<figure><img src="image_67.png" /></figure>

only provides the guidance through which it should be done, and specific implementation can be selected on a case-by-case basis.

One of the key features, here, can provide different degrees of access control and log data access, which helps keep track of the chain of custody of data. This not only makes the data access transparent but also provides an opportunity for monetization.

Another service which can be provided is to keep some commonly-used clean data in the semantic layer for easy access. This helps the products get the benefits of using pre-processed data from a central source. That said, this does not bind the data product to the limitations of the central data source, as the data products can always use the same data from the original source.

### 3.3.3. Data product integration

Enabling access to farm data through one uniform set of protocols requires that all data domains agree on the specified ontology. One of the key functions of the semantic layer data product is to add and update new data products in the CowMesh. To enable such integration of the data products, each product needs to provide access to their data in such a way that it complies with the data ontology and the access protocol set by

the semantic layer. For example, cow milk property information may be stored in two data domains, one public and one private, which are stored in different formats. To integrate these two organizations into data domains in the CowMesh, the data products should be able to access them through protocols set by the semantic layer while providing the necessary access control and privacy.

The integration of data sources can be achieved by introducing the role of a Knowledge Scientist (KS; Fletcher et al., 2020), who will perform this integration. A KS is a person who represents a bridge between the underlying data and the business requirements. In our case, a KS would communicate with the two data domains, to understand the structure of the data which they are willing to make a part of the CowMesh. The KS will have a detailed understanding of the CowMesh and the semantic layer and the required knowledge of the data source through communication. This translation can be implemented at the semantic layer end or the data product end, which can be decided on a case-by-case basis and is implementation-specific.

It is important to emphasize that the KS does not need to be an expert in the dairy industry or know specific properties about the data or data cleaning. The key role of the KS is to
have basic knowledge about the concepts related to dairy industry data, knowledge about CowMesh, the ontology, and the role of the semantic layer, ability to understand requirements from communications with the data domains, and understanding the related technologies of the specific implementation.

The data sources in the CowMesh do not necessarily need to represent a large public or private organization. Smaller and independent data hosts can also participate easily in the process by following the solid protocol,<fnref n="13" /> guided by the protocols set by the semantic layer. A solid pod is a personal online data store that empowers users with control over their data, following the principles of the solid project for decentralized and secure data management on the web. The basic idea behind solid is to allow users to store their data in a “pod,” which is a personal online data store that they control. Users can, then, grant access to their data to apps and services as they see fit, rather than having their data silo-ed in different apps and services controlled by large corporations. Solid aims to provide a more open, decentralized, and user-controlled web, where individuals have more autonomy over their personal data and are empowered to choose which apps and services they want to use and share their data with. Therefore, instead of having full infrastructure like an organization, an individual (e.g., a farmer) with data can contribute to the CowMesh by making their data accessible as data products through solid pods, which are easy to deploy. Services such as Inrupt<fnref n="14" /> can be used to deploy a solid pod easily, with the assistance of a KS following the protocols set by the semantic layer.

### 3.3.4. Governance

The governance of data, data access, and ownership is simplified by the CowMesh architecture through the semantic layer. The data domains own their data, and they decide whether to keep the data within the semantic layer or rather to keep them on their private server. Each data domain and data product can have their own governance. However, to be a part of the CowMesh, the data domains, and data products need to conform to the standards and protocols set by the semantic layer. This provides two levels of governance. The data products in the data mesh as an independent unit may have their own governance. In addition, by being a part of the CowMesh, they fall under the uniform set of protocols and standards. This streamlines the governance of the entire CowMesh. Better governance will also encourage the opening of controlled channels from private organizations through CowMesh, which can help to facilitate greater collaboration between the dairy industry and academic researchers.

One open question revolves around who will govern the CowMesh. While the answer will be specific to the context in which CowMesh is being implemented, some possibilities are as follows: (1) one of the member organizations of the CowMesh can take responsibility for governance; (2) several members of the organizations of the CowMesh can form a governance forum; (3) a neutral organization can act as a governing body. One successful example of such a governing body is The Open Subsurface Data

Universe (OSDU),<fnref n="15" /> which regulates how oil and gas companies manage and analyze subsurface data. The goal of OSDU is to create a common data platform that allows the member companies to share and collaborate on subsurface data while also providing secure and scalable access to the data. OSDU is under the guidance of The Open Group,<fnref n="16" /> a global consortium that brings together industry, government, and academia to develop open standards and best practices for technology.

## 4. Use case implementations

In this section, we will present two use cases which can benefit from the proposed architecture and overcome current challenges in data-driven dairy farming. Figure 3 shows an example of CowMesh architecture in the context of the Irish agricultural sector. Here, we extend Figure 1 to demonstrate data domains and data products, together with their interactions in Ireland. In this example, the semantic layer is owned by the VistaMilk data domain, and a data product “CowReport,” which provides a periodic summary insight into the data from different sources. Such a report can directly help the farming industry analyze the data from a higher level perspective and help the stakeholders to take informed decisions.

Teagasc is the Agriculture and Food Development Authority in Ireland and has a data product PastureBase (Hanrahan et al., 2017) related to countrywide grassland management. The Irish Cattle Breeding Federation (ICBF) is a non-government organization that provides a large data repository for several areas related to dairy farms. Each academic institution or research center involved in agriculture research can be a data domain. For instance, we show some data products within the Insight Centre for Data Analytics<fnref n="17" /> data domain. The possible products within this domain and how they interact are described in the following subsections.

### 4.1. Mastitis prediction

Mastitis is an inflammatory response of the udder in the cow's mammary gland caused due to microorganism infections. Mastitis is divided into two types, namely, (a) clinical mastitis, where symptoms are visible to the naked eye; and (b) sub-clinical mastitis, where the symptoms are not visible but can be measured though testing. Both of the variants compromise the health and wellbeing of the cows which results in negative impact on milk production volume and quality (Halasa et al., 2007), increased veterinary costs (Cavero et al., 2007), and an increased risk of culling. Mastitis is one of the most common infections on dairy farms globally, with ~20–30% of cows in any herd likely to become infected annually (Heringstad et al., 2000). Therefore, the ability to predict the onset of clinical or sub-clinical mastitis in cows ahead of time will be of great benefit on dairy farms.

<footnote n="13">13 https://solidproject.org/ (visited August 17, 2023).</footnote>

<footnote n="14">14 https://www.inrupt.com/ (visited August 17, 2023).</footnote>

<footnote n="15">15 https://osduforum.org/ (visited August 17, 2023).</footnote>

<footnote n="16">16 https://www.opengroup.org/ (visited August 17, 2023).</footnote>

<footnote n="17">17 https://www.insight-centre.org/ (visited September 19, 2023).</footnote>
<figure><img src="image_68.png" /></figure>

### 4.1.1. Predicting mastitis: traditional process

Prediction of mastitis (both clinical and sub-clinical) has been previously performed using data-driven statistical and machine learning methods (Ebrahimi et al., 2019; Anglart et al., 2020; Bobbo et al., 2021). A recent study by Pakrashi et al. (2023) addressed this issue by predicting sub-clinical mastitis in Irish dairy cows up to 7 days ahead of time. Pakrashi et al. (2023) use machine learning algorithms to train prediction models using the data from seven Irish research farms spanning 9 years of data, which consisted of the following information:

* Daily milk yield and other milking information;

* Milk composition (e.g., fat, lactose, protein, and somatic cell count)

* General cow features available at the farm (genetic information of the cow, how many times the cow has given birth, etc.)

* Other derived variables from the above information (e.g., how many times a cow has been diagnosed before, if a cow was treated before, mean, and standard deviation of the change in milk composition in the last 7 and 15 days)

The final delivery of the data for the study conducted by Pakrashi et al. (2023) was in the form of CSV<fnref n="18" /> files sent via email. The general cow features were included in one CSV file, and milk composition data in another CSV file. Each of the CSV files required special attention for data cleaning, and then, they were joined to make the dataset required. In addition, for the specific task in hand, several derived variables were created for the project, which was not a part of the provided data. This was, then, analyzed, and a machine learning model was trained. While the development was performed, a new batch of data from a subsequent year was available and transferred through email in a similar set of CSV files, which was again combined after fixing a few issues due to incompatibilities with the previous data received.

The above process, if considered in isolation, is relatively straightforward. However, deploying the pilot project in a practical real-world scenario presents a number of problems as follows:

1. As farms generate new data, accessing data via email or through a single central data repository is cumbersome.

<footnote n="18">18 Comma Separated Value file format.</footnote>
2. Different teams working on the data coming from the same source had different variable names assigned by the corresponding research teams. There was no clear data description, therefore it was hard to communicate between the teams about the problems.

3. The same data cleaning and transformation were performed independently by the different teams using the same data, leading to redundancy and duplication of effort.

4. The final training dataset prepared by Pakrashi et al. (2023) was found to be useful to other teams. However, processed datasets were sent as a zipped set of CSV files *via* email. This data generation and processing consume a significant amount of time. Additionally, when data are sent *via* email, any updates or changes in processing or data require resending the files, which are often overlooked, leading to continued use of outdated data by other teams.

5. Different teams worked on predicting mastitis through different approaches (Jin et al., 2023). The main target is to predict mastitis in cows using different data available from a dairy farm. Therefore, these projects and the predictive models can be combined to complement each other to build a better mastitis predictor. However, without a shared framework, it is extremely difficult to integrate the products coming from different frameworks.

6. The exact details around data ownership were not clearly defined.

The above issues show that, although valuable data were available from a central source, the processing and analysis work were scattered across silo-ed teams, and the products the teams built (mastitis predictor) were also bound within the team. Aligning with the major challenges defined by WEF (see Section 2), point 1 is a fragmentation issue, while points 2, 3, 5, and 6 relate to standardization. The problems mentioned in points 1, 4, and 5 represent access issues.

### 4.1.2. Mastitis predictor as a data product in CowMesh

Our proposed data mesh architecture helps address these issues. To demonstrate this, we describe how a collection of projects related to mastitis research can be integrated into the data mesh in an Irish context.

First, the required data mentioned previously (e.g., daily milk data and milk composition data) are aligned with an ontology defined by the semantic layer. This is performed with the help of the data product team and the KS. Such an ontology is shown in Figure 2.

Figure 3 shows how the set of mastitis-related products could be incorporated into the CowMesh. The data domain is named "SCM1_Data" (SCM stands for sub-clinical mastitis), and the six data products are named "SCM1Pred," "SCM1DataAccess," "SCM2Pred," "SCM2DataProcess," "CM1Pred" (CM stands for clinical mastitis), and "HealthMonitor." The roles of these products are explained below.

The "SCM1Pred" product is developed by a research group which is focused on predicting sub-clinical mastitis from daily

milking information and historical data about milk and cows. This data product consumes data through protocols set by the semantic layer, by using the API, following the ontology. Therefore, given the ontology (such as Figure 2), this domain will be mainly working with the concepts, such as "Cow," "Milk," and "Disease." The outputs of this product are the predictions and the trained machine learning model. Such a product sharing the outputs of the predictions can be incorporated into a report such as the "CowReport" data product or directly sent to the farmer to assist in decision-making. In addition, potentially the trained machine learning model may be required by other research teams so that they can build another product on top of this (e.g., explaining the predicted outcome). Some additional variables were developed in this project, which were found to be useful to other teams. Therefore, the data product "SCM1DataAccess" provides an API through which the additional variables are accessible.

The data product "CM1Pred" is developed by a research team which works on predicting clinical mastitis. This product accesses data through the semantic layer and uses the additional variables through "SCM1DataAccess," as well as the predictions from "SCM_Pred" as required. Here, "CM1Pred" does not have to recompute the additional variables or receive the variables through cumbersome CSV files.

On the other hand, "SCM2Pred" is a data product which performs sub-clinical mastitis prediction but uses a different perspective and a different set of variables. In this project, some of the data, "SCM2Pred," which are owned privately and are not allowed to be kept. The data pre-processing logic is provided as a service through the "SCM2DataProcess" data product. Therefore, this product can be accessed to use the same data pre-processing while accessing the actual data through the semantic layer from the same sources. The access and sharing of the data, which might be privately held, are governed by the semantic layer. The sharing protocol and other agreements would have been already done while integrating the related data domains.

The data product "HealthMonitor" can be a dashboard summarizing the cows' health in the farms. This takes data from the semantic layer, summarizing the outputs of "CM1Pred," "SCM1Pred," and "SCM2Pred."

The results of these products are also consumed by the report generation product of the semantic layer data domain, such as "CowReport." Including such information in the report can provide dairy farmers and stakeholders with a high-level picture of the health of large herds. Additionally, incorporating mastitis prediction information into the report can prevent farm losses, address cattle welfare, and improve milk production by enabling farmers to take preventive measures ahead of time, before a cow shows the signs of clinical or sub-clinical mastitis.

Issues 1, 2, and 3 are addressed by the ontology and the uniform API provided by the semantic layer and central cleaned accessible data kept with the semantic layer. Issues 4 and 5 are addressed by the data products "SCM1DataAccess" and "SCM1Pred," respectively, which enable the use of the data sharing. These data products, like any other data products in the mesh, would also adhere to the semantic layer ontology.

As each of the data domains is responsible for managing their data by adhering to the semantic layer ontology, cleaning,
pre-processing, generating derived attributes, and keeping them
updated, the data quality issue is addressed. The access of the data
is limited through the API and governed by the semantic layer.
Therefore, access can be controlled and the chain of ownership of
the data can be tracked, resulting in better data transparency and
clear ownership boundaries, thus also addressing issue 6.

To be a part of the CowMesh, it is necessary to interact with the
KS to ensure compliance with the protocols. The KS, working with
the corresponding products, will assist the relevant team to become
integrated into the CowMesh.

## 4.2. MilkMap

Ireland has a seasonal milk supply influenced by changing weather, soil nutrition, and a host of other environmental and societal factors. In addition to milk yield, the primary change in milk across a season is its composition, i.e., variation in macro (protein, fat, and lactose) and micro (minerals, vitamins, and other bio-actives) constituents. Milk composition determines the yield of dairy products produced by a farm or processor, which can be logistically complex given the fractionation, fermentation, and preservation techniques, and end applications being employed across the sector. Moreover, the compositional makeup of milk determines its functionality, processability, and ultimately its final end use (i.e., as a consumer food or ingredient in another food). For example, consistent manufacture of milk gel-based products such as cheese, casein, or yogurt is highly dependent on the protein and mineral composition of milk. Another example is the relationship between nutrient composition (i.e., protein, minerals, and other ionic species) and heat stability of milk.

### 4.2.1. MilkMap: traditional process

The MilkMap system is designed to visually represent dairy processing in Ireland. This specialized tool necessitates significant custom processing of the aforementioned raw dairy values. This mapping application is designed to provide additional analytical capabilities in an agricultural context, including the monitoring of dairy production and the provision of time series forecasting (e.g., for yield and composition). While the architecture employed in the MilkMap system considers generalized geographical patterns over long period of time, it also provides the means to drill down into specific localized regions to explore the trends and patterns specific to each region. The final mapping application is ultimately made available to stakeholders across the organization, allowing them to leverage the full potential of the data originally collected. The processed data used in the mapping project are also available in a form that can easily be employed in different systems within the organization.

The delivery of this specialized application for dairy processing in Ireland necessitates significant custom processing of the aforementioned raw dairy values. For instance, the anonymization of dairy data was achieved through transformation of latitude and longitude coordinates to lower resolution H3 hexagonal (Brodsky, 2018) values. These data were transferred from dairy processors to a central repository, allowing for a distributed utilization of this

information in application development. No sensitive information
is available to either the MilkMap application or the data processing
of dairy values needed for it. Other data sources could also be
potentially integrated into the MilkMap system. For instance,
mid-infra-red (MIR) data taken from existing instruments located
across processing plants in Ireland, and grass growth data retrieved
from a source such as PastureBase (Hanrahan et al., 2017).

During the development of the MilkMap, several challenges
were encountered as follows:

1. Obtaining access to the data was a major hurdle, particularly from the dairy cooperatives, due to legal and privacy concerns. Extensive negotiations and agreements were required to obtain access to the information necessary for the successful implementation of the MilkMap.

2. Combining data from multiple sources was complex and time-consuming, as each source presented its own obstacles to access. Updating and accessing the data were also problematic and necessitated a special Secure File Transfer Protocol (SFTP) connection set up in each case.

3. Noisy data, missing values, and a lack of standardization in data formats presented additional issues, as did the inability to directly communicate with dairy farms or cooperatives, with interaction predominantly passing through the ICBF. Outlier detection was necessary to handle anomalous values, however, pinpointing their root cause proved difficult due to the involvement of numerous intermediaries in the data sharing pipeline.

4. Privacy concerns limited the specificity of the data, making it infeasible to drill down to the level of individual farms when performing detailed analysis.

### 4.2.2. MilkMap as a data product in CowMesh

Overcoming these challenges required careful consideration of issues around data cleaning, standardization, governance, and security measures to ensure the accuracy and completeness of the data. To reduce the overhead in terms of time and effort, we propose that the MilkMap system could be implemented as a data product within the data mesh, which would help mitigate the issues listed above. The specific benefits of this approach are as follows:

1. To clarify data access issues, such as those around privacy and legal requirements, the semantic layer would prove useful. Since CowMesh provides increased trust and transparency around data usage, we believe that the data sources are more likely to join the mesh and supply the required data.

2. The issue of cumbersome processing of data coming from different sources with different access protocols and data structures can be addressed by the uniform data ontology and data access protocols set by the semantic layer.

3. The issue around noisy and inconsistent data can be handled at the semantic layer when integrating the data domain into the CowMesh. By conforming to the semantic layer protocols, consistent practices for data cleaning, and standardization can be enforced. In addition, since the data domain must now adhere to the data ontology, each feature in the data will be documented (e.g., in terms of range and relationship to other features).
4. The privacy and anonymization issues can be handled at the semantic layer or at the corresponding data domain. In either case, the data handling pipeline can be organized such that data products only have access to a subset of data that is necessary and sufficient as controlled by the data domain.

## 5. Advantages and opportunities for CowMesh

The objective of the CowMesh is to add value to the dairy industry through intelligent data processing. To do this, the products need access to clean data from the farms to act upon. CowMesh provides the following main advantages which encourage farms and data sources to share data, such that products can provide analytical and predictive insights, which adds value to the dairy farms.

* **Trust:** Data domains and data products can rely on the accuracy and quality of the data they are working with, as well as the security of the data and the trustworthiness of the data sources. By establishing trust in the data and the data sources, organizations can make better decisions and extract more value from the data.

* **Privacy and transparency:** By using the decentralized property of the data mesh, data can be privately held. The nature of the CowMesh allows the private data domains to decide how much data they want to make accessible to the CowMesh. In addition, the chain of ownership can be tracked and audited, ensuring transparency and compliance.

* **FAIR data:** FAIR refers to Findable, Accessible, Interoperable, and Reusable. By ensuring that data follow the data ontology and the access is standardized through the set of access protocols and interfaces (e.g., well-defined APIs), organizations can make it easier for users to discover and access relevant data, as well as combine and analyze data from multiple sources. This can lead to more accurate insights and better decision-making.

* **Decentralized interoperability:** CowMesh combines the contrasting characteristics of data mesh and data fabric and enables data and products to be decentralized while providing one central protocol to be followed, therefore enabling interoperability.

* **Governance:** Governance becomes easier, as the CowMesh follows the standards and protocols set by the semantic layer. This makes the different products follow the standards and protocols easily.

