Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Climate impacts and adaptation in US dairy systems 1981–2018


Animal-level responses to weather variability in US dairy systems are well described, but the potential of housing and other farm management practices (for example, fans and sprinklers) to moderate the impacts of weather remains uncertain. Here we assess the influence of historical variation in the temperature–humidity index (THI) on milk yields using monthly state-level yield data and high-resolution daily weather data over 1981–2018. We find that milk yields are compromised by exposure to both extreme heat (>79 THI) and cold (<39 THI), causing average daily yield decreases of around 3.7% and 6.1%, respectively, relative to optimal conditions (65–69 THI). Colder regions are more sensitive to heat extremes, and warm regions are more sensitive to cold extremes. Sensitivity to THI has reduced dramatically over time. Climate trends contributed modestly (around 0.1% over 38 years) to rising yields in most states via alleviating cold stress, although more extreme future conditions may negate these benefits.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Dairy cow populations and milk yield trends.
Fig. 2: The relationship between THI and milk yield and heterogeneity across space and time.
Fig. 3: State- and time-specific milk yield models.
Fig. 4: Trends in THI exposure from 1981 to 2018.
Fig. 5: Disaggregating the yield effects of changes in climate and THI sensitivity.

Data availability

USDA data on monthly milk yields and dairy cow populations are publicly available from USDA NASS Quickstats ( Weather data from PRISM are also publicly available ( In addition, compiled datasets utilized for the findings of this study are available on Zenodo with the identifier

Code availability

Code utilized for the findings of this study is available on Zenodo with the identifier


  1. Dairy Production and Products: Milk and Milk Products (FAO, 2013);

  2. Background: Corn and Other Feedgrains (USDA ERS, 2018);

  3. National Agricultural Statistics Service (US Department of Agriculture);

  4. Capper, J. L., Cady, R. A. & Bauman, D. E. The environmental impact of dairy production: 1944 compared with 2007. J. Anim. Sci. 87, 2160–2167 (2009).

    Article  CAS  Google Scholar 

  5. Niles, M. T. & Wiltshire, S. Tradeoffs in US dairy manure greenhouse gas emissions, productivity, climate, and manure management strategies. Environ. Res. Commun 1, 075003 (2019).

    Article  Google Scholar 

  6. Field, T. G. & Taylor, R. E. Scientific Farm Animal Production: An Introduction, Eleventh Edition (Pearson, 2018).

  7. Fuquay, J. W. Heat stress as it affects animal production. J. Anim. Sci. 52, 164–174 (1981).

    Article  CAS  Google Scholar 

  8. St-Pierre, N. R., Cobanov, B. & Schnitkey, G. Economic losses from heat stress by US livestock industries. J. Dairy Sci. 86, E52–E77 (2003).

    Article  Google Scholar 

  9. Kadzere, C. T., Murphy, M. R., Silanikove, N. & Maltz, E. Heat stress in lactating dairy cows: a review. Livest. Prod. Sci. 77, 59–91 (2002).

    Article  Google Scholar 

  10. Bouraoui, R., Lahmar, M., Majdoub, A., Djemali, M. & Belyea, R. The relationship of temperature–humidity index with milk production of dairy cows in a Mediterranean climate. Anim. Res. 51, 479–491 (2002).

    Article  Google Scholar 

  11. West, J. W. Effects of heat-stress on production in dairy cattle. J. Dairy Sci. 86, 2131–2144 (2003).

    Article  CAS  Google Scholar 

  12. Vitali, A. et al. Seasonal pattern of mortality and relationships between mortality and temperature–humidity index in dairy cows. J. Dairy Sci. 92, 3781–3790 (2009).

    Article  CAS  Google Scholar 

  13. Pragna, P. et al. Heat stress and dairy cow: impact on both milk yield and composition. Int. J. Dairy Sci. 12, 1–11 (2017).

    Article  CAS  Google Scholar 

  14. Hoffmann, I. Climate change and the characterization, breeding and conservation of animal genetic resources. Anim. Genet. 41, 32–46 (2010).

    Article  Google Scholar 

  15. Qi, L., Bravo-Ureta, B. E. & Cabrera, V. E. From cold to hot: a preliminary analysis of climatic effects on the productivity of Wisconsin dairy farms. AgEconSearch (2014).

  16. Bohmanova, J., Misztal, I. & Cole, J. B. Temperature–humidity indices as indicators of milk production losses due to heat stress. J. Dairy Sci. 90, 1947–1956 (2007).

    Article  CAS  Google Scholar 

  17. Field, C. B. et al. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (IPCC, 2021);

  18. Mueller, N. D. et al. Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Chang. 6, 317–322 (2016).

    Article  ADS  MathSciNet  Google Scholar 

  19. Seneviratne, S. I., Donat, M. G., Mueller, B. & Alexander, L. V. No pause in the increase of hot temperature extremes. Nat. Clim. Chang. 4, 161–163 (2014).

    Article  ADS  Google Scholar 

  20. Dairy 2014: Dairy Cattle Management Practices in the United States, 2014 (USDA, APHIS, NAHMS, 2016);

  21. Mondaca, M. R. & Cook, N. B. Modeled construction and operating costs of different ventilation systems for lactating dairy cows. J. Dairy Sci. 102, 896–908 (2019).

    Article  CAS  Google Scholar 

  22. Ferreira, F. C., Gennari, R. S., Dahl, G. E. & De Vries, A. Economic feasibility of cooling dry cows across the United States. J. Dairy Sci. 99, 9931–9941 (2016).

    Article  CAS  Google Scholar 

  23. Hayhoe, K. et al. Emissions pathways, climate change, and impacts on California. Proc. Natl Acad. Sci. USA 101, 12422–12427 (2004).

