using an e-commerce platform to optimize SCM and service capabilities. This platform is seen as an ideal environment to gain insight into the supply chain concept, and research suggests that the core competency of agricultural e-commerce platforms is providing high-quality supply chain services by managing these three flows.

5.2. Managerial Contributions

The findings of this study offer valuable managerial insights into the potential impact of an agricultural e-commerce platform on rural development. The ASC e-commerce platform studied here demonstrated its ability to reduce the risk of the agricultural supply chain, improve the quality of agricultural products, and promote collaboration among stakeholders. By converging three streams, the platform created a more efficient and effective system for the entire agricultural product life cycle, including production, processing, distribution, and financing. This led to not only reduced risk in the agricultural supply chain, but also improved quality and competitiveness of farmers and the sector as a whole. Additionally, the platform facilitated cooperation and mutual benefits among farmers, distributors, and consumers. These insights emphasize the importance of leveraging technology and innovative solutions in addressing the challenges faced by the agricultural industry, and highlight the potential for the platform to facilitate rural revitalization. This study contributes to the existing literature that supports the viability of rural e-commerce for promoting and developing various agricultural products in developing countries [ 72 , 73 , 74 ]. As a mature e-commerce platform for potatoes, Tudouec showcases a successful business model and supply chain management approach that has the potential to be replicated and adapted for other agricultural contexts. Overall, this study has important implications for rural management.

5.3. Limitation and Future Research Directions

This study was limited in several ways that should be addressed in future research. Firstly, the sample size of the present study was limited to a single entity, Tudouec. Further research is needed to expand the sample size and conduct multiple case studies. Secondly, the time period of this study was relatively short, so may not provide a comprehensive picture of the situation. Longitudinal studies with a longer time frame are required to better understand the long-term operation and development of the platform. Thirdly, this study mainly employed a qualitative research approach, leading to more subjective findings. Future research should aim to increase the level of objectivity by incorporating quantitative methods. Fourthly, the focus of this study was on a potato e-commerce platform in the Inner Mongolia region. Research in different geographical and agricultural contexts is needed to generalize the results. Additionally, future research should explore various types of rural e-commerce platforms not covered in this study and compare the results with those presented in this paper.

Funding Statement

This research was supported by the National Natural Science Foundation of China (Grant Number 71932002 and 72274010), the Youth Beijing Scholars Program, and China Scholarship Council (202206540068).

Author Contributions

J.L.: conceptualization, methodology, funding acquisition support, writing—original draft, Writing—review & editing. X.Y.: software, Methodology, data curation, investigation, verification, writing—original draft. Y.L.: formal analysis, Visualization. X.D.: conceptualization, supervision, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review

• LIFE CYCLE SUSTAINABILITY ASSESSMENT
• Published: 02 March 2022

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• Cecilia Silvestri   ORCID: orcid.org/0000-0003-2528-601X 1 ,
• Luca Silvestri   ORCID: orcid.org/0000-0002-6754-899X 2 ,
• Michela Piccarozzi   ORCID: orcid.org/0000-0001-9717-9462 1 &
• Alessandro Ruggieri 1  

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A Correction to this article was published on 24 March 2022

This article has been updated

The implementation of sustainability and circular economy (CE) models in agri-food production can promote resource efficiency, reduce environmental burdens, and ensure improved and socially responsible systems. In this context, indicators for the measurement of sustainability play a crucial role. Indicators can measure CE strategies aimed to preserve functions, products, components, materials, or embodied energy. Although there is broad literature describing sustainability and CE indicators, no study offers such a comprehensive framework of indicators for measuring sustainability and CE in the agri-food sector.

Starting from this central research gap, a systematic literature review has been developed to measure the sustainability in the agri-food sector and, based on these findings, to understand how indicators are used and for which specific purposes.

The analysis of the results allowed us to classify the sample of articles in three main clusters (“Assessment-LCA,” “Best practice,” and “Decision-making”) and has shown increasing attention to the three pillars of sustainability (triple bottom line). In this context, an integrated approach of indicators (environmental, social, and economic) offers the best solution to ensure an easier transition to sustainability.

Conclusions

The sample analysis facilitated the identification of new categories of impact that deserve attention, such as the cooperation among stakeholders in the supply chain and eco-innovation.

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Avoid common mistakes on your manuscript.

1 Introduction

A key principle of the circular economy (CE) is the establishment of circular loops that can maintain the highest value of materials and energy as possible within production-consumption systems (Moraga et al. 2019 ).

In recent decades, economists have analyzed the independence between environment and economic systems (Pearce and Turner 1990 ), introducing the concept of circularity (Ghisellini et al. 2016 ; De Pascale et al. 2021 ). Based on an analogy applying a thermodynamic law stating that, in a closed system, the total energy remains constant (Yigitcanlar 2010 ), the purpose of CE should be to realize closed loops for recycling and reusing primary materials instead of virgin resources (Georgescu-Roegen 1971 ; De Pascale et al. 2021 ).

For achieving this goal and for ensuring the deployment of a sustainable CE, the regeneration of natural systems and the proper organization of waste flows are mandatory aspects, as is the reduction of the pollution in water, air, and soil caused by related activities (MacArthur 2017 ; Pauliuk 2018 ).

However, according to De Schoenmakere and Gillabel ( 2017 ), a transition toward a circular economy demands fundamental changes in consumption and production systems, going well beyond the efficiency of the use of resources and recycling of waste. Indeed, the persistence of some environmental key issues, such as the loss of biodiversity, climate change, and the depletion of natural resources, is a present and systemic challenge. Systems of production and consumption as well as specific products that can satisfy the essential needs of a company, such as energy, food, mobility, water, and shelter, represent the largest part of the environmental burdens in terms of resources, waste generation, and emissions (De Schoenmakere and Gillabel 2017 ).

In particular, for the food sector, the expected growth of 2 billion people from 2020 to 2050, especially in the currently low- and middle-income countries, will further increase food-related environmental burdens (FAO 2020 ).

By 2050, the additional land required for agricultural production has been estimated to be about 100 million hectares (FAO 2017 ). The demand for agricultural land will result not only in excess deforestation, but also in the adoption of unsustainable agricultural practices that involve the loss of biodiversity, desertification, erosion, salinization of agricultural land, and pollution of fresh and marine water (Hjeresen and Gonzales 2020 ).

In 2011, a FAO report ( 2011 ) estimates that, between 1900 and 2000, 75% of agricultural crop varieties were lost, and three-quarters of the global food depend on only twelve plant species and five animal species. The agricultural production results in 69% of water consumption, and for this reason, approximately three million people will not have easy access to water by 2030 (Porkka et al. 2016 ). Together with other actors involved in the food system, agricultural production is responsible for around 30% of greenhouse gas emissions (Fassio and Tecco 2019 ).

For these reasons, the attention on this issue is increasing. During recent decades, academics, policymakers, and other stakeholders have stressed the need to find possible paths and developments for sustainable food production (Gaitán-Cremaschi et al. 2017 ). Indeed, such a challenge requires changes in the way in which food is produced, which should be in line with growing awareness and concerns of social, environmental, food safety, and health costs related to food production processes (Ilbery and Maye 2005 ; Gaitán-Cremaschi et al. 2017 ).

In this domain, the implementation of sustainability and CE models in the agri-food production can assist in resource efficiency, reducing environmental burdens, and ensuring improved and socially responsible systems (Hamam et al. 2021 ; Salimi 2021 ).

Beyond the concept definition, the CE implementation can be guided through specific action plans supported by specific indicators. According to Moraga et al. ( 2019 ), indicators can measure CE strategies aimed “to preserve functions, products, components, materials, or embodied energy; additionally, indicators can measure the linear economy as a reference scenario” (p. 452).

Indeed, the continuous intensification of the agricultural production has contributed to the development of a variety of environmental indicators (Niemeijer and de Groot 2008 ; Jorgensen et al. 2013 ; Huffman 2015 ), which represent a necessary guide for supporting the main actors of the agri-food system (Coteur et al. 2019 ).

For enterprises and systems is essential to achieve a balance between their monetary cost–benefit (sustainability economic) and how society considers their way to address social, economic, and environmental issues (Lehmann et al. 2011 ). In this context, a proper set of indicators represents a necessary tool for assessing the dynamic developments of enterprises and systems (Gunasekaran et al. 2001 , 2004 ; Gerbens-Leenes et al. 2003 ).

In 2013, Wheaton and Kulshreshtha ( 2013 ) analyzed the indicators developed by the Agriculture and Agri-Food Canada (AAFC) that have been developed to understand the relationship between agriculture and the environment. These indicators are based on mathematical models required for integrating “information on soils, climate, and landscape with agricultural activity or practice information from the Canadian Census of Agriculture” (p. 99).

According to Hellweg et al. ( 2014 ), to apply environmental impact indicators in the agri-food systems, the main barrier is represented by the fragmentation between stakeholders of the agri-food system. “Farmers grow crops, manufacturers make food, retailers sell it, users waste and consume it, and society (and the planet) pays the consequences” (Horton et al. 2016 , 167). This issue was addressed by Horton et al. ( 2016 ), which proposed “a theoretical framework for integrated solutions based upon mapping of whole agri-food systems” (p. 164). In 2016, Gaitán-Cremaschi et al. ( 2017 ) applied the total factor productivity (TFP) approach for comparing the sustainability performance of the agri-food supply chain, introducing the price-related productivity and distance-function-based productivity indicators.

D’Eusanio et al. ( 2018 ) investigated the social indicators to analyze the multifunctionality of products and the positive social dimension. In 2020, Mitchell et al. introduced eight indicators for understanding how multiple ecosystem services vary across both space and time at regional-to-national scales in Canada.

In particular, for the sustainable management of the agri-food system, it is necessary that the indicators and evaluation tools address the entire life cycle of the product and the social context of reference (De Schoenmakere and Gillabel 2017 ). An integrated sustainability assessment must be based on a life-cycle approach that allows for an in-depth analysis of the entire system or supply chain (Hospido et al. 2010 ; Parent and Lavallée 2011 ). The life cycle assessment (LCA) is a key tool for performing an environmental sustainability analysis of products and technologies (Guinée et al. 2011 ), providing a systematic path for measuring improvements in resource productivity and being an effective tool for promoting cleaner production (Strazza et al. 2011 ). The standard ISO ( 2006 ) defines LCA as a standardized methodological tool to assess the major environmental impacts of a product “from the cradle to the grave.” For this reason, Royo et al. ( 2016 ) and Ferreira et al. ( 2019 ) define LCA as an extraordinary tool. Its application in the agri-food system facilitates study of environmental behavior “from production to consumption” (Perez Neira, 2016 , p. 2561). The LCA methodology can be applied to assess numerous environmental implications in the value chain of fruits and vegetables (Accorsi et al. 2015 ; Ferreira et al. 2019 ), and numerous studies have shown the significant impact of the agri-food system in terms of energy consumption, greenhouse gas emissions, and other environmental impacts (Perez Neira 2016 ).

Life cycle thinking (LCT) is a central approach on which policies of the European Commission are currently based. This method can help companies and consumers within the agri-food sector to understand and quantify the environmental impacts of food and beverage products and support more informed choices (Del Borghi et al. 2018 ).

Nazir ( 2017 ) defines LCT as “a holistic approach and a fundamental concept for ensuring the transition towards more sustainable production and consumption patterns” (p. 1862).

Among the main tools of the LCT, LCA has been widely used to evaluate agricultural systems, food processing, production activities, and compare alternatives “from field to table” and food waste management (Nazir 2017 ; Notarnicola et al. 2017 ; Del Borghi et al. 2018 ). However, in order to achieve complete sustainability based on the principles of the CE, it is necessary also to apply other LCT tools, such as life cycle costing (LCC) (for the evaluation of the economic impact) and social-LCA (for the evaluation of the social impact) (UNEP 2020 ).

