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Global needs for nitrogen fertilizer to improve wheat yield under climate change

Nature Plants volume 10, pages 1081–1090 (2024)Cite this article

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Abstract

Increasing global food demand will require more food production1 without further exceeding the planetary boundaries2 while simultaneously adapting to climate change3. We used an ensemble of wheat simulation models with improved sink and source traits from the highest-yielding wheat genotypes4 to quantify potential yield gains and associated nitrogen requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved sink and source traits increased yield by 16% with current nitrogen fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential—a 52% increase in global average yield under a mid-century high warming climate scenario (RCP8.5), fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil nitrogen availability and nitrogen use efficiency, along with yield potential.

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Fig. 1: Simulated and measured N-unlimited wheat grain yield response to high-yielding traits at 34 representative high-rainfall or irrigated global sites in the major wheat production regions.
Fig. 2: Analysis of the phenotypic space of yield-related traits.
Fig. 3: Simulated global yield response to N application for locally adapted cultivars without and with high-yielding traits under baseline and climate change scenarios.

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Data availability

The measured and simulated crop data and model inputs for the New Zealand and South America field experiment data are available at https://doi.org/10.7910/DVN/XA4VA2 (ref. 54) and https://doi.org/10.7910/DVN/VKWKUP (ref. 55) respectively. The simulation protocols, model inputs and simulation results for the 34 global locations are available at https://doi.org/10.7910/DVN/6KBBI3 (ref. 56). National wheat production statistics and area for the period 2016 to 2019 were obtained from the FAO public database available at https://www.fao.org/faostat. Daily weather data were obtained from the AgMERRA (https://data.giss.nasa.gov/impacts/agmipcf/agmerra/) and NASA POWER (https://power.larc.nasa.gov/) climate datasets.

Code availability

The data analysis scripts were developed with R (v.4.1.3) and are available in GitHub at https://github.com/pmartre/AgMIPWheat4 (ref. 57). Documentation and codes of the crop models used in this study are available from the links or email addresses given in Supplementary Table 1.

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Acknowledgements

This study was a part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4. The experimental work conducted at Valdivia, Chili by J. Herrera (UACh) is appreciated. P.M. and S.D. acknowledge support from the metaprogram Agriculture and forestry in the face of climate change: adaptation and mitigation (CLIMAE) of INRAE. This work was supported by the French National Research Institute for Agriculture, Food and Environment (INRAE); the International Maize and Wheat Improvement Center (CIMMYT) and the International Wheat Yield Partnership (IWYP, grant IWYP115 to P.M., S.A. and F.E.), CIMMYT and the Chilean Technical and Scientific Research Council (CONICYT-ANID) through FONDECYT (grant 1141048 to D. Calderini); the Foundation for Food and Agricultural Research (to M.R.); the German Federal Ministry of Education and Research (BMBF) through the BonaRes project ‘I4S’ (grant 031B0513I to K.C.K.); the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (grant CZ.02.1.01/0.0/0.0/16_019/000797 to K.C.K. and C.N.); the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy (grant EXC 2070 – 390732324 to F.E. and T.G.) and the Collaborative Research Centre DETECT (grant No. SFB1502/1–2022 -450058266 to T.G.); the JPI-FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food and Forestry Policies (grant 24064/7303/15 to R.F. and G.P.) and the SYSTEMIC project funded by JPI-HDHL, JPI-OCEANS and FACCE-JPI under ERA-NET (grant 696295 to R.F. and G.P.); and BMBF in the framework of the funding measure ‘Soil as a Sustainable Resource for the Bioeconomy—BonaRes’, project BonaRes (Module A): BonaRes Center for Soil Research, subproject ‘Sustainable Subsoil Management—Soil3’ (grant 031B0151A to A.K.S.) and COINS (grant 01LL2204C to A.K.S.). A.C.R. received support from the National Aeronautics and Space Administration (NASA) Earth Science Division grant for the NASA Goddard Institute for Space Studies Climate Impacts Group. J.-P.C. and J.-C.D. received support from the CASDAR and Intercéréales funds.

