Abstract
Agricultural development is facing the dual challenges of intensifying natural resource constraints and global warming, making the development of low-carbon agriculture imperative. We adopted a meta-analytic framework integrating multilevel meta-analysis, mixed-effects meta-regression, and random forest analysis to investigate the overall effect sizes of agronomic measures and environmental factors on the carbon footprint intensity (CFI) within the narrow-sense crop production boundary. Using 181 independent effect sizes, the overall evidence indicated an average CFI reduction of 14.11% across the examined factors. In the multi-moderator model, organic material return (OMR) was identified as a key moderator. In particular, returning plant-based organic inputs and applying biochar were associated with marked decreases in CFI, by about 22.60% and 49.50%, respectively. Conservation tillage (CT) showed a decreasing trend (23.40%), but this was not statistically significant overall. The synergistic combination of OMR and CT, two advantageous agronomic measures, significantly decreased CFI by 32.66%. Random forest analysis provided a clue interpretation regarding the regulation of the environment (R² = 0.0811). Temperature and precipitation exhibited regulatory potential, and soil pH also showed notable importance. Three-way interactions among soil pH/temperature, OMR, and CT were not significant, whereas precipitation showed a marginally significant three-way interaction with OMR and CT, suggesting that the effect of OMR on CFI depended on both precipitation amount and CT regime. This study not only assessed factors contributing to the carbon footprint of crop production but also identified interactive effects between advantageous agronomic measures and environmental factors, providing theoretical support for the precise management of green, low-carbon agriculture.
Data availability
Data will be made available on request.
References
Prasad, P. V. V., Bheemanahalli, R. & Jagadish, S. V. K. Field crops and the fear of heat stress—Opportunities, challenges and future directions. Field Crops Res. 200, 114–121. https://doi.org/10.1016/j.fcr.2016.09.024 (2017).
Intergovernmental Panel On Climate Change. Climate Change 2014: Mitigation of Climate Change: Working Group III Contribution to the IPCC Fifth Assessment Report 1st ed. (Cambridge University Press, 2015). https://doi.org/10.1017/CBO9781107415416.
Ming, A. et al. Key messages from the IPCC AR6 climate science report. (2021). https://doi.org/10.33774/coe-2021-fj53b
Pryor, S. W., Smithers, J., Lyne, P. & Van Antwerpen, R. Impact of agricultural practices on energy use and greenhouse gas emissions for South African sugarcane production. J. Clean. Prod. 141, 137–145. https://doi.org/10.1016/j.jclepro.2016.09.069 (2017).
Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992. https://doi.org/10.1126/science.aaq0216 (2018).
Ravani, M., Georgiou, K., Tselempi, S., Monokrousos, N. & Ntinas, G. Carbon footprint of greenhouse production in EU—How close are we to Green Deal goals?. Sustainability 16, 191. https://doi.org/10.3390/su16010191 (2023).
Chambers, J. Q. et al. Hurricane Katrina’s carbon footprint on U.S. Gulf Coast Forests. Science 318, 1107–1107. https://doi.org/10.1126/science.1148913 (2007).
Wiedmann, T. & Minx, J. A Definition of “Carbon Footprint,” In (ed. Pertsova, C. C.) (2008).
Gao, T., Liu, Q. & Wang, J. A comparative study of carbon footprint and assessment standards. Int. J. Low-Carbon Tech. 9, 237–243. https://doi.org/10.1093/ijlct/ctt041 (2014).
Ozlu, E., Arriaga, F. J., Bilen, S., Gozukara, G. & Babur, E. Carbon footprint management by agricultural practices. Biology 11, 1453. https://doi.org/10.3390/biology11101453 (2022).
Gan, Y., Liang, C., Wang, X. & McConkey, B. Lowering carbon footprint of durum wheat by diversifying cropping systems. Field Crops Res. 122, 199–206. https://doi.org/10.1016/j.fcr.2011.03.020 (2011).
