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Clumped canopy architecture raises global crop yield and reduces N2O emissions

Nature Plants volume 12, pages 49–61 (2026)Cite this article

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Abstract

Producing more food with reduced environmental impact remains a critical challenge. Previous agricultural management strategies have predominantly emphasized crop varieties, fertilization and irrigation, often requiring substantial resource inputs and technical expertise. However, the role of crop canopy architecture, which remarkably influences plant growth and ecosystem processes, has been largely overlooked. Here we integrate satellite-based and field observations to assess the global impacts of canopy architecture on crop yield and nitrous oxide (N2O) emissions for rice, wheat, maize and soybean during the past two decades. Our findings reveal that crops with clumped canopy architectures achieve higher yields and lower N2O emissions, a pattern consistently observed across all four major crops, even though soil properties also critically regulate N2O emissions. This effect is possibly driven by enhanced light interception and gross primary production, along with increased canopy nitrogen demand. Aligning crop canopy architecture with the global average can potentially increase crop production by 336 million tons annually, generating economic benefits of US$108 billion per year while simultaneously reducing N2O emissions by 41.6% globally. These results highlight the critical role of canopy architecture in global food security and present a novel strategy for enhancing agricultural productivity and sustainability on a global scale.

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Fig. 1: Relationships between CI and crop yields and between CI and N2O emissions at the global scale.
Fig. 2: Underlying mechanisms of the CI impacts on crop yield and N2O emissions.
Fig. 3: Potential increases in crop yield and decreases in N2O emissions with improved crop architecture globally.
Fig. 4: Increases in global total production and reductions in N2O emissions with optimized CI at the 50th scenario.
Fig. 5: Schematic illustration of the multi-benefits of clumped crop architecture.

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Data availability

The MODIS CI are available at https://doi.pangaea.de/10.1594/PANGAEA.884994; the global crop yield data from the Global Dataset of Historical Yield (GDHY) are available at https://doi.org/10.20783/DIAS.564; the global crop yield from M3Crops dataset are available at http://www.earthstat.org/harvested-area-yield-175-crops/; the global crop yield from Spatial Production Allocation Model dataset are available at https://doi.org/10.7910/DVN/PRFF8V; the country-level crop yield statistics from the FAO are available at https://www.fao.org/faostat; the filed N2O observations data are available from the link in ref. 9; the climatic data from the TerraClimate product are available at https://www.climatologylab.org/; the global soil properties are available at https://data.isric.org/; the global field application rates of N, P and K fertilizers are available at https://doi.org/10.11888/Terre.tpdc.300446; the global satellite-based APAR and GPP product are available via Dryad at https://doi.org/10.5061/dryad.dfn2z352k (ref. 75); the global canopy N demand dataset are available from the link in ref. 33.

Code availability

The codes used in this study are publicly available via Figshare at https://figshare.com/s/4f4170369c238cb0b694 (ref. 76).

References

  1. Wheeler, T. & Von Braun, J. Climate change impacts on global food security. Science 341, 508–513 (2013).

    Article  CAS  PubMed  Google Scholar 

  2. Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Van Dijk, M., Morley, T., Rau, M. L. & Saghai, Y. A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nat. Food 2, 494–501 (2021).

    Article  PubMed  Google Scholar 

  4. Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8, e66428 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Leff, B., Ramankutty, N. & Foley, J. A. Geographic distribution of major crops across the world. Global Biogeochem. Cycles 18, GB1009 (2004).

    Article  Google Scholar 

  6. Adalibieke, W., Cui, X., Cai, H., You, L. & Zhou, F. Global crop-specific nitrogen fertilization dataset in 1961–2020. Sci. Data 10, 617 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Tian, H. et al. Global nitrous oxide budget 1980–2020. Earth Syst. Sci. Data Discuss. 2023, 1–98 (2023).

    Google Scholar 

  8. Tian, H. et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature 586, 248–256 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Cui, X. et al. Global mapping of crop-specific emission factors highlights hotspots of nitrous oxide mitigation. Nat. Food 2, 886–893 (2021).

