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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Data that support these findings are available via GitHub (https://github.com/FMS321/soilquality_climatechange_paper.git).
Codes for processing the data are available via GitHub (https://github.com/FMS321/soilquality_climatechange_paper.git).
Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050. The 2012 Revision (FAO, 2012).
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).
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).
Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486–489 (2014).
Fan, M. S. et al. Improving crop productivity and resource use efficiency to ensure food security and environmental quality in China. J. Exp. Bot. 63, 13–24 (2012).
Godfray, H. C. J. et al. Food security: the challenge of feeding 9 billion people. Science 327, 812–818 (2010).
Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).
Porter, J. R. et al. Food Security and Food Production Systems (Cambridge Univ. Press, 2014).
Ray, D. K. & Foley, J. A. Increasing global crop harvest frequency: recent trends and future directions. Environ. Res. Lett. 8, 044041 (2013).
Lal, R. Restoring soil quality to mitigate soil degradation. Sustainability 7, 5875–5895 (2015).
Wall, D. & Six, J. Give soils their due. Science 347, 695 (2015).
Ray, D. K. et al. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).
Battisti, D. S. & Naylor, R. L. Historical warnings of future food insecurity with unprecedented seasonal heat. Science 323, 240–244 (2009).
Nelson, G. C. et al. Climate Change: Impact on Agriculture and Costs of Adaptation (International Food Policy Research Institute, 2009).
Challinor, A. J., Koehler, A. K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).
Schlenker, W., Hanemann, M. & Fisher, A. Will US agriculture really benefit from global warming? Accounting for irrigation in the hedonic approach. Am. Econ. Rev. 95, 395–406 (2005).
Piao, S. L. et al. The impacts of climate change on water resources and agriculture in China. Nature 467, 43–51 (2010).
Ray, D. K. et al. Climate change has likely already affected global food production. PLoS ONE 14, e0217148 (2019).
Ramankutty, N. et al. The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Glob. Ecol. Biogeogr. 11, 377–392 (2002).
Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).
Lobell, D. B. & Burke, M. B. On the use of statistical models to predict crop yield responses to climate change. Agr. For. Meteorol. 150, 1443–1452 (2010).
Auffhammer, M. & Schlenker, W. Empirical studies on agricultural impacts and adaptation. Energy Econ. 46, 555–561 (2014).
Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872 (2016).
Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3, 827–832 (2013).
Basso, B. et al. Soil organic carbon and nitrogen feedbacks on crop yields under climate change. Agr. Environ. Lett. 3, 180026 (2018).
Mϋller, C. et al. Implication of climate mitigation for future agricultural production. Environ. Res. Lett. 10, 125004 (2015).
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H. O. et al.) (Cambridge Univ. Press, 2022).
Zhang, W. et al. Closing yield gaps in China by empowering smallholder farmers. Nature 537, 671–674 (2016).
Cui, Z. L. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363–368 (2018).
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 3632 (2018).
Müller, C. et al. Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).
Jamieson, P. D., Porter, J. R. & Wilson, D. R. A test of the computer simulation model ARC-WHEAT on wheat crops grown in New Zealand. Field Crops Res. 27, 337–350 (1991).
Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI–MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).
Xiong, W. et al. The Impacts of Climate Change on Chinese Agriculture—Phase II National Level Study Final Report (AEA Group, 2008).
Liu, B. et al. Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Clim. Change 6, 1130–1136 (2016).
Tao, F. et al. Global warming, rice production, and water use in China: developing a probabilistic assessment. Agr. For. Meteorol. 148, 94–110 (2008).
Xiong, W. et al. Different uncertainty distribution between high and low latitudes in modelling warming impacts on wheat. Nat. Food 1, 63–69 (2020).
Fernandez-Illescas, C. P., Porporato, A., Laio, F. & Rodriguez-Iturbe, I. The ecohydrological role of soil texture in a water-limited ecosystem. Water Resour. Res. 37, 2863–2872 (2001).
