EP4695745A1

EP4695745A1 - Method for handling agricultural product data

Info

Publication number: EP4695745A1
Application number: EP24719156.2A
Authority: EP
Inventors: Abhijeet Sharma
Original assignee: BASF SE
Current assignee: BASF SE
Priority date: 2023-04-14
Filing date: 2024-04-12
Publication date: 2026-02-18
Also published as: WO2024213729A1

Abstract

Disclosed is a computer-implemented method for handling agricultural product data, the method comprising, for each of a plurality of agricultural products, receiving, at a data processing system, real-time agricultural product data from an application running on an external device, the real-time agricultural product data associated with an agricultural product associated with a value chain, the data processing system initiating writing the real-time agricultural product data in a first blockchain, receiving, at the data processing system, additional agricultural product data associated with the agricultural product, the additional agricultural product data being obtained at a later time than the real-time agricultural product data, and the data processing system initiating storing final agricultural product data associated with the agricultural product in a final blockchain, the final agricultural product data being based on the real-time agricultural product data and the additional agricultural product data.

Description

METHOD FOR HANDLING AGRICULTURAL PRODUCT DATA

TECHNICAL FIELD

The present disclosure relates to a method for handling agricultural product data, a data processing system, a computer program product, a computer-readable medium, and a use.

TECHNICAL BACKGROUND

The present disclosure relates, in general terms, to a method for handling agricultural product data. Handling of agricultural product data, particularly spanning the value chain (also referred to as a product chain) associated with the agricultural product data, is quite complex. For example, an issue may be agricultural data’s variability over time and/or the diversity and/or complexity of value chains associated with agricultural product data.

However, handling of agricultural product data requires accuracy and accountability. A significant risk associated with agricultural product data is that current handling methods thereof are prone to tampering and/or inaccuracy.

There is a general need to improve handling of agricultural product data, particularly in the light of accommodating the special requirements associated with agricultural data as outlined above.

SUMMARY OF THE INVENTION

In one aspect, disclosed is a computer-implemented method for handling agricultural product data. The method comprises, for each of a plurality of agricultural products: receiving, at a data processing system, real-time agricultural product data from an application running on an external device, the real-time agricultural product data associated with an agricultural product associated with a value chain, the data processing system initiating writing the real-time agricultural product data in a first blockchain, receiving, at the data processing system, additional agricultural product data associated with the agricultural product, the additional agricultural product data being obtained at a later time than the real-time agricultural product data, and the data processing system initiating storing final agricultural product data associated with the agricultural product in a final blockchain, the final agricultural product data being based on the real-time agricultural product data and the additional agricultural product data.

In one aspect, the computer implemented method is used in a distributed (computing) system comprising at least two blockchains, in particular at least three blockchains, wherein each blockchain includes multiple member nodes.

In another aspect, a data processing system is disclosed, the data processing system (also referred to as computing system) configured to carry out the method of the present disclosure, in particular comprising one or more processing units configured to carry out the method of the present disclosure.

In another aspect, a computer program product is provided comprising instructions which, when the program is executed by a data processing system, cause the data processing system to carry out the method of the present disclosure.

In another aspect, a computer-readable medium is provided comprising instructions which, when executed by a data processing system, cause the data processing system to carry out the method of the present disclosure.

In another aspect, a use of any of the blockchains employed in the method, particularly the first, second, and/or final blockchain, for auditing and/or use of the final blockchain for sharing final agricultural product data with third parties associated with the value chain is provided.

The data processing system according to the present disclosure may comprise one or more computing nodes and one or more computer-readable media having thereon computerexecutable instructions configured to, when executed by the one or more computing nodes, cause the data processing system to perform the steps of a method according to the present disclosure.

As will be recognized from the above, the present invention allows for leveraging the trustworthiness of real-time data being written (immutably) into a blockchain and at the same time accommodating the needs of the value chain associated an agricultural product, which require that there is a possibility to correct or update originally provided data at a later date.

Real-time data may not be complete or entirely correct when being written into the first blockchain. As an example, during harvesting of an agricultural product, harvesting data may be fed, in real-time, into the first blockchain. The harvesting data may be provided by an application running on a mobile device associated with a harvester and/or a device incorporated in a machine being part of the harvesting process. The harvesting data may, example, comprise weight or volume data representative of the weight or volume of the harvested agricultural product data. Such data may be based on sensor data, e.g., from scales measuring a weight of harvested agricultural product, for example when the agricultural product is collected for storage. Such harvesting data might be seen as representative of the harvested amount of agricultural product and thus could be seen as the amount of agricultural product that will be introduced into the value chain.

It is advantageous to keep this data for later use in an immutable manner, which the invention proposed to achieve by writing the real-time data into a blockchain. However, at some later point it may turn out that the data was incomplete or flawed. In such a case, a correction of the data would be justified. For example, it may turn out that additional product was harvested or that the weighing process was flawed or that the initially harvested amount cannot be introduced into the value chain, e.g. for quality reasons.

Apart from this, real-time data as received from an application may comprise surplus data that should or need not be made available to third parties associated with the value chain, for example exact GPS coordinates or data revealing identities, and/or may be in a format that is incompatible with other data formats. When storing data at a later point in time, said data may be based on the real-time data and possibly based on corrected or additional data, the data having been consolidated, e.g., by discarding surplus data, by combining data points (e.g., by summing weights of agricultural product), and/or by bringing the data into compatible formats.

The invention suggests storing agricultural product data from a defined later point in time in a different blockchain than the first blockchain. This blockchain may be seen having a later moment of truth. Moreover, it can be seen as having a higher level of truth, as some time has passed during which changes (by addition of data or correction of data) could have been made if necessary.

The additional blockchain, thus, improves the overall result, as it ensures that data having a high level of truth is provided and at the same time allows for reducing the risk of data tampering. This may be done with two blockchains, the first and the final blockchain. As will be seen below, additional blockchains between the first and the final blockchain may be provided, providing stepwise increase in level of truth and/or data consolidation.

Particularly, the handling of the method is less prone to tampering of data and still provides good accuracy even in the light of the difficulties associated with agricultural product data, particularly the need to retroactively make changes to the data.

It is noted that the time that lies between agricultural product data to be written into the first block chain and agricultural product data written into further blockchains may be determined based on the expected harvesting cycle of the agricultural product and/or the respective expected timespans for processing steps associated with the agricultural product. In another aspect, the method further comprises making only agricultural product data in the final blockchain fully accessible for third parties associated with the value chain.

In other words, agricultural product data in the first and any potential other blockchain associated with the value chain, may not be fully accessible for third parties associated with the value chain, e.g., only be accessible for selected, particularly authorized third parties. Thus, data that have a low level of truth and/or low consolidation may be protected from access by other value chain participants or unauthorized third parties, while remaining accessible for authorized parties, e.g., for an audit. The data may be accessed from the final blockchain or another blockchain to which data from the final blockchain was transferred, e.g., referred to as an external blockchain.

In yet another aspect, the method further comprises initiating writing updated agricultural product data in a second blockchain, the updated agricultural product data based is at least in part on agricultural product data associated with the agricultural product and obtained at a later time than the real-time agricultural product data and at an earlier time than the additional agricultural product data.

Thus, at least three blockchains may be employed having mutually different moments of truth and/or levels of truth and/or levels of data consolidation.

