Skip to main content

Climate change uncertainty among American farmers: an examination of multi-dimensional uncertainty and attitudes towards agricultural adaptation to climate change

Climatic Change volume 162, pages 1047–1064 (2020)Cite this article

Abstract

A large survey of corn farmers in twelve US midwestern states (n = 6849) was used to determine the role of multiple dimensions of uncertainty on prior experience with climate change, attitudes towards climate adaptation, and use of climate outlooks in agricultural decision-making. Epistemic uncertainty refers to a perception about the level of information about a phenomenon. Aleatoric uncertainty is a perception that a phenomenon occurs at random and no new information will reduce uncertainty while response uncertainty refers to the perception of the efficacy of an action to reduce a risk. Epistemic and response uncertainty explained a large portion of variance of farmers’ attitudes towards adaptation and their willingness to use weather and climate outlook tools. Aleatoric uncertainty however did not add or added only a small portion of variance explaining farmers’ attitudes climate adaptation or use of climate tools. Our results indicate that climate scientists should not treat farmers’ uncertainty as a monolithic concept, but instead embrace its multidimensionality. We also suggest that reception of expert-led presentations or tools that have a lot of modeling data, which are often layered with statistical uncertainty, can negatively influence farmers’ model uncertainty.

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

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

References

  • Ainsworth EA, Ort DR (2010) How do we improve crop production in a warming world? Plant Physiol 154(2):526–530

    Article  Google Scholar 

  • Arbuckle JG, Prokopy LS, Haigh T, Hobbs J, Knoot T, Knutson C et al (2013) Climate change beliefs, concerns, and attitudes toward adaptation and mitigation among farmers in the Midwestern United States. Clim Chang 117(4):943–950. https://doi.org/10.1007/s10584-013-0707-6

    Article  Google Scholar 

  • Arbuckle JG, Hobbs J, Loy A, Morton LW, Prokopy LS, Tyndall J (2014) Understanding Corn Belt farmer perspectives on climate change to inform engagement strategies for adaptation and mitigation. J Soil Water Conserv 69(6):505–516

    Article  Google Scholar 

  • Arbuckle JG, Tyndall JC, Morton LW, Hobbs J (2017) Climate change typologies and audience segmentation among Corn Belt farmers. J Soil Water Conserv 72(3):205–214

  • Beddington JR, Asaduzzaman M, Fernandez A, Clark M, Guillou M, Jahn M, Scholes R (2012) Achieving food security in the face of climate change: final report from the Commission on Sustainable Agriculture and Climate Change

  • Biehl LL, Zhao L, Song CX, Panza CG (2017) Cyberinfrastructure for the collaborative development of U2U decision support tools. Clim Risk Manag 15:90–108

    Article  Google Scholar 

  • Bradley R, Drechsler M (2014) Types of uncertainty. Erkenntnis 79(6):1225–1248. https://doi.org/10.1007/s10670-013-9518-4

    Article  Google Scholar 

  • Brechin SR (2012) Shaping US climate opinion. Nat Clim Chang 2(4):236–237

  • Burke M, Emerick K (2016) Adaptation to climate change: Evidence from US agriculture. Am Econ J Econ Pol 8(3):106–140

  • Charles D (2017) A farm journalist tells farmers what They’d rather not hear about climate change. National Public Radio. http://www.npr.org/sections/thesalt/2017/06/02/531223294/a-farm-journalist-tells-farmers-what-theyd-rather-not-hear-about-climate-change

  • Clayton C (2015) The elephant in the cornfield: the politics of agriculture and climate change. Amazon (ebook), Seattle

    Google Scholar 

  • Curry JA, Webster PJ (2011) Climate science and the uncertainty monster. Bull Am Meteorol Soc 92(12):1667–1682

    Article  Google Scholar 

  • Dantan JY, Gayton N, Qureshi AJ, Lemaire M, Etienne A (2013) Tolerance analysis approach based on the classification of uncertainty (aleatory/epistemic). Proc CIRP 10:287–293

    Article  Google Scholar 

  • Diggs DM (1991) Drought experience and perception of climatic change among Great Plains farmers. Great Plains Res, 114–132

  • Dillman DA, Smyth JD, Christian LM (2014) Internet, phone, mail, and mixed-mode surveys: the tailored design method. John Wiley & Sons

  • Eanes FR, Singh AS, Bulla BR, Ranjan P, Prokopy LS, Fales M, Wickerham B, Doran PJ (2017) Midwestern US farmers perceive crop advisers as conduits of information on agricultural conservation practices. Environ Manag 60(5):974–988

    Article  Google Scholar 

  • Eanes FR, Singh AS, Bulla BR, Ranjan P, Fales M, Wickerham B, Doran PJ, Prokopy LS (2019) Crop advisers as conservation intermediaries: perceptions and policy implications for relying on nontraditional partners to increase US farmers’ adoption of soil and water conservation practices. Land Use Policy 81:360–370

    Article  Google Scholar 

  • Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M et al (2011) Solutions for a cultivated planet. Nature 478(7369):337–342

    Article  Google Scholar 

  • Glas ZE, Getson JM, Gao Y, Singh AS, Eanes FR, Esman LA et al (2019) Effect of monetary incentives on mail survey response rates for Midwestern farmers. Soc Nat Resour 32(2):229–237

    Article  Google Scholar 

  • Gramig BM, Barnard JM, Prokopy LS (2013) Farmer beliefs about climate change and carbon sequestration incentives. Clim Res 56.2(2013):157–167

    Article  Google Scholar 

  • Haigh T, Morton LW, Lemos MC, Knutson C, Prokopy LS, Lo YJ, Angel J (2015) Agricultural advisors as climate information intermediaries: exploring differences in capacity to communicate climate. Weather, Climate, and Society 7(1):83–93

    Article  Google Scholar 

  • Hatfield JL, Boote KJ, Kimball BA, Ziska LH, Izaurralde RC, Ort D et al (2011) Climate impacts on agriculture: implications for crop production. Agron J 103(2):351–370

    Article  Google Scholar 

  • Helton JC, Burmaster DE (1996) Guest editorial: treatment of aleatory and epistemic uncertainty in performance assessments for complex systems. Reliab Eng Syst Saf 54(2–3):91–94. https://doi.org/10.1016/S0951-8320(96)00066-X

    Article  Google Scholar 

  • Huang H, Khanna M (2010) An econometric analysis of US crop yield and cropland acreage: implications for the impact of climate change. In AAEA annual meeting, Denver, Colorado (pp. 25-27)

  • IPCC (2015) Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Climate Change, Food, and Agriculture [Mastrandrea, M.D., K.J. Mach, V.R. Barros, T.E. Bilir, D.J. Dokken, O. Edenhofer, C.B. Field, T. Hiraishi, S. Kadner, T. Krug, J.C. Minx, R. PichsMadruga, G.-K. Plattner, D. Qin, Y. Sokona, T.F. Stocker, M. Tignor (eds.)]. World Meteorological Organization, Geneva, Switzerland, 68 pp

  • Johnson DR, Creech JC (1983) Ordinal measures in multiple indicator models: a simulation study of categorization error. Am Sociol Rev, 398–407

  • Kahan DM, Jenkins-Smith H, Braman D (2011) Cultural cognition of scientific consensus. J Risk Res 14(2):147–174

    Article  Google Scholar 

  • Kahneman D, Tversky A (1982) Variants of uncertainty. Cognition 11(2):143–157. https://doi.org/10.1016/0010-0277(82)90023-3

    Article  Google Scholar 

  • Kucharik CJ, Serbin SP (2008) Impacts of recent climate change on Wisconsin corn and soybean yield trends. Environ Res Lett 3(3):034003

    Article  Google Scholar 

  • Le Dang H, Li E, Nuberg I, Bruwer J (2014) Understanding farmers’ adaptation intention to climate change: a structural equation modelling study in the Mekong Delta, Vietnam. Environ Sci Pol 41:11–22

    Article  Google Scholar 

  • Leiserowitz AA (2005) American risk perceptions: is climate change dangerous? Risk Anal 25(6):1433–1442

