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Soil quality both increases crop production and improves resilience to climate change

Abstract

Interactions between soil quality and climate change may influence the capacity of croplands to produce sufficient food. Here, we address this issue by using a new dataset of soil, climate and associated yield observations for 12,115 site-years representing 90% of total cereal production in China. Across crops and environmental conditions, we show that high-quality soils reduced the sensitivity of crop yield to climate variability leading to both higher mean crop yield (10.3 ± 6.7%) and higher yield stability (decreasing variability by 15.6 ± 14.4%). High-quality soils improve the outcome for yields under climate change by 1.7% (0.5–4.0%), compared to low-quality soils. Climate-driven yield change could result in reductions of national cereal production of 11.4 Mt annually under representative concentration pathway RCP 8.5 by 2080–2099. While this production reduction was exacerbated by 14% due to soil degradation, it can be reduced by 21% through soil improvement. This study emphasizes the vital role of soil quality in agriculture under climate change.

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Fig. 1: Geographical distribution of on-farm trials.
Fig. 2: Projected yield change in high- and low-quality soils in future climate change.
Fig. 3: Climate change-driven change in cereal production.
Fig. 4: Schematic representation of the pattern of soil quality moderating the yield resilience to climate variability and change.

Data availability

Data that support these findings are available via GitHub (https://github.com/FMS321/soilquality_climatechange_paper.git).

Code availability

Codes for processing the data are available via GitHub (https://github.com/FMS321/soilquality_climatechange_paper.git).

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Acknowledgements

We thank J. Pan in Chinese Academy of Agricultural Sciences for her help in projecting future climate by using the global gridded climate data of 0.5° × 0.5° horizontal resolution of five ESMs. We thank J. Yang, M. He and P. Hou for their help in categorizing types of crop varieties. We also thank Sustainable Agriculture Innovation Network for organizing a workshop on soil quality, climate change and food security and discussing an early version of the manuscript. This work was financially supported by the National Key Research and Development Programme of China (2017YFD0200108) and the National Natural Science Foundation of China (31972520) for M.F., L.Q., H.C., Y.M., H.Y., Y.H. and W.L. The input of P.S. contributes to the Newton Fund/UKRI-funded project N-Circle (BB/N013484/1). The input of B.E. was supported by the Newton Fund/UKRI-funded project CINAg project (BB/N013468/1).

Author information

Authors and Affiliations

Authors

Contributions

M.F. designed the research. M.F., L.Q., J.F., R.L., H.C., S.L., F.Z., Y.M., Y.H., R.J., H.Y. and W.L. collected data. M.F., L.Q., X.W., P.S., H.C., Y.W. and Y.M. contributed to data analysis. M.F. and L.Q. wrote the manuscript with edits from X.W., P.S., Y.L., B.E., S.D., T.B., S.P. and C.M. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mingsheng Fan.

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The authors declare no competing interests.

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Nature Climate Change thanks David Makowski and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Yield under best management practices (YieldBMPs).

a–c, wheat, d-f, maize, and g-i rice. The filled orange corresponds to a bar for each individual YieldBMPs site-year (ranked from high to low, Numbers shown on each panel). The blue dashed lines indicate mean YieldBMPs. YieldBMPs (Mg/ha) are shown as mean (±SD, standard deviation); Coefficient of variation (CV, %) calculated by dividing mean yield by SD; N refers to the number of site-years of on-farm trials in major cropping systems in China. W-NCP, winter wheat in North China Plain; W-YZB, winter wheat in Yangtze River Basin; W-NWC, winter wheat in Northwest China; M-NEC, rainfed maize in Northeast China; M-NCP, maize in North China Plain; M-SWC, rainfed maize in Southwest China; SR-YZB, single rice in Yangtze River Basin; ER-SC, early rice in South China; LR-SC, late rice in South China.

Extended Data Fig. 2 The relative contribution (%) of explaining variables to yields under best management practices.

Assessment was conducted by GBRT models based on the primary data comprising all on-farm trials in major cropping systems in China. Orange, green and blue bars indicate climate, soil and management variables respectively. Tmax and Tmin, maximum and minimum temperature; PRE, precipitation; GDD, growing degree days; RAD, solar radiation; SOM, soil organic matter; Olsen-P and Avail-K, soil available phosphorus and potassium. W-NCP, winter wheat in North China Plain; W-YZB, winter wheat in Yangtze River Basin; W-NWC, winter wheat in Northwest China; M-NEC, rainfed maize in Northeast China; M-NCP, maize in North China Plain; M-SWC, rainfed maize in Southwest China; SR-YZB, single rice in Yangtze River Basin; ER-SC, early rice in South China; LR-SC, late rice in South China.

