Soil Organic Matter Cycling and Greenhouse Gas Accounting

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Chapter 17

Soil Organic Matter Cycling and Greenhouse Gas Accounting Methodologies S. J. Del Grosso,*,1,2 S. M. Ogle, and W. J. Parton2 1USDA,

Agricultural Research Service, 2150 Centre Ave, Bldg. D, Ste. 100, Fort Collins, CO 80526 2Natural Resource Ecology Laboratory, Colorado State University, 1231 East Drive, Fort Collins, CO 80526 *E-mail: [email protected]

Soil organic matter (SOM) transformations play an important role in regulating the atmospheric concentrations of the three primary biogenic greenhouse gases (GHG), methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2). Soils are a source and sink of CH4 and CO2, but are usually only a source for N2O. Decomposition of SOM under anaerobic conditions leads to CH4 emissions while aerobic decomposition results in CO2 emissions. The microbial processes that result in N2O emissions involve transformations of inorganic nitrogen (N) that are coupled with SOM cycling. Different methodologies of varying complexity are used to quantify these transformations and associated GHG emissions. Simple methods use regression equations that relate land management practices to GHG emissions. For example, Tier 1 Intergovernmental Panel on Climate Change (IPCC, 12) methodology uses default emission factors and activity data on N inputs, general land use and climate categories to calculate GHG emissions. Tier 2 methodology uses country or region-specific emission factors and more detailed activity data. Tier 3 methodology involves more complex process-based models that simulate the plant-soil-atmosphere system. The United States uses a Tier 3 approach to estimate soil CO2 and N2O fluxes from most agricultural lands for its national inventory while most other nations use Tier 1 approaches. Although higher tier approaches appear to give better estimates, uncertainty is large © 2011 American Chemical Society Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.

and improvements in model algorithms and activity data are required to more reliably account the soil GHG emissions reported in national inventories.


Introduction Soil organic matter (SOM) transformations are a major source of nutrients in soil and important gaseous compounds in the atmosphere. Annually, the decomposition of plant residues and SOM adds approximately 10 times more CO2 to the atmosphere than fossil fuel and industrial sources (1). This large CO2 source is balanced by a roughly equivalent amount of uptake by photosynthesis. Decomposition of SOM under anaerobic conditions is a leading source of atmospheric CH4 while oxidation of atmospheric CH4 by microbes in aerobic soils is an important sink. Soil microbial activity involving nitrogen (N) transformations results in release of N2O, another important biogenic greenhouse gas (GHG). It is important to better understand the processes that control SOM transformations because small changes in these rates could have large impacts on atmospheric concentrations of CO2, CH4, N2O, soil C storage, and N cycling rates. In this chapter, we explain the soil organic matter transformations that influence GHG concentrations, how these transformations are quantified, and methods used to account GHG fluxes at regional and greater scales.

Figure 1. Flow diagram of soil organic matter transformations. Fixation includes symbiotic and non-symbiotic N fixation. 332 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


Soil Organic Matter Cycling Soil organic matter transformations involve immobilizing and mineralizing nutrients and carbon (C). Plants and microbes immobilize nutrients and C when they assimilate mineral compounds into organic matter; nutrients and C are mineralized when biological and chemical processes decompose organic matter and release compounds in the mineral form. The major processes involved in SOM cycling are net primary productivity (NPP), biomass senescence, decomposition of senesced pant litter and SOM, nitrification, denitrification, methanogenesis, and methane oxidation (Figure 1). The following sections describe these processes and how they are quantified. Net Primary Productivity and Senescence It is important to consider net primary productivity (NPP) because it provides the raw material (senesced biomass) that is converted to SOM upon decomposition. In addition to fixing atmospheric CO2, NPP assimilates mineral N (NH4, NO3) from the soil (Figure 1) and thus influences the supply of nutrients available for soil microbial processes. Instead of directly measuring CO2 assimilation, NPP is usually quantified by measuring the biomass produced during the growing season at small scales (e.g. plot level). But in addition to NPP, the biomass pool is also influenced by herbivory so assumptions must be made to infer NPP. Estimating below ground NPP is even more problematic because destructive sampling is required and it is difficult to entirely separate roots from microbes that feed on them. Analogous to the above ground situation, only biomass is measured, herbivory and root exudates are discounted, thus assumptions must be made to estimate NPP. Major controls on NPP are temperature, water status, nutrient availability, and disturbance regime (2). As temperature and water stress are alleviated, nutrients cycle faster and NPP increases. Disturbance events, such as fire, can increase or decrease NPP. Fire quickly mineralizes nutrients and can increase NPP in the short term (3). However, systems that are subject to high fire frequency tend to have lower NPP in the long run than less disturbed systems because some of the nutrients mineralized during fire events are lost from the plant-soil system via volatilization and leaching (2). Decomposition Decomposition of plant litter and SOM results in the mineralization of C which is released as CO2 and, depending on the nutrient concentration of the material being decomposed, mineralization or immobilization of soil nutrients (Figure 1). Plant litter, except for that from legumes, typically has a low N content so decomposers must immobilize N from the mineral soil pool leading to net immobilization of nutrients during this phase of decomposition. Once litter has passed through or been incorporated into microbial biomass it is referred to as SOM. Soil organic matter has higher N concentration than litter so further decomposition results in release of N to the soil mineral N pool and net mineralization of N. Potential decomposition and net mineralization rates 333 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