    Article  Google Scholar 

  • Lobell DB, Asner GP (2003) Climate and management contributions to recent trends in US agricultural yields. Science 299(5609):1032–1032

    Article  Google Scholar 

  • Marx SM, Weber EU, Orlove BS, Leiserowitz A, Krantz DH, Roncoli C, Phillips J (2007) Communication and mental processes: experiential and analytic processing of uncertain climate information. Glob Environ Chang 17(1):47–58. https://doi.org/10.1016/j.gloenvcha.2006.10.004

    Article  Google Scholar 

  • Mase AS, Gramig BM, Prokopy LS (2017) Climate change beliefs, risk perceptions, and adaptation behavior among Midwestern US crop farmers. Clim Risk Manag 15:8–17

    Article  Google Scholar 

  • McCarl BA (2010) Analysis of climate change implications for agriculture and forestry: an interdisciplinary effort. Clim Chang 100(1):119–124

    Article  Google Scholar 

  • Melillo JM, Richmond T (TC), and Yohe GW, Eds. (2014) Climate change impacts in the United States: the Third National Climate Assessment. U.S. Global Change Research Program, 841 pp. doi:https://doi.org/10.7930/J0Z31WJ2

  • Menapace L, Colson G, Raffaelli R (2015) Climate change beliefs and perceptions of agricultural risks: an application of the exchangeability method. Glob Environ Chang 35:70–81

    Article  Google Scholar 

  • Morton LW, McGuire JM, Cast AD (2017) A good farmer pays attention to the weather. Clim Risk Manag 15:18–31

    Article  Google Scholar 

  • National Research Council (NRC) (2010) Adapting to the impacts of climate change: America’s climate choices. National Academies Press, Washington, DC

    Google Scholar 

  • Niles MT, Lubell M, Brown M (2015) How limiting factors drive agricultural adaptation to climate change. Agric Ecosyst Environ 200:178–185

  • Niles MT, Brown M, Dynes R (2016) Farmer’s intended and actual adoption of climate change mitigation and adaptation strategies. Clim Chang 135(2):277–295

    Article  Google Scholar 

  • Nilsen T, Aven T (2003) Models and model uncertainty in the context of risk analysis. Reliab Eng Syst Saf 79(3):309–317. https://doi.org/10.1016/S0951-8320(02)00239-9

    Article  Google Scholar 

  • Niyogi D, Jacobs EM, Liu X, Kumar A, Biehl L, Rao PSC (2017) Assessment of a long-term high-resolution hydroclimatic dataset for the US Midwest. Earth Interact 21(4):1–31

    Article  Google Scholar 

  • Norman G (2010) Likert scales, levels of measurement and the “laws” of statistics. Adv Health Sci Educ 15(5):625–632

    Article  Google Scholar 

  • Paté-Cornell ME (1996) Uncertainties in risk analysis: six levels of treatment. Reliab Eng Syst Saf 54(2):95–111. https://doi.org/10.1016/S0951-8320(96)00067-1

    Article  Google Scholar 

  • Prokopy LS, Haigh T, Mase AS, Angel J, Hart C, Knutson C et al (2013) Agricultural advisors: a receptive audience for weather and climate information? Weather, Climate, and Society 5(2):162–167

    Article  Google Scholar 

  • Prokopy LS, Arbuckle JG, Barnes AP, Haden VR, Hogan A, Niles MT, Tyndall J (2015) Farmers and climate change: a cross-national comparison of beliefs and risk perceptions in high-income countries. Environ Manag 56(2):492–504

    Article  Google Scholar 

  • Prokopy LS, Carlton JS, Haigh T, Lemos MC, Mase AS, Widhalm M (2017) Useful to usable: developing usable climate science for agriculture. Clim Risk Manag 15:1–7

    Article  Google Scholar 

  • Prokopy LS, Floress K, Arbuckle JG, Church SP, Eanes FR, Gao Y, et al (2019) Adoption of agricultural conservation practices in the United States: Evidence from 35 years of quantitative literature. J Soil Water Conserv 74(5):520–534

