From traceability to provenance of agricultural products through blockchain

Abstract

As China’s agricultural output has improved, the national and local monitoring system of agricultural product safety has become much better, and monitoring standards have become increasingly strict. Despite this, there are agricultural product safety incidents which have caused consumer panic. One way to address this is by properly establishing tracking systems so that agricultural product logistics in China can be tracked and monitored. We explored this research objective with agricultural traceability and security in mind. One option that could be considered is the blockchain technology. Blockchain could also be used to ascertain the provenance of agricultural products to increase the quality and safety of the Chinese agricultural supply chain. In this context, this research converged on big data and technology, platforms and other means for product quality and safety of agricultural products traceability. In order to verify the accuracy of these three convergence, regression analysis were used to construct five models for verification of three hypothesis. The results show that based on “Internet+”, using big data, big technology and big platform can significantly increase the accuracy of agricultural products traceability system hence improve consumer acceptance of the safety of agricultural products.

Keywords

Internet plus agricultural products supply chain traceability blockchain provenance food safety

1. Introduction

1.1. Motivation

The quality and safety of agricultural products is the most important concern for end users and has a great impact on the stable development of national economy and society. In recent years, the Chinese government has put great efforts to improve the quality and safety of agricultural products. In 2017, the Ministry of Agriculture quadrupled their agricultural product quality and safety monitoring testing, the qualified rate of major agricultural products was 97.8%, up 0.3% from the same period in 2016. The qualified rates were 97.00%, 98.00%, 98.90%, 99.50%, 96.30% and 99.80%, respectively for vegetables, fruits, tea, poultry products, aquatic products, and livestock products (also called “lean meat essence”). In 2018, the government proposed to implement a food safety strategy in a document of the Central Committee of the Government. At present, the comprehensive agricultural production capacity of China has been greatly improved, the national and local monitoring system of agricultural product safety has become more effective and the monitoring standards have become increasingly strict. However, there are still some agricultural product safety incidents that have dented consumer confidence in Chinese agricultural products.

From 2013 to 2017 in China, there were more than 170000 agricultural products quality and safety incidents and the government investigated and dealt with 68000 cases concerning the quality and safety of agricultural products. These incidents have raised public awareness of products in the food supply and have caused concerns about the business and supply management practices of producers. Therefore, there is still additional work to required to improve the quality and safety of Chinese agricultural products. Strengthening quality and safety monitoring of agricultural products and building a system that allows product traceability could form part of this quality process.

There is no doubt that government supervision is important to ensure the quality of agricultural products, but the full co-operation of producers, sellers and processors in the supply chain is necessary for an effective regulatory response. In 2002, China began to study the food traceability system; and in 2009, promulgated and implemented the Food Safety Law. The product quality traceability and responsibility traceability were formally clarified in the form of law. Nowadays, there is considerable amount of research on the traceability of agricultural products in China.

Many developed countries, such as the USA, Canada, and Japan; have established standardised food quality and safety traceability systems. In the European Union, as early as 2002, the “general food law” traceability systems have been enforced. The EU Food and Feed Safety Regulation, issued and implemented in 2006, stipulates that the whole process of food from farm to table must be controlled, managed and traceable. In the United States, food safety is subject to mandatory regulation through the issue of Food Safety tracking regulations, and in 2006, all food-related enterprises established traceability system for product quality. Compared with the developed countries, research on the traceability system of agricultural products supply chain in China is still in early stage. This motivates us to propose a new supply chain traceability system by integrating the Blackchain technology.

1.2. Objectives and contributions

Internet Plus (“Internet+”) is proposed by China’s Prime Minister Li Keqiang in his Government Work Report on March 5, 2015 [16]. “Internet+” represents a new economic form. It refers to the use of Internet-based information technology to achieve the integration of the Internet and traditional industries. This could have transformative economic effects through the optimization of production factors, update of business systems, and reconstruction of business models. “Internet+” could integrate various Internet based technologies (e.g., mobile Internet, cloud computing, big data or Internet of Things) into existing industries and would aid business development in China [21].

