Abstract
Fungicide seed treatments (FSTs) are widely used in Midwest soybean production due to perceived disease risk. While some studies report significant yield increases, overall economic and environmental benefits remain unclear. This study utilized randomized controlled trial (RCT) and observational data to estimate the causal FST effect on soybean yield. Analysis of the RCT data revealed a modest average yield increase of 22.2 kg/ha attributable to FST. Observational data also indicated a small average yield gain of approximately 36 kg/ha. Monte Carlo simulations showed that yield gains often do not offset the seed treatment costs, with financial benefit likely only under low FST costs and high soybean prices. Given the limited economic return and concerns about the potential negative impacts of widespread FST use on soil microbes and non-target organisms, our research suggests that FSTs may not be necessary in Midwest soybean production, and growers should carefully evaluate their use based on individual farm gate economics as well as ecological considerations.
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Data availability
The code necessary to reproduce the analyses and figures shown in this study is available at https://doi.org/10.5281/zenodo.15546368. To respect the privacy of growers, field locations in the associated data files have been de-identified (i.e., reported by state only and not by latitude and longitude).
References
Deines, J. M., Wang, S. & Lobell, D. B. Satellites reveal a small positive yield effect from conservation tillage across the US Corn Belt. Environ. Res. Lett. 14 https://doi.org/10.1088/1748-9326/ab503b (2019).
NASS & Quick Stats, A. P. I. USDA Natl. Agric. Stat. Serv. (2025). https://quickstats.nass.usda.gov
Matcham, E. G. et al. Management strategies for early- and late-planted soybean in the north-central United States. Agron. J. 112, 2928–2943 (2020).
Mourtzinis, S., Specht, J. E. & Conley, S. P. Defining optimal soybean sowing dates across the US. Sci. Rep. 9 https://doi.org/10.1038/s41598-019-38971-3 (2019).
Nleya, T., Schutte, M., Clay, D., Reicks, G. & Mueller, N. Planting date, cultivar, seed treatment, and seeding rate effects on soybean growth and yield. Agrosystems Geosci. Environ. 3, e20045. https://doi.org/10.1002/agg2.20045 (2020).
Rattalino Edreira, J. I. et al. Assessing causes of yield gaps in agricultural areas with diversity in climate and soils. Agric. Meteorol. 247, 170–180 (2017).
Robinson, A. P., Conley, S. P., Volenec, J. J. & Santini, J. B. Analysis of high yielding, early-planted soybean in Indiana. Agron. J. 101, 131–139 (2009).
Vossenkemper, J. P. et al. Early planting, full-season cultivars, and seed treatments maximize soybean yield potential. Crop Forage Turfgrass Manag. 1, 1–9 (2015).
Severo Silva, T. et al. Soybean yield response to management practices (4–40 years) and soil health parameters. Field Crops Res. 329, 109959. https://doi.org/10.1016/j.fcr.2025.109959 (2025).
Rowntree, S. C. et al. Genetic gain × management interactions in soybean: I. Planting date. Crop Sci. 53, 1128–1138 (2013).
Lamichhane, J. R., You, M. P., Laudinot, V., Barbetti, M. J. & Aubertot J.-N. Revisiting sustainability of fungicide seed treatments for field crops. Plant. Dis. 104, 610–623 (2020).
Matthiesen, R. L. & Robertson, A. E. Effect of infection timing by four Pythium spp. on soybean damping-off symptoms with and without cold stress. Plant. Dis. 107, 3975–3983 (2023).
Scott, K., Eyre, M., McDuffee, D. & Dorrance, A. E. The efficacy of ethaboxam as a soybean seed treatment toward Phytophthora, Phytopythium, and Pythium in Ohio. Plant. Dis. 104, 1421–1432 (2020).
Bandara, A. Y., Weerasooriya, D. K., Bradley, C. A., Allen, T. W. & Esker, P. D. Dissecting the economic impact of soybean diseases in the United States over two decades. PLOS ONE. 15, e0231141. https://doi.org/10.1371/journal.pone.0231141 (2020).