The second blockchain may have a moment of truth that is later than the moment of truth of the first blockchain. Alternatively or in addition, it may have a level of truth that is higher than the level of truth than the first blockchain. Additional further blockchains are conceivable, e.g., with increasing levels of truth and/or later moments of . The second blockchain and any further blockchains may have a moment of truth that is earlier than the moment of truth of the final blockchain. Alternatively or in addition, the first blockchain and any further blockchains may have a level of truth that is lower than the level of truth than the final blockchain. Different blockchains associated with the value chain may also have different levels of data consolidation. In particular, data consolidation may be carried out on data prior to writing data into the second block chain and/or prior to writing data into the final blockchain.

The method may entail that there are exactly three (subsequent) blockchains, each associated with the value chain, the first, the second, and the final blockchain, particularly where the agricultural product is cotton. Three blockchains with different moments of truth are be particularly suitable in the context of the value chain of agricultural products, particularly in the context of a cotton-related value chain.

In yet another aspect, the additional agricultural product data are second additional agricultural product data, and the method further comprises, after receiving the real-time agricultural product data and prior to receiving the second additional agricultural product data, receiving, at the data processing system, first additional agricultural product data associated with the agricultural product, in particular the first additional agricultural product data being obtained at a later time than the real-time agricultural product data and at an earlier time than the second additional agricultural product data. In this aspect, the method comprises the data processing system initiating writing updated agricultural product data associated with the agricultural product into a second blockchain.

In this aspect, the updated agricultural product data are based at least on the real-time agricultural product data and the first additional agricultural product data. Moreover, in this aspect, the final agricultural product data are based at least on the real-time agricultural product data, the first additional agricultural product data, and the second additional agricultural product data.

In the present disclosure, the final agricultural product data being based on the real-time agricultural product data may entail that the final agricultural product data are determined directly from said real-time agricultural product data and/or is determined from data derived from the real-time agricultural product data, e.g., converted and/or consolidated data, particularly obtained from a preceding blockchain, e.g., the second blockchain. Similarly, the final agricultural product data being based on the first additional agricultural product data may entail that the final agricultural product data are determined directly from said first additional agricultural product data and/or is determined from data derived from first additional agricultural product data, e.g., converted and/or consolidated data, particularly as obtained from a preceding blockchain, e.g., the second blockchain. In other words, being “based on data”, in the present disclosure, is to be understood as being derived directly or indirectly from said data. This similarly applies for updated agricultural product data in the second blockchain or any other additional blockchain.

In other words, any updated agricultural product data and/or the final agricultural product data may take into account the previously obtained data, e.g., from the preceding blockchains, in addition to newly received data.

In yet another aspect, the final agricultural product data are based on the additional agricultural product data and/or the updated agricultural product data are based on the first additional agricultural product data.

In yet another aspect, the final agricultural product data comprise consolidated data obtained at least from the (second) additional agricultural product data and at least one of: the real-time agricultural product data, first additional agricultural product data, and updated agricultural product data.

The final agricultural product data may be obtained by one or more stages of consolidation. As an example, the final agricultural product data may be obtained by only one stage of consolidation that entails consolidating a dataset comprising at least part of the real-time agricultural product data and at least part of the (second) additional agricultural product data or entails consolidating a dataset comprising at least part of the real-time agricultural product data and/or at least part of the first additional agricultural product data and/or at least part of the updated agricultural product data and or at least part of the (second) additional agricultural product data.

Alternatively, the final agricultural product data may be obtained by at least two stages of consolidation, wherein a first stage of consolidation consolidates real-time product data and optionally first additional agricultural product data, for example thereby obtaining the updated product data, and wherein a second stage of consolidation consolidates the (second) additional agricultural product data with the data obtained by the first stage of consolidation. Additional stages are conceivable.

In other words, consolidation of agricultural product data may be carried out between writing into a blockchain and a subsequent blockchain, either between each pair of subsequent blockchains or between only some of the pairs of subsequent blockchains.

In an aspect, the updated agricultural product data comprise consolidated data obtained at least from the first additional agricultural product data and the real-time agricultural product data.

In an aspect, the updated agricultural product data comprise first up-to-date agricultural product data obtained by replacing and/or correcting the real-time agricultural product data based on the first additional agricultural product data. Alternatively or in addition, the updated agricultural product data comprise first consolidated data obtained by consolidating a dataset comprising the real-time agricultural product data and/or the first additional agricultural product data.

That is, in order to obtain first up-to-date agricultural product data, real-time agricultural product data may be modified to obtain first up-to-date agricultural product data by correcting parts of the real-time agricultural product data based on the first additional agricultural product data, which may, for example, comprise corrected and/or updated values, e.g. amounts of agricultural product. Alternatively, in order to obtain first up-to-date agricultural product data, part of or the entire real-time agricultural product data may be replaced based on the first agricultural product data.

For example, the first additional agricultural product data may comprise the full set of the realtime agricultural product data with updates and/or corrections applied or a selected subset of the real-time agricultural product data with updates and/or corrections applied, and the real-time agricultural product data may be replaced by the first additional agricultural product data. The selected subset of the real-time agricultural product data may be selected so as to only maintain data that require being carried over to the next blockchain. For example, where the real-time agricultural product data comprised an overhead of information, such an overhead may be removed in this manner.

As an example, the real-time agricultural product data may be consolidated with the first additional agricultural product data and the first up-to-date agricultural product data may comprise the consolidated data. The up-to-date agricultural product data, in this case, may not comprise the real-time agricultural product data as such, in which case the real-time agricultural product data has been replaced.

In an aspect, the final agricultural product data comprise second up-to-date agricultural product data obtained by replacing and/or correcting the real-time agricultural product data and/or first additional agricultural product data based on the (second) additional agricultural product data.

That is, in order to obtain second up-to-date agricultural product data, real-time agricultural product data and/or first additional agricultural product data may be modified to obtain second up-to-date agricultural product data by correcting parts of the real-time agricultural product data and/or first additional agricultural product data based on the (second) additional agricultural product data, which may, for example, comprise corrected and/or updated values, e.g. amounts of agricultural product. Alternatively, in order to obtain second up-to-date agricultural product data, part of or the entire real-time agricultural product data and/or first additional agricultural product data may be replaced based on the (second) agricultural product data.

For example, the second additional agricultural product data may comprise the full set of the real-time agricultural product data and/or first additional agricultural product data with updates and/or corrections applied or a selected subset of the real-time agricultural product data and/or first additional agricultural product data with updates and/or corrections applied, and the realtime agricultural product data and/or first additional agricultural product data may be replaced by the (second) additional agricultural product data. The selected subset of the real-time agricultural product data and/or first additional agricultural product data may be selected so as to only maintain data that require being carried over to the next blockchain. For example, where the real-time agricultural product data and/or first additional agricultural product data comprised an overhead of information, such an overhead may be removed in this manner.

As an example, the real-time agricultural product data and/or first additional agricultural product data may be consolidated with the (second) additional agricultural product data and the second up-to-date agricultural product data may comprise the consolidated data. The up-to-date agricultural product data, in this case, may not comprise the real-time agricultural product data and/or first additional agricultural product data as such, in which case the real-time agricultural product data and/or first additional agricultural product data has been replaced. In an aspect, the final agricultural product data comprise second consolidated data obtained by consolidating a dataset comprising the real-time agricultural product data and/or the first additional agricultural product data and/or the second additional agricultural product data.

