One of the most critical steps in applying the Welfare Footprint Framework is deciding how Affective Experiences should be defined for analysis. This step determines the resolution of the welfare quantification—too broad, and important nuances may be lost; too detailed, and the analysis may become unmanageable or unsupported by data. The challenge lies in avoiding both oversimplification and unnecessary complexity.

Not enough detail

It is important to recognize that the appropriate level of detail will depend on the objective of the analysis. In some cases, the goal may require rapidly estimating the overall affective state of an animal during a given phase of life, such as the rearing or production phase. In such cases, it may be valid to use broad categories of experience—such as general discomfort or well-being—without distinguishing between every individual disease, deprivation, or positive event the animal may encounter. However, this type of broad estimation cannot be directly supported by empirical evidence on intensity and duration. That is because the scientific literature and field data provide validated indicators of intensity and duration only for specific conditions, such as fractures, respiratory infections, social bonding, or restricted movement.

To ensure that the analysis remains evidence-based, it is often necessary to define affective experiences at a more granular level. This means anchoring them in specific biological outcomes and pathways that are known to result in distinct experiences. Pain from a fractured limb, pleasure from affiliative interactions, or fear from predator exposure are all examples of experiences with documented affective relevance and measurable properties. These experiences are not only more scientifically tractable—they also tend to mark events that are especially meaningful in the life of the animal. They help characterize key welfare risks and opportunities.

Too much detail

At the other end of the spectrum, one may be inclined to define affective experiences in an overly detailed manner. This often happens when attempting to distinguish between every minor variation in symptom presentation or disease etiology, believing that more granularity necessarily equates greater accuracy. However, excessive granularity can make the analysis cumbersome and can exceed the resolution supported by available data. For example, differentiating between every possible cause of lameness, or treating slight differences in respiratory pathogen strains as separate experiences, may not yield meaningful distinctions in what the animal feels.

A similar issue arises when users define affective experiences based on symptoms (e.g., fever, coughing). This approach may appear detailed or clinically grounded, but it introduces unnecessary complexity and limits the usefulness of the analysis. Symptoms can indeed be part of an affective experience—such as coughing that results from a painful airway irritation —but they are often not reliable units of analysis on their own. Symptoms reflect observable aspects of a disease process, and their presence may suggest a certain intensity or progression.

Moreover, symptoms (or indicators – see this figure) do not provide a dependable basis for quantification. There is often no validated data on the duration and prevalence of individual symptoms in a population. For example, we might know how many animals suffer from tracheitis, and for how long, but we typically lack systematic data on how many animals cough, how frequently, and for how long in a way that can be used for quantification. Because symptoms are part of the unfolding of a disease over time, they are best viewed as indicators or inputs—useful in clinical judgment or diagnostic pathways. Additionally, the same symptom can appear across a range of very different conditions, and its affective relevance depends entirely on context. Coughing could reflect a temporary irritation or a necrotizing infection. Treating symptoms as affective experiences overlooks this variability and masks the differences in what the animal may actually feel.

Classifications based on Aetiology

Another common tendency is to define Affective Experiences in terms of the specific aetiology of a condition—for example, as “respiratory disease caused by respiratory syncytial virus (RSV),” or “respiratory disease caused by influenza virus,” or “respiratory disease caused by metapneumovirus.” While this level of detail might reflect a clinical interest in diagnosis, it is not helpful for affective experience analysis. First, population-level data on the prevalence of these specific viral causes is rarely available, as diagnostic testing is typically not performed routinely, especially in large-scale production systems. Second, these aetiologies often result in highly similar symptoms, and the affective experience they produce (such as irritation, air hunger, or fatigue) is often indistinguishable. In human health, a common practice is to refer to these overlapping presentations as “Influenza-like illness” (ILI), recognizing that the syndrome is more relevant to the patient’s experience than the precise pathogen. The same logic applies here: rather than defining separate affective experiences by each pathogen, it is better to define them at the level of the syndrome or biological outcome they share. This allows for more realistic, evidence-based estimates of intensity and duration, and avoids artificial fragmentation of experience categories.