    Article  CAS  PubMed  Google Scholar 

  10. Summary for policymakers. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2021).

  11. Murchie, E. H. & Burgess, A. J. Casting light on the architecture of crop yield. Crop Environ. 1, 74–85 (2022).

    Article  Google Scholar 

  12. Valladares, F. & Niinemets, Ü. The architecture of plant crowns: from design rules to light capture and performance. In Functional Plant Ecology (eds Pugnaire, F. & Valladares, F.) 101–149 (CRC Press, 2007).

  13. Richards, R. Selectable traits to increase crop photosynthesis and yield of grain crops. J. Exp. Bot. 51, 447–458 (2000).

    Article  CAS  PubMed  Google Scholar 

  14. Cheng, Y. et al. High canopy photosynthesis before anthesis explains the outstanding yield performance of rice cultivars with ideal plant architecture. Field Crops Res. 306, 109223 (2024).

    Article  Google Scholar 

  15. Evans, J. R. & Clarke, V. C. The nitrogen cost of photosynthesis. J. Exp. Bot. 70, 7–15 (2019).

    Article  CAS  PubMed  Google Scholar 

  16. Jiang, Y. et al. Optimizing rice plant photosynthate allocation reduces N2O emissions from paddy fields. Sci. Rep. 6, 29333 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schützenmeister, K. et al. N2O emissions from plants are reduced under photosynthetic activity. Plant-Environ. Interact. 1, 48–56 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ort, D. R. et al. Redesigning photosynthesis to sustainably meet global food and bioenergy demand. Proc. Natl Acad. Sci. USA 112, 8529–8536 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Tian, J. et al. Maize smart-canopy architecture enhances yield at high densities. Nature 632, 576–584 (2025).

    Article  Google Scholar 

  20. Fang, H. Canopy clumping index (CI): A review of methods, characteristics, and applications. Agric. For. Meteorol. 303, 108374 (2021).

    Article  Google Scholar 

  21. Chen, J. M. et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Global Biogeochem. Cycles 26, GB1019 (2012).

    Article  Google Scholar 

  22. Li, F. et al. Vegetation clumping modulates global photosynthesis through adjusting canopy light environment. Glob. Chang. Biol. 29, 731–746 (2023).

    Article  CAS  PubMed  Google Scholar 

  23. Wei, S., Fang, H., Schaaf, C. B., He, L. & Chen, J. M. Global 500 m clumping index product derived from MODIS BRDF data (2001–2017). Remote Sens. Environ. 232, 111296 (2019).

    Article  Google Scholar 

  24. Iizumi, T. & Sakai, T. The global dataset of historical yields for major crops 1981–2016. Sci. Data 7, 97 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Yu, Q. et al. A cultivated planet in 2010–part 2: the global gridded agricultural-production maps. Earth Syst. Sci. Data 12, 3545–3572 (2020).

    Article  Google Scholar 

  26. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles 22, GB1022 (2008).

    Article  Google Scholar 

  27. Yang, Y. et al. Soil nitrous oxide emissions by atmospheric nitrogen deposition over global agricultural systems. Environ. Sci. Technol. 55, 4420–4429 (2021).

    Article  CAS  PubMed  Google Scholar 

  28. Chen, J. M., Liu, J., Leblanc, S. G., Lacaze, R. & Roujean, J.-L. Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sens. Environ. 84, 516–525 (2003).

    Article  Google Scholar 

  29. Chen, W., Pan, W. & Xu, Z. Current situation and trends in production of Japonica rice in China. J. Shenyang Agric. Univ. 37, 801–805 (2006).

    Google Scholar 

  30. Grogan, D., Frolking, S., Wisser, D., Prusevich, A. & Glidden, S. Global gridded crop harvested area, production, yield, and monthly physical area data circa 2015. Sci. Data 9, 15 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  31. He, L., Chen, J. M., Pisek, J., Schaaf, C. B. & Strahler, A. H. Global clumping index map derived from the MODIS BRDF product. Remote Sens. Environ. 119, 118–130 (2012).