In yet another aspect, the updated agricultural product data and/or the final agricultural product data are received from and/or confirmed by an authorized entity, for example a decentralized autonomous organization, DAO. Alternatively or in addition, the updated agricultural product data and/or the final agricultural product data are obtained based on a smart contract.

Thus, there may be a step of authorizing which data may be written into the blockchain(s), particularly for any data that are not real-time agricultural product data. The authorizing may be performed by a DAO or by means of a smart contract. If this is the case, the data may be obtained, by the data processing system, from the DAO or from a system running the smart contract.

In yet another aspect, the first blockchain, the final blockchain and optionally the second blockchain each represent a different moment of truth and/or a different level of truth.

In yet another aspect, any data of the first blockchain and/or second blockchain that is to be replaced with updated and/or final agricultural product data are nullified and/or labelled as less reliable.

In yet another aspect, agricultural product data comprise harvesting quantity data for the agricultural product and/or production related data for the agricultural product, e.g., intake quantity and/or output quantity associated with the production, and/or expected demand for the agricultural product and/or qualitative data associated with the agricultural product, e.g. representative of product origin, sustainability or the like.

In yet another aspect, real-time agricultural product data comprise harvesting data received from an application running on a device associated with a harvesting party and/or comprise production output data from a production factory. The harvesting party and the production factory are examples for value chain participants.

In yet another aspect, the agricultural product data are time-stamped data.

In yet another aspect, wherein the agricultural product comprises cotton and/or cottoncomprising product, for example yarn, fabric, garments.

In yet another aspect, participants of the value chain (also referred to as value chain participants) comprise cotton harvesters, cotton aggregators, ginning factories providing raw cotton, spinning mills providing yarn, providers of cotton-comprising products like fabric or garments, for example weaving, knitting, dyeing, finishing factories, wholesale traders of cotton or cotton-comprising products, retailers of cotton or cotton-comprising products. Any disclosure, embodiments, features, technical effects, and advantages described in the present disclosure in the context of the method also apply to the apparatus, the use, and the computer element of the present disclosure and vice versa. The benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples and vice versa.

TERMINOLOGY

In the following, terminology as used herein and/or the technical field of the present disclosure will be outlined by ways of definitions and/or examples. Where examples are given, it is to be understood that the present disclosure is not limited to said examples.

In the present disclosure, the term “agricultural product” is to be understood broadly and may, for example, comprise products directly obtained by agricultural harvesting and products containing or derived from products directly obtained by agricultural harvesting. In one exemplary embodiment, the agricultural product may comprise raw cotton and one or more products containing or derived from said raw cotton.

The agricultural product being associated with a value chain is to be understood broadly. It may refer to any agricultural product that is intended for being (further) processed by one or more participants of the value chain prior to being sold as a final product to consumers and/or agricultural product obtained by processing agricultural product directly obtained by agricultural harvesting.

As an example, agricultural product being associated with a value chain may comprise raw cotton designated for processing to obtain fabric, may comprise fabric obtained from said cotton, and/or may comprise clothes made from said fabric.

Multiple products, for example the aforementioned cotton, fabric and optionally clothes, may be associated with a shared or common value chain associated with the agricultural product. An agricultural product may also be associated with multiple value chains. For example, fibers made from cotton may be part of a value chain associated with clothing made from said fibers and may also be part of a value chain associated with packaging made from said fibers or potentially cotton swabs.

According to the present disclosure, all value chain participants may talk to the first blockchain. The data may be changed and/or consolidated on the second blockchain and all these participants may get a view from a DAO to see the proposed changed and/or consolidated data and agree on thereon. For example, a ginner and a farmer may agree on what the farmer delivered and what ginner received. For the first blockchain, the respective apps of the participants, e.g. farmer and ginner, may directly feed this data. The above may happen for all the value chain participants. For the second blockchain, the data is updated, e.g. in agreement with all the value chain participants, as they are interdependent and have to come to an agreement. The participants can then see the consolidated data and/or changed in the second/third blockchain. Then, the participants again have to agree on data to be written into the final chain. It is of course possible to omit the second block chain, i.e. to use only the first and the final blockchain (no intermediate blockchains). Although the method allows for using only two blockchains, three blockchains may give additional points of intervention, e.g., to add additional data that was initially missing.

According to the present disclosure, the data written in the final blockchain may be made available to the public only after transferring the data to an outer/external blockchain, to which the public may be given access.

Data written into the second and/or final blockchain may have different data types, e.g., having data points that are more publicly digestible, easy to understand, and/or specific. For example, in the first blockchain CO2 emissions associated with a value chain participant may be in fragmented format, whereas the final blockchain a total CO2 value may be provided, which may be more easily digestible.

In the present disclosure, the term “third parties associated with the value chain” is to be understood broadly and may entail parties actively participating in the value chain, e.g., by supplying agricultural product, processing agricultural product, and/or purchasing agricultural product, but not being the origin of the respective agricultural product data. It may also entail parties not participating in the value chain as such but, for example, a government, a regulatory body, an audit organization or the like.

In the present disclosure, the term “agricultural product data” is to be understood broadly and may refer to any data associated with an agricultural product. Agricultural product data, as an example, may be data representative of an amount of agricultural product and/or a type of agricultural product and/or properties of the agricultural product including physical characteristics, like product composition, and/or non-physical characteristics, like origin.

In the present disclosure, the term “real-time agricultural product data” is to be understood broadly and may comprise agricultural product data transmitted by an external device (e.g., external to the data processing system of the present disclosure) immediately upon collecting the data. For example, it may comprise data transmitted by the external device without intentional time delay after collection of the agricultural product data. As an example, a harvester may harvest an agricultural product and, while harvesting, an application on an external device may monitor the harvesting to collect data representative of an amount of harvested agricultural product and transmit the collected data immediately. The term “updated agricultural product data” is to be understood broadly and may comprise agricultural product data that are based on the real-time product data and additional agricultural product data received after the real-time product data, e.g. complementing the real-time product data, and/or is based on corrected agricultural product data, e.g. by replacing part of the realtime agricultural product data. It may refer to data having undergone consolidation.

The term “additional agricultural product data” is to be understood broadly and may comprise agricultural product data that supplement the real-time agricultural product data and/or agricultural product data that correct and/or at least partially replace the real-time agricultural product data. Obtaining the additional agricultural product data may entail receiving additional agricultural product data and/or processing previously received agricultural product data.

The additional agricultural product data having been obtained at a later time than the real-time agricultural product data is to be understood broadly and may comprise that at least part of the additional agricultural product data are more up-to-date than the real-time product data, for example comprises complementary data and/or corrected data.

The term “final agricultural product data” is to be understood broadly any may refer to any agricultural product data stored in the final blockchain. The final agricultural product data, according to the present disclosure, is based on the real-time agricultural product data and (second) additional agricultural product data. As an example, the final agricultural product data may be the result of the real-time agricultural product data having been corrected and/or updated and/or complemented based on updated and/or (e.g. first and/or second) additional agricultural product data, e.g. in one or more iterations. The final agricultural product data may comprise agricultural product data having been consolidated, e.g. in one or more iterations.

As an example, the final agricultural product data, while still based on the real-time agricultural product data, may be the result of the real-time agricultural product data having been corrected and/or updated and/or having been consolidated in one or more iterations.

In the present disclosure, data being based on other data may entail that data are derived directly or indirectly from the other data. For example, the data may comprise at least part of the other data in its original form. Alternatively, the other data may be consolidated and/or otherwise modified and the data may comprise at least part of the consolidated and/or otherwise modified data.

