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Oscillation-induced yield loss in China partially driven by migratory pests from mainland Southeast Asia

Nature Food volume 6, pages 681–691 (2025)Cite this article

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Abstract

Large-scale climate oscillations are recognized as skilful predictors of variations in global and regional crop yield. However, the mechanisms linking climate oscillations to crop yield variations remain unclear and are widely assumed to result from crop physiological responses to oscillation-induced local climate variations. Here we assessed the pattern of oscillation-induced yield variations in China over the past four decades and found that El Niño/Southern Oscillation (ENSO) is the primary climatic oscillation associated with extreme yield anomalies, particularly in southern China. These ENSO-related extreme yield anomalies are driven not only by local climate anomalies but also by greater occurrences of crop pests and diseases. Interestingly, the greater occurrence of crop pests is not triggered by local climate anomalies but is linked to ENSO-forced climate anomalies in mainland Southeast Asia, the source region of these pests, fuelled by the ENSO-driven circulation pattern facilitating their migration to China. Given the projected increase in the frequency of ENSO events in a warming future, effectively mitigating such oscillation-induced crop failures requires cross-border collaboration between the source and receiving countries of crop pests.

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Fig. 1: Spatial pattern of correlation between \({Y}_{{\mathrm{TB}}}^{\,{\mathrm{Q}}}\) and main climate oscillations, and response of yield anomalies to the Niño 3.4 index.
Fig. 2: Pathways for oscillations affecting extreme yield variability in southern China.
Fig. 3: Evidence for the pathway by which ENSO affects \({Y}_{{\mathrm{TB}}}^{{\mathrm{Q}}}\) in southern China by modulating the occurrence of CPD.
Fig. 4: Mechanisms of additional migratory rice pests invading southern China caused by ENSO.

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Data availability

For all oscillation index data, please refer to the literature cited in the Article. Monthly scale meteorological data are available via the CRU dataset at https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.02/). The monthly scale SST and wind field data are available via the NCEP/NCAR reanalysis at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. Relevant datasets of pests and diseases in China are detailed in the following links. The occurrence ratio anomalies of two migratory pests at the provincial scale in southern China are available via figshare at https://doi.org/10.6084/m9.figshare.28147505.v1 (ref. 59). Occurrence ratio anomalies of two main migratory pests at the national scale for China are available via figshare at https://doi.org/10.6084/m9.figshare.28147508.v1 (ref. 60). The pest trapping data at the China–Vietnam joint monitoring sites from 2013 to 2015 are available via fFigshare at https://doi.org/10.6084/m9.figshare.28151327.v1 (ref. 61). The occurrence area data of rice migratory pests in Vietnam are available via figshare at https://doi.org/10.6084/m9.figshare.28151348.v1 (ref. 62). Agricultural statistics for China are available via the National Bureau of Statistics of China at http://www.stats.gov.cn/english/. Rice phenology data for the China region are available via figshare at https://doi.org/10.6084/m9.figshare.8313530.v7 (ref. 63), and rice phenology data for the MSA region are available via RiceAtlas at https://doi.org/10.7910/DVN/JE6R2R. Gridded data on pesticide application rates from the dataset PEST-CHEMGRIDS_v1.01 are available via figshare at https://doi.org/10.6084/m9.figshare.7764014.v6 (ref. 64). Source data are provided with this paper.

Code availability

All data were processed using MATLAB v2021b and RStudio (R version 4.1). Most of the statistical analysis was carried out in MATLAB and R. We used the lavaan package in R to build an SEM and the earth package to build a MARS model. We used Python 3.8 for causal analysis with package tigramite, which is available via GitHub at https://github.com/jakobrunge/tigramite. The figures were produced in Origin Pro 2021 and ArcGIS 10.8. Other codes are available upon request.

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Acknowledgements

We are grateful to S. Ainiwaer for her support and contribution to the causal analysis in our research, and we also thank S. Yang, for her detailed comments and suggestions. We thank Q. Fu and H. Jiang from the China National Rice Research Institute (CNRRI) for their suggestions on figure presentations and support in editing this paper. This study has received funding from the National Natural Science Foundation of China (grant nos. 42361144876 and 42171096), Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 101154967 and the Leibniz Female Professorship Award (application no. P102/2020).

Author information

Authors and Affiliations

  1. Institute of Carbon Neutrality, Sino‐French Institute for Earth System Sciences, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Chenzhi Wang, Xuhui Wang, Yuxing Sang, Quanbo Zhao, Liangliang Zhang, Yahai Lu, Feng Zhou, Hongyan Liu & Shilong Piao

  2. Leibniz Centre for Agricultural Landscape Research, Müncheberg, Germany

    Chenzhi Wang & Diane Cooke

  3. Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany

    Christoph Müller

  4. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, China

    Yao Huang

  5. Laboratoire de Météorologie Dynamique, CNRS, Sorbonne Université, Paris, France

    Laurent Li

  6. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China

    Fulu Tao

  7. University of Chinese Academy of Sciences, Beijing, China

    Fulu Tao

  8. Natural Resources Institute Finland (Luke), Helsinki, Finland

    Fulu Tao

  9. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China

    Tao Lin

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  1. Chenzhi Wang
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Contributions

C.W. and X.W. designed the study; C.W. collected data and performed the analyses; C.W. and X.W. wrote the first draft of the manuscript, with inputs and suggestions from Y.S., C.M., Y.H., L.L, D.C., Q.Z., L.Z., Y.L., F.Z., H.L., F.T., T.L. and S.P.

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Correspondence to Xuhui Wang.

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Extended data

Extended Data Fig. 1 The average proportion of different crop loss caused by CPD and rice production loss caused by specific rice CPD since 1980S.

Top, Results of national level. Bottom, results of paddy-field crops dominated regions (southern China).

Source data

Extended Data Fig. 2 Relationship between ENSO and rice migration pest occurrence condition in Vietnam.

a, The correlation between winter Niño 3.4 and occurrence area of two rice migration pests in Vietnam from 2010 to 2016. b, The distribution of aggregated occurrence area of two rice migration pests (rice planthopper and rice leaf folder) under two different winter ENSO conditions. The symbol * shows they are different at 90% significant level after Mann-Whitney U two-sided test and the p-value is 0.067. c, Estimated correlation between average occurrence area of rice planthopper and rice leaf folder in Vietnam and winter Niño 3.4 index value from 2010 to 2016 based on bootstrap method.

Source data

Extended Data Fig. 3 Spring wind field anomalies at 850 hPa and mean sea level pressure anomalies during the yield-loss years when the winter ENSO index (Nino 3.4) is positive.

The area with grey slash represents the difference tested by the composite analysis at 95% significant level.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–23 and Tables 1–10.

Reporting Summary

Source data

Source Data Fig. 1

This is a zip compressed file, in which the tiff file is the data of Fig. 1a. It is recommended to use ArcGIS or Qgis to open it; the data of Fig. 1b and c are in the Fig1b_and_c.xlsx file.

Source Data Fig. 2

The file contains two sheets corresponding to the coefficients of the latent variables in the SEM model and the path coefficients of the regression. All coefficients are standardized.

Source Data Fig. 3

This file includes five sheets corresponding to the subplots of Fig. 3. It is important to note that the data in Fig. b,c in the sheets are a matrix, not a structured table.

Source Data Fig. 4

The zip file of Figure 4 includes seven files. Figure 4a includes an .xlsx and a .tiff file. The tiff file is raster data of MSA precipitation anomalies, and the xlsx file is data of longitude, latitude and rice planting area grade data. Figure 4b includes a .tiff file of rice planting area and a .xlsx file for box plots. Figure 4c is two .tiff files of two SST data (winter and spring SST) and a .tiff file of rice harvesting area. We recommend to use ArcGIS or Qgis to open .tiff files.

Source Data Extended Data Fig. 1

There are two sheets corresponding to the pie charts a and b in Extended Data Fig. 1.

Source Data Extended Data Fig. 2

There are three sheets corresponding to a (scatter plot), b (box-and-line plot), and c (histogram) in Extended Data Fig. 2.

Source Data Extended Data Fig. 3

This table contains five columns for latitude, longitude, sea level pressure field anomalies and U- and V-shaped wind anomaly at 850 hPa. When plotting, the U and V need to be vector-summed to get the final direction of the wind field.

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Wang, C., Wang, X., Sang, Y. et al. Oscillation-induced yield loss in China partially driven by migratory pests from mainland Southeast Asia. Nat Food 6, 681–691 (2025). https://doi.org/10.1038/s43016-025-01158-3

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