    Article  Google Scholar 

  32. Jiao, Z. et al. An algorithm for the retrieval of the clumping index (CI) from the MODIS BRDF product using an adjusted version of the kernel-driven BRDF model. Remote Sens. Environ. 209, 594–611 (2018).

    Article  Google Scholar 

  33. Dong, N. et al. Rising CO2 and warming reduce global canopy demand for nitrogen. New Phytol. 235, 1692–1700 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Dong, N. et al. Leaf nitrogen from first principles: field evidence for adaptive variation with climate. Biogeosciences 14, 481–495 (2017).

    Article  CAS  Google Scholar 

  35. Luo, X. et al. Global variation in the fraction of leaf nitrogen allocated to photosynthesis. Nat. Commun. 12, 4866 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Dreccer, M. F. Nitrogen use at the leaf and canopy level: a framework to improve crop N use efficiency. J. Crop Improv. 15, 97–125 (2006).

    Article  Google Scholar 

  37. Gu, J. et al. Canopy light and nitrogen distributions are related to grain yield and nitrogen use efficiency in rice. Field Crops Res. 206, 74–85 (2017).

    Article  Google Scholar 

  38. Braghiere, R. K., Quaife, T., Black, E., He, L. & Chen, J. Underestimation of global photosynthesis in Earth system models due to representation of vegetation structure. Global Biogeochem. Cycles 33, 1358–1369 (2019).

    Article  CAS  Google Scholar 

  39. Chen, J., Menges, C. & Leblanc, S. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sens. Environ. 97, 447–457 (2005).

    Article  Google Scholar 

  40. Béland, M. & Baldocchi, D. D. Vertical structure heterogeneity in broadleaf forests: effects on light interception and canopy photosynthesis. Agric. For. Meteorol. 307, 108525 (2021).

    Article  Google Scholar 

  41. Chen, S., Shao, B., Impens, I. & Ceulemans, R. Effects of plant canopy structure on light interception and photosynthesis. J. Quant. Spectrosc. Radiat. Transf. 52, 115–123 (1994).

    Article  CAS  Google Scholar 

  42. Nilson, T. A theoretical analysis of the frequency of gaps in plant stands. Agric. Meteorol. 8, 25–38 (1971).

    Article  Google Scholar 

  43. Baldocchi, D. D., Wilson, K. B. & Gu, L. How the environment, canopy structure and canopy physiological functioning influence carbon, water and energy fluxes of a temperate broad-leaved deciduous forest—an assessment with the biophysical model CANOAK. Tree Physiol. 22, 1065–1077 (2002).

    Article  CAS  PubMed  Google Scholar 

  44. Stewart, D. et al. Canopy structure, light interception, and photosynthesis in maize. Agron. J. 95, 1465–1474 (2003).

    Article  Google Scholar 

  45. Mathur, S., Jain, L. & Jajoo, A. Photosynthetic efficiency in sun and shade plants. Photosynthetica 56, 354–365 (2018).

    Article  CAS  Google Scholar 

  46. Williams, I. N., Riley, W. J., Kueppers, L. M., Biraud, S. C. & Torn, M. S. Separating the effects of phenology and diffuse radiation on gross primary productivity in winter wheat. J. Geophys. Res. Biogeosci. 121, 1903–1915 (2016).

    Article  Google Scholar 

  47. Braghiere, R. K. et al. Characterization of the radiative impact of aerosols on CO2 and energy fluxes in the Amazon deforestation arch using artificial neural networks. Atmos. Chem. Phys. 20, 3439–3458 (2020).

    Article  CAS  Google Scholar 

  48. Niinemets, U. Photosynthesis and resource distribution through plant canopies. Plant Cell Environ. 30, 1052–1071 (2007).

    Article  CAS  PubMed  Google Scholar 

  49. Reay, D. S. et al. Global agriculture and nitrous oxide emissions. Nat. Clim. Change 2, 410–416 (2012).

    Article  CAS  Google Scholar 

  50. Liu, Y. et al. Localized nitrogen management strategies can halve fertilizer use in Chinese staple crop production. Nat. Food 5, 825–835 (2024).