These points address the three main challenges (fragmentation, standards, and access) defined by WEF in the agricultural domain as mentioned in Section 2. Trust, Privacy and Transparency, and Governance can bring cultural changes with respect to how data are shared, which can encourage more organizations and individuals to share their data, addressing the challenge of “standards,” whereas FAIR Data and Decentralized Interoperability address the challenges of “fragmentation” and “access” issues.

Several resources and infrastructures need to be maintained to run the CowMesh including designing the specific architecture, hiring a KS, maintaining the APIs and the semantic layer, and maintaining security of the systems and servers. This would require some funds to be spent on CowMesh. As CowMesh is a service which the data domains and data products will use; several aspects of the CowMesh can be monetized, which can help maintain the framework. In addition, through monetization, the individual owners of data (e.g., farmers) and the larger organizations can benefit by charging for the data in an on-demand fashion. This monetary incentive may help more data sources to contribute to CowMesh and get compensated for their data while keeping the data ownership to the corresponding source and maintaining transparency. Some of the ways in which CowMesh can be monetized are as follows:

* **Data use:** The data are accessed through the API, and the use is tracked by the semantic layer. Using this, the total data used by each data product can be tracked and charged. This charge can, then, be distributed to the farmers and the institutions generating the data.

* **Data access:** The uniform data access, interoperability, and also possibly cleaned data are provided by the semantic layer of the CowMesh. Therefore, this service provided by the semantic layer can be monetized. For example, the access to the API calls (not the data) as well as the integration to the CowMesh can be monetized.

* **Report or dashboard consultation:** The semantic layer can generate a periodic report data insights and the analytical components of the CowMesh, for which the organizations and the farmers can pay. This can be a direct value added to the farming industry to see the higher level picture and enable the farmers and the organizations to take updated and informed decisions.

* **Predictive analytics:** Advanced products, such as predicting mastitis, lameness, or ketosis in cows, can be treated as add-on services, which can be offered to farmers on a subscription basis. This is a direct benefit to the farm, as these predictive data products consume data from the farms and then feed back predictive insights to help the farmers.

While the directions above indicate the potential opportunities that the CowMesh can bring, a detailed analysis of the monetization of the CowMesh is outside the scope of the current study.

The different consumers of CowMesh include farmers, researchers, commercial dairy organizations, and veterinary institutions. Farmers can contribute data to CowMesh through data products or the semantic layer. Research organizations, veterinary institutions, and commercial dairy organizations execute specific projects aligned with dairy industry needs and farmers' requirements. Project results benefit farmers through reports and other data products. Research findings can also be shared with commercial dairy organizations for commercialization or as reports. Each party can seamlessly integrate via CowMesh,
retaining data ownership and transparency of usage, while benefiting from services and products offered, contributing to the improvement of the dairy industry at a national level.

## 6. Discussion

This study presents a data architecture, CowMesh, designed to unify disparate dairy industry data under a uniform, interoperable, and decentralized framework, thus enabling the products using the data to create value for the dairy farmers and the dairy industry. CowMesh is a combination of data mesh and data fabric. The data mesh's functionality helps the different data products using the dairy data to operate in a manner that is independent and decentralized. The central data product semantic layer is a data fabric, which provides a single unified data model and protocol. This enables connection to and integration of data from different sources within the data mesh. This enables uniform governance, creates trust, promotes data transparency, and keeps the data and the data products decentralized while providing interoperability within the data products. In future, a similar framework can be developed for other agricultural industries tailored for their specific requirements. In addition, specific details about the governance and semantic layer can be explored in an Irish context. Finally, a pilot project to implement CowMesh should be explored in future.

## Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

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## Author contributions

CG conceptualized the architecture. AP and BM enhanced the architecture and specific cases. AP, CG, and BM contributed to conception and the design of the architecture and designed the mastitis prediction case study. DW and DG designed and wrote the MilkMap case study. AP wrote and finalized the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

## Funding

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and the Department of Agriculture, Food, and Marine on behalf of the Government of Ireland under grant numbers [16/RC/3835] and [12/RC/2289_P2].

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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<figure><img src="image_69.png" /></figure>

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## OPEN ACCESS

EDITED BY
Matthew McCabe,
King Abdullah University of Science and
Technology, Saudi Arabia

REVIEWED BY
Shailesh Tripathi,
Tampere University of Technology, Finland
Damon M. Dooley,
Simon Fraser University, Canada

*CORRESPONDENCE

Baptiste Darnala

<figure><img src="image_70.png" /></figure>

baptiste.darnala@elzeard.co

Clément Jonquet

<figure><img src="image_71.png" /></figure>

clément.jonquet@inrae.fr

RECEIVED 15 March 2023

ACCEPTED 28 August 2023

PUBLISHED 16 October 2023

## CITATION

Darnala B, Amardeilh F, Roussey C, Todorov K and Jonquet C (2023) C3PO: a crop planning and production process ontology and knowledge graph. *Front. Artif. Intell.* 6:1187090. doi: 10.3389/frai.2023.1187090

## COPYRIGHT

© 2023 Darnala, Amardeilh, Roussey, Todorov and Jonquet. This is an open-access article distributed under the terms of the *Creative Commons Attribution License (CC BY)*. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# C3PO: a crop planning and production process ontology and knowledge graph

Baptiste Darnala¹,²*, Florence Amardeilh², Catherine Roussey³, Konstantin Todorov¹ and Clément Jonquet¹,³*

¹LIRMM, CNRS, University of Montpellier, Montpellier, France, ²Elzeard, Bordeaux, France, ³MISTEA, INRAE, Institut Agro, University of Montpellier, Montpellier, France

Vegetable crop farmers diversify their production by growing a range of crops during the season on the same plot. Crop diversification and rotation enables farmers to increase their income and crop yields while enhancing their farm sustainability against climatic events and pest attacks. Farmers must plan their agricultural work per year and over successive years. Planning decisions are made on the basis of their experience regarding previous plans. For the purpose of assisting farmers in planning decisions and monitoring, we developed the *Crop Planning and Production Process Ontology (C3PO)*, i.e., a representation of agricultural knowledge and data for diversified crop production. C3PO is composed of eight modules to capture all crop production dimensions and complexity for representing farming practices and constraints. It encodes agricultural processes and farm plot organization and captures common agricultural knowledge. C3PO introduces a representation of technical itineraries, i.e., sequences of technical farming tasks to grow vegetables, from soil identification and seed selection to harvest and storage. C3PO is the backbone of a knowledge graph which aggregates data from heterogeneous related semantic resources, e.g., organism taxonomies, chemicals, reference crop listings, or development stages. C3PO and its knowledge graph are used by the Elzeard enterprise to develop knowledge-based decision support systems for farmers. This article describes how we built C3PO and its knowledge graph—which are both publicly available—and briefly outlines their applications.

## KEYWORDS

ontology, semantic resource, knowledge representation, knowledge graph, agriculture, crop planning, crop production process, digital farming

## 1. Introduction

Agricultural work is complex, farmers need to take various factors such as weather, seasonality, commercial demand, and plant life cycles into account when planning production. Moreover, consumers' and farmers' behavior and practices are changing, with greater consideration for ecological and economic aspects. After the WWII, single-crop farming and the use of chemical inputs were highly promoted, but this led to reduced soil fertility and chemical contamination of soil and crops. In recent years, farmers have begun adopting agroecological practices. Agroecology offers a way of designing farming production systems that rely on agroecosystem functionalities. Crop diversity is a key aspect of this approach. Several scientific studies (Isbell et al., 2017; Paut et al., 2019) have shown that, in both spatial and temporal terms, crop plant diversity (i) improves risk management under changing weather and economic conditions; (ii) delivers a natural defense system against
diseases and pest attacks; and (iii) increases agroecosystem stability and resilience. Farmers must consider different stages of development, treatments, and biological interactions between plants. However, these new agroecological practices increase farmers' workload, management complexity, and mental burden (Morel and Léger, 2015; Dumont, 2021). Between 2019 and 2020, the SME Elzeard conducted over 150 interviews with vegetable farmers, agricultural advisors, teachers, and researchers.<fnref n="1" /> Elzeard identified several technological barriers, including the need for knowledge sharing and new operational tools to assist vegetable farmers in their daily crop management, i.e., optimizing crop rotations, yields, and finding alternatives to chemical inputs.

Agricultural knowledge-i.e., consensual information used by farmers to make decisions and to take actions-is currently scattered webwide, in books, archives, and databases while also conveyed informally through interpersonal communication and cultural practices. As well, climate change is increasing an important factor to mitigate in agriculture. Climate has a direct impact, with rainfall fluctuations and heat waves, affecting plant growth and promoting disease emergence (Mendelsohn, 2009; Arora, 2019). To help farmers face this new challenge and its impact on agriculture, it is necessary to share and discover new knowledge. Climate change and the adoption of agroecology approaches now call for the development of novel knowledge-based systems. Knowledge needs to be formalized and shared with anyone who needs it, requiring a common formalization. Moreover, quality knowledge must be readily findable, accessible, interoperable, and reusable (FAIR) for users (Wilkinson et al., 2016). In that respect, semantic web technologies facilitate the development of robust knowledge-based systems by enabling formalization and sharing of knowledge for validation, enrichment, and discovery with native reasoning. Common formalization also facilitates the aggregation of diverse resources and provenance verification to control the quality of shared knowledge.

Semantic Web technologies are used to build knowledge graphs (KG) and ontologies in agriculture (crops and livestock farming, etc.) for both experimental and agricultural purposes (Drury et al., 2019). For instance, AgroPortal (Jonquet et al., 2018) hosts approximately 150 ontologies and vocabularies (as of January 2023), many of which focus on agrifood-environment issues, such as the Agronomy ontology (Devare et al., 2016) and FoodOn (Dooley et al., 2018). However, diversified vegetable farming has not been extensively explored, and there is a substantial need for research in this area. Diversified vegetable crop farmers define technical itineraries (“itinéraire cultural” in French, abbreviated in the domain as ITK) as sequences of technical farming tasks to grow vegetables from soil identification and seed selection to harvest and storage. In these sequences, every task and their timing depend on each other and other parameters such as the cropping mode (open field or under cover) or the climatic conditions. As an illustration, planting an onion crop in winter can lead to a spring harvest if cultivated under cover. However, if the onions are cropped in an open field, they will be harvested in the summer. Farmers and agricultural experts define the technical itineraries and draw up plans for the following year(s) based on what happened

previously in the fields. They can share their technical itineraries with other farmers who will adapt them to their specific agricultural context, as defined by parameters such as soil type and climate, the number of farm workers or the diversity of crops. To the best of our knowledge, technical itineraries have not been represented using Semantic Web technologies.

In this study, we developed the Crop Planning and Production Process Ontology (C3PO) and populated it into a KG. The couple jointly captures vegetable crop farming management concepts and knowledge to support multiple applications in diversified crop planning. The ontology incorporates the representation of technical itineraries for farm planning and management, as well as the representation of plants, plot organization, and chemical products and equipment. The KG is aligned with other agricultural Semantic Web resources to integrate reference data dispersed in organization systems. Our goal is to represent interactions between living organisms, farmers' actions, and input products. Each type of entity is identified and managed under different web standards. This study presents the methodology by which the ontology and related KG have been developed. A subpart of the C3PO knowledge graph will soon be publicly available on La Serre des Savoirs, a web portal that pools integrated and harmonized knowledge about plants and farming practices. In addition to these knowledge assets, we are building multiple applications such as two knowledge-based decision support systems to assist farmers: Elzeard, a web and mobile application to plan and monitor crop production in vegetable farming systems; and La Pépinière, a free web application to help beginners to design their farms and their future production system.

The rest of the study is organized as follows: Section 2 presents related work in ontology development methodology, agriculture, and semantics; Section 3 outlines the methodology implemented to build the ontology and knowledge graph; Section 4 presents the ontology and knowledge graph; Section 5 presents applications based on the ontology and knowledge graph; Section 6 discusses the problems encountered and limitations of our study; and Section 7 concludes and presents the perspectives.

## 2. Related work

Many ontology and knowledge graph development methodologies have been created to support ontology development since the 1990s. In this section, we present those used in the development of our resource. The NeOn methodology (Suárez-Figueroa et al., 2012) presents nine flexible scenarios to build an ontology and “ontology networks” based on the reuse of semantic resources, the transformation of non-semantic resources, and the reuse of ontology design patterns. An ontology network is a collection of ontologies linked via relations such as mapping, import, or version. By this methodology, we designed C3PO in multiple ontology modules to address different scenarios. The list of C3PO’s modules is presented in Section 4.1. According to the NeOn methodology definition, an ontology module is “a part of the ontology that defines a relevant set of terms”. However, although NeOn offers interesting guidelines to organize the ontology development, the methodology is time-consuming due to the required quantity of documentation and the NeOn toolkit,

<footnote n="1">1 https://elzeard.co</footnote>
i.e., the integrated development environment proposed with the methodology, is no longer updated. The Agile methodology was originally used for the development of systems and applications, then later also for ontologies, which implies iterative development and publication and continuous collaboration with consumers. SAMOD (Peroni, 2016) is an Agile methodology with which ontologists develop small ontology iterations for describing a particular use-case and addressing competency questions. After review, the iteration is added to the main ontology representing the whole domain. We adopted this approach for the development of each C3PO module. The Linked Open Terms (LOT) methodology (Poveda-Villalón et al., 2022) is another Agile methodology describing each ontology development step, from specification to publication. LOT is focused on industrial projects as the aim to be compatible with software development methodologies with iterative steps. Moreover, a set of tools is provided as well as examples of how they may be used in ontology development. We built C3PO by combining the development steps presented in LOT and the iteration development process presented in SAMOD.

We studied models focused on agronomy and agriculture. We queried AgroPortal to identify ontologies and vocabularies to represent plant knowledge, agricultural tasks, and plot organization and identified multiple semantic resources: the French Crop Usage thesaurus (FCU) (Roussey, 2018), a list of cultivated plants organized by agriculture uses in France; the Agroecology Knowledge Management application (GECO, in French), a research information system for the GECO data graph (Soulignac et al., 2019) to design innovate agroecology-oriented crop systems; TAXREF-LD (Michel et al., 2017), a linked data representation of the national repository of fauna and flora of France; the NCBI Taxonomy, a curated classification and nomenclature for organisms; Plant Ontology (Jaiswal et al., 2005), a structured vocabulary and database resource that links plant anatomy, morphology, growth, and development to plant genomics data; Crop Ontology (Arnaud et al., 2012), a vocabulary of observable characteristics of common crops for food and agriculture; and the AgroLD knowledge graph focused on plant biology data (Larmande and Todorov, 2021).<fnref n="2" /> Each of these resources is based on a specific viewpoint but cannot be used alone to represent plant knowledge in agriculture. However, we have combined some of them in a coherent integrated knowledge graph that is presented later. Other ontologies and vocabularies available in AgroPortal-but which we did not directly used in our work- to represent agricultural processes include: the Agronomy Ontology (AGRO) (Devare et al., 2016), which represents agronomic experiments by recording precise observations concerning experiments on agricultural plots but is not geared toward agricultural planning and monitoring. The DEMETER Agriculture Information Model (Palma et al., 2022) focuses on smart farming solutions using sensors to monitor crops, which is currently beyond our scope.

Semantic Web technologies may represent processes which could be used to represent tasks in agriculture. The Provenance Ontology (Prov-O) (Lebo et al., 2013) traces the provenance and evolution of activities, interacting with involving agents and entities. Prov-O is an interesting ontology that needs to be

specialized to represent a domain, but it does not address all of our needs, especially with respect to temporal aspects required for representing technical itineraries. Otherwise, it is essential to represent theoretical dates, i.e., dates not related to a year (e.g. 04/25), which is not possible. We, thus, opted to use the Time Ontology (Hobbs and Pan, 2006) and extended it to fulfill our needs. However, Prov-O is used to track KG updates, as explained in Section 3.4. We also studied ValueFlows, an ontology that describes economic value flows according to three representation layers (Knowledge, Plan, and Observation).<fnref n="3" /> Knowledge represents plan specification to make something: an ordering set of tasks (e.g., a cooking recipe which specify step-by-step the recommended quantity of ingredients to cook); plan represents the planning of these tasks by an agent and the choice made to implement them (e.g., the actual recipe steps with the quantity of ingredients planned to be used); observation represents the plan execution of the tasks (e.g., the actual recipe steps followed by the cook with the quantities of ingredients used). We used these three layers to conceptualize the principles underlying the representation of technical itineraries: plan specification, plan, and plan execution.

To the best of our knowledge, there are currently no ontology available to help diversified vegetable crop farms in planning and managing their farming tasks. Existing ontologies either only represent sub-parts of the problem or focus on other agricultural sectors, such as cereal cropping or livestock farming. However, the representation of technical itineraries for diversified vegetable agriculture and their use for agricultural planning has not been addressed in an adequate and complete ontology.

## 3. Requirements and methodology for FAIR ontology building and sharing

As mentioned previously, we built C3PO using a combination of LOT and SAMOD. We followed the LOT workflow, consisting of the ontology: (i) requirement specification, (ii) implementation, (iii) publication, and (iv) maintenance. The methodology also includes the knowledge graph construction and maintenance procedures. In the development, we built a component that meets current needs before adding it to the ontology, as recommended by SAMOD. Combining these methodologies brought us: (i) a general process regarding the construction of the ontology, due to LOT; (ii) a process regarding the update of the ontology through iterative development, thanks to SAMOD. We recommend this combination to any ontology development project related to an application development using agile methodology, with multiple viewpoints, multiple subdomains, and that integrate several and heterogeneous data sources. The main actors are the Domain Expert (DE), i.e., who offers the domain knowledge covered by the ontology and the overall vision of the work, and the Ontology Expert (OE), who has expertise in ontology and knowledge representation methods. Figure 1 presents the C3PO implementation steps, people involved, and tools used.

<footnote n="2">2 https://www.ncbi.nlm.nih.gov/taxonomy</footnote>

<footnote n="3">3 https://lab.allmende.io/valueflows</footnote>
<figure><img src="image_72.png" /><figcaption>FIGURE 1
C3PO implementation steps in the LOT/SAMOD methodologies and tools used.</figcaption></figure>

# 3.1. Ontology requirement specification

## 3.1.1. Use-case specification

This specification involves collecting the requirements for the ontology. The DEs present the needs in terms of data and queries required with informal text and schemas. This study defines the scope of the domain of the ontology should address and specify the use-cases. Moreover, DEs and OEs can extract multiple sub-domains for the ontology, which leads to the creation of several ontology modules. OEs review the document to gain insight into the data need, constraints, and logical inference expected and split the use-case regarding the different domains. Moreover, OEs also distinguish between static qualitative data (e.g., in our case plant families, inputs, crops, physical locations, and laws) and user input data. This difference is important as the implementation needs are not the same: user data will be collected *via* forms or sensors, then curated and eventually analyzed, whereas qualitative data that are relevant for the domain will have to be found by OEs in relevant external knowledge sources in the form of open data, existing ontologies or KGs, community standards, and norms. When such standards do not exist, OEs will have to build them. For instance, the way farmers organize their plots is typically user data, while the taxonomic representation of plants represented in TAXREF-LD is a relevant knowledge source that has been integrated into the C3PO knowledge graph.

## 3.1.2. Functional ontological requirements

OEs describe each use-case with a title, an informal description, and competency questions related to this use-case to support the ontology development process. Examples of use-case and some competency questions for C3PO are presented in Table 1. Some

competency questions are also described in the ontology metadata and in the documentation.<fnref n="4" />

# 3.2. Ontology implementation

## 3.2.1. Ontology conceptualization

To conceptualize an ontology module that fulfills the requirements, we opted to use the SAMOD methodology because it offers the possibility to build an ontology module for some use-cases before integration in the main ontology. The proposition at the end of the conception phase is a list of concepts, relations, and queries.