The “Internet+” based agricultural security traceability system could collect information about agricultural products in the production and circulation stages then transmit this information between the main bodies of supply chain. In this context, “Internet+” could effectively solve the problem of information asymmetry during agricultural products trading and therefore improve the quality and safety level of agricultural products.

Blockchain technology allows digital information to be distributed, but not copied, meaning that a blockchain is a linking of a series of time-stamped immutable records of data that is not owned by a single entity. This makes the data stored on the block secure and yet transparent. This technology addresses the issues of trust as the immutable ledger is shared and replicated across all nodes. Benefits of this technology include the development and use of smart contracts, and yet remains secure to participants who hold accounts to access the blockchain in real-time.

The objectives of this study are to explore the emergence and implementation of product traceability systems in the agricultural products supply chain in China. In particular, to understand what benefits that a traceability system that based on“Internet+” can provide to the agricultural products supply chain in China and to explore the provenance of agricultural products through Blockchain.

The current study makes contributions to improving the supply chain traceability system, in particular in Chinese agricultural products supply chain context, creating a new framework that combines the big data and blockchain technologies. Having a more accurate and efficient traceability system will allow a better management of agricultural products supply chain.

The rest of the paper is organized as follows: in Section 2, we briefly introduce some related work. We study a traceability System that is based on “Internet+” (i.e. based on big data, big technology and big platform) in Section 3. We will then discuss our vision of the blockchain-enabled traceability system in Section 4. Finally, conclusions and future works are provided in Section 5.

2. Related work

In this section, we briefly introduce some related concepts, technology and review some related works.

2.1. Definitions of traceability

There are different definitions of traceability in international standards, national legislation, and scientific and technical articles. According to the International Standardization Organization (ISO) 8402 [10], traceability is defined as: “The ability to trace the history, application or location of an entity by means of recorded identifications.” The Codex Alimentarius Commission Procedural Manual [7] defines traceability as “the ability to follow the movement of a food through specified stage(s) of production, processing and distribution”. In this definition, traceability is simply following of the movement of the food through the agricultural supply chain. The EU General Food Law [22] defines traceability as “the ability to trace and follow a food, feed, food-producing animal or substance intended to be, or expected to be incorporated into a food or feed, through all stages of production, processing and distribution”. The most cited definition of traceability in scientific and technical papers is “the ability to track a product batch and its history through the whole, or part, of a production chain from harvest through transport, storage, processing, distribution and sales” [19].

The International Food Standards Committee defines traceability as“the ability to track any process, such as processing, storage, circulation, etc. in order to maintain the integrity and sustainability of the food supply chain information flow.” In the Chinese guidelines for traceability of agricultural products (NYR / T 1431-2007), traceability is defined as “the ability to identify the source of a product or product component from the end of the supply chain (product user) to the beginning (product producer or raw material supplier) that means the ability to trace the history, location, etc. of agricultural products by recording or marking.” The commonality in all these definitions and the underlying rationale behind adopting them is that traceability systems are meant to preserve information about the origins of agricultural products throughout the whole process from farm to fork.

2.2. Traceability systems

Since the 1990s, the relationship between agricultural product safety and supply chain management has been an important topic of interest in related research fields, including governance relationship, signal transmission, traceability systems and so on. At the governance research level, Thankappan [24] used the case of the fresh fruit and vegetable industry to discuss the impact of private sector and consumer requirements on food regulation in the UK. Their research results show that businesses, private interests, Consumers and other stakeholders have an important function of accountability for agricultural food supply chain management in the UK. At the level of signal transmission, many scholars generally agree that brands can effectively solve the problem of information transmission of agricultural products. Sonesson et al. [23] gave the detailed descriptions, life cycle assessment (LCA) evaluations, and consequence assessments of the supply chains for six commodities, i.e., milk, cheese, beef, pork, chicken, and bread, in a Swedish region. The results for the pork supply chain show that a change in the supply chain can easily lead to the change of all kinds of properties of products in the systems [23].

Countries such as the United States, Canada, Japan, the United Kingdom, France, and the Netherlands have already established standardized food quality and safety traceability systems which provides information on the origin of food, processing processes, logistics, transportation and market flow, etc., to track the whole supply chain. Traceability systems in this context reduce the loss of food producers and ensure the reasonable rights and interests of consumers [3,17].

Considering the influence of the“Internet” on the agricultural supply chain leads to the construction of the “trinity” of Internet based quality supervision. The information service used by the supply chain affects the quality of behavior in the supply chain so the construction of a unified network system platform are put forward. Dabbene [5] based on the in-depth review of the latest food situation, elaborated the concept of traceability of food supply chain in detail on the influence of demand and its using technology on modern supply chain management.

Dong Yude [6] in the Jiangyin, Jiangsu Province, applied the technologies of QR codes as well as database to the supply chain network information to systematically as a foray into the use of traceability systems to ensure agricultural product safety in the Chinese context. Feng et al. [8] developed a cattle/beef traceability system that integrated RFID technology with PDA and barcode printer in China. The real-time and accurate data can be obtained and transmitted through this tracking system and the high efficiency of information tracking and tracing across the cattle/beef supply chain has been realized.

2.3. Blockchain for traceability and provenance

Blockchain plays an important role in meeting key supply chain management objectives [14]. They are ideal for applications where trust and value are essential [26]. Blockchains are systems that no single centralised party can manage and take control of [12]. They are peer-to-peer networks where non-trusting members can interact with each other without a trusted intermediary [4]. Every member involved in the supply chain, from growers to consumers and every party in-between, would be able to participate. With agreements from the participants, key transaction data from the traceability systems, such as price, date, location, quantity, certification, and other relevant information, can be stored on the distributed ledger. The records on the blockchain become the indisputable single source of truth for the provenance and all transactions of the products along the supply chain.

There are multiple ways to integrate the blockchain with the management information systems of each member. The Ethereum based consortium blockchain proposed in [2] is an appropriate solution for supply chain provenance.

There are many blockchain use cases reported in the literature [11,13,15], including the implementation of a traceability system for agri-food supply chain management using blockchain and IoT [1]. One major challenge in building a blockchain supply chain traceability system is to get the agreement and cooperation of the large number of members in forming a blockchain consortium.

3. Traceability systems based on “Internet+”

In this section, we investigate a traceability system of agricultural product safety based on “Internet+”. In this paper, “Internet+” technology includes big data, big technology and big platform.

3.1. Research hypothesis

3.1.1. Big data and agricultural products safety traceability system

To address the problem of asymmetric agricultural product information areal-time monitoring and warning system for agricultural products has been constructed This has the potential to create social and ecological benefits for the quality of agricultural products and brand effects to promote industrial transformation and upgrading to achieve significant economic benefits, to increase the added value of agricultural products and market competitiveness to promote the enthusiasm of farmers in scientific production. Strict control of pesticides and fertilizer use in order to benefit the sustainability of the agricultural ecological environment. Accordingly, the following assumptions have been made:

H1.
Internet big data supports agricultural product safety traceability system.
3.1.2. Big technology and agricultural safety traceability system

When agricultural enterprises form irreplaceable technological advantages in the market, they can effectively reduce their costs and increase their sales volume. The value of technology in the Internet era to the safety traceability system of agricultural products is mainly reflected in the integration of the Internet technology into the agricultural products business process and operation. It can be measured by the degree of application and accuracy of traceability of agricultural products. So, if you want to use the big technology of the Internet in agriculture, for enterprises in goods and services, there will be higher precipitating costs and lower incremental costs in the initial development of major Internet technologies; in subsequent operations, Enterprises will apply the Internet technology to agricultural products scale service to make the marginal returns of agricultural products increase continuously and realize the profit of agricultural products through strong scale effect. Accordingly, the following assumptions are made:

