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•  Organization of the evaluation tree.

The three main branches of the evaluation tree are the three pillars of sustainability (1^st hierarchical level) environmental, economic and social (ENV, ECO and SOC respectively). Each pillar is divided into smaller branches called ‘components’ (2^nd level), themselves subdivided into smaller branches called ‘criteria’ (3^rd level) and ‘sub-criteria’ (4^th level) that define them. The calculated data at the end of a branch is called an ‘indicator’ (5^th and last level). An indicator is the synthesis or simplification of data deemed relevant to report the impacts of a practice. Causal indicators report on practices and effect indicators report on impacts. 

•  Pilars, Componants and indicators

More than 40 indicators are organized in a tree that includes three main branches corresponding to the pillar of sustainability –social, environmental and economic-. 

• Focus on the food security component 

Three criteria were considered to evaluate food security: i) production of human edible proteins and energy at farm level in order to estimate the capacity of farms to feed a large number of people per unit of agricultural land, ii) competition between animals and human food production in order to assess whether the production system is efficient in using resources that could be directly used for human food and that are used for beef production, and iii) production costs of beef, protein and energy that give an indication of the economic accessibility of this food for the population. Some indicators were calculated at farm gate and took into account all inputs and outputs from the farm and included milk and crops sold so that it assessed the contribution of the whole farm to food security. Other indicators were calculated at beef production level to track the factors that could improve the beef production. These indicators only took into account the inputs used to produce meat (including inputs used to produce feed on the farm) based on allocation rules.

Notes: in grey: farm gate indicators, in white: meat production level indicators that include purchased inputs and inputs to produce the feed produced on the farm; HEE: Human Edible Energy and HEP Human Edible Protein; UAA: Utilised Agricultural Area; TL, nTL, LFP are resp. Tillable Land, non-Tillable Land and Land equivalent for the purchased feed; J joule.

• Farm gate protein and energy production

The calculation of the total quantity of food protein and energy produced by each farm that was edible by humans took into account all agricultural production on the farm (beef but also milk, cereals, etc., Table 2). It was evaluated on a per hectare of utilised agricultural area (UAA) basis, which included the UAA of the holding as well as the areas corresponding to feed purchases (Table 3).  For each animal product, the share of human edible protein and the share of human edible energy are defined as a percentage of the gross protein or gross energy of the agricultural product according to Laisse et al. (2019). Meat production depends on carcass yield, which varies according to breed and category of cattle (Table 4). Giblets and human edible by-products which are also produced when slaughtering beef are included in the meat production estimate.

• Feed-food competition

Feed-food competition was assessed by two indicators estimated at the beef production scale, the efficiency of conversion of edible resources in edible animal products, and the use of agricultural land. The ratio of human edible proteins (or energy) produced and used evaluated the net efficiency of conversion of plant proteins (or energy) into beef protein (or energy). An efficiency greater than 1 means that the system produces more human edible protein (or energy) than it consumes. Conversely, an efficiency between 0 and 1 means that the production of meat is a net consumer of protein (or energy). The use of agricultural land is assessed though the amount of tillable and non-tillable land required to produce one kilogram of meat carcass. Non-tillable land corresponds to permanent grassland. These areas are not currently in competition with human food because they may be of low productivity or not accessible by machinery and (or) European Agricultural policy restricts their cultivation (European Commission, 2020). Nonetheless, higher pressure on arable land or climate change might lead to conversion of a part of these permanent grasslands into tillable lands in the future (Havlík et al., 2012). The arable areas (cereals for feeds, temporary grassland, fodder crops, etc.) are considered to be in direct competition with the production of human food. It was assumed that the land required to produce the purchased feed is arable land (including fodder).

• Production costs

The third criterion used to characterize food security is the production cost of agricultural products that reflects the potential price at farm gate. This was calculated at the beef production level per 1 kg of carcass produced, and also at farm level per 1kg of human edible protein and 1 MJ of human edible energy produced. The production cost of a product was estimated considering all farm costs over an annual production cycle and assigning them to a given product. They encompassed current costs (structural costs and costs related to the herd, crops and forage areas), depreciation (wear and tear and discounting of equipment and buildings) and supplementary costs (remuneration of labour and borrowed capital). The remuneration of farm labour was estimated on the basis of the number of worker units provided in the farm case studies multiplied by the median net wage, for 2016, per country available on the European statistics website Eurostat.

• Aggregation

It is necessary to weight and aggregate the indicators in order to be able to summarize all the information in a single note and thus conclude on the sustainability of the systems. However, aggregation has several methodological pitfalls (Schärlig, 1985), including: incommensurability, i.e., the fact that there is no common unit for all indicators, the subjectivity of weights, compensation between indicators and the loss of information when aggregating.

We propose to use The CONTRA® tool to aggregate indicators. This tool combines the advantages of the hierarchical decision trees () and uses the fuzzy logic that ensures transparency, adaptability, flexibility and limitation of compensations (Bockstaller et al., 2017). Details can found in the deliverable. Aggregation has not  been applied yet to the system analyzed.