In particular, the launch of the United Nations (UN) Sustainable Development Goals (SDGs) (United Nations 2015 ) and the approval of The Guiding Principles on Business and Human Rights by the United Nations Human Rights Council (United Nations 2011 ) have emphasized the importance of the research and application of methodologies that can better understand and reflect the negative and positive social impacts of the value chain. Fourteen of the seventeen objectives of SDGs concerning social impacts have evident links to the S-LCA framework (UNEP 2020 ). However, to date, few case studies have been conducted in this field (Zamagni et al. 2011 ), and many sectors are still not explored (D’Eusanio et al. 2018 ). In particular, in the agri-food sector, the social dimension is often analyzed, but there are few applications in terms of S-LCA as well as the LCC. Of the 84 case studies analyzed by Stillitano et al. ( 2021 ) on the application of LCT tools in agri-food processes, only eight studies (9.5%) deal with the LCC approach in combination with other analyses. Finally, no document to date has explored S-LCA.

The close relationship between agricultural activities and the impact on the three dimensions of sustainability (environmental, social, and economic) makes the use of indicators for measuring agricultural activities a sine qua non condition to ensure real sustainability based on CE principles.

However, the greatest difficulty for agri-food companies is represented by the large variety of potential indicators connected to sustainability and the main related actors (Sonesson et al. 2010 ; Lehmann et al. 2011 ). Indeed, according to Bele et al. ( 2018 ), the development of sustainable agricultural models is often characterized by the use of indicators that widely differ from the conventional ones. For example, biodiversity is not always captured by ecological sustainability indicators, despite the crucial role that biodiversity plays in ensuring long-term sustainability (Bele et al. 2018 ). Also, with regard to the social dimension, there is no universal consensus on the use of a specific indicator, causing confusion and ambiguity. This situation has led to the development of a framework for identifying the key social indicators of the CE through qualitative (Delphi) and quantitative (fuzzy logic) tools (Padilla-Rivera et al. 2021 ).

Although there is a broad literature on sustainability and CE indicators, to the best of the authors’ knowledge, there is no study that offers such a thorough framework of indicators for measuring the sustainability and CE in the agri-food sector.

This article aims to fill this gap by performing a systematic literature review (SLR), thereby providing a thorough overview of the sustainability and CE indicators in the agri-food sector, classified according to the strategic goal for which they are applied. The purpose is to understand how these indicators are used and their final objectives. The authors intend to outline a guideline for companies, thus providing a clear synthesis of the main tools available for assessing sustainability and CE. For this purpose, the study focuses on the indicators proposed and discussed in the literature with no regard for the tools with which the indicators are being applied.

Furthermore, this study investigates how such indicators are applied regarding the three dimensions of sustainability (economic, social, and environmental) as well as most debated topics directly connected to the selected indicators and in relation to the three main LCT tools.

Starting with this main research gap, the aim of this study is twofold: to find the strategic goals of the main indicators to measure the sustainability in the agri-food sector and, on the basis of these findings, to understand how they are used and for what specific purposes. In particular, the study is focused on how the identified indicators are applied to the triple bottom line (TBL) pillars.

Finally, new potential dimensions that could be used to extend impact categories for identified indicators have been investigated.

2 Relationship among sustainability, the circular economy, and LCA indicators

Sustainability and the CE are generating increased interest among governments, investors, businesses, and civil society (Pieroni et al. 2019 ). However, the conceptual relationship between sustainability and CE remains unclear (Geissdoerfer et al. 2017 ).

The concept of sustainability is older than that of CE (Nikolaou and Tsagarakis 2021 ). In its modern conception, sustainability originated in silviculture and was then transferred to the context of ecology, understood as nature’s ability to regenerate itself (Geissdoerfer et al. 2017 ). In the 1960s, attention to the issue of environmental risks on a global scale (such as climate change, biodiversity loss) led scholars to question linear development models based on production-consumption logic (Rockström et al. 2009 ), highlighting the close interconnections between environment, society, and economy (Kates et al. 2005 ). In 2007, the estimated number of sustainability concept definitions was approximately 3000 (Johnston et al. 2007 ), and to date, it is still a developing framework for scientific research and environmental management (Ruggerio, 2021 ). Sustainability can be defined as a conceptual construct applied to real-world systems (Gallopín 2003 ; Gallopín et al. 2014 ). Human activity should be conducted to preserve the functions of terrestrial ecosystems (ISO 15392, 2008 ). Sustainability is thus capable of transforming human lifestyles in order to optimize the likelihood of ensuring sustainable living conditions in terms of safety, well-being, and health (Geissdoerfer et al. 2017 ) while maintaining the provision of non-substitutable goods and services (McMichael et al. 2003 ) or through the indefinite perpetuation of all life forms (Ehrenfeld 2005 ).

Sustainability is related to another concept that arose in the 1980s, namely sustainable development. In fact, this concept was introduced in the Brundtland Report. The report highlighted the need to implement a strategy capable of integrating the needs of development and the environment, stating that sustainable development is such when it allows meeting “the needs of current generations without compromising the ability of future generations to meet their own needs” (WCED 1987 , p. 43). The verb “sustain” assumes the maintenance of unspecified characteristics over time, while the concept of “development” can be subject to multiple interpretations that vary depending on the values, interests, and disciplinary beliefs of reference (Geissdoerfer et al. 2017 ). Indeed, needs are defined by different cultures. The syllogism “sustainable development is necessary for all of us” (Redclift 2005 , p. 13) can be interpreted and conceived differently for each individual as well as for each culture. For this reason, according to several schools of thought, sustainable development is a contradictory concept due to the impossibility of sustaining infinite economic growth on a planet with limited resources (Beckerman 1992 ; Redclift 2005 ) and with apparent contradictions in its goals (Redclift 2005 ; Spaiser et al. 2017 ).

Despite the apparent contradictions and problems, the literature agrees on the need to define a new development paradigm that facilitates overcoming the old one (Ruggerio 2021 ) and embraces the three pillars of sustainability: people, profit, and planet (Elkington 1997 ). Triple bottom line (TBL) is the most commonly used approach to describe sustainability (Correia  2019 ); is based on the balanced integration of economic, environmental, and social performance (Geissdoerfer et al. 2017 ); and provides a framework to measure business performance and organizational success using, precisely, the three lines (Alhaddi 2015 ). The three “spheres”(Geissdoerfer et al. 2017 , p. 579) act “as interdependent and mutually reinforcing pillars” (Assembly 2005 p. 12) through processes of mutual causation and positive feedback (McKelvey 2002 ). The TBL recognizes that companies add economic value but can also impact environmental and social value (Lee 2007 ). Thus, the TBL is the model on which companies making the transition to sustainability are based (Chabowski et al. 2011 ; Svensson and Wagner 2015 ). From a market perspective, to achieve competitiveness, sustainability performance must therefore require a high connection between economic, environmental, and social performance (Yee et al. 2021 ).

Based on the discussion and literature review in this paper, the term sustainability is defined as “the balanced and systemic integration of intra and intergenerational economic, social, and environmental performance,” as defined by Geissdoerfer et al. ( 2017 , p. 579).

Regarding sustainability, CE is a relatively new concept that has only recently attracted academic attention (Calisto Friant et al. 2020 ). CE has mainly been developed by government and private sector actors, and for that reason, the primary beneficiaries seem to be economic actors (Suárez-Eiroa et al. 2019 ). CE tends to privilege the economic and environmental dimensions, providing only implicit benefits for the social dimension (Geissdoerfer et al. 2017 ). CE arises, in fact, in response to an unsustainable linear production-consumption system in which resources become increasingly scarce with evident negative repercussions for economies and the environment (Suárez-Eiroa et al. 2019 ). The earth can be defined as a closed system with limited assimilative capacities (Boulding  1966 ). That is why the basic idea of CE is the creation of closed and circular circuits in which recycling and reuse of raw materials replace the use of virgin resources (Georgescu-Roegen 1971 ; De Pascale et al. 2021 ). The main objective of CE is to adapt the production-consumption system to environmental sustainability requirements (Suárez-Eiroa et al. 2019 ). In this context, CE can be seen as a means to achieve sustainability (Geissdoerfer et al. 2017 ) and a central development model (Korhonen et al. 2018 ) for promoting sustainable development (Avilés-Palacios and Rodríguez-Olalla 2021 ), but with a narrower focus on the economic and environmental dimensions (Geissdoerfer et al. 2017 ). However, not all authors agree with this narrow view of CE (Opferkuch et al. 2021 ). Indeed, several authors argue that tying the concept of CE only to resource efficiency does not help promote a systems approach, thus preventing companies from considering the impacts of CE strategies from a broader sustainability perspective (Webster 2013 ). The transition to CE involves several challenges not only in environmental and economic terms but also in social terms through effective management of all stakeholders in the closed system, sharing values with consumers, and overcoming organizational barriers (Ritzén and Sandström 2017 ; Stewart and Niero 2018 ; Opferkuch et al. 2021 ).

Based on what has been discussed and from the analysis of the literature, in this article, the term CE refers to a central development model whose goal is “to achieve sustainability through closed cycles to accomplish the balance between economic valuation, social inclusiveness, and environmental resilience” (Avilés-Palacios and Rodríguez-Olalla 2021 , p. 3).

The debate on the concept of CE has also led to difficulties defining standard indicators for measuring CE (Rigamonti and Mancini 2021 ). To date, no harmonized method exists to assess whether a specific CE strategy contributes to sustainable consumption and production (Peña et al. 2021 ). However, the need to implement an efficient CE strategy has led several authors to propose different classifications of circularity indicators (Elia et al. 2017 ; Corona et al. 2019 ; Sassanelli et al. 2019 ).

In this context, several authors agree that LCA is an excellent tool to assess the sustainability impacts of CE strategies (Niero and Kalbar 2019 ; Peña et al. 2021 ; Rigamonti and Mancini 2021 ; Roos Lindgreen et al. 2021 ). In addition, the use of LCA-based measurement indicators is particularly effective when CE assessment occurs at the micro-level (Roos Lindgreen et al. 2021 ). In this context, the goal is to measure the circularity of a product (to assess the ability to conserve both the quantity and quality of material) or system (to assess the ability of a company to implement circular patterns) (Bracquené et al. 2020 ).

LCA is a standardized (ISO 14040: 2006 ; ISO 14040: 2006 ) and scientific methodology that is based on the concept of eco-efficiency (Niero and Kalbar 2019 ) and aims to assess the impacts associated with the life cycle of a product or service (Peña et al. 2021 ). LCA can support decision-making by providing a holistic perspective. Indeed, the comprehensive application of all impact categories allows for the assessment of the effects on the biophysical environment and the social and economic environment (Peña et al. 2021 ). For this reason, “LCA can be applied to build more consistent and robust CE strategies by considering potential upstream and downstream impacts and encompassing all relevant resources and impact categories” (Rigamonti and Mancini 2021 , p. 2).

3 Research questions and goals of the research study

The literature on sustainability and the circular economy indicators in the agri-food sector is not very extensive. Today (March 1, 2021), it is possible to find six literature reviews that investigated such relation. However, it is interesting to analyze the previous literature reviews in order to understand their differences with the present research work.

In particular, the first three articles focus on agri-food and sustainability, while the other two study the topic of agri-food and CE. This initial analysis already shows that there are no studies that simultaneously analyze both CE and sustainability indicators.

In relation to the study of agri-food and sustainability, the first published paper, by Barth et al. ( 2017 ), implemented a systematic literature review to understand sustainable business model innovation in the agri-food sector. The authors propose a conceptual framework to implement an innovative and useful business model to achieve sustainability goals and investigate the literature regarding the three pillars of sustainability but do not address the issue of measurement and indicators.

Stone and Rahimifard ( 2018 ) focus on resilience in agri-food supply chains. The authors implement a SLR to identify multidisciplinary aspects of resilience applicable to the agri-food supply chains to build a theoretical framework. The authors identify 40 elements of resilience in the literature but do not discuss the indicators.

Luo et al. ( 2018 ) also study the agri-food supply chain by identifying six main clusters into which the literature is divided as follows: short/alternative supply chains, food supply chain sustainability, food supply chain modelling, global agri-food supply chains, transparency/traceability, and food supply chain relationships/vertical coordination/networks. In particular, the sustainability cluster focuses on agri-food supply chains’ sustainability performance indicators, mentioning the value chain analysis (VCA), LCA, and ecological embedding measurements. The authors focus on future research directions in the study of agri-food supply chain.