Consolidating a dataset, according to the present disclosure may comprise, among others, combining data of the dataset, removing duplicates from the dataset, discarding selected data of the dataset, and/or converting data. The term “consolidated data” as used herein is to be understood broadly and may refer to data obtained by consolidating other data, particularly by consolidating data of a dataset, particularly a dataset resulting from combining at least parts of two or more other datasets. Non-limiting examples for methods of data consolidation are merging of data, removal of overhead data, removal of duplicates, or the like.

Receiving agricultural product data is to be understood broadly and may comprise receiving agricultural product data directly from a value chain participant and/or receiving agricultural product data from a third party. In case agricultural product data are received from a third party, the third party may have received the agricultural product data and verified that writing it into the blockchain is permissible and/or the third party may have created the agricultural product data by modifying agricultural product data received at the third party, e.g., to consolidate the received agricultural product data. Preferably, receiving the real-time agricultural product data entails receiving the real-time agricultural product data directly from a value chain participant, e.g., from a harvester.

In the present disclosure, the data processing system initiating writing data into a blockchain is to be understood broadly. As an example, the data processing system may receive and directly forward or process an external request to write data into the blockchain and/or it may itself issue a request to write data into a blockchain, particularly in response to an external request. An external request may be received from the same party as the agricultural product data to be written into the blockchain. Optionally, the external request may be from a party giving permission to write said data into the blockchain, which may be different from the party from which the data originated (e.g. the value chain participant).

The first blockchain and the final blockchain, as well as the optional second blockchain and optionally further blockchains employed in the method of the present disclosure, may together form a set of blockchains associated with a common or single value chain, for example a value chain associated with one final (agricultural) product. The blockchains of one set of blockchains may have mutually different levels of truth and/or moments of truth. Each of said blockchains, optionally with the exception of the final blockchain, may be part of multiple sets of blockchains. As an example, a blockchain associated with an agricultural product directly obtained by agricultural harvesting may be part of several sets of blockchains associated with different final products comprising or made from the directly obtained agricultural product. A final product may be associated with several sets of blockchains, e.g. where the final product comprises or is made from multiple agricultural products.

The term “level of truth” as used herein is to be understood broadly and may refer to an accuracy of data of a dataset, for example as stored in a blockchain. A higher level of truth may, for example, be brought about by a dataset being more up-to-date than another data set, particularly comprises more recent data points, and/or by a dataset having one or more corrected datapoints as compared to another data set.

The term “moment of truth” as used herein may refer to a moment in time or a timespan at which data in the blockchain is accurate or, in other words, the time or time span at which the data in the blockchain is up-to-date.

In the context of the present disclosure, e.g. for agriculture products, a time difference between the moments of truth of the different blockchains may, for example be on the scale of several weeks, particularly several months. The time difference between the moments of truth of the different blockchains may, for example, be less than a year. The time difference between the moments of truth of the different blockchains may depend on the agricultural product and/or the timespans associated with different stations in the value chain, e.g., harvesting and/or processing times or the like.

The term "level of consolidation” as used herein may refer to the degree to which data has been consolidated. The level of consolidation may be increased by performing consolidation steps. For example, consolidation steps may comprise merging data and/or removing duplicates from a dataset and/or removing an overhead of data. Each of these consolidation steps may be seen as increasing the degree of consolidation. Accordingly, after performing one or more of said steps, the data will have a higher level of consolidation.

The term “first blockchain” is to be understood broadly and may entail a blockchain having, among the blockchains employed in the method of the present disclosure, have the lowest level of truth and/or the earlies moment of truth. It will be understood that this, to some degree, results from the first blockchain comprising real-time agricultural product data.

The term “final blockchain” is to be understood broadly and may entail a blockchain having, among the blockchains employed in the method of the present disclosure, the highest level of truth and/or the latest moment of truth. The “final blockchain” may be the only blockchain in a series of blockchains associated with an agricultural product value chain that may be fully exposed to third parties associated with the value chain.

The term “second blockchain” is to be understood broadly and may, for example, entail any blockchain having a level of truth and/or moment of truth between the level of truth and/or moment of truth of the first blockchain and the final blockchain.

According to the present disclosure the first blockchain and the final blockchain may be separate blockchains. Any further blockchains of the present disclosure, for example the second blockchain, may be separate blockchains, particularly separate from each other and from the first blockchain and the final blockchain. The separate blockchains may be configured such that they allow for mutual data retrieval.

The computer implemented method of the present disclosure may involve decentral networks comprising distributed ledgers. Specifically, according to the present disclosure, the distributed ledgers may be blockchains. A distributed ledger may be seen as a shared, replicated, and synchronized database among member nodes of a decentralized network, such as a P2P network. In general, a distributed ledger may record transactions between participants of the network and may, thus, provide an immutable history of transactions. Updates of the distributed ledger may be performed based on a consensus algorithm. When an update occurs, all nodes update themselves with the proper updated copy of the ledger. Blockchain applications are a specific example of a distributed ledger application. The nature of the distributed ledger is that there is no centralized authority, e.g., a clearing house. A distributed ledger may comprise a distributed ledger application. The distributed ledger application performs computing steps associated with the distributed ledger. The distributed ledger application may be stored on each member node of the distributed ledger.

As used herein, the term “smart contract” may be a computer program or a transaction protocol that automatically executes and/or controls and/or documents events and/or actions according to an agreement, e.g. a contract. For example, the computer program or transaction protocol may automatically carry out steps in response to predetermined conditions derived from and/or laid down in the agreement being met.

As used herein, the term “authorized entity” is to be understood broadly and may refer to any centrally organized or de-centrally organized entity having been granted authority to take decisions concerning data to be written into one or more of the blockchains. An authorized entity may, for example be or comprise a decentralized autonomous organization (DAO).

As used herein ..determining" also includes ..initiating or causing to determine", “generating" also includes ..initiating and/or causing to generate" and “providing” also includes “initiating or causing to determine, generate, select, send and/or receive”. “Initiating or causing to perform an action” includes any processing signal that triggers a computing node or device to perform the respective action.

In the claims as well as in the description the word “comprising” does not exclude other elements or steps and the indefinite article “a” or “an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation. BRIEF DESCRIPTION OF THE DRAWINGS

In the following, the present disclosure is further described with reference to the enclosed figures:

Fig. 1a illustrates example embodiments of a centralized computing environment with computing nodes.

Fig. 1 b illustrates example embodiments of a decentralized computing environment with computing nodes.

Fig. 1c illustrates an example embodiment of a distributed computing environment.

Fig. 2 shows an example of a distributed ledger.

Fig. 3 is a flow chart illustrating an example embodiment of the method according to the present disclosure.

Fig. 4 is a flow chart illustrating an example embodiment of the method according to the present disclosure.

Fig. 5 illustrates the method of the present disclosure and exemplary parties associated with the method.

DETAILED DESCRIPTION OF EMBODIMENTS

The following embodiments are mere examples for implementing the method, the system or application device disclosed herein and shall not be considered limiting.

In order to provide context for the method and apparatus according to the present disclosure, different computing environments, central, decentral and distributed, are illustrated in Figs. 1a to 1c and described herein below.

The present disclosure may be implemented in decentral or at least partially decentral computing environments, specifically, decentral networks, which may reflect the decentral nature of transfer of agricultural product between multiple independent parties.

