Fruit and vegetables

The fruit and vegetables supply chain plays a central role in French food and agriculture. It covers a wide diversity of plant productions, intended for fresh or processed consumption, and mobilises varied value chains, from agricultural production to the agri-food industry. This chain is characterised by strong seasonality, highly perishable products and a multiplicity of forms of recovery. It also generates significant flows of co-products and losses, which are a major issue for the efficiency and sustainability of food systems.

The flow diagram presenting the results for this supply chain, produced as part of the SOCLE project, offers a complete view of how material circulates through the chain, from supplies to final outlets. It is a Sankey-type representation: the thickness of the arrows is proportional to the quantities (in kilotonnes, kt), making it possible to grasp at a glance the relative weight of each segment.

Complete view of the results

This diagram aims to represent the supply chain as a whole at the most detailed level possible. Although some data could be mobilised at a finer level of detail than the one shown, it did not make it possible to guarantee complete data all along the chain: such data is therefore shown in aggregated form on the diagram.

The diagram represents all the material flows within the fruit and vegetables supply chain in France, from supply sources to the various final uses, for the 3 years studied in the SOCLE project: 2015, 2019 and 2023 (select the desired year in the timeline below the diagram).

Supplies include imports and the agricultural production of fruit and vegetables, distinguishing the different crop categories and the losses occurring right from the production stage. The intermediate market splits between the fresh market, which directly feeds final consumption, and primary processing and agri-food processing activities. This part highlights the diversity of processed products (canned, frozen, juices, prepared products) as well as the generation of co-products and sorting rejects. The outlets bring together human food, animal feed, non-food uses and exports. The diagram also highlights downstream losses and statistical discrepancies, providing an overall view of the recovery routes and potential inefficiencies of the system.

The relative thickness of the flows highlights the predominant role of the fresh market, the significant place of processed products and the importance of losses and co-products in the overall balance of the chain.

AF-Filières methodology: general principle and illustration through the diagrams

The AF-Filières (Supply Chain Flow Analysis) methodology, implemented in the SOCLE project, aims to reconstruct all the flows of a supply chain by aggregating, structuring and analysing all the available information. It consists in gathering all the relevant data about a given sector into a single, hierarchical model that links every piece of information together. This systematic cross-checking makes two essential operations possible: 1. checking the internal consistency of the data and, where necessary, reconciling it; 2. determining missing values, either precisely or as intervals when some indeterminacy remains.

The Sankey diagrams presented in the "Method and sources" section illustrate each step of this process, relying directly on the data actually used in the SOCLE model. They provide a concrete view of how the supply chain is reconstructed, step by step.

Part 1 — Data collection, qualification and structural completion of the model

(Associated diagram: Sources)

This first phase starts with an inventory of all the relevant public and sector-specific sources describing the agricultural supply chain in France (available years, nature of the data: direct values, min/max, coefficients, etc.). The associated diagram shows, for each documented flow, the sources used and their characteristics, giving an immediate reading of the well-documented areas and of those where information is scarcer. The diagram also makes it possible to display the type of data collected in the legend (use the colour selector to show it): direct value, minimum or maximum, or allocation and yield coefficients, which express a relationship between two flows rather than an absolute value. It is also possible to consult the raw data from the source directly, by referring to the corresponding sheet of the model's Excel file (the sheet name is available in the tooltip of each flow).

Because the collected data generally does not cover the whole supply chain, this step also includes structural completion: additional flows, not yet documented, are added to the model to ensure the continuity of the processing chains and to materialise the segments lacking information.

At the end of this phase, the whole knowledge perimeter is mapped: the flows for which data exists and those that will have to be determined later.

Part 2 — Validation, reconciliation and determination of the flows

(Associated diagram: Method)

Based on this complete structure, the flows for which reliable data exists are consolidated. When several sources converge, the collected values are retained as they are. When inconsistencies are detected (contradictory material balances, incompatible yields, etc.), a statistical discrepancy flow is added to the model: its value represents the difference between the collected data, making it easy to assess its magnitude relative to the flows involved, as well as the product or sector concerned.

Once the model has been made consistent, the missing flows can be determined using material balances, structural constraints and relationships between flows. Some flows thus obtain a precise value (called "determined"); others can only be defined through a min–max range when a lack of information remains. The associated diagram makes all of these situations visible: collected values, statistical discrepancies, computed values and indeterminacy intervals, where applicable.

Part 3 — Overall synthesis and confidence level

(Associated diagram: Confidence level)

The last diagram offers an overall view of the results, enriched with a global reliability indicator for each flow. This reliability is based on several criteria: quality and precision of the sources, closeness of the collected data to the project's perimeter, magnitude of the observed statistical discrepancies, width of the indeterminacy intervals, etc.

How to read the reliability levels:

Reliable: data with a very low level of uncertainty, drawn from validated and consistent sources.
Robust: solid data, confirmed by several elements or methods, usable with confidence.
Probable: plausible data, based on serious elements but carrying notable uncertainty.
Approximate: data giving the right order of magnitude, but without any guarantee of precision.
Indicative: data provided for information only, with a high level of uncertainty.

This diagram makes it possible to identify at a glance the segments of the supply chain that are well documented and those that remain exploratory.