H2.
Internet technology supports agricultural safety traceability system.
3.1.3. Big platforms and agricultural safety traceability system

At present, the agricultural product safety traceability system platform is mainly established by government departments. These bodies mainly function as repositories of agricultural product data and engage in information transmission and release but critically lack communication and contact with consumers. Even when the consumers demonstrate a demand for agricultural product traceability, the capacity of government departments to solve the problem appears lacking. The proposal of “Internet+” integration would enable the establishment of a large interactive platform allowing the dissemination of supply chain information amongst government management, production and sales, and social consumption. This would essentially make transparent the existing repositories of information of government information to engage consumer need and would reduce the operating costs of production and sales as well as improve the efficiency of logistics and distribution. Consumers would enjoy increased product quality and price diversification as a direct result of this newfound transparency. Accordingly, the following assumptions are made:

H3.
Internet big platform supports agricultural product safety traceability system.
3.2. Materials and methods

3.2.1. Data samples and sources

The Internet-based agricultural products enterprises in the Shanghai and Shenzhen stock markets from 2013 to 2017 were selected as the sample population. The data is from the “Wind” database and the annual reports published by the Shanghai and Shenzhen stock exchanges. Given that the problems such as mixed good and bad enterprises, and incomplete information disclosure for some enterprises, the sample data is processed as follows:

those enterprises with the incomplete data are eliminated

those enterprises with losses for three consecutive years are eliminated

those enterprises that generated revenue from the Internet less than 10% are eliminated

A total of 78 enterprises were selected, including 30 productive enterprises, 28 processing enterprises and 20 service enterprises. Statistic of sample data is displayed in Table 1.

Table 1
Statistic of sample data

Type of Enterprise Productive Processing Service

Numbers of Enterprises 30 28 20

Type of Enterprise	Productive	Processing	Service
Numbers of Enterprises	30	28	20

3.2.2. Selection and definition of variables

In this section, we selected suitable variables and define said variables.

Dependent variable: traceability accuracy. It is used to measure the traceability accuracy of the agricultural products in the supply chain when the benefit of the parties is maximized, and is expressed by the cost function of the responsibility.

Independent variable: big data, if the enterprise has established or uploaded the agricultural product information database to take 1, otherwise take 0; the big platform, if the enterprise has established or joined the agricultural product interactive platform to take 1, otherwise takes 0; the big technology, if the enterprise has the research and development input to take 1, otherwise takes 0.

Control variable: enterprise size and capital structure. It is represented by the natural logarithm of the total assets of the enterprise at the end of the year.

3.2.3. Establishment of regression model

Referring to the research methods of Yang [28], this paper selects the following models to determine the factors influencing the traceability of agricultural products in the supply chain: $\begin{array}{l} ln (\frac{P_{i}}{1 - P_{i}}) = α_{0} + α_{1} x_{1 j} + α_{2} x_{2 j} \\ + \dots + α_{i} x_{i j} + ε_{i} \\ (1) & where P_{i} = \frac{1}{1 + {exp}^{- (α_{0} + \sum_{i = 1}^{n} α_{i} x_{i j})}} + ε_{i} \end{array}$ $P_{i}$ is the degree of accuracy of traceability information; $x_{i j}$ denotes the j index in the i factor; $α_{0}$ is constant, $α_{i}$ is the regression coefficient of independent variable; $ε_{i}$ is a random error. When $α_{i} > 0$ , the j index has a positive effect on the traceability of agricultural products whereas $α_{i} < 0$ , the j index has a negative effect on the traceability degree of agricultural products.

3.3. Empirical study

3.3.1. Statistical analysis

The SPSS software is used to analyse the sample data and the statistical characteristic results are reported in Table 2. As can be seen from Table 2: 1) the average cost of liability in the overall sample is 0.883, and only one sample has a cost value of responsibility greater than 1 and a frequency of $1.28 %$ , indicating that most of the Internet-based agricultural products enterprises tend to apply traceability systems. 2) $74.3 %$ , $76.5 %$ and $77.7 %$ of the total sample of productive, processing and service enterprises respectively are willing to integrate the data, technologies and platforms with traceability systems and keen to increase R&D investment to maintain stronger market competition.

Table 2
Traceability statistical results matrix for Internet agricultural enterprises

Index Responsibility cost Big data Big technology Big platform ES&CS

Responsibility Cost 1.000

Big Data 0.583 1.000

Big Technology 0.617 0.713 1.000

Big Platform 0.734 0.537 0.632 1.000

ES&CS −0.354 −0.542 −0.315 −0.238 1.000

Mean Value 0.883 0.734 0.765 0.777 0.693

Standard Deviation 0.541 0.435 0.531 0.573 0.397

Index	Responsibility cost	Big data	Big technology	Big platform	ES&CS
Responsibility Cost	1.000
Big Data	0.583	1.000
Big Technology	0.617	0.713	1.000
Big Platform	0.734	0.537	0.632	1.000
ES&CS	−0.354	−0.542	−0.315	−0.238	1.000
Mean Value	0.883	0.734	0.765	0.777	0.693
Standard Deviation	0.541	0.435	0.531	0.573	0.397

* ES&CS = Enterprise Size and Capital Structure.

Table 3

Results of regression analysis of Internet agricultural products enterprises

Independent variable	Responsibility cost
	Model one	Model two	Model three	Model four	Model five
Big data	–	0.436	–	–	0.343
Big technology	–	–	0.383	–	0.274
Big platform	–	–	–	0.389	0.395
ES&CS	−0.427	−0.434	−0.319	−0.342	−0.287
$R^{2}$	0.387	0.466	0.501	0.491	0.683
Revised $R^{2}$	0.376	0.464	0.498	0.479	0.674
F statistics	15.438	14.292	13.457	12.177	12.733
Whether hypotheses are established	–	H1 yes	H2 yes	H3 yes	–

* ES&CS = Enterprise Size and Capital Structure.

3.3.2. Regression analysis

As can be seen from Table 2, the correlation coefficient of each indicator is between 0.537–0.734, which indicates that the differentiation between indicators is obvious, the possibility of index variation is relatively small, and the relevant data can better reflect the content of their respective indicators. Three hypotheses are tested by regression analysis, and the results of regression analysis are shown in Table 3. From Table 3, we can see that:

Model One was built based on the control variable of enterprise size and capital structure and a regression analysis of responsibility cost can be made to explain the variation of 37.6% of the variables.

On this basis, big data index was added and form Model Two. The regression analysis with Model Two can explain the variation of 46.4% variables, and the standardized regression coefficient is 0.343 that has strong significance and indicates that the hypothesis H1 can be confirmed on the basis of Model One.

Big technology index was added into Model One and form Model Three. The regression analysis with Model Three can explain the variation of 49.8% of variables, and the standardized regression coefficient is 0.274 that has strong significance and indicates that hypothesis H2 can be established.

on the basis of Model One, Model Four is formed by adding big platform indicators. The regression analysis with Model Four can explain the variation of 47.9% variables and standardized regression coefficient is 0.395 that has strong significance and shows that the hypothesis H3 can be verified.

Similarly, in order to further verify the above hypothesis, three indicators of big data, big technology, big platform and the control index of enterprise size and capital structure are added to Model One then form Model Five, which can explain the variation of 67.4% variables and indicates that Model Five effectively supports the regression analysis results of the above four models.

