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# Estimating livestock biomass across diverse populations and data ecosystems

Y. Li* (1, 2), D. Mayberry (1, 2) & J. Rushton (1, 3)

(1) Global Burden of Animal Diseases (GBADs) Programme, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, 146 Brownlow Hill, Liverpool, L3 5RF, United Kingdom (https://animalhealthmetrics.org)

(2) Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, 306 Carmody Road, St Lucia, QLD 4067, Australia

(3) Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, 146 Brownlow Hill, Liverpool, L3 5RF, United Kingdom

*Corresponding author: yin.li@csiro.au

## Summary

Estimates of livestock biomass can be used as denominators in disease burden estimates, in addition to informing assessments of resource use and environmental impacts. This article explores the challenges of accurately estimating biomass across different scales and data ecosystems, with a particular focus on the use of biomass in the Global Burden of Animal Diseases programme. The greatest of these challenges is a lack of subgroup (breed, age, sex)-specific data on populations and liveweight at national and subnational level. This can be overcome by using global datasets and generic estimates of liveweight for each species, though this approach fails to account for the diversity of livestock systems.

## Keywords

Biomass – Disease burden – GBADs – Global Burden of Animal Diseases – Liveweight – Population.

## Introduction

The Global Burden of Animal Diseases (GBADs) programme aims to estimate, compare and attribute the burden of animal disease across diverse livestock production systems, providing vital insights for effective investments in animal health [1,2]. For example, outputs from GBADs could be used to help prioritise investments to provide the greatest societal or production benefits, or for ex post evaluation on the effectiveness of previous investments. The use of a consistent methodology across countries and case studies to estimate the burden of disease allows for comparisons between countries and multiple species.

One of the challenges faced by the GBADs programme involves how to compare disease burdens between different species. Given the substantial differences in body sizes and corresponding resource requirements among species, direct head-to-head comparisons may lack fairness. In this context, livestock biomass, representing the aggregate liveweight of a given livestock population, emerges as a more equitable

variable. It functions as a normalisation factor for comparing inputs, outputs and production losses per unit across varying species, production systems and countries. Livestock biomass is most frequently used in the assessment of natural resource use (especially grazing [3]) and environmental impacts [4], and in reporting the use of veterinary antimicrobials [5]. It can also be used as an indicator for gauging livestock development and food security, providing a benchmark for monitoring progress and trends in these domains [6].

However, despite its utility, there are no standardised methods for calculating animal biomass, and different approaches often provide widely varying estimates [7,8]. This lack of standardisation is perhaps a result of the evolution of different approaches in response to specific research questions or data availability. Hence, this article explores the challenges inherent in estimating livestock biomass at different scales, focusing on the liveweight of current livestock populations. It also provides guidance on the selection of methodologies for biomass estimation depending on the type and granularity of data available.
Challenges in estimating livestock
biomass

The computation of livestock biomass involves aggregating the liveweight of individual animals. In an ideal scenario, this aggregation would include accurate liveweight data for each animal, capturing differences due to species, breed, class (e.g. age, sex), health status and production system factors. However, acquiring such data is constrained by the labour-intensive nature of individually identifying and weighing livestock in a population, especially since the liveweight of individuals changes over time. It is therefore necessary to use average values for a given population and time period; the average liveweight of an individual within a population is then multiplied by the total number of animals, yielding the aggregate biomass. This can be done either for an entire species population or, in cases where liveweight and population data for specific age/sex groups are available, for specific livestock classes. While the latter approach implies greater accuracy, the result is affected in the inherent variability in liveweight across subgroups, as discussed below.

There is substantial variation in the liveweight of animals within a species based on breed and sex [4,9]. This is il-lustrated in Ethiopia using data from the Domestic Animal Diversity Information System, known as DAD-IS, which cat-alalogues around 35 cattle breeds kept for beef, dairy and draught power [10]. Among the 14 breeds with reported liveweight values, the average liveweight is 271 kg, with averages by breed and sex ranging from 153 kg (female Abergelle cattle) to 400 kg (male Fogera cattle). On aver-age, males are 20% heavier than females (Fig. 1).

Within breeds, liveweight is influenced by genetics, age, management, physiological status and climate [11,12]. These variables drive much of the differences in liveweight between different countries and production systems. For example, Fordyce et al. [13] reported average liveweight of mature Bos indicus cross cows in northern Australia ranging from 351 kg

(non-pregnant cows, Northern Forest region) to 518 kg (pregnant cows, Central Forest region). Similar variation was reported for heifers and first lactation cows, and between lactating and non-lactating animals. Data at this level are most often reported in the literature stemming from research projects and local surveys. However, the liveweight of animals in different age groups can be estimated based on known adult liveweight using growth curve formulas [14].

