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

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Report on Spatiotemporal Co-optimization of Agricultural Management Practices

Introduction

This report presents a study on the spatiotemporal co-optimization of agricultural management practices aimed at achieving climate-smart agriculture. The focus is on optimizing fertilizer application, irrigation, and residue management to maximize crop yield and minimize greenhouse gas emissions (GHG) in the North China Plain.

Methodology

The study employs a hybrid approach combining agricultural system modeling, machine learning, and life cycle assessment. This methodology facilitates the co-optimization of multiple management practices over time and space.

Findings

The study’s key findings are as follows:

1. The optimal fertilizer application rate and irrigation for the historical period (1995–2014) are lower than both local farmers’ practices and trial-derived recommendations.
2. With optimized practices, the projected annual requirement for fertilizer, irrigation water, and residue inputs across the North China Plain in the period 2051–2070 is reduced by:
   • 16% (14–21%) for fertilizer
   • 19% (7–32%) for irrigation water
   • 20% (16–26%) for residue inputs
3. These reductions are accompanied by substantial decreases in GHG emissions.

Sustainable Development Goals (SDGs)

This study aligns with several Sustainable Development Goals (SDGs), specifically:

• SDG 2: Zero Hunger – By optimizing agricultural practices, the study aims to achieve higher crop yields, contributing to food security.
• SDG 6: Clean Water and Sanitation – The reduction in irrigation water usage supports sustainable water management.
• SDG 13: Climate Action – Minimizing GHG emissions directly addresses climate change mitigation.
• SDG 12: Responsible Consumption and Production – Efficient use of fertilizers and residues promotes sustainable agricultural practices.

Conclusion

The study demonstrates the potential of spatiotemporal co-optimization of multiple management practices in agriculture. It also provides digital mapping of these practices as a benchmark for site-specific management across the region, contributing significantly to the achievement of SDGs.

Data Availability

Daily historical climate data are available at https://data.cma.cn/. The soil database can be accessed 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 model 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.

Acknowledgements

This study was supported by the national key research programme of the Ministry of Science and Technology of China (grant no. 2021YFE0114500 to Z.L.), 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.).

References

1. Shang, Z. et al. Can cropland management practices lower net greenhouse emissions without compromising yield? Glob. Change Biol. 27, 4657–4670 (2021).
2. Cui, Z. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363–366 (2018).
3. Chen, X.-P. et al. Integrated soil–crop system management for food security. Proc. Natl Acad. Sci. USA 108, 6399–6404 (2011).

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, 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

Corresponding Author:

Correspondence to Zhongkui Luo (

Fuente: nature.com

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