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Conventional agriculture increases global warming while decreasing system sustainability

Nature Climate Change volume 15, pages 110–117 (2025)Cite this article

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Abstract

Intensification of farming since the Green Revolution has led to large increases in yield but has also increased anthropogenic greenhouse gas emissions. Here, by providing a global comprehensive cradle-to-gate quantification from seed to yield, we show that the global warming potential (GWP) of conventional agriculture of grain crops has increased eightfold from 1961 to 2020, whereas the sustainability index (SI) has decreased threefold. Tillage, synthetic fertilizers and irrigation together accounted for 90% of the increased GWP, linked to tenfold increases in fertilization and groundwater use and more than doubled mechanized and irrigated areas. We highlight regions with high GWP and low SI, such as South Asia, and project further threefold increases in agriculture GWP by 2100 compared with 2020 (3.3 ± 0.73 PgCO2e) driven by declined use efficiency of the inputs. Green energy and climate-smart agriculture techniques can reduce the projected GWP in 2100 to 2.3 PgCO2e and increase the SI fourfold.

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Fig. 1: LCA of the global warming of conventional agriculture.
Fig. 2: GWP of the conventional agriculture during 1961–2020.
Fig. 3: Variations in the global warming potential and system sustainability at country level in 2020.
Fig. 4: Global and regional warming of the conventional agriculture and its system sustainability.
Fig. 5: Future global warming of the conventional agriculture and its SI until 2100.

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Data availability

All data generated or analysed during this study are included in this article and its Supplementary Information and Supplementary Data. The data for fertilizer use in agriculture and agronomic efficiency were retrieved from refs. 4,5,113 and using the databases of FAO6 and ref. 7. The data for seed consumption and harvested area were extracted from FAO databases1,55. The application rate of pesticides was adopted from FAO79. Data for the equipped area for irrigation and the fraction of the water source are available in FAO-AQUASTAT92. Supplementary datasets are also available via Figshare at https://doi.org/10.6084/m9.figshare.25997251(ref. 114). Source data are provided with this paper.

Code availability

Data collection and processing were performed in Microsoft Excel. Code developed for data processing in R in this study is available via Figshare at https://doi.org/10.6084/m9.figshare.25997251 (ref. 114).

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Acknowledgements

This study was supported by the Key-Area Research and Development Program of Guangdong Province (2021B0202030002) to J.Z., the Guangdong Laboratory for Lingnan Modern Agriculture (NT2021010) to J.Z., the Science and Technology Planning Project of Guangdong Province of China (grant no. 2019B030301007) to J.Z. and the RUDN University Strategic Academic Leadership Program to Y.K.

Author information

Authors and Affiliations

  1. Department of Ecology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou, China

    Ahmed I. Abdo, Daolin Sun, Zhaoji Shi & Jiaen Zhang

  2. Guangdong Provincial Key Laboratory of Utilization and Conservation of Food and Medicinal Resources in Northern Region, College of Biology and Agriculture, Shaoguan University, Shaoguan, China

    Ahmed I. Abdo

  3. Soil Science Department, Faculty of Agriculture, Zagazig University, Zagazig, Egypt

    Ahmed I. Abdo & Mohamed K. Abdel-Fattah

  4. Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong Provincial Key Laboratory of Eco-circular Agriculture, South China Agricultural University, Guangzhou, China

    Daolin Sun, Zhaoji Shi & Jiaen Zhang

  5. Guangdong Engineering Technology Research Centre of Modern Eco-agriculture and Circular Agriculture, Guangzhou, China

    Daolin Sun, Zhaoji Shi & Jiaen Zhang

  6. Key Laboratory of Agro-Environment in the Tropics, Ministry of Agriculture and Rural Affairs, South China Agricultural University, Guangzhou, China

    Jiaen Zhang

  7. Peoples Friendship University of Russia (RUDN University), Moscow, Russia

    Yakov Kuzyakov

  8. Department of Soil Science of Temperate Ecosystems, Department of Agricultural Soil Science, University of Goettingen, Göttingen, Germany

    Yakov Kuzyakov

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  1. Ahmed I. Abdo
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Contributions

A.I.A. designed and coordinated the study, conducted data analysis and synthesis and wrote the paper. D.S. and Z.S. collected and arranged the data. M.K.A.-F. collected the data, carried out the case study and wrote the paper. J.Z. designed and coordinated the study, conducted the data analysis and revised the paper. Y.K. conducted the data analysis and revised the paper. All the co-authors reviewed and commented on the paper.

Corresponding author

Correspondence to Jiaen Zhang.

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The authors declare no competing interests.

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Nature Climate Change thanks Berien Elbersen, Stefano Menegat 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 The main explanatory variables affecting the regional GWP and SI of conventional agriculture.

a, Fertilizer application rate (kg ha−1). b, Grain yield (Mg ha−1). c, Fertilizer agronomic efficiency (kg kg−1). d, Seeding rate (kg ha−1). e, Pesticide rate (kg ha−1). f, Machinery value. g, Energy emission factor (kgCO2 GJ−1). h, Fraction of the equipped area for irrigation. i, Fraction of the groundwater in irrigation. CIS is the Commonwealth of Independent States.

Source data

Extended Data Fig. 2 The forecasted future changes in the population by FAO (a) and the interpreted grain production (b) during 1961–2020.

The values on the columns are the global values as sum of regional values. The future grain production was interpreted using the two-sided linear regression model of future population based on the current grain demand in 2020.

Source data

Extended Data Fig. 3 The global warming of all inputs in the conventional agriculture during 2020.

a, b, and c, the GWP values of seeds, fertilizers, and pesticides, respectively, including production, packaging, transportation, and application to the field. d, the GWP of tillage including emissions of machines and soil. e, the GWP of irrigation including emissions of installations and water lifting, pumping and degassing. f, the GWP of harvesting machines (that is combine, baler, and tub grinder).

Source data

Extended Data Fig. 4 The system sustainability index (SI) at country level for the years 1961, 1980 and 2000.

a, 1961. b, 1980. c, 2000. The SI is unitless and was calculated based on the biomass C and emitted C during biomass production.

Source data

Supplementary information

Supplementary Information

Supplementary Methods 1–4, Tables 1–14, Figs. 1–3 and References.

Reporting Summary

Supplementary Data 1–21

The whole inputs and outputs data.

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Abdo, A.I., Sun, D., Shi, Z. et al. Conventional agriculture increases global warming while decreasing system sustainability. Nat. Clim. Chang. 15, 110–117 (2025). https://doi.org/10.1038/s41558-024-02170-4

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