Malith S. Sooriya Patabendige
Md Elias Uddin- Department of Animal Science, University of Connecticut, Storrs, CT, United States
Livestock significantly contribute to biodiversity loss, primarily due to changes in land use, overexploitation of natural resources, pollution, and climate change. Intensive farming systems that depend heavily on resource inputs accelerate the decline of species and exert immense pressure on biodiversity, ultimately making the industry unsustainable. Identifying hotspots and quantifying their impacts along the value chain of animal products help producers and policymakers make informed decisions and provide insights to guide consumers toward more environmentally conscious purchasing. Life cycle assessment (LCA) provides a holistic approach to assessing the environmental footprint (EF) throughout the life cycle of a product or service. However, ecosystem services, such as biodiversity, are often not integrated into LCA, particularly in the context of livestock systems. Existing studies and methodologies frequently fail to illustrate the impacts of biodiversity under various management practices. In addition, the majority of these studies focus on a single midpoint impact category related to land use change, which is based on the species–area relationship (SAR) and metrics at the species level. However, due to the dynamic and complex nature of biodiversity, relying a single midpoint impact or metric alone is insufficient to capture the full spectrum, and it does not provide a comprehensive understanding of the impacts. In addition, the lack of consensus on characterization factors (CFs), limitations in data availability (i.e., conservation status of taxa at local and regional levels), and challenges in assigning weights to taxa and ecological functions based on their significance are key limitations that need to be addressed in future LCA studies.
1 Introduction
An unprecedented level of biodiversity loss has been observed in recent decades, with a decline of 69% in biodiversity between 1970 and 2018 (WWF, 2022). Habitat loss caused by the conversion of natural ecosystems for food and agricultural production, as well as habitat homogenization and degradation arising from agricultural intensification, has been identified as the primary reason for this trend (IPBES, 2019). Approximately 45% of the habitable Earth’s surface has been transformed into agricultural land (Ritchie and Roser, 2019), with more than two-thirds of this area utilized for pasture and livestock feed production (Benton et al., 2021). Increased reliance on large amounts of fertilizers and machinery, overgrazing, and higher stocking densities in modern intensive livestock production systems also accelerate the loss of biodiversity (Benton et al., 2021). Furthermore, the livestock sector indirectly contributes to the decline of species and biodiversity by altering the global climate through the emission of nearly 12% of anthropogenic greenhouse gasses (GHG) from global animal protein production (FAO, 2022; IPBES, 2019), polluting and contaminating water and land (i.e., eutrophication and acidification) (FAO, 2006; FAO, 2018), and contributing to the spread of diseases and invasive species (FAO, 2006; FAO, 2018). On the contrary, livestock systems are also known to perform an often-overlooked role in promoting diversity and shaping ecosystem functioning. For instance, studies have shown that well-managed rangelands with moderate grazing levels foster greater diversity compared to under-grazed or overgrazed systems, highlighting the significance of the interplay between biodiversity and livestock (Geß, 2020; Mathewos et al., 2023). However, the increasing pressure to meet the growing demand for animal-sourced products of the expanding human population continue to strain local biodiversity and its ecological functioning, jeopardizing the sustainability of these production systems (FAO, 2018).
It is essential to identifying and quantifying biodiversity under various production and management scenarios along the product value chain using life cycle assessment (LCA) to gain a comprehensive understanding of sustainability and to make informed decisions. However, the current LCA methodology often overlooks the multifunctionality (e.g., draft power, financial asset and savings, social status, and biodiversity) of smallholder extensive or semi-intensive livestock farming systems, resulting in a greater footprint per unit of product compared to intensive systems (Gerber et al., 2013). Despite the presence of numerous LCA impact assessment methods, such as LC-IMPACT (Verones et al., 2020) and Impact World+ (Bulle et al., 2019), incorporating biodiversity, recent literature points out several limitations associated with these models and their applications. For instance, Teillard et al. (2016) emphasized the importance of improved assessments in the context of agricultural production with the inclusion of concepts from landscape ecology, such as landscape heterogeneity and habitat fragmentation, and the use of agro-ecological models to account for agricultural intensity in biodiversity loss. Similarly, Souza et al. (2015) highlighted the need for consensus on life cycle impact indicators, particularly in land use modeling. However, the literature on incorporating biodiversity into LCA models is still evolving, and only a few regional studies have utilized and applied these concepts to livestock production systems (Mueller et al., 2013; McClelland et al., 2023). Our goal was to perform an extensive literature review in order to identify viable biodiversity indicators and indices that are appropriate for livestock production, particularly for dairy and beef systems. We also aimed to provide an overview of current LCA impact assessment methods that incorporate biodiversity within the context of livestock, such as ReCiPe 2016 (Huijbregts et al., 2016). We also discussed the challenges and shortcomings associated with these methods and suggested potential future directions. Building on recent reviews (Teillard et al., 2016; Damiani et al., 2023) and placing particular emphasis on livestock species, this mini-review serves as a summary guide to different approaches for tailoring existing LCA methodologies, particularly by using multiple ecosystem indicators as midpoint impacts and regional characterization factors (CFs), which indicate the biodiversity damage per unit area,. This framework will allow for a comparison of the ecological footprints associated with different livestock farming practices.