Analyzing the topic of agri-food and the CE, Esposito et al. ( 2020 ) investigate the adoption of circular economy models and tools along the agri-food chain and stressed that it is almost utopian to define a single circular economy model for the whole sector. It is interesting to note that the study provides a comparative analysis between environmental assessment tools and the type of supply chain emerging form the paper analyzed. In this domain, the authors studied the industrial symbiosis on the basis of the three levels of CE identified in the literature: the macro-level (international), the meso-level (state, province, and city), and the micro-level (organization) (Salomone and Ioppolo 2012 ; Su et al. 2013 ). Authors state that, at the micro-level, in agriculture production, the assessment tools proposed are as follows: LCA, Water Footprint Assessment (WFA), and Carbon Footprint Analysis (CFA). In relation to the meso-level, the research identifies in the LCA the most appropriate tool used by scholars to assess the environmental impacts of both agriculture and the livestock sector.

The paper from Joshi et al. ( 2020 ) aims to identify the key dimensions of the circular economy in the agri-tourism industry. The authors, through a review of the literature and the support of some experts in the field, define nine dimensions of performance measurement in agri-tourism industry: network design, product design and visibility, traceability and transparency, co-creation, destination attractiveness, adoption of climate change, governance, market linkage, local community contribution and sustainable livelihoods, food security and self-efficacy.

Finally, Stillitano et al. ( 2021 ) propose an SLR addressing the state-of-art of LCT applications from a CE point of view, highlighting how researcher are adopting the LCT approach to measure the empirical circular paths of agri-food systems. Their study focuses on three LCT tools - LCA, LCC, and S-LCA - not considering the various indicators that do not fit into these approaches. In addition, the study only analyzes case studies and not conceptual works as well.

These three papers address the issue of CE and indicators in agri-food or agri-tourism sector but do not extend the analysis to sustainability aspects.

Table 1 shows the main details of the articles analyzed above. In particular, the table facilitates better synthesis and comparison of all of the articles in their main characteristics of analysis and therefore provides a better understanding of the differences from the present research work.

Within this context, the purpose of this article is to investigate sustainability measurement indicators to understand the strategic purpose of their use. The research aims to analyze how such indicators are used and for what purpose in business management toward a sustainable approach. In particular, we want to investigate in depth the application of these indicators with respect to the three pillars of sustainability defined by the triple bottom line (TBL) to understand if there is an integrated vision of the three dimensions of sustainability (environmental, economic, and social). Furthermore, the article seeks to verify if the “trilemma challenge” is considered and what level of investigation it receives.

This study can provide an important overview of the issue of sustainability in agri-food to understand how companies are moving toward the TBL objectives. Indeed, as can be seen from previous articles, there is no systematic review focusing on a comprehensive view of sustainability and the circular economy in the agri-food sector, particularly emphasizing the most appropriate measurement tools.

Thus, to achieve the aims of the paper, we tried to answer the following research questions:

RQ1. What are the main strategic purposes for the sustainability measurement indicators applied in the agri-food sector?

RQ2. What is the most recurring topic related to the sustainability measurement indicators?

RQ3. What are the new dimensions that indicators need to consider in the sustainability measurement process?

RQ4. How much of the three pillars of TBL do we find in the LCT indicators?

This SLR aims to answer these questions and to analyze a significant part of the literature concerning the agri-food sector, sustainability, circular economy, and their measurement. In order to fully investigate and understand the link between the various topics, a systematic literature review was implemented.

4 Research method

4.1 review methodology.

A systematic literature review approach (SLR) was used to answer the research questions. The aim of SLR is “to identify, evaluate, and interpret research relevant to a determined topic area, research question, or phenomenon of interest” (Kitchenham and Charters  2007 ; Muller et al. 2019 , p. 398).

In methodological terms, a literature review allows investigation of a given topic through both qualitative and quantitative content analysis (Hill 1995 ; Seuring and Muller 2008 ; Silvestri et al. 2021 ).

This method can reduce literature bias, providing considerable evidence for a phenomenon across various settings and empirical methods (Denyer and Tranfield 2009 ; Durach et al. 2017 ; Adjei-Bamfo et al. 2019 ).

For this reason, the SLR methodology born in medicine (Saade et al. 2020 ) has successively spread in other fields, such as social sciences, engineering (Bastas and Liyanage 2018 ; Sassanelli et al. 2019 ), business and economics (Colicchia and Strozzi 2012 ; Merli et al. 2018 ), and environmental science (Alshqaqeeq et al. 2020 ; Silvestri et al. 2021 ).

According to Denyer and Tranfield ( 2009 ), SLR is a specific methodology based on four steps: (1) identification of existing studies, (2) selection and evaluates of contributions, (3) analysis and synthesize data, and (4) description of the results in such a way that provides clear conclusions. Also, the process model proposed by Mayring ( 2004 ) comprised four steps: (1) material collection, (2) descriptive analysis, (3) category selection, and (4) material evaluation.

Following the guideline proposed by Mayring ( 2004 ) and Denyer and Tranfield ( 2009 ), this review process has been structured in four steps, as shown in Fig.  1 :

Revision process. Authors’ elaboration. Notes: The figure shows the review process followed by the authors in developing the SLR

4.2 Material collection

The Scopus database was used for material collection. Scholars consider Scopus to be among the best databases to produce a reliable bibliometric survey (Durán-Sánchez et al. 2018 ). Scopus offers a high level of singularity (Sánchez et al. 2017 ) and broad data coverage (Salim et al. 2019 ), making it one of the most comprehensive and comprehensive scientific databases (Chadegani et al. 2017 ).

The search strings used in Scopus are “Agri-food” AND “Sustainability” AND “Indicator*” OR “Assessment” OR “Measurement” OR “Performance”; “Agri-food” AND “Circular Economy” AND “Indicator*” OR “Assessment” OR “Measurement” OR “Performance.” In the Scopus search, the research criteria were “Title, Keywords, Abstract.” The keyword “assessment” automatically includes all articles dealing with LCA case studies, since this term is already included in the acronym of life cycle assessment (LCA). However, the authors verified this assumption, repeating the research process and confirming that the number of LCA papers remains the same.

For research purposes, this analysis focused only on papers in peer-reviewed scientific journals in English (Seuring and Muller 2008 ; Adjei-Bamfo et al. 2019 ; Alshqaqeeq et al. 2020 ; Merli et al. 2020 ). The collection of articles ended March 1, 2021, the process of article analysis and study ended April 15, 2021, and the article writing was completed June 1, 2021.

The initial number of collected papers from Scopus was 256. By eliminating duplicated articles and through the analysis of abstracts, the remaining articles were 130 from Scopus. The reading of the articles allowed the researchers to eliminate another 28. The total of the final sample is 99 papers (Fig.  2 ).

Identification of relevant articles process. Authors’ elaboration. Notes: The figure shows the process of selecting articles for the research topic analysis

4.3 Category selection

In this phase, the filtered articles were analyzed, synthesized, and classified according to specific categories (Wijewickrama et al. 2021 ). Based on the Mayring ( 2004 ) model and considering the research questions, several structural dimensions were identified. Subsequently, for each of them, the analytical categories identified to evaluate the material.

Both structural dimensions and categories can be derived deductively or inductively (Mayring 2004 ; Seuring and Muller 2008 ; Merli et al. 2020 ). The deductive approach, dimensions, and categories are defined based on a specific theory or an existing model (Polit and Beck 2004 ). Therefore, they defined these aspects before proceeding with the material’s analysis (Seuring and Muller 2008 ). In the inductive approach, the dimensions and categories are defined by analyzing the material, thus shifting the focus from the specific to the general (Silvestri et al. 2021 ).

In this research, five structural dimensions were defined using the deductive approach, while three dimensions applied the inductive approach.

In the inductive approach, identification of dimensions and related categories was based on content analysis (Tranfield et al. 2003 ; Lueddeckens et al. 2020 ). An iterative process was then applied, which involves first familiarizing oneself with the literature and the sample of articles selected (Dixon-Woods 2011 ) and then allowing themes to develop and emerge organically, as opposed to selecting predetermined themes or frameworks, and then categorizing the data accordingly (Prajapati et al. 2019 ).

Specifically, this approach was used for the cluster dimension, which consisted of three analytical categories, categorized as follows:

Assessment-LCA: This cluster includes all articles that used the life cycle assessment (LCA) method to measure the environmental impact of activities carried out in the agri-food system. Many of the articles in this cluster mainly analyze the environmental dimension of sustainability.

Best practices: This includes articles that aim to define guidelines for the agri-food system actors to achieve global sustainability.

Decision-making: This cluster includes articles that aim to support agricultural producers and policymakers in the process of transitioning toward sustainability. The measurement indicators are tools capable of helping agricultural producers and policymakers understand the impact of their sustainability.

After familiarization with the sample, in order to increase the reliability and quality of the content analysis, the study was conducted in three phases. An initial researcher developed an initial classification of the articles. The analysis was also based on the search for specific keywords that supported the categorization process (e.g., the keywords LCA, Assessment, Case study, and/or Measurement supported the identification of the category Assessment-LCA; the keywords Practice, Guidelines, Framework, and/or Model supported the identification of the category Best practices; the keywords Decision, Collaboration, and/or Recommendations supported the identification of the category Decision-making). The validation of the keywords was based on their contextualization within the article and for the three selected categories. The results were then refined by a second researcher and then moved to a combined discussion by the two researchers to review and finalize the categorization and classifications of the selected articles (Prajapati et al. 2019 ; Wijewickrama et al. 2021 ).

The first cluster comprises qualitative empirical studies (case studies that applied the LCA method). The Best practices cluster is mainly composed of conceptual studies, proposing frameworks, or models to support the implementation of sustainability through guidelines. The third cluster articles are all empirical and propose both qualitative (case studies) and quantitative (questionnaires) studies (see Sect.  5.2 ).

For the Keyword dimension (for which an inductive approach was used), the iterative process was developed by VOSviewer software (see Sect.  5.3 ), while for the Scientific Field dimension (for which an inductive approach was used), the content analysis was developed by integrating the information obtained from the Subject area suggested by Scopus.com (see Sect.  5.4 ).

In the deductive approach, the identification of dimensions and related categories was based on the literature’s analysis of models and frameworks.

In particular, for the structural dimensions Sustainability and Indicators, the classification was based on the TBL model (see Sects.  5.5 and 5.6 ).

For the LCA Impact category, the analysis has been more complex. LCA is based on applying principles, models, and characterization factors that allow an LCA practitioner to calculate the characterization results for a given impact category (Tobergte and Curtis 2013 ). Thus, an LCA practitioner can choose to apply several different methods and related impact categories in their research. To classify the LCA indicators applied in the sample under analysis, we relied on the one proposed by Acero et al. ( 2017 ) (see Sect.  5.7 ).

Following the guidelines proposed by Muijs ( 2010 ), MacInnis ( 2011 ), Flick ( 2014 ) and Jaakkola ( 2020 ), MacInnis ( 2011 ), the articles were classified into conceptual and empirical. An additional analysis was performed on each macro-group. Conceptual articles are classified under Framework, Model, Review, and Index (MacInnis  2011 ). Empirical articles are divided into Qualitative (based on non-numerical data) and Quantitative (based on statistically analyzed numerical data) (Flick  2014 ; Muijs  2010 ). Based on these studies, the structural dimension Research Methodology was then defined (see Sect.  5.8 ).

Finally, for Geographical Focus, we relied on the science of geography (see Sect.  5.9 ).

Table a 1  in the Appendix shows the considered approach for each category and structural dimension, which can be inductive or deductive synthesizing, and includes the criteria for defining the dimensions and categories.

The identification of the dimensions and their categories allows the analysis of the articles from different points of view, contributing to a greater understanding of the topic and therefore answering the research questions of this article in a more in-depth way. The categorization process allows us to (1) study the topic of sustainability and circularity measurement indicators by looking at them from the point of view (stakeholder, TBL, LCA Impact category, etc.); (2) analyze in which scientific and geographic areas sustainability and circularity measurement indicators are most developed; and (3) study the methodologies used for their analysis.

Before analyzing the eight structural dimensions, a general description of the sample was performed.

5.1 Distribution of times and sources

Ninety-nine articles were analyzed to answer the research questions of this study. Articles and results were evaluated and sorted into a database. Articles were analyzed based on the structural dimensions and categories identified. The data collected in the database allow the development of a descriptive analysis of the sample, about (1) year of publication, (2) journal title, and (3) number of authors for each article.