    Article  CAS  PubMed  Google Scholar 

  51. You, L., Ros, G. H., Chen, Y., Zhang, F. & de Vries, W. Optimized agricultural management reduces global cropland nitrogen losses to air and water. Nat. Food 5, 995–1004 (2024).

    Article  CAS  PubMed  Google Scholar 

  52. Gu, B. et al. Cost-effective mitigation of nitrogen pollution from global croplands. Nature 613, 77–84 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Duan, J. et al. Agricultural management practices in China enhance nitrogen sustainability and benefit human health. Nat. Food 5, 378–389 (2024).

    Article  PubMed  Google Scholar 

  54. Food and Agriculture Organization, International Fund for Agricultural Development, UNICEF, World Food Programme. The State of Food Security and Nutrition in the World 2024. (WHO).

  55. Food Price Monitoring and Analysis (Food and Agriculture Organization).

  56. Li, J.-Y., Wang, J. & Zeigler, R. S. The 3,000 rice genomes project: new opportunities and challenges for future rice research. Gigascience 3, 2047–217X (2014).

    Article  Google Scholar 

  57. Zhao, H. et al. An inferred functional impact map of genetic variants in rice. Mol. Plant 14, 1584–1599 (2021).

    Article  CAS  PubMed  Google Scholar 

  58. Wang, Z., Schaaf, C. B., Sun, Q., Shuai, Y. & Román, M. O. Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. Remote Sens. Environ. 207, 50–64 (2018).

    Article  Google Scholar 

  59. Kim, K.-H., Doi, Y., Ramankutty, N. & Iizumi, T. A review of global gridded cropping system data products. Environ. Res. Lett. 16, 093005 (2021).

    Article  Google Scholar 

  60. Yang, Y. et al. Enhanced nitrous oxide emissions caused by atmospheric nitrogen deposition in agroecosystems over China. Environ. Sci. Pollut. Res. 28, 15350–15360 (2021).

    Article  CAS  Google Scholar 

  61. Laborte, A. G. et al. RiceAtlas, a spatial database of global rice calendars and production. Sci. Data 4, 1–10 (2017).

    Article  Google Scholar 

  62. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 1–12 (2018).

    Article  Google Scholar 

  63. Batjes, N. H. ISRIC-WISE Global Data Set of Derived Soil Properties on a 0.5 by 0.5 Degree Grid (ver. 3.0) (ISRIC, 2005).

  64. Zhang, K., Li, X., Zheng, D., Zhang, L. & Zhu, G. Estimation of global irrigation water use by the integration of multiple satellite observations. Water Resour. Res. 58, e2021WR030031 (2022).

    Article  Google Scholar 

  65. Bi, W. et al. A global 0.05 dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020. Sci. Data 9, 213 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Wang, S., Zhang, Y., Ju, W., Qiu, B. & Zhang, Z. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Sci. Total Environ. 755, 142569 (2021).

    Article  CAS  PubMed  Google Scholar 

  67. Stocker, B. D. et al. P-model v1. 0: an optimality-based light use efficiency model for simulating ecosystem gross primary production. Geosci. Model Dev. 13, 1545–1581 (2020).

    Article  Google Scholar 

  68. Shen, R., Peng, Q., Li, X., Chen, X. & Yuan, W. CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution. Earth Syst. Sci. Data 17, 2193–2216 (2025).

    Article  Google Scholar 

  69. USDA National Agricultural Statistics Service Cropland Data Layer. Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/ (USDA-NASS).

  70. Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Braghiere, R. K., Gérard, F., Evers, J. B., Pradal, C. & Pagès, L. Simulating the effects of water limitation on plant biomass using a 3D functional–structural plant model of shoot and root driven by soil hydraulics. Ann. Bot. 126, 713–728 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Walters, D. et al. The Met Office Unified Model global atmosphere 4.0 and JULES global land 4.0 configurations. Geosci. Model Dev. 7, 361–386 (2014).

