Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun 22;9(6):211573.
doi: 10.1098/rsos.211573. eCollection 2022 Jun.

Understanding the relative risks of zoonosis emergence under contrasting approaches to meeting livestock product demand

Harriet Bartlett  1   2 , Mark A Holmes  2 , Silviu O Petrovan  1   3 , David R Williams  4 , James L N Wood  2 , Andrew Balmford  1
Affiliations

Understanding the relative risks of zoonosis emergence under contrasting approaches to meeting livestock product demand

Harriet Bartlett et al. R Soc Open Sci. .

Abstract

It has been argued that intensive livestock farming increases the risk of pandemics of zoonotic origin because of long-distance livestock movements, high livestock densities, poor animal health and welfare, low disease resistance and low genetic diversity. However, data on many of these factors are limited, and analyses to date typically ignore how land use affects emerging infectious disease (EID) risks, and how these risks might vary across systems with different yields (production per unit area). Extensive, lower yielding practices typically involve larger livestock populations, poorer biosecurity, more workers and more area under farming, resulting in different, but not necessarily lower, EID risks than higher yielding systems producing the same amount of food. To move this discussion forward, we review the evidence for each of the factors that potentially link livestock production practices to EID risk. We explore how each factor might vary with yield and consider how overall risks might differ across a mix of production systems chosen to reflect in broad terms the current livestock sector at a global level and in hypothetical low- and high-yield systems matched by overall level of production. We identify significant knowledge gaps for all potential risk factors and argue these shortfalls in understanding mean we cannot currently determine whether lower or higher yielding systems would better limit the risk of future pandemics.

Keywords: agriculture; biodiversity; emergence; livestock; spillover; zoonoses.

PubMed Disclaimer

Conflict of interest statement

We declare we have no competing interests.

Figures

Figure 1.
Figure 1.
Contrasting beef systems in Brazil. (a) low-yielding pasture adjacent to rainforest and (b) a high-yielding feedlot system. To understand the relative EID risks of each system, it is important to consider the risks associated with both livestock management and land use. Photos reprinted with permission from Fábio Nascimento.
Figure 2.
Figure 2.
Schematic diagram of the different EID risk factors associated with livestock production for a mix of production systems chosen to broadly reflect the current livestock sector at a global level. Green areas represent natural habitat, grey areas represent farmland and turquoise bands are the ecotones between them. Turquoise squares represent microhabitats. This scenario is used as a baseline, with the size or width of red bars, lines and arrows (describing the approximate magnitude of risk factors and contact rates) set to reflect intermediate levels of risk. ‘Natural habitat quality’, ‘health and welfare’, ‘disease resistance’, ‘genetic diversity’ and ‘biosecurity’ are negatively associated with relative EID risk, so relative EID risks are greater (and red bars, lines and arrows larger) when these attributes are lower (e.g. when natural habitat quality, and health and welfare are poorer). The opposite is the case for all other risk factors—so risks (and red symbols) are greater as, for example, ‘ecotone extent’, ‘human contact with wild hosts' and ‘population size’ increase.
Figure 3.
Figure 3.
Schematic diagram of how different EID risk factors associated with livestock production might vary under contrasting approaches to meeting the same level of livestock product demand as figure 2, which is taken as a baseline. The dashed grey lines serve as a reminder of the relative EID risks of the figure 2 baseline. These two panels show how EID risks might change if instead the same level of demand was met using low-yield (a) or high-yield systems (b). Relative EID risks are described by the size or width of red bars, lines and arrows.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, Daszak P. 2008. Global trends in emerging infectious diseases. Nature 451, 990-993. (10.1038/nature06536) - DOI - PMC - PubMed
    1. Plowright RK, Parrish CR, McCallum H, Hudson PJ, Ko AI, Graham AL, Lloyd-Smith JO. 2017. Pathways to zoonotic spillover. Nat. Rev. Microbiol. 15, 502-510. (10.1038/nrmicro.2017.45) - DOI - PMC - PubMed
    1. Johnson CK, Hitchens PL, Pandit PS, Rushmore J, Evans TS, Young CC, Doyle MM. 2020. Global shifts in mammalian population trends reveal key predictors of virus spillover risk. Proc. R. Soc. B 287, 20192736. (10.1098/rspb.2019.2736) - DOI - PMC - PubMed
    1. FAOSTAT. 2018 Food and agriculture data (1 November 2018). See https://www.fao.org/faostat/en/#home. (accessed in 2021).
    1. Daszak P, Cunningham AA, Hyatt AD. 2001. Anthropogenic environmental change and the emergence of infectious diseases in wildlife. Acta Trop. 78, 103-116. (10.1016/S0001-706X(00)00179-0) - DOI - PubMed