OEs analyze the requirements and extract a list of concepts and relations. They propose a name, URI, and definition for each one. As an example of the use-case in Table 1, an extractable concept is a “crop”, however we choose to create the “CultivatedPlant” class with an URI and associated definition: “Vegetal organism cultivated by human beings”. Then, an ontology module is built with the classes and properties proposed with Chowlk notation (Chávez-Feria et al., 2022), i.e., an UML-based notation to build ontology diagrams in Diagrams.net.<fnref n="5" /> The diagram is composed of the classes, properties, and an instantiated data example. An example of a diagram produced and related to the example in Table 1 is presented in Figure 2, this is a representation of the onion, its families, and labels. The individuals of the plants and their families are both typed skos:Concept and owl:Class.

<footnote n="4">4 https://www.elzeard.co/ontologies/c3po/</footnote>

<footnote n="5">5 https://www drawio.com/</footnote>
<table><caption>TABLE 1 Use-case description of the representation of plants and their families.</caption><thead><tr><td>Title</td><td>Representation of the organization of plants and their groups</td></tr></thead><tbody><tr><td>Description</td><td>Plants are organized in several families (group of plants that share some common characteristics). These families can describe botanical characteristics, or can describe their usage in agriculture or consumption. Representing only the plant and the families is not sufficient as plants have cultivars that could be split in multiple categories (called varietal types) regarding their physical characteristics. For example, onion can be divided into yellow onion and red onion. The need is to get a representation of the whole plant taxonomy to enhance the farmers' knowledge about plant characteristics.</td></tr><tr><td>Competency Questions</td><td><ol><li>What are the botanical and usage families of a given crop? Botanical family of the onion is amaryllidaceae and usage family is bulb vegetables.</li><li>Are these two given crops from the same family? Onion and garlic are from the same botanical family.</li><li>What are the crops in a given botanical or usage family? Onion, garlic, and shallot are in the amaryllidaceae botanical family.</li></ol></td></tr></tbody></table>

## 3.2.2. Ontology conceptualization validation

To validate the conceptualization, we present modeling diagrams to DE validation and refinement. Class and property names are validated with DEs to check their existence in the domain.

## 3.2.3. Ontology encoding

After validation, OEs integrate the diagram in C3PO diagrams to generate a formal representation in OWL using Chowlk Converter.<fnref n="6" /> The class and property definition are written on the side in another tabular file so that they can be collaboratively edited by DEs and OEs. We generate the OWL file of the definition with OntoRefine, i.e., a tool to transform a tabular file into an RDF file using a template.<fnref n="7" /> We combine the Chowlk output file and definition file in Protégé (Musen, 2015) to consolidate and export a complete OWL file for C3PO module that we will use in our KG, exchanges with external parties, or publish in AgroPortal.

We supplement the C3PO KG with terms from controlled vocabularies used for property values. This “vocabulary part” of our KG contains information such as climate, unit of measure, and irrigation mode. To ease collaboration, it is maintained with VocBench (Stellato et al., 2015), an application that allows us to build ontology and thesaurus.

## 3.2.4. Ontology evaluation

To evaluate the ontology module: (i) First, to validate the domain representation, we write SPARQL queries matching the competency questions and executed them on the ontology. The competency questions and the SPARQL queries are available.<fnref n="8" /> (ii)

Second, to validate the structure and syntax, we use OOPS (Poveda-Villalón et al., 2014) to detect any classic ontology pitfalls. (ii) Third, to validate the embedded logic, we run the Pellet (Sirin et al., 2007) and Hermit (Glimm et al., 2014) reasoners to check the consistency of the ontology regarding the domain. (iv) Finally, to validate the usability, we explicitly used the ontology as a data model for an implemented application, which does run and fulfill its requirements (see Section 5 for details).

## 3.2.5. FAIR ontology publication

The ontology publication phase consists of providing the OWL files and the documentation online according to FAIR principles. Regarding these principles, we published C3PO on GitLab in the form of a set of ontology module files, as presented in Section 4. We produced the documentation of each module using Widoco (Garijo, 2017), a tool that generates an HTML documentation from an OWL file. The publication phase also consists in adding metadata to the ontology to improve its description. We upload C3PO in AgroPortal and declare some metadata using the AgroPortal metadata schema named MOD (Dutta et al., 2015). To evaluate C3PO’s fairness level (i.e., to which level our ontology adhere to the FAIR Principles), we used O’FAIR (Amdouni et al., 2022), an ontology fairness evaluator for semantic resource proposed by AgroPortal. C3PO reaches 59% of fairness. More information about metadata standard is described in Section 4.2.

# 3.3. Knowledge graph construction

The knowledge graph constructed under C3PO consists of several heterogeneous data sources. The type of source impacted the way we imported data, as described hereafter.

## 3.3.1. Domain expert data

As DEs are not Semantic Web experts, we support them in populating the C3PO KG with tabular files. Then, we use OntoRefine as was done when building the ontology to produce RDF files. The process is used to import characteristics of the cultivated plants and technical itineraries in the knowledge graph.

## 3.3.2. Relational database import

Some agricultural databases that we wanted to be in C3PO KG were not available in RDF. We, thus, use tabular formats of the databases to which we apply a preprocessing, e.g., to produce URIs. In the tabular to RDF transformation process, value sets are encoded directly in our vocabulary that is built with SKOS (Miles and Bechhofer, 2009). Then, the files are transformed in OWL format with OntoRefine to produce an RDF dataset to be imported in the C3PO KG. We use this process for the integration of Basagri, a database containing information on agricultural chemical products and their uses with plants distributed by the Lexagri company.<fnref n="9" /> The database is updated daily, but it is not freely available. In section 4.1, we present the whole process and how

<footnote n="6">6 https://chowlk.linkeddata.es/</footnote>

<footnote n="7">7 https://www.ontotext.com/products/ontotext-refine/</footnote>

<footnote n="8">8 https://gitlab.com/serre-des-savoirs/c3po/-/blob/master/C3PO-Competencyagyestd.xlsx</footnote>

<footnote n="9">9 https://www.lexagri.com/products/basagri/</footnote>
<figure><img src="image_73.png" /><figcaption>FIGURE 2
Representation of plants and their families.</figcaption></figure>

we used the E-PHY ontology (Bouazzouni and Jonquet, 2021) to represent the Basagri chemical data.

### 3.3.3. RDF linking

Semantic Web resources exist in agriculture, as presented in the related work. We manually linked our knowledge graph with other KGs such as TAXREF-LD and FCU to enhance the representation of plants with botanical and usage information (Darnala et al., 2022). The process involved DEs to produce and validate the set of links.

### 3.3.4. Application data

As the ontology is designed to be used for crop planning, part of the data are from user input from applications (Elzeard, La Serre des Savoirs and la Pépinière).

The final C3PO KG is built by importing all the previously revised data in the same RDF database, i.e., GraphDB in our case.<fnref n="10" /> GraphDB was chosen to meet our requirements with respect to ease in dealing with RDF data directly, write and test SPARQL queries, create named graphs and reasoning features.

## 3.4. Ontology and knowledge graph maintenance

The ontology is updated each time a new use-case appears and requires an ontology development. Updates are also done to fix bugs remaining in the ontology or the knowledge graph. Moreover,

as the ontology is published on GitLab, the submission of regarding bugs or improvements is possible. To update the knowledge graph, we implemented pipelines to produce RDF graphs from CSV files to enable continuous data development by DEs and improvement of the knowledge graph without an extensive need of an OE. To prevent direct insertion of triples in the knowledge graph and possible errors, we promote building of a new named graph for static data, i.e., in our case plant and input knowledge, each time a new batch of data is imported in the current knowledge graph. However, we could have problems of changing ids between two versions of the knowledge graph, so a backup of each version of the knowledge graph is required.

Regarding user data present in the knowledge graph, we use Prov-O to track updates and provenance. Each update lists the modified instances, the user involved, and the time of the update. The update description is saved in JSON format as a value of a data property in the knowledge graph. Tracking updates allows us to know who performs the update and recover from previous timestamps if needed.

# 4. The C3PO ontology and knowledge graph

In this section, we describe the current version of the C3PO and specific development strategies for both the ontology source file and the knowledge graph. We divided the ontology in several modules, each representing a specific sub-domain of interest for vegetable agriculture planning. We chose such modular representation as we identified/extracted from the conceptualization step several sub-domains, which could work independently but still strongly related. Furthermore, modules helped during the conceptualization

<footnote n="10">10 https://graphdb.ontotext.com/</footnote>
<table><caption>TABLE 2 C3PO modules and their URIs and namespaces.</caption><thead><tr><th>Module name</th><th>Module URI</th><th>Module namespace</th></tr></thead><tbody><tr><td colspan="3"><strong>Support modules</strong></td></tr><tr><td>Time</td><td>http://www.elzeard.co/ontologies/c3po/time</td><td>c3potime</td></tr><tr><td>Vocabulary</td><td>http://www.elzeard.co/ontologies/c3po/vocabulary</td><td>c3povocab</td></tr><tr><td>Parameter</td><td>http://www.elzeard.co/ontologies/c3po/parameter</td><td>c3poparam</td></tr><tr><td colspan="3"><strong>Domain modules</strong></td></tr><tr><td>Plant</td><td>http://www.elzeard.co/ontologies/c3po/plant</td><td>c3poplant</td></tr><tr><td>Plot</td><td>http://www.elzeard.co/ontologies/c3po/plot</td><td>c3poplot</td></tr><tr><td>Crop Management</td><td>http://www.elzeard.co/ontologies/c3po/cropManagement</td><td>c3pocm</td></tr><tr><td>Admin</td><td>http://www.elzeard.co/ontologies/c3po/admin</td><td>c3poadmin</td></tr><tr><td>Supply</td><td>http://www.elzeard.co/ontologies/c3po/supply</td><td>c3posupply</td></tr><tr><td>Sale</td><td>http://www.elzeard.co/ontologies/c3po/sale</td><td>c3posale</td></tr></tbody></table>

to divide the work and focus on sub-domains instead of the whole area. As an example, we divided the representation of plant knowledge and plot organization into two distinct modules. We divided the modules between support modules and domain modules. Support modules are used in almost all the domain modules to improve reusability between the modules. Domain modules are representing sub-domains of the C3PO domain. Table 2 presents the module, their namespace, and the color used in the Figures of Section 4. We published the competency questions for Plant, CropManagement and Plot module on the documentation, and the SPARQL queries.<sup>11,12</sup>

# 4.1. Ontology modules and knowledge graph

## 4.1.1. Support modules

### Time module

The Time module extends the Time Ontology to be able to represent `c3potime:RelativePropertInterval` composed of `time:RelativeInstant`, as shown in Figure 3, while the respective representations of time instants are not placed in a specific year. This is important for representing cultivation dates for any crop that might occur in a different year. An example of a `c3potime:RelativePropertInterval` could be the date interval between two `time:RelativeInstant`: the 16th of September and the following 2nd of February. This

allows to create patterns reusable every year. Moreover, it allows sharing of information that could be reused at any time without reference of the year. `time:RelativeInstant` has the data property `c3potime:inRDate` with a type `c3potime:rdate` formatting `_Y_M_F_W_D` where `_` represent a number, "Y" a year, "M" a month, "F" a fortnight, "W" a week, and "D" a day. The previous example should be represented as "0Y9M16D" for the 16th of September and "1Y2M2D" for the 2nd of February.

### Vocabulary module

The Vocabulary module corresponds to several closed lists of qualitative values represented as SKOS thesaurus. The SKOS thesaurus has several top concepts dedicated to a specific list: units, climate characteristics, culture modes, etc. Any skos:Concept instances are linked to instances from other C3PO modules using specific object properties. For example, the instance of the class `c3poparam:Parameter` is linked to any narrower concepts of `c3povoc:Unit` using the property `c3poparam:hasParameterUnit`. Those skos:Concept instances representing unit are partially aligned with QUDT (Hodgson et al., 2014) instances, i.e., a knowledge graph representing the various standard quantity kind and unit.

### Parameter module

The Parameter module corresponds to a representation of numeric parameters such as weight and volume. The module is composed of a class `c3poparam:Parameter` with a measured, minimun, and maximum value. The class is specialized for each measurement type. Each parameter class have a constraint to specify its unit, defined in the Vocabulary module.

For example, the class `c3poparam:Yield` will store all the yield measurement with the associated unit `c3povoc:YieldUnit` and is defined as follows:

c3poparam:Yield rdf:type owl:Class ;
    rdfs:subClassOf c3poparam:Parameter ,
    [ rdf:type owl:Restriction ;
        owl:onProperty c3poparam:hasParameterUnit ;
        owl:allValuesFrom [owl:intersection
            (skos:Concept
                [ rdf:type owl:Restriction ;
                    owl:onProperty skos:broader ;
                    owl:hasValue c3povoc:YieldUnit]) ;
            rdf:type owl:Class]
        ] .

## 4.1.2. Domain modules

### Plant module

The Plant module represents cultivated plant taxonomy from a farmers' viewpoints. Plants are described by the class `c3poplant:CultivatedPlant` with characteristics such as crop seasons, watering needs, or nutritional requirements. Plants are hierarchically organized under a taxonomy representing as a SKOS thesaurus. This taxonomy has different levels as follows: plant family, cultivated plant, varietal type, and cultivar, as shown in Figure 4. Varietal type is an intermediate level between cultivated plant and cultivar that represents some physical characteristics of crops that farmers refer to while defining the market outlets. This level does not belong to a botanical scientific taxonomy. Again using the onion example of the previous section, the botanical and usage families of the onion are, respectively, Amaryllidaceae

<footnote n="11">11 https://www.elzeard.co/ontologies/c3po/</footnote>

<footnote n="12">12 https://gitlab.com/serre-des-savoirs/c3po</footnote>
<figure><img src="image_74.png" /><figcaption>FIGURE 3
Time module representation.</figcaption></figure>

and bulb vegetables. We defined several varietal types such as yellow onions and red onions. Fiamma is an example of a red onion cultivar. The module also represents crop succession and association information. Figure 4 presents the main classes of the Plant module instantiated with the onion example. A Plant knowledge graph was built under the Plant module with the help of agronomists and is linked with FCU and TAXREF-LD. In future, the module will be extended to improve the representation of trees, which will be useful with regard to fruit crops.

## Plot module

The Plot module represents the spatial organization of plots on the farms. It contains the representation of a `c3poplot:ProductionCell`, which is an occupation of legally registered land with an address and an id. In addition, the farmer-driven spatial organization is represented with the possibility of creating `c3poplot:CultivablePlot` and `c3poplot:CultivableBed` within the plots, which may be useful for diversified vegetable crop farmers growing multiple crops on the same plot or the same row. Irrigation systems and landscape elements such as meadows present on farms are also represented. Figure 5 presents the main Plot module classes.

In future, the module will be extended to improve the representation of irrigation systems and landscape elements by taking into account all the specific features of the lands, especially in areas of ecological interest that are required to the farm to get different certifications.

## CropManagement module

The CropManagement module represents technical itineraries and farming processes for task planning and recording. As noted previously, we studied the three ValueFlows representation layers: plan specification, plan and plan execution. Plan specification is built by instantiating the `c3pocm:CropItinerary` class, representing generic technical itineraries created by farmers and agronomic experts. A `c3pocm:CropItinerary` consist of a set of tasks. One generic task is represented as a `c3pocm:TechnicalOperation`. These generic technical itineraries are linked to a `c3poplant:CultivatedPlant` from the plant module and have parameters such as season and soil type. `c3pocm:TechnicalOperation` are farming processes such as planting or harvesting, which are described with `c3potime:RelativeProperInterval` to give a

time range where the task could be applied. Plan is built by instantiating the `c3pocm:ProductionProcess` class for each crop. `c3pocm:ProductionProcess` is composed of a set of `c3pocm:OperationalTask` planned in the farmer's calendar. Farmers often rely on series principles, i.e., they grow the same type of crops with (more or less) the same set of tasks, but on a different plot and at a different time to achieve a continuous flow of crop production. As shown in Figure 6, we integrate the `c3pocm:Series` classes to represent this principle. Plan execution is built by instantiating the `c3pocm:Activity` class. An `c3pocm:Activity` represents one task carried out. `c3pocm:OperationalTask` and `c3pocm:Activity` are tasks that happened on crops and land, so we add a property named `c3pocm:concernsPosition` to link a task and the instance of the `c3poplot:LandUse` class from Plot module. As an example to present the difference between `c3pocm:OperationalTask` and `c3pocm:Activity`, a possible instanciation of `c3pocm:OperationalTask` could be a harvest happened between 4 July 2022 and 6 July 2022, with a certain estimated yield. During the execution, an instantiation of `c3pocm:Activity` is made for each day (4 July 2022, 5 July 2022, and 6 July 2022), with the real yield obtained per day. The three types of task `c3pocm:TechnicalOperation`, `c3pocm:OperationalTask` and `c3pocm:Activity` are linked to instance of `c3pocm:FarmingPractice`. `c3pocm:FarmingPractice` is specialized in many sub-classes, representing various farming tasks such as harvesting or planting. Each sub-class has its own parameters. The three layers help farmers to analyze their production and decide what should be changed the following year to improve their productivity. Figure 6 presents the main classes of the CropManagement module.

The CropManagement knowledge graph built under the C3PO CropManagement module has been partially populated with the help of agronomists, especially regarding the integration of instances of `c3pocm:CropItinerary`.

In future, the module will be extended to link the harvested crops with the Sales module.

## Admin module

The Admin module represents agents and organizations and their administrative information as users of the applications. It
<figure><img src="image_75.png" /><figcaption>FIGURE 4
Plant module representation.</figcaption></figure>

relies on existing standard ontology modules or vocabularies, such as FOAF (Graves et al., 2007) for the representation of people and organizations and Event Ontology (Raimond and Abdallah, 2007) for events. We created subclasses from these resources to integrate `c3poadmin:Farm`, `c3poadmin:Producer` and `c3poadmin:Cooperative` representations.

## Supply module

The Supply module represents agricultural input and equipment that farmers could use. The module allows users to represent input as `ephy:Intrant` and their usages as `ephy:Usage`. A `ephy:Usage` is the combination of a product, plant, or family, a targeted pest and the application method of the product. This combination defines properties such as the maximum dosage allowed or the number of possible applications. Figure 7 presents the main classes of the CropManagement module. The Supply module currently extends the E-PHY ontology to integrate Basagri data. The E-PHY ontology is an ontology produced to represent the French E-Phy catalog of plant protection products. Basagri is a private dataset containing information regarding regulatory data on agricultural inputs in France. The dataset is proposed as a set of files in CSV format. We implemented a pipeline to transform the CSV format into an RDF knowledge graph under the E-PHY

ontology as it fulfilled our requirements regarding agricultural inputs. We extracted different information such as the dosage authorized for a product regarding a plant or the number of days required before the farmers return to the plot or harvest, from the Basagri files. We, then, built URIs for input using their marketing authorization (AMM, a code delivered France for authorized chemical products) as in the E-PHY proposition. We created vocabularies using SKOS thesaurus representations for closed lists such as the type of product function (insecticide, herbicide, etc.). We also compared the list of crops of Basagri and C3PO to find similarities based on labels and connect `ephy:Usage` to instance of `c3poplant:CultivatedPlant` or `c3poplant:CultivatedFamily` from the plant module. We used the Levenshtein distance (Levenshtein et al., 1966) to deal with slight differences such as singular/plural names. In future, the module will be extended to improve the representation of farming equipment. Moreover, the alignment of Basagri and C3PO crops will also be improved to enhance the number of similarities between the two databases.

## Sale module

The Sale module organizes the stocks and the product delivery. The module is still under construction and will be updated and combined with the DataFoodConsortium ontology which
<figure><img src="image_76.png" /><figcaption>FIGURE 5
Plot module representation.</figcaption></figure>

represents the supply chain and delivery process in the food distribution system.<fnref n="13" />

## 4.2. Statistics and availability

The C3PO knowledge graph currently consists of 4,647 axioms, 236 classes, 211 object properties, 71 data properties, and 270 individuals mostly contained in the Vocabulary module. Moreover, the KG is currently composed of 8,402,495 triples, of which 3,025,790 are explicit and 5,376,790 are implicit. All of the ontologies are available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Figure 8 presents different components of C3PO's knowledge graph (module, inter-module relations, and data source).

A sub-part of the knowledge graph containing the Plant module and the CropManagement module is available on GitLab under an Attribution-ShareAlike 4.0 International license (CC BY-SA 4.0). The other parts are also not public because they concern users' data or are licensed, e.g., Basagri.

According to the MIRO guidelines (Matentzoglu et al., 2018), hereafter in Table 3, we present the current version (1.0) of C3PO.

Only the MIRO “basic” guidelines are reported here, but we have incorporated as much metadata as possible in the C3PO OWL source file, according to the MOD specifications (Dutta et al., 2015).

C3PO and its modules are uploaded on AgroPortal. We published each module as a view of the ontology in AgroPortal project. We edited some metadata on the global level: C3PO. We improve the FAIR score of the ontology by following the AgroPortal guidelines during the Metadata AgroHackathon in August 2022.<fnref n="14" />

# 5. The ontology and knowledge graph in use

We are developing the ontology and knowledge graphs in the context of multiple application development for knowledge sharing and crop planning. These applications are built by Elzeard and developed in the framework of the MESCLUN DURAB, PACON (Morel et al., 2023), and D2KAB (Aubin et al., 2019) research projects.<fnref n="15" /> These applications help to assess the ontology

<footnote n="13">13 https://www.datafoodconsortium.org/en/</footnote>

<footnote n="14">14 https://agrohackathon2022.workshop.inrae.fr/</footnote>

<footnote n="15">15 https://www.picleg.fr/Projets/Les-projets-en-cours/MESCLUN-DURAB</footnote>
<figure><img src="image_77.png" /><figcaption>FIGURE 6
CropManagement module representation.</figcaption></figure>

<figure><img src="image_78.png" /><figcaption>FIGURE 7
Supply module representation.</figcaption></figure>
<figure><img src="image_79.png" /><figcaption>TABLE 3 C3PO information following basics MIRO guidelines.</figcaption></figure>

<table><thead><tr><td>Basics MIRO guidelines</td><td>C3PO information</td></tr></thead><tbody><tr><td>Admin</td><td>http://www.elzeard.co/ontologies/c3po/admin</td></tr><tr><td>A.1 Ontology name</td><td>Crop planning and production process ontology</td></tr><tr><td>A.2 Ontology owner</td><td><ul><li>Baptiste Darnala (Elzeard)</li><li>Florence Amardeilh (Elzeard)</li></ul></td></tr><tr><td>A.3 Ontology license</td><td>Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)</td></tr><tr><td>A.4 Ontology URL</td><td>http://www.elzeard.co/ontologies/c3po</td></tr><tr><td>A.5 Ontology repository</td><td>https://gitlab.com/serre-des-savoirs/c3po<br>https://agroportal.lirmm.fr/ontologies/C3PO</td></tr></tbody></table>

consistency regarding the domain and the quality of the data integrated in the knowledge graph.

## 5.1. "Serre des Savoirs"

The so called "Serre des Savoirs" web portal is under construction to access open data in the C3PO KG related to cultivated plants and technical itineraries. The knowledge graph content is described with the Plant and CropManagement modules.

For the CropManagement module, the application will query only the CropItinerary and TechnicalOperation instances. The web portal will directly query the knowledge graph and make it accessible for non-Semantic Web experts such as farmers and agronomists. The development of the application has impacted the development of the Plant and CropManagement module and the needs in terms of information required in the application.

A screenshot of the descriptive page of the tomato is presented in Figure 9. The information displayed provides a general description of this vegetable including the species scientific name, the cultivating families, and varietal types described in Section 3.2. Moreover, information on the cultivation context is provided, such as the irrigation and nutrition needs of the plant. Several competency questions of plant module are used to build this page presented as follows: <fnref n="16" />

1. What is the plant's botanical species?

2. What is the plant's botanical family?

3. What is the scientific name of the botanical taxon (species or family)?

4. What is the plant's usage family?

5. What are the varietal types of a plant?

6. How much does the seed of a plant weigh (seeds per gram)?

7. How much water does a plant need?

<footnote n="16">16 https://www.elzeard.co/ontologies/c3po/EN/EN_Plant.html</footnote>
8. How deep does a plant's root system go?

9. How much nutrients does a plant need?

10. How long does it take for a plant to return to the plot?

To illustrate the querying of the C3PO KG, the SPARQL queries
corresponding to CQ2 and CQ10 are presented as follows:

```sql
PREFIX c3poplant: <http://www.elzeard.co/ontologies/c3po/
plant#>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>

select ?CultivatedPlant ?BotanicalFamily where {
    ?CultivatedPlant a c3poplant:CultivatedPlant .
    ?BotanicalFamily a c3poplant:BotanicalFamily .
    ?CultivatedPlant skos:broader+
    ?BotanicalFamily .}

PREFIX c3poplant: <http://www.elzeard.co/ontologies/c3po/
plant#>
PREFIX c3poparam: <http://www.elzeard.co/ontologies/c3po/
parameter#>

select ?CultivatedPlant ?PlotReturnTime
    ?parameterValue ?minValue ?maxValue
    ?ParameterUnit where {
        ?CultivatedPlant c3poplant:hasPlotReturnTime
        ?PlotReturnTime .
        Optional (?PlotReturnTime
c3poparam:parameterValue ?parameterValue .)
        Optional (?PlotReturnTime c3poparam:minValue
        ?minValue .)
        Optional (?PlotReturnTime c3poparam:maxValue
        ?maxValue .)
        ?PlotReturnTime c3poparam:hasParameterUnit
        ?ParameterUnit .}
```

## 5.2. Decision support applications

In addition, the C3PO KG was used to build two applications for farmers: *Elzeard* and *La Pépinière*. *Elzeard* is a web application where farmers describe their farms with their locations and plot organization. The farmers can, then, build the plan for their crops and related tasks, organize their farm workers' schedules, and choose the inputs to use. C3PO is used as the data model for the web application. The knowledge graph built with plant, technical itineraries, and input knowledge is queried to help farmers access decision-support information. C3PO is used in *Elzeard*. Figure 10 presents a screenshot of a technical itinerary in *Elzeard*. The list of competency questions involved in building this webpage come from the CropManagement module:<fnref n="17" />

1. How long does it take for a plant to emerge in this technical itinerary?

2. How long does a plant grow in this technical itinerary?

3. How long does it take to harvest a plant in this technical itinerary?

4. What is the shelf life of a plant in this technical itinerary?

5. What is the estimated overall workload for this technical itinerary?

6. What are the tasks involved in this technical itinerary?

7. When is the best time to plant in this technical itinerary?

8. Over what period will I be able to spread out the harvests for this technical itinerary?

9. Which task must be carried out before or after another task in this technical itinerary?

10. What is the forecast yield for this technical itinerary?

11. Which varieties are recommended for this technical itinerary?

12. What is the expected yield for this technical itinerary?

To illustrate the querying of the C3PO KG, the SPARQL query
corresponding to CQ7 is presented as follows:

```sql
PREFIX c3pocm: <http://www.elzeard.co/ontologies/c3po/
cropManagement#>
PREFIX c3potime: <http://www.elzeard.co/ontologies/c3po/
time#>
select ?CropItinerary ?FarmingPractice
    ?beginningDate ?endingDate
    where {
        ?CropItinerary a c3pocm:CropItinerary .
        ?CropItinerary c3pocm:hasOperationMember
        ?Operation .
        ?Operation c3pocm:implies ?FarmingPractice .
        ?FarmingPractice a c3pocm:PlantingProcess.
    }
    Optional {
        ?Operation c3pocm:hasRelativePeriod ?Period .
        ?Period c3potime:hasBeginning ?Beginning .
        ?Beginning c3potime:inRDate ?beginningDate .
        ?Period c3potime:hasEnding ?Ending .
        ?Ending c3potime:inRDate ?endingDate .
    }
}
```

Figure 11 presents a screenshot of a planning made by a farmer, both made in the application *Elzeard*. Figure 10 is composed of several pieces of information such as the cropping period and the cultivating tasks. Figure 11 gives an overview of all the crops of a farmer in a period and the commercial needs in terms of harvested crop. The list of competency questions involved in building this webpage come from the CropManagement module:<fnref n="18" />

1. How many series have I planned for this crop?

2. What is the period of each series that I have planned for this crop?

3. Which variety is associated with this series?

4. What is the surface area associated with this series?

5. What are the planting distances between my seedlings or plants for this series?

*La Pépinière* is an application under development to help beginner farmers design their farms and future productions. The application has an educative feature, whereas *Elzeard* is production-oriented purpose. *La Pépinière* uses the Plant, CropManagement, and Plot modules as data models and has access to the same knowledge present in *La Serre des Savoirs* to help farmers. Figure 12 presents a screenshot of the *La Pépinière* application. The competency questions used are the same as presented in Figure 11.