Shao, G. et al. Carbon footprint of maize-wheat cropping system after 40-year fertilization. Sci. Total Environ. 926, 172082. https://doi.org/10.1016/j.scitotenv.2024.172082 (2024).
Zhang, M.-Y., Wang, F.-J., Chen, F., Malemela, M. P. & Zhang, H.-L. Comparison of three tillage systems in the wheat-maize system on carbon sequestration in the North China Plain. J. Clean. Prod. 54, 101–107. https://doi.org/10.1016/j.jclepro.2013.04.033 (2013).
Zhang, W. et al. An integrated straw-tillage management increases maize crop productivity, soil organic carbon, and net ecosystem carbon budget. Agric. Ecosyst. Environ. 340, 108175. https://doi.org/10.1016/j.agee.2022.108175 (2022).
Liu, C., Cutforth, H., Chai, Q. & Gan, Y. Farming tactics to reduce the carbon footprint of crop cultivation in semiarid areas. A review. Agron. Sustain. Dev. 36, 69. https://doi.org/10.1007/s13593-016-0404-8 (2016).
Han, X. et al. Combining slow-release fertilizer and plastic film mulching reduced the carbon footprint and enhanced maize yield on the Loess Plateau. J. Environ. Sci. 147, 359–369. https://doi.org/10.1016/j.jes.2023.12.001 (2025).
Wang, C., Zhao, J., Gao, Z., Feng, Y. & Chu, Q. Cleaner tillage and irrigation options for food-water-energy-carbon synergism in wheat−maize cropping systems. Environ. Res. 242, 117710. https://doi.org/10.1016/j.envres.2023.117710 (2024).
Ogle, S. M. et al. Climate and soil characteristics determine where no-till management can store carbon in soils and mitigate greenhouse gas emissions. Sci. Rep. 9, 11665. https://doi.org/10.1038/s41598-019-47861-7 (2019).
Fan, X. et al. Effects of substituting synthetic nitrogen with organic amendments on crop yield, net greenhouse gas emissions and carbon footprint: A global meta-analysis. Field Crops Res. 301, 109035. https://doi.org/10.1016/j.fcr.2023.109035 (2023).
He, L., Zhang, A., Wang, X., Li, J. & Hussain, Q. Effects of different tillage practices on the carbon footprint of wheat and maize production in the Loess Plateau of China. J. Clean. Prod. 234, 297–305. https://doi.org/10.1016/j.jclepro.2019.06.161 (2019).
Sun, W. et al. Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture. Glob. Change Biol. 26, 3325–3335. https://doi.org/10.1111/gcb.15001 (2020).
Liu, C., Lu, M., Cui, J., Li, B. & Fang, C. Effects of straw carbon input on carbon dynamics in agricultural soils: A meta-analysis. Glob. Change Biol. 20, 1366–1381. https://doi.org/10.1111/gcb.12517 (2014).
Liu, Q. et al. Carbon footprint of rice production under biochar amendment – A case study in a Chinese rice cropping system. GCB Bioenergy 8, 148–159. https://doi.org/10.1111/gcbb.12248 (2016).
Wallace, B. C. et al. Open<Emphasis Type=“ItalicSmallCaps”>MEE</Emphasis> : Intuitive, open-source software for meta‐analysis in ecology and evolutionary biology. Methods Ecol. Evol. 8, 941–947. https://doi.org/10.1111/2041-210X.12708 (2017).
Cauvy-Fraunié, S. & Dangles, O. A global synthesis of biodiversity responses to glacier retreat. Nat. Ecol. Evol. 3, 1675–1685. https://doi.org/10.1038/s41559-019-1042-8 (2019).
Nakagawa, S. & Santos, E. S. A. Methodological issues and advances in biological meta-analysis. Evol. Ecol. 26, 1253–1274. https://doi.org/10.1007/s10682-012-9555-5 (2012).
Burda, B. U., O’Connor, E. A., Webber, E. M., Redmond, N. & Perdue, L. A. Estimating data from figures with a web-based program: Considerations for a systematic review. Res. Synth. Methods 8, 258–262. https://doi.org/10.1002/jrsm.1232 (2017).
