China can be self-sufficient in maize production by 2030 with optimal crop management

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China can be self-sufficient in maize production by 2030 with optimal crop management
  • 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).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bodirsky, B. L. et al. The ongoing nutrition transition thwarts long-term targets for food security, public health and environmental protection. Sci. Rep. 10, 19778 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1–7 (2012).

    Article 
    CAS 

    Google Scholar 

  • Agnolucci, P. et al. Impacts of rising temperatures and farm management practices on global yields of 18 crops. Nat. Food 1, 562–571 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Food and Agriculture Organization of the United Nations. Crops and livestock products Accessed 28 March 2022.

  • General Administration of Customs of the People’s Republic of China. Customs Statistics Accessed 30 March 2022.

  • United Nation. Transforming our world: the 2030 Agenda for Sustainable Development. (2015).

  • Duvick, D. Genetic progress in yield of United States maize (Zea mays L.). Maydica 50, 193 (2005).

    Google Scholar 

  • Rizzo, G. et al. Climate and agronomy, not genetics, underpin recent maize yield gains in favorable environments. Proc. Natl Acad. Sci. USA 119, e2113629119 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tollenaar, M. & Wu, J. Yield improvement in temperate maize is attributable to greater stress tolerance. Crop Sci. 39, 1597–1604 (1999).

    Article 

    Google Scholar 

  • Duvick, D. N. The contribution of breeding to yield advances in maize (Zea mays L.). Adv. Agron. 86, 83–145 (2005).

    Article 

    Google Scholar 

  • Assefa, Y. et al. Analysis of long term study indicates both agronomic optimal plant density and increase maize yield per plant contributed to yield gain. Sci. Rep. 8, 1–11 (2018).

    Article 
    ADS 

    Google Scholar 

  • Sangoi, L., Gracietti, M. A., Rampazzo, C. & Bianchetti, P. Response of Brazilian maize hybrids from different eras to changes in plant density. Field Crops Res. 79, 39–51 (2002).

    Article 

    Google Scholar 

  • Derieux, M. et al. Estimation du progrès génétique réalisé chez le maïs grain en France entre 1950 et 1985. Agronomie 7, 1–11 (1987).

  • Deng, J. et al. Models and tests of optimal density and maximal yield for crop plants. Proc. Natl Acad. Sci. USA 109, 15823–15828 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hörbe, T. A. N., Amado, T. J. C., Ferreira, A. O. & Alba, P. J. Optimization of corn plant population according to management zones in Southern Brazil. Precis. Agric. 14, 450–465 (2013).

    Article 

    Google Scholar 

  • Assefa, Y. et al. Yield responses to planting density for US modern corn hybrids: a synthesis‐analysis. Crop Sci. 56, 2802–2817 (2016).

    Article 
    CAS 

    Google Scholar 

  • Feng, P., Wang, B., Liu, D. L. & Yu, Q. Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia. Agric. Syst. 173, 303–316 (2019).

    Article 

    Google Scholar 

  • Feng, P. et al. Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia. Environ. Res. Lett. 15, 084051 (2020).

    Article 
    ADS 

    Google Scholar 

  • Guilpart, N., Iizumi, T. & Makowski, D. Data-driven projections suggest large opportunities to improve Europe’s soybean self-sufficiency under climate change. Nat. Food 3, 255–265 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Meng, Q. et al. Growing sensitivity of maize to water scarcity under climate change. Sci. Rep. 6, 2045–2322 (2016).

    Google Scholar 

  • National Bureau of Statistics (NBS). China Municipal Statistical Yearbook Accessed 1 May 2022.

  • Sangoi, L. Understanding plant density effects on maize growth and development: an important issue to maximize grain yield. Cienc. Rural 31, 159–168 (2001).

    Article 

    Google Scholar 

  • Testa, G., Reyneri, A. & Blandino, M. Maize grain yield enhancement through high plant density cultivation with different inter-row and intra-row spacings. Eur. J. Agron. 72, 28–37 (2016).

    Article 

    Google Scholar 

  • Cheng, M. et al. Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China. Agric. Meteorol. 323, 109057 (2022).

    Article 

    Google Scholar 

  • Tao, F., Zhang, L., Zhang, Z. & Chen, Y. Designing wheat cultivar adaptation to future climate change across China by coupling biophysical modelling and machine learning. Eur. J. Agron. 136, 126500 (2022).

    Article 

    Google Scholar 

  • Liakos, K. G., Busato, P., Moshou, D., Pearson, S. & Bochtis, D. Machine learning in agriculture: A review. Sensors 18, 2674 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Liu, G. et al. Reducing maize yield gap by matching plant density and solar radiation. J. Integr. Agric. 20, 363–370 (2021).

    Article 

    Google Scholar 

  • Luo, N. et al. Agronomic optimal plant density for yield improvement in the major maize regions of China. Crop Sci. 60, 1580–1590 (2020).

    Article 

    Google Scholar 

  • Woli, K. P., Burras, C. L., Abendroth, L. J. & Elmore, R. W. Optimizing corn seeding rates using a field’s corn suitability rating. Agron. J. 106, 1523–1532 (2014).

