Valentin Bellassen
email: valentin.bellassen@inrae.fr

Filippo Arfini
email: filippo.arfini@unipr.it

Federico Antonioli
email: federico.antonioli1@gmail.com

Antonio Bodini
email: antonio.bodini@unipr.it

Michael Boehm
email: boehm@ecozept.de

Ružica Brečić

Sara Chiussi
email: sara.chiussi@gmail.com

Peter Csillag
email: csillag.peter@ecosensus.hu

Michele Donati
email: michele.donati@unipr.it

Liesbeth Dries
email: liesbeth.dries@wur.nl

Marion Drut
email: marion.drut@agrosupdijon.fr

Matthieu Duboys De Labarre
email: matthieu.duboys-de-labarre@inrae.fr

Hugo Ferrer
email: hugo.ferrer@upc.edu

Jelena Filipović
email: jfilipovic@ekof.bg.ac.rs

Lisa Gauvrit
email: gauvrit@ecozept.com

José M Gil

Matthew Gorton
email: matthew.gorton@ncl.ac.uk

Viet Hoàng
email: viet.hoang@ueh.edu.vn

Mohamed Hilal
email: mohamed.hilal@inra.fr

Knutsen Kamilla
email: kamilla-knutsen.steinnes@sifo.hioa.no

Steinnes

Apichaya Lilavanichakul
email: apichaya.l@ku.ac.th

Agata Malak-Rawlikowska
email: agata_malak_rawlikowska@sggw.pl

Edward Majewski
email: edward_majewski@sggw.pl

Sylvette Monier-Dilhan
email: sylvette.monier@inrae.fr

Paul Muller
email: paul.muller@univ-lorraine.fr

Orachos Napasintuwong
email: orachos.n@ku.ac.th

Kalliroi Nikolaou

Mai Nguyen
email: mainguyen1012@gmail.com

An Nguyễn Quỳnh

Ho Ueh

Chi

Ioannis Papadopoulos

Jack Peerlings
email: jack.peerlings@wur.nl

Aron Török
email: aaron.torok@gmail.com

Bojan Ristic
email: risticbj@gmail.com

Burkhard Schaer
email: schaer@ecozept.com

Zaklina Stojanovic
email: zaklina@ekof.bg.ac.rs

Barbara Tocco
email: barbara.tocco@newcastle.ac.uk

Marina Tomic Maksan

Gunnar Vitterso
email: gunnar.vitterso@sifo.hioa.no

Sustainability Performance of Certified and Non-certified Food

Keywords:

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction and Research Problem

EU and national food quality policies have witnessed recent reforms. In 2007, the EU agreed on a new Council Regulation (Council Regulation (ec) No. 834/2007) setting out the principles, aims, and overarching rules of organic production and defining how organic products were to be labelled. In 2012, the Quality Package (Regulation (EU) No. 1151/2012) was passed to improve and promote the operation of schemes to protect Geographical Indications (gi s) for agri-food products. The Regulation details the rationale for establishing/ bellassen et al.

promoting gi s as a means to generate a fair return for farmers and producers for the qualities of particular goods and to enable consumers to make better-informed purchasing decisions through effective labelling. The diversity and quality of EU agricultural and fisheries production are one of its main strengths in both domestic and international markets. Supporting Food Quality Schemes (fqs s) -here understood as Protected Designation of Origin (pdo), Protected Geographical Indications (pgi) and organic products -is thus regarded as consistent with Europe 2020 policy priorities for 'sustainable and inclusive growth' , which seek to achieve competitive and high employment economies (economically sustainable) delivering social and territorial cohesion (socially sustainable), while paying attention to the burden placed on the environment and natural resources (environmentally sustainable). But are fqs s really more sustainable than other food products?

To answer this question and as part of the H2020 Strength2Food project, we gathered raw data on 54 food value chains spanning over 13 countries. The sampling design is paired: 27 certified -pdo, pgi, or organic -products and 27 reference products (products similar to the certified value chain but not certified). This raw data allows for the estimation of 25 performance indicators covering the three sustainability pillars: 9 economic indicators, 7 environmental indicators and 9 social indicators.