Extended Data Fig. 3 Geographical distribution of paired on-farm trials under high- and low-quality soils.

Symbols of blue dot and red triangle indicate on-farm trials conducted in high- and low-quality soils in major cropping systems in China, respectively. Paired on-farm trials were conducted in high-(N = 1665) and low - (N = 1676) quality soils. W-NCP, winter wheat in North China Plain; W-YZB, winter wheat in Yangtze River Basin; W-NWC, winter wheat in Northwest China; M-NEC, rainfed maize in Northeast China; M-NCP, maize in North China Plain; M-SWC, rainfed maize in Southwest China; SR-YZB, single rice in Yangtze River Basin; ER-SC, early rice in South China; LR-SC, late rice in South China.

Extended Data Fig. 4 Comparison of climate variables between locations of paired on-farm trials conducted in high- and low- quality soils.

a–d, average maximum temperature (a), average minimum temperature (b), cumulative precipitation (c) and cumulative radiation (d) between locations, where paired on-farm trials were conducted in high-(N = 1665) and low - (N = 1676) quality soils. W-NCP, winter wheat in North China Plain; W-YZB, winter wheat in Yangtze River Basin; W-NWC, winter wheat in Northwest China; M-NEC, rainfed maize in Northeast China; M-NCP, maize in North China Plain; M-SWC, rainfed maize in Southwest China; SR-YZB, single rice in Yangtze River Basin; ER-SC, early rice in South China; LR-SC, late rice in South China. *** refers to significance at p = 0.001.

Extended Data Fig. 5 Projected yield change in future climate change.

a,b, projected yield changes under RCP 2.6 (a) and RCP 8.5 (b) pathways up to 2040–2059; c,d, projected yield changes under RCP 2.6 (c) and RCP 8.5 (d) pathways up to 2080–2099. Projections were based on Gradient Boosted Regression Tree (GBRT) model trained on the primary dataset comprising all on-farm trials for major cropping systems in China. Boxes represent variability across each cropping system over the 2040–2099 periods. Solid lines and diamonds in this figure indicate median and mean yields, respectively; the boundary of the box indicates the 25th and 75th percentile; whisker caps denote the 90th and 10th percentiles. W-NCP, winter wheat in North China Plain; W-YZB, winter wheat in Yangtze River Basin; W-NWC, winter wheat in Northwest China; M-NEC, rainfed maize in Northeast China; M-NCP, maize in North China Plain; M-SWC, rainfed maize in southwest China; SR-YZB, single rice in Yangtze River Basin; ER-SC, early rice in South China; LR-SC, later rice in South China.

Extended Data Fig. 6 Projected yield changes and their difference between high- and low-quality soils.

a–d, projected yield changes and their difference between high- and low-quality soils under climate change in RCP 2.6 (a) and RCP 8.5 (b) up to 2040–2059, and RCP 2.6 (c) and RCP 8.5 (d) up to 2080–2099. Projected yield changes were estimated based on the primary dataset comprising all on-farm trials, differences of yield changes were estimated based on sub-datasets comprising on-farm trials with paired high- and low-quality soils in major cropping systems in China. Horizontal and vertical error bars (standard deviation, SD) represent interannual variations of yield changes and difference of yield change between high- and low-quality soils, respectively. Solid lines represent significant difference in yield change between high- and low-quality soil at p = 0.10, while dashed lines represent no significant difference. W-NCP, winter wheat in North China Plain; W-YZB, winter wheat in Yangtze River Basin; W-NWC, winter wheat in Northwest China; M-NEC, rainfed maize in Northeast China; M-NCP, maize in North China Plain; M-SWC, rainfed maize in Southwest China; SR-YZB, single rice in Yangtze River Basin; ER-SC, early rice in South China; LR-SC, late rice in South China.

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Qiao, L., Wang, X., Smith, P. et al. Soil quality both increases crop production and improves resilience to climate change. Nat. Clim. Chang. 12, 574–580 (2022). https://doi.org/10.1038/s41558-022-01376-8

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