Fig. 1a illustrates an example embodiment of a centralized computing system 100 comprising a central computing node 101 (filled circle in the middle) and several peripheral computing nodes 101.1 to 101. n (denoted as filled circles in the periphery).

The term “computing system" is defined herein broadly as including one or more computing nodes, a system of nodes or combinations thereof. The term “computing node” is defined herein broadly and may refer to any device or system that includes at least one physical and tangible processor, and a physical and tangible memory capable of having thereon computer-executable instructions that are executed by a processor. Computing nodes are now increasingly taking a wide variety of forms. Computing nodes may, for example, be handheld devices, production facilities, sensors, monitoring systems, control systems, appliances, laptop computers, desktop computers, mainframes, data centers, or even devices that have not conventionally been considered a computing node, such as wearables (e.g., glasses, watches or the like). The memory may take any form and depends on the nature and form of the computing node.

In this example, the peripheral computing nodes 101.1 to 101. n may be connected to one central computing system (or server). In another example, the peripheral computing nodes 101.1 to 101.n may be attached to the central computing node via e.g. a terminal server (not shown). The majority of functions may be carried out by, or obtained from the central computing node (also called remote centralized location). One peripheral computing node 101.n has been expanded to provide an overview of the components present in the peripheral computing node. The central computing node 101 may comprise the same components as described in relation to the peripheral computing node 101.n.

Each computing node 101 , 101.1 to 101.n may include at least one hardware processor 102 and memory 104. The term “processor” may refer to an arbitrary logic circuitry configured to perform basic operations of a computer or system, and/or, generally, to a device which is configured for performing calculations or logic operations. In particular, the processor, or computer processor may be configured for processing basic instructions that drive the computer or system. It may be a semiconductor-based processor, a quantum processor, or any other type of processor configures for processing instructions. As an example, the processor may comprise at least one arithmetic logic unit ("ALU"), at least one floating-point unit ("FPU)", such as a math coprocessor or a numeric coprocessor, a plurality of registers, specifically registers configured for supplying operands to the ALU and storing results of operations, and a memory, such as an L1 and L2 cache memory. In particular, the processor may be a multicore processor. Specifically, the processor may be or may comprise a Central Processing Unit ("CPU"). The processor may be a (“GPU”) graphics processing unit, (“TPU”) tensor processing unit, ("CISC") Complex Instruction Set Computing microprocessor, Reduced Instruction Set Computing ("RISC") microprocessor, Very Long Instruction Word ("VLIW") microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing means may also be one or more special-purpose processing devices such as an Application-Specific Integrated Circuit ("ASIC"), a Field Programmable Gate Array ("FPGA"), a Complex Programmable Logic Device ("CPLD"), a Digital Signal Processor ("DSP"), a network processor, or the like. The methods, systems and devices described herein may be implemented as software in a DSP, in a micro-controller, or in any other side-processor or as hardware circuit within an ASIC, CPLD, or FPGA. It is to be understood that the term processor may also refer to one or more processing devices, such as a distributed system of processing devices located across multiple computer systems (e.g., cloud computing), and is not limited to a single device unless otherwise specified.

The memory 104 may refer to a physical system memory, which may be volatile, non-volatile, or a combination thereof. The memory may include non-volatile mass storage such as physical storage media. The memory may be a computer-readable storage media such as RAM, ROM, EEPROM, CD-ROM, or other optical disk storage, magnetic disk storage, or other magnetic storage devices, or any other physical and tangible storage medium which can be used to store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by the computing system. Moreover, the memory may be a computer-readable media that carries computer- executable instructions (also called transmission media). Further, upon reaching various computing system components, program code means in the form of computer-executable instructions or data structures can be transferred automatically from transmission media to storage media (or vice versa). For example, computer-executable instructions or data structures received over a network or data link can be buffered in RAM within a network interface module (e.g., a “NIC”), and then eventually transferred to computing system RAM and/or to less volatile storage media at a computing system. Thus, it should be understood that storage media can be included in computing components that also (or even primarily) utilize transmission media.

The computing nodes 101 , 101 .1 ...101 .n may include multiple structures 106 often referred to as an “executable component or computer-executable instructions”. For instance, memory 104 of the computing nodes 101 , 101.1...101. n may be illustrated as including executable component 106. The term “executable component” may be the name for a structure that is well understood to one of ordinary skill in the art in the field of computing as being a structure that can be software, hardware, or a combination thereof or which can be implemented in software, hardware, or a combination. For instance, when implemented in software, one of ordinary skill in the art would understand that the structure of an executable component includes software objects, routines, methods, and so forth, that is executed on the computing nodes 101 , 101.1...101. n, whether such an executable component exists in the heap of a computing node 101 , 101 .1 ...101 .n, or whether the executable component exists on computer-readable storage media. In such a case, one of ordinary skill in the art will recognize that the structure of the executable component exists on a computer-readable medium such that, when interpreted by one or more processors of a computing node 101 , 101 .1 ...101 .n (e.g., by a processor thread), the computing node 101 , 101 .1 ...101 n is caused to perform a function. Such a structure may be computer-readable directly by the processors (as is the case if the executable component were binary). Alternatively, the structure may be structured to be interpretable and/or compiled (whether in a single stage or in multiple stages) so as to generate such binary that is directly interpretable by the processors. Such an understanding of example structures of an executable component is well within the understanding of one of ordinary skill in the art of computing when using the term “executable component”. Examples of executable components implemented in hardware include hardcoded or hard-wired logic gates, that are implemented exclusively or near- exclusively in hardware, such as within a field- programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC), or any other specialized circuit. In this description, the terms “component”, “agent”, “manager”, “service”, “engine”, “module”, “virtual machine” or the like are used synonymous with the term “executable component.

The processor 102 of each computing node 101 , 101.1...101.n direct the operation of each computing node 101 , 101.1...101.n in response to having executed computer- executable instructions that constitute an executable component. For example, such computer-executable instructions may be embodied on one or more computer-readable media that form a computer program product. The computer-executable instructions may be stored in the memory 104 of each computing node 101 , 101 .1 ...101 .n. Computer-executable instructions comprise, for example, instructions and data which, when executed at a processor 101 , cause a general purpose computing node 101 , 101.1...101. n, special purpose computing node 101 , 101.1...101. n, or special purpose processing device to perform a certain function or group of functions. Alternatively or in addition, the computer-executable instructions may configure the computing node 101 , 101.1...101. n to perform a certain function or group of functions. The computer executable instructions may be, for example, binaries or even instructions that undergo some translation (such as compilation) before direct execution by the processors, such as intermediate format instructions such as assembly language, or even source code.

Each computing node 101 , 101.1...101. n may contain communication channels 108 that allow each computing node 101 .1 ...101 .n to communicate with the central computing node 101 , for example, a network (depicted as solid line between peripheral computing nodes and the central computing node in Fig. 1a). A “network” may be defined as one or more data links that enable the transport of electronic data between computing nodes 101 , 101.1...101. n and/or modules and/or other electronic devices. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computing node 101 , 101 .1 ...101 .n, the computing node 101 , 101 .1 ...101 .n properly views the connection as a transmission medium. Transmission media can include a network and/or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general- purpose or special-purpose computing nodes 101 , 101.1...101.n. Combinations of the above may also be included within the scope of computer-readable media. The computing node(s) 101 , 101.1 to 101.n may further comprise a user interface system 110 for use in interfacing with a user. The user interface system 110 may include output mechanisms 110A as well as input mechanisms 11 OB. The principles described herein are not limited to the precise output mechanisms 110A or input mechanisms 110B as such will depend on the nature of the device. However, output mechanisms 110A might include, for instance, displays, speakers, displays, tactile output, holograms and so forth. Examples of input mechanisms 110B might include, for instance, microphones, touchscreens, holograms, cameras, keyboards, mouse or other pointer input, sensors of any type, and so forth.