3.4. Summary

The traceability system of agricultural product supply chain should be able to set up each link of agricultural product supply chain in detail from many levels, through the understanding of each link information. After collection, a highly sensitive database related to changes in agricultural product market demand is established to increase the collation and control of the agricultural product supply chain, and to optimize the supply basis of agricultural products. Therefore, under the influence of the concept of information sharing, when designing the incentive mechanism of traceability system of agricultural product supply chain, we should emphasize strengthening the overall consciousness of all partners in agricultural product supply chain, and after using the traceability system within their respective business scope, it can be quickly and accurately traced the supply links of agricultural production through sales. This will allow them to find the varieties, scale and reasons of the problems in the operation of the supply chain of agricultural products, and create corresponding solutions through the analysis thereof.

The accuracy of records in the traceability systems plays a critical role in the safety of agricultural products. Blockchain technology has been recognised as the potential solution to fundamentally address these issues [27]. While traceability systems are designed to record and manage vast amount of data obtained from the participants along the supply chain, accuracy and immutability of data, and transparency available to all relevant parties, cannot be guaranteed. Blockchains enable the creation of transparent and immutable supply chains [9]. As a decentralised and distributed ledger that embeds encryption in its core [18], blockchain creates verifiable records and proves identity [27]; and therefore, ensures accurate and immutable provenance of goods and products.

4. Discussion

In the context of “Internet+”, we highly recommend adoption of blockchain in conjunction with big data, big platform, and big technology, due to the immeasurable benefits that blockchain technology can bring to all industries and the nation as a whole. Blockchain provides a mechanism that inherently facilitates transparency and immutability, and hence ensures integrity in business practice. In the long run, the problem of counterfeit high value goods and unsafe agricultural products will be eliminated. The value added to a brand and the company is enormous if the genuinity of its product can be guaranteed and verified by its customers.

Imagine one day that all goods and products produced or made in China can be easily verified and traced online. For the nation, blockchains will help raise the image and reputation of China as an honest and ethical player. For all the parties along the supply chain, blockchains will help improve business ethics and raise the industry standard. For consumers in China and overseas, blockchains will help improve their confidence and make informed buying decisions as provenance and transparent traceability records are readily available. Consumers will not need to worry about getting counterfeit products or unsafe agricultural food products.

Fig. 1.

Blockchain for provenance, traceability, and transparency in supply chain.

As shown in Fig. 1, our vision of an industry wide agricultural products blockchain will involve many parties, such as farmers, processing plants, distributors, transports, retailers, consumers; and government departments and agencies such as the State Administration of Taxation, Law enforcement, and other certification bodies. The blockchain-enabled traceability system will be deployed on a cloud platform, with IoT tracking movements of products and streaming real time data into the big data platform [20,25]. Smart contracts resided on the blockchain are executed automatically to carry out financial transactions or other processes [4]. The records kept on the blockchain becomes the single source of truth for the provenance and history of products.

5. Conclusions

In this research, we built five models by means of regression analysis and used real world dataset to verify three hypothesis regarding “Internet+” base agricultural security traceability systems in the agricultural products supply chain in China. To increase the quality and safety of the Chinese agricultural supply chain we also proposed to integrate Blockchain into traceability systems in the future work.

Our finding demonstrated that based on “Internet+”, big data, big technology and big platform can significantly affect the traceability accuracy of the product safety quality of the Internet agricultural products enterprises. The main reason is that big data can provide socialized and networked information, big technology can increase knowledge value orientation and social capital, and large platform can make various industries realize more interactive experience.

Another finding is that in reality, many enterprises still have a one-sided understanding of “Internet”, so that the understanding of relevant data, technology and platform is even less.

In this paper, we discussed the traceability accuracy of product quality and safety only from the responsibility cost of enterprises, and fails to consider other factors that affect the traceability precision. Considering other factors to further improve the traceability system is one of the future research works. Another direction for our future research work is to exploit the blockchain technology to develop more reliable and better quality systems for agricultural products supply chain.

We believe that the new agricultural products blockchain framework proposed in this research will not only contribute to supply chain management theory but also contribute to improve consumer acceptance of the safety of agricultural products in practical.

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