Methods

Disclaimer: being a summarized description of the method used to estimate sustainability indicators, this article largely draws from two existing documents from the same authors: [START_REF] Bellassen | Sustainability of European food quality schemes: Multi-performance, structure, sustainability performance[END_REF] and Bellassen et al. (2016). More technical details on the Methods are available in the data repository.

-Sustainability Performance of Certified and Non-certified Food deposited at Data inrae -doi:www.doi.org/10.15454/OP51SJ

2.1.

General Points on Indicators and Their Analysis

2.1.1.
Overview of Indicators and Minimal Systematic Comparison The choice of indicators was made on the basis of the safa methodology (Sustainability Assessment of Food and Agriculture systems) developed by the Food and Agriculture Organization of the United Nations (2013) to measure the sustainability of food production. safa provides guidelines on how to consider each sustainability dimension, including which indicators could be sustainability performance relevant and useful indications on how to implement them. safa, however, is primarily focused on processing firms and stops short of formulating a complete method which goes from primary data collection to indicator estimation and interpretation.

The indicators presented in this document operationalise a subset of safa indicators, complementing them along the following three lines: -Most safa indicators cannot be directly implemented from the safa indicators report. They require the definition of specific data to be collected and calculation or aggregation methods which are not explicated in the report, although the report sometimes refers to existing tools for doing this. Our method defines all necessary data and variables, and provides associated calculators or aggregation methods, together with a data storage and source traceability system. -Because they were designed to be collected for a single firm, many safa indicators require a substantial amount of data. This makes it difficult to cover more than a few indicators for an entire value chain within 3 person-months. Our method simplifies indicators by prioritising data collection on the key drivers of the indicators, by providing default values for many non-key but necessary variables and, where necessary, by restricting the scope of an original safa indicator down to the scope for which data is most accessible. As a result, it is possible in most cases to estimate 25 sustainability indicators across the three sustainable development pillars for both a specific product produced by several firms and a generic reference product in 3 person-months. -Finally, several safa indicators rely only on the subjective views of specific stakeholders. Where stakeholder views are a necessary part of the indicator (e.g. bargaining power distribution), our indicators combine stakeholder views with objective data.

To make the collection of information and the subsequent analysis on the twenty-seven case studies efficient, operational choices were made concerning the type of indicators and their management. One of the most important choices is the distinction between "systematic indicators" which should be computed on all case studies and "complementary indicators" which concern only a subset of case studies, often based on data availability. There was a total of 13 systematic indicators (four economic; four environmental; five social), and a total of eleven complementary indicators (five economic; three environmental; four social). Around 150 variables were collected and refined into the 25 indicators (see Table 1). andSocial Sciences 6 (2021) 1-22 2.1.2. Relative Difference and Value Chain Averages Indicators are estimated at each level of the value chain (farm level, processing level and, where relevant, retail level). To control for country and product specificities, we analyse relative differences between the fqs and its reference product rather than absolute values.

Equation ( 1) is used, where rel_diffj is the relative difference for an indicator at level j of the value chain, and indic FQS, j and indic FQS, j are the indicator value at level j of the value chain for the fqs and the reference product respectively.

rel_diff = indic indic indic j - FQS,j R EF,j REF,j (1)
For environmental indicators and for bargaining power distribution, the opposite of the relative difference is used in the analysis so that a positive difference consistently indicates higher performance of fqs (e.g., more added value, more employment, lower carbon footprint).

In a second step, to assess the difference in performance for the entire value chain, we compute aggregated values or "value chain averages as shown in equation ( 2

+ indic x,proc 1 + coproducts proc ( )                V Value chain stability: average = cum indic cum indic c VC FQS REF - u um indic cum indic = min indic x, farm , indic x,proc REF X ( )                                           (2)
bellassen et al.