The accuracy of biomass calculations is also affected by population data, and liveweight of different classes of stock is not useful without equivalent data on herd structures. Official country statistics and international databases commonly house reported livestock population figures. These statistics predominantly stem from surveys, owing to the impracticality of directly counting every animal within a country. However, this approach introduces potential errors and bias. For example, Fordyce et al. [15] used a static herd modelling approach to validate reported Australian cattle population data generated from surveys. They found that national statistics likely underestimate the national cattle population by a substantial amount, largely due to a combination of low survey return rates (possible selection bias) and consistent under-reporting of herd size by individual businesses (measurement errors). National surveys may also exclude certain farm businesses, particularly smallholder, informal and backyard production systems. Again, drawing on the Australian example, the Australian Bureau of Statistics rural environment and agricultural commodity survey used to estimate livestock populations includes only farms registered with the Australian Tax Office and whose estimated value of agricultural operations is greater than AUS$ 40,000 [16], excluding an unknown number of farms and livestock. Owing to the logistical challenge in conducting regular livestock population censuses or surveys, national data on livestock numbers are also not always available for every country every year. As such, global datasets such as the Food and Agriculture Organization (FAO) Corporate Statistical Database (FAOSTAT) report 'imputed', 'estimated' or 'unofficial' population data for some countries and species [17].

The detail of population reporting varies between data sources. For global datasets, population data are usually limited to the total number of each species type. National statistics are more likely to include data for subgroups based on breed, sex and age, though not all countries include such data. National statistics are also more likely to provide geographical disaggregation of populations, but this is typically based on political boundaries (e.g. state borders) rather than production systems. This distinction is important in the context of GBADs because differences in animal liveweight and production losses associated with disease are associated with production systems.

For both liveweight and population data, the challenge of accessing accurate information is likely to be greatest for

<figure><img src="image_2.png" /><figcaption>Figure 1
Distribution of average liveweight for male (grey) and female (orange) cattle of the 14 breeds of cattle in Ethiopia</figcaption></figure>

Source: FAO [10]
animals kept in informal and/or extensive production systems, and those with limited presence within a country. In comparison, detailed data on populations and liveweight are often collected for intensive and integrated industries such as pork and poultry production. However, these data are not readily shared, and incentives may be required to facilitate access to this information.

## Data and methods for estimating livestock biomass

As indicated above, data on livestock weights and populations are available from a range of sources, including international databases, national statistics, industry data and literature, and availability of these data varies by country and production system. These sources differ in the specificity, and therefore accuracy, of their information and their suitability for calculating biomass at different scales (Table I). In response, the GBADs programme has adopted methodological tiers to guide the selection of different methods for

estimating livestock biomass. This approach aligns with the Intergovernmental Panel on Climate Change's tier approach for calculating greenhouse gas emissions, in which a tier represents a level of methodological complexity: Tier 1 is the basic method, Tier 2 intermediate and Tier 3 the most demanding in terms of complexity and data requirements.

For biomass calculations, Tier 1 methods rely on coarse estimates of livestock populations (e.g. global data from FAOSTAT [17]) and generic liveweight values. These methods can be used to estimate biomass at a global scale or in countries without national data. Where country-specific data on liveweight is not available, livestock conversion units such as tropical livestock units (TLUs) [18] may be used. A TLU (250 kg liveweight) is based on the average weight of a camel, with conversion ratios for common terrestrial livestock species shown in Table II. For a given species, livestock weight is estimated as 250 kg multiplied by the appropriate conversion factor. For example, the average liveweight of cattle estimated using this method would be 175 kg, while the