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156. https://doi.org/10.1890/0012-9658(1999)080%255B1150:TMAORR%255D2.0.CO;2 (1999).
Cheng, K. et al. Carbon footprint of China’s crop production—An estimation using agro-statistics data over 1993–2007. Agric. Ecosyst. Environ. 142, 231–237. https://doi.org/10.1016/j.agee.2011.05.012 (2011).
Schimel, D. Radiative Forcing of Climate Change, in: Climate Change 1995: The Science of Climate Change. Cambridge University Press, 65–131. (1996).
Viechtbauer, W. Conducting Meta-Analyses in. metafor Package J. Stat. Soft. 36. https://doi.org/10.18637/jss.v036.i03 (2010).
Calcagno, V. & Mazancourt, C. D. : An. Package Easy Automated Model. Selection (Generalized) Linear Models J. Stat. Soft. 34. https://doi.org/10.18637/jss.v034.i12 (2010). glmulti.
Civitello, D. J. et al. Biodiversity inhibits parasites: Broad evidence for the dilution effect. Proc. Natl. Acad. Sci. U. S. A. 112, 8667–8671. https://doi.org/10.1073/pnas.1506279112 (2015).
Crawford, K. M. et al. When and where plant-soil feedback may promote plant coexistence: A meta‐analysis. Ecol. Lett. 22, 1274–1284. https://doi.org/10.1111/ele.13278 (2019).
Deng, B. & Sun, W. Herbal medicine for hand–foot syndrome induced by fluoropyrimidines: A systematic review and meta-analysis. Phytother. Res. 32, 1211–1228. https://doi.org/10.1002/ptr.6068 (2018).
Nakagawa, S. et al. OrchaRd 2.0: An R package for visualising meta-analyses with orchard plots. Methods Ecol. Evol. 14, 2003–2010. https://doi.org/10.1111/2041-210X.14152 (2023).
Wickham, H. ggplot2: elegant graphics for data analysis, Second edition. ed, Use R! Springer, Switzerland. (2016).
Breiman, L. Random Forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).
Van Lissa, C. J. Small Sample Meta-Analyses. In Small Sample Size Solutions 186–202 (Routledge, 2020). https://doi.org/10.4324/9780429273872-16.
Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603. https://doi.org/10.1038/s41586-021-03306-8 (2021).
Van Lissa, C. J. metaforest: Exploring Heterogeneity in Meta-Analysis using Random Forests. (2017). https://doi.org/10.32614/CRAN.package.metaforest
Nakagawa, S., Noble, D. W. A., Senior, A. M. & Lagisz, M. Meta-evaluation of meta-analysis: Ten appraisal questions for biologists. BMC Biol. 15, 18. https://doi.org/10.1186/s12915-017-0357-7 (2017).
Al-Mansour, F. & Jejcic, V. A model calculation of the carbon footprint of agricultural products: The case of Slovenia. Energy 136, 7–15. https://doi.org/10.1016/j.energy.2016.10.099 (2017).
Singh, B. P., Setia, R., Wiesmeier, M. & Kunhikrishnan, A. Agricultural Management Practices and Soil Organic Carbon Storage. In Soil Carbon Storage 207–244 (Elsevier, 2018). https://doi.org/10.1016/B978-0-12-812766-7.00007-X.
Vogel, H.-J. et al. A systemic approach for modeling soil functions. SOIL 4, 83–92. https://doi.org/10.5194/soil-4-83-2018 (2018).
Bolinder, M. A. et al. The effect of crop residues, cover crops, manures and nitrogen fertilization on soil organic carbon changes in agroecosystems: A synthesis of reviews. Mitig. Adapt. Strateg. Glob. Change 25, 929–952. https://doi.org/10.1007/s11027-020-09916-3 (2020).
Singh Yadav, S. P. et al. Biochar application: A sustainable approach to improve soil health. J. Agric. Food Res. 11, 100498. https://doi.org/10.1016/j.jafr.2023.100498 (2023).
Usman, A. R. A. et al. Conocarpus biochar induces changes in soil nutrient availability and tomato growth under saline irrigation. Pedosphere 26, 27–38. https://doi.org/10.1016/S1002-0160(15)60019-4 (2016).
Muhammad, N., Aziz, R., Brookes, P. C. & Xu, J. Impact of wheat straw biochar on yield of rice and some properties of Psammaquent and Plinthudult. J. Soil Sci. Plant Nutr. 17, 808–823. https://doi.org/10.4067/S0718-95162017000300019 (2017).
Tomczyk, A., Sokołowska, Z. & Boguta, P. Biochar physicochemical properties: Pyrolysis temperature and feedstock kind effects. Rev. Environ. Sci. Biotechnol. 19, 191–215. https://doi.org/10.1007/s11157-020-09523-3 (2020).
Dang, Y. P., Page, K. L., Dalal, R. C. & Menzies, N. W. No-till Farming Systems for Sustainable Agriculture: An Overview In (eds Dang, Y. P. et al.) (2020).
Lal, R. Soil Organic Matter and Feeding the Future: Environmental and Agronomic Impacts 1st ed. (CRC Press, 2021). https://doi.org/10.1201/9781003102762.
Freitag, M., Friedrich, T. & Kassam, A. The carbon footprint of Conservation Agriculture. International Journal of Agricultural Sustainability 2331949. https://doi.org/10.1080/14735903.2024.2331949 (2024).
Gattinger, A. et al. Enhanced top soil carbon stocks under organic farming. Proc. Natl. Acad. Sci. U. S. A. 109, 18226–18231. https://doi.org/10.1073/pnas.1209429109 (2012).
Holka, M., Kowalska, J. & Jakubowska, M. Reducing carbon footprint of agriculture—Can organic farming help to mitigate climate change?. Agriculture 12, 1383. https://doi.org/10.3390/agriculture12091383 (2022).
Zhang, L. et al. Integrative effects of soil tillage and straw management on crop yields and greenhouse gas emissions in a rice–wheat cropping system. Eur. J. Agron. 63, 47–54. https://doi.org/10.1016/j.eja.2014.11.005 (2015).
Cui, Y. et al. Effects of no-till on upland crop yield and soil organic carbon: A global meta-analysis. Plant Soil 499, 363–377. https://doi.org/10.1007/s11104-022-05854-y (2024).
Husson, O. et al. Conservation agriculture systems alter the electrical characteristics (Eh, pH and EC) of four soil types in France. Soil Tillage Res. 176, 57–68. https://doi.org/10.1016/j.still.2017.11.005 (2018).
Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–582. https://doi.org/10.1038/nature08930 (2010).
Reiff, J. et al. Permaculture enhances carbon stocks, soil quality and biodiversity in Central Europe. Commun. Earth Environ. 5, 305. https://doi.org/10.1038/s43247-024-01405-8 (2024).
Jiang, T., Wang, X., Afzal, M. M., Sun, L. & Luo, Y. Vegetation productivity and precipitation use efficiency across the Yellow River Basin: Spatial patterns and controls. Remote Sens. 14, 5074. https://doi.org/10.3390/rs14205074 (2022).
Qin, J. et al. Global energy use and carbon emissions from irrigated agriculture. Nat. Commun. 15, 3084. https://doi.org/10.1038/s41467-024-47383-5 (2024).
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TQ: Methodology, Investigation, Visualization, Writing-Original Draft; ZY: Methodology, Software; ZH: Conceptualization, Writing – Review & Editing; ZC: Conceptualization, Methodology, Supervision, Writing – Review & Editing. All authors reviewed the manuscript.
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Tao, Q., Zhang, Y., Zhang, H. et al. Synergistic effects of advantageous agronomic measures and sensitive environmental factors on the carbon footprint of crop production based on a meta-analysis.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-45933-z
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DOI: https://doi.org/10.1038/s41598-026-45933-z
Keywords
- Carbon footprint
- Meta-analysis
- Agronomic measures
- Environmental factor
Source: Ecology - nature.com