For most indicators, these aggregated values are simply averages across value chain levels for which the indicator could be estimated (farm, processing and, where relevant, retail). There are, however, two exceptions. The first exception concerns indicators expressed on a per ton basis, that is the environmental indicators and the labour to production ratio. Because these indicators follow a life cycle assessment logic, and in particular because they use a functional unit (one ton of product), aggregated values over the value chain must be calculated cumulatively. If one ton of cheese requires 10 tons of milk, the aggregated indicator sums the footprint of 10 tons of milk at farm level and 1 ton of cheese at processing level rather than averaging the footprints of one ton of milk and one ton of cheese. This cumulative process also allocates the footprint to all products (e.g., milk and meat at farm level) based on their relative economic value. For environmental indicators, this is already done in the estimation of the indicator. For labour to production ratio, the formula is provided in equation ( 2).

The second exception concerns the indicator on value chain stability for which the aggregated value is the minimum across value chain levels. In equation (2), the vc average is the aggregated performance difference for the entire value chain, rel_diffj is the relative difference in performance at level j of the value chain (see equation ( 1)), n is the lowest level of the value chain where the indicator could be estimated (most often the processing level), cum_indicX is the cumulative indicator over different value chain levels for product X (either fqs or reference), indic X,farm and indic X,proc are the indicator value for product X at the farm and processing levels respectively, final_prod_ratio is the amount of raw product at farm level (e.g., milk) necessary for one ton of final product (e.g., cheese), and coproducts_farm and coproducts_proc are the value of coproducts (e.g., meat) expressed as a percentage of the value of the main product (e.g., milk) at farm and processing levels respectively.

2.2.

Reference, Data Collection and Metadata Documentation 2.2.1.

Selection of a Reference Product/Case: Elements of Guidance To provide a basis for comparison, each sustainability indicator has been estimated for the same product category (for example cheese) in two different value chains: specific quality (organic or geographical indication) and generic quality (reference product). To define the reference, the following guidance, composed of two objectives and three constraints, was applied. The two objectives are: -comparability of contexts: the two cases (food quality scheme and its standard reference) should be produced in territorial contexts (in terms of location) as similar as possible;

sustainability performance -comparability of the products: the two products/basket of products (food quality scheme and their standard reference) should be as comparable as possible. These objectives should be sought until one of the three following constraints are met: -data resolution limit: data for the reference are only available at a larger scale than for the case studied; -confusion of the case and its reference: for example, for an apple under geographical indication (gi), the reference would ideally be the production of "standard" apples in the same area. Nevertheless, if almost all the apple production of that area is under gi, a reference should be chosen at a larger scale (regional or even national scale); -the case studied is the only one of its type: with the example of an apple under gi, the ideal reference would be a standard apple of the same variety. Nevertheless, as mentioned for geographic scale, data may be scarce at this detailed level (variety), or even all the apples of this variety may be sold under gi. In this case, a suitable reference would be one, or a mix of, the main varieties. In practice, the choice of a relevant reference by case study conductors will strongly depend on data availability, so that a national average can be used if a more suited reference cannot be documented. Moreover, a mix of specific references and national averages can be used. For example, looking at the Comté cheese, some variables (e.g., price of milk, price of cheese, …) may be specific to Emmental, a non-certified ripened, hard, cow-milk based cheese, while national averages are used for other variables (e.g., quantity of mineral fertilizer per hectare, share of exports over total production, …) for which Emmental-specific data are not readily available.

Note that the use of the reference is primarily to interpret the results from the case, so even if the reference presents some peculiarities, this can be accounted for in the discussion of results. An extreme case of such peculiarities is Sjenica cheese in Serbia. Because it is almost the only sheep cheese produced in the country, the reference product is a conventional cow cheese. But as a result, many differences between Sjenica cheese and its reference are better explained by the difference between sheep and cow than by the technical specifications or the terroir of Sjenica cheese. For this reason, Sjenica should be excluded from most cross-comparisons. To the contrary of many performance assessments, we thus opted for real relative references as opposed bellassen et al.

to normative references, that is references which correspond to fictive cases or to targets to be reached (Acosta-Alba & Van der Werf, 2011).

2.2.2.

Data Collection 2.2.2.1.

Value Chain Diagram The first step in data collection was to identify the firms which belong to the value chain (see the box text for the criteria) and to classify them into different levels (e.g., farm level, processing level, retail level). This first step resulted in a value chain diagram which is inserted in the second sheet of each data file and provides the code of each value chain level.

Box.

Criteria Used to Identify Which Firms Belong to the Value Chain When firms are making only part of their turnover from the fqs product (- e.g., a freezing plant which is freezing and packaging all kind of fruits, including the fqs (organic raspberries) -criteria are needed to determine whether they belong to the fqs value chain. The key recommended criterion is that the firm makes at least 50% of its turnover from the fqs product. As such, most firms at retail level will be excluded. However, a few systematic or ad hoc exceptions are made:) -The retail level is included for two economic indicators, namely price premium and export; -A firm/value chain level can be retained on an ad hoc basis when its impact on an indicator is substantial (e.g., impact of freezing on the carbon footprint of frozen raspberries); -A firm/value chain level can be retained on an ad hoc basis when stakeholders consider it as part of the value chain despite it making less than 50% of its turnover from the product.

2.2.2.2.

Two Angles of Prioritisation Two distinctions were made to convey a sense of priority for data collection: -Systematic versus complementary indicators: systematic indicators were to be computed for all case studies while complementary ones could be restricted to a subset of particularly interesting cases; -Key versus secondary variables: a reasonable approximation of the indicator can be obtained from key variables data while obtaining values for secondary variables would create even more precise estimates.

sustainability performance

In other words, most of the data collection/gathering effort should be spent on key variables which contribute to systematic indicators, while the rest should only be provided if data is readily available, and should not be the object of a dedicated data collection effort.

2.2.2.3.

Relying on Existing Sources of Information In general, given the resource and time constraints, most variables were designed to be common enough to be obtained from existing studies, reports and databases. A good strategy for a comprehensive overview of existing sources may be to conduct a few (3-5) interviews with key stakeholders in the chosen case study's value chain.

2.2.2.4.

Default Values In parallel to case-by-case data collection, an effort was made to obtain national average values for as many variables as possible, and cover all the sectors studied (dairy, meat products, seafood/fish, cereals, fruits & vegetables). These values do not refer to specific products but to larger product categories which can be identified in systematic surveys. For this purpose, databases with pan-European coverage, such as the Farm Accountancy Data Network (fadn) and different surveys and datasets available via Eurostat database (i.e., Farm Structure Survey, Structural Business Statistics, Labour Force Survey, etc.) have been explored.

These default values were used in three different manners: -to check that the collected data for the case and/or its reference is of a reasonable order of magnitude; -to estimate indicators for a "national average" reference product; -to save time on data collection when there is evidence (e.g., expert judgement) that a given variable is not significantly different from the national average. This last option was infrequently used and, in all cases, data sources for each variable and product are transparently documented in the data repository.

2.2.3.

Quality Checks in Data Collection and Indicator Estimation 2.2.3.1.

Principles Considering the scale and the complexity of data collection (measuring the sustainability level of 54 products using 25 indicators referring to the environmental, economic and social dimensions of sustainability), an organizational model was developed. This thorough quality check procedure was bellassen et al.

implemented to limit the risk of misreporting data. The three key aspects of this procedure were 1) to record all data, their date and source in a shared spreadsheet, 2) to separate the person who collected data from the person who estimated the indicator, and 3) to come up with a written and consensual interpretation of the results between these people.

The most important principle of the procedure for data collection and indicator estimation is an early and repeated interaction between the case study conductor and the indicator coordinator (see Figure 1). The case study conductor is responsible for collecting the data and ensuring its traceability, which implies creating a repository with all source files and intermediary calculations. The indicator coordinator is responsible for the quality check of the data provided (e.g., verifying, together with the case study conductor, the source when an order of magnitude seems wrong, etc.) and for providing the case study conductor with the estimated indicator(s). Both are responsible for interpreting the results. Results are considered valid only when both the case study conductor and the indicator coordinator agree that the estimated resulting indicators are plausible and, should a large difference occur between the certified product and its conventional reference, that they have plausible ways of explaining this large difference. Those conducting case studies received initial guidance and tips for data collection, and regular online meetings were organized to share data collection practices and problems and thus ensure consistency across case studies.

2.2.4.