<table><caption>**Table I**

Different sources of liveweight data and their utility in biomass calculations</caption><thead><tr><td>Data source</td><td>Differentiated by breed, age and sex groups</td><td>Disaggregated by production purpose</td><td>Disaggregated by country</td><td>Accuracy</td><td>Data scale</td><td>Use in methods</td><td>Pros for biomass estimation</td><td>Constraints for biomass estimation</td></tr></thead><tbody><tr><td>Transformed biomass concepts such as tropical livestock units [18]</td><td>No</td><td>No</td><td>No</td><td>Poor</td><td>Can be applied at all scales, but may be regionally specific</td><td>1</td><td>Easily scalable for national, regional and global analyses</td><td>Most of these units are based on energy requirements, not liveweight. Intended for use in grazing systems only</td></tr><tr><td>Domestic Animal Diversity Information System database [10]</td><td>Disaggregated by breed and sex</td><td>Sometimes</td><td>Yes</td><td>Good</td><td>National; global</td><td>1, 2</td><td>Suitable for regional and national analyses</td><td>Incomplete data for many breeds. Not all countries are represented in dataset</td></tr><tr><td>Slaughter weight from Food and Agriculture Organization of the United Nations [19]</td><td>No</td><td>No</td><td>Yes</td><td>Poor</td><td>National; global</td><td>1, 2</td><td>Suitable for national, regional and global analyses</td><td>May overestimate the biomass of a species as not all individuals are slaughtered at adult ages</td></tr><tr><td>National statistics</td><td>Sometimes</td><td>Yes</td><td>Yes</td><td>Fair</td><td>National; subnational</td><td>1, 2, 3</td><td>Suitable for subnational and national analyses; may support analysis by production system</td><td>Often not open access</td></tr><tr><td>Private industry data</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Good</td><td>Farm or production system</td><td>1, 2, 3</td><td>High level of accuracy</td><td>Not publicly available</td></tr><tr><td>Literature</td><td>Sometimes</td><td>Yes</td><td>Yes</td><td>Variable</td><td>Subnational</td><td>1, 2, 3</td><td>Studies often focus on specific production systems; may support analysis by production system</td><td>Often small studies in which the representativeness of live body weights is arguable. Data from multiple studies will usually be required to scale up to national level</td></tr></tbody></table>
average liveweight of chickens would be 2.5 kg. These conversion ratios provide a convenient way to estimate the average liveweight of a species and are not specific to breeds, production systems or countries.

<table><caption>**Table II**

Tropical livestock unit conversion ratios</caption><thead><tr><td>Species</td><td>TLU conversion ratio</td></tr></thead><tbody><tr><td>Asses</td><td>0.5</td></tr><tr><td>Camels</td><td>1</td></tr><tr><td>Cattle and buffalo</td><td>0.7</td></tr><tr><td>Goats</td><td>0.1</td></tr><tr><td>Horses</td><td>0.8</td></tr><tr><td>Mules</td><td>0.7</td></tr><tr><td>Pigs</td><td>0.2</td></tr><tr><td>Chickens</td><td>0.01</td></tr><tr><td>Sheep and goats</td><td>0.1</td></tr></tbody></table>

TLU: tropical livestock unit

Source: Jahnke [18]

Although TLUs provide easy estimates of liveweight for different species, caution is advised when utilising these units. TLUs are typically based on the metabolic energy requirements of different livestock species and are used to estimate grazing pressure. Although TLUs have been used elsewhere to estimate livestock biomass [6], this is a deviation from their intended application and may under- or overestimate the liveweight of some species. They are also often regionally specific, with the TLU and conversion ratio based on African production systems. Thus, application of these units outside their intended geography may lead to errors [18]. Application of similar units such as livestock units [20] and adult equivalents [21], which were designed to calculate equivalent liveweight of different classes of stock, faces similar limitations.

Tier 2 methods use country-specific data on livestock populations and liveweight, and where sufficient data are available, such methods may be scaled to provide continental or regional estimates of livestock biomass. Country-specific liveweight data may be sourced from national statistics or the FAO Technical Conversion Factors for Agricultural Commodities [19].

Proxy data such as slaughter weight may be used where liveweight data are not available, as slaughter data are often collected by national agencies. However, it should be noted that this approach introduces additional errors since slaughtered animals are probably not representative of the broader population. Firstly, in most countries, slaughtered animals are mainly adults, and the age/sex structure of the slaughtered animal group may differ from that of the same species on the farm. Secondly, body condition, and therefore liveweight, may vary for slaughtered animals versus those remaining

on a farm. For example, slaughtered beef cattle from intensive commercial farms in the United Kingdom would have a higher liveweight than the average on-farm liveweight of beef cattle. Conversely, slaughtered cattle in Ethiopia are mainly the old cattle from small herds and may have lower body condition and liveweight compared to the growing and healthy cattle on a farm. Given that the source of slaughtered animals varies between countries, using slaughter weight as a proxy for average liveweight of a species would introduce positive or negative bias in estimating livestock biomass for different countries.

