‘Adapt to endure’ has become a necessity in agriculture, but the means to do so remain largely undefined. The aim of this literature review is to analyse how the herd contributes to a livestock farming system's capacity to adapt to a changing world and evolve when the future is uncertain. We identify six categories of elements linked to the herd, called ‘sources of flexibility’, that are used to manage perturbation. The first three are: using the animal's adaptive capacities, using the diversity of species and breeds and combining the diversity of animal products. The last three are: organising the mobility of animals and livestock farmers, juggling the herd numbers and mastering the balance between productivity and herd survival. These sources of flexibility are described in the literature by studying the different ways in which they are used. For example, the ‘juggle herd numbers’ source is described by volume, categories of animals, type of transfer, such as births, purchases or gifts, and timing of use, especially linked to the timing of the perturbation. Identified studies also compare or rank sources and analyse the connections between them. The flexibility framework (management science) is used for this analysis according to the levels of organisation of a livestock farming system: a strategic level referring to long-term options and to the capacity to modify the system structure, and an operational level referring to adjustment decisions during the productive cycle, the presence or absence of intervention by the livestock farmer, and the time scales involved. We conclude that the decision to use one or another source (in terms of modalities, alternatives, scheduling and combinations) is made according to the production objectives, the structural means, the type/frequency/intensity of perturbations and the context/environment. Consequently, the flexibility of a livestock farming system cannot be assessed in absolute terms. Enhancing flexibility needs management of all elements and scales involved (and not only the herd), and requires diversity to be organised at different scales.

Finding ways of increasing the efficiency of production systems is a key issue of sustainability. System efficiency is based on long-term individual efficiency, which is highly variable and management driven. To study the effects of management on herd and individual efficiency, we developed the model simulation of goat herd management (SIGHMA). This dynamic model is individual-based and represents the interactions between technical operations (relative to replacement, reproduction and feeding) and individual biological processes (performance dynamics based on energy partitioning and production potential). It simulates outputs at both herd and goat levels over 20 years. A farmer’s production project (i.e. a targeted milk production pattern) is represented by configuring the herd into female groups reflecting the organisation of kidding periods. Each group is managed by discrete events applying decision rules to simulate the carrying out of technical operations. The animal level is represented by a set of individual goat models. Each model simulates a goat’s biological dynamics through its productive life. It integrates the variability of biological responses driven by genetic scaling parameters (milk production potential and mature body weight), by the regulations of energy partitioning among physiological functions and by responses to diet energy defined by the feeding strategy. A sensitivity analysis shows that herd efficiency was mainly affected by feeding management and to a lesser extent by the herd production potential. The same effects were observed on herd milk feed costs with an even lower difference between production potential and feeding management. SIGHMA was used in a virtual experiment to observe the effects of feeding strategies on herd and individual performances. We found that overfeeding led to a herd production increase and a feed cost decrease. However, this apparent increase in efficiency at the herd level (as feed cost decreased) was related to goats that had directed energy towards body reserves. Such a process is not efficient as far as feed conversion is concerned. The underfeeding strategy led to production decrease and to a slight feed cost decrease. This apparent increase in efficiency was related to goats that had mobilised their reserves to sustain production. Our results highlight the interest of using SIGHMA to study the underlying processes affecting herd performance and analyse the role of individual variability regarding herd response to management. It opens perspectives to further quantify the link between individual variability, herd performance and management and thus further our understanding of livestock farming systems.