**5. Discussion and conclusion**

The foundations of resilience analysis have progressively shifted towards the foundations of flexibility analysis. Our assertion is based on qualifying the set of properties that will enable a system to secure sustainability by restricting the use of the two terms to different levels of organization (**Figure 5**): "flexibility" to cover the level overarching the entire production system and "resilience" to cover the underlying level of the biophysical (or operant learning) system. The terms used at the next level down, comprising the organic system entities such as plants and animals, would be "plasticity" and "adaptive capacity" as employed in Ref. [42]. The three examples of production systems highlighted earlier share a common denominator in that they are all "extensive" systems, that is, where productivity per surface unit of land is not maximized compared to intensive systems. A clear pattern emerged, wherein the adaptive capacities of these systems are perceived differently under the two scenarios. The design and development of intensive systems (high production per surface unit of land) consisted then, as now, in targeting measures capable of absorbing the negative effects of increasing perform‐ ance. This means that for the animals, the primary property needed is "robustness," that is, the ability to produce a lot and regularly, regardless of the environmental disturbances.

intensified fodder system gradient, ranging from extensive 100% grassland systems to

Depending on the flexibility leverage deployed by the farmer [7], both the system compo‐ nents (structural dimensions) and their interplays (functional dimensions) will take on a certain measure of specificity. Furthermore, this distinction picks up on the distinction made by Alcaras and Lacroux [16] between the stability of an organization's structure and the stability of an organization's target objectives: (i) the "size" lever: reproductive capaci‐ ties, useful lifespan and carcass yield, for animals that farmers can no longer select to work with once they opt to increase the size of their holding through internal growth (zero buy‐ in); (ii) the "responsiveness" lever (short‐range opportunity‐taking): versatility, ability to handle change (feed type and volume), malleability, breed mix, capacities for out‐of‐season production; (iii) the "collective workflows/technicity" lever: quantitative performance, standardized high‐tech information system, records; (iv) the "room for manoeuvre" lever:

**Figure 5.** Descriptors assigned to adaptive capacities according to level of organization in the functional analysis of

The foundations of resilience analysis have progressively shifted towards the foundations of flexibility analysis. Our assertion is based on qualifying the set of properties that will enable a system to secure sustainability by restricting the use of the two terms to different levels of organization (**Figure 5**): "flexibility" to cover the level overarching the entire production

intensive corn silage‐based systems.

10 Livestock Science

versatility, simplicity, hardiness.

production systems.

**5. Discussion and conclusion**

The levers that farmers can deploy to protect their management systems against market uncertainty will differ depending on farmer standpoints, objectives, lessons learned, the collective organizations they work with, the standards and specifications they work to, etc. Therefore, in order to properly analyse the attributes of systems that make them less vulnerable to unknowns, the focus should be directed towards the information systems employed by farm system managers [45]. It is equally important to identify the interplays between overarching and underlying scale levels for the system studied (panarchy) and to hone in on the dynamics at work during periods of transition.

Literature review combined with the examples compiled reveals that studies directed at developments and changes in farm systems harnessing ecological‐biological (animals, plants, etc.) and human‐social (farmers' strategies and objectives) dimensions can use the notion of flexibility to gain a sharper and more explicit analysis of the interactions between these dimensions.

The move to revitalize the analytical framework governing livestock farming systems has to explicitly factor in dimensions stemming from interactions between animal production science and social sciences (formalization of livestock farmer strategies, workflow organization; [46]) as well as between ecology (resilience) and management science (flexibility). The target is to combine the analytical perspectives on (i) the regulatory properties of management‐led biological systems (such as the herd, whose dynamics are shaped by interactions between human decisions and the biological functions of the animals; [43, 47]) and the leverages capable of parrying the effects of climatic risks and economic unknowns (types of product, relations with downstream factors, socio‐technical networks).

There has been a key turning point in the way agronomics researchers have addressed the issue of performance in farm production systems. There has been a move away from focusing on ways to control or increase quantitative performance metrics (although there are shades of ecological intensification policy that still encourage this kind of outlook; [48]) and towards other rationales, such as "multicriteria" system design and assessment frameworks. Looking at the issues left unresolved and the various standpoints on offer, we have identified at least two courses of action:


Approaches based on concepts and theories borrowed from disciplines such as ecology and management science are particularly fruitful for fuelling reflective thinking and reframing analyses in agronomics science when the aim is to investigate the dynamics of change and the adaptability of farm in response to situations of uncertainty.

For farmers, the art of farm management resides in tackling head‐on how they define and readjust the production objectives set, how they lead negotiations with other farm stakeholders in order to achieve these objectives given the resources available, how they tackle uncertainty and how they tackle opportunity. These are all complex adaptation processes occurring at the interface between the farm and its environment, which emerge not only in the decisions taken but also in the short‐term and long‐term practices that we have termed "flexibility." Our analysis of these processes applied to three real‐world systems enabled us to highlight a handful of principles governing farm business flexibility. First, the situational contextualiza‐ tion: flexibility is dependent not only on the technical features of the production system components (plasticity) but also on the socio‐economic environment in which the businesses evolve; second comes the collectiveness component: flexibility becomes greater as the business integrates the collective dimension of farm activity, even if the overriding aim is to maintain decision‐making autonomy over the production system. Finally, from the methodology standpoint, trials led at our experimental farm station have prompted us to continue investi‐ gations into methods for qualifying and if possible even quantifying the sustainability of farm structures in interaction with their environment, factoring in the different farm‐structuring organizational levels. This research will ultimately be used for inter‐farm comparisons integrating on‐farm production system adaptability over time.