Figure  3 shows the temporal distribution of the articles up to March 1, 2021. The highest peak of publications about the sustainability measurement indicator topic in the agri-food sector occurred in 2020 (21 articles), followed by 2019 (20 articles), and 2018 and 2017 (15 articles each). The focus on the topic is, therefore, recent. Between 2017 and March 1, 2021, 74% of the entire sample articles were published. In only the first month of 2021, there are three articles.

Source: Authors’ elaboration. Notes: The graph shows the temporal distribution of the articles under analysis

Temporal distribution of selected articles.

The Journal of Cleaner Production has the most significant number of papers (20.0%), followed by Sustainability (13%) and Journal of Industrial Ecology (4%), and the three most prolific journals account for 37% of records (Table a 2  in Appendix).

Figure  4 shows the temporal distribution of the three main journals over time. The focus on this topic in Journal of Cleaner Production is constant over time. The publications in the journal Sustainability in contrast became more concentrated in recent years (from 2018 to February 1, 2021). The Journal of Industrial Ecology is the first of the three to focus on this theme (the first article was published in 2011), while in subsequent years, it presents a non-constant trend (it was published in 2016, 2017, and then 2019).

Source: Authors’ elaborations. Notes: The graph shows the time distribution of articles from the three major journals

Temporal distribution of the first three journals.

Finally, Table a 3  in Appendix shows the number of authors per article. A total of 32.3% of articles have a number of authors greater than or equal to 5, and 21.2% of papers have between 1 and 2 authors. Articles with four authors represent 26.3% of the total, following by 20.2% of articles, which have three authors.

5.2 Cluster

All the articles collected can be presented in three clusters within the following items:

Assessment-LCA

Best practices

Decision-making

Figure  5 shows the division of the sample into the three clusters. The largest cluster is the Decision-making cluster (39 articles out of 99), followed by the Best practices cluster (33 articles out of 99), and finally the Assessment-LCA cluster (27 articles out of 99).

Source: Authors’ elaboration. Notes: The graph shows the composition of the sample according to the three clusters identified by the analysis

Composition of the sample in clusters.

Figure  6 shows the temporal distribution of the three clusters. Articles belonging to the Best practices cluster are present in all years, starting in 2007. Although the Best practices cluster is not the most numerous, its distribution over time is more homogeneous and constant, unlike the other two clusters. This data demonstrates the significant role played by theoretical studies aimed at defining guidelines for the correct and complete implementation of sustainability.

Source: Authors’ elaboration. Notes: The graph shows the distribution of articles over time by cluster

Temporal distribution of clusters.

5.2.1 Assessment-LCA

The first cluster consists of 27 articles published from 2011 to February 1, 2021. The articles’ objective is to measure the environmental impact of activities carried out in the agri-food system through the application of the LCA tool. The cluster is mainly composed of qualitative empirical articles and develop case studies.

The most thoroughly analyzed aspect of LCA studies is evaluating the energy and environmental efficiency of the agri-food system (7 articles out of 27). The agri-food system consumes a significant amount of energy due to agricultural machinery, irrigation, chemical use, transportation, and processing (Garofalo et al. 2017 ). Laso et al. ( 2018 ) and Pérez-Neira & Grollmus-Venegas ( 2018 ) studied the energy assessment of the Spanish agri-food systems. Laso et al. ( 2018 ) focused on the energy and environmental efficiency assessment of the Spanish agri-food system using the LCA/DEA methodology. Pérez-Neira and Grollmus-Venegas ( 2018 ), on the other hand, developed a case study focusing on peri-urban horticulture, demonstrating the strategic role of the LCA tool in the evaluation of alternatives. The topic of sustainable agriculture in the peri-urban context is the research developed by Caputo et al. ( 2020 ). The authors evaluated the development of an innovative food hub as a process of urban regeneration, analyzing the LCA tool’s energy-environmental effects. Instead, the comparative study approach is used by Muradin et al. ( 2018 ) to evaluate the eco-efficiency of two alternative agricultural biogas plants. Notarnicola et al. ( 2017 ) used the LCA tool to assess the sustainability of 21 types of bread, estimating the embodied energy and equivalent GHG emissions of each type of bread (considering mass, nutritional value, and functional unit based on price). Also, Goucher et al. ( 2017 ) analyzed the sustainability of the bread supply chain but focused on the environmental impact of fertilizers used to produce raw materials and which are the cause of one-third of the total greenhouse gas emissions. Smetana et al. ( 2019 ) highlighted the need to improve the feed production industry for a more energy-efficient one. Ferreira et al. ( 2019 ) focused on evaluating energy efficiency in the olive oil extraction process, while Khounani et al. ( 2021 ) applied the LCA tool to evaluate different biorefinery platforms that valorize olive waste. Finally, while Perez Neira ( 2016 ) analyzed the energy sustainability of Ecuadorian cocoa export, Recanati et al. ( 2018 ) studied the environmental impacts of chocolate, from the bean’s sourcing its distribution in the market, with particular attention to the issue of environmental labels. On the topic of environmental labels and packaging, Del Borghi et al. ( 2018 ) developed their research and, using the LCA tool, evaluated the design of environmentally friendly packaging systems focusing on a case study of legumes. Blanc et al. ( 2019 ) instead evaluated the use of bioplastics in fruit and vegetable packaging, focusing on the entire fruit supply chain. On the other hand, the honey jar evaluated in the study developed by D’Eusanio et al. ( 2018 ). The authors proposed a social and socio-economic assessment of a honey jar by applying social life cycle assessment in the honey sector. Del Borghi et al. ( 2014 ) developed an environmental sustainability assessment in the environmental industry, focusing on the tomato product supply chain. Moreover, tomato assessment is always the subject of research by both Garofalo et al. ( 2017 ) and Ingrao et al. ( 2019 ). Both studies are developed in Italy, as is the research developed by Lo Giudice et al. ( 2014 ), Tasca et al. ( 2017 ); Tassielli et al., ( 2018 ); Martucci et al. ( 2019 ) and Roselli et al. ( 2020 ). The different authors have proposed assessing different agri-food products’ environmental sustainability, consistently applying the LCA tool. Belaud et al. ( 2019 ), Harun et al. ( 2021 ), and Rezaei et al. ( 2021 ) have instead focused their attention on assessing environmental sustainability in rice production. Specifically, Belaud et al. ( 2019 ) demonstrated the benefits of using digital technologies (such as big data) in improving both sustainability and management of agricultural waste from rice. Tsangas et al. ( 2020 ) proposed a sustainability assessment of the agri-food sector to demonstrate the importance of the LCA tool’s business strategy level. Finally, Aramyan et al. ( 2011 ) presented a study on the European supply chain in sustainable production.

5.2.2 Best practice

The second cluster consists of 33 articles published from 2007 to 2020. The articles’ objective is to outline guidelines for agri-food system actors and policymakers to achieve global sustainability. The cluster is predominantly composed of conceptual studies (25 articles out of 33) that propose models or frameworks. Empirical articles (8 out of 33) are predominantly qualitative, proposing case studies as models for achieving sustainability (for details, see Table a4 in the Appendix).

In this cluster, the LCA tool is often used as a reference model for monitoring environmental policy interventions in the agri-food sector. Gava et al. ( 2018 ) developed a conceptual model highlighting that LCA is an excellent tool for monitoring performance at the farm or sector level against the United Nations 2030 Sustainable Development Goals in agri-food. Woodhouse et al. ( 2018 ) used LCA to develop a qualitative sustainability checklist, demonstrating how this tool can help companies overcome the assessment process’s challenges. Teixeira and Pax ( 2011 ) analyzed the evolution of the LCA tool in the agri-food field, while Soussana ( 2014 ) highlighted the potential of the LCA tool in sustainability assessment in food systems.

Crenna et al. ( 2017 ) and Nazir ( 2017 ) focused on different aspects and stages of LCA. Nazir ( 2017 ) described life cycle thinking (LCT) and the applications of LCT in agri-food. The author defined the challenges an LCA practitioner faces in applying the tool in sustainable agri-food systems. Crenna et al. ( 2017 ), on the other hand, developed his research based on life cycle impact assessment (LCIA) models, providing recommendations on how to direct future research, improve current models, and develop new sustainability indicators. Bonisoli et al. ( 2018 ) analyzed several agriculture sustainability frameworks, demonstrating the complexity of identifying a priori the best framework to rely on to achieve sustainability. However, the authors stated that combining different approaches could prove to be the best way to help farmers achieve sustainability goals.

Also, Morrissey and Dunphy ( 2015 ) highlighted the need to apply integrated models of sustainability. Food production and consumption systems’ transition to a sustainable low-carbon future is a complex issue involving many aspects. Only an integrated view of sustainability measurement models can enable its full achievement (Morrissey & Dunphy, 2015 ). This dynamic dimension of the agri-food system also emerged from Aivazidou et al. ( 2015 ). Gaitán-Cremaschi et al. ( 2017 ), on the other hand, proposed a single global metric to allow the comparison of the sustainability performance of agri-food supply chains by applying a total factor productivity (TFP) approach. Also, Green et al. ( 2020 ) used metrics to assess the sustainability of agribusiness production systems quantitatively. The authors highlighted the need to develop more robust methods of assessment. Indeed, agri-food production systems are significant drivers of global sustainability challenges, including climate change, freshwater scarcity, micronutrient deficiencies, and cardiovascular disease.

The theoretical framework proposed by Horton et al. ( 2016 ) focuses precisely on food security and human health. According to the authors, the lack of integrated decision-making across the agri-food system is the biggest obstacle to global food security. The theoretical framework presented by Horton et al. ( 2016 ) aims to address this challenge by proposing a methodology based on the integrated assessment of all processes involved in food production and consumption, from land ecology to nutrition and health. In this context, information services play a crucial role in ensuring the integration of the various stakeholders in the agri-food chain. Lehmann et al. ( 2011 ) presented a framework for the development of information services, proposing three examples of application by selecting for the social dimension of sustainability the theme of food security, for the economic dimension the theme of quality, and for the environmental dimension the theme of global warming potential. The quality dimension is the subject of study by Saitone and Sexton ( 2017 ).

According to the authors, modern consumers focus heavily on food’s ability to meet multiple dimensions of quality, including that of sustainability. For this reason, Stone and Sexton ( 2017 ) analyzed the food chain’s evolution by assessing its capabilities in addressing modern challenges. Also, the authors analyzed the impacts of these demands on the well-being of the various actors in the supply chain and assessed the effectiveness of related agricultural policies. Moreover, it is precisely the policy dimension that the study conducted by Ruiz-Almeida and Rivera-Ferre ( 2019 ) focuses on. The authors stated that existing frameworks often lack the policy dimension. For this reason, Ruiz-Almeida and Rivera-Ferre ( 2019 ) proposed a quantitative methodology that allows the analysis of the functioning of food systems while also considering the effects produced by agricultural policies at the international level.

While Saitone and Sexton ( 2017 ) developed the theme of quality, Wheaton and Kulshreshtha ( 2013 ) focused on the link between agriculture and climate change. Specifically, the authors explored the effects of climate change on agri-environmental sustainability. By applying mathematical models, the authors demonstrated how agri-environmental indicators could be used to measure (and possibly predict) direct impacts on climate change. Higgins et al. ( 2015 ) focused on agri-food GHG emissions and developed farm sustainability metrics capable of positively influencing GHG reductions. Bilali et al. ( 2020 ) returned to the topic of the link between agriculture and climate change. The authors selected a set of indicators proposing a robust and user-friendly sustainability measurement approach in line with the principle of continuous improvement and innovation. Rabadán et al. ( 2019 ) developed a study highlighting the decisive role that eco-innovation technology plays in improving companies’ sustainability performance in the agribusiness sector.

