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Global crop production increase by soil organic carbon

Nature Geoscience volume 16, pages 1159–1165 (2023)Cite this article

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Abstract

Soil organic carbon sequestration has been promoted to combat climate change while improving soil fertility. However, its quantitative contribution to crop productivity has proven elusive. Using data from 13,662 controlled field trials with 66,593 treatments across a broad range of soils, climates and management practices, we here show that yields increase with increased soil organic carbon, until no further increase (p < 0.05) occurs above mean optimum soil organic carbon of 43.2–43.9 g kg−1 for maize, 12.7–13.4 g kg−1 for wheat and 31.2–32.4 g kg−1 for rice. Sequestering soil organic carbon is one-fifth as effective (that is, 80% less) as nitrogen fertilization for improving crop yield where soil management is optimized. By increasing soil organic carbon beyond current technology to optimum levels, global production of the three most important staple crops increases by 4.3% (sufficient to provide calories for 640 million people). However, currently available management practices would increase crop production by only 0.7% once other production constraints have already been addressed. Therefore, yield improvements under currently available technologies are unlikely to drive adoption of soil organic carbon sequestration globally, except in hot-spot regions where crop production benefits most, or unless novel practices that allow greater soil organic carbon sequestration beyond current limitations can further increase yields cost-effectively.

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Fig. 1: Causal effect of SOC on crop yield and SOC yield gap.
Fig. 2: Drivers of crop yield.
Fig. 3: Drivers of SOC yield gap.
Fig. 4: Global potential to increase yields by increasing SOC.

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Data availability

Data that support these findings are available via figshare (https://doi.org/10.6084/m9.figshare.24137280).

Code availability

Codes for processing the data are available via figshare (https://doi.org/10.6084/m9.figshare.24137280).

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Acknowledgements

We thank the National Key Research and Development Program of China (2017YFD0200108) and the National Natural Science Foundation of China (31972520) for providing financial support to Y.M., M.F. and L.Q. We thank the Cornell Institute for Digital Agriculture (CIDA RIF2019) for supporting D.W. and AI-CLIMATE (NIFA-2023-67021-39829) for supporting J.L. on this project. We thank the China Scholarship Council for providing funds to Y.M. to pursue his study at Cornell University. We thank L. M. Johnson from the Cornell Statistical Consulting Unit for supporting the data analysis work. We thank all agricultural scientists and extension personnel who provided local technical assistance and management during the project.

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Author notes

  1. These authors contributed equally: Yuqing Ma, Dominic Woolf.

Authors and Affiliations

  1. State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China

    Yuqing Ma & Mingsheng Fan

  2. Soil and Crop Sciences, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA

    Yuqing Ma, Dominic Woolf & Johannes Lehmann

  3. Cornell Institute for Digital Agriculture (CIDA), Cornell University, Ithaca, NY, USA

    Dominic Woolf & Johannes Lehmann

  4. Cornell Atkinson Center for Sustainability, Cornell University, Ithaca, NY, USA

    Dominic Woolf & Johannes Lehmann

  5. Key Laboratory of Arable Land Quality Monitoring and Evaluation, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China

    Lei Qiao

  6. Cultivated Land Quality Monitoring and Protection Center, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Beijing, China

    Rong Li

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Contributions

Y.M., D.W., M.F. and J.L. designed the research; Y.M., D.W., M.F., L.Q., R.L. and J.L. collected field and other research data; Y.M., D.W., M.F. and J.L. contributed to methodology and data analysis; Y.M. and D.W. contributed to visualization; D.W., M.F. and J.L. supervised the work; Y.M. and D.W. wrote the first manuscript; Y.M., D.W., M.F. and J.L. edited the manuscript; all authors read and approved the final manuscript.

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Correspondence to Mingsheng Fan or Johannes Lehmann.

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Nature Geoscience thanks Marcel van der Heijden and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Distribution of field trials for maize, wheat and rice.

a–c, Maize (a), wheat (b) and rice (c). The round symbols on the three top panels represent the locations of field trials. The bottom panel indicates the eight IPCC climate zones (large fractions of climate zones where the three studied crops distribute are covered by field trials). Numbers in brackets under the climate name indicate the number of field trials for each climate zone of each crop. Numbers in brackets under the crop name indicate the total number of field trials for each crop. Basemap reproduced from Resource and Environment Science and Data Center, Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences.

Extended Data Fig. 2 Distribution of climate-and-soil specific optimum SOC for maize, wheat and rice.

a–c, Maize (a), wheat (b) and rice (c). The optimum SOC is the value above which no additional increase in crop yield can be achieved by increasing SOC. Basemap reproduced from ArcGIS/Esri.

Extended Data Fig. 3 Causal effect of soil organic carbon (SOC) on crop yield under different nitrogen input rates.

a, three crops combined. b, maize. c, wheat, d, rice. The solid lines represent mean partial dependence of crop yield on SOC, and the ribbons represent 95% confidence intervals. N0, N0.5, N1.0 and N1.5 indicate 0, 50%, 100% and 150% of optimum nitrogen input rates, respectively, with optimum phosphorus and potassium input rates. Control means no fertilizer inputs. For three crops combined, predicted yield was normalized as the ratio of on-farm measured yield in each trial to predicted minimum SOC yield gap for each crop under the same treatment. Predicted maximum SOC yield gap is the difference between predicted yield under optimum SOC and predicted minimum yield.