Wang, E. L. et al. Capacity of soils to buffer impact of climate variability and value of seasonal forecasts. Agr. For. Meteorol. 149, 38–50 (2009).
Vereecken, H. et al. Modeling soil processes: review, key challenges, and new perspectives. Vadose Zone J. 15, 1–57 (2016).
Myers, R. J. K. et al. in The Biological Management of Tropical Soil Fertility (eds Woomer, P.I. & Swift, M.J.) Ch. 4 (Wiley, 1994).
Smith, P. & Gregory, P. J. Climate change and sustainable food production. P. Nutr. Soc. 72, 21–28 (2013).
Khasawneh, F. E., Sample, E. C. & Kamprath, E. J. The Role of Phosphorus in Agriculture (American Society of Agronomy, 1980).
FAOSTAT (Statistics Division of the Food and Agriculture Organization of the United Nations, 2006); http://www.fao.org/faostat/en/#home
Fan, M. S. et al. Plant-based assessment of inherent soil productivity and contributions to China’s cereal crop yield increase since 1980. PLoS ONE 8, e74617 (2013).
Liu, X. & Chen, F. Farming System in China (China Agriculture Press, 2005).
Chen, X. P. in Fertilization Technology Highlights, (ed. Zhang, F. S) Ch. 6 (Chinese Agricultural Univ. Press, 2006).
Zhang, F. et al. Integrated nutrient management for food security and environmental quality in China. Adv. Agron. 116, 1–40 (2012).
Bünemann, E. K. et al. Soil quality—a critical review. Soil Biol. Biochem. 120, 105–125 (2018).
National Soil Survey Office. Chinese Soil (China Agriculture Press, 1998) .
Jiang, R. F. & Cui, J. Y. in Fertilization Technology Highlights, (ed. Zhang, F. S.) Ch. 5 (China Agricultural Univ. Press, 2006).
Cramer, W. P. & Solomon, A. M. Climatic classification and future global redistribution of agricultural land. Clim. Res. 3, 97–110 (1993).
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).
Friedman, J. H. Stochastic gradient boosting. Comput. Stat. Data 38, 367–378 (2002).
Buston, P. M. & Elith, J. Determinants of reproductive success in dominant pairs of clownfish: a boosted regression tree analysis. J. Anim. Ecol. 80, 528–538 (2011).
Friedman, J. H. & Meulman, J. J. Multiple additive regression trees with application in epidemiology. Stat. Med. 22, 1365–1381 (2003).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
Kuhn, M. & Johnson, K. Applied Predictive Modeling (Springer, 2013).
Yang, J. M., Yang, J. Y., Liu, S. & Hoogenboom, G. An evaluation of the statistical methods for testing the performance of crop models with observed data. Agric. Syst. 127, 81–89 (2014).
Loague, K. & Green, R. E. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contamin. Hydro. 7, 51–73 (1991).
Akinremi, O. O. et al. Evaluation of LEACHMN under Dryland conditions. I. Simulation of water and solute transport. Can. J. Soil Sci. 85, 223–232 (2005).
Palosuo, T. et al. Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur. J. Agron. 35, 103–114 (2011).
Deng, N. et al. Closing yield gaps for rice self-sufficiency in China. Nat. Commun. 10, 1725 (2019).
Correndo, A. A. et al. Assessing the uncertainty of maize yield without nitrogen fertilization. Field Crops Res. 260, 107985 (2021).
Rattalino Edreira, J. I. et al. Spatial frameworks for robust estimation of yield gaps. Nat. Food 2, 773–779 (2021).
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
IPCC Climate Change 2014: Climate Change: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).
Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction—the ISI-MIP approach. Earth Syst. Dynam. 4, 219–236 (2013).
Chen, H., Sun, J., Lin, W. & Xu, H. Comparison of CMIP6 and CMIP5 models in simulating climate extremes. Sci. Bull. 65, 1415–1418 (2020).
China Agriculture Yearbook (China Agriculture Press, 2005).
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).
The authors declare no competing interests.
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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.
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