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Navigating the information landscape: public and private information source access by midwest farmers

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Abstract

Timely and accurate information is vital to the success of row crop farmers in the United States. Information access is also critical to conservation efforts due to its influence on best management practice adoption. Public information sources like extension educators have been declining in importance for farmers, raising concerns around what information farmers receive on conservation practices and the accessibility of agronomic information. In this study we investigate farmers’ changing information source consultation by broadly considering the agricultural information landscape, exploring whether farmers have displayed clear trends in access between public and private sources and whether certain farmer or operational characteristics predict public or private source access. We utilize data from a 2018 survey of farmers in four Corn Belt states to examine farmers’ information seeking behaviors and predict the number of total, public, and private sources accessed using structural equation modeling with latent variables. Our findings elaborate on the public-to-private source shift and reveal that farmers continue to seek information from both private and public sources, though the frequency, mode of contact, and types of farmers contacting these sources differ. Results suggest public information sources are still influential, but they are accessed less frequently, tend to appeal to farmers with stronger environmental concerns, and have less appeal to older farmers compared to private information sources. Our findings indicate the potential for extension and other public sources to diversify modes of communication to further their reach.

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Notes

  1. The component fit of an acceptable latent variable has standardized and unstandardized factor loadings close to one another to show that the included measures are valid and reliable (the former above .4 and the latter around 1). Overall model fit statistics for an acceptable latent variable include a non-significant chi-square value (indicating that the estimated model is not significantly different from the data), values for the Incremental Fit Index (IFI), Comparative Fit Index (CFI), and Tucker-Lewis Index (TLI) above .95, and a Root Mean Square Error of Approximation (RMSEA) below .05 (West et al. 2012).

  2. We thank an anonymous reviewer for this point.

Abbreviations

SEMLV:

Structural equation modeling with latent variables

USDA:

United States department of agriculture

SWCD:

Soil and water conservation district

CFA:

Confirmatory factor analysis

IFI:

Incremental fit index

TLI:

Tucker–Lewis index

CFI:

Comparative fit index

RMSEA:

Root mean square error of approximation

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Acknowledgements

Support for this research was provided by the National Science Foundation (NSF) Long Term Ecological Research Program (Grant No. DEB 1832042) at the Kellogg Biological Station, by the USDA Long-Term Agroecosystem Research (LTAR) Program, and by Michigan State University AgBioResearch and College of Social Science. The authors thank two anonymous reviewers for their feedback, members of the survey research team, and the survey respondents, without whom this work would not be possible.

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Authors and Affiliations

  1. Department of Geography, Environment and Spatial Sciences, Michigan State University, 673 Auditorium Rd., East Lansing, MI, 48824, USA

    Kristina Beethem

  2. Department of Geography, Environment and Spatial Sciences and Department of Political Science, Michigan State University, 673 Auditorium Rd., East Lansing, MI, 48824, USA

    Sandra T. Marquart-Pyatt

  3. Department of Sociology, The University of Vermont, 31 South Prospect Street, Burlington, VT, 05401, USA

    Jennifer Lai

  4. Department of Human Dimensions of Natural Resources, Colorado State University, 1480 Campus Delivery, Fort Collins, CO, 80523-1480, USA

    Tian Guo

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Correspondence to Kristina Beethem.

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Appendix

Appendix

See Tables 4, 5, 6, and 7.

Table 4 Component fit and overall model fit statistics for latent variables
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Table 5 Survey questions for variables in empirical models
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Table 6 SEMLV results for models of public information use by mode of access (n = 1718)
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Table 7 SEMLV results for models of private information use by mode of access (n = 1718)
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Beethem, K., Marquart-Pyatt, S.T., Lai, J. et al. Navigating the information landscape: public and private information source access by midwest farmers. Agric Hum Values 40, 1117–1135 (2023). https://doi.org/10.1007/s10460-022-10411-5

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