Munkvold, G. P. Seed pathology progress in academia and industry. Annu. Rev. Phytopathol. 47, 285–311 (2009).
Cox, W. J. & Cherney, J. H. Location, variety, and seeding rate interactions with soybean seed-applied insecticide/fungicides. Agron. J. 103, 1366–1371 (2011).
Gaspar, A. P. et al. Response of broad-spectrum and target-specific seed treatments and seeding rate on soybean seed yield, profitability, and economic risk. Crop Sci. 57, 2251–2262 (2017).
Navi, S. S., Huynh, T., Mayers, C. G. & Yang, X. B. Diversity of Pythium spp. associated with soybean damping-off, and management implications by using foliar fungicides as seed treatments. Phytopathol. Res. 1, 8. https://doi.org/10.1186/s42483-019-0015-9 (2019).
Lamichhane, J. R., Corrales, D. C. & Soltani, E. Biological seed treatments promote crop establishment and yield: a global meta-analysis. Agron. Sustain. Dev. 42 https://doi.org/10.1007/s13593-022-00761-z (2022).
Dorrance, A. E. et al. Integrated management strategies for Phytophthora sojae combining host resistance and seed treatments. Plant. Dis. 93, 875–882 (2009).
Bradley, C. A. Effect of fungicide seed treatments on stand establishment, seedling disease, and yield of soybean in North Dakota. Plant. Dis. 92, 120–125 (2008).
Bradley, C. A., Wax, L. M., Ebelhar, S. A., Bollero, G. A. & Pedersen, W. L. The effect of fungicide seed protectants, seeding rates, and reduced rates of herbicides on no-till soybean. Crop Prot. 20, 615–622 (2001).
Cox, W. J., Shields, E. & Cherney, J. H. Planting date and seed treatment effects on soybean in the Northeastern United States. Agron. J. 100, 1662–1665 (2008).
Lueschen, W. E. et al. Soybean production as affected by tillage in a corn and soybean management system: II. Seed treatment response. J. Prod. Agric. 4, 580–585 (1991).
Schulz, T. J. & Thelen, K. D. Soybean seed inoculant and fungicidal seed treatment effects on soybean. Crop Sci. 48, 1975–1983 (2008).
Esker, P. D. & Conley, S. P. Probability of yield response and breaking even for soybean seed treatments. Crop Sci. 52, 351–359 (2012).
Gaspar, A. P., Marburger, D. A., Mourtzinis, S. & Conley, S. P. Soybean seed yield response to multiple seed treatment components across diverse environments. Agron. J. 106, 1955–1962 (2014).
Gaspar, A. P., Mitchell, P. D. & Conley, S. P. Economic risk and profitability of soybean fungicide and insecticide seed treatments at reduced seeding rates. Crop Sci. 55, 924–933 (2015).
Orlowski, J. M. et al. High-input management systems effect on soybean seed yield, yield components, and economic break-even probabilities. Crop Sci. 56, 1988–2004 (2016).
Kandel, Y. R. et al. Integration of host resistance, seed treatment, and seeding rate for management of sudden death syndrome, a disease of soybean caused by Fusarium virguliforme. Plant. Health Prog. 24, 445–452 (2023).
Byrnes, J. E. K. & Dee, L. E. Causal inference with observational data and unobserved confounding variables. Ecol. Lett. 28, e70023. https://doi.org/10.1111/ele.70023 (2025).
Kimmel, K., Dee, L. E., Avolio, M. L. & Ferraro, P. J. Causal assumptions and causal inference in ecological experiments. Trends Ecol. Evol. 36, 1141–1152 (2021).
Yang, Y. et al. Publication bias impacts on effect size, statistical power, and magnitude (Type M) and sign (Type S) errors in ecology and evolutionary biology. BMC Biol. 21, 71. https://doi.org/10.1186/s12915-022-01485-y (2023).
van Zwet, E., Schwab, S. & Greenland, S. Addressing exaggeration of effects from single RCTs. Significance 18, 16–21 (2021).