Extended Data Fig. 4 Causal effect of soil organic carbon (SOC) on crop yield across climate zones for maize, wheat and rice.

a-d, cool dry (a), cool moist (b), warm dry (c), and warm moist (d). The solid lines represent the mean partial dependence of normalized crop yield on SOC, and the ribbons represent 95% confidence intervals. Optimum SOC is the SOC level beyond which no significant (p < 0.05) yield increases were observed. The significance of the yield increase was estimated by two-sided t-tests. Normalized predicted yield was calculated as the ratio of predicted yield from partial dependence to predicted minimum mean yield for each crop under each climate zone. Normalized predicted maximum SOC yield gap is the difference between normalized predicted yield under optimum SOC and normalized predicted minimum yield.

Extended Data Fig. 5 Distribution of yield increases derived from soil organic carbon (SOC) for three studied crops.

Regions on the maps are derived from Fig. 4 and classified into quartiles. a, c, e, technical potential represents yield gains when SOC is increased to current technical potential SOC levels for maize (a), wheat (c), and rice (e). Current technical potential SOC levels can be achieved using a combination of high organic matter inputs and zero tillage; b, d, f, unconstrained represents yield gains when SOC increased to optimum levels for (b), wheat (d), and rice (f). Basemap reproduced from ArcGIS/Esri.

Extended Data Fig. 6 Relative importance (%) of variables for the global extrapolation of yield models and its predicted standard errors for each model.

a,b,c, bar charts indicate relative importance (permutation method; see Methods) of variables as predictors of random forest extrapolation yield models. d,e,f, line charts represent the predictions of standard errors for maize (d), wheat (e), and rice (f) estimated by the jackknife method (See methods; Uncertainty analysis of global extrapolations). NPK fertilization = nitrogen, phosphorus and potassium fertilizer rate; PET = potential evapotranspiration; PRE, Tmax and Tmin = precipitation, maximum mean temperature and minimum mean temperature; AWC = available water capacity.

Extended Data Fig. 7 Correspondence of distributions of climate zones where the majority of field trials are located and producing regions of the three studied crops.

a, maize, b, wheat, c, rice. Climate zones were divided based on IPCC criteria43. Ntreat is the number of treatments rather than the number of field trials in each climate zones. The distributions of maize, wheat and rice producing regions were obtained from EarthStat44. The shaded regions are the global producing regions for the three studied crops. Climate zones where only few field trials distribute are excluded from the map including maize tropical dry zone (Ntreat = 10), rice tropical dry zone (Ntreat = 15) and rice cool temperate dry zone (Ntreat = 15). Basemap reproduced from ArcGIS/Esri.

Extended Data Fig. 8 Global yield increases of maize, wheat and rice by increasing soil organic carbon (SOC) in each country.

Yield increases of the three crops by increasing SOC in each country was normalized to total yield increases of the three crops from SOC relative to annual current crop production of the three crops (a, b), to the yield increases of the three crops from SOC per unit harvest area (c, d), and to the total yield increases of the three crops from SOC relative to annual crop production of the three crops at best management (e, f). Total yield increase relative to annual current crop production was calculated by using yield increases of the three crops by increasing SOC in each country under technical potential and unconstrained increase in SOC divided by its current annual crop production of the three crops in each country. Yield increases of the three crops per unit harvested area was calculated by using the yield increase of the three crops by increasing SOC in each country under the technical potential and unconstrained SOC increase divided by the total harvested area of the three crops in each country. Total yield increases relative to annual crop production at best management was calculated by using yield increases of the three crops by increasing SOC in each country under technical potential and unconstrained increase in SOC divided by annual crop production of the three crops at best management in each country.

Extended Data Fig. 9 Depth of food deficit, prevalence of undernourishment and the proportions of food deficit addressed by improving SOC under the technical potential or unconstrained SOC increases.

Depth of food deficit (a) is the average calorie deficiency per capita per day below the minimum dietary daily energy requirement in each country. Prevalence of undernourishment (b) is the ratio of total undernourished population to the total population in each country that experiences national food deficits. Proportions of food deficit addressed were estimated by using the kilocalories provided by a daily increase of maize, wheat and rice per undernourished person resulting from a SOC increase under either the technical potential (c,e) or unconstrained SOC increases (d,f) for each country divided by the daily food deficit in kilocalories per undernourished person (Supplementary Table 6). The countries (c-f) were grouped based on the Global Food Security Index50 and classified as ‘weak and moderate food security environment’ or ‘good and very good food security environment’ (Supplementary Table 6). The technical potential represented yield gains when SOC increased to levels that are achieved using a combination of high organic matter inputs and zero tillage; an unconstrained increase in SOC represented yield gains when SOC increased to optimum levels.

Supplementary information

Supplementary Information

Supplementary Text 1–4, Figs. 1 and 2 and Tables 1–6.

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Ma, Y., Woolf, D., Fan, M. et al. Global crop production increase by soil organic carbon. Nat. Geosci. 16, 1159–1165 (2023). https://doi.org/10.1038/s41561-023-01302-3

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