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Does adopting a nitrogen best management practice reduce nitrogen fertilizer rates?

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Abstract

Technical best management practices are the dominant approach promoted to mitigate agriculture’s significant contributions to environmental degradation. Yet very few social science studies have examined how farmers actually use these practices. This study focuses on the outcomes of farmers’ technical best management practice adoption related to synthetic nitrogen fertilizer management in the context of Midwestern corn agriculture in the United States. Moving beyond predicting the adoption of nitrogen best management practices, I use structural equation modeling and data from a sample of over 2500 farmers to analyze how the number of growing season applications a farmer uses influences the rate at which synthetic nitrogen is applied at the field-level. I find that each additional application of N during the growing season is associated with an average increase of 2.4 kg/ha in farmers’ average N application rate. This result counters expectation for the outcome of this practice and may suggest that structural pressures are leading farmers to use additional growing season applications to ensure sufficiently high N rates, rather than allowing them to reduce rates. I conclude by discussing the implication of this study for future research and policy.

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Notes

  1. Percentage of US total hectares in each state is as follows: Michigan (2.5%); Indiana (6.5%); Illinois (13.2%); Ohio (3.8%) (NASS 2016).

  2. See the ARMS survey at: https://www.ers.usda.gov/data-products/arms-farm-financial-and-crop-production-practices/

  3. The average item missingness was approximately 36%. Total N application rate and total number of growing season applications had the highest at approximately 47% and 43% missing respectively. Question length likely explains higher rates of missingness. These two variables were asked as part of a detailed, page-length table on nutrient management. Future work should simplify these questions to single items to encourage higher response rates.

  4. FIML has been shown to produce relatively unbiased estimates at 75% missing data even in small sample sizes (e.g., n = 300 complete cases) and it is often compared favorably to other missing data techniques, including multiple imputations (Allison 2012; Enders and Bandalos 2001; Newman 2003). While FIML is appropriate for this analysis, for reliability’s sake the model was examined using three missing data techniques: FIML, multiple imputations (w/ OLS regression), and listwise deletion. Results suggest the FIML analysis presented here is robust: in every case, the relationship between growing season applications and N rate was significant, positive, and had approximately the same coefficient shown in this paper.

  5. The decisions were ultimately based on the author’s existing knowledge of farmers’ N management.

  6. Visual inspection of a dot plot of the yield variable was used to detect and drop outliers. A total of 8 cases were dropped.

  7. For those in rotations other than corn-soy (i.e., they did not grow corn and thus didn’t have corn yields to provide) 2016 corn yields were imputed when possible.

  8. Intraclass correlation coefficient (ICC) test showed that 2.3% and 5.5% of the variability in farmers’ N rate and number of growing season applications was attributed to the state-level, respectively.

Abbreviations

BMP:

Best management practice

N:

Nitrogen

SEMLV:

Structural equation modeling with latent variables

FIML:

Full information maximum likelihood

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Acknowledgements

I’d like to thank Dr. Sandra Marquart-Pyatt, Dr. Riva Denny, Dr. Scott Swinton, and Dr. Diana Stuart for their helpful comments and insightful suggestions for how to improve this manuscript and analysis. To the farmers who completed this survey, thank you for your invaluable input. Finally, I greatly appreciate the input of the four anonymous reviewers and the editor. They went above and beyond in contributions to the writing and ideas behind this article. Their thoughtful comments significantly enhanced the quality of this manuscript. Thank you!

Funding

This work was supported by the NSF's Kellogg Biological Station Long Term Ecological Research Site. Grant Number (DEB 1027253) and the Environmental Resilience Institute, funded by Indiana University’s Prepared for Environmental Change Grand Challenge initiative.

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    Matthew Houser

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Table 4 Sample versus population characteristics
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Table 5 Model variables and descriptions
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Houser, M. Does adopting a nitrogen best management practice reduce nitrogen fertilizer rates?. Agric Hum Values 39, 79–94 (2022). https://doi.org/10.1007/s10460-021-10227-9

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