    Article  Google Scholar 

  73. Slevin, D., Tett, S. F., Exbrayat, J.-F., Bloom, A. A. & Williams, M. Global evaluation of gross primary productivity in the JULES land surface model v3. 4.1. Geoscientific Model Dev. 10, 2651–2670 (2017).

    Article  Google Scholar 

  74. Sellers, P. J. Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sens. 6, 1335–1372 (1985).

    Article  Google Scholar 

  75. Bi, W. & Zhou, Y. A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020). Dryad https://doi.org/10.5061/dryad.dfn2z352k (2022).

  76. Yan, Y. & Wang, S. Code for CI-crop manuscript. Figshare https://figshare.com/s/4f4170369c238cb0b694 (2025).

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (32322064 and 32471675), the Fundamental Research Funds for the Central Universities (KYCXJC2025006), the National Key R&D Program of China (2022YFF0803100), the Jiangsu Provincial Natural Science Foundation for Distinguished Young Scholars (BK20220083) and the Nanjing U35 Project. C.D. was supported by the Postdoctoral Fellowship Program of CPSF under grant GZC20252644 and Jiangsu Province Outstanding Postdoctoral Program under grant 2025ZB155. J.P. was supported by the Spanish government grants PID2022-140808NB-I00 funded by MICIU/AEI/10.13039/501100011033 and FEDER, EU, the Catalan Government grants SGR 2021–1333 and AGAUR2023 CLIMA 00118, and the EU grant CONCERTO (HORIZON-CL5-2024-D1-01). L.L. was supported by the National Natural Science Foundation of China (42125103). Y.K. thanks the RUDN University Strategic Academic Leadership Program. We also extend our sincere thanks to all data providers for their continuous efforts and for sharing their data.

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Author notes

  1. These authors contributed equally: Yuli Yan, Chaoya Dang, Lei Liu.

Authors and Affiliations

  1. College of Agriculture, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China

    Yuli Yan, Chaoya Dang, Zihao Wang, Liyuan Chen, Yu Jiang, Yanfeng Ding & Songhan Wang

  2. Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China, Nanjing Agricultural University, Nanjing, China

    Yuli Yan, Chaoya Dang, Zihao Wang, Liyuan Chen, Yu Jiang, Yanfeng Ding & Songhan Wang

  3. State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China

    Lei Liu & Qing Zhu

  4. College of Urban and Environmental Sciences, Peking University, Beijing, China

    Zhenong Jin & Feng Zhou

  5. Department of Soil Science of Temperate Ecosystems, Department of Agricultural Soil Science, University of Gottingen, Gottingen, Germany

    Yakov Kuzyakov

  6. Peoples Friendship University of Russia (RUDN University), Moscow, Russia

    Yakov Kuzyakov

  7. Department of Geography and Planning, University of Toronto, Toronto, Ontario, Canada

    Jing M. Chen

  8. International Institute for Earth System Sciences, Nanjing University, Nanjing, China

    Yanlian Zhou

  9. Center for Earth System Science and Global Sustainability, Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA, USA

    Hanqin Tian

  10. State Key Laboratory of Nutrient Use and Management, Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, China

    Xuejun Liu

  11. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China

    Ziyin Shang

  12. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Baojing Gu

  13. CSIC, Global ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain

    Josep Peñuelas

  14. CREAF, Cerdanyola del Vallès, Spain

    Josep Peñuelas

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Contributions

S.W. designed the research; Y.Y., C.D., Z.W. and L.C. performed the analysis and conducted the field experiment; S.W., B.G. and Y.D. drafted the paper; L.L., J.P., Z.J., Y.K., J.M.C., F.Z., Y.Z., H.T., X.L., Q.Z., Y.J. and Z.S. provided the data and contributed to the interpretation of the results and to the writing.

Corresponding authors

Correspondence to Baojing Gu, Yanfeng Ding or Songhan Wang.

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Yan, Y., Dang, C., Liu, L. et al. Clumped canopy architecture raises global crop yield and reduces N2O emissions. Nat. Plants 12, 49–61 (2026). https://doi.org/10.1038/s41477-025-02172-w

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