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Spatiotemporal co-optimization of agricultural management practices towards climate-smart crop production

Nature Food volume 5, pages 59–71 (2024)Cite this article

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Abstract

Co-optimization of multiple management practices may facilitate climate-smart agriculture, but is challenged by complex climate–crop–soil management interconnections across space and over time. Here we develop a hybrid approach combining agricultural system modelling, machine learning and life cycle assessment to spatiotemporally co-optimize fertilizer application, irrigation and residue management to achieve yield potential of wheat and maize and minimize greenhouse gas emissions in the North China Plain. We found that the optimal fertilizer application rate and irrigation for the historical period (1995–2014) are lower than local farmers’ practices as well as trial-derived recommendations. With the optimized practices, the projected annual requirement of fertilizer, irrigation water and residue inputs across the North China Plain in the period 2051–2070 is reduced by 16% (14–21%) (mean with 95% confidence interval), 19% (7–32%) and 20% (16–26%), respectively, compared with the current supposed optimal management in the historical reference period, with substantial greenhouse gas emission reductions. We demonstrate the potential of spatiotemporal co-optimization of multiple management practices and present digital mapping of management practices as a benchmark for site-specific management across the region.

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Fig. 1: A simulation framework enables spatiotemporal optimization of multiple management practices.
Fig. 2: Spatial pattern of yield, SOC changes (ΔSOC), net GHG emissions and optimized management practices under the target of maximizing yield and minimizing GHG emissions.
Fig. 3: Distributions of simulated yield, ΔSOC and GHG emissions under the target of maximizing yield and minimizing GHG emissions across the study region (that is, the NCP).
Fig. 4: Distributions of optimized management practices under the target of maximizing yield and minimizing GHG emissions across the study region (that is, the NCP).
Fig. 5: Coefficients of the predictors of a linear mixed-effects regression for optimized management practices.

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Data availability

Daily historical climate data are available at https://data.cma.cn/. The soil database is freely available at http://poles.tpdc.ac.cn/zh-hans/data/8ba0a731-5b0b-4e2f-8b95-8b29cc3c0f3a/?tdsourcetag=s_pctim_aiomsg. The raw datasets from CMIP6 simulations are available at https://esgf-node.llnl.gov/projects/cmip6/. The bias correction method is available at https://www.isimip.org/gettingstarted/isimip3b-bias-adjustment/. The APSIM Classic is freely available at https://www.apsim.info/download-apsim/. The data needed to regenerate the results in this study are publicly available at https://figshare.com/articles/figure/_b_Data_for_Spatiotemporal_co-optimization_of_agricultural_management_practices_b_/24471919.

Code availability

The code used to generate the results can be accessed at https://figshare.com/articles/figure/_b_Data_for_Spatiotemporal_co-optimization_of_agricultural_management_practices_b_/24471919.

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Acknowledgements

We acknowledge financial support from the national key research programme of Ministry of Science and Technology of China (grant no. 2021YFE0114500 to Z.L.) and the National Natural Science Foundation of China (grant no. 42001105 to L.X.) and the Fundamental Research Funds for the Central Universities (XUEKEN2023012, KYLH2023005 to L.X.).

Author information

Authors and Affiliations

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

    Liujun Xiao, Jinfeng Chang, Ping Zhang, Hangxin Zhou, Yuchen Wei, Haoyu Zhang, Zhou Shi & Zhongkui Luo

  2. National Engineering and Technology Center for Information Agriculture, Engineering Research Center of Smart Agriculture, Ministry of Education, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing, China

    Liujun Xiao & Yan Zhu

  3. Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Guocheng Wang

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

    Enli Wang

  5. School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China

    Shengli Liu

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Contributions

Z.L. conceived the study; L.X., G.W., P.Z. and H.Z. compiled the data; L.X., G.W. and H.Z. led the data assessment with the contributions of S.L.; L.X. conducted mapping; Z.L., L.X., G.W., E.W. and J.C. interpreted the results with the contribution of all authors; Z.L. and L.X. led manuscript writing with substantial contributions of all authors; Z.L., L.X. and Y.Z. contributed to the manuscript revision.

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Correspondence to Zhongkui Luo.

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Nature Food thanks Fanqiao Meng, Jordi Sardans and Zhenling Cui for their contribution to the peer review of this work.

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Xiao, L., Wang, G., Wang, E. et al. Spatiotemporal co-optimization of agricultural management practices towards climate-smart crop production. Nat Food 5, 59–71 (2024). https://doi.org/10.1038/s43016-023-00891-x

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