Finally, Tier 3 methods require data on population size and structure and on animal liveweight at subnational levels. Estimates of biomass for each subgroup can be summed to calculate biomass at larger scales (e.g. production system or national level). These data are sometimes available in national livestock census reports (e.g. from the Central Statistics Agency of Ethiopia [22]) but may need to be supplemented with farm survey data or information from literature. Synthetic data on population structures and cohorts can also be generated using mathematical and population dynamics models, based on known reproduction, mortality and offtake rates [23], with liveweight estimated from growth curve formulas if no other data exist.

## Methods in practice: comparing livestock biomass estimations using different data sources in Ethiopia

To illustrate the variation in biomass estimations using different methods and data sources, the stock biomass of cattle in Ethiopia in 2018 was estimated using different methods (Table III). While some methods gave similar estimates, the overall range (11–16 billion kg) was wide. Assuming the Tier 3 (most detailed) method provides the greatest level of accuracy, it becomes apparent that using TLUs underestimates average cattle liveweight, while application of average values across a population overestimates liveweight if a country has a large proportion of young livestock.

## Conclusions

As illustrated above, due to the variation in availability and quality of liveweight and population data, no single method can be recommended to calculate livestock biomass. The choice of methodology, guided by tiers, is influenced by factors such as resource availability, the scope of analysis and the desired level of accuracy. While Tier 3 methods may provide greater accuracy given the disaggregation of livestock population and liveweight data by class and locality, useful estimates can still be derived using less detailed data and Tier 1 or 2 approaches.

As such, the implementation of methods varies across the GBADs programme. For initial global estimates of biomass,
Table III

Total biomass of cattle in Ethiopia in 2018 using different methods and data sources

<table><thead><tr><td>Data and method</td><td>Method tier</td><td>Biomass (billion kg)</td><td>Difference</td></tr></thead><tbody><tr><td>National statistics on livestock populations [23] and liveweight by age/sex/breed</td><td>3</td><td>12.8</td><td>Reference method</td></tr><tr><td>National statistics on livestock populations [23] and mean liveweight (250 kg)</td><td>2</td><td>15.4</td><td>+20%</td></tr><tr><td>FAOSTAT data on populations [17] and liveweight data [19]</td><td>1</td><td>15.7</td><td>+23%</td></tr><tr><td>FAOSTAT data on populations [17] and TLU conversion ratios [6]</td><td>1</td><td>11.0</td><td>-14%</td></tr></tbody></table>

FAOSTAT: Food and Agriculture Organization Corporate Statistical Database

TLU: tropical livestock unit

Tier 1 methods utilising FAOSTAT data are currently the only suitable approach, and this resolution is consistent with initial global estimates of disease burden. Within case studies, Tier 2 and 3 methods are being utilised, depending on the species and country of interest. These biomass estimates are being used as inputs into calculations of total economic value [24], to highlight potential areas of high resource use and risk of disease transmission for policy-makers [25], and as denominators for comparing estimates of livestock value [26] and disease burdens between production systems and species [27]. As the GBADs programme evolves, biomass and disease burden estimates from individual countries will

be combined to give regional and global estimates of higher accuracy. Consistent with this goal, the standardisation of biomass estimation methodologies will be important in ensuring cross-study and cross-regional comparability.

## Acknowledgements

This study was funded by the Bill and Melinda Gates Foundation and the United Kingdom's Foreign, Commonwealth and Development Office under the Global Burden of Animal Diseases (GBADs) programme (Grant Agreement Investment ID: INV-005366).

# Comment estimer la biomasse du bétail en tenant compte de la diversité des populations et des écosystèmes de données

Y. Li, D. Mayberry & J. Rushton

## Résumé

Les estimations de la biomasse du bétail peuvent servir de dénominateurs lors des estimations de l'impact des maladies, en plus d'apporter des éléments d'appréciation pour évaluer l'utilisation des ressources et les impacts sur l'environnement. Les auteurs analysent les difficultés qui se posent lorsqu'il s'agit d'obtenir une estimation précise de la biomasse à différentes échelles et en utilisant divers écosystèmes de données, et se penchent plus particulièrement sur l'utilisation de la biomasse dans le programme « Impact mondial des maladies animales ». La difficulté majeure est l'absence de données spécifiques aux sous-groupes (en fonction de la race, de l'âge, du sexe) dans les populations et par poids vif à l'échelle nationale et infranationale. Ce problème pourrait être résolu en recourant à des jeux de données mondiaux et à des estimations génériques du poids vif par espèce, mais cette approche ne parvient pas à rendre compte de la diversité des systèmes d'élevage.