Andrade, J. F. et al. Assessing the influence of row spacing on soybean yield using experimental and producer survey data. Field Crops Res. 230, 98–106 (2019).
Mourtzinis, S. et al. Assessing benefits of artificial drainage on soybean yield in the North Central US region. Agric. Water Manag. 243, 106425. https://doi.org/10.1016/j.agwat.2020.106425 (2021).
Shah, D. A. et al. A machine learning interpretation of the contribution of foliar fungicides to soybean yield in the north-central United States. Sci. Rep. 11, 18769. https://doi.org/10.1038/s41598-021-98230-2 (2021).
Siegel, K. & Dee, L. E. Foundations and future directions for causal inference in ecological research. Ecol. Lett. 28, e70053. https://doi.org/10.1111/ele.70053 (2025).
Mourtzinis, S. et al. Neonicotinoid seed treatments of soybean provide negligible benefits to US farmers. Sci. Rep. 9 https://doi.org/10.1038/s41598-019-47442-8 (2019).
Smith, M. J. et al. Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial. Stat. Med. 41, 407–432 (2022).
Hirt, J., Nordhausen, T., Fuerst, T., Ewald, H. & Appenzeller-Herzog, C. Guidance on terminology, application, and reporting of citation searching: The TARCiS statement. BMJ 385, e078384. https://doi.org/10.1136/bmj-2023-078384 (2024).
Hitaj, C. et al. Sowing uncertainty: What we do and don’t know about the planting of pesticide-treated seed. BioScience 70, 390–403 (2020).
Igelström, E. et al. Causal inference and effect estimation using observational data. J. Epidemiol. Community Health. 76, 960–966 (2022).
VanderWeele, T. J. & Hernan, M. A. Causal inference under multiple versions of treatment. J. Causal Inference. 1, 1–20 (2013).
VanderWeele, T. J. Concerning the consistency assumption in causal inference. Epidemiology 20, 880–883 (2009).
Larsen, A. E., Meng, K. & Kendall, B. E. Causal analysis in control–impact ecological studies with observational data. Methods Ecol. Evol. 10, 924–934 (2019).
Imbens, G. W. & Rubin, D. B. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (Cambridge University Press, 2015).
Moccia, C. et al. Machine learning in causal inference for epidemiology. Eur. J. Epidemiol. 39, 1097–1108 (2024).
van der Laan, M. J. & Rose, S. Targeted Learning: Causal Inference for Observational and Experimental Data (Springer, 2011).
Athey, S. & Wager, S. Estimating treatment effects with causal forests: An application. Obs Stud. 5, 37–51 (2019).
Hahn, P. R., Murray, J. S. & Carvalho, C. M. Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion). Bayesian Anal. 15, 965–1056 (2020).
Hill, J. L. Bayesian nonparametric modeling for causal inference. J. Comput. Graph Stat. 20, 217–240 (2011).
van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super learner. Stat. Appl. Genet. Mol. Biol. 6 https://doi.org/10.2202/1544-6115.1309 (2007).
Rohrer, J. M. Thinking clearly about correlations and causation: Graphical causal models for observational data. Adv. Methods Pract. Psychol. Sci. 1, 27–42 (2018).
DÁgostino McGowan, L. tipr: An R package for sensitivity analyses for unmeasured confounders. J. Open. Source Softw. 7, 4495. https://doi.org/10.21105/joss.04495 (2022).
Dorie, V., Harada, M., Carnegie, N. B. & Hill, J. A flexible, interpretable framework for assessing sensitivity to unmeasured confounding. Stat. Med. 35, 3453–3470 (2016).
Athey, S., Tibshirani, J. & Wager, S. Generalized random forests. Ann. Stat. 47, 1148–1178 (2019).
Rehill, P. How do applied researchers use the causal forest? A methodological review. Int. Stat. Rev. 93, 288–316 (2025).
van Zwet, E. W., Tian, L. & Tibshirani, R. Evaluating a shrinkage estimator for the treatment effect in clinical trials. Stat. Med. 43, 855–868 (2024).