Fig. 1 b illustrates an example embodiment of a decentralized computing environment 100’ with several computing nodes 101.1 ’ to 101 .n’ denoted as filled circles. In contrast to the centralized computing environment 100 illustrated in Fig. 1a, the computing nodes 101.T to 101 .n’ of the decentralized computing environment are not connected to a central computing node 101 and are thus not under control of a central computing node. Instead, resources, both hardware and software, may be allocated to each individual computing node 101. T...101. n’ (local or remote computing system) and data may be distributed among various computing nodes 101.T...101 .n’ to perform the tasks. Thus, in a decentral system environment, program modules may be located in both local and remote memory storage devices. One computing node 10T has been expanded to provide an overview of the components present in the computing node 10T. In this example, the computing node 10T comprises the same components as described in relation to Fig. 1a.

Fig. 1c illustrates an example embodiment of a distributed computing environment 103. In this description, “distributed computing” may refer to any computing that utilizes multiple computing resources. Such use may be realized through virtualization of physical computing resources. One example of distributed computing is cloud computing. “Cloud computing” may refer a model for enabling on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). When distributed, cloud computing environments may be distributed internationally within an organization and/or across multiple organizations. In this example, the distributed cloud computing environment 103 may contain the following computing resources: mobile device(s) 114, applications 116, databases 118, data storage 120 and server(s) 122. The cloud computing environment 103 may be deployed as public cloud 124, private cloud 126 or hybrid cloud 128. A private cloud 124 may be owned by an organization and only the members of the organization with proper access can use the private cloud 126, rendering the data in the private cloud at least confidential. In contrast, data stored in a public cloud 126 may be open to anyone over the internet. The hybrid cloud 128 may be a combination of both private and public clouds 124, 126 and may allow to keep some of the data confidential while other data may be publicly available. As explained in detail above, the method and apparatus of the present disclosure may make use of blockchains, which are types of a distributed ledger. Blockchains organize data into blocks. Blocks are chained together in an append-only manner. An exemplary distributed ledger 200, as may be employed in the present disclosure, is shown in Fig. 2.

A distributed ledger 200 is a shared, replicated, and synchronized database among member nodes 201 of a decentralized network, such as a P2P network. A distributed ledger application performs computing steps associated with the distributed ledger. In general, a distributed ledger may record transactions between participants of the network and may, thus, provide an immutable history of transactions. Updates of the distributed ledger are performed based on consensus algorithm. When an update occurs, all nodes update themselves with the proper updated copy of the ledger. Blockchain applications are a specific example of a distributed ledger application. The nature of the distributed ledger is that there is no centralized authority, e.g., a clearing house.

A distributed ledger, more specifically, the transactions thereof, may represent material flows in a material network like a supply chain, e.g., introducing of material into a material network or transfer of material within the material network. In that case, the distributed ledger may allow for searching transactions, which in turn may enable lookup across multiple intermediate steps in the material network, e.g., the supply chain, which may ensure traceability of material flow and accountability of each material owner and recipient. Specifically, the material may be an agricultural product.

In Fig. 2, as a specific example, each node is shown as having a database layer 201a, also referred to as Database API, and a distributed ledger control layer 201 b, which includes the distributed consensus algorithm and serves as a distributed ledger anchoring, e.g., blockchain anchoring. Providing separate layers may be advantageous, as database functions are geared at high throughput, e.g., for data loading and retrieval, access and querying, whereas distributed ledger functions usually provide lower throughput, yet ensure data immutability, tamper resistance, evidence, decentralized consensus over state, and replication of state across diverse nodes. However, a separation into separate layers is optional.

A distributed ledger as illustrated in Fig. 2, may be configured such that the database access and query commands on each node are implemented as part of the database layer and only few essential database commands may be implemented by the control layer.

An exemplary method according to the present disclosure where two blockchains associated with the value chain are employed is described below making reference to Fig. 3. The method comprises, in step S11 , receiving, at a data processing system, real-time agricultural product data from an application running on an external device, the real-time agricultural product data associated with an agricultural product associated with a value chain.

In step S12, the data processing system initiates writing the real-time agricultural product data into a first blockchain. Writing may, for example, be initiated by a request to write the real-time agricultural product data into the blockchain. The real-time agricultural product data may then be written into the first blockchain. Agricultural product data may be received from different participants of the value chain and at different times. As is the nature of blockchain, this can only cause chaining of data blocks, but existent data blocks remain immutable.

In step S13, additional agricultural product data associated with the agricultural product are received at the data processing system, the additional agricultural product data being obtained at a later time than the real-time agricultural product data. As an example, the additional agricultural product data may, for example, comprise corrections to the real-time agricultural product data and/or supplementary agricultural product data.

Based on the real-time agricultural product data and the additional agricultural product data, final agricultural product data may be generated, which may, for example, comprise combining and/or consolidating agricultural product data.

The final agricultural product data may comprise up-to-date agricultural product data obtained by replacing and/or correcting the real-time agricultural product data based on the additional agricultural product data.

The final agricultural product data may, alternatively or in addition, comprise consolidated data obtained by consolidating a dataset comprising the real-time agricultural product data and/or the additional agricultural product data.

It is noted that retrieval of the real-time agricultural product data for use in generating the final agricultural product data may, particularly, be made by means of a request to read the data from the first blockchain.

The additional or final agricultural product data may be received from and/or confirmed by an authorized entity, for example a decentralized autonomous organization, DAO, and/or obtained based on a smart contract. This is advantageous as, compared to real-time data, it is more difficult to avoid tampering at this stage.

In step S14, the data processing system initiates storing the final agricultural product data associated with the agricultural product in a final blockchain, the final agricultural product data being based on the real-time agricultural product data and the additional agricultural product data. Writing may, for example, be initiated by a request to write the final agricultural product data into the blockchain.

In optional step S15, only agricultural product data in the final blockchain is made fully accessible for third parties associated with the value chain. For example, agricultural product data may be queried by means of a request from the third party and, potentially dependent on an authorization of the third party, access to selected data from the final blockchain may be provided to the third party. However, the third party may not have access to data in the first blockchain. The third party may be a seller of a final product for example.

Optionally, any data of the first blockchain and/or second blockchain that is to be replaced with final agricultural product data may be nullified and/or labelled as less reliable. This may be done, for example, upon successfully writing the final agricultural product data into the final blockchain.

The first blockchain and the final blockchain may represent a different moment of truth and/or a different level of truth. For example, any data obtained before a time constituting the moment of truth may be written into the first blockchain and any data obtained after said time may be written into the final blockchain.

Another exemplary method according to the present disclosure where three blockchains associated with the value chain are employed is described below making reference to Fig. 4. It is to be understood that the method may be extended to additional blockchains applying the same principle, so a detailed description of the use of four or more blockchains will be omitted.

As will be seen below, first and second additional agricultural product data may be obtained at different times.

The method comprises, in step S21 , receiving, at a data processing system, real-time agricultural product data from an application running on an external device, the real-time agricultural product data associated with an agricultural product associated with a value chain.

