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

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

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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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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.

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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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.

Zvi Hochman

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

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

Pierre Martre & Sibylle Dueri

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

Jose Rafael Guarin

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

Jose Rafael Guarin

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

Jose Rafael Guarin, Yujing Gao & Gerrit Hoogenboom

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

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

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

Frank Ewert, Thomas Gaiser & Amit Kumar Srivastava

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

Heidi Webber

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

Daniel Calderini

KWS Momont Recherche, Lille, France

Gemma Molero

CIMMYT, Texcoco, Mexico

Matthew Reynolds

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

Daniel Miralles & Guillermo Garcia

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

Hamish Brown, Mike George & Rob Craigie

ARVALIS, Loireauxence, France

Jean-Pierre Cohan

ARVALIS, Villiers-le-Bâcle, France

Jean-Charles Deswarte

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

Gustavo Slafer

Catalonian Institution for Research and Advanced Studies, Lleida, Spain

Gustavo Slafer

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

Francesco Giunta

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

Davide Cammarano

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

Roberto Ferrise & Gloria Padovan

CSIRO Agriculture and Food, Brisbane, Queensland, Australia

Zvi Hochman & Peter Thorburn

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

Gerrit Hoogenboom

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

Leslie A. Hunt

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

Kurt C. Kersebaum

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

Kurt C. Kersebaum & Claas Nendel

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

Claas Nendel

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

Alex C. Ruane

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

Iwan Supit

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

Enli Wang & Zhigan Zhao

Plant Production Systems, Wageningen University, Wageningen, the Netherlands

Joost Wolf

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

Chuang Zhao

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

Zhigan Zhao

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

Senthold Asseng

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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.

Correspondence to Pierre Martre.

The authors declare no competing interests.

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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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.

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.

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.

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.

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.

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.

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.

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.

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.

Supplementary Tables 1–8 and Figs. 1–8.

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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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Received: 07 March 2023

Accepted: 04 June 2024

Published: 04 July 2024

Issue Date: July 2024

DOI: https://doi.org/10.1038/s41477-024-01739-3

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