Co-definition and evaluation of SUSTAINable BEEF farming systems based on resources non edible by humans
A growing world population and changing consumption patterns continue to increase the demand for animal products. In this context, ruminant-based systems have the potential advantage of using resources non edible by humans and converting these into high quality food. However, there is also public debate on issues concerning the cattle sector such as food quality and safety, its impact on climate change, animal welfare and competition between feed and food production.
In that context, we hypothesize that beef farming systems which rely mainly on grasslands and agro-industrial resources non-edible by humans are more sustainable than specialized systems which use feedstuffs that could also be directly used as food or of which the production competes with food production. Sustainability here includes environmental and social dimensions as well as economic perspectives taking into account the services delivered by these systems. Furthermore, ruminant systems relying more on grassland and other biomass non-edible by humans could contribute to a circular economy.
A first core step of SUSTAINBEEF was the definition of beef farm types along with the identification of innovations improving system sustainability while considering feed-food competition. Defining typical systems was beneficial to identify (1) targeted improvements of FarmDyn to assess beef farming systems in their regional contexts and (2) the data needed to clearly define and assess the innovations. Thirteen case studies were identified and described across regions. These data are used to parameterize and subsequently validate the model for the specific case studies.
As a second important step, twenty innovations were identified based on both literature work and interaction with stakeholders and clearly documented in description sheets, aiming at identifying levers and barriers on their implementation on farms. These innovations were subsequently discussed during focus group with farmers, advisers and others actors along the value chain. The related identification of barriers and levers is also important to model the constraints linked to each innovation and to estimate their impacts on the considered beef farming system.
Based on the fuzzy decision methodology, a sustainable evolution tree, including 42 indicators covering all three pillars of sustainability, was developed. A related tool was developed to calculate indicators’ values and implemented into FarmDyn.
List of innovation
Three steps were needed to identify innovation reducing feed-food competition. First, a literature review was performed to identify innovations and describe barriers and levers of their implementation. In parallel, experts were interviewed to complete the list. A first list of twenty innovations was established. Innovations were classified according to two levels: ESR (efficiency, replacement and redesign theory) method and management impact. Thereafter, this list was presented to farmers and advisers to (1) validate the innovations, (2) identify gaps and (3) characterize barriers and levers to their implementation at farm level.
Case studies
In a collaborative approach, case studies across regions were characterized according to a standardized and clearly structured description covering general, herd related and economics data. Data on diet are provided in detail for different animal categories.
Each team was free to develop their case studies based on either: (1) identification of a single farm by experts and use data of this farm or (2) use on a statistical approach to identify the most representative farms. Special attention was paid to the complementarity of case studies selected across regions.
Knowledge products
Legein L., Melchior A., Mertens A., Hennart S., Reding E., Produire de la viande de manière autonome. inWallonie Elevages, novembre 2018 – Livestock magazine
S. Hennart, A. Melchior, L. Legein, A. Mertens, 2019. SUSTAINBEEF : Quelles innovations afin d’optimiser l’utilisation de ressources non comestibles par l’humain lors de la production de viande bovine ? CRA-Info – News brief for Farmers, advisers and administration
J. Balouzat, A. Vaudaux and P. Madrange, Description of beef production (D2.1), 109 p.
Knowledge networks
Knowledge exchange can be possible through different channels:
Other projects, at regional, national or European level, for example INNO-4-GRASS (Be),
Non-governmental organisations involved in farm advising, as Fourrages Mieux (Be),
Farmers unions: FWA (Be), FUGEA (Be),
Coordinated by: Dr. Didier Stilmant - Walloon Agricultural Research Centre (BELGIUM) - Contact
Funded by: SPW-Wallonia, ANR, BMEL, DAFM/Taegasc and MILPAAF as part of the ERA-NET Cofund SusAn through a virtual common pot model including EU Top-Up funding from the European Union´s Horizon 2020 research and innovation programme (grant agreement no 696231).
7 research partners:
BELGIUM: Walloon Livestock Association
FRANCE: Institut National de la Recherche Agronomique and Institut de l’Elevage
GERMANY: University of Bonn
IRELAND: Teagasc and University College Dublin
ITALY: Council for Agriculture Research and Economics
More information:
The SUSTAINBEEF project started on 1 September 2017 and runs until 31 August 2020.
Co-definition and evaluation of SUSTAINable BEEF farming systems based on resources non edible by humans
In that context, we hypothesize that beef farming systems which rely mainly on grasslands and agro-industrial resources non-edible by humans are more sustainable than specialized systems which use feedstuffs that could also be directly used as food or of which the production competes with food production. Sustainability here includes environmental and social dimensions as well as economic perspectives taking into account the services delivered by these systems. Furthermore, ruminant systems relying more on grassland and other biomass non-edible by humans could contribute to a circular economy.
A first core step of SUSTAINBEEF was the definition of beef farm types along with the identification of innovations improving system sustainability while considering feed-food competition. Defining typical systems was beneficial to identify (1) targeted improvements of FarmDyn to assess beef farming systems in their regional contexts and (2) the data needed to clearly define and assess the innovations. Thirteen case studies were identified and described across regions. These data are used to parameterize and subsequently validate the model for the specific case studies.
As a second important step, twenty innovations were identified based on both literature work and interaction with stakeholders and clearly documented in description sheets, aiming at identifying levers and barriers on their implementation on farms. These innovations were subsequently discussed during focus group with farmers, advisers and others actors along the value chain. The related identification of barriers and levers is also important to model the constraints linked to each innovation and to estimate their impacts on the considered beef farming system.
Based on the fuzzy decision methodology, a sustainable evolution tree, including 42 indicators covering all three pillars of sustainability, was developed. A related tool was developed to calculate indicators’ values and implemented into FarmDyn.
List of innovation
Three steps were needed to identify innovation reducing feed-food competition. First, a literature review was performed to identify innovations and describe barriers and levers of their implementation. In parallel, experts were interviewed to complete the list. A first list of twenty innovations was established. Innovations were classified according to two levels: ESR (efficiency, replacement and redesign theory) method and management impact. Thereafter, this list was presented to farmers and advisers to (1) validate the innovations, (2) identify gaps and (3) characterize barriers and levers to their implementation at farm level.
Case studies
In a collaborative approach, case studies across regions were characterized according to a standardized and clearly structured description covering general, herd related and economics data. Data on diet are provided in detail for different animal categories.
Each team was free to develop their case studies based on either: (1) identification of a single farm by experts and use data of this farm or (2) use on a statistical approach to identify the most representative farms. Special attention was paid to the complementarity of case studies selected across regions.
Knowledge products
Knowledge networks
Knowledge exchange can be possible through different channels:
Coordinated by: Dr. Didier Stilmant - Walloon Agricultural Research Centre (BELGIUM) - Contact
Funded by: SPW-Wallonia, ANR, BMEL, DAFM/Taegasc and MILPAAF as part of the ERA-NET Cofund SusAn through a virtual common pot model including EU Top-Up funding from the European Union´s Horizon 2020 research and innovation programme (grant agreement no 696231).
7 research partners:
More information: