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Emerging weed resistance increases tillage intensity and greenhouse gas emissions in the US corn–soybean cropping system


Tillage is a common agricultural practice that helps prepare the soil and remove weeds. However, it remains unknown how tillage intensity has evolved and its effect on net greenhouse gas (GHG) emissions. Here, using a process-based modelling approach with a multi-source database, we examined the change in tillage intensity across the US corn–soybean cropping systems during 1998–2016 and the impact of tillage intensity on soil GHG emissions. We found that tillage intensity first decreased and then, after 2008, increased, a trend that is strongly correlated with the adoption of herbicide-tolerant crops and emerging weed resistance. The GHG mitigation benefit (−5.5 ± 4.8 TgCO2e yr−1) of decreasing tillage intensity before 2008 has been more than offset by increased GHG emissions (13.8 ± 5.6 TgCO2e yr−1) due to tillage reintensification under growing pressure of weed resistance. As weed resistance persists or grows, tillage intensity is anticipated to continue rising, probably increasing GHG emissions. Our results imply that farmers’ choices in managing herbicide resistance may help mitigate agricultural GHG emissions, underscoring the importance of an alternative strategy to control weeds.

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Fig. 1: Conceptual depiction of the hypothetical GHG fluxes in response to tillage intensity changes affected by HT crop adoption and emergence of herbicide-resistant weeds.
Fig. 2: Annual changes of crop acreage under each tillage practice since 1998 and possible factors regulating the tillage intensity shift.
Fig. 3: Model-estimated impacts of tillage practices on GHG emissions in the US corn–soybean cropping system during 1998–2016.
Fig. 4: Tillage intensity change-induced soil GHG emissions.
Fig. 5: Spatial patterns of the model estimated GHG fluxes due to the historical use of tillage across the US corn–soybean cropping system.
Fig. 6: Accumulated GHG fluxes (gCO2e m2) due to tillage intensity changes across the US corn–soybean cropping system.

Data availability

The tillage maps used in this study were developed from a proprietary national survey conducted annually by Kynetec Group. The purchase agreement requires that the data remain confidential. Source data supporting the figures are provided with this paper. Source data are provided with this paper.

Code availability

The code used to perform analyses in this study is generated in ENVI/IDL and is available upon request.


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This work is supported by NSF grants (1903722, 1945036), the new faculty start-up fund of Iowa State University, and Michigan State University’s Elton R. Smith Endowment for the promotion of academic programmes in food and agricultural policy. This work was initiated while D.A.H. and H.F. were employed at Michigan State University. D.H. was supported by NSF grants (1919897, 2000058). We acknowledge the editor and four reviewers for providing constructive comments and suggestions to improve this manuscript.

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Authors and Affiliations



C.L., D.A.H. and H.F. conceived and designed the research. C.L. and Z.Y. developed tillage data sets, carried out model simulations, analysed the model results and wrote the manuscript. D.A.H. synthesized and interpreted the farmers’ survey data and contributed to the tillage data development. H.F., H.T. and D.H. helped with model validation, result interpretation and discussion. All co-authors reviewed and contributed to the manuscript.

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Correspondence to Chaoqun Lu.

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Nature Food thanks Moakes Simon, Magdalena Necpalova, Zhangcai Qin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Figs. 1–9, Table 1, information on model representation, input data and simulation experiment design.

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Source data

Source Data Fig. 2

Time-series data of crop acreage under tillage practices, HT crop adoption percentage and species number of weeds resistant to herbicide.

Source Data Fig. 3

Model-estimated impacts of historical tillage practices on GHG fluxes in the US corn–soybean cropping system during 1998–2016.

Source Data Fig. 4

Model-estimated impacts of tillage intensity change on annual and accumulated GHG fluxes during 1998–2016.

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Lu, C., Yu, Z., Hennessy, D.A. et al. Emerging weed resistance increases tillage intensity and greenhouse gas emissions in the US corn–soybean cropping system. Nat Food 3, 266–274 (2022).

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