Abstract
Denitrification, a major source of gaseous nitrogen emissions from agricultural soils, is influenced by management. Practices promoting belowground diversity are suggested to support sustainable agriculture, but how they modulate nitrogen losses via denitrification remains inconclusive. Here we sampled 106 cereal fields spanning a 3000 km North-South gradient across Europe and compiled 56 associated climatic, soil, microbial and management variables. We show that increased denitrification potential was associated with higher proportion of time with crop cover over the last ten years and was best predicted by microbial biomass and microbial functional guilds involved in nitrogen cycling, in particular denitrification. We also demonstrate that several diversification practices affect the variation in denitrification potential predictors, suggesting a trade-off between agricultural diversification and nitrogen losses via denitrification. However, increased crop diversity in rotations improved yield-scaled denitrification, highlighting the potential of this practice to minimize nitrogen losses while contributing to sustainable food production.
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Increasing crop rotational diversity can enhance cereal yields
Crop cover is more important than rotational diversity for soil multifunctionality and cereal yields in European cropping systems
Aggregation of activity data on crop management can induce large uncertainties in estimates of regional nitrogen budgets
Data availability
Data and OTU tables used in this study as well as source data for the figures are available at Zenodo (https://doi.org/10.5281/zenodo.14760398).
Code availability
The R code used in this study is available at Zenodo (https://doi.org/10.5281/zenodo.14760398).
References
Sutton, M. A. et al. Too much of a good thing. Nature 472, 159–161 (2011).
Ludemann, C. I. et al. A global FAOSTAT reference database of cropland nutrient budgets and nutrient use efficiency (1961–2020): nitrogen, phosphorus and potassium. Earth Syst. Sci. Data 16, 525–541 (2024).
Richardson, K. et al. Earth beyond six of nine planetary boundaries. Sci. Adv. 9, eadh2458 (2023).
Scheer, C. et al. Estimating global terrestrial denitrification from measured N2O:(N2O + N2) product ratios. Curr. Opin. Environ. Sustain. 47, 72–80 (2020).
Tian, H. et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature 586, 248–256 (2020).
Bowles, T. M. et al. Addressing agricultural nitrogen losses in a changing climate. Nat. Sustain. 1, 399–408 (2018).
Sinha, E. et al. Eutrophication will increase during the 21st century as a result of precipitation changes. Science 357, 405–408 (2017).
Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).
You, L. et al. Optimized agricultural management reduces global cropland nitrogen losses to air and water. Nat. Food 5, 995–1004 (2024).
Garland, G. et al. Crop cover is more important than rotational diversity for soil multifunctionality and cereal yields in European cropping systems. Nat. Food 2, 28–37 (2021).
Kim, N. et al. Do cover crops benefit soil microbiome? A meta-analysis of current research. Soil Biol. Biochem. 142, 107701 (2020).
Tamburini, G. et al. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6, eaba1715 (2020).
Smith, M. E. et al. Increasing crop rotational diversity can enhance cereal yields. Commun. Earth Environ. 4, 89 (2023).
Kremen, C. et al. Ecosystem services in biologically diversified versus conventional farming systems: benefits, externalities, and trade-offs. Ecol. Soc. 17, 40 (2012).
Domeignoz-Horta, L. A. et al. Peaks of in situ N2O emissions are influenced by N2O-producing and reducing microbial communities across arable soils. Glob. Change Biol. 24, 360–370 (2017).
Wang, J. et al. No-till increases soil denitrification via its positive effects on the activity and abundance of the denitrifying community. Soil Biol. Biochem. 142, 107706 (2020).
Pan, B. et al. A global synthesis of soil denitrification: driving factors and mitigation strategies. Agric. Ecosyst. Environ. 327, 107850 (2022).
Philippot, L. et al. Ecology of denitrifying prokaryotes in agricultural soil. Adv. Agron. 96, 249–305 (2007).
Fageria, N. K. et al. Role of cover crops in improving soil and row crop productivity. Commun. Soil Sci. Plant Anal. 36, 2733–2757 (2005).
Duan, Y. F. et al. Catch crop residues stimulate N2O emissions during spring, without affecting the genetic potential for nitrite and N2O reduction. Front. Microbiol. 9, 2629 (2018).
Olesen, J. E. et al. Challenges of accounting nitrous oxide emissions from agricultural crop residues. Glob. Change Biol. 29, 6846–6855 (2023).
Kravchenko, A. N. et al. Hotspots of soil N2O emission enhanced through water absorption by plant residue. Nat. Geosci. 10, 496–500 (2017).
Højberg, O. et al. Denitrification in soil aggregates analyzed with microsensors for nitrous oxide and oxygen. Soil Sci. Soc. Am. J. 58, 1691–1698 (1994).
MacLaren, C. et al. Long-term evidence for ecological intensification as a pathway to sustainable agriculture. Nat. Sustain. 5, 770–779 (2022).
Costa, A. et al. Crop rotational diversity can mitigate climate-induced grain yield losses. Glob. Change Biol. 30, e17298 (2024).
Renard, D. et al. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).
Fageria, N. K. et al. Enhancing nitrogen use efficiency in crop plants. Adv. Agron. 88, 97–185 (2005).
Yi, B. et al. Diversified cropping systems with limited carbon accrual but increased nitrogen supply. Nat. Sustain. 8, 152–161 (2025).
Gelfand, I. et al. Long-term nitrous oxide fluxes in annual and perennial agricultural and unmanaged ecosystems in the upper Midwest USA. Glob. Change Biol. 22, 3594–3607 (2016).
Putz, M. et al. Relative abundance of denitrifying and DNRA bacteria and their activity determine nitrogen retention or loss in agricultural soil. Soil Biol. Biochem. 123, 97–104 (2018).
Thompson, K. A. et al. Soil microbial communities as potential regulators of in situ N2O fluxes in annual and perennial cropping systems. Soil Biol. Biochem. 103, 262–273 (2016).
Dai, Z. et al. Long-term nitrogen fertilization decreases bacterial diversity and favors the growth of Actinobacteria and Proteobacteria in agro-ecosystems across the globe. Glob. Change Biol. 24, 3452–3461 (2018).
Jones, C. M. et al. Reactive nitrogen restructures and weakens microbial controls of soil N2O emissions. Commun. Biol. 5, 273 (2022).
Isobe, K. et al. Phylogenetic conservation of bacterial responses to soil nitrogen addition across continents. Nat. Commun. 10, 2499 (2019).
Thompson, R. L. et al. Acceleration of global N2O emissions seen from two decades of atmospheric inversion. Nat. Clim. Change 9, 993–998 (2019).
Charles, A. et al. Global nitrous oxide emission factors from agricultural soils after addition of organic amendments: a meta-analysis. Agric. Ecosyst. Environ. 236, 88–98 (2017).
Graf, D. R. H. et al. Intergenomic comparisons highlight modularity of the denitrification pathway and underpin the importance of community structure for N2O emissions. PLoS ONE 9, e114118 (2014).
Saghaï, A. et al. Diversity and ecology of NrfA-dependent ammonifying microorganisms. Trends Microbiol. 32, 602–613 (2024).
Saghaï, A. et al. Phyloecology of nitrate ammonifiers and their importance relative to denitrifiers in global terrestrial biomes. Nat. Commun. 14, 8249 (2023).
Morrissey, E. M. et al. Phylogenetic organization of bacterial activity. ISME J. 10, 2336–2340 (2016).
Jones, C. M. et al. Phylogenetic analysis of nitrite, nitric oxide, and nitrous oxide respiratory enzymes reveal a complex evolutionary history for denitrification. Mol. Biol. Evol. 25, 1955–1966 (2008).
Shoun, H. et al. Fungal denitrification and nitric oxide reductase cytochrome P450nor. Philos. Trans. R. Soc. B Biol. Sci. 367, 1186–1194 (2012).