Also, Notarnicola et al. ( 2012 ) highlighted the decisive role that research plays in making the agri-food sector more sustainable. Negra et al. ( 2020 ) focused on the benefits of a collaborative approach between scientists and companies in co-developing sustainability indicators. According to the authors, a collaboration between the two unlocks significant sustainable investments in the food and agriculture sector, even on a large scale. Marotta et al. ( 2017 ) presented a case study demonstrating that corporate social responsibility (CSR) promotes sustainable innovations, thus generating value. Ross et al. ( 2015 ) also analyzed the sustainability initiatives of US companies applying CSR. The results showed some difficulty among companies in linking sustainable initiatives to their strategies, particularly in food chain logistics. Bloemhof et al. ( 2015 ) developed their research on this topic. The authors presented a framework for food chain logistics, proposing performance indicators, metrics, and improvement opportunities to measure and potentially enhance sustainability performance. Gold et al. ( 2017 ), on the other hand, developed a model identifying the main barriers to sustainability that companies in the agri-food chain must overcome to implement integration processes within them. Raut et al. ( 2019 ) examined the hard and soft indicators for evaluating green management practices, thus exploring the relationship between green practices and agri-food business performance of Indian firms. Also, Priyadarshini and Abhilash ( 2020 ) focused their attention on the Indian agri-food arena, developing best practices to best support the transition to sustainable agriculture, focusing on agricultural policies’ role. The focus on agricultural policies’ role is also the subject of analysis by Lynch et al. ( 2019 ). The authors developed a review of recent developments in agricultural sustainability monitoring. The article provides an overview of potential developments in Irish agricultural sustainability assessment.

The purpose is to support researchers and agricultural stakeholders involved in program design to meet and exceed the agricultural policies’ requirements. Gésan-Guiziou et al. ( 2020 ) proposed using multi-criteria decision analysis (MCDA) methods as a tool for sustainability transition, highlighting its main advantages. Also, Iocola et al. ( 2020 ) proposed a valuation framework based on the multi-criteria approach proposing a set of 32 indicators to support companies transitioning to a sustainable agri-food system. Risku-Norja and Mäenpää ( 2007 ) instead proposed a model based on the material flow approach (MFA) to assess food production and consumption’s economic and environmental consequences. Finally, Duru and Therond ( 2015 ) described and evaluated the sustainability of two different livestock systems, highlighting the crucial role of the implementation of ecological forms of modernization in achieving sustainability. Kuisma and Kahiluoto ( 2017 ) have, on the other hand, shown that agribusiness efficiency requires loss prevention and the circular use of resources.

5.2.3 Decision-making cluster

The third cluster consists of 39 articles published from 2012 to March 1, 2021. The articles’ objective is to support agricultural producers and policymakers in the process of transitioning to sustainability. The cluster consists of both qualitative and quantitative empirical studies. Qualitative research makes up the most significant percentage of the sample (30 articles out of 39). The remaining nine articles are quantitative studies.

The continued intensification of agricultural production affects environmental sustainability, leading to various environmental indicators (Niemeijer and de Groot 2008 ; Jorgensen et al. 2013 ; Huffman et al. 2015 ). Sustainability measurement indicators are an essential guide to support stakeholders in the agri-food system (Coteur et al. 2019 ). Based on this need, Van Asselt et al. ( 2014 ) proposed a protocol for selecting and quantifying indicators that can be used to discuss the sustainability of agri-food production systems. The protocol is primarily aimed at policymakers, representing a valuable tool for assessing agri-food production systems’ sustainability. The model, based on twelve indicators, is classified into four groups of sustainability attributes (productivity, stability, equity, and autonomy), as proposed by Chaparro-Africano ( 2019 ). It is primarily aimed at stakeholders in the agri-food supply chain. The purpose of the model is to provide an easily applicable methodology capable of supporting the system’s actors by ensuring effective use in the management of sustainability.

Direct stakeholder involvement plays a crucial role in defining sustainability indicators (Coteur et al. 2019 ). Peano et al. ( 2014 ) proposed a set of indicators selected based on reliable criteria obtained through the direct involvement of farmers. In defining the indicators, the authors considered economical, ecological, social, and cultural aspects and quality. The theme of quality is also developed by Pattey and Qiu ( 2012 ), who analyzed the impact of agricultural practices on air quality in their study. At the sectoral level, agriculture is the cause of several environmental impacts, such as water consumption, energy, waste production, greenhouse gas emissions, and soil degradation (Pagotto and Halog 2016 ). In terms of the last aspect, the research of Huffman et al. ( 2015 ) developed, which proposes a set of indicators to improve and evaluate soil cover for Canada’s agricultural land. Pagotto & Halog ( 2016 ) proposed a set of indicators capable of evaluating various sub-sectors eco-efficiency performance in Australian agri-food systems, considering all environmental impacts caused by agriculture. The study aims to support decision-makers by informing them of the advantages of moving from a traditional linear system to a circular production system. This could create a sustainable and efficient circular economy in the Australian food industry. Vasa et al. ( 2017 ) proposed a similar study. The authors developed a comparative analysis of the development of circular agriculture in Albania, Macedonia, and Serbia, summarizing the implications of various performance indicators that guide circular agriculture development. According to Coteur et al. ( 2019 ), to create a sustainable economy in the agri-food sector, the leaders need a good set of indicators capable of (1) assessing its sustainability and (2) identifying the need for interaction between the actors in the chain. Indicators and actors are essential factors in sustainable development, and understanding how actors interfere at the chain level helps guide transition processes toward sustainable development better.

The importance of cooperation and collaboration in agri-food supply chains also emerges from studies conducted by Allaoui et al. ( 2019 ), Azevedo et al. ( 2018 ), do Canto et al. ( 2020 ), dos Santos & Guarnieri ( 2020 ), and Trivellas et al. ( 2020 ). For the authors, the level of cooperation played a crucial role in achieving sustainability and considered an indicator of social sustainability. Cooperation allows for the development of a network of relationships and strategies, enabling the strengthening of the values of solidarity and long-term commitment and innovation (Van der Ploeg 2014 ). In this context, Chams et al. ( 2020 ) developed a study to assess the social impact of innovation and research in building a sustainable agribusiness sector. The results highlighted the crucial role of both innovation and research, identifying essential implications for farm decision-makers. According to Galdeano-Gómez et al. ( 2017 ), the policymakers involved in regional development should promote innovation and training in farms. The study of the interactions between the intensity of agriculture, environmental impact, and the role of innovation requires new, more sophisticated indicators. The goal is thus to quantify the interactions between the three dimensions.

For this reason, the use of aggregated indicators to arrive at one composite measure is necessary (Ryan et al. 2016 ). This point of view is also shared by Arfini et al. ( 2019 ), who presented a holistic framework to assess the sustainability of food quality systems and proposed synthetic indicators to provide an overall picture of the evolution of a specific production system (see Parmigiano-Reggiano PDO).

The close link between agricultural activities, the use of non-renewable natural resources, and the provision of basic goods for society drives the study of indicators. For this reason, the topic of eco-efficiency is receiving increasing interest as a sustainability indicator, as it links environmental and economic performance in productive activities (Godoy-Durán et al. 2017 ). In this context, Banasik et al. ( 2019 ) developed a stochastic model, quantifying trade-offs between environmental and economic indicators. This approach is a valuable decision support tool for companies that need to balance environmental and economic objectives. In addition, Godoy-Durán et al. ( 2017 ) studied eco-efficiency, developing a case study on small family farms in Spanish horticulture. The authors, upon applying data envelopment analysis (DEA), studied the combinations of environmental, economic, and social indicators. The results showed significant inefficiency, particularly in waste management. On this topic, Vergine et al. ( 2017 ) developed a study on wastewater reuse for irrigation in the agri-food sector. The authors demonstrated the positive impact of wastewater reuse in terms of environmental and economic sustainability and the importance of new technologies in achieving sustainable goals. Moreover, by combining the Internet of Things and edge computing, Pérez-Pons et al. ( 2021 ) developed a real-world scenario to make farms more profitable and sustainable. Increasing resource efficiency and becoming more environmentally friendly are just some of the challenges that agri-food companies face. In this context, technology can serve as a valuable tool. In particular, the issue of climate change associated with agricultural activities is the most studied aspect. Chandrakumar et al. ( 2019 ) created a model called “absolute sustainability-based life cycle assessment” (p. 906) to provide information in absolute terms on the climate impacts of an agri-food system focusing on the New Zealand system. The model represents a good tool to support companies by encouraging the development of a series of technical and management initiatives oriented to global sustainability. Based on the model “water-energy-food nexus,” Tortorella et al. ( 2020 ) proposed an integrated methodological approach to analyze the effects of climate change in the agri-food sector. The aim is to support decision-making related to climate change mitigation. Also, Irabien and Darton ( 2016 ) used the water-energy-food nexus model to analyze the level of sustainability in local tomato production in Spain. The authors pointed out that the approach based on life cycle assessment indicators is a handy decision support tool. Peano et al. ( 2015 ) proposed an interpretive structure, “Sustainable Agri-Food Evaluation Methodology,” capable of guiding the assessment of sustainability in small farms, supporting their decision-making process. Other authors such as Coppola et al. ( 2020 ) and Conca et al. ( 2021 ) studied CSR’s role in achieving sustainability, while Bonisoli et al. ( 2019 ) and Dietz et al. ( 2019 ) focused on the role of farm certifications. Both management approaches aim to support management in achieving sustainability goals. According to Verdecho et al. ( 2020 ), modern companies need adequate tools that support the integration of sustainability strategy into their operations, primarily in the supplier selection process. In this context, the authors proposed a methodology based on the multi-criteria technique to support companies in this process: supplier selection. Mangla et al. ( 2018 ) and Naseer et al. ( 2019 ) focused their attention on the entire agribusiness supply chain, identifying key factors and constraints in sustainable management. Simultaneously, Saputri et al. ( 2019 ) proposed a model to assess the sustainability of GMO and non-GMO agribusiness supply chain performance. The multi-criteria technique is also used by Pronti and Coccia ( 2020 ) to perform a comparative analysis of agroecological and conventional small coffee crops. The study results showed that agroecological systems support the socio-economic sustainability of the rural areas under study, promoting the sustainable development of the entire eastern region of Minas Gerais (Brazil). Pelletier ( 2018 ) also study the socio-economic dimension. The author proposed a model to assess Canadian egg production facilities’ social sustainability, highlighting the risks and benefits of such an approach. Also, in Canada, Mitchell et al. ( 2020 ) analyzed ecosystem service indicators and proposed a model to support business decisions. The research showed that, by improving understanding of the spatial patterns and temporal dynamics of ecosystem services, a better understanding of the underlying processes and sustainability goals is possible. Finally, Bele et al. ( 2018 ) developed an analysis on sustainability indicators focusing on biodiversity, while Miglietta and Morrone ( 2018 ) proposed a study on water sustainability management, highlighting the need for better alignment of water policy with agricultural policy.

5.3 Keyword co-occurrence analysis

The VOSviewer software ( www.vosviewer.com ; Van Eck & Waltman  2019 ) was used to analyze keywords. This software helps users visualize data maps of bibliometric networks that are based on Visualization of Similarities (VOS) (Van Eck and Waltman 2019 ; Merli et al. 2020 ) and are generally used for performing literature review in research studies (Jin et al. 2019 ; Li et al. 2020 ). The co-occurrence analysis facilitates the counting of articles that were simultaneously published (Merli et al.  2020 ). The distance among nodes (keywords) “is approximately inversely proportional to the similarity (relatedness in terms co-occurrence) of the keywords” (Bornmann et al. 2018 , p. 430). Furthermore, “A smaller distance generally indicates a stronger relation” (van Eck and Waltman  2010 , p. 525). Through this logic, the software builds a network of keywords (adjectives and nouns) that occur in more than one paper, visualizing them in a 2-D map. In addition, it also has a clustering tool that connects keywords to clusters according to their recurrence (Bornmann et al. 2018 ; Van Eck and Waltman 2019 ). In this study, the considered setup consists of binary counting, using only keywords that are repeated at a minimum of five times (Merli et al. 2020 ). Keywords that did not match with this setting were manually excluded, and the interpretability criterion has been used for determining the number of clusters (Bornmann et al. 2018 ). Words included in the papers abstract were removed (e.g., “design/methodology/approach,” “originality value”). The analysis produced a total of 953 keywords, and 47 keywords matched with the inclusion criteria.

Figure  7 shows the network visualization in which a label represents a label and a circle with different sizes. Sizes were determined according to their weight. The most recurring keyword was “Sustainable development” (40 occurrences, 303 total link strength, and 45 links), followed by “Sustainability” (53 occurrences, 267 total link strength, 44 links), “Life cycle” (27 occurrences, 231 total link strength, 39 links), “Life cycle assessment” (21 occurrences, 146 total link strength, 34 links), and “Environmental impact” (24 occurrences, 187 total link strength, 40 links).