Author information

Author notes

  1. Zvi Hochman

    Present address: University of Melbourne, Melbourne, Victoria, Australia

Authors and Affiliations

  1. LEPSE, Univ Montpellier, INRAE, Institut Agro Montpellier, Montpellier, France

    Pierre Martre & Sibylle Dueri

  2. Center for Climate Systems Research, Columbia University, New York, NY, USA

    Jose Rafael Guarin

  3. NASA Goddard Institute for Space Studies, New York, NY, USA

    Jose Rafael Guarin

  4. Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA

    Jose Rafael Guarin, Yujing Gao & Gerrit Hoogenboom

  5. Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany

    Frank Ewert, Heidi Webber, Kurt C. Kersebaum, Claas Nendel, Amit Kumar Srivastava & Tommaso Stella

  6. Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany

    Frank Ewert, Thomas Gaiser & Amit Kumar Srivastava

  7. Brandenburg University of Technology Faculty of Environment and Natural Sciences, Cottbus, Germany

    Heidi Webber

  8. Institute of Plant Production and Protection, Austral University of Chile, Valdivia, Chile

    Daniel Calderini

  9. KWS Momont Recherche, Lille, France

    Gemma Molero

  10. CIMMYT, Texcoco, Mexico

    Matthew Reynolds

  11. Department of Plant Production, University of Buenos Aires, IFEVA-CONICET, Buenos Aires, Argentina

    Daniel Miralles & Guillermo Garcia

  12. The New Zealand Institute for Plant and Food Research Limited, Lincoln, New Zealand

    Hamish Brown, Mike George & Rob Craigie

  13. ARVALIS, Loireauxence, France

    Jean-Pierre Cohan

  14. ARVALIS, Villiers-le-Bâcle, France

    Jean-Charles Deswarte

  15. Department of Agricultural and Forest Sciences and Engineering, University of Lleida, AGROTECNIO-CERCA Center, Lleida, Spain

    Gustavo Slafer

  16. Catalonian Institution for Research and Advanced Studies, Lleida, Spain

    Gustavo Slafer

  17. Department of Agricultural Sciences, University of Sassari, Sassari, Italy

    Francesco Giunta

  18. Department of Agroecology, iClimate, CBIO, Aarhus University, Tjele, Denmark

    Davide Cammarano

  19. Department of Agriculture, Food, Environment and Forestry, University of Florence, Florence, Italy

    Roberto Ferrise & Gloria Padovan

  20. CSIRO Agriculture and Food, Brisbane, Queensland, Australia

    Zvi Hochman & Peter Thorburn

  21. Global Food Systems Institute, University of Florida, Gainesville, FL, USA

    Gerrit Hoogenboom

  22. Department of Plant Agriculture, University of Guelph, Guelph, Ontario, Canada

    Leslie A. Hunt

  23. Tropical Plant Production and Agricultural Systems Modelling, University of Göttingen, Göttingen, Germany

    Kurt C. Kersebaum

  24. Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

    Kurt C. Kersebaum & Claas Nendel

  25. Global Change Research Institute, Academy of Sciences of the Czech Republic, Brno, Czech Republic

    Claas Nendel

  26. Climate Impacts Group, National Aeronautics and Space Administration Goddard Institute for Space Studies, New York, NY, USA

    Alex C. Ruane

  27. Earth Systems and Global Change Group, Wageningen University, Wageningen, the Netherlands

    Iwan Supit

  28. CSIRO Agriculture and Food, Canberra, Australian Capital Territory, Australia

    Enli Wang & Zhigan Zhao

  29. Plant Production Systems, Wageningen University, Wageningen, the Netherlands

    Joost Wolf

  30. College of Resources and Environmental Sciences, China Agricultural University, Beijing, China

    Chuang Zhao

  31. Department of Agronomy and Biotechnology, China Agricultural University, Beijing, China

    Zhigan Zhao

  32. Technical University of Munich, Department of Life Science Engineering, Digital Agriculture, HEF World Agricultural Systems Center, Freising, Germany

    Senthold Asseng

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  1. Pierre Martre
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Contributions

P.M., S.A., F.E. and H.W. conceived the research. P.M. designed the study. D. Calderini, G.M., M.R., D.M., G.G., H.B., M.G., R.C. J.-P.C. and J.-C.D. conducted the field experiments. P.M., S.D. and J.R.G. analysed the data. P.M. produced the figures and wrote the first draft of the manuscript. All authors contributed to the revision of the manuscript. Authors D. Cammarano, R.F., T.G., Y.G., Z.H., G.H., L.A.H., K.C.K., C.N., G.P., A.C.R., A.K.S., T.S., I.S., P.T., E.W., J.W., C.Z., M.B. and Z.Z. performed the crop model simulations and are listed in alphabetical order.

Corresponding author

Correspondence to Pierre Martre.

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The authors declare no competing interests.

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Nature Plants thanks Ebrahim Jahanshiri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Simulated global nitrogen (N) unlimited grain yield and protein concentration for cultivars with and without high-yielding traits.

Simulated global average N unlimited (a) grain yield and grain protein concentration (b) for locally adapted cultivars without (open boxes) and with (closed boxes) high-yielding traits for the 1981–2010 baseline period (yellow) and the 2040–2069 period under RCP4.5 (pink) and RCP8.5 (blue For each box plot, the vertical bars represent 1.5 times the interquartile range, the box represents the interquartile, and the horizontal lines inside the box represents the median. Data are the average of 30 years of yield simulated with 12 wheat crop growth models, and for future climate scenarios of five global climate models, using region-specific soils, cultivars, and sowing data and unlimited N supply.

Extended Data Fig. 2 Comparison of measured and simulated yield, yield components, and phenology of the spring wheat cultivar Bacanora and the best yielding line of the Bacanora x Weebil cross.

Measured and simulated (a) grain yield, (b) total above ground biomass, (c) harvest index, (d) grain number, (e) average grain dry mass, (f) days between sowing anthesis, and (g) grain filling duration for the spring wheat cultivar Bacanora and the best yielding doubled haploid line of the Bacanora x Weebil cross sown in the field in Valdivia, Chile (VA) in 2008 and 2009, in Buenos Aires, Argentina (BU) in 2009, and in Ciudad Obregon, Mexico (CO) in 2009, and for the average of the four experimental years (Average). Data are the average of 3 to 4 independent replicates for each year and location of measurements and the ensemble median of 12 wheat crop growth models.

Extended Data Fig. 3 Simulated relative climate change adaptation for global average wheat production, nitrogen (N) demand, and grain protein concentration for high-yielding traits at different rates of N fertilizer application.

Adaptation to climate change of (a) global production, (b) N demand, and (c) grain protein concentration for mid-century RCP4.5 and RCP8.5 climate scenarios relative to the baseline average for 1981–2010. Adaptation to climate change is calculated at the average N fertilization rate currently used by farmers in each country (yellow bars) and at N rates that maximize grain yield at each location considering (blue bars) or not (pink bars) a minimum grain protein concentration of 12%. Data are for the average of 30 years of yield simulated by 12 wheat crop growth models and five climate models. Relative adaptation values calculated at 34 representative global sites were weighed and aggregated to the globe based on national wheat production data. For each box plot, the vertical bars represent 1.5 times the interquartile range, the box represents the interquartile, and the horizontal lines inside the box represents the median.

Extended Data Fig. 4 Simulated grain yield response to nitrogen (N) fertilizer rate at 34 representative high rainfall or irrigated global sites.

Simulations are for the baseline period (1981–2010) for locally adapted cultivars without (black circles) and with high-yielding traits given in Supplementary Table 2 (red circles), and for mid-century RCP4.5 (blue circles) and RCP8.5 (green circles) climate scenarios with the high-yielding trait only. Data are the average of 30 years simulations with 12 wheat crop growth models, plus five global climate models for the climate change scenarios, using region-specific soils and sowing data. Red crosses show the current national average grain yield against the current national average N fertilizer application rate from reported national data, respectively.

