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

摘要/Abstract

摘要：

农田作为陆地生态系统中重要的温室气体源和汇，其科学管理为减缓气候变化提供了重要潜力。开发农田土壤固碳和温室气体排放计量方法对于准确量化农业对碳中和战略的贡献至关重要，也是温室气体清单编制、固碳减排效应评估、自愿减排交易、涉碳相关认证的必然要求。本文系统梳理了农田土壤固碳与温室气体排放计量方法，对比了不同计量方法在数据需求、获取成本、估算精度等方面的差异及面临的挑战，重点探讨了这些方法在不同应用场景下的适用性及其优化路径。农田土壤固碳和温室气体排放计量方法主要包括两期差减法（仅针对土壤固碳）、数据集成法（仅针对土壤固碳）、参数法/排放因子法、经验模型和过程模型。两期差减法和数据集成法是采用实测数据直接估算土壤有机碳变化量的重要方法，但其估算精度直接受到两期数据的匹配程度以及数据量的影响。参数法/排放因子法能够以最少的数据需求估算土壤固碳和温室气体排放，其估算精度依赖于参数/排放因子的本土化及细化程度。由于方法的限制，参数法/排放因子法更适合在区域尺度下进行低分辨率的估算，而无法准确捕捉高分辨率下的空间变异。经验模型和过程模型综合考虑了气象、土壤和管理因素对土壤固碳和温室气体排放的影响，适用于高分辨率的空间模拟和田块尺度的计量。经验模型的性能依赖于建模数据库的数据数量、质量及变量筛选的准确性。相比之下，过程模型在参数本土优化的前提下能够捕捉复杂条件下土壤有机碳和温室气体的时空动态变化。鉴于不同方法面临的挑战，本文强调了在特定应用场景下平衡数据需求、成本及估算精度的重要性，并指出模型模拟法将成为未来农田碳计量的主要方法。如何针对不同应用场景，权衡数据需求、获取成本及估算精度以选取和研发适宜的计量方法是当前面临的重要挑战。为进一步提升计量准确性与适用性，未来研究应聚焦于构建完善的田间监测网络、推动标准化数据库建设及共享、深化排放因子的本土化与精细化，并探索机器学习在多算法集成和过程模型参数调优中的创新应用。

关键词: 土壤固碳, 温室气体, 农田, 排放因子, 模型模拟, 碳中和

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

孙建飞, 程琨. 农田土壤固碳与温室气体排放计量研究进展[J]. 中国农业科学, 2025, 58(11): 2190-2205.

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.

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图/表 6

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表5

中国农田GHG排放经验模型研究汇总"

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]

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表6

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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