Source: Authors’ elaboration. Notes: The graph shows the network visualization

Network visualization.

Furthermore, the software identifies 4 clusters and more strong relations in terms of co-occurrences among keywords represented by “Sustainability” and “Sustainable development” (link strength 20), “Sustainable development” and “Life cycle assessment” (link strength 20), “Sustainable development” and “Supply chains” (link strength 17), “Sustainability” and “Environmental impact” (link strength 8), “Sustainability” and “Agriculture” (link strength 11), “Agriculture” and “Environmental impact” (link strength 11), and “Climate change” and “Life cycle assessment” (link strength 6).

Cluster 1 contains 17 items and highlights the relationship between sustainable development and the agri-food system from the point of view of tools for measuring and evaluating environmental impact. In particular, the cluster shows the relationship of the tool of “Life cycle assessment,” with categories of environmental impacts such as “Greenhouse gases” (link strength 9) or “Gas emissions” (link strength 9). The focus is then oriented to the whole agri-food chain (“Life cycle” and “Supply chains,” link strength 12).

Cluster 2 includes 12 items and shows the relationship between “Agriculture” and “Sustainability” but from the point of view of companies and strategic decisions. Cluster 2 shows how sustainability must be, first and foremost, a political choice of companies, which is increasingly oriented toward sustainability that is environmental, economic, and social (this cluster includes the keyword “social sustainability”). The link strength between “Sustainability” and “Decision making” is 8, while “Decision making” and “Food supply” have a link strength of 7. Finally, the instruments for measuring sustainability in cluster 2 are indicators. Cluster 2 confirms what has already been stated about the “Decision-making” cluster, made up of articles suggesting that companies use indicators to support strategic choices oriented toward sustainability.

Cluster 3 is made up of 9 items and shows the relationship between the agri-food system, climate change, and innovation. Cluster 3 also focuses on companies in the agri-food chain, but from the point of view of the impact of their activities on the problem of climate change. The presence of the keyword “Innovation” highlights the critical role of innovation in achieving a genuinely sustainable agri-food system.

Finally, cluster 4, made up of 4 items, shows the relationship between agri-food systems and Italy. The composition of cluster 4 confirms what was stated in the previous paragraph (5.7 Geographical Focus). Italy is the country with the highest number of published items due to the critical role that the agri-food system plays in both the national and EU economies.

The “Overlay visualization” (Fig.  8 ) “is identical to the network visualization except that the items are colored differently” (Van Eck and Waltman  2019 , p.9). Specifically, Fig.  12 represents keywords by years, allowing us to understand their longevity level of innovativeness. The longest-lived keywords are “Indicators” (Adv. pub. Years 2016), “Environment” (Adv. pub. Years 2014), and “Food production” (Adv. pub. Years 2014), while the most recent keywords are “Artificial life” (Adv. pub. Years 2018), “Environmental impact” (Adv. pub. Years 2018), “Alternative agriculture” (Adv. pub. Years 2019), and “Agri-food” (Adv. pub. Years 2019). The data highlights that awareness of the environmental impact in the agri-food sector is relatively recent. Also, the analysis shows how scholars identify new digital tools as a solution to achieving sustainability.

Source: Authors’ elaboration. Notes: The graph shows the overlay visualization

Overlay visualization.

Between the two extremes, we find keywords such as “Sustainability” (Adv. pub. Years 2017), “Life cycle assessment” (Adv. pub. Years 2017), “Climate change” (Adv. pub. Years 2017), and “Innovation” (Adv. pub. Years 2017). In particular, this last keyword has links to recent keywords such as “Alternative agriculture” or “Agri-food.” The link shows that the idea of applying Industry 4.0 digital tools in agri-food to promote “Alternative agriculture” and sustainability is a current theme.

5.4 Scientific field

The articles were classified according to the scientific approach used to analyze sustainability in the agri-food sector. Different approaches or perspectives exist through which a phenomenon can be observed (Esposito et al. 2020 ). For this reason, an analysis of the scientific field of each article was performed. A joint approach was used to identify the scientific field. Based on the “Subject area” suggested by Scopus.com, we proceeded to read the articles to understand which was the prevailing scientific field. Figure  13 shows the classification of articles based on the scientific field.

Figure  9 shows that the most recurrent scientific field is “Environmental science” (51 articles out of 99), followed by “Business, Management and Account” (25 articles out of 99), and “Agricultural and Biological Sciences” (12 articles out of 99). From this graph, the strong link between the environmental aspect and the business world is evident.

Source: Authors’ elaboration. Notes: The graph shows the classification of articles by scientific field

Article classification based on the scientific field.

Figure  10 shows, instead, the classification of particles according to their cluster of belonging. In the cluster “Decision-making,” almost half of the articles fall within the scientific file, “Business, Management, and Account.” The result shows that a large part of these articles seek to support companies in the decision-making process to facilitate a better transition toward sustainability. The scientific field of “Social science” is found only in the clusters “Decision-making” and “Best practices.” The social dimension is, in fact, an aspect that must be considered in the decision-making processes and political choices of companies. Finally, the scientific field “Energy” is present only in the “ Assessment-LCA ” cluster. In the cluster analysis above, particular attention was paid to the theme of energy, a resource that is widely used in the agri-food sector.

Source: Authors’ elaboration. Notes: Article classification based on their cluster to which they belong and scientific field

Article classification based on their cluster to which they belong.

5.5 Sustainability

The TBL approach often used to analyze sustainability in SRL studies developed within the sustainability context (Sassanelli et al. 2019 ; Ülgen et al. 2019 ; Franciosi et al. 2020 ; Silvestri et al. 2021 ).

Figure  11 shows that the theme of sustainability according to the TBL approach is the most studied (50 articles out of 99), followed by the environmental dimension (22 articles out of 99), the binomial environment and economy (17 articles out of 99), and finally the combination of Economics and Social (10 articles out of 99). The result is very significant. The analysis shows that, in applying the sustainability indicators, the authors have favored a global vision of sustainability, studying environmental, and economic and social dimensions.

Source: Authors’ elaboration

The TBL dimensions. Notes: Env (environmental), Env Ec (Environmental and Economic), Env Ec Soc (Environmental, economic and social), Env Soc (Environmental and social).

Figure  12 shows that the TBL approach in the use of indicators is constant over time, starting from 2011. The three dimensions are often studied together. However, the single environmental dimension is the most often studied.

Source: Authors’ elaboration. Notes: The graph shows the distribution of items over time based on TBL

Temporal distribution of papers that involve (TBL).

5.6 Indicators

Sustainability measurement indicators were also classified based on the TBL approach. The sustainability measurement indicators of this structural dimension fall into the “Decision-making” cluster. The purpose of the studies in this cluster is to demonstrate the effectiveness of the measurement indicators in order to support companies’ strategic decisions regarding sustainability.

The average number of indicators used in the articles is 20, while the average number of dimensions analyzed is 3. Thirty-nine percent of articles classify indicators on the basis of the three pillars of TBL, 16% classify them considering two pillars, and 13% consider only one dimension (the environmental one). The remaining 32% use more than three dimensions to classify indicators, exploding the three pillars of TBL (Vasa et al. 2017 ; Bonisoli et al. 2019 ) or adding new ones such as the eco-innovation dimension (Ryan et al. 2016 ; Arfini et al. 2019 ; Chams et al. 2020 ).

All indicators were ranked based on the three pillars of TBL (Table 2 ). The most used indicator is “Profitability and Investment” (6.06%), followed by “Water Quality” (5.79%) and “Atmosphere” (5.51%). The economic dimension therefore seems to acquire great importance in the analysis of sustainability, which is on par with environmental sustainability. The first dimension relative to social sustainability is “Quality of Life” (5.51%), which is in fourth place, tied with “Atmosphere” (5.51%). This result is important because it demonstrates the increasing attention paid to the social aspect. Development, in fact, must not only be harmonious and respectful of the environment, but must contribute to improving individual and collective well-being (Gazzola and Querci 2017 ). Achieving environmental sustainability can facilitate social sustainability through people’s well-being and attachment to their place of living, helping to make them a satisfied person (Moser 2009 ).

Based on the collected results, a Pareto analysis has been performed in order to understand which indicators are the most important and used in this field of research, the so-called vital few (Karuppusami and Gandhinathan 2006 ).

The Pareto analysis has been developed as a quality control tool for processes, although several authors used this tool also in systematic literature reviews (Karuppusami and Gandhinathan 2006 ; Aquilani et al. 2016 ; Bajaj et al. 2016 ; Silvestri et al. 2021 ). Pareto analysis is based on the 80/20 rule. Eighty percent of the results are the “vital few” category that occupy a substantial amount of the cumulative percentage. The remaining 20% of the outcomes are the “useful many” category (Karuppusami and Gandhinathan, 2006 ).

The graph in Fig.  13 shows the indicators analyzed on the basis of the Pareto diagram. The graph shows a clear pointer that overlays the line graph at 80%, identifying the remaining 20% as the least used indicators. The “vital few” category is composed of 8 indicators of social sustainability and 7 indicators of environmental and economic sustainability, respectively. The Pareto analysis shows the increasing focus on the social aspect of sustainability. Sustainability can help to improve the quality of life of citizens (also understood as “Human safety,” “Equity,” “Culture/Ethics”), provided that culture and territory are integrated into industrial development models (Lepage 2009 ; Mella and Gazzola 2011 ). This explains why the social component is always studied.

Source: Authors’ elaboration. Notes: The graph shows the Pareto diagram highlighting the most used indicators in literature for measuring sustainability in the agri-food sector

Pareto analysis.

5.7 LCA impact category

Table 3 shows the impact categories and related methods that emerged from the sample analysis.

The most analyzed impact category is the “Climate change” (18.8% of the sample), measured mainly by the global warming potential (GWP) indicator (21 articles out of 99). Climate change is one of the leading environmental effects of economic activity. It can be defined as the change in global temperature caused by the greenhouse effect released by human activity (Acero et al. 2017 ). The study of the climate change category is not a coincidence. The agri-food system is characterized by a significant consumption of energy that increases the emission of greenhouse gases (GHGs), mainly carbon dioxide (CO2), methane (CH4), and dinitrogen monoxide (N2O) (Horne et al. 2009 ; Garofalo et al. 2017 ). The second category is “Non-renewable resources” (17.4% of the sample), which measures the decrease in the availability of non-biological (non-renewable) resources due to their unsustainable use (Acero et al. 2017 ). The need for energy in the agri-food system implies, as a consequence, the exploitation of non-renewable resources, such as oil. This situation has led over the years to an increase in the costs of both oil and natural gas and the rise of concerns about the increasingly limited availability of these non-renewable resources, hindering efforts made globally to meet an ever-increasing demand for food (FAO 2012 ). The third most analyzed category in LCA studies is “Eco-toxicity” (16.1%), which measures the emission of certain substances, such as heavy metals, into both water (both fresh and marine) and soil. These substances can have significant negative impacts on the ecosystem. According to Acero et al. ( 2017 ), this impact category provides a method to describe the exposure and effects of toxic substances on the environment. In the agri-food system, fertilizers have caused negative impacts on both water and soil quality over time (Garnett 2013 ). Millions of people worldwide suffer from environmental health problems resulting from the use of agrochemicals (pesticides and fertilizers) and other pollutants in groundwater (such as manure) (Hawkes and Ruel 2006 ). This is followed by “Ozone layer depletion” (11.4% of the sample), which measures the impact of some harmful gases on the Ozone layer, and “Eutrophication” (8.1% of the sample), which measures the impact of chemicals (such as fertilizers) on the ecosystem.

The relationship between ozone depletion and food production is strong. UV radiation disrupts developmental and physiological processes by decreasing crop productivity and causing a food shortage problem for humans (Anwar et al. 2016 ). Assessing the impact of agricultural activities on ozone depletion is essential to monitor food production’s negative consequences (Newsham and Robinson 2009 ). The link between “Eutrophication” and agri-food is also strong. Water is essential for agriculture, manufacturing, and other miscellaneous uses. If clean drinking water is contaminated, human health and many ecosystems are at risk (Nazari-Sharabian et al. 2018 ).

Table a 5 in the Appendix lists all of the LCA indicators used for the purpose of the analysis and, for each indicator, the stage in which it is been applied.

