1 Introduction
Life cycle assessment (LCA) is a decision support tool to evaluate the environmental impacts of a product or service throughout its life cycle (Hellweg and Milà i Canals
2014). There is a growing interest by producers and consumers to broaden LCA to the economic and social dimensions of sustainability. It is demanded in general (Hellweg and Milà i Canals
2014) and specifically in the food sector (Nemecek et al.
2016). Although methods for life cycle costing (Swarr et al.
2011) and databases for social impact assessment (Benoit-Norris et al.
2012) exist, they are rarely applied. With the increasing consumption of animal products (Kearney
2010), also animal welfare becomes more and more relevant for sustainability assessments of food, but is even more neglected.
Animal welfare refers to the physical and mental well-being of non-human animals (Carenzi and Verga
2009). In this respect, the Farm Animal Welfare Council defined five freedoms that need to be provided to achieve animal welfare: (1) freedom from thirst, hunger and malnutrition; (2) freedom from discomfort; (3) freedom from pain, injury and disease; (4) freedom to express normal behaviour; and (5) freedom from fear and distress (Farm Animal Welfare Council
1993). Since the emotional state of animals cannot be directly measured (Carenzi and Verga
2009; Chan
2011), welfare is in practice assessed based on the satisfaction of needs. Bartussek, for example, developed an animal needs index for pigs (Bartussek
1995b), laying hens (Bartussek
1995a) and cattle (Bartussek
1996) distinguishing 30 to 38 criteria grouped into five areas of influence: (1) possibility of movement, (2) social contact, (3) floor condition, (4) climate, and (5) care intensity. He assigns scores for defined intervals of each criterion, which results in a qualitative assessment of animal welfare.
A search in Web of Science of scientific articles with the terms ‘(“life cycle assessment” OR “life cycle sustainability assessment” OR “LCA” OR “LCSA”) AND (“animal product*” OR “meat” OR “milk” OR “egg*” OR “fish*” OR “seafood” OR “diet*”)’ in their title, abstract, and keywords resulted in 1117 publications, whereas the same search with the additional term “animal welfare” resulted in 20 publications out of which only nine actually assessed animal welfare (< 1%). Seven articles assessed animal welfare for a single product type, mostly dairy products (van Asselt et al.
2015; Meul et al.
2012; Mueller-Lindenlauf et al.
2010; Del Prado et al.
2011; Schmitt et al.
2016; Zucali et al.
2016), and once poultry (Castellini et al.
2012). Two articles examined multiple products, including meat, eggs, and dairy products (Head et al.
2014; Röös et al.
2014). Each study uses different criteria to assess animal welfare. Most rely on qualitative and relative scores that do not allow for comparisons across studies. More complex approaches like the animal needs index mentioned above (van Asselt et al.
2015) or approaches that require farm visits and laboratory measurements of blood samples (Castellini et al.
2012) are not applicable at large scales. The two studies that assessed multiple product types aimed to provide a communication tool that guides consumers. However, their approaches are very generic. They either use a traffic light system for qualitative scores based on information from literature (Röös et al.
2014) or even rely on expert opinions to assign scores (Head et al.
2014). These scores are subjective and not related to a functional unit, as required for life cycle assessment. Only one study considers slaughter conditions (Röös et al.
2014) and one study considers the slaughter age (Schmitt et al.
2016) in their assessments.
Previous approaches only assessed animal welfare of livestock and rarely of fish, but ignored animal welfare of other aquatic animals and of insects, which are both food sources, too. While farm animals are already widely acknowledged as sentient beings (Carenzi and Verga
2009), the sentience of other species such as fishes and insects is debated. Although fishes are unlikely to experience the same complexity of emotions as human beings, still several studies suggest that there is anatomical, physiological, and behavioural evidence for fish sentience (Ashley
2007; Hastein et al.
2005). The increasing demand for proteins suggests that also alternative protein sources to traditional animal products will have to be exploited more in the future, and insects represent such a potential novel protein source (Boland et al.
2013; Rumpold and Schlüter
2013). Although more uncertain, research suggests that invertebrates (including insects) are sentient, in which case their welfare should be accounted for (Chan
2011; Elwood
2011).
Overall, this study aims at providing a quantitative framework for animal welfare assessment compatible with life cycle assessment. While we acknowledge that wild animals can accidentally be killed for crop and feed cultivation as well as livestock farming (see discussion), the scope of this study is limited to animals that are intentionally killed for human food consumption. It is applied to various animal-based food products including aquatic animals and insects. Finally, the impacts on animal welfare are compared for various diets.
2 Methods
2.1 Framework
Besides the intensity of distress, the duration (Morton and Hau
2010) and the number of affected animals (Chan
2011) should be taken into account in animal welfare assessments. The latter especially applies when comparing diverse animal products. Then, the welfare of invertebrates, whose sentience and consciousness is more uncertain, might gain in importance due to the large number of affected animals. At the same time, the number of affected animals allows to relate the impacts to a functional unit, such as 1 Mcal of the food product. Furthermore, the assessment should go beyond the farm gate and include the slaughter conditions to complete the life cycle. Finally, a quantitative assessment in absolute terms is preferred, as it allows for comparisons across studies. In summary, we recommend that the assessment:
1.
considers the quality of an animal’s life, the lifetime, and the number of animals affected,
2.
considers the conditions during farm life and slaughter (including transport to the slaughterhouse), and
3.
is quantitative and related to a functional unit.
We suggest three alternative welfare indicators meeting these requirements: animal life years suffered (ALYS), loss of animal lives (AL), and loss of morally adjusted animal lives (MAL). The indicators differ in the valuation of the time lost due to premature death (lives lost). Providing three alternatives allows to choose an indicator according to the preferred ethical view or to compare the resulting impacts of all three for sensitivity analyses.
2.1.1 Animal life years suffered (indicator 1)
Indicator 1 disregards the premature death of the animals, because, for animals living a life full of suffering, death might even mean a salvation from that suffering. The duration of suffering is then the focus and welfare loss is expressed as animal life years suffered (ALYS):
$$ \mathrm{Animal}\ \mathrm{welfare}\ \mathrm{loss}=\mathrm{number}\ \mathrm{affected}\times \left(\left[\mathrm{life}\ \mathrm{duration}\hbox{--} \mathrm{slaughter}\ \mathrm{duration}\right]\times \left[1\hbox{--} \mathrm{life}\ \mathrm{quality}\right]+\mathrm{slaughter}\ \mathrm{duration}\right) $$
2.1.2 Loss of animal lives (indicator 2)
Besides possible proximate interests such as avoiding pain, an animal ultimately strives for survival and reproduction, although not all animals might be conscious of that goal (Chan
2011). Therefore, it can be argued that not only the animal’s life quality should be considered in animal welfare assessment, but also their years of life lost. This is in line with the assessment of life cycle impacts on human health, which are typically expressed as disability-adjusted life years (DALYs), a disease burden indicator developed by the World Health Organization (Murray
1994). DALYs are composed of (1) years lost due to disability, which corresponds to the frustration of animal needs assessed in previous animal welfare studies and (2) years of life lost due to premature mortality, which is also against animals’ interest. Since animal species differ in life expectancies, we consider life fractions instead of an absolute duration. Consequently, welfare loss in
indicator 2 is expressed in number of animal lives (AL) lost:
$$ \mathrm{Animal}\ \mathrm{welfare}\ \mathrm{loss}=\mathrm{lives}\ \mathrm{lost}\ \left(\mathrm{LL}\right)+\mathrm{lives}\ \mathrm{with}\ \mathrm{disability}\ \left(\mathrm{LD}\right) $$
where
$$ \mathrm{Lives}\ \mathrm{lost}\ \left(\mathrm{LL}\right)=\mathrm{number}\ \mathrm{affected}\times \left(1\hbox{--} \mathrm{life}\ \mathrm{fraction}\right) $$
and
$$ \mathrm{Lives}\ \mathrm{with}\ \mathrm{disability}\ \left(\mathrm{LD}\right)=\mathrm{number}\ \mathrm{affected}\times \left(\left[\mathrm{life}\ \mathrm{fraction}\hbox{--} \mathrm{slaughter}\ \mathrm{fraction}\right]\times \left[1\hbox{--} \mathrm{life}\ \mathrm{quality}\right]+\mathrm{slaughter}\ \mathrm{fraction}\right) $$
In contrast to
indicator 1, a shorter life is considered worse in this case. This was also assumed by Schmitt et al. (
2016). Indicator
2 also implies that the lives of all animals are equally valued. This can be justified by assuming that all the analysed animals are sentient to some extent, and that all sentient beings have an interest in continuing to live (Francione
2010).
2.1.3 Loss of morally adjusted animal lives (indicator 3)
Alternatively, the lives of different animal species can be valued gradually, depending on their degree of self-awareness and their sense of time. Therefore, we introduce a moral value to
indicator 3 and express welfare loss as morally adjusted animal lives (MAL):
$$ \mathrm{Lives}\ \mathrm{lost}\ \left(\mathrm{LL}\right)=\mathrm{number}\ \mathrm{affected}\times \left(1\hbox{--} \mathrm{life}\ \mathrm{fraction}\right)\times \mathrm{moral}\ \mathrm{value} $$
We do not introduce the same moral value for lives with disability, because the sentience of animals is considered in the life quality. Thus, lives with disability are estimated in the same way as in indicator 2.
2.2 Criterion 1: life quality
Animals have many needs and the frustration of any of them can lead to a loss in animal welfare. Since information about the satisfaction of all those needs is scarce, not all can be considered. While we recommend covering several needs in future studies, we select here only one criterion for each animal product as a proxy for overall life quality. Focussing on criteria with the highest availability allows to apply the indicator at large scales and facilitates comparisons across studies. The score for life quality ranges from a minimum quality of 0 to a maximum quality of 1.
For dairy cattle, life quality is approximated by the number of days per year on pasture. According to the Welfare Quality® assessment protocol for cattle (Welfare Quality®
2009a), first an index
I is calculated:
$$ I=100\times \mathrm{days}\ \mathrm{on}\ \mathrm{pasture}/365 $$
This index is then transformed to a quality score using spline functions. If
I is lower or equal to 50:
$$ \mathrm{Quality}=\left(1.7756\times I-0.00093197\times {I}^2-0.00010556\times {I}^3\right)/100 $$
Otherwise,
$$ \mathrm{Quality}=\left(-37.324+4.0151\times I-0.045721\times {I}^2+0.00019303\times {I}^3\right)/100 $$
For beef cattle, life quality is also approximated by the number of days per year on pasture, using the same index
I as for dairy cattle. Two cases are distinguished for transforming the index to a quality score, depending on access to pasture before fattening. If the cattle had no access to pasture prior to fattening and
I is lower or equal to 10:
$$ \mathrm{Quality}=\left(4.0025\times I-0.28112\times {I}^2+0.0092976\times {I}^3\right)/100 $$
Otherwise without access to pasture:
$$ \mathrm{Quality}=\left(9.3096+1.2096\times I-0.0018293\times {I}^2-0.000011980\times {I}^3\right)/100 $$
If the cattle had access to pasture prior to fattening and
I is lower or equal to 10:
$$ \mathrm{Quality}=\left(3.9875\times I-0.22139\times {I}^2+0.0068822\times {I}^3\right)/100 $$
Otherwise,
$$ \mathrm{Quality}=\left(6.8136+1.9435\times I-0.016979\times {I}^2+0.000068633\times {I}^3\right)/100 $$
If the conditions before fattening are unknown, we suggest to take the average score of both cases.
For pigs, life quality is approximated by the surface area available for each animal (m
2/100 kg). According to the Welfare Quality® assessment protocol for pigs (Welfare Quality®
2009b), the index
I is calculated as:
$$ I=\left(10.3\times \mathrm{surface}\ \mathrm{area}\right)-3.09 $$
If
I is lower or equal to 20,
$$ \mathrm{Quality}=\left(12.306\times I-0.58370\times {I}^2+0.0096231\times {I}^3\right)/100 $$
Otherwise,
$$ \mathrm{Quality}=\left(76.822+0.78238\times I-0.0075336\times {I}^2+0.000020276\times {I}^3\right)/100 $$
For broilers and laying hens, life quality is approximated by the stocking density (kg/m
2). According to the Welfare Quality® assessment protocol for poultry (Welfare Quality®
2009c), the index
I is calculated as:
$$ I=2.5\times \left(44-\mathrm{stocking}\ \mathrm{density}\right) $$
If
I is lower or equal to 30:
$$ \mathrm{Quality}=\left(2.6077\times \mathrm{I}-0.051672\times {\mathrm{I}}^2+0.00050863\times {\mathrm{I}}^3\right)/100 $$
Otherwise,
$$ \mathrm{Quality}=\left(12.019+1.4058\times \mathrm{I}-0.011609\times {\mathrm{I}}^2+0.000063483\times {\mathrm{I}}^3\right)/100 $$
While the assessment protocol (Welfare Quality®
2009c) distinguishes between broilers and laying hens and does not yet provide scores for the latter, we use the same approach for laying hens as for broilers.
For the above five animal products, as the animal products most commonly consumed in Western societies, we also test alternative life quality scores (see
Electronic Supplementary Material).
For Atlantic salmon, life quality is approximated by the stocking density (kg/m
3). We define two boundary values, which are obtained from Turnbull et al. (
2005). Outside of this range, animal welfare is not affected anymore because maximum or minimum welfare are already reached. Within the range, we fit a linear regression line from a minimum quality of 0 to a maximum quality of 1:
$$ \mathrm{Quality}=4.67-0.17\times \mathrm{stocking}\ \mathrm{density} $$
For shrimps, we assumed maximum life quality of 1, as, in our case studies, they were not farmed but wild-caught and, consequently, their life quality is not affected by human interference until their premature death.
For insects, we assumed a life quality of 0.999. On the one hand, insect rearing is not regulated (de Goede et al.
2013) and, as a result, insect treatment is likely to be much more inhumane than for livestock. We assume a twice as bad treatment as for chickens (e.g. half the space allowance in proportion to their body size). On the other hand, insects have a roughly 2000 times lower sentience (and moral value, see criterion 4) than chickens as the livestock closest in size and sentience. Therefore, their life quality is less affected. The maximum suffering, which is 1 for chickens, would then be 2/2000 = 0.001 for insects, and life quality is then 0.999.
Slaughter of the animals—including associated operations such as transport of livestock to the slaughterhouse—are assigned a life quality of 0, independent of the animal and production system. Stunning before slaughter is not always practiced due to religious beliefs; when it is practiced, it is not always successful at first attempt (Grandin
2010); and even if it is successful, animals are exposed to several other factors that cause immense stress during the pre-slaughter period (Terlouw et al.
2008). What differs in our assessment is the duration of suffering.
2.3 Criterion 2: number affected
The number of animals affected per functional unit (e.g. 1 kg of meat) depends on the yield:
$$ \mathrm{Number}\ \mathrm{affected}=1/\mathrm{yield}=1/\left(\mathrm{live}\ \mathrm{weight}\times \mathrm{product}\ \mathrm{fraction}\right) $$
In case of meat or fish (as opposed to milk and eggs), the live or slaughter weight is usually specified and has to be converted to the product weight that is ready to be cooked and eaten with the product fractions given in Table
1. For shrimps and insects, we assume a product fraction of 100%. Further allocation of different meat qualities can be done in subsequent modelling.
Table 1
Product fractions
Cattle (beef) | 0.353 | 0.679 | |
Pigs | 0.417 | 0.528 | |
Chickens (broilers) | 0.699 | 0.724 | |
Atlantic salmons | 0.560 | 0.620 | (Bencze Rørå et al. 1998) |
More than one animal can be affected by a product, such as male chicks in egg production and bobby calves in milk production. Moreover, when catching shrimps, other species are accidentally caught of which some are discarded and not used as food. Such additional life loss is also attributed to the respective product and, as such, reduces the product yield per animal killed. However, we disregarded the possible use of animal products such as fishmeal and fish oil as animal feed (Shepherd and Jackson
2013).
Contrariwise, one animal can produce more than one product. This was accounted for by converting the by-products to equivalents of the main product, and this increases the yield per animal. For beef cattle, a monetary value fraction of 0.83 was assumed for the meat, while other profit can be made from edible offal, semen, and leather (Mekonnen and Hoekstra
2010). We assumed dairy cows to weigh 500 kg at the end of their life by selecting a low value of reported live weights of beef cattle. Bobby calves were assumed to have a slaughter weight of 17 kg (Flysjö et al.
2011). We used a ratio of 11:1 as price ratio per kg between meat and milk (More O’Ferrall
1982). For eggs, value fractions were derived from the revenue of eggs compared to that of meat from spent hens which are provided for different egg production systems (Dekker et al.
2011). When catching shrimps, some of the accidentally caught other species are not discarded but used as additional food (by-catch) (Ziegler et al.
2011). We assumed equal values per mass for shrimps and by-catch.
Since calories better represent the function of food than mass does, we convert the number of affected animals per kg to the number of affected animals per Mcal (1000 food calories). The caloric contents are displayed in Table
2.
Table 2
Caloric content of animal products
Beef | 2760 | |
Pork | 2630 | |
Poultry | 2150 | |
Eggs | 1430 | |
Milk | 610 | |
Salmon | 2080 | |
Shrimp | 850 | |
Cricket | 1200 | |
Mealworm | 2060 | |
2.4 Criterion 3: time
The slaughter age defines the life duration, while the life fraction is derived from the ratio of slaughter age to life expectancy:
$$ \mathrm{Life}\ \mathrm{fraction}=\mathrm{slaughter}\ \mathrm{age}/\mathrm{life}\ \mathrm{expectancy} $$
Life spans and life expectancies are compiled in Table
3. Life span is here defined as the maximum number of years an animal of that species can live, while life expectancy is the average number of years that an animal is expected to live at birth. Premature death can also happen in wildlife, for example, caused by natural predators such as wolves, pumas, and leopards. This reduces the life expectancy compared to the life span. Such differences can be substantial: for example, buffalos (wild relatives of domestic cattle) have a natural life span of 20–23 years, but live, on average, for only 4.3 to 6.3 years; and warthogs (wild relatives of domestic pigs) live, on average, less than 3 years despite a natural life span of 17 years (Spinage
1972). Although farmed animals can be victims of predators as well (Treves and Karanth
2003), captive animals often have higher life expectancies than free-living animals (Mason
2010) if they would not be slaughtered. As a conservative approach, we still assume the life expectancy of an animal in wildlife as the reference for our analysis, however, without granting benefits when a farm animal lives longer (i.e. the life fraction is limited to 1), which can happen for dairy cows in some systems.
Table 3
Demographic characteristics of animals
Cattle (beef and dairy cattle) | 20 | 5a
| |
Pigs | 15 | 3a
| |
Chickens (broilers and laying hens) | 7.5 (5–10) | 3 | (Delgado et al. 2017, Komiyama et al. 2004) |
Atlantic salmons | 13 | 6 | (Kalman and Sjonger 2007) |
Southern pink shrimps | | 1.67 | (García-Isarch et al. 2013) |
Mealworm (Tenebrio molitor) | | 0.5 | |
House cricket (Acheta domesticus) | | 0.21 | |
If more than one animal is affected by a product, the weighted average of slaughter ages is taken. This concerns egg and milk production. Male chicks are culled at the age of 1 day because they cannot lay eggs, while laying hens are slaughtered after more than 1 year (Aerts et al.
2009). Similarly, in some milk production systems, bobby calves are slaughtered a few days after being born because not all calves can be raised for economic profit, whereas the dairy cow lives for a few years (Flysjö et al.
2011).
The time from catching the animals to be slaughtered until they actually die is described here as the slaughter duration, while slaughter fraction is relative to the life span. For livestock, it includes the loading of animals into a transport vehicle, the journey to the slaughterhouse, and the waiting in the slaughterhouse. The FAO recommends that cattle should not be transported for longer than 36 h (Chambers et al.
2001), and the EU limits the transport of poultry without water to 12 h (Eyes on Animals
2013). Loading the truck beforehand and waiting for slaughter afterwards can each take another couple of hours (Eyes on Animals
2013). Based on that, we assumed a slaughter duration of 1 day for livestock. In extreme cases, transport can even take a month (Independent
2008). Wild aquatic animals usually die from suffocation, which takes some minutes. The operation before that, for example trawling with a net, can take several hours (Braithwaite
2010). Only shrimps are concerned by wild catch in our study, and we assumed a slaughter duration of 1 h. In aquaculture, in our case for salmon production, we assumed fish is killed by a gill cut or by stunning with carbon dioxide followed by a gill cut, which takes, on average, 5–6 min until brain function is lost (van de Vis et al.
2003). Adding time for pre-slaughter management, we assumed a slaughter duration of 10 min. In contrast, insects are often killed by freezing, and we assumed that it takes 10 min (Roscoe et al.
2016), although it can even take 1 h (Lo Pinto et al.
2013) (Table
4).
Table 4
Slaughter duration
2.5 Criterion 4: moral value
Scientific evidence shows that pigs, cattle, and some birds are self-aware and plan for the future. Also chickens recognise individuals and plan at least for the near future (Marino
2017). Fishes are less likely to be self-aware, but demonstrated their ability of remembering the past by still remembering a hole in a net after almost a year (Singer
2011). Insects are much less understood and might not be self-aware. However, it would be misleading to draw conclusions from their brain size about their intelligence. Social insects such as ants and bees were found to be much more intelligent than previously thought (Chittka and Niven
2009). Although intelligence is not a measure of self-awareness, we give them the benefit of the doubt (Singer
2011) and assume that they are to some extent self-aware. Still, the degree of self-awareness and sense of time of all the animals under investigation is lower than of human beings who are the only ones with a biographical sense, who tell stories about their past and hope to achieve something in the far future (Singer
2011).
We assigned each species a moral value based on their expected intelligence relative to a human being (Table
5). We approximated intelligence either by brain mass, number of total neurons, or number of cortical neurons, depending on data availability. Among these, the number of cortical neurons seems to be the best measure of intelligence and is the only one that can explain the superior cognitive abilities of human beings. An elephant’s brain, for example, is about three times larger and contains three times more neurons than the average human brain, but only a third of the cortical neurons as found in human brains (Herculano-Houzel et al.
2014).
Table 5
Moral valuation of animal lives
Humana
| – | 16 billion | 86 billion | 1508 g | 1 |
Cattleb
| – | | 3 billion | | 0.035 |
Pigc
| – | 432 million | | | 0.027 |
Chickend
| Red junglefowl | 61 million | | | 0.0038 |
Salmone
| Shark | | | 1.8 g | 0.0012 |
Shrimpf
| Lobster | | 100,000 | | 1.2 × 10−6
|
Cricketg
| Fruit fly and ant | | 250,000 | | 2.9 × 10−6
|
Mealwormg,h
| Fruit fly, ant and zebrafish | | 25,000 | | 2.9 × 10−7
|
2.6 Case study
To illustrate the application of the framework, we perform a case study comparing different types of animal products and different diets. The data for the evaluation are obtained from a literature review. Most information is retrieved from LCA studies on environmental aspects of animal production, which provide parameters relevant for an animal welfare assessment. In total, our database covers 50 cases for eight animal products.
The sensitivity of animal welfare to changes in the parameters describing the criteria was tested. Individual input parameters were halved and the effect on animal welfare (output) was quantified using (MacLeod et al.
2002):
$$ S=\frac{\Delta \mathrm{Output}/\mathrm{output}}{\Delta \mathrm{Input}/\mathrm{input}} $$
We estimated animal welfare for the world average per capita consumption of animal products in the year 2011 based on data from FAOSTAT (FAO
2015). The consumption of beef, pig, and chicken meat was scaled up to compensate for the neglected consumption of mutton, goat, and other meat. Although it is a large simplification, all fish consumption was represented by salmon and all seafood consumption by shrimps. A hypothetical diet without seafood, a diet without birds (poultry and eggs) and an ovo-lacto-vegetarian diet were constructed by replacing all missing animal proteins by proteins from the remaining animal products. An additional vegetarian diet was constructed by assuming that missing animal proteins are substituted by proteins from plants, without the need to increase the consumption of milk and eggs (Table
6).
Table 6
Animal product composition in kg/(a × capita) of different diets with equal protein intake
Beef | 10.1 | 12.7 | 18.7 | – | – |
Pork | 16.7 | 21.0 | 31.0 | – | – |
Poultry | 15.6 | 19.6 | – | – | – |
Milk | 90.7 | 114 | 168 | 259 | 90.7 |
Eggs | 8.95 | 11.2 | – | 25.6 | 8.95 |
Salmon | 14.2 | – | – | – | – |
Shrimps | 4.93 | – | – | – | – |
Following the LCA framework, the life cycle inventory—the diets—was multiplied with the impact characterisation factor—the developed welfare indicator—to yield the total impact on animal welfare caused by the respective diet.
5 Conclusions
This study proposes a framework for animal welfare assessment. While the assessment is simplified, it allows for a direct integration into life cycle sustainability assessment. There is a trade-off between applicability and indicator complexity, but even a simple estimate of animal welfare is much better than ignoring the issue, as is the common practice in life cycle sustainability assessments. The framework aims to enable routine assessments of animal welfare. Three alternative animal welfare indicators are suggested: (1) animal life years suffered (ALYS), (2) loss of animal lives (AL), and (3) loss of morally adjusted animal lives (MAL). The indicators all consider three criteria: (1) the life quality of an animal on the farm and during the slaughter process, (2) the slaughter age either as life duration or life fraction, and (3) the number of animals affected for providing a product unit. One of the indicators additionally takes into account a moral value assigned to animals based on the number of neurons or brain mass as a proxy for their intelligence and self-awareness. The indicators differ in the valuation of the time lost due to premature death. Providing multiple alternatives allows to choose an indicator according to the preferred ethical view or to conduct sensitivity analyses.
The indicators are applied to eight products: beef, pork, poultry, milk, eggs, salmon, shrimps, and insects. Animal welfare loss is most influenced by the number of animals affected. Consequently, the difference in animal welfare is often larger for different animal products than for different production systems of the same product. While milk reduces animal welfare the least according to most indicators and criteria, insects perform worst despite a much lower assumed sentience. The sentience as a proxy for the moral value is the most normative choice and requires special attention in future research comparing welfare across species. As a note of caution, the investigated case studies might not be representative for global production. Future research should extend the database to improve the representativeness and the product coverage. If more welfare-relevant variables are reported in the future, the life quality criterion can also be elaborated to account for further animal needs.
In Western societies with a high meat consumption, a reduced intake of animal products benefits their health, the environment, and the production animals. Still, consumers often face high personal barriers. Besides a reduction, our study shows a further opportunity to improve animal welfare: a shift to other products, usually derived from larger animals.
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