## Mots-clés

Biomasse – GBADs – Impact des maladies – Impact mondial des maladies animales – Poids vif – Population.
# Estimación de la biomasa del ganado en diversas poblaciones y ecosistemas de datos

Y. Li, D. Mayberry & J. Rushton

## Resumen

Las estimaciones de la biomasa del ganado pueden utilizarse como denominadores en las estimaciones del impacto de las enfermedades, además de servir de base para evaluar el uso de los recursos y los efectos ambientales. En este artículo se analizan los desafíos que plantea la estimación precisa de la biomasa en diferentes escalas y ecosistemas de datos, prestando especial atención al uso de la biomasa en el programa sobre el Impacto Global de las Enfermedades Animales. El mayor de estos desafíos radica en la falta de datos específicos por subgrupos (raza, edad, sexo) sobre poblaciones y pesos vivos a escala nacional y subnacional. Esta dificultad se puede resolver utilizando conjuntos de datos globales y estimaciones genéricas del peso vivo para cada especie, aunque este enfoque no tiene en cuenta la diversidad de los sistemas pecuarios.

## Palabras clave

Biomasa – GBADs – Impacto de las enfermedades – Impacto Global de las Enfermedades Animales – Peso vivo – Población.

## References

[1] Rushton J, Bruce M, Bellet C, Torgerson P, Shaw A, Marsh T, et al. Initiation of Global Burden of Animal Diseases Programme. Lancet. 2018;392(10147):538-40. https://doi.org/10.1016/S0140-6736(18)31472-7

[2] Rushton J, Huntington B, Gilbert W, Herrero M, Torgerson PR, Shaw APM, et al. Roll-out of the Global Burden of Animal Diseases programme. Lancet. 2021;397(10279):1045-6. https://doi.org/10.1016/S0140-6736(21)00189-6

[3] Addy BL, Thomas D. Stocking rate and the productivity of Rhodes grass pastures on the Lilongwe Plain, Malawi. Trop. Anim. Health Prod. 1978;10(1):11-7. https://doi.org/10.1007/BF02235294

[4] Wilson RT. Biodiversity of domestic livestock in the Republic of Yemen. Trop. Anim. Health Prod. 2003;35(1):27-46. https://doi.org/10.1023/a:1022075604669

[5] Góchez D, Raicek M, Pinto Ferreira J, Jeannin M, Moulin G, Erlacher-Vindel E. OIE annual report on antimicrobial agents intended for use in animals: methods used. Front. Vet. Sci. 2019;6:317. https://doi.org/10.3389/fvets.2019.00317

[6] Rothman-Ostrow P, Gilbert W, Rushton J. Tropical Livestock Units: re-evaluating a methodology. Front. Vet. Sci. 2020;7:556788. https://doi.org/10.3389/fvets.2020.556788

[7] Callaghan CT, Nakagawa S, Cornwell WK. Global abundance estimates for 9,700 bird species. PNAS. 2021;118(21):e2023170118. https://doi.org/10.1073/pnas.2023170118

[8] Bulut E, Ivanek R. Comparison of different biomass methodologies to adjust sales data on veterinary antimicrobials in the USA. J. Antimicrob. Chemother. 2022;77(3):827-42. https://doi.org/10.1093/jac/dkab441

[9] Ruchay A, Gritsenko S, Ermolova E, Bochkarev A, Ermolov S, Guo H, Pezzuolo A. A comparative study of machine learning methods for predicting live weight of Duroc, Landrace, and Yorkshire pigs. Animals. 2022;12(9):1152. https://doi.org/10.3390/ani12091152

[10] Domestic Animal Diversity Information System (DAD-IS). Rome (Italy): Food and Agriculture Organization of the United Nations; 2022. Available at: https://www.fao.org/dad-is/en (accessed on 29 January 2024).