  • Ramankutty N, Foley JA, Norman J, McSweeney K (2002) The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Glob Ecol Biogeogr 11(5):377–392

    Article  Google Scholar 

  • Regan HM, Colyvan IM, Burgman MA (2002) A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl 12(2):618–628

    Article  Google Scholar 

  • Rejesus RM, Mutuc-Hensley M, Mitchell PD, Coble KH, Knight TO (2013) US agricultural producer perceptions of climate change. J Agric Appl Econ 45(4):701–718

    Article  Google Scholar 

  • Roesch-McNally GE, Gordon Arbuckle J, Tyndall JC (2016) What would farmers do? Adaptation intentions under a Corn Belt climate change scenario Agric Hum Values, 1–14. https://doi.org/10.1007/s10460-016-9719-y

  • Savage LJ (1954) The foundations of statistics. Wiley, New York

    Google Scholar 

  • Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc Natl Acad Sci 106(37):15594–15598

    Article  Google Scholar 

  • Simon HA (1972) Theories of bounded rationality. Decision Organ 1(1):161–176

    Google Scholar 

  • Simon HA (1997) Models of bounded rationality: empirically grounded economic reason. MIT press

  • Slovic P (1987) Perception of risk. Science 236(4799):280–285

    Article  Google Scholar 

  • Safi SA, Smith J, Liu Z (2012) Rural Nevada and climate change: vulnerability, beliefs, and risk perception. Risk Anal 32(6):1041–1059

    Article  Google Scholar 

  • Sullivan GM, Artino AR Jr (2013) Analyzing and interpreting data from Likert-type scales. J Grad Med Educ 5(4):541–542

    Article  Google Scholar 

  • Tabuchi H (2017) In America’s heartland, discussing climate change without saying ‘climate change’. New York Times January 28, 2017. Accessed 26 June 2017. https://www.nytimes.com/2017/01/28/business/energy-environment/navigating-climate-change-in-americas-heartland.html

  • Tversky A, Kahneman D (1991) Loss aversion in riskless choice: A reference-dependent model. Q J Econ 106(4):1039–1061

  • Tversky A, Simonson I (1993) Context-dependent preferences. Manag Sci 39(10):1179–1189

  • Weber MG, Flannigan MD (1997) Canadian boreal forest ecosystem structure and function in a changing climate: impact on fire regimes. Environ Rev 5(3–4):145–166

    Article  Google Scholar 

  • Weber EU, Johnson EJ (2009) Economic and Neuroeconomic explanations of risk preference risk preference : the historical context. Brain, 127–144. https://doi.org/10.1016/B978-0-12-374176-9.00010-5

  • Weber EU, Stern PC (2011) Public understanding of climate change in the United States. Am Psychol 66(4):315

    Article  Google Scholar 

Download references

Funding

Support for this research was provided by USDA National Institute of Food and Agriculture.

(NIFA) Award Number 2011-68002-30220, project titled “Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers.”

Author information

Authors and Affiliations

  1. CSU, Sacramento, CA, USA

    Ajay S. Singh

  2. Bates College, Lewiston, ME, USA

    Francis Eanes

  3. Purdue University, West Lafayette, IN, USA

    Linda S. Prokopy

Authors
  1. Ajay S. Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francis Eanes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linda S. Prokopy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ajay S. Singh.

Additional information

Data

Singh, A., Getson, J., Esman, L., Koundinya, V., Klink, J. L., Mase, A., Haigh, T., Widhalm, M. J.

Prokopy, L. S. (2018). Farmers' Climate Risk Perceptions and Use of Climate Information: 2016

Survey Data

Purdue University Research Repository doi:10.4231/R78W3BBV

Publisher’s note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Singh, A.S., Eanes, F. & Prokopy, L.S. Climate change uncertainty among American farmers: an examination of multi-dimensional uncertainty and attitudes towards agricultural adaptation to climate change. Climatic Change 162, 1047–1064 (2020). https://doi.org/10.1007/s10584-020-02860-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10584-020-02860-w

Keywords

  • Climate change
  • Climate adaptation
  • Uncertainty
  • Climate communication
  • Agriculture
  • Food security