In step S22, the data processing system initiates writing the real-time agricultural product data in a first blockchain. Writing may, for example, be initiated by a request to write the real-time agricultural product data into the blockchain. The real-time agricultural product data may then be written into the first blockchain. Agricultural product data may be received from different participants of the value chain and at different times. As is the nature of blockchain, this can only cause chaining of data blocks, but existent data blocks remain immutable.

In step S23, after receiving the real-time agricultural product data and prior to receiving the second additional agricultural product data, receiving first additional agricultural product data associated with the agricultural product are received at the data processing system, the first additional agricultural product data, in particular, being obtained at a later time than the real- time agricultural product data and at an earlier time than the second additional agricultural product data. As an example, the additional agricultural product data may, for example, comprise corrections to the real-time agricultural product data and/or supplementary agricultural product data.

Based on the real-time agricultural product data and the first additional agricultural product data, updated agricultural product data may be generated, which may comprise combining and/or consolidating agricultural product data.

The updated agricultural product data may comprise first up-to-date agricultural product data obtained by replacing and/or correcting the real-time agricultural product data based on the first additional agricultural product data, for example.

Obtaining first consolidated data may comprise consolidating a dataset comprising the real-time agricultural product data and/or the first additional agricultural product data.

It is noted that retrieval of the real-time agricultural product data for use in generating the updated agricultural product data may, particularly, be made by means of a request to read the data from the first blockchain.

The updated agricultural product data may be received from and/or confirmed by an authorized entity, for example a decentralized autonomous organization, DAO, and/or obtained based on a smart contract. This is advantageous as, compared to real-time data, it is more difficult to avoid tampering at this stage.

In step S24, the data processing system initiates storing the updated agricultural product data associated with the agricultural product in a second blockchain, the updated agricultural product data being based at least in part on the first additional agricultural product data and the realtime agricultural product data. Writing may, for example, be initiated by a request to write the final agricultural product data into the blockchain.

In step S25, after receiving the first additional agricultural product data, second additional agricultural product data associated with the agricultural product are received at the data processing system, in particular the second additional agricultural product data being obtained at a later time than the real-time agricultural product data and the second additional agricultural product data. As an example, the second additional agricultural product data may comprise corrections to the real-time agricultural product data and/or may comprise supplementary agricultural product data.

Based at least on the real-time agricultural product data, the first additional agricultural product data and the second additional agricultural product data, final agricultural product data may be generated, which may comprise combining and/or consolidating agricultural product data. The final agricultural product data may, for example, comprise consolidated data obtained at least from the second additional agricultural product data and at least one of: the real-time agricultural product data, first additional agricultural product data, updated agricultural product data.

The final agricultural product data may comprise second up-to-date agricultural product data obtained by replacing and/or correcting the real-time agricultural product data and/or first additional agricultural product data based on the second additional agricultural product data.

The final agricultural product data may, alternatively or in addition, comprise second consolidated data obtained by consolidating a dataset comprising the real-time agricultural product data and/or the first additional agricultural product data and/or the second additional agricultural product data.

It is noted that retrieval of the real-time agricultural product data and/or the updated product data for use in generating the final agricultural product data may, particularly, be made by means of a request to read the data from the first blockchain and/or the second blockchain.

The final agricultural product data are received from and/or confirmed by an authorized entity, for example a decentralized autonomous organization, DAO, and/or obtained based on a smart contract. This is advantageous as, compared to real-time data, it is more difficult to avoid tampering at this stage.

In step S26, the data processing system initiates storing the final agricultural product data associated with the agricultural product in a final blockchain, the final agricultural product data being based at least on the real-time agricultural product data, the first additional agricultural product data, and the second additional agricultural product data. Writing may, for example, be initiated by a request to write the final agricultural product data into the blockchain.

In optional step S27, only agricultural product data in the final blockchain is made fully accessible for third parties associated with the value chain. For example, agricultural product data may be queried by means of a request from the third party and, potentially dependent on an authorization of the third party, access to selected data from the final blockchain may be provided to the third party. However, the third party may not have access to data in the first or second blockchain.

Optionally, any data of the first blockchain and/or second blockchain that is to be replaced with updated and/or final agricultural product data may be nullified and/or labelled as less reliable. This may be done, for example, upon successfully writing the updated and/or final agricultural product data into the second blockchain or final blockchain. In an embodiment, the first blockchain, the second blockchain, and the final blockchain each represent a different moment of truth and/or a different level of truth.

In the methods of the present disclosure, for example as outlined in the context of Figures 3 and 4, agricultural product data may comprise harvesting quantity data for the agricultural product and/or production related data for the agricultural product, e.g., intake quantity and/or output quantity associated with the production, and/or expected demand for the agricultural product and/or qualitative data associated with the agricultural product, e.g. representative of product origin, sustainability or the like.

In the methods of the present disclosure, for example as outlined in the context of Figures 3 and 4, real-time agricultural product data may comprise harvesting data received from an application running on a device associated with a harvesting party and/or production output data from a production factory (i.e. , devices associated with a value chain participant).

In the methods of the present disclosure, for example as outlined in the context of Figures 3 and 4, the agricultural product data may be time-stamped data. This is particularly advantageous for tracking the moment of truth.

In the methods of the present disclosure, for example as outlined in the context of Figures 3 and 4, the agricultural product may comprise cotton and/or cotton-comprising product, for example yarn, fabric, garments.

In the methods of the present disclosure, for example as outlined in the context of Figures 3 and 4, particularly where agricultural product may comprise cotton and/or cotton-comprising product, for example yarn, fabric, garments, the participants of the value chain may comprise cotton harvesters, cotton aggregators, ginning factories providing raw cotton, spinning mills providing yarn, providers of cotton-comprising products like fabric or garments, for example weaving, knitting, dyeing, finishing factories, wholesale traders of cotton or cotton-comprising products, retailers of cotton or cotton-comprising products.

Fig. 5 illustrates a method according to the present disclosure in the context of potential parties involved and using three blockchains as an example. For illustration, a cotton-related value chain is described. However, the method may similarly apply to any other agricultural product.

A first participant 501 of the value chain may provide real-time agricultural product data 511 to be written into the first blockchain 610. For example, the first participant may be a harvester and the real-time agricultural product data may be harvesting data. The real-time agricultural product data are written into the first blockchain.

A second participant 502 of the value chain may be a cotton aggregator and the real-time agricultural product data 512 may be input and/or output of cotton. A third participant 503 associated with the value chain may also provide real-time agricultural product data 513 to be written into the first blockchain. For example, the third participant may be a ginning factory and the real-time agricultural product data may be input and/or output into/out of the ginning process.

At a later point in time, the cotton harvester may notice that some amount of harvested product was bad quality and discarded, for example, and may submit corrected agricultural product data, i.e., first additional agricultural product data. That is, the first participant may submit first additional agricultural product data 521. Updated agricultural product data 531 may then be generated and written into the second blockchain 620.

Similarly, at a later point in time, the second and third participant may submit corrected or complementary agricultural product data, i.e., first additional agricultural product data 522, 523. Updated agricultural product data 532, 533 may then be generated and written into the second blockchain.

Moreover, agricultural product data 534, 535, 536 associated with additional participants of the value chain may also be written into the second blockchain (the data as received from the participants are denoted by 523, 525, and 526). As examples, spinning mills 504, providers of cotton-comprising products like fabric or garments 505, for example weaving, knitting, dyeing, finishing factories, and traders/retailers 506 of cotton or cotton-comprising products, are shown.