6 Research methodology

Of 99 articles, 76 are empirical, and 23 are conceptual. Figure  14 shows the time distribution of papers classified into empirical and conceptual. Most of the empirical papers are qualitative (64 out of 76), and only 11 are quantitative. The conceptual papers propose a framework (10 out of 23) and model (7 out of 23). Figure  15 shows the general classification of the articles.

Source: Authors’ elaboration. Notes: The graph shows the distribution over time of articles divided into conceptual and empirical

Temporal distribution of the papers: conceptual vs empirical.

Source: Authors’ elaboration. Notes: The graph shows the classification of articles, divided into conceptual and empirical, in-depth analysis

Classification of the articles.

Case studies are the most used tool for developing qualitative empirical research, both for Sect. 5.2.1  and “Decision-making.”

In the Sect. 5.2.1  cluster, the use of case studies is crucial to measure the impact of agricultural activities on the environment and, in some cases, also on the economic and social dimensions. In particular, Recanati ( 2018 ); Blanc et al. ( 2019 ) and Martucci et al. ( 2019 ) focused on all dimensions of TBL, highlighting the importance of using an integrated approach in the assessment process. Other authors, such as Tassielli et al. ( 2018 ), Belaud et al. ( 2019 ) and Ferreira et al. ( 2019 ) analyzed only two dimensions, environment and economy.

Also, in the “Decision-making” cluster, the application of case studies was necessary to apply indicators to measure sustainability. The indicators were often used to demonstrate the link between the climate change problem and the activities carried out in the agri-food sector. Banasik et al. ( 2019 ), for example, highlighted how creating sustainable agri-food supply chains relies on an integrated approach. Assessment tools must consider both environmental and economic performance and the relationship and trade-off between these competing goals. According to the author, this is the only way to limit climate change and the natural resource depletion caused by the agri-food system. Vasa et al. ( 2017 ) instead suggested the development of circular agriculture based on the principles of the circular economy.

Quantitative studies based on the development of questionnaires are present in both the “Decision-making” cluster (Godoy-Durán et al. 2017 ; Bonisoli et al. 2019 ; Dietz et al. 2019 ; Naseer et al. 2019 ; Coppola et al. 2020 ; Trivellas et al. 2020 ) and the “Best practices” cluster (Rabadán et al. 2019 ; Raut et al. 2019 ). These are mainly studies that aim to measure the level of sustainable performance of multiple companies operating in the agri-food sector through direct interviews with entrepreneurs or managers.

The “Best practices” cluster is mainly composed of conceptual studies. The purpose is to propose models or frameworks to outline guidelines for actors in the agri-food system. Gava et al. ( 2018 ) proposed a model based on LCA to help the selection of interventions toward a more sustainable agri-food system. Soussana ( 2014 ) proposed a model based on LCA to select the environmental research priorities related to the activities carried out in the agri-food system. Authors such as Crenna et al. ( 2017 ), Higgins et al. ( 2015 ), Horton et al. ( 2016 ), and Lehmann et al. ( 2011 ) instead proposed frameworks capable of combining sustainability in the agri-food sector with political and social needs.

6.1 Geographical focus

In order to assess the geographic areas in which studies related to sustainability and circularity indicators in the agri-food sector are most applied, the sample of articles was classified through a geographic focus. In particular, the classification was structured on three levels: (1) at the country level; (2) at the region level; and (3) at the continent level. The geographical origin of articles was obtained by considering the country of the first author (Fig.  16 ). Italy is the country with the highest number of publications (29 articles out of 99), followed by Spain (11 out of 99), and the Netherlands (7 out of 99). With 74 articles out of 99, Europe has most studied and developed this topic. North and South America come second, with 13 articles out of 99, of which eight were published by North American scholars and the remaining five articles by South American scholars. Asia and Oceania are very lacking in terms of publications on this topic, while Africa has no publications (Fig.  17 ). These results are in line with the European Commission’s statistical data and the political choices made by the EU in recent years.

Source: Authors’ elaboration. Notes: The graph shows the geographical distribution of the authors

Number of articles per country of the first author.

Source: Authors’ elaboration. Notes: The graph shows the distribution of authors according to the continent from which they originate

Number of articles per continents of the first author.

Data published by the report of the European Commission ( 2018 ), also in 2018, indicate that the EU was confirmed as the world leader in exports of agri-food products, and in October 2019, the EU recorded a new record in exports, at + 12% compared to the same month of the previous year, reaching 14.7 billion euros (European Commission 2019 ). Despite the decline in farms, the EU had 10.3 million farms in 2016, occupying 156.7 million hectares of land for agricultural production and representing 38.2% of the EU’s total land area. In recent years, EU policies have focused on sustainability and the fight against climate change. In fact, in 2021, according to a study developed by Burck et al. ( 2021 ) measuring the “Climate Change Performance Index” (CCPI), the EU improved its environmental policy, gaining six positions and ranking 16th in the CCPI. This finding is also confirmed by the graph in Fig.  18 , which shows the publications’ temporal trend. From 2017 to 2020, there is strong growth in terms of publications in Europe. This is a sign of increased awareness of the activities carried out in the agri-food sector.

Source: Authors’ elaboration. Notes: The graph shows the time distribution of publication of authors according to the continent from which they originate

Temporal distribution of articles per continents.

Therefore, the concentration of scientific production in the EU is justified both by the leadership role that the EU has in the agri-food sector and by the increasing attention to sustainability issues. This combination justifies the concentration of publications in Europe.

Italy, in this context, plays an important role. According to data Istat ( 2019 ), Italy is confirmed as the first European country for added value (32.2 billion euros) in the agri-food sector and the second for production value. Italy provides almost one-fifth of the entire EU agricultural system’s added value. Out of an estimated total of 182.3 billion in 2018, Italy contributes 17.7%, while France contributes 17.6%, Spain 16.6%, and Germany 9.2% (Edison Fondazione 2019 ). The agricultural value added in Italy generated by the agri-food sector amounts to 34,357 million euros and represents 2.2% of the national value-added (CREA 2020 ).

7 Discussion

Analysis of the articles allows us to answer the various research questions formulated in the initial phase.

RQ1. What are the main strategic purposes for which sustainability measurement indicators are applied in agri-food?

RQ2. What is the most recurring topic related to sustainability measurement indicators?

7.1 RQ1. What are the main strategic purposes for which sustainability measurement indicators are applied in agri-food?

The sample analysis allowed us to classify the items into three clusters. The study of the three clusters allowed us to understand the strategic purposes for which the indicators are applied. The articles in the Sect. 5.2.2 cluster highlight the need, on the part of researchers, to provide the agri-food sector with guidelines to better support the transition toward a more sustainable system. The use of indicators is part of a broader framework, within which the authors have proposed models or conceptual frameworks. In this context, measurement indicators are tools that can be used to address, in a holistic way, issues related to environmental and human health risks associated with food production and consumption.

The aim is to promote an integrated view of both measurement indicators with respect to business strategies and the entire supply chain, highlighting the need to stimulate cooperation with the scientific world as well. Negra et al. ( 2020 ), for example, proposed a model in which the scientific world and businesses co-develop new indicators based on science and other decision-making tools. The aim is to provide new strategic approaches that allow companies to integrate the sustainability of the agri-food system into business management.

The cluster analysis also highlights the need to standardize internationally the different frameworks used to define and assess sustainability according to a perspective of “complex socio-ecological systems” (Ruiz-Almeida and Rivera-Ferre  2019 , p. 1321). The indicators identified by the authors are based on the application of a quantitative methodology that facilitates the analysis of the functioning of agri-food systems at the international level, also considering the political dimension. Integration is not only between the scientific and business worlds but also with the policy world. Policy integration is essential for achieving sustainability throughout the agri-food supply chain. According to Priyadarshini and Abhilash ( 2020 ), at times there is a lack of coherence between policy agendas and the goals of the Sustainable Development Goals (SDGs) defined in 2015 by the United Nations. This inconsistency is one of the main obstacles in achieving sustainability, which must be overcome.

In the “Best practices” cluster, indicators appear as tools within broader models and frameworks. In the “Decision-making” cluster, indicators are still tools, but their application is intended to demonstrate how their use can support companies in their decision-making processes, orienting them more toward sustainability. In this cluster emerges the need to apply an integrated approach, in which indicators simultaneously measure all three pillars of sustainability (TBL). The focus is not only on the individual company but also on the entire supply chain. According to Allaoui et al. ( 2019 ), planning and coordination among all actors help the whole agri-food system achieve sustainability. The approach must be based on the three pillars of TBL, and for that reason, indicators must measure the three dimensions simultaneously. Van Asselt et al. ( 2014 ), for example, proposed a protocol to select and quantify indicators to be used to discuss and communicate the sustainability of agri-food production systems. The list of indicators includes social, environmental, and economic dimensions. Even in this cluster, the role of policy is crucial. According to Van Asselt et al. ( 2014 ), policymakers can define the weight of different indicators and the extent to which they can offset each other at the level of individual indicators or overall sustainability. The role of policy is crucial, as are synergies between actors in the supply chain. Among the recipients of scientific results from the articles in the analyzed sample, policymakers are mentioned in 42 out of 99 articles, while supply chain stakeholders are mentioned in 46 out of 99 articles.

In the cluster, several indicators are also used to demonstrate the benefits of efficient waste management, according to a Circular Economy approach (Pagotto and Halog 2016 ; Vergine et al. 2017 ; Pronti and Coccia 2020 ; Tortorella et al. 2020 ). Again, research findings aim to inform decision-makers about the benefits of switching from a traditional system to a circular production system. The shift could create a sustainable circular economy capable of making the agri-food system more efficient.

In the third cluster, “Assessment-LCA,” indicators are applied within a specific tool, LCA, and measure the categories of impact of agricultural activities with respect to the environment. The impact on energy consumption is the most studied aspect (Perez Neira 2016 ; Notarnicola et al. 2017 ; Laso et al. 2018 ; Muradin et al. 2018 ; Ferreira et al. 2019 ; Caputo et al. 2020 ). In fact, the agri-food system consumes a significant amount of energy due to the operation of agricultural machinery, irrigation, chemical use, transportation, and processing (Garofalo et al. 2017 ).

7.2 RQ2. What is the most recurring topic related to sustainability measurement indicators?

7.2.1 rq3. what are the new dimensions that indicators need to consider in the sustainability measurement process.

Relative to RQ2, the problem of climate change falls under what Harvey and Pilgrim ( 2011 ) and Morrissey and Dunphy ( 2015 ) call the “trilemma challenge” (Morrissey and Dunphy  2015 , p. 42). Land availability, energy, and climate change are critical issues for the sustainable development of the world’s economies. In addition, the combination with an ever-increasing demand for food means that further study of these relationships is needed (Morrissey and Dunphy  2015 ).

Agricultural activities have many effects on the environment (soil quality and quantity, air, contamination of wildlife habitat) and for this reason, there is a need to provide robust indicators capable of providing information on the environmental conditions in the agri-food sector and their trends (Wheaton and Kulshreshtha 2013 ). The theme of the “trilemma challenge” is central to the sample of articles analyzed. Fifty-five out of 99 articles apply sustainability measurement indicators to understand the impact agricultural activities have on the three critical environmental issues. However, the research specifically focuses on the climate change issue. Agricultural activities emit large amounts of greenhouse gases, and deforestation (to create new space for agriculture) releases significant amounts of CO2, as does the entire food chain and related activities. This situation places agriculture as one of the activities that contribute the most to climate change (Campbell et al. 2017 ). Furthermore, climate change will itself affect the conditions of agriculture. According to the Report FAO ( 2016 ), the impacts of climate change on agriculture and the implications for food security are alarming. For this reason, more than half of the articles in the sample analyze the link between agricultural activities and climate change, thereby proposing new models or indicators capable of supporting agri-food system stakeholders in achieving global sustainability and CE.