    Article  ADS  CAS  Google Scholar 

  24. Klinedinst, P. L., Wilhite, D. A., Hahn, L. G. & Hubbard, K. G. The potential effects of climate change on summer seasonal dairy cattle milk production and reproduction. Clim. Chang. 23, 21–36 (1993).

    Article  ADS  Google Scholar 

  25. Mauger, G., Bauman, Y., Nennich, T. & Salathé, E. Impacts of climate change on milk production in the United States. Prof. Geogr. 67, 121–131 (2015).

    Article  Google Scholar 

  26. Key, N. & Sneeringer, S. Potential effects of climate change on the productivity of U.S. dairies. Am. J. Agric. Econ. 96, 1136–1156 (2014).

    Article  Google Scholar 

  27. Ortiz-Bobea, A., Knippenberg, E. & Chambers, R. G. Growing climatic sensitivity of U.S. agriculture linked to technological change and regional specialization. Sci. Adv. 4, eaat4343 (2018).

    Article  ADS  Google Scholar 

  28. Butler, E. E., Mueller, N. D. & Huybers, P. Peculiarly pleasant weather for US maize. Proc. Natl Acad. Sci. USA 115, 11935–11940 (2018).

    Article  CAS  Google Scholar 

  29. Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).

    Article  ADS  CAS  Google Scholar 

  30. Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. Proc. Natl Acad. Sci. U. S. A. 115, 6644–6649 (2018).

    Article  ADS  Google Scholar 

  31. PRISM Climate Data (Oregon State Univ., 2019);

  32. Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. (2008).

  33. National Research Council. Nutrient Requirements of Dairy Cattle, Seventh Revised Edition (National Academies Press, 2001).

  34. Auldist, M. J., Walsh, B. J. & Thomson, N. A. Seasonal and lactational influences on bovine milk composition in New Zealand. J. Dairy Res. 65, 401–411 (1998).

    Article  CAS  Google Scholar 

  35. Lobell, D. B. Climate change adaptation in crop production: beware of illusions. Glob. Food Sec. 3, 72–76 (2014).

    Article  Google Scholar 

  36. Mukherjee, D., Bravo-Ureta, B. E. & De Vries, A. Dairy productivity and climatic conditions: econometric evidence from South-eastern United States. Aust. J. Agric. Resour. Econ. 57, 123–140 (2013).

    Article  Google Scholar 

  37. Milk Cost of Production Estimates: Cost-of-Production Estimates-2016 Base (USDA ERS, 2021);

  38. Liang, X. Z. et al. Determining climate effects on US total agricultural productivity. Proc. Natl Acad. Sci. USA 114, E2285–E2292 (2017).

    Article  CAS  Google Scholar 

  39. Malikov, E., Miao, R. & Zhang, J. Distributional and temporal heterogeneity in the climate change effects on U.S. agriculture. J. Environ. Econ. Manage. 104, 102386 (2020).

    Article  Google Scholar 

  40. MacDonald, J. M., Law, J. & Mosheim, R. Consolidation in U.S. Dairy Farming Economic Research Report No. 274 (ERS, USDA, 2020);

  41. Hemme, T. & Otte, J. Pro-Poor Livestock Policy Initiative Status and Prospects for Smallholder Milk Production a Global Perspective (Food and Agriculture Organization of the United Nations, 2010).

  42. Osei-Amponsah, R. et al. Heat stress impacts on lactating cows grazing Australian summer pastures on an automatic robotic dairy. Animals 10, 869 (2020).

    Article  Google Scholar 

  43. Chang-Fung-Martel, J., Harrison, M. T., Rawnsley, R., Smith, A. P. & Meinke, H. The impact of extreme climatic events on pasture-based dairy systems: a review. Crop Pasture Sci 68, 1158 (2017).

    Article  Google Scholar 

  44. Livestock Hot Weather Stress. Operations Manual (NOAA, 1976);

  45. Pinheiro J., Bates D., Debroy S. S. D. Linear and nonlinear mixed effects models, R package nlme version 3.1-152 (2021).

  46. Conley, T. G. GMM estimation with cross sectional dependence. J. Econom. 92, 1–45 (1999).

    Article  MathSciNet  Google Scholar 

  47. Borchers, H. W. pracma: practical numerical math functions, version–393 (2019).

  48. Colin Cameron, A., Gelbach, J. B. & Miller, D. L. Robust inference with multiway clustering. J. Bus. Econ. Stat. 29, 238–249 (2011).

    Article  MathSciNet  Google Scholar 

  49. Zeileis, A., Köll, S. & Graham, N. Various versatile variances: an object-oriented implementation of clustered covariances in R. J. Stat. Softw. (2020).

Download references


The authors thank S. Davis, L. Sloat and J. Dillon for helpful comments. This work is supported by the Interdisciplinary Engagement in Animal Systems Program (2021-68014-34141 to N.D.M.) from the USDA National Institute of Food and Agriculture.

Author information

Authors and Affiliations



N.D.M., M.G.-Q. and A.H. designed the study. M.G.-Q. and A.H conducted the analyses with support from N.D.M. and B.M. on the analytical framework and interpretation of model results, A.J.R. on weather data processing and E.K. and M.T.N. on interpretation regarding dairy cattle physiology and management. M.G.-Q., N.D.M. and A.H. led the writing with input from all co-authors.

Corresponding authors

Correspondence to Maria Gisbert-Queral or Nathaniel D. Mueller.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Food thanks Michelle Tigchelaar, Matthew Harrison and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1 and 2 and Tables 1–11.

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gisbert-Queral, M., Henningsen, A., Markussen, B. et al. Climate impacts and adaptation in US dairy systems 1981–2018. Nat Food 2, 894–901 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing