农田土壤固碳与温室气体排放计量研究进展

doi:10.3864/j.issn.0578-1752.2025.11.009

Abstract

Abstract:

As significant sources and sinks of greenhouse gases in terrestrial ecosystems, cropland can effectively mitigate climate change through scientific management practices. Developing methods for quantifying soil carbon sequestration and greenhouse gas emissions is crucial for accurately measuring the contribution of agriculture to carbon neutrality strategies. These methods are also essential for greenhouse gas inventory preparation, evaluating carbon sequestration and emission reduction effects, voluntary emission reduction trading, and carbon-related certification. This paper systematically reviewed the methods for quantifying soil carbon sequestration and greenhouse gas emissions in cropland, comparing the differences among various methods in terms of data requirements, acquisition costs, estimation accuracy, and the challenges faced. It focused on the applicability of these methods in different scenarios and their optimization pathways. The methods discussed for soil carbon sequestration and greenhouse gas emissions included the Two-Period Difference Method (for soil carbon sequestration only), Data Integration Method (for soil carbon sequestration only), Parameter Method/Emission Factor Method, Empirical Models, and Process Models. The Two-Period Difference Method and Data Integration Method estimate changed in soil organic carbon using empirical data, but their accuracy was directly affected by the matching of datasets and the amount of data. The Parameter Method/Emission Factor Method estimated soil carbon sequestration and greenhouse gas emissions with minimal data requirements, but its accuracy depended on the localization and refinement of parameters/emission factors. Due to these limitations, the Parameter Method/Emission Factor Method was more suitable for low-resolution estimates at regional scales and could not accurately capture high-resolution spatial variations. Empirical Models and Process Models considered the effects of meteorological, soil, and management factors on soil carbon sequestration and greenhouse gas emissions, making them suitable for high-resolution spatial simulations and field-scale measurements. The performance of empirical models depended on the quantity, quality, and accuracy of variable selection in the modeling database. In contrast, Process-based Models could capture the spatiotemporal dynamics of soil organic carbon and greenhouse gases under complex conditions, but required local parameter optimization for accurate performance. Given the challenges of different methods, this paper highlighted the importance of balancing data requirements, costs, and estimation accuracy in specific-scenarios. It noteed that model simulation methods would become the main trend in future agricultural carbon quantification. Addressing how to balance data requirements, acquisition costs, and estimation accuracy for different scenarios was a significant challenge. Future research should focus on building comprehensive field monitoring networks, promoting standardized database construction and sharing, deepening the localization and refinement of emission factors, and exploring innovative applications of machine learning in multi-algorithm integration and process model parameter adjustment.

Key words: soil carbon sequestration, greenhouse gas, cropland, emission factor, model simulation, carbon neutrality

SUN JianFei, CHENG Kun. A Review of Soil Carbon Sequestration and Greenhouse Gas Emissions Quantification in Cropland[J].Scientia Agricultura Sinica, 2025, 58(11): 2190-2205.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2025.11.009

https://www.chinaagrisci.com/EN/Y2025/V58/I11/2190

Figures/Tables 6

Table 1

Table 2

Table 3

Table 4

Table 5

Summary of empirical models for greenhouse gas emissions in Chinese croplands"

温室气体 GHG	建模数据量 Data volume	方法 Method	R²	考虑变量 Considered variable	估算结果 Estimation	参考文献 Reference
稻田CH₄ CH₄ from paddy field	53个地点 53 sites	多元回归 Multiple regression	0.687 (模型Model)	SOC含量、土壤pH、季前和当季水分管理方式、气候区、有机物料类型和投入量 SOC content, soil pH, water regime, climate zone, organic material type and input rate	/	[54]
稻田CH₄ CH₄ from paddy field	122个地点1089组数据 1089 observations from 122 sites	多元回归 Multiple regression	0.50 (模型Model)	SOC含量、土壤pH、季前和当季水分管理方式、气候区、有机物料类型和投入量 SOC content, soil pH, water regime, climate zone, organic material type and input rate	/	[38]
稻田CH₄ CH₄ from paddy field	67个地点495个数据 495 observations from 67 sites	多元回归 Multiple regression	0.35-0.72 (模型Model)	纬度、当季水分管理方式、有机肥施用量、化学氮肥施用量、土壤pH、土壤黏粒含量、土壤砂粒含量、土壤C/N、温度（生育期） Latitude, water regime, organic fertilizer application rate, mineral nitrogen fertilizer application rate, soil pH, soil clay content, soil sand content, soil C/N ratio, temperature (growth period)		[50]
稻田CH₄ CH₄ from paddy field	112篇文献835组数据 835 observations from 112 articles	多元回归 Multiple regression	0.29-0.76 (模型Model) 0.15-0.70 (验证Validation)	纬度、季前和当季水分管理方式、有机物料投入量、土壤pH、土壤容重 Latitude, water regime, organic material input rate, soil pH, soil bulk density	4.75 Tg CH₄	[48]
稻田CH₄ CH₄ from paddy field	1110组数据 1110 observations	随机森林 Random forest	0.64 (验证Validation)	有机物料类型及投入量；季前及当季水分管理方式；生育期的温度、降雨量及空气相对湿度；SOC含量；土壤黏粒含量；化学氮肥投入量；大气CO₂浓度 Types and input of organic materials; water regime; temperature, rainfall and relative air humidity during the growth period; SOC content; clay content; mineral nitrogen fertilizer dosage; atmospheric CO₂ concentration	6.12 Tg CH₄	[53]
农田N₂O N₂O from cropland	206组数据 206 observations	多元回归 Multiple regression	0.58 (模型Model)	年降雨量、氮肥用量 Annual rainfall and nitrogen fertilizer dosage	167 Gg N₂O-N	[56,57]
农田N₂O N₂O from cropland	104个地点853组数据 853 observations from 104 sites	多元回归 Multiple regression	0.48 (验证Validation)	氮肥类型和用量、作物类型、温度、土壤黏粒含量 Nitrogen fertilizer type and dosage, crop type, temperature, soil clay content	31 Gg N₂O-N （稻田Paddy field）	[62]
稻田N₂O N₂O from paddy field	221组数据 221 observations	多元回归 Multiple regression	0.607 (模型Model)	氮肥投入类型及投入量、水稻类型、当季水分管理方式、气候区、土壤pH Types and amounts of nitrogen fertilizer input, rice types, water regime, climate zones, soil pH	22.48 Gg N₂O-N （稻田Paddy field）	[58,59]
稻田N₂O N₂O from paddy field	578组数据 578 observations	多元回归 Multiple regression	0.42 (模型Model) 0.30 (验证Validation)	化学氮肥投入量、SOC含量、水分管理方式、有机物料类型和投入量、经度、纬度、大气CO₂浓度 Mineral nitrogen fertilizer input, SOC content, water regime, organic material type and input, longitude, latitude, atmospheric CO₂ concentration	/	[23]
稻田N₂O N₂O from paddy field	578组数据 578 observations	随机森林 Random forest	0.59 (验证Validation)	化学氮肥类型及投入量；有机物料类型及投入量；季前及当季水分管理方式；SOC含量；土壤黏粒含量；生育期温度、降雨、和相对湿度；大气CO₂浓度 Mineral nitrogen fertilizer type and input, organic material type and input, water regime, SOC content, soil clay content, temperature, rainfall and relative air humidity during the growth period, atmospheric CO₂ concentration	23.21 Gg N₂O-N （稻田Paddy field）	[53]

Table 5

Table 6

References 3

[1]	IPCC. Climate Change 2023:Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland, 2023.
[2]	MINASNY B, MALONE B P, MCBRATNEY A B, ANGERS D A, ARROUAYS D, CHAMBERS A, CHAPLOT V, CHEN Z S, CHENG K, DAS B S, FIELD D J, GIMONA A, HEDLEY C B, HONG S Y, MANDAL B, MARCHANT B P, MARTIN M, MCCONKEY B G, MULDER V L, O’ROURKE S, RICHER-DE-FORGES A C, ODEH I, PADARIAN J, PAUSTIAN K, PAN G X, POGGIO L, SAVIN I, STOLBOVOY V, STOCKMANN U, SULAEMAN Y, TSUI C C, VÅGEN T G, VAN WESEMAEL B, WINOWIECKI L. Soil carbon 4 per mille. Geoderma, 2017, 292: 59-86.
[52]	DORMANN C F, ELITH J, BACHER S, BUCHMANN C, CARL G, CARRÉ G, MARQUÉZ J R G, GRUBER B, LAFOURCADE B, LEITÃO P J, MÜNKEMÜLLER T, MCCLEAN C, OSBORNE P E, REINEKING B, SCHRÖDER B, SKIDMORE A K, ZURELL D, LAUTENBACH S. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 2013, 36(1): 27-46.
[53]	SUN J F, CHEN L B, OGLE S, CHENG K, XU X R, LI Y P, PAN G X. Future climate change may pose pressures on greenhouse gas emission reduction in China’s rice production. Geoderma, 2023, 440: 116732.
[54]	YAN X Y, CAI Z C, OHARA T, AKIMOTO H. Methane emission from rice fields in mainland China: Amount and seasonal and spatial distribution. Journal of Geophysical Research: Atmospheres, 2003, 108(D16): 4505.
[55]	黄耀, 张稳, 郑循华, 韩圣慧, 于永强. 基于模型和GIS技术的中国稻田甲烷排放估计. 生态学报, 2006, 26(4): 980-988.
	HUANG Y, ZHANG W, ZHENG X H, HAN S H, YU Y Q. Estimates of methane emission from Chinese rice paddies by linking a model to GIS database. Acta Ecologica Sinica, 2006, 26(4): 980-988. (in Chinese)
[78]	QIAN H Y, ZHANG N, CHEN J J, CHEN C Q, HUNGATE B A, RUAN J M, HUANG S, CHENG K, SONG Z W, HOU P F, ZHANG B, ZHANG J, WANG Z, ZHANG X Y, LI G H, LIU Z H, WANG S H, ZHOU G Y, ZHANG W J, DING Y F, VAN GROENIGEN K J, JIANG Y. Unexpected parabolic temperature dependency of CH₄ emissions from rice paddies. Environmental Science & Technology, 2022, 56(8): 4871-4881.
[79]	U.S. Environmental Protection Agency (EPA). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015. EPA 430-R- 17-001.https://www.epa.gov/ghgemissions/inventory-us-Greenhouse-gas-emissions-and-sinks.
[80]	CARVALHO D V, PEREIRA E M, CARDOSO J S. Machine learning interpretability: A survey on methods and metrics. Electronics, 2019, 8(8): 832.
[81]	YU Q Y, JI W J, PRIHODKO L, ROSS C W, ANCHANG J Y, HANAN N P. Study becomes insight: Ecological learning from machine learning. Methods in Ecology and Evolution, 2021, 12(11): 2117-2128. doi: 10.1111/2041-210X.13686 pmid: 35874972
[82]	XU X R, OUYANG X, GU Y N, CHENG K, SMITH P, SUN J F, LI Y P, PAN G X. Climate change may interact with nitrogen fertilizer management leading to different ammonia loss in China’s croplands. Global Change Biology, 2021, 27(24): 6525-6535.
[83]	GUILPART N, IIZUMI T, MAKOWSKI D. Data-driven projections suggest large opportunities to improve Europe’s soybean self- sufficiency under climate change. Nature Food, 2022, 3: 255-265.
[84]	SMITH P, DAVIES C A, OGLE S, ZANCHI G, BELLARBY J, BIRD N, BODDEY R M, MCNAMARA N P, POWLSON D, COWIE A, VAN NOORDWIJK M, DAVIS S C, DE B RICHTER D, KRYZANOWSKI L, VAN WIJK M T, STUART J, KIRTON A, EGGAR D, NEWTON-CROSS G, ADHYA T K, BRAIMOH A K. Towards an integrated global framework to assess the impacts of land use and management change on soil carbon: Current capability and future vision. Global Change Biology, 2012, 18(7): 2089-2101.
[85]	LI C S, QIU J J, FROLKING S, XIAO X M, SALAS W, MOORE B III, BOLES S, HUANG Y, SASS R. Reduced methane emissions from large-scale changes in water management of China’s rice paddies during 1980-2000. Geophysical Research Letters, 2002, 29(20): 33-37.
[77]	YANG Y Y, LIU L, ZHANG F, ZHANG X Y, XU W, LIU X J, WANG Z, XIE Y W. Soil nitrous oxide emissions by atmospheric nitrogen deposition over global agricultural systems. Environmental Science & Technology, 2021, 55(8): 4420-4429.
[76]	GURUNG R B, OGLE S M, BREIDT F J, PARTON W J, DEL GROSSO S J, ZHANG Y, HARTMAN M D, WILLIAMS S A, VENTEREA R T. Modeling nitrous oxide mitigation potential of enhanced efficiency nitrogen fertilizers from agricultural systems. The Science of the Total Environment, 2021, 801: 149342.
[75]	LI Y M, SHAH S H H, WANG J Y. Modelling of nitrification inhibitor and its effects on emissions of nitrous oxide (N₂O) in the UK. The Science of the Total Environment, 2020, 709: 136156.
[74]	YUE Q, CHENG K, OGLE S, HILLIER J, SMITH P, ABDALLA M, LEDO A, SUN J F, PAN G X. Evaluation of four modelling approaches to estimate nitrous oxide emissions in China’s cropland. The Science of the Total Environment, 2019, 652: 1279-1289.
[73]	LI Y, WHITE R, CHEN D L, ZHANG J B, LI B G, ZHANG Y M, HUANG Y F, EDIS R. A spatially referenced water and nitrogen management model (WNMM) for (irrigated) intensive cropping systems in the North China Plain. Ecological Modelling, 2007, 203(3/4): 395-423.
[72]	GRANT R F, PATTEY E. Modelling variability in N₂O emissions from fertilized agricultural fields. Soil Biology and Biochemistry, 2003, 35(2): 225-243.
[71]	DELGROSSO S, MOSIER A, PARTON W, OJIMA D. DayCent model analysis of past and contemporary soil NO and net greenhouse gas flux for major crops in the USA. Soil and Tillage Research, 2005, 83(1): 9-24.
[70]	CAI Z C, SAWAMOTO T, LI C S, KANG G D, BOONJAWAT J, MOSIER A, WASSMANN R, TSURUTA H. Field validation of the DNDC model for greenhouse gas emissions in East Asian cropping systems. Global Biogeochemical Cycles, 2003, 17(4): 1107.
[69]	LIAO Q J H, YAN X Y. Models of N₂O emission from agricultural fields: A review. Journal of Agro-Environment Science, 2010, 29(5): 817-825. (in Chinese)
	廖千家骅, 颜晓元. 农业土壤氧化亚氮排放模型研究进展. 农业环境科学学报, 2010, 29(5): 817-825.
[68]	ZHANG W, SUN W J, LI T T. Uncertainties in the national inventory of methane emissions from rice cultivation: Field measurements and modeling approaches. Biogeosciences, 2017, 14(1): 163-176.
[67]	WANG Z, ZHANG X Y, LIU L, WANG S Q, ZHAO L M, WU X D, ZHANG W T, HUANG X J. Estimates of methane emissions from Chinese rice fields using the DNDC model. Agricultural and Forest Meteorology, 2021, 303: 108368.
[66]	LI C S, FROLKING S, FROLKING T A. A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. Journal of Geophysical Research: Atmospheres, 1992, 97(D9): 9759-9776.
[65]	ZHANG W, HUANG Y, ZHENG X H, LI J, YU Y Q. Modeling methane emission from rice paddies: Model and modification. Acta Ecologica Sinica, 2004, 24(11): 2347-2352. (in Chinese)
	张稳, 黄耀, 郑循华, 李晶, 于永强. 稻田甲烷排放模型研究: 模型及其修正. 生态学报, 2004, 24(11): 2347-2352.
[64]	CHENG K, OGLE S M, PARTON W J, PAN G X. Predicting methanogenesis from rice paddies using the DayCent ecosystem model. Ecological Modelling, 2013, 261: 19-31.
[63]	HUANG Y, SASS R L, FISHER F M. A semi-empirical model of methane emission from flooded rice paddy soils. Global Change Biology, 1998, 4(3): 247-268.
[62]	YUE Q, LEDO A, CHENG K, ALBANITO F, LEBENDER U, SAPKOTA T B, BRENTRUP F, STIRLING C M, SMITH P, SUN J F, PAN G X, HILLIER J. Re-assessing nitrous oxide emissions from croplands across Mainland China. Agriculture, Ecosystems & Environment, 2018, 268: 70-78.
[61]	STEHFEST E, BOUWMAN L. N₂O and NO emission from agricultural fields and soils under natural vegetation: Summarizing available measurement data and modeling of global annual emissions. Nutrient Cycling in Agroecosystems, 2006, 74(3): 207-228.
[60]	BOUWMAN A F, BOUMANS L J M, BATJES N H. Modeling global annual N₂O and NO emissions from fertilized fields. Global Biogeochemical Cycles, 2002, 16(4): 28-38.
[59]	LIAO Q J H, WANG S W, YAN X Y. Estimation of N₂O emissions from paddy fields during rice growing season in China. Journal of Agro-Environment Science, 2012, 31(1): 212-218. (in Chinese)
	廖千家骅, 王书伟, 颜晓元. 中国稻田水稻生长季N₂O排放估算. 农业环境科学学报, 2012, 31(1): 212-218.
[58]	LIAO Q J H, YAN X Y. Statistical analysis of factors influencing N₂O emission from paddy fields in Asia. Environmental Science, 2011, 32(1): 38-45. (in Chinese)
	廖千家骅, 颜晓元. 亚洲地区稻田N₂O排放影响因子统计模拟分析. 环境科学, 2011, 32(1): 38-45.
[57]	LU Y Y, HUANG Y, ZOU J W, ZHENG X H. An inventory of N₂O emissions from agriculture in China using precipitation-rectified emission factor and background emission. Chemosphere, 2006, 65(11): 1915-1924.
[56]	LU Y Y, HUANG Y, ZHENG X H. N₂O emission factor for agricultural soils. Chinese Journal of Applied Ecology, 2005, 16(7): 1299-1302. (in Chinese)
	卢燕宇, 黄耀, 郑循华. 农田氧化亚氮排放系数的研究. 应用生态学报, 2005, 16(7): 1299-1302.
[51]	SHRESTHA N. Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 2020, 8(2): 39-42.
[50]	WEI H P, SUN W J, HUANG Y. Statistical analysis of methane emission from rice fields in China and the driving factors. Scientia Agricultura Sinica, 2012, 45(17): 3531-3540. doi: 10.3864/j.issn. 0578-1752.2012.17.009. (in Chinese)
	魏海苹, 孙文娟, 黄耀. 中国稻田甲烷排放及其影响因素的统计分析. 中国农业科学, 2012, 45(17): 3531-3540. doi: 10.3864/j.issn. 0578-1752.2012.17.009.
[49]	DAOUD J I. Multicollinearity and regression analysis. Journal of Physics: Conference Series, 2017, 949: 012009.
[48]	SUN J F, WANG M H, XU X R, CHENG K, YUE Q, PAN G X. Re-estimating methane emissions from Chinese paddy fields based on a regional empirical model and high-spatial-resolution data. Environmental Pollution, 2020, 265(Pt A): 115017.
[47]	CARLSON K M, GERBER J S, MUELLER N D, HERRERO M, MACDONALD G K, BRAUMAN K A, HAVLIK P, O’CONNELL C S, JOHNSON J A, SAATCHI S, WEST P C. Greenhouse gas emissions intensity of globalcroplands. Nature Climate Change, 2017, 7: 63-68.
[46]	CHEN H, ZHU Q A, PENG C H, WU N, WANG Y F, FANG X Q, JIANG H, XIANG W H, CHANG J, DENG X W, YU G R. Methane emissions from rice paddies natural wetlands, and lakes in China: synthesis and new estimate. Global Change Biology, 2013, 19(1): 19-32. doi: 10.1111/gcb.12034 pmid: 23504718
[45]	YAN X Y, AKIYAMA H, YAGI K, AKIMOTO H. Global estimations of the inventory and mitigation potential of methane emissions from rice cultivation conducted using the 2006 Intergovernmental Panel on Climate Change Guidelines. Global Biogeochemical Cycles, 2009, 23(2): GB2002.
[44]	KIM D G, HERNANDEZ-RAMIREZ G, GILTRAP D. Linear and nonlinear dependency of direct nitrous oxide emissions on fertilizer nitrogen input: A meta-analysis. Agriculture, Ecosystems & Environment, 2013, 168: 53-65.
[43]	SHCHERBAK I, MILLAR N, ROBERTSON G P. Global meta analysis of the nonlinear response of soil nitrous oxide (N₂O) emissions to fertilizer nitrogen. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(25): 9199-9204.
[42]	YUE Q, WU H, SUN J F, CHENG K, SMITH P, HILLIER J, XU X R, PAN G X. Deriving emission factors and estimating direct nitrous oxide emissions for crop cultivation in China. Environmental Science & Technology, 2019, 53(17): 10246-10257.
[41]	ZOU J W, HUANG Y, ZHENG X H, WANG Y S. Quantifying direct N₂O emissions in paddy fields during rice growing season in mainland China: Dependence on water regime. Atmospheric Environment, 2007, 41(37): 8030-8042.
[40]	NIKOLAISEN M, HILLIER J, SMITH P, NAYAK D. Modelling CH₄ emission from rice ecosystem: A comparison between existing empirical models. Frontiers in Agronomy, 2023, 4: 1058649.
[39]	VO T B T, WASSMANN R, MAI V T, VU D Q, BUI T P L, VU T H, DINH Q H, YEN B T, ASCH F, SANDER B O. Methane emission factors from Vietnamese rice production: pooling data of 36 field sites for meta-analysis. Climate, 2020, 8(6): 74.
[38]	WANG J Y, AKIYAMA H, YAGI K, YAN X Y. Controlling variables and emission factors of methane from global rice fields. Atmospheric Chemistry and Physics, 2018, 18(14): 10419-10431.
[37]	YAN X Y, YAGI K, AKIYAMA H, AKIMOTO H. Statistical analysis of the major variables controlling methane emission from rice fields. Global Change Biology, 2005, 11(7): 1131-1141.
[36]	GURUNG R B, OGLE S M, BREIDT F J, WILLIAMS S A, PARTON W J. Bayesian calibration of the DayCent ecosystem model to simulate soil organic carbon dynamics and reduce model uncertainty. Geoderma, 2020, 376: 114529.
[35]	DING W H, CHANG N J, ZHANG G L, KANG J H, YI X P, ZHANG J, ZHANG J F, WANG L G, LI H. Soil organic carbon changes in China’s croplands: A newly estimation based on DNDC model. The Science of the Total Environment, 2023, 905: 167107.
[34]	TANG H J, QIU J J, VAN RANST E, LI C S. Estimations of soil organic carbon storage in cropland of China based on DNDC model. Geoderma, 2006, 134(1/2): 200-206.
[33]	LI C S, ZHUANG Y H, FROLKING S, GALLOWAY J, HARRISS R, MOORE B III, SCHIMEL D, WANG X K. Modeling soil organic carbon change in croplands of China. Ecological Applications, 2003, 13(2): 327-336.
[32]	YU Y Q, HUANG Y, ZHANG W. Changes in rice yields in China since 1980 associated with cultivar improvement, climate and crop management. Field Crops Research, 2012, 136: 65-75.
[31]	GOTTSCHALK P, SMITH J U, WATTENBACH M, BELLARBY J, STEHFEST E, ARNELL N, OSBORN T J, JONES C, SMITH P. How will organic carbon stocks in mineral soils evolve under future climate Global projections using RothC for a range of climate change scenarios. Biogeosciences, 2012, 9(8): 3151-3171.
[30]	CHENG K, OGLE S M, PARTON W J, PAN G X. Simulating greenhouse gas mitigation potentials for Chinese croplands using the DayCent ecosystem model. Global Change Biology, 2014, 20(3): 948-962. doi: 10.1111/gcb.12368 pmid: 23966349
[29]	OGLE S M, BREIDT F J, EASTER M, WILLIAMS S, KILLIAN K, PAUSTIAN K. Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process-based model. Global Change Biology, 2010, 16(2): 810-822.
[28]	ADHIKARI K, OWENS P R, LIBOHOVA Z, MILLER D M, WILLS S A, NEMECEK J. Assessing soil organic carbon stock of Wisconsin, USA and its fate under future land use and climate change. The Science of the Total Environment, 2019, 667: 833-845. doi: S0048-9697(19)30930-1 pmid: 30852437
[27]	WEST T O, SIX J. Considering the influence of sequestration duration and carbon saturation on estimates of soil carbon capacity. Climatic Change, 2007, 80(1): 25-41.
[26]	HAN D R, WIESMEIER M, CONANT R T, KÜHNEL A, SUN Z G, KÖGEL-KNABNER I, HOU R X, CONG P F, LIANG R B, OUYANG Z. Large soil organic carbon increase due to improved agronomic management in the North China Plain from 1980s to 2010s. Global Change Biology, 2018, 24(3): 987-1000. doi: 10.1111/gcb.13898 pmid: 29035007
[25]	YU Y Y, GUO Z T, WU H B, KAHMANN J A, OLDFIELD F. Spatial changes in soil organic carbon density and storage of cultivated soils in China from 1980 to 2000. Global Biogeochemical Cycles, 2009, 23(2): GB2021.
[24]	JORDAN M I, MITCHELL T M. Machine learning: Trends, perspectives, and prospects. Science, 2015, 349(6245): 255-260. doi: 10.1126/science.aaa8415 pmid: 26185243
[23]	SUN J F. Modeling of carbon sequestration and greenhouse gas emission from paddy fields in China and assessment of carbon sequestration and emission reduction potential[D]. Nanjing: Nanjing Agricultural University, 2022. (in Chinese)
	孙建飞. 中国稻田固碳和温室气体排放模型构建与固碳减排潜力评估[D]. 南京: 南京农业大学, 2022.
[22]	QIN Z C, HUANG Y. Quantification of soil organic carbon sequestration potential in cropland: A model approach. Science China Life Sciences, 2010, 53(7): 868-884.
[21]	IPCC. Guidelines for National Greenhouse Gas Inventories. National Greenhouse Gas Inventories Programme. Intergovernmental Panel on Climate Change. Hayama, Japan, 2006.
[20]	JIN L, LI Y E, GAO Q Z, LIU Y T, WAN Y F, QIN X B, SHI F. Estimate of carbon sequestration under cropland management in China. Scientia Agricultura Sinica, 2008, 41(3): 734-743. doi: 10.3864/j.issn.0578-1752.2008.03.014. (in Chinese)
	金琳, 李玉娥, 高清竹, 刘运通, 万运帆, 秦晓波, 石锋. 中国农田管理土壤碳汇估算. 中国农业科学, 2008, 41(3): 734-743.doi: 10.3864/j.issn.0578-1752.2008.03.014.
[19]	PAN G X, XU X W, SMITH P, PAN W N, LAL R. An increase in topsoil SOC stock of China’s croplands between 1985 and 2006 revealed by soil monitoring. Agriculture, Ecosystems & Environment, 2010, 136(1/2): 133-138.
[18]	CHENG K, PAN G X, TIAN Y G, LI L Q. Changes in topsoil organic carbon of China’s cropland evidenced from the national soil monitoring network. Journal of Agro-Environment Science, 2009, 28(12): 2476-2481. (in Chinese)
	程琨, 潘根兴, 田有国, 李恋卿. 中国农田表土有机碳含量变化特征: 基于国家耕地土壤监测数据. 农业环境科学学报, 2009, 28(12): 2476-2481.
[17]	ZHANG S H, ZHOU X B, CHEN Y S, DU F, ZHU B. Soil organic carbon fractions in China: Spatial distribution, drivers, and future changes. The Science of the Total Environment, 2024, 919: 170890.
[16]	LIN B J, LI R C, LIU K C, OLADELE O P, XU Z Y, LAL R, ZHAO X, ZHANG H L. Management-induced changes in soil organic carbon and related crop yield dynamics in China’s cropland. Global Change Biology, 2023, 29(13): 3575-3590.
[15]	YU Y Q, HUANG Y, ZHANG W. Modeling soil organic carbon change in croplands of China, 1980-2009. Global and Planetary Change, 2012, 82/83: 115-128.
[14]	WANG X Y, XU X P, QIU S J, ZHAO S C, HE P. Deep tillage enhanced soil organic carbon sequestration in China: A meta-analysis. Journal of Cleaner Production, 2023, 399: 136686.
[13]	SUN W J, CANADELL J G, YU L J, YU L F, ZHANG W, SMITH P, FISCHER T, HUANG Y. Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture. Global Change Biology, 2020, 26(6): 3325-3335. doi: 10.1111/gcb.15001 pmid: 31953897
[12]	HUANG Y, SUN W J. Variation trend of organic carbon content in farmland topsoil in Chinese mainland in recent 20 years. Chinese Science Bulletin, 2006, 51(7): 750-763. (in Chinese)
	黄耀, 孙文娟. 近20年来中国大陆农田表土有机碳含量的变化趋势. 科学通报, 2006, 51(7): 750-763.
[11]	SMITH P, CHAPMAN S J, SCOTT W A, BLACK H I J, WATTENBACH M, MILNE R, CAMPBELL C D, LILLY A, OSTLE N, LEVY P E, LUMSDON D G, MILLARD P, TOWERS W, ZAEHLE S, SMITH J U. Climate change cannot be entirely responsible for soil carbon loss observed in England and Wales, 1978-2003. Global Change Biology, 2007, 13(12): 2605-2609.
[10]	PAN G X, LI L Q, WU L S, ZHANG X H. Storage and sequestration potential of topsoil organic carbon in China’s paddy soils. Global Change Biology, 2004, 10(1): 79-92.
[9]	XIE Z B, ZHU J G, LIU G, CADISCH G, HASEGAWA T, CHEN C M, SUN H F, TANG H Y, ZENG Q. Soil organic carbon stocks in China and changes from 1980s to 2000s. Global Change Biology, 2007, 13(9): 1989-2007.
[8]	ZHAO Y C, WANG M Y, HU S J, ZHANG X D, OUYANG Z, ZHANG G L, HUANG B, ZHAO S W, WU J S, XIE D T, ZHU B, YU D S, PAN X Z, XU S X, SHI X Z. Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(16): 4045-4050.
[7]	YAN X Y, CAI Z C, WANG S W, SMITH P. Direct measurement of soil organic carbon content change in the croplands of China. Global Change Biology, 2011, 17(3): 1487-1496.
[6]	SUN W J, HUANG Y, ZHANG W, YU Y Q. Carbon sequestration and its potential in agricultural soils of China. Global Biogeochemical Cycles, 2010, 24(3): GB3001.
[5]	U.S.Environmental Protection Agency (EPA). (2023). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2021. EPA 430-R- 23-003. Retrieved from https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks.
[4]	IPCC,2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, 2019. https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/.
[3]	XIA L L, CAO L, YANG Y, TI C P, LIU Y Z, SMITH P, VAN GROENIGEN K J, LEHMANN J, LAL R, BUTTERBACH-BAHL K, KIESE R, ZHUANG M H, LU X, YAN X Y. Integrated biochar solutions can achieve carbon-neutral staple crop production. Nature Food, 2023, 4: 236-246. doi: 10.1038/s43016-023-00694-0 pmid: 37118263

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

年份 Year	第二期数据量 Data volume for the second period	估算结果 Estimation Result（Tg C·a^-1）	参考文献 Reference
1980s-2000s	国家级耕地土壤监测数据 National-level cropland soil monitoring data	12（稻田Paddy field）	[10]
1980s-2000s	132篇文献60000余个数据 Over 60000 data from 132 articles	15.6-20.1	[12]
1980s-2000s	84篇文献526个数据 526 data from 84 articles	23.6（稻田Paddy field: 3.4 ）	[9]
1980s-2007	1394个重采样数据 1394 resampled data	9.6	[7]
1980s-2011	4060个重采样数据 4060 resampled data	18.1	[8]

年份 Year	数据量 Data volume	估算结果 Estimation result	参考文献 Reference
1988-2007	299 个国家级耕地土壤监测点 299 national-level cropland soil monitoring sites	年均变化率 Annual change rate: 3.61%	[18]
1985-2006	130篇文献 130 articles	年均变化Annual change: 25.5 (22.2-27.6) Tg C·a^-1	[19]
1980-2000	146篇文献 146 articles	年均变化Annual change: 23 (18.6-27.8) Tg C·a^-1 生物物理固碳潜力Biophysical potential: 2-2.5 Pg C	[6]
1970s-2000s	160篇文献 160 articles	化肥、有机肥、有机无机肥配施、秸秆还田和免耕土壤固碳潜力 SOC sequestration potential under mineral fertilizers, organic fertilizers, organic-inorganic fertilizers, straw returning, and no tillage: 0.129, 0.545, 0.889, 0.597, and 0.765 t C·hm^-2	[20]

建模数据量 Data volume for model	方法 Method	考虑变量 Considered variable	估算结果 Estimation result (Tg C·a^-1)	参考文献 Reference
280组数据 280 data	人工神经网络 Artificial neural network	经度、纬度、海拔、土壤类型、土地利用类型和初始土壤有机碳含量 Longitude, latitude, altitude, soil type, land use type, and initial soil organic carbon content	13	[25]
708组重采样数据 708 resampled data	随机森林 Random forest	作物残茬碳投入量、降雨、pH、土壤盐分、土壤质地、地形湿润指数、坡度、肥料施用量、灌溉模式 Crop residual carbon input, rainfall, pH, soil salinity, soil texture, terrain moisture index, slope, fertilizer use, irrigation mode	10.9 （华北平原 North China）	[26]
102篇文献638组数据 638 data from 102 articles	随机森林 Random forest	有机物料类型及投入量、试验年限、土壤有机碳、大气CO₂浓度、生育期降雨、生育期温度、土壤pH、化学氮肥投入量、种植制度、耕作方式 Types and input amounts of organic materials, experimental duration, soil organic carbon, atmospheric CO₂ concentration, rainfall during the growth period, temperature during the growth period, soil pH, mineral nitrogen fertilizer input amount, crop rotation, and tillage method	4.88（3.95-5.81）	[23]

方法 Method	数据需求 Data requirement	成本 Cost	分辨率 Resolution	适用场景 Applicable scenario
参数法/排放因子法 Parameter method/Emission factor method	最关键管理因素、气候区 The most critical management factor and climate zone	低 Low	国家/地区 National/regional assessment	国家/地区温室气体清单编制 National/regional GHG inventory
经验模型 Empirical model	主要土壤、种植管理和气象数据 Main soil, planting management, and meteorological data	中 Medium	田块尺度/高空间分辨率 Field scale/high spatial resolution	温室气体清单编制，区域现状/潜力空间分布特征分析，田块尺度评估 GHG inventory, analysis of spatial distribution characteristics, and assessment of field scale
过程模型 Process based model	详细土壤、种植管理和气象数据 Detailed soil, planting management, and meteorological data	高 High	田块尺度/高时间-空间分辨率 Field scale/high temporal spatial resolution	温室气体清单编制，高时空分辨率区域现状/潜力分析，田块尺度评估 GHG inventory, analysis of high spatiotemporal resolution areas, and assessment of field scale

A Review of Soil Carbon Sequestration and Greenhouse Gas Emissions Quantification in Cropland

RichHTML

PDF

Abstract

Cite this article

share this article

Figures/Tables 6

References 3

Related Articles 15

Metrics

Comments

Recommended 0

温室气体 GHG	排放因子细化情况 Details on emission factor refinement	估算结果 Estimation result (Tg CH₄·a^-1/Gg N₂O-N·a^-1)	参考文献 Reference
稻田CH₄ CH₄ from paddy field	水稻当季及季前不同水分状况、有机添加物类型和数量、土壤类型和水稻品种 Water regime, types and quantities of organic additives, soil types, and rice varieties	全球Global：25.6（14.8-41.7）中国China：7.41	[45]
稻田CH₄ CH₄ from paddy field	水稻当季及季前不同水分状况、有机添加物类型和数量、土壤类型和水稻品种 Water regime, types and quantities of organic additives, soil types, and rice varieties	中国China：8.11（5.2-11.36）	[46]
稻田CH₄ CH₄ from paddy field	水稻当季及季前不同水分状况、有机添加物类型和数量、土壤类型和水稻品种 Water regime, types and quantities of organic additives, soil types, and rice varieties	全球Global：28.3	[47]
稻田N₂O N₂O from paddy field	氮肥投入量、水分管理方式 Nitrogen input and water regime	中国China：29.0	[41]
农田N₂O N₂O from cropland	作物类型、肥料类型、区域特征 Crop type, fertilizer type, regional characteristics	中国China：194	[42]

[1]	ZHANG SuXin, SHEN Ge, YU QiangYi, WU WenBin. Remote Sensing Detection of Cropping System and Its Spatial-Temporal Pattern in China [J]. Scientia Agricultura Sinica, 2024, 57(8): 1469-1489.
[2]	GAO ShangJie, LIU XingRen, LI YingChun, LIU XiaoWan. Effects of Biochar and Straw Return on Greenhouse Gas Emissions and Global Warming Potential in the Farmland [J]. Scientia Agricultura Sinica, 2024, 57(5): 935-949.
[3]	LUO YuHong, HUANG YuShu, ZHU Na, LI Le, CHENG YanBin, LIU JiaHui, ZHANG JingMin, BAO YuFan, XU Nuo, YAN YuChun. Effects of Cultivation and Cropland Afforestation on Soil Particle- Size Distribution and Soil Nutrients in the Typical Steppe of Xilingol League [J]. Scientia Agricultura Sinica, 2024, 57(24): 4919-4932.
[4]	NIU KunYu, GE RuoHao, CHEN MeiAn, JIN ShuQin, LIU Jing. Structural Characteristics, Development Trends, and International Comparison of Greenhouse Gas Emissions in China’s Agri-Food System Under the Dual-Carbon Objectives [J]. Scientia Agricultura Sinica, 2024, 57(21): 4290-4307.
[5]	ZHOU YuanQing, DONG HongMin, ZHU ZhiPing, WANG Yue, LI NanXi. Review on Carbon Footprint Assessment of Pig Farming System [J]. Scientia Agricultura Sinica, 2024, 57(2): 379-389.
[6]	WANG WenJun, LIANG AiZhen, ZHANG Yan, CHEN XueWen, HUANG DanDan. Model Simulation Research of Soil Organic Carbon Dynamics of Long-Term Conservation Tillage in Black Soil [J]. Scientia Agricultura Sinica, 2024, 57(10): 1943-1960.
[7]	GONG ShiFei, XIAO NengWu, DING WuHan, JU XueHai, WU PingHua, YU YongSong, LI Hu. Environmental Risks Assessment of Livestock and Poultry Non- Point Source Pollution in Shiyan City Based on Arable Land Carrying Capacity [J]. Scientia Agricultura Sinica, 2023, 56(5): 920-934.
[8]	XIE Jun, YIN XueWei, WEI Ling, WANG ZiFang, LI QingHu, ZHANG XiaoChun, LU YuanYuan, WANG QiuYue, GAO Ming. Effects of Control Irrigation on Grain Yield and Greenhouse Gas Emissions in Ridge Cultivation Direct-Seeding Paddy Field [J]. Scientia Agricultura Sinica, 2023, 56(4): 697-710.
[9]	LIU GaoYuan, HE AiLing, DU Jun, LÜ JinLing, NIE ShengWei, PAN XiuYan, XU JiDong, LI Jue, YANG ZhanPing. Effect of Organic Fertilizer Replacing Chemical Fertilizer on Nitrous Oxide Emission from Wheat-Maize Rotation System in Lime Concretion Black Soil [J]. Scientia Agricultura Sinica, 2023, 56(16): 3156-3167.
[10]	SONG BoYing, GUO YanJie, WANG WenZan, LÜ ZeNan, ZHAO YuQing, LIU Lu, ZHANG LiJuan. Effects of Biochar Combined with Dicyandiamide on Greenhouse Gases Emissions from Facility Vegetable Soil [J]. Scientia Agricultura Sinica, 2023, 56(10): 1935-1948.
[11]	LI XiaoLi,HE TangQing,ZHANG ChenXi,TIAN MingHui,WU Mei,LI ChaoHai,YANG QingHua,ZHANG XueLin. Effect of Organic Fertilizer Replacing Chemical Fertilizers on Greenhouse Gas Emission Under the Conditions of Same Nitrogen Fertilizer Input in Maize Farmland [J]. Scientia Agricultura Sinica, 2022, 55(5): 948-961.
[12]	ZHANG XueLin, WU Mei, HE TangQing, ZHANG ChenXi, TIAN MingHui, LI XiaoLi, HOU XiaoPan, HAO XiaoFeng, YANG QingHua, LI ChaoHai. Effects of Crop Residue Decomposition on Soil Inorganic Nitrogen and Greenhouse Gas Emissions from Fluvo-Aquic Soil and Shajiang Black Soil [J]. Scientia Agricultura Sinica, 2022, 55(4): 729-742.
[13]	CHEN XuHao,GAO Qiang,CHEN XinPing,ZHANG WuShuai. Temporal and Spatial Characteristics of Resources Input and Environmental Effects for Maize Production in the Three Provinces of Northeast China [J]. Scientia Agricultura Sinica, 2022, 55(16): 3170-3184.
[14]	MAO AnRan,ZHAO HuBing,YANG HuiMin,WANG Tao,CHEN XiuWen,LIANG WenJuan. Effects of Different Mulching Periods and Mulching Practices on Economic Return and Environment [J]. Scientia Agricultura Sinica, 2021, 54(3): 608-618.
[15]	ZHANG WeiJian, YAN ShengJi, ZHANG Jun, JIANG Yu, DENG Aixing. Win-Win Strategy for National Food Security and Agricultural Double-Carbon Goals [J]. Scientia Agricultura Sinica, 2021, 54(18): 3892-3902.