Bösch, Y. et al. Distribution and environmental drivers of fungal denitrifiers in global soils. Microbiol. Spectr. 11, e00061–23 (2023).
FAL et al. Referenzmethoden Der Eidg. Landwirtschaftlichen Forschungsanstalten. 1. Bodenuntersuchung Zur Düngeberatung (Zürich- Reckenholz, 1996).
Gregorich, E. G. et al. Soil Sampling and Methods of Analysis (CRC Press, 2007).
Hijmans, R. J. Raster: Geographic Data Analysis and Modeling https://doi.org/10.32614/CRAN.package.raster (2024).
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (Vienna, Austria 2024).
Jin, Y. et al. V.PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).
Oksanen, J. et al. Vegan: community ecology package. https://doi.org/10.32614/CRAN.package.vegan (2018).
Magurran, A. et al. Explaining the excess of rare species in natural species abundance distributions. Nature 422, 714–716 (2003).
Hubbell, S. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton University Press, Princeton, NJ, USA, 2001).
Krebs, C. J. Ecological Methodology (Addison-Wesley Educational Publishers, Inc: New York, NY, USA, 1999).
Pell, M. et al. Potential denitrification activity assay in soil – With or without chloramphenicol? Soil Biol. Biochem. 28, 393–398 (1996).
Philippot, L. et al. Importance of denitrifiers lacking the genes encoding the nitrous oxide reductase for N2O emissions from soil. Glob. Change Biol. 17, 1497–1504 (2011).
van Groenigen, J. W. et al. Towards an agronomic assessment of N2O emissions: a case study for arable crops. Eur. J. Soil Sci. 61, 903–913 (2010).
Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2017).
Marra, G. et al. Practical variable selection for generalized additive models. Comput. Stat. Data Anal. 55, 2372–2387 (2011).
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B. 73, 3–36 (2011).
Breheny, P. et al. Visualization of regression models using visreg. R. J. 9, 56–71 (2017).
Genuer, R. et al. VSURF: an R package for variable selection using random forests. R. J. 7, 19–33 (2015).
Liaw, A. et al. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
Molnar, C. et al. iml: an R package for interpretable machine learning. J. Open Source Softw. 3, 786 (2018).
Apley, D. W. et al. Visualizing the effects of predictor variables in black box supervised learning models. J. R. Stat. Soc. Ser. B Stat. Methodol. 82, 1059–1086 (2020).
Acknowledgements
The Digging Deeper project was funded through the 2015–2016 BiodivERsA call, with national funding from the Swiss National Science Foundation (grant 31BD30-172466 to M.G.A.v.d.H), the Deutsche Forschungsgemeinschaft (grant 317895346 to M.C.R.), the Swedish Research Council Formas (grant 2016-0194 to S.H. and 2018-02321 to R.B.), the Spanish Ministerio de Economía y Competitividad (grant PCIN-2016-028 to F.T.M.) and the Agence Nationale de la Recherche (grant ANR-16-EBI3-0004-01 to L.P.). We thank Claudia von Brömssen (Swedish University of Agricultural Sciences) for advice on the generalized additive models.
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S.H., M.G.A.v.d.H., F.T.M., L.P., and M.C.R. initiated the study, planned the field work, and contributed materials. A.S., S.B., F.D., A.E., P.G-P., G.G., C.H., D.S.P., and S.R. contributed to data collection. A.S. and M.E.S. performed the analyses, and A.S., M.E.S., G.V., R.B., and S.H. interpreted the results. A.S., M.E.S., and S.H. drafted the manuscript. All authors commented on and approved the final manuscript.
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Saghaï, A., Smith, M.E., Vico, G. et al. Diverse crop rotations offset yield-scaled nitrogen losses via denitrification.
Commun Earth Environ (2025). https://doi.org/10.1038/s43247-025-03116-0
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DOI: https://doi.org/10.1038/s43247-025-03116-0
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