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Occurrence of crop pests and diseases has largely increased in China since 1970

  • 1.

    Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).

    ADS 
    CAS 

    Google Scholar 

  • 2.

    The Future of Food and Agriculture—Alternative Pathways to 2050 (Food and Agriculture Organization of the United Nations, 2018).

  • 3.

    Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 4.

    Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).

    ADS 
    CAS 

    Google Scholar 

  • 5.

    Zhang, W. et al. Closing yield gaps in China by empowering smallholder farmers. Nature 537, 671–674 (2016).

    ADS 
    CAS 

    Google Scholar 

  • 6.

    Chakraborty, S. & Newton, A. C. Climate change, plant diseases and food security: an overview. Plant Pathol. 60, 2–14 (2011).

    Google Scholar 

  • 7.

    Oerke, E. C. Crop losses to pests. J. Agri. Sci. 144, 31–43 (2005).

    Google Scholar 

  • 8.

    Bebber, D. P., Ramotowski, M. A. T. & Gurr, S. J. Crop pests and pathogens move polewards in a warming world. Nat. Clim. Change 3, 985–988 (2013).

    ADS 

    Google Scholar 

  • 9.

    Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 10.

    Delcour, I., Spanoghe, P. & Uyttendaele, M. Literature review: impact of climate change on pesticide use. Food Res. Int. 68, 7–15 (2015).

    Google Scholar 

  • 11.

    Ziska, L. H. Increasing minimum daily temperatures are associated with enhanced pesticide use in cultivated soybean along a latitudinal gradient in the mid-western United States. PLoS ONE 9, e98516 (2014).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 12.

    Lamichhane, J. R. et al. Robust cropping systems to tackle pests under climate change. A review. Agron. Sustain. Dev. 35, 443–459 (2014).

    Google Scholar 

  • 13.

    Bebber, D. P. et al. Many unreported crop pests and pathogens are probably already present. Glob. Change Biol. 25, 2703–2713 (2019).

    ADS 

    Google Scholar 

  • 14.

    Bale, J. S. et al. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).

    ADS 

    Google Scholar 

  • 15.

    Garrett, K. A., Dendy, S. P., Frank, E. E., Rouse, M. N. & Travers, S. E. Climate change effects on plant disease: genomes to ecosystems. Annu. Rev. Phytopathol. 44, 489–509 (2006).

    CAS 

    Google Scholar 

  • 16.

    Hruska, A. J. Fall armyworm (Spodoptera frugiperda) management by smallholders. CAB Rev. 14, 1–11 (2019).

    Google Scholar 

  • 17.

    Sutherst, R. W. et al. Adapting to crop pest and pathogen risks under a changing climate. Wiley Interdiscip. Rev. Clim. Change 2, 220–237 (2011).

    Google Scholar 

  • 18.

    Donatelli, M. et al. Modelling the impacts of pests and diseases on agricultural systems. Agric. Syst. 155, 213–224 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 19.

    Jones, J. W. et al. Toward a new generation of agricultural system data, models, and knowledge products: state of agricultural systems science. Agric. Syst. 155, 269–288 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 20.

    Miller, S. A., Beed, F. D. & Harmon, C. L. Plant disease diagnostic capabilities and networks. Annu. Rev. Phytopathol. 47, 15–38 (2009).

    CAS 

    Google Scholar 

  • 21.

    Bebber, D. P., Holmes, T., Smith, D. & Gurr, S. J. Economic and physical determinants of the global distributions of crop pests and pathogens. New Phytol. 202, 901–910 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 22.

    Savary, S. et al. The global burden of pathogens and pests on major food crops. Nat. Ecol. Evol. 3, 430–439 (2019).

    Google Scholar 

  • 23.

    An early warning news about the mirgating condition of Fall Armyworm in China from National Agro-Tech Extension and Service Center https://www.natesc.org.cn/News/des?id=eaf064ae-6582-47c1-a9f3-a58969fd47b3&kind=HYTX (in Chinese, available in Nov.2021).

  • 24.

    Piao, S. et al. The impacts of climate change on water resources and agriculture in China. Nature 467, 43–51 (2010).

    ADS 
    CAS 

    Google Scholar 

  • 25.

    Chown, S. L., Sorensen, J. G. & Terblanche, J. S. Water loss in insects: an environmental change perspective. J. Insect Physiol. 57, 1070–1084 (2011).

    CAS 

    Google Scholar 

  • 26.

    Bjorkman, A. D. et al. Plant functional trait change across a warming tundra biome. Nature 562, 57–62 (2018).

    ADS 
    CAS 

    Google Scholar 

  • 27.

    National Agricultural Technology Extension and Service Center. Technical Specification Manual of Major Crop Pest and Disease Observation and Forecast in China (China Agriculture Press, 2010).

  • 28.

    Olfert, O., Weiss, R. M. & Elliott, R. H. Bioclimatic approach to assessing the potential impact of climate change on wheat midge (Diptera: Cecidomyiidae) in North America. Can. Entomol. 148, 52–67 (2015).

    Google Scholar 

  • 29.

    Savary, S., Teng, P. S., Willocquet, L. & Nutter, F. W. Quantification and modeling of crop losses: a review of purposes. Annu. Rev. Phytopathol. 44, 89–112 (2006).

    CAS 

    Google Scholar 

  • 30.

    Chakraborty, S. Migrate or evolve: options for plant pathogens under climate change. Glob. Change Biol. 19, 1985–2000 (2013).

    ADS 

    Google Scholar 

  • 31.

    Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 32.

    Chaloner, T. M., Gurr, S. J. & Bebber, D. P. Plant pathogen infection risk tracks global crop yields under climate change. Nat. Clim. Change 11, 710–715 (2021).

    ADS 

    Google Scholar 

  • 33.

    Carvalho, J. L. N. et al. Agronomic and environmental implications of sugarcane straw removal: a major review. Glob. Change Biol. Bioenergy 9, 1181–1195 (2017).

    CAS 

    Google Scholar 

  • 34.

    Savary, S., Horgan, F., Willocquet, L. & Heong, K. L. A review of principles for sustainable pest management in rice. Crop Prot. 32, 54–63 (2012).

    Google Scholar 

  • 35.

    Frolking, S. et al. Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China. Glob. Biogeochem. Cycles 16, 38-31–38-10 (2002).

    Google Scholar 

  • 36.

    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).

    Google Scholar 

  • 37.

    Harvell, C. D. et al. Climate warming and disease risks for terrestrial and marine biota. Science 296, 2158–2162 (2002).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 38.

    Scherm, H. Climate change: can we predict the impacts on plant pathology and pest management? Can. J. Plant Pathol. 26, 267–273 (2004).

    Google Scholar 

  • 39.

    Cheke, R. A. & Tratalos, J. A. Migration, patchiness, and population processes illustrated by two migrant pests. Bioscience 57, 145–154 (2007).

    Google Scholar 

  • 40.

    Eyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    ADS 

    Google Scholar 

  • 41.

    O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    ADS 

    Google Scholar 

  • 42.

    van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).

    ADS 

    Google Scholar 

  • 43.

    Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12, 3055–3070 (2019).

    ADS 

    Google Scholar 

  • 44.

    Gregory, P. J., Johnson, S. N., Newton, A. C. & Ingram, J. S. Integrating pests and pathogens into the climate change/food security debate. J. Exp. Bot. 60, 2827–2838 (2009).

    CAS 

    Google Scholar 

  • 45.

    Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. Crop Evapotranspiration—Guidelines for Computing Crop Water Requirements FAO irrigation and drainage paper 56 (FAO, 1998).

  • 46.

    Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 47.

    Kahiluoto, H. et al. Decline in climate resilience of European wheat. Proc. Natl Acad. Sci. USA 116, 123–128 (2019).

    CAS 

    Google Scholar 

  • 48.

    Folke, C. et al. Regime shifts, resilience, and biodiversity in ecosystem management. Annu. Rev. Ecol. Evol. Syst. 35, 557–581 (2004).

    Google Scholar 

  • 49.

    Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).

    ADS 
    CAS 

    Google Scholar 

  • 50.

    Clark, J. S. Why environmental scientists are becoming Bayesians. Ecol. Lett. 8, 2–14 (2005).

    Google Scholar 

  • 51.

    Clark, J. S. & Gelfand, A. E. A future for models and data in environmental science. Trends Ecol. Evol. 21, 375–380 (2006).

    Google Scholar 

  • 52.

    Gelfand, A. E. & Smith, A. F. M. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85, 398–409 (1990).

    MathSciNet 
    MATH 

    Google Scholar 

  • 53.

    Lunn, D., Spiegelhalter, D., Thomas, A. & Best, N. The BUGS project: evolution, critique and future directions. Stat. Med. 28, 3049–3067 (2009).

    MathSciNet 

    Google Scholar 

  • 54.

    Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).

    MathSciNet 

    Google Scholar 


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