Extended Data Fig. 5 Simulated grain nitrogen (N) use efficiency response to N fertilizer rate at 34 representative high rainfall or irrigated global sites.

Simulations are for the baseline period (1981–2010) for locally adapted cultivars without (black circles) and with high yielding traits given in Supplementary Table 2 (red circles), and for mid-century RCP4.5 (blue circles) and RCP8.5 (green circles) climate scenarios with the high-yielding trait only. Data are the average of 30 years simulations with 12 wheat crop growth models, plus five global climate models for the climate change scenarios, using region-specific soils and sowing data.

Extended Data Fig. 6 Simulated grain protein concentration response to nitrogen (N) fertilizer rate at 34 representative high rainfall or irrigated global sites.

Simulations are for the baseline period (1981–2010) for locally adapted cultivars without (black circles) and with high-yielding traits given in Supplementary Table 2 (red circles), and for mid-century RCP4.5 (blue circles) and RCP8.5 (green circles) climate scenarios with the high-yielding traits only. Data are the average of 30 years simulations with 12 wheat crop growth models, plus five global climate models for the climate change scenarios, using region-specific soils and sowing data.

Extended Data Fig. 7 Comparison of simulated and historical national average grain yield and N fertilizer rate for 23 countries.

In (a) Simulated national average grain yield at each studied location was interpolated on the relationship between simulated grain yield and N fertilizer application rate (Extended Data Fig. 3) at the N fertilizer application rate equal to the reported national application rate. In (b) at each studied location N fertilizer rate was interpolated on the relationship between simulated grain yield and N fertilizer application rate at the grain yield equal to the reported national average grain yield. Simulated data are means of 30 years (1981-20210) simulations with an ensemble of 12 wheat crop growth models. Error bars show 50% of the models (interquartile). Countries are represented with their two-letter code. Historical national yields are the average for the 2016 to 2019 harvests. Dotted line is the 1:1 relationship and solid line is linear regression. RRMSE, NU, LC, and SB are relative root mean squared error, non-unity slope, lack or correlation, and squared bias, respectively. Note that systematic biases (overestimation of national average yields and underestimation of the national average rate of nitrogen fertilization) are expected due to the impact of pests, diseases and weeds on yield, or to negative responses to certain extreme climatic impact factors23 that are not taken into account in the crop models used in this study.

Extended Data Fig. 8 Simulated impact of high-yielding traits on grain protein concentration with and without adaptation of N fertilizer application rate at 34 representative global sites.

Simulations are for the 1981–2010 baseline period (yellow) and for mid-century RCP4.5 (pink) and RCP8.5 (blue) climate scenarios. The relative change in N use efficiency are calculated at the average N fertilization rate currently used by farmers in each country (yellow bars) and at N rates that maximize grain yield at each location considering (blue bars) or not (pink bars) a minimum grain protein concentration of 12%. Data are the average of 30 years of yield simulated with 12 wheat crop growth models and five climate models using region-specific soils and sowing data. For each box plot, the vertical bars represent 1.5 times the interquartile range, the box represents the interquartile, and the horizontal lines inside the box represents the median.

Extended Data Fig. 9 Simulated impact of high-yielding traits on grain yield with and without adaptation of nitrogen (N) fertilizer application rate at 34 representative global sites.

Simulations are for the 1981–2010 baseline period (yellow) and for mid-century RCP4.5 (pink) and RCP8.5 (blue) climate scenarios. The relative change in grain yield are calculated at the average N fertilization rate currently used by farmers in each country (yellow bars) and at N rates that maximize grain yield at each location considering (blue bars) or not (pink bars) a minimum grain protein concentration of 12%. Data are the average of 30 years of yield simulated with 12 wheat crop growth models and five climate models using region-specific soils and sowing data. For each box plot, the vertical bars represent 1.5 times the interquartile range, the box represents the interquartile, and the horizontal lines inside the box represents the median.

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Martre, P., Dueri, S., Guarin, J.R. et al. Global needs for nitrogen fertilizer to improve wheat yield under climate change. Nat. Plants 10, 1081–1090 (2024). https://doi.org/10.1038/s41477-024-01739-3

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