1.1 Database search and selection of new article
A preliminary literature survey was conducted using the scientific databases such as Google Scholar and Web of Science, with keywords such as “Biodiversity metrics,” “Biological footprint,” “Ecological footprint,” “Life cycle assessment,” “Livestock farming,” “Ruminant systems,” and “Sustainable farming”. A total of 328 peer-reviewed articles published in English were filtered, and only 38 studies related to LCA incorporating biodiversity and biodiversity metrics in the context of livestock farming, particularly ruminant systems, were selected after excluding duplicates and irrelevant studies (i.e., non-LCA) by scanning the titles and the abstracts. In addition, five peer-reviewed journal articles published in English were identified through snowballing the above-mentioned articles, and nine more articles were included based on consultation with experts. In total, 52 articles were selected for this mini-review.
2 Importance of biodiversity in livestock production
Biodiversity provides several direct and indirect services that contribute to ecosystem functions. According to the Millennium Ecosystem Assessment (MEA) (2005), these services are classified into four categories, namely, provisional, regulating, cultural, and supporting services, all of which are essential for human existence. Biodiversity plays a key role in shaping the food system, including livestock production, by providing biomass that accounts for 18 and 37% of the global human calorie and protein supply, respectively (Benton et al., 2021). Regulating services, such as water regulation, pollination, and pest control, and supporting services, such as soil formation and nutrient recycling, fulfill the natural resource requirements for continuous livestock and livestock feed production (Teillard et al., 2016). For example, 35% of global crops including important livestock feed crops, such as Alfalfa (Medicago sativa), are pollinated by animals (Klein et al., 2006), whereas pest and weed controlling agents such as soil microbes and soil nematodes are essential to maintain and manage rangelands and pastures for livestock grazing (Bale et al., 2007). Similarly, other ecosystem services, such as climate change regulation through carbon sequestration, water purification, and air quality regulation, buffer the impacts, such as climate change and pollution (M.E.A., 2005), arising from livestock production. However, dependence on biodiversity and its ecological services varies across different production systems. For example, extensive production systems, such as crop-livestock integrated systems and organic production systems, directly rely on biodiversity and its ecological services, such as nutrient recycling, pollination, and natural pest control (Pfiffner and Stoeckli, 2023). However, intensive livestock farming practices greatly depend on human inputs and have a low reliance on biodiversity, except for a few indirect services such as feedstock and biomass production.
3 Incorporating biodiversity in life cycle assessment studies
As with other environmental footprints (EFs), such as carbon and water footprints, a biodiversity footprint (BF) accounts for trade-offs related to biodiversity along the supply and consumption chain of a product or a service. This provides a wealth of information on the impacts on biodiversity resulting from resource utilization and emissions associated with obtaining the end product. Thereby, a BF helps reveal the contribution of a particular organization or sector to biodiversity decline. However, an ecological footprint that quantifies the ecological impact in terms of biologically productive land required to compensate for anthropogenic resource usage and GHG emissions does not necessarily reflect or suggest a correlation with the biodiversity footprint (Hanafiah et al., 2012).