<footnote n="17">17 https://www.elzeard.co/ontologies/c3po/EN/EN_CM.html</footnote>

<footnote n="18">18 https://www.elzeard.co/ontologies/c3po/EN/EN_CM.html</footnote>
<figure><img src="image_80.png" /><figcaption>FIGURE 9
Descriptive page of the tomato in *La Serre des Savoirs*.</figcaption></figure>

## 6. Discussion and difficulties

We encountered several difficulties during C3PO development process. Concerning C3PO development, the diversified vegetable agricultural domains and the multiple viewpoints were complex to represent in a single ontology. The division into modules eases the development by allowing to work in multiple subdomains independently. However, naming problems of classes shared between multiple modules could arise and should be checked when updating the ontology. In addition, this complexity required the involvement of multiple domain experts such as farmers, agronomists, taxonomists, and retailers to have a better vision of the domain and the needs the ontology should meet.

As illustrated, C3PO, even if not yet perfect, was developed primarily to serve multiple applications, and thus, a usable ontology had to be produced quickly. The Agile development methodology, which eases the release of several iterative ontology versions already usable by application developers, was suitable. However, new domain discoveries could impact previous development

choices and lead to refinement of the ontology impacting the knowledge graph structure. Moreover, our methodology helps us to keep track all steps of development process. The files (Chowlk schema, text description,...) produced during the specification and conceptualization steps are used to document C3PO.

Regarding cooperation with the development team and domain experts, we had to use tools that are easily understandable by non-Semantic Web users. The graphic notation proposed by Chowlk helped to produce schemes that would be understandable by different actors and directly convertible into OWL format, but this generated a more complex ontology engineering workflow, otherwise we would have simply used Protégé in group.

In the documentation writing process, we included definitions of classes and properties in shared documents to improve collaboration between DEs and OEs. It was important to provide understandable detailed definitions to enhance the model reusability.

During the C3PO conceptualization process, various difficulties were encountered according to the concerned module. For the
<figure><img src="image_81.png" /><figcaption>FIGURE 10
Technical itinerary of the beet in Elzeard.</figcaption></figure>

<figure><img src="image_82.png" /><figcaption>FIGURE 11
Crop planning of a farmer in Elzeard.</figcaption></figure>

Plant module, our aim was to create a module that represents a plant taxonomy with multiple viewpoints (botanical and agricultural), to have agricultural information on plants and ease

the link with different heterogeneous knowledge graphs. This led to the typing of instances as skos:Concept and owl:Class. SKOS offers the possibility of creating the plant organization and
<figure><img src="image_83.png" /><figcaption>FIGURE 12
Crop planning of a farmer in La Pépinière.</figcaption></figure>

links with other skos:Concept, while OWL helps to create classes and properties. For the Supply module, the transformation of an existing ontology (E-PHY) led to checking if the ontology was usable and the changes required to meet our needs. The most important changes were made when we opted to change the domain or the range of a property. In that case, we chose to create a new property. The main drawback of this method is the E-PHY ontology, and its properties are provided in French while C3PO is built in English, so the URIs of the module are not in the same languages. Linguistic uniformization could be applied in future.

During the C3PO KG building process, multiple and heterogeneous sources of data required us to build multiple data integration pipelines. For the Plant module, we had to test different data integration scenarios with domain experts. As previously noted, we ended by using tabular files. This method eases the integration of domain expert data but could lead to problems. As multiple spreadsheets are used, problems of misspelled URIs can occur and lead to missing connections in the graph. We overcame this problem by creating a list of SPARQL queries to check the consistency of the graph, find errors, and fix them before importing the data in the graph. We also had difficulty connecting our instances with TAXREF-LD as taxons are represented with owl:Class, and scientific names are represented by instances of skos:Concept. We decide to link our C3PO instances with skos:Concept using specific C3PO's properties. Notably, a link between a class and an instance is possible only with the property

rdf:type. Regarding the data imported from the applications, problems were encountered due to wrong data integration or failed knowledge graph updates. For instance, properties were duplicated instead of being renamed after an ontology update. Here, we used SPARQL queries to address these issues. We recommend to update the knowledge graph by exporting the data, applying the change, and importing in a new knowledge graph. Thus, we keep track of changes to be able to roll back. In this way, the knowledge graph is not updated directly.

About the reasoning aspect, we define some constraints on C3PO's classes to check the quality of users' input data (c3poparam:Parameter). However, we do not check the expert data extracted from reference sources (TAXREF-LD, FCU, etc.). Unfortunately, mistakes may happen on this source that will cause inconsistencies. Thus, we should apply reasoning and other checking processes on those part of the graph in future.

The knowledge graph is not fully opened. A subpart regarding information about plants and technical itineraries is accessible in GitHub and through a SPARQL endpoint. User information saved in the Plot, Admin and CropManagement modules remain private. Sharable information will be accessible through the “La Serre des Savoirs” Web Portal. This portal will be enriched with data already aggregated, and users will have the possibility to enrich “La Serre des Savoirs” directly.

Regarding the interoperability and reusability challenges, we applied different workflows. First, we linked a subpart of our plant
instances with other knowledge graphs (FCU and TAXREF-LD). However, we recreate the concepts, we aggregate a subpart of the knowledge such as labels, and we keep the alignment. We process as well to control the management of the terminology and to enrich it with multiple sources. In addition, we are using core domains ontologies such as FOAF, Prov-O, or Time ontology. Regarding future development to reuse or align with domain ontologies, we prefer to align our concepts instead of import external concepts. This choice is made regarding the context of industrial development, as we cannot ensure that external ontologies will be sustainable. Align instead of import offer the possibility to keep the control on C3PO. In addition to that, major concepts such as technical itineraries do not exist in other ontologies, which reduce the possibility of reusing this part of the graph. Finally, domain ontologies reused in C3PO are stored in AgroPortal repository and were found through this repository. Thus, ontologies not declared in the repository were not studied during our conceptualization step. However, we may miss some interesting ontologies such as PestOn (Medici et al., 2022), which means that we should update regularly our state-of-the-art research.

# 7. Conclusion and future work

Diversified vegetable farming is complex, and many parameters have to be taken into account for decision support. We built the Crop Planning and Production Ontology (C3PO) and its Knowledge Graph to help farmers in their choices. The ontology is divided into several modules to represent a specific part of the domain. The knowledge graph is created from heterogeneous data sources (other knowledge graphs, relational databases, or user/expert data). The C3PO KG is the backbone of three web applications and aims to give farmers access to information to support their planning and monitoring decisions. The open part of the knowledge graph brings novel aspect as no representation of technical itineraries exists for vegetables farmers. This knowledge has not been formalized yet and serve as a basis for reusability of common technical itineraries shared in different sources. Future studies will be continued to the referencing and sharing of technical itineraries to create a collaborative knowledge base through our web portal “La Serre des Savoirs” currently in development.

These applications—not all yet in production—already pre-validate C3PO as an “application ontology”, but future reuses will also validate C3PO as a “domain ontology”. The methodology presented in this study is based on LOT and SAMOD methodologies, and we highlighted how we implemented each process in an application development operation. Various domain expert partners were included in our approach to assess and identify the main concepts and properties: scientists (from the D2KAB, MESCLUN DURAB research project) and agricultural professionals (crop farmers, networks of agricultural advisors, and teachers). C3PO is available on GitLab as an open source project that can be reused and contributed to and published in AgroPortal to facilitate its discovery and reuse. In future studies, we will extend and improve the ontology to include equipment,

farm components (e.g., irrigation structures or meadows currently present in the ontology but need refinement), and pests and diseases. We will also improve the ability of the ontology to make inferences on the data based on agricultural knowledge. We will improve the interoperability of C3PO and create alignment with other semantic resources. Finally, we will extend the scope of the ontology and knowledge graph in order to be able to model other types of crop production, such as arboriculture or agroforestry.

# Data availability statement

TAXREF-LD: The version 15.2 of TAXREF-LD graph used for this study can be found in the AgroPortal repository https://agroportal.lirmm.fr/ontologies/TAXREF-LD. The github repository is https://github.com/frmichel/taxref-ld. The SPARQL EndPoint is https://taxref.mnhn.fr/sparql. FCU: The version 3.3 of the FCU thesaurus used for this study can be found in the AgroPortal repository: https://agroportal.lirmm.fr/ontologies/ CROPUSAGE. The gitlab repository is https://gitlab.irstea.fr/ copain/frenchcropusage. The SPARQL EndPoint is http://ontology. inrae.fr/frenchcropusage/sparql. C3PO KB: The version 1.0 of the C3PO KB can be found in the gitlab repository https://gitlab. com/serre-des-savoirs/c3po-kb. The associated ontology can be found on the AgroPortal repository https://agroportal.lirmm.fr/ ontologies/C3PO/?p=summary. The SPARQL EndPoint is https:// graph.elzeard.co/sparql.

# Author contributions

BD supervised the building of C3PO and its KG and used within applications and wrote the manuscript. BD and FA built the ontology, the knowledge graph, and implemented the process with domain experts. BD, FA, and CR wrote the definitions of the ontology. CJ and CR helped in the modeling of some modules of the ontology and provided general knowledge engineering expertise. All authors contributed to the manuscript, read, and approved the final version.

# Funding

The authors would like to acknowledge support from the National Office for Biodiversity with MesclunDurab grant and I-NOV grant. This study was also partially achieved with support from the *Data to Knowledge in Agronomy and Biodiversity* (D2KAB - www.d2kab.org) project that received funding from the French National Research Agency (ANR-18-CE23-0017) and the project Partages de Connaissances (PACON) of the transverse program MetaBio funded by INRAE. This work also received fund from Agence Nationale de la Recherche under the France 2030 program, reference ANR-16-CONV-0004.
## Acknowledgments

The authors would like to thank Dr. Kevin Morel (INRAE), Juliette Raphel (Elzeard), and Guillaume Turlier (Elzeard) for help as domain experts and all contributors from MesclunDurab and D2KAB projects for their constructive feedback.

## Conflict of interest

BD and FA were employed by Elzeard, Bordeaux.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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