In this context, CE is crucial for supporting a low-carbon and climate-friendly economy (Mehmood et al. 2021 ). Implementing the CE framework in the agri-food sector shows several advantages (Nattassha et al. 2020 ). However, a truly circular system requires a systematic transformation characterized by sustainable local supply chains with zero waste (MacArthur 2013 ). The agri-food chain includes all stages from cultivation to final consumption, and according to a report (FAO 2014 ), along the chain, about one-third of all food produced in the world is lost or wasted globally. In CE, waste is considered an input for the following cycle/process (Mehmood et al. 2021 ). For this reason that CE is perceived to be of considerable importance (Nattassha et al. 2020 ). Pagotto and Halog ( 2016 ) suggest implementing “sustainable resource management” (p. 8) for the entire supply chain based on an integrated system capable of managing material/resource life cycles in order to achieve economic and environmental sustainability (Fiksel 2006 ). Integrated waste management, however, requires a systemic vision that looks at the entire supply chain.

Green et al. ( 2020 ) highlighted the need to develop more robust assessment methods based on a holistic and quantitative approach. The impacts of agricultural actions on climate change and vice versa also put food safety and thus human health at serious risk (Gaitán-Cremaschi et al. 2017 ). In this context, Horton et al. ( 2016 ) asserted that the lack of integrated decision-making across the agri-food system is the greatest barrier to food security. Green et al. ( 2020 ) also emphasized that creating a sustainable future will require all actors in the system to co-develop and co-implement interventions. The analysis reveals the central role that cooperation plays in achieving global sustainability. In fact, other studies in the literature have already highlighted the benefits of the “collaboration-sustainability” binomial in agri-food (Doukidis et al. 2007 ; León-Bravo et al. 2017 ; Dania et al. 2018 ). In this context, Azevedo et al. ( 2018 ) showed that the achievement of sustainability is achieved if actors are involved in the “collaborative paradigm” (p. 2). To demonstrate this, they studied the influence of collaborative initiatives on the sustainability indicators of a fruit and vegetable product. do Canto et al. ( 2020 ) also showed that the achievement of sustainability has as a prerequisite the existence of a network between the actors in the chain. In particular, the authors argue that social capital, with its mechanisms, can encourage partners to develop strategic initiatives for sustainability, especially if managers share key factors for the adoption of eco-innovations and the overall sustainability of the chain. Indeed, collaboration allows firms to share information (Soylu et al. 2006 ), a sine qua non for developing, applying, and establishing new innovative ideas and practices (Schiefer et al. 2015 ).

In addition to collaboration, innovation also plays a decisive role in achieving sustainability. According to the Report FAO ( 2016 ), the “trilemma challenge” requires urgent research to promote innovation and change in agri-food systems. The integrated approach based on the collaboration of actors is the winning key. Innovation in the agri-food sector is complex, precisely because it requires the involvement of multiple partners and collaborative programs (Rabadán et al. 2019 ). In this context, innovation relies on lasting relationships developed with different actors in the chain, such as the distribution or acquisition sector. Relationship building and the ability to network are crucial elements to develop and introduce innovations as well as key elements for the sustainable development and survival of the companies themselves (Capitanio et al. 2010 ). In this context, Rabadàn et al. ( 2019 ), through the application of specific indicators, showed that eco-innovation generated by cooperation between actors in the agri-food chain improves companies’ performance, regardless of their size.

In the new economic context in which globalization has introduced new technological paradigms and digital tools of Industry 4.0, the agri-food sector is also called upon to employ these cutting-edge technologies to expand its possibilities. In the context of Smart Agriculture or Agri-Food Industry 4.0, Pérez-Pons et al. ( 2021 ) demonstrated through a case study that new digital technologies contribute to make businesses more efficient and sustainable. In particular, the authors combined the use of both environmental and economic indicators with digital tools (Edge-IoT). In particular, IoT is an effective tool for businesses to share information with food regulators and other stakeholders in the chain. The application of IoT allows managers to improve the effectiveness of their enterprise in managing both upstream and downstream activities and ensuring greater food safety and chain sustainability (Yadav et al. 2021 ). Kamilaris et al. ( 2017 ) referred to Agri-IoT (p. 442) as the framework proposed for the application of IoT-based smart agriculture. The use of new technologies allows the agri-food sector to better adapt to the current market through the efficient use of resources while respecting the environment (Yadav et al. 2021 ). Technological improvements along with a drastic reduction in the use of non-renewable resources at the economy and agriculture level help address climate change and the increase in natural hazards that affect both ecosystems and human life (FAO  2016 ).

In this context, indicators for measuring sustainability must include new categories of analysis, namely the level of cooperation between actors and the level of innovation.

The transition to a sustainable system requires an exploration of new modes of production and consumption, new technologies, innovations, and new regulatory and institutional infrastructures to coordinate change. For this reason, Morrissey and Dunphy ( 2015 ) suggested sustainability assessment processes to introduce a variable to measure innovation. Also, Chams et al. ( 2020 ) highlighted this aspect, focusing mainly on the analysis of the impact of innovation on society and the ecosystem.

7.3 RQ4. How much of the three pillars of TBL do we find in the LCT indicators?

Indeed, the indicators need to measure not only environmental but also social and economic impacts. The SLR showed how the three pillars of TBL were analyzed through the sample articles. The environmental aspect is the most analyzed. However, as many as 50 articles out of 99 apply sustainability indicators to measure all three pillars of TBL. This figure is significant because it shows how the environmental aspect tends to be increasingly associated with the other two dimensions. This approach reinforces the idea of greater awareness of the concept of global sustainability. Godoy-Durán et al. ( 2017 ) stated that eco-efficiency is gaining increasing interest as an indicator of sustainability, as it links environmental and economic performance in production activities. In the agri-food sector, these indicators and their determinants are crucial, as they analyze the link between agricultural activities and the use of often-limited natural resources and the provision of basic goods for society (Godoy-Durán et al. 2017 ). Eco-efficiency is also an indicator used to measure the level of circularity of agri-food systems. In fact, Muradin et al. ( 2018 ) developed an analysis focused on the comparative evaluation of eco-efficiency in biogas production based on the efficient use of agri-food industry waste.

However, the social aspect is also gaining increasing attention. The problem of climate change also involves human welfare and health. Report FAO ( 2020 ) estimates that the food-related social cost of greenhouse gas emissions associated with the current dietary patterns is more than $1.7 trillion per year by 2030. Adoption of healthy diets would facilitate up to 97% reduction in direct and indirect health care costs. Green et al. ( 2020 ) are of the same thought. Indeed, according to the authors, nutritionally responsible food production can alleviate sustainability challenges.

The social aspect considers not only human health but also the quality of life, defined “as global satisfaction seen by citizens” (Lepage  2009 , p. 109). According to Moser ( 2009 ), in order to promote human well-being, it is necessary to focus not only on the individual effects of environmental characteristics but also to consider the general relationship of people with their environment. This concept applied in the agri-food sector translates into as follows: (1) well-being for the community that hosts agri-food chain businesses and (2) new job opportunities with a focus on women and young farmers (Chams et al. 2020 ). Through the modernization of the agricultural system and the introduction of new techniques of land use, Chams et al. ( 2020 ) showed the possibility of improving the prosperity of the sector, ensuring self-employed farmers a decent standard of living and the continuity of their family activities. Indeed, it should not be forgotten that small family farms represent the heart of the agricultural sector. Globally, out of a sample of 167 countries surveyed, there are 570 million farms, or more than 90% of all farms (Guiomar et al. 2018 ). In the EU, more than two-thirds (69.1%) of all farms are small (Eurostat 2018 ).

Among the tools to assess the social dimension is the social life cycle assessment (S-LCA), applied by D’Eusanio et al. ( 2018 ), to assess the social impact of a honey pot. In particular, the authors suggested the use of life cycle thinking tools to achieve and measure sustainability goals. LCA assesses the environmental dimension, life cycle costing (LCC) assesses the economic dimension, and social life cycle assessment relates to the social dimension. In particular, S-LCA methodology evaluates social and socio-economic performance to the extent that it directly affects, positively or negatively, the stakeholders involved in the product life cycle (UNEP/SETAC 2009 ).

Compared to the LCA tool, introduced in the 1960s, the S-LCA is a younger approach (Petti et al. 2018 ). However, it emerges as a valuable tool to measure the social dimension of sustainability in the agri-food sector, in which the application of the LCA tool is widely performed. Among the various impact categories of S-LCA, “Supplier relationships” (Wu et al. 2014 ) need further investigation. Cooperation among supply chain stakeholders represents a social dimension that could find appropriate application in S-LCA.

The LCC is a tool to support business decisions based on the internal and external costs incurred by the company. This tool has close ties to LCA. The use of both tools would help leaders consider the economic consequences of production in terms of emissions, resource use, and environmental and human health effects (Fathollahi and Coupe 2021 ). Costs associated with eco-innovation could be included as an impact category of LCC.

In this context, the need for a holistic approach to the use of sustainability and circularity measurement tools in the agri-food sector is clearly emerging.

Cooperation among all stakeholders in the system is conditio sine qua non to stimulate the innovation process. New technologies (IoT) improve the tradition toward a truly sustainable and circular system. Measurement tools play a crucial role. Their use must be based on an integrated approach in which the tools of LCA, S-LCA, and LCC are applied simultaneously. It is necessary to broaden the impact categories, considering also the indicators capable of measuring the level of innovation and cooperation in the system. To this end, the indicators used in the literature for non-LCA purposes can represent a good basis for further investigation and new areas of research. For the social dimension, indicators such as “Human Safety and Health,” “Culture/Ethics,” and “Cooperatives/Supplier Relationships” should be considered. For the economic dimension, crucial indicators are certainly “Profitability and Investment,” “Productivity/Yield/Development,” and “Innovation/R&D proactivity.” Sustainability measurement indicators and impact categories of LCA, S-LCA, and LCC tools should be integrated in order to provide stakeholders with best practices as guidelines and tools to support both decision-making and measurement. Figure  19 shows the framework.

Source: Authors’ elaboration. Notes: Sustainability measurement indicators and impact categories of LCA, S-LCA, and LCC tools should be integrated in order to provide stakeholders with best practices as guidelines and tools to support both decision-making and measurement, according to the circular economy approach

8 Conclusion

The research aimed to investigate sustainability measurement indicators to understand the strategic purposes of their use. In particular, the research, through SLR, sought to understand how the indicators were applied with respect to the three pillars of the TBL and what was the most recurring topic related to sustainability measurement indicators.

Analysis of the sample revealed three different purposes for using the indicators: (1) in the Sect. 5.2.1 cluster, the indicators are applied within a specific tool, LCA, and measure the categories of impact of agricultural activities with respect to the environment; (2) in the “Best practices” cluster, the indicators appear as tools within broader models and frameworks; (3) in the “Decision-making” cluster, the indicators are still tools, but their application is intended to demonstrate how their use can support businesses in decision-making processes, orienting them more toward sustainability.

The analysis of the sample has shown increasing attention to the three pillars of sustainability. In this context, an integrated approach of indicators (environmental, social, and economic) is the best solution to ensure an easier transition to sustainability and CE.

In particular, the analysis of the sample identifies new categories of impact, which need attention and cooperation between stakeholders in the supply chain and innovation. However, policy also plays a crucial role.

From a scientific point of view, the research aims to provide an overview of the indicators for measuring sustainability in the agri-food sector, providing ideas for further investigation in relation to the issues of cooperation and innovation.

From a managerial point of view, the research aims, again, to provide an overview of indicators for measuring sustainability in the agri-food sector, providing insights into the benefits of this approach.

The main limitation is not having access to information from agri-food companies. Understanding how companies in the agri-food system use indicators is crucial, because it allows us to understand the real level of use of these tools as well as the main obstacles to their application. Listening to the voice of the agricultural companies would provide a complete vision of the whole system and help verify the gaps existing between the theoretical models of the academic world and the real models of the companies.

For future research, the authors suggest that future scholars complete the overview of indicators obtained from the analysis of scientific research, with one in the field evaluating the level of application of the indicators by stakeholders in the agri-food sector.

Change history

24 march 2022.

A Correction to this paper has been published: https://doi.org/10.1007/s11367-022-02038-9

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Cecilia Silvestri, Michela Piccarozzi & Alessandro Ruggieri

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Silvestri, C., Silvestri, L., Piccarozzi, M. et al. Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review. Int J Life Cycle Assess (2022). https://doi.org/10.1007/s11367-022-02032-1

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Received : 15 June 2021

Accepted : 16 February 2022

Published : 02 March 2022

DOI : https://doi.org/10.1007/s11367-022-02032-1

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