LCA serves as a holistic tool for assessing the EF of livestock products, although most LCA studies do not include ecosystem services or biodiversity in their assessments (Teillard et al., 2016; Myllyviita et al., 2019; Crenna et al., 2020). For instance, the most recent livestock LCA conducted in the USA focused on carbon footprint (Thoma et al., 2013; Naranjo et al., 2020; Uddin et al., 2021; Aguirre-villegas et al., 2022). Therefore, excluding biodiversity from LCA often makes the sustainability assessment biased. However, recent developments in biodiversity LCA methods have identified ways to incorporate biodiversity into LCA models to assist in uncovering impacts on biodiversity from both on-farm and off-farm activities (e.g., feed production, energy generation) (Curran et al., 2016; Lindner et al., 2021; Marques et al., 2021; Damiani et al., 2023). In addition, such assessments will permit comparisons of different products and services (Winter et al., 2017), deriving from various management practices and regions.
LCA methodology for assessing biodiversity oversees the impact pathways leading to biodiversity loss at both regional and global levels (FAO, 2020). Each inventory item (i.e., the land area transformed or occupied) is translated into its damage impacts by CFs through a life cycle impact assessment model to provide comparable impact units (Souza et al., 2015) expressed in relation to a functional unit and within a defined system boundary. For example, studies in the context of livestock production often express biodiversity impacts per liter of milk, kilogram of carcass, or kilogram of protein, where system boundaries are set at a farm-gate level (FAO, 2020) to account for all stages of production. Due to the significant impact of livestock farming on habitat conversion, the majority of these life cycle impact assessment models rely on land use for evaluating the impacts of biodiversity loss (Souza et al., 2015; Teillard et al., 2016). These models are based on the species–area relationship (SAR) ecological model (Souza et al., 2015; Teillard et al., 2016), which assumes a positive correlation between the number of species and land area and uses species richness as a proxy for biodiversity (Lindner et al., 2021). Several methodologies, such as ReCiPe (Huijbregts et al., 2016), have been designed by incorporating multiple midpoint impact categories—parameters that are between inventory data and the endpoint of the cause-effect chain for a particular impact category (Bare et al., 2000)—such as climate change, eutrophication, water use, ecotoxicity, invasive species, and land use. These methodologies aim to provide a comprehensive understanding of the endpoint impacts—environmental damage aggregated and summarized from midpoint impacts (Hardaker et al., 2022) of biodiversity loss—particularly in the context of livestock systems (Table 1; Teillard et al., 2016; Crenna et al., 2020).
Table 1. Life cycle assessment (LCA) methodologies that incorporate biodiversity in the context of livestock.
4 Biodiversity metrics as indicators
Understanding and assessing changes in biodiversity are fundamental to conservation and investigating the sustainability of any ecosystem. Biotic indicators, defined as species or groups of species that reflect the state of an environment (Gerhardt, 2002), provide insights and offer quantifiable and interpretable means of assessing the impacts arising from disturbances or changes in the land use pattern, and they also provide an effective way to evaluate and follow up on conservation efforts (Duelli and Obrist, 1998; Büchs, 2003; Clergué et al., 2005; McClelland et al., 2023). However, due to the dynamic nature and complexity of biodiversity, as well as cost and time constraints, interpretations rely on a limited set of key indicators, selected based on scientific reliability, repeatability, and the underlying conservation goals (Büchs, 2003; Clergué et al., 2005). For example, McClelland et al. (2023) developed a biodiversity integrity index using indicators from eight thematic categories: habitat protection, habitat change, wildlife conservation, invasive species, aquatic biodiversity, off-farm feed, landscape heterogeneity, and ecosystem services to assess the performance of the ecosystem. To gain a comprehensive understanding of biodiversity, one should incorporate indicators to represent the principal components of biodiversity (genetic, species, and habitat) at every three dimensions (composition, structure, and function) (Curran et al., 2016; Teillard et al., 2016; McClelland et al., 2023), as well as indicators to reflect management practices (i.e., livestock density), particularly for human-dominated landscapes (Herzog et al., 2013).
Species richness (the number of species within a defined region) and species evenness (which accounts for the number of species and their relative abundance within a community) are widely used direct measures of biodiversity (Moore, 2013). Since sampling the entire biotic community is practically infeasible, taxa that are ecologically important and sensitive to environmental factors are often selected as suitable representatives of biodiversity correlates (Büchs, 2003). For example, in a livestock system, representatives of major ecological functions, such as primary production (e.g., vascular plants), decomposition and nutrient recycling (e.g., soil microbes, nematodes, and earthworms), and pest control (e.g., spiders), could provide a better understanding of ecosystem functioning (Herzog et al., 2013). In addition, indicator species—those that respond to changes in the environment (Clergué et al., 2005)—such as aquatic and terrestrial invertebrates, algae, and plankton, provide insights into the health of the ecosystem, which is threatened by excessive discharge of nutrients, chemicals, and manure from livestock farming. Furthermore, arthropods in general receive widespread recognition as key taxa for reflecting overall species diversity, as they account for approximately 65% of all multicellular species (Duelli and Obrist, 1998; Herzog et al., 2013).
Primarily based on species composition, a number of biodiversity metrics have been designed with the intention of assessing the state of and the influence on biodiversity. For example, metrics such as the mean species abundance (MSA) and the living planet index serve as state indicators of local ecosystem intactness and global decline in species, respectively (Teillard et al., 2016; Rossberg, 2022). In contrast, potential disappearance fraction (PDF), which compares original species richness to the fraction remaining after human intervention, is widely used as a footprint metric, particularly in the context of LCA studies with multiple midpoint impact categories (Goedkoop et al., 2008; Table 1). Although the MSA was initially developed in the context of the GLOBIO3 modeling framework as a state indicator, it has also been used in LCA to translate several midpoint impact categories into biodiversity impacts at the end point, especially within the livestock sector (Alkemade et al., 2009; Teillard et al., 2016). Despite high spatial variation (regional and global) of these metrics in pressure-state-response (PSR) and LCA frameworks, numerous metrics are being tailored and developed to suit different methodologies and management practices, such as confined and grazing systems of ruminants.
5 Research gaps, limitations, and future developments
One of the major limitations of current LCA models is that most account only for a single midpoint impact category, particularly land use change, in assessing biodiversity impacts (Souza et al., 2015; Teillard et al., 2016) in livestock systems. Although the livestock sector is a primary contributor to habitat change, other impacts associated with livestock production, such as climate change, eutrophication, overexploitation of resources, and spread of invasive species, are overlooked in LCA studies (Teillard et al., 2016). Moreover, land use ecological models that depend on the SAR are criticized for being oversimplified (Souza et al., 2015) and fail to account for positive contributions to biodiversity (Teillard et al., 2016). For instance, in well-managed semi-natural grasslands, extrapolation of reduction in land area to species loss can result in incorrect interpretations (Souza et al., 2015). In addition, the use of potential natural vegetation as a land cover reference to estimate the impacts on biodiversity fails to distinguish between relative intrinsic values (e.g., diversity, ecosystem services) and productivity, assigning the same weight to land use change impacts across different habitat types (Curran et al., 2016; Teillard et al., 2016). Another disadvantage is that these models rely on a single metric (e.g., MSA, PDF), primarily at the species level (Table 1; Winter et al., 2017; Crenna et al., 2020; Lindner et al., 2021), and rarely account for functional attributes (e.g., species functional traits and ecosystem services) (Teillard et al., 2016). Therefore, they are unable to capture the full scope of biodiversity (Lindner et al., 2021; Marques et al., 2021), including species extinction (Teillard et al., 2016). Furthermore, these indicators do not account for ecological or conservational values, such as endemism and rarity, to demonstrate the individual contribution to biodiversity (Chaudhary and Brooks, 2018) and to illustrate taxonomic significance. Few LCA models and indices have been developed to suggest higher biodiversity and conservation value for rare and unique species compared to common species. However, the development of these models is constrained due to limitations in ecological data at the regional and global scales, challenges in assigning weighting factors for indicators based on their relative ecological importance (Herzog et al., 2013), and the lack of consensus on CFs (Chaudhary and Brooks, 2018). For example, the IUCN Red List, which serves as a database for ecological studies, is less reliable at the global scale, and only a few species have been evaluated and listed at the regional level (Duelli and Obrist, 2003). Frischknecht and Jolliet (2016) recommended the determination of CFs at different levels (e.g., global, regional) and for different ecosystem types, while other studies have suggested the use of multiple indicators and drivers to investigate impacts on biodiversity across different dimensions and to include spatial details in impact assessments (Crenna et al., 2020), aiming to address the existing weaknesses in current LCA studies. Alternatively, other approaches and tools, such as the PSR model, are widely used in assessing the impact on biodiversity at the local scale, where causal indicators translate the pressure and state of the environment (FAO, 2020). Although the PSR framework facilitates interpretation and decision-making, it fails to provide information on impacts across the life cycle of a product, especially off-farm impacts on biodiversity such as livestock feed production (Teillard et al., 2016), and is therefore unable to provide insights into BF. However, the recent Biodiversity Multi-Scale Assessment of Product Systems (BioMAPS) framework proposed by Maier (2023) integrates ecological, conservational, and LCA requirements, including land use types and management practices, to assess impacts and risks at regional and global levels by scaling up from the local scale.