[11] Bayou E, Haile A, Gizaw S, Mekasha Y. Evaluation of non-genetic factors affecting calf growth, reproductive performance and milk yield of traditionally managed Sheko cattle in southwest Ethiopia. SpringerPlus. 2015;4:568. https://doi.org/10.1186/s40064-015-1340-9

[12] Abera M, Eshetu M, Mummed YY, Pilla F, Wondifraw Z. Impact of climatic variability on growth performance of Fogera cattle in Northwestern Ethiopia. J. Anim. Behav. Biometeorol. 2021;9(4):2137. https://doi.org/10.31893/JABB.21037

[13] Fordyce G, Barnes TS, McGowan MR, Perkins NR, Smith DR, McCosker KD. Defining the primary business measure of liveweight production for beef cows in northern Australia. Anim. Prod. Sci. 2023;63(4):395-409. https://doi.org/10.1071/AN20096

[14] Shumye M. Growth curve analysis of body weight in crossbred dairy cattle in central Ethiopia. Livest. Res. Rural Dev. 2021;33(7):95. Available at: https://www.lrrd.org/lrrd33/7/3395molla.html (accessed on 29 January 2024).

[15] Fordyce G, Shephard R, Moravek T, McGowan MR. Australian cattle herd: a new perspective on structure, performance and production. Anim. Prod. Sci. 2023;63(4):410-21. https://doi.org/10.1071/AN20342

[16] Australian Bureau of Statistics (ABS). Agricultural commodities, Australia methodology. Canberra (Australia): ABS; 2023. Available at: https://www abs.gov.au/methodologies/agricultural-commodities-australia-methodology/2021-22 (accessed on 29 January 2024).
[17] FAOSTAT corporate statistical database. Rome (Italy): Food and Agriculture Organization of the United Nations; 1997. Available at: https://www.fao.org/faostat/en/#home (accessed on 29 January 2024).

[18] Jahnke HE. Livestock production systems and livestock development in tropical Africa. Kiel (Germany): Kieler Wissenschaftsverlag Vauk; 1982. 253 p. Available at: https://pdf.usaid.gov/pdf_docs/pnaan484.pdf (accessed on 29 January 2024).

[19] Food and Agriculture Organization of the United Nations (FAO). Technical conversion factors for agricultural commodities. Rome (Italy): FAO; 2021. 346 p. Available at: https://www.fao.org/fileadmin/ templates/ess/documents/methodology/tcf.pdf (accessed on 29 January 2024).

[20] Glossary: livestock unit (LSU). Luxembourg: Eurostat; 2013. Available at: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Livestock_unit_(LSU) (accessed on 29 January 2024).

[21] McLennan SR, McLean I, Paton C. Re-defining the animal unit equivalence (AE) for grazing ruminants and its application for determining forage intake, with particular relevance to the northern Australian grazing industries. North Sydney (Australia): Meat and Livestock Australia Limited; 2020. 36 p. Available at: https://era.daf.qld.gov.au/id/eprint/7964 (accessed on 29 January 2024).

[22] Central Statistical Agency of Ethiopia. Agricultural sample survey 2020/21 [2013 E.C.], volume II: report on livestock and livestock characteristics. Statistical Bulletin 589. Addis Ababa: Central Statistical Agency of Ethiopia; 2021. 199 p. Available at: https://www.statsethopia.gov.et/wp-content/uploads/2021/05/2013.LIVESTOCK-REPORT.FINAL_.pdf (accessed on 29 January 2024).

[23] O'Connor J, More SJ, Griffin JM, O'Leary EO. Modelling the demographics of the Irish cattle population. Prev. Vet. Med. 2009;89(3-4):249-54. https://doi.org/10.1016/j.prevetmed.2009.02.011

[24] Central Statistics Agency of Ethiopia. Socioeconomic survey 2018-2019. Addis Ababa: Central Statistical Agency of Ethiopia; 2020. https://doi.org/10.48529/k739-c548

[25] Schrobback P, Dennis G, Li Y, Mayberry D, Shaw A, Knight-Jones T, et al. Approximating the global economic (market) value of farmed animals. Glob. Food Sec. 2023;39:100722. https://doi.org/10.1016/j.gfs.2023.100722

[26] Smith D, Ilham N, Putri R, Widjaja E, Nugroho WS, Cooper TL, et al. Calculation of livestock biomass and value by province in Indonesia: key information to support policymaking [pre-print]. Social Science Research Network; 2023. https://doi.org/10.2139/ssrn.4603739

[27] Li Y, Mayberry D, Jemberu W, Schrobback P, Herrero M, Chaters G, et al. Characterizing Ethiopian cattle production systems for disease burden analysis. Front. Vet. Sci. 2023;10:1233474. https://doi.org/10.3389/fvets.2023.1233474

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