Metadata Documentation For each variable value, two metadata items were documented: -the source/reference for the values (e.g., "Dupond et al., 2010"); -to which time period the variable's values correspond. Time periods should be as recent as possible, and to the extent possible, similar between different variables. When relevant and available, time-series and/or multi-year averages can be used. In addition, all original documents from which the data are sourced and the intermediary calculations (e.g., excel or word documents) have been stored in an online repository, so that both the case study conductor and the indicator coordinator can go back to them easily to double-check some values or interpret the results.

2.3.

Description of Indicators, Their Purpose and Their Estimation Method

The exhaustive list of the raw data collected and the technical details of the method to estimate the 25 performance indicators based on this raw data are provided in the full documentation posted in the data repository.

Table 2 sets out the sample characteristics where the sectors are highlighted (red, green and blue lines for animal, vegetal and seafood respectively). The indicated turnover is either at processing or farm level, whichever is higher. Arfini & Bellassen (2019) provides a detailed description of each value chain, its structure, its governance and its sustainability performance.

As a result of the quality check procedure described in 2.2.3, the applicant-pgi Sjenica sheep cheese was removed from the sample: its reference product is a cow cheese, and the difference between cow and sheep was identified as the main drivers for the differences in performance. The procedure also resulted in the exclusion of employment indicators at processing level for pgi Doi Chaang coffee and pgi tkr Hom Mali rice, for which differences between certified food and its reference were both high and unexplained. bellassen et al. sustainability performance

Reuse Potential

We anticipate two major avenues for reuse of the dataset Sustainability performance of certified and non-certified food. First, the analysis we have made so far and submitted to academic journals is not exhaustive. Sector-specific or standard-specific analysis are only sketched and have not been synthesized. Other cross-comparisons could also be envisaged, based on geographical or cultural proximity for example, and more systematic sensibility studies could be performed. Second, other food value chains may be willing to assess their sustainability using the same method. Having access to our detailed dataset will allow them to better understand the method -seeing it applied to a broad set of examples -and to undertake detailed quality checks (e.g., identifying outliers in both raw data and indicator values). Finally, should such an assessment be conducted, we encourage these fellow researchers to enrich the dataset by sending us their results. Such a virtuous cycle could, over time, lead to further interesting analyses, such as intertemporal comparisons or testing past results on a higher sample. and governance of PDO,PGI,. Springer International Publishing. https://www.doi.org/10.1007/978-3-030-27508-2_2. Bellassen, V., Giraud, G., Hilal, M., Arfini, F., Barczak, A., Bodini, A.,

  ):All indicators except environmental indicators and labour t to production : average

figure 1

 1 figure 1 Organization of data collection and indicator estimation and interpretation

sustainability assessment Sustainability pillar Indicator type Indicator sub-type (code) Level of analysis along the value chain

	sustainability performance								bellassen et al.
	One value per level of	the value chain		Single value for the	whole value chain		Single value for the	whole value chain							One value per level of	the value chain
	Price premium (Ec1.1, eur kg-1)	Gross Operating Margin (Ec1.3, % of	turnover)	Share of value exported within Europe	(Ec1.5, % of turnover)	Local multiplier (Ec2.1, no unit)	Carbon footprint per unit of product	(En1.1, kgco2e ton-1)	Distance travelled per unit of product	(En2.1, ton.km ton-1)	Blue water footprint (ground water	consumption, En 3.3, m3 kg-1)	Grey water footprint (water pollution by	nitrates, En 3.2, m3 kg-1)	Labour to production ratio (So 1.1, awu	ton-1)
	table 1 List of indicators for Systematic Economic Price premium	Profitability and value	added distribution	Trade		Local multiplier	Environmental Carbon footprint		Foodmiles		Water footprint				Social Employment