At yet a later point in time, any of the value chain participants may submit corrected or complementary agricultural product data, i.e., second additional agricultural product data 541 , 542, 543, 544, 545, 546.

Final agricultural product data 551 , 552, 553, 554, 555, 556 may then be generated and written into the final blockchain 630.

It is noted that in the simplified illustration of the Fig. 5, as an example, data written into the second blockchain (431 to 536) and second additional agricultural product data 541 to 546 are shown as being processed together to obtain the final agricultural product data 551 to 556. However, an example is shown for data associated with the first participant, wherein the final agricultural product data is calculated based on the second additional agricultural product data and at least one of: the updated agricultural product data 531 , the first agricultural product data 521 , the first additional agricultural product data 541. Specifically, it may be advantageous to process, in addition to the second additional agricultural product data, the updated agricultural product data as retrieved from the second blockchain and optionally the first agricultural product data 521 as retrieved from the first blockchain. The same principles apply for each of the final agricultural product data sets 551 to 561 . As part of generating the updated and/or the final agricultural product data, any of the participants and/or a third party may collect data, e.g. comprising retrieving data from the first and/or second blockchain, select data, modify data, and/or consolidate data. As the generating may be a source of data tampering, preferably any modification of data from previous blockchains may, at some point prior to being committed to the blockchain, be authorized by a DAO or smart contract.

Whether data are to be written into the first blockchain or the second blockchain or the final blockchain, is time-dependent. For example, there may be a cutoff time until which data to be written into the second or final blockchain will be taken into account. The cutoff time is selected in such a manner as to take into account the value chain processes and timelines associated with the respective agricultural product, e.g., harvesting cycles.

As such, whether agricultural product data are to be considered first or second additional agricultural product data may be defined by whether the agricultural product data was obtained prior to or after the cutoff time.

When data in the first or second blockchain become invalid, flags or the like may be provided to indicate this.

The above are only some examples of ecosystems of parties/entities in handling agricultural product data and/or parties/entities associated with value chains associated with the agricultural product.

The present disclosure has been described in conjunction with preferred embodiments and examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed invention, from the studies of the drawings, this disclosure and the claims. Notably, in particular, the any steps presented can be performed in any order, i.e. the present invention is not limited to a specific order of these steps. Moreover, it is also not required that the different steps are performed at a certain place or at one node of a distributed system, i.e. each of the steps may be performed at different nodes using different equipment/data processing.

Claims

1. Computer-implemented method for handling agricultural product data, the method comprising, for each of a plurality of agricultural products: receiving (S11 , S21 ), at a data processing system, real-time agricultural product data from an application running on an external device, the real-time agricultural product data associated with an agricultural product associated with a value chain, the data processing system initiating (S12, S22) writing the real-time agricultural product data in a first blockchain (610), receiving (S13, S25), at the data processing system, additional agricultural product data associated with the agricultural product, the additional agricultural product data being obtained at a later time than the real-time agricultural product data, the data processing system initiating (S14, S26) storing final agricultural product data associated with the agricultural product in a final blockchain (630), the final agricultural product data being based on the real-time agricultural product data and the additional agricultural product data.

2. The method of claim 1 , comprising making (S27) only agricultural product data in the final blockchain (630) fully accessible for third parties associated with the value chain.

3. The method of claim 1 or 2, further comprising initiating (S24) writing updated agricultural product data in a second blockchain (620), the updated agricultural product data based at least in part on agricultural product data associated with the agricultural product and obtained at a later time than the real-time agricultural product data and at an earlier time than the additional agricultural product data.

4. The method of claim 1 or 2, wherein the additional agricultural product data are second additional agricultural product data, and wherein the method further comprises, after receiving the real-time agricultural product data and prior to receiving the second additional agricultural product data, receiving (S23), at the data processing system, first additional agricultural product data associated with the agricultural product, in particular the first additional agricultural product data being obtained at a later time than the real-time agricultural product data and at an earlier time than the second additional agricultural product data, and the data processing system initiating (S24) writing updated agricultural product data associated with the agricultural product in a second blockchain (620), the updated agricultural product data being based at least on the real-time agricultural product data and the first additional agricultural product data, wherein the final agricultural product data are based at least on the real-time agricultural product data, the first additional agricultural product data and the second additional agricultural product data.

5. The method of any of the preceding claims, wherein the final agricultural product data are based on the additional agricultural product data and/or wherein the updated agricultural product data are based on the first additional agricultural product data.

6. The method of claim 5, wherein the final agricultural product data comprise consolidated data obtained at least from the additional agricultural product data and at least one of: the real-time agricultural product data, second additional agricultural product data, updated agricultural product data, and/or wherein the updated agricultural product data comprise consolidated data obtained at least from the first additional agricultural product data and the real-time agricultural product data.

7. The method of any of the preceding claims, wherein the updated agricultural product data comprise: first up-to-date agricultural product data obtained by replacing and/or correcting the realtime agricultural product data based on the first additional agricultural product data, and/or first consolidated data obtained by consolidating a dataset comprising the real-time agricultural product data and/or the first additional agricultural product data; and/or wherein the final agricultural product data comprise: second up-to-date agricultural product data obtained by replacing and/or correcting the real-time agricultural product data and/or first additional agricultural product data based on the (second) additional agricultural product data, and/or second consolidated data obtained by consolidating a dataset comprising the real-time agricultural product data and/or the first additional agricultural product data and/or the second additional agricultural product data.

8. The method of any of the preceding claims, wherein the updated agricultural product data and/or the final agricultural product data are received from and/or confirmed by an authorized entity, for example a decentralized autonomous organization, DAO, and/or wherein the updated agricultural product data and/or the final agricultural product data are obtained based on a smart contract.

9. The method of any of the preceding claims, wherein the first blockchain (610), the final blockchain (630) and optionally the second blockchain (620) each represent a different moment of truth and/or a different level of truth.

10. The method of any of the preceding claims, wherein any data of the first blockchain (610) and/or second blockchain (620) that is to be replaced with updated and/or final agricultural product data are nullified and/or labelled as less reliable.

11 . The method of any of the preceding claims, wherein agricultural product data comprise harvesting quantity data for the agricultural product and/or production related data for the agricultural product, e.g., intake quantity and/or output quantity associated with the production, and/or expected demand for the agricultural product and/or qualitative data associated with the agricultural product, e.g. representative of product origin, sustainability or the like; and/or wherein real-time agricultural product data comprise harvesting data received from an application running on a device associated with a harvesting party and/or production output data from a production factory; and/or wherein the agricultural product data are time-stamped data.

12. The method of any of the preceding claims, wherein the agricultural product comprises cotton and/or cotton-comprising product, for example yarn, fabric, garments, and/or wherein participants in the value chain comprise cotton harvesters, cotton aggregators, ginning factories providing raw cotton, spinning mills providing yarn, providers of cottoncomprising products like fabric or garments, for example weaving, knitting, dyeing, finishing factories, wholesale traders of cotton or cotton-comprising products, retailers of cotton or cotton-comprising products.

13. A data processing system configured to carry out the method of any of the preceding claims, in particular comprising one or more processing units configured to carry out the method of any of the preceding claims.

14. A computer program product comprising instructions which, when the program is executed by a data processing system, cause the data processing system to carry out the method of any of claims 1 to 12.

15. A computer-readable medium comprising instructions which, when executed by a data processing system, cause the data processing system to carry out the method of any of claims 1 to 12.