Ioannidis, J. P. A. Why most discovered true associations are inflated. Epidemiology 19, 640–648 (2008).
Stuart, E. A. Matching methods for causal inference: A review and a look forward. Stat. Sci. 25, 1–21 (2010).
Baetsen-Young, A. M., Swinton, S. M. & Chilvers, M. I. Economic impact of fluopyram-amended seed treatments to reduce soybean yield loss associated with sudden death syndrome. Plant. Dis. 105, 78–86 (2021).
Mourtzinis, S. et al. Field-level yield benefits and risk effects of intensive soybean management across the US. Field Crops Res. 301, 109012. https://doi.org/10.1016/j.fcr.2023.109012 (2023).
Dangal, N. K., Wiggs, S., Mueller, B., Smith, D. L. & Mueller, D. S. Addition of nanofertilizers and nitrogen fertilizer in combination with fluopyram helps in SDS management and protecting yield in soybean. PhytoFrontiers 5, 464–471 (2025).
Dangal, N. K. et al. Soybean seed treatment evaluation under various levels of sudden death syndrome and populations of soybean cyst nematode. Plant Dis. https://doi.org/10.1094/PDIS-05-25-1114-RE.
Dorrance, A. E. et al. Picarbutrazox effectiveness added to a seed treatment mixture for management of oomycetes that impact soybean in Ohio. Plant. Dis. 108, 2330–2340 (2024).
Truscott, J. E. & Gilligan, C. A. The effect of cultivation on the size, shape, and persistence of disease patches in fields. Proc. Natl. Acad. Sci. 98, 7128–7133 (2001).
Ayesha, M. S., Suryanarayanan, T. S., Nataraja, K. N., Prasad, S. R. & Shaanker, R. U. Seed treatment with systemic fungicides: Time for review. Front. Plant. Sci. 12, 654512. https://doi.org/10.3389/fpls.2021.654512 (2021).
Leadbeater, A., McGrath, M. T., Wyenandt, C. A. & Stevenson, K. L. An overview of fungicide resistance and resistance management: History and future trends in Fungicide Resistance in North America 2nd edn 3–19 (APS Press, 2019).
Berg, G. & Raaijmakers, J. M. Saving seed microbiomes. ISME J. 12, 1167–1170 (2018).
Cordovez, V., Dini-Andreote, F., Carrión, V. J. & Raaijmakers, J. M. Ecology and evolution of plant microbiomes. Annu. Rev. Microbiol. 73, 69–88 (2019).
Bandara, A. Y., Weerasooriya, D. K., Conley, S. P., Allen, T. W. & Esker, P. D. Modeling the relationship between estimated fungicide use and disease-associated yield losses of soybean in the United States II: Seed-applied fungicides vs seedling diseases. PLOS ONE. 15, e0244424. https://doi.org/10.1371/journal.pone.0244424 (2020).
Acknowledgements
This work was supported by the Wisconsin Soybean Marketing Board, the North Central Soybean Research Program (Project 204331), the United Soybean Board (Project 24-210-S-A-1-A / 2411-210-0101), and by USDA National Institute of Food and Agriculture Hatch Appropriations to PDE (Project PEN04836 and Accession 7005075). Any findings, opinions or recommendations are those of the authors and do not necessarily reflect the views of the funding agencies.
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Study conceptualization (PE, SC), Formal analysis (DS), Funding acquisition (PE, PG, SC), Initial investigation (PG, SC), Methodology (PE, DS), Project administration (PE), Supervision (PE), Visualization (DS), Writing—original draft (PE, DS), Writing—review & editing (all authors). All authors reviewed and approved the manuscript before submission.
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Esker, P.D., Shah, D.A., Mourtzinis, S. et al. Marginal causal effect of fungicide seed treatments on soybean yield and uncertain profitability in the US Midwest. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47390-0
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DOI: https://doi.org/10.1038/s41598-026-47390-0