Sustainability pillar Indicator type Indicator sub-type (code) Level of analysis along the value chain

	sustainability performance					bellassen et al.
		Level of analysis along	the value chain	Single value for the One value per level of	whole value chain the value chain	One value per level of	the value chain		One value per level of	the value chain	One value per level of	the value chain	Single value for the	whole value chain
		Indicator sub-type (code)		Bargaining power distribution (So2.1, no Carbon footprint per hectare (En1.2,	unit) kgco2e ha-1)	Educational attainment (So3.1, no unit) Emissions from transportation per unit of Generational change (So5.1, no unit) product (En2.2, kgco2e ton-1) Gender equality (So5.2, no unit) Green water footprint (total -rain +	Gross Value-added (Ec1.2, % of turnover) groundwater -water requirements, En3.1,	m3 kg-1)	Net result (Ec1.4, % of turnover) Turnover to labour ratio (So1.2,	eur awu-1)	Share of value exported outside Europe Stability of the value chain level (So2.2, no unit) (Ec1.6, % of turnover)	Share of volume exported within Europe Wage level (So3.2, eur awu-1) (Ec1.7, % of production) Gender equality index (So5.3, no unit)	Share of volume exported outside Europe	(Ec1.8, % of production)
	table 1 table 1 List of indicators for sustainability assessment (cont.) List of indicators for sustainability assessment (cont.)	Sustainability Indicator type	pillar	Governance Environmental Carbon footprint		Social capital Foodmiles	Water footprint	Complementary Economic Profitability and value	added distribution	Profitability and value Social added distribution Employment	Trade Governance	Social capital

Research Data Journal for the Humanities

table 2

 2 Sample characteristics with sectors highlighted

	Product name Country Product	Type of	Processed? Turnover	Reference product
			description	fqs		of fqs (M€
						yr-1)
	Dalmatian	Croatia Dry pork ham pgi	Yes	4.20 Local non-pgi firm
	prosciutto
	pdo olive oil	Croatia Olive oil	pdo	Yes	0.25 National average
	Comte cheese France Hard pressed	pdo	Yes	504.19 Similar uncertified
			cooked				cheese (Emmental) or
			cheese from				national average (cow
			cow milk				cheese)
	Organic flour France Wheat flour Organic Yes	34.80 National average
	Saint-Michel	France Mussels	pdo	No	25.45 National average (tsg
	bay bouchot		produced on				Bouchot mussels)
	mussels		"bouchots"
	Organic rice	France Rice	Organic Yes	17.64 Non-organic rice
							(mostly PGI)
	Organic pork	Germany Raw meat	Organic Yes	69.00 National average
	Organic yoghurt Germany Organic	Organic Yes	387.00 National average
			yoghurt from
			cow milk
	Zagora apples Greece Apple	pdo	No	10.11 Kissavos apples (non-gi
							apples from another
							region)
	Kastoria	Greece Apple	pgi	No	7.50 Kissavos apples (non-gi
	apples						apples from another
							region)
	Gyulai	Hungary Sausage	pgi	Yes	55.00 Non-pgi Hungarian
	sausage						sausage
	Kalocsai	Hungary Paprika	pdo	Yes	10.75 Imported Chinese
	paprika		powder				pepper milled in
	powder						Hungary
	Parmigiano	Italy	Hard pressed	pdo	Yes	1,009.94 Biraghi cheese (similar
	Reggiano		cooked				non-pdo cheese)
	cheese		cheese from
			cow milk

  Brennan, M., Drut, M., Duboys de Labarre, M.,Gorton, M., Hartmann, M., Majewski, E., Muller, P., Monier-Dilhan, S., Poméon, T., Tocco, B., Tregear, A., Veneziani, M., Vergote, M.-H., Vitterso G., Wavresky, P. & Wilkinson, A. (2016). Methods and indicators for measuring the social, environmental and economic impacts of food quality schemes, short food supply chains and varying public sector food procurement policies on agrifood chain participants and rural territories (Strength2Food project, deliverable 3.2). inra. Food and Agriculture Organization of the United Nations. (2013). SAFA indicators. Sustainability performance of certified and non-certified food (2021). [Dataset]. https:// data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/OP51SJ. bellassen et al.

Research Data Journal for the Humanitiesand Social Sciences 6 (2021) 1-22

Acknowledgements

This research was funded by the European Commission under the H2020 Strength2Food project, grant number 678024.

Product name Country Product description

Type of fqs

Processed? Turnover of fqs (M€ yr-1)

Reference product

Lofoten stockfish