6 Conclusion
Biodiversity assessment is an important part of evaluating the sustainability of livestock production, as livestock farming has a significant impact on biodiversity depletion. Studies incorporating biodiversity in the LCA framework allow the identification of hotspots and quantification of biological impacts across the life cycle of animal products from various origins. Despite the availability of several models and methodologies, capturing the full scope of biodiversity in these studies is challenging due to its complexity and the limitations and weaknesses associated with these models. Regardless, these approaches offer valuable insights for potential interventions and mitigation strategies. Incorporating multiple indicators for different ecosystem functioning and management practices to reflect multiple dimensions of biodiversity would improve the reliability of LCA studies when comparing various livestock products.
Author contributions
MS-P: Investigation, Methodology, Writing – original draft, Writing – review & editing. MU: Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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References
Aguirre-villegas, H. A., Larson, R. A., Rakobitsch, N., Wattiaux, M. A., and Silva, E. (2022). Farm level environmental assessment of organic dairy systems in the U.S. J. Clean. Prod. 363:132390. doi: 10.1016/j.jclepro.2022.132390
Alkemade, R., Van Oorschot, M., Miles, L., Nellemann, C., Bakkenes, M., and Brink, B. T. (2009). GLOBIO3: a framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems 12, 374–390. doi: 10.1007/s10021-009-9229-5
Bale, J. S., Van Lenteren, J., and Bigler, F. (2007). Biological control and sustainable food production. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 363, 761–776. doi: 10.1098/rstb.2007.2182
Bare, J. C., Hofstetter, P., Pennington, D. W., and De Haes, H. A. U. (2000). Midpoints versus endpoints: the sacrifices and benefits. Int. J. Life Cycle Assess. 5, 319–326. doi: 10.1007/bf02978665
Benton, T., Bieg, C., Harwatt, H., Wellesley, L., and Pudasaini, R. (2021). Food system impacts on biodiversity loss Three levers for food system transformation in support of nature. Available online at: https://doi.org/10.13140/RG.2.2.34045.28640.
Büchs, W. (2003). Biotic indicators for biodiversity and sustainable agriculture—introduction and background. Agric. Ecosyst. Environ. 98, 1–16. doi: 10.1016/s0167-8809(03)00068-9
Bulle, C., Margni, M., Patouillard, L., Boulay, A., Bourgault, G., De Bruille, V., et al. (2019). Impact world+: a globally regionalized life cycle impact assessment method. Int. J. Life Cycle Assess. 24, 1653–1674. doi: 10.1007/s11367-019-01583-0
Chaudhary, A., and Brooks, T. M. (2018). Land use intensity-specific global characterization factors to assess product biodiversity footprints. Environ. Sci. Technol. 52, 5094–5104. doi: 10.1021/acs.est.7b05570
Clergué, B., Amiaud, B., Pervanchon, F., Lasserre-Joulin, F., and Plantureux, S. (2005). Biodiversity: function and assessment in agricultural areas. A review. Agronomie 25, 1–15. doi: 10.1051/agro:2004049
Crenna, E., Marques, A., La Notte, A., and Sala, S. (2020). Biodiversity assessment of value chains: state of the art and emerging challenges. Environ. Sci. Technol. 54, 9715–9728. doi: 10.1021/acs.est.9b05153
Curran, M., De Souza, D. M., Antón, A., Teixeira, R. F. M., Michelsen, O., Vidal-Legaz, B., et al. (2016). How well does LCA model land use impacts on biodiversity? A comparison with approaches from ecology and conservation. Environ. Sci. Technol. 50, 2782–2795. doi: 10.1021/acs.est.5b04681
Damiani, M., Sinkko, T., Caldeira, C., Tosches, D., Robuchon, M., and Sala, S. (2023). Critical review of methods and models for biodiversity impact assessment and their applicability in the LCA context. Environ. Impact Assess. Rev. 101:107134. doi: 10.1016/j.eiar.2023.107134
Duelli, P., and Obrist, M. K. (1998). In search of the best correlates for local biodiversity in cultivated areas. Biodivers. Conserv. 7, 297–309. doi: 10.1023/A:10088735108171998
Duelli, P., and Obrist, M. К. (2003). Biodiversity indicators: the choice of values and measures. Agric. Ecosyst. Environ. 98, 87–98. doi: 10.1016/s0167-8809(03)00072-0
FAO. (2006). Livestock’s long shadow: Environmental issues and options. Available online at: https://www.europarl.europa.eu/climatechange/doc/FAO%20report%20executive%20summary.pdf (accessed February 26, 2025).
FAO. World livestock: Transforming the livestock sector through the sustainable development goals. Rome. (2018). Available online at: https://doi.org/10.4060/ca1201en.
FAO Biodiversity and the livestock sector – Guidelines for quantitative assessment – Version 1 Rome Livestock Environmental Assessment and Performance Partnership (FAO LEAP) (2020). Available online at: https://doi.org/10.4060/ca9295en.
FAO. (2022). Greenhouse gas emissions from agrifood systems. Global, regional and country trends, 2000–2020. FAOSTAT Analytical Brief Series No. 50. Rome. Available online at: https://openknowledge.fao.org/server/api/core/bitstreams/121cc613-3d0f-431c-b083-cc2031dd8826/content (accessed February 26, 2025).
Frischknecht, R., and Jolliet, O. (2016). Global guidance for life cycle impact assessment indicators volume 1. Available online at: https://www.ecocostsvalue.com/EVR/img/references%20others/global-guidance-lcia-v.1-1.pdf (accessed February 26, 2025).
Frischknecht, R., Steiner, R., Arthur, B., Norbert, E., and Gabi, H., (2006). Swiss Ecological Scarcity Method: The New Version 2006. Available online at: https://treeze.ch/fileadmin/user_upload/downloads/Publications/Methodology/Life_Cycle_Impact_Assessment/Frischknecht-2006-EcologicalScarcity-Paper.pdf (accessed February 26, 2025).
Gerber, P., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., et al. (2013). Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. In Food and agriculture Organization of the United Nations (FAO) eBooks. Available online at: https://portals.iucn.org/library/node/29403.
Gerhardt, A. (2002). Bioindicator species and their use in biomonitoring. Environ. Monit. 1, 77–123. Available at: https://eolss.net/sample-chapters/c09/E6-38A-01-07.pdf (Accessed February 26, 2025).
Geß, A. (2020). Biodiversity impact assessment of grazing sheep. In Sustainable production, life cycle engineering and management (pp. 227–239). Available online at: https://doi.org/10.1007/978-3-030-50519-6_16.
Goedkoop, M., Heijungs, R., Huijbregts, M., Schryver, A., Struijs, J., and Zelm, R. (2008) ReCiPE 2008: A life cycle impact assessment method which comprises harmonised category indicators at the midpoint and the endpoint level.
Hanafiah, M. M., Hendriks, A. J., and Huijbregts, M. A. J. (2012). Comparing the ecological footprint with the biodiversity footprint of products. J. Clean. Prod. 37, 107–114. doi: 10.1016/j.jclepro.2012.06.016
Hardaker, A., Styles, D., Williams, P., Chadwick, D., and Dandy, N. (2022). A framework for integrating ecosystem services as endpoint impacts in life cycle assessment. J. Clean. Prod. 370:133450. doi: 10.1016/j.jclepro.2022.133450
Herzog, F., Jeanneret, P., Ammari, Y., Angelova, S., Arndorfer, M., Bailey, D., et al. (2013). Measuring farmland biodiversity. HAL (Le Centre Pour La Communication Scientifique Directe). Available online at: https://hal.archives-ouvertes.fr/hal-01738188.
Huijbregts, M. A. J., Steinmann, Z. J. N., Elshout, P. M. F., Stam, G., Verones, F., Vieira, M., et al. (2016). ReCiPe2016: a harmonised life cycle impact assessment method at midpoint and endpoint level. Int. J. Life Cycle Assess. 22, 138–147. doi: 10.1007/s11367-016-1246-y
IPBES (2019) in Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the intergovernmental science-policy platform on biodiversity and ecosystem services. eds. S. Díaz, J. Settele, E. S. Brondízio, H. T. Ngo, M. Guèze, and J. Agard, et al. (Bonn, Germany: IPBES Secretariat), 56.
Jeanneret, P., Baumgärtner, D., Knuchel, R. F., Koch, B., and Gaillard, G. (2014). An expert system for integrating biodiversity into agricultural life-cycle assessment. Ecol. Indic. 46, 224–231. doi: 10.1016/j.ecolind.2014.06.030
Klein, A., Vaissière, B., Cane, J. H., Steffan-Dewenter, I., Cunningham, S. A., Kremen, C., et al. (2006). Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. Lond. B Biol. Sci. 274, 303–313. doi: 10.1098/rspb.2006.3721
Lindner, J. P., Eberle, U., Knuepffer, E., and Coelho, C. R. V. (2021). Moving beyond land use intensity types: assessing biodiversity impacts using fuzzy thinking. Int. J. Life Cycle Assess. 26, 1338–1356. doi: 10.1007/s11367-021-01899-w
M.E.A. (2005). A Report of the Millennium Ecosystem Assessment. Ecosystems and Human Well-Being. Island Press, Washington DC. Available online at: https://www.millenniumassessment.org/documents/document.356.aspx.pdf.
Maier, S. (2023). Biodiversity multi-scale assessments of product systems - the BioMAPS method. Fraunhofer Verlag. Available online at: https://doi.org/10.24406/publica-2648.
Marques, A., Robuchon, M., Hellweg, S., Newbold, T., Beher, J., Bekker, S., et al. (2021). A research perspective towards a more complete biodiversity footprint: a report from the world biodiversity forum. Int. J. Life Cycle Assess. 26, 238–243. doi: 10.1007/s11367-020-01846-1
Mathewos, M., Sisay, A., and Berhanu, Y. (2023). Grazing intensity effects on rangeland condition and tree diversity in Afar, northeastern Ethiopia. Heliyon 9:e22133. doi: 10.1016/j.heliyon.2023.e22133
McClelland, S. C., Haddix, J. D., Azad, S., Boughton, E. H., Boughton, R. K., Miller, R. S., et al. (2023). Quantifying biodiversity impacts of livestock using life-cycle perspectives. Front. Ecol. Environ. 21, 275–281. doi: 10.1002/fee.2636
Moore, J. C. (2013). Diversity, taxonomic versus functional. In Elsevier eBooks (pp. 648–656). Available online at: https://doi.org/10.1016/b978-0-12-384719-5.00036-8.
Mueller, C., De Baan, L., and Koellner, T. (2013). Comparing direct land use impacts on biodiversity of conventional and organic milk—based on a Swedish case study. Int. J. Life Cycle Assess. 19, 52–68. doi: 10.1007/s11367-013-0638-5
Myllyviita, T., Sironen, S., Saikku, L., Holma, A., Leskinen, P., and Palme, U. (2019). Assessing biodiversity impacts in life cycle assessment framework - comparing approaches based on species richness and ecosystem indicators in the case of Finnish boreal forests. J. Clean. Prod. 236:117641. doi: 10.1016/j.jclepro.2019.117641
Naranjo, A., Johnson, A., Rossow, H., and Kebreab, E. (2020). Greenhouse gas, water, and land footprint per unit of production of the California dairy industry over 50 years. J. Dairy Sci. 103, 3760–3773. doi: 10.3168/jds.2019-16576
Pfiffner, L., and Stoeckli, S. (2023). Agriculture and biodiversity. Impacts of different farming systems on biodiversity. Available online at: https://doi.org/10.5281/zenodo.7743951.
Ritchie, H., and Roser, M. (2019). Half of the world’s habitable land is used for agriculture. Published online at http://OurWorldInData.org. Available online at: https://ourworldindata.org/global-land-for-agriculture (accessed February 26, 2025).
Rosenbaum, R. K., Bachmann, T. M., Gold, L. S., Huijbregts, M. A. J., Jolliet, O., Juraske, R., et al. (2008). USEtox--the UNEP-SETAC toxicity model: recommended characterization factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment. Int. J. Life Cycle Assess. 13, 532–546. doi: 10.1007/s11367-008-0038-4
Rossberg, A. G. (2022). Quantifying Biodiversity Impact-Relations amongst local and global metrics, why they matter, and how to offset impacts. London: Tech. rep., Queen Mary University of London, 16.
Souza, D. M., Teixeira, R. F. M., and Ostermann, O. (2015). Assessing biodiversity loss due to land use with life cycle assessment: are we there yet? Glob. Change Biol. 21, 32–47. doi: 10.1111/gcb.12709
Teillard, F., Anton, A., Dumont, B., Finn, J. A., Henry, B., Souza, D. M., et al. (2016). A review of indicators and methods to assess biodiversity--Application to livestock production at global scale. Livestock Environmental Assessment and Performance (LEAP) Partnership. Rome, Italy: FAO.
Thoma, G., Popp, J., Nutter, D., Shonnard, D., Ulrich, R., Matlock, M., et al. (2013). Greenhouse gas emissions from milk production and consumption in the United States: a cradle-to-grave life cycle assessment circa 2008. Int. Dairy J. 31, S3–S14. doi: 10.1016/j.idairyj.2012.08.013
Uddin, M. E., Aguirre-Villegas, H. A., Larson, R. A., and Wattiaux, M. A. (2021). Carbon footprint of milk from Holstein and Jersey cows fed low or high forage diet with alfalfa silage or corn silage as the main forage source. J. Clean. Prod. 298:126720. doi: 10.1016/j.jclepro.2021.126720
Verones, F., Hellweg, S., Antón, A., Azevedo, L. B., Chaudhary, A., Cosme, N. M. D., et al. (2020). Lc-impact: a regionalized life cycle damage assessment method. J. Ind. Ecol. 24, 1201–1219. doi: 10.1111/jiec.13018
Verones, F., Kuipers, K., Núñez, M., Rosa, F., Scherer, L., Marques, A., et al. (2022). Global extinction probabilities of terrestrial, freshwater, and marine species groups for use in life cycle assessment. Ecol. Indic. 142:109204. doi: 10.1016/j.ecolind.2022.109204
Weidema, B., Wesnaes, M., Hermansen, J., Kristensen, T., and Halberg, N. (2008). Environmental Improvement Potentials of Meat and Dairy Products (EUR 23491). Available online at: https://doi.org/10.2791/38863.
Winter, L., Lehmann, A., Mikosch, N., and Finkbeiner, M. (2017). Including biodiversity in life cycle assessment--state of the art, gaps and research needs. Environ. Impact Assess. Rev. 67, 88–100. doi: 10.1016/j.eiar.2017.08.006
WWF (2022). Living planet report 2022 – Building a nature positive society. R. E. A. Almond, M. Grooten, D. Juffe Bignoli, and T. Petersen (Eds). WWF, Gland, Switzerland. Available online at: https://wwfint.awsassets.panda.org/downloads/embargo_13_10_2022_lpr_2022_full_report_single_page_1.pdf (accessed February 26, 2025).
Keywords: biodiversity, livestock farming, biological footprint, life cycle assessment, ecosystem services
Citation: Sooriya Patabendige MS and Uddin ME (2025) Incorporating biodiversity into the sustainability assessment of livestock systems using comprehensive life cycle assessment: A mini-review. Front. Sustain. Food Syst. 9:1422922. doi: 10.3389/fsufs.2025.1422922
Edited by:
Eduardo Aguilera, Spanish National Research Council (CSIC), SpainReviewed by:
Andreas Gess, University of Stuttgart, GermanyCopyright © 2025 Sooriya Patabendige and Uddin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Md Elias Uddin, bXVkZGluMkB1Y29ubi5lZHU=