    Article 

    Google Scholar 

  • Liu, G. et al. Canopy characteristics of high-yield maize with yield potential of 22.5 Mg ha-1. Field Crops Res. 213, 221–230 (2017).

    Article 
    ADS 

    Google Scholar 

  • Ming, B. et al. Changes of maize planting density in China. Sci. Agric. Sin. 50, 1960–1972 (2017).

    Google Scholar 

  • Li, S. & Wang, C. Analysis on change of production and factors promoting yield increase of corn in China. J. Maize Sci. 4, 26–30 (2008).

    Google Scholar 

  • Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 344, 516–519 (2014).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Liu, B., Chen, X., Meng, Q., Yang, H. & van Wart, J. Estimating maize yield potential and yield gap with agro-climatic zones in China Distinguish irrigated and rainfed conditions. Agric. Meteorol. 239, 108–117 (2017).

    Article 

    Google Scholar 

  • Wu, A., Hammer, G. L., Doherty, A., von Caemmerer, S. & Farquhar, G. D. Quantifying impacts of enhancing photosynthesis on crop yield. Nat. Plants 5, 380–388 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Meng, Q., Liu, B., Yang, H. & Chen, X. Solar dimming decreased maize yield potential on the North China Plain. Food Energy Secur. 9, e235 (2020).

    Article 

    Google Scholar 

  • Bu, L. et al. The effect of adapting cultivars on the water use efficiency of dryland maize (Zea mays L.) in northwestern China. Agric. Water Manag. 148, 1–9 (2015).

    Article 

    Google Scholar 

  • Li, J., Lammerts van Bueren, E. T., Jiggins, J. & Leeuwis, C. Farmers’ adoption of maize (Zea mays L.) hybrids and the persistence of landraces in Southwest China: implications for policy and breeding. Genet. Resour. Crop Evol. 59, 1147–1160 (2012).

    Article 

    Google Scholar 

  • Xue, J. et al. Effects of light intensity within the canopy on maize lodging. Field Crops Res. 188, 133–141 (2016).

    Article 

    Google Scholar 

  • Tian, J. et al. Teosinte ligule allele narrows plant architecture and enhances high-density maize yields. Science 365, 658–664 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Meng, Q., Cui, Z., Yang, H., Zhang, F. & Chen, X. EstabliShing High-yielding Maize System For Sustainable Intensification in China. Adv. Agron. 148, 85–109 (2018).

    Article 

    Google Scholar 

  • Zhu, P. & Burney, J. Untangling irrigation effects on maize water and heat stress alleviation using satellite data. Hydrol. Earth Syst. Sci. 26, 827–840 (2022).

    Article 
    ADS 

    Google Scholar 

  • Ciampitti, I. A. & Vyn, T. J. Physiological perspectives of changes over time in maize yield dependency on nitrogen uptake and associated nitrogen efficiencies: a review. Field Crops Res. 133, 48–67 (2012).

    Article 

    Google Scholar 

  • Chen, X. et al. Integrated soil-crop system management for food security. Proc. Natl Acad. Sci. USA 108, 6399–6404 (2011).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Feng, P., Wang, B., Liu, D. L., Waters, C. & Yu, Q. Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia. Agric. Meteorol. 275, 100–113 (2019).

    Article 

    Google Scholar 

  • Rohatgi, A. WebPlotDigitizer user manual version 3.4. 1–18 (2014).

  • Oldfield, E. E. et al. Positive associations of soil organic matter and crop yields across a regional network of working farms. Soil Sci. Soc. Am. J. 86, 384–397 (2022).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Butler, E. E., Mueller, N. D. & Huybers, P. Peculiarly pleasant weather for US maize. Proc. Natl Acad. Sci. USA 115, 11935–11940 (2018).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article 
    MATH 

    Google Scholar 

  • Angstrom, A. Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Q. J. Roy. Meteorol. Soc. 50, 121–126 (1924).

    Article 
    ADS 

    Google Scholar 

  • Soil SubCenter, National Earth System Science Data Center, National Science & Technology Infrastructure of China. China High-resolution National Soil Information Grid Basic Attribute Dataset (2010–2018) Accessed 17 January 2022.

  • Allison, L. E. Organic Carbon. Methods Soil Anal. 9, 1367–1378 (1965).

    CAS 

    Google Scholar 

  • China Meteorological Data Service Center. National Meteorological Information Center Accessed 28 March 2021.

  • Liu, D. L. & Zuo, H. Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia. Clim. Change 115, 629–666 (2012).

    Article 
    ADS 

    Google Scholar 

  • Meng, Q. et al. Understanding production potentials and yield gaps in intensive maize production in China. Field Crops Res. 143, 91–97 (2013).

    Article 

    Google Scholar 

  • United States Department of Agriculture. Foreign Agricultural Service Accessed 22 Aug 2022.

  • Department of Economic and Social Affairs. World Urbanization Prospects 2018 Accessed 23 Aug 2022.

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