can be measured using controlled incubations. Rates measured in this manner are considered potentials because environmental conditions are controlled and growing vegetation is not present to compete for nutrients. Buried litter bags in situ that are sampled through time are subject to ambient environmental conditions and are used to quantify decomposition and net nutrient mineralization rates using the principle of mass balance. Recent analysis showed that lignin content, lignin to N ratio, and environmental conditions explained the majority of variability in net mineralization and decomposition rates for buried leaf litter in various global biomes (4, 5). SOM decomposition rates can also be inferred from measurements of soil CO2 emissions but this is confounded with autotrophic respiration, unless measurements are taken from fallow soils or when plants are not active. Nitrification and Denitrification Nitrification is the aerobic oxidation of ammonium (NH4) to NO3 where N2O, NO, and NO2 are produced as intermediate species (Figure 1). Major controls are soil O2 status, water content, temperature, and NH4 availability. Most nitrified NH4 is converted to NO3, with the portion lost as N2O gas less than 10%, but this fraction varies considerably based on O2 availability and other factors (6). Denitrification is the anaerobic reduction of NO3 to N2O and N2 (Figure 1). Major controls are soil O2 status, water content, temperature, NO3, and labile C availability (most denitrifiers are heterotrophs). The majority of denitrified NO3-N can be emitted as N2O or N2. As conditions become more anaerobic and the supply of electron donor (labile C) relative to initial electron acceptor (NO3) increases, the portion of N2 relative to N2O emitted also increases (7). Nitrification and denitrification rates can be measured reliably for incubation studies. But in field situations, often soil N2O flux measurements are the only indicator of nitrification and denitrification rates and N2O from nitrification cannot be distinguished from N2O from denitrification. Furthermore, it is difficult to accurately quantify N2O emissions rates in the field because they are highly variable in space and time and often respond non-linearly to the key drivers. Methods used to estimate soil gas fluxes to the atmosphere can be divided into two broad classes known as bottom up and top down approaches. Bottom up approaches calculate soil surface gas flux rates using ground based chambers that trap gases emitted from the soil surface while top down approaches infer gas flux based on changes in the atmospheric concentration of gases in space or time. Theses methods are describes in detail in Chapter 1 of this volume. Methanogenesis and Methane Oxidation Methanogenesis is a form of anaerobic respiration, often representing the final step in the decay of organic matter, where the terminal electron acceptor is carbon compound such as acetic acid (C3COOH) or CO2 (Figure 1). Controls on methane production include O2 status, temperature, pH, and C availability (8, 9), while CH4 emissions from soil is also influenced by gas diffusivity and plant species, as plant transport is often the dominant pathway for soil CH4 emissions. Oxidation of atmospheric methane, as well as methane created in deeper soil layers, is carried 334 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


out by methanotrophic bacteria in aerated surface soil layers (Figure 1). Major controls are soil gas diffusivity, temperature, and CH4 availability (10). Both methanogenesis and methane oxidation likely occur in most soils. In drained, upland soils, methane oxidation dominates and these soils are net CH4 sinks, while in low lying, saturated soils methanogenesis dominates and these soils are net sources. As with N2O, both ground based chambers and top down methods are used to measure CH4 fluxes from soils. Methods used to measure the rates of soil organic matter transformations are not perfect because usually only pool sizes can be directly measured and rates must be inferred. Field sampling of vegetation and soil processes using bottom up methods requires varying degrees of disturbance of the plant-soil system. Top down methods require little, if any, disturbance, but sample across large areas so rates of processes occurring at small scales are confounded. Also, more than one process often influences what is measured. For example, both nitrification and denitrification contribute to measured N2O fluxes. Consequently, models are often used to estimate rates of these processes. Models have advantages of not requiring disturbance and simulating flows of material for different process (e.g., NPP, N gas fluxes for nitrification and denitrification individually) as well as pool sizes (e.g., standing biomass, soil organic carbon). However, models are limited in that they are simplifications of reality and are constrained by measurements, which themselves are imperfect. One reason it is difficult to model soil processes is that the controlling factors interact in different ways. For example, heavy livestock grazing is expected to increase mineral N availability in soil, and hence N2O emissions. But in arid temperate grasslands, grazing can decrease snow retention (and thus soil water content) due to reduced vegetation cover such that N2O emissions during the spring thaw period can be greatly reduced compared to ungrazed systems (11, 12).

GHG Accounting Methodologies Because it is not feasible to measure GHG emissions from different sources at regional and larger scales, methodologies involving models of varying complexity have been developed. We discuss these methodologies in the context of national greenhouse gas inventories. Signatory nations of the United Nations Framework Convention on Climate Change (UNFCCC) agree to report their national GHG emissions annually to the UNFCCC using agreed upon accounting methodologies developed by the Intergovernmental Panel on Climate Change (IPCC). The IPCC Good Practice Guidelines define three methodological Tiers for calculating GHG emissions for national inventories (13). Tier 1 methods are the easiest to use and employ default emission factors and country specific activity data to estimate emissions. Emission factors define GHG emissions per unit of agricultural activity; e.g., 1 kg N2O-N is emitted directly from soils for every 100 kg of N fertilizer added (Figure 2). Tier 2 methods use the same approach as Tier 1 but apply country or region specific emission factors and require more disaggregated activity data. Tier 3 methods use process-based simulation models and/or inventory monitoring systems (e.g., (14, 15)). In addition to soil C stock 335 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


changes, and direct soil N2O and CH4 emissions, the IPCC Guidelines recommend accounting for indirect N2O emissions (13). Indirect N2O results from N that left the agricultural system in a form other than N2O (e.g., gaseous NOx and NH3, dissolved NO3 leached into ground water) and converted to N2O offsite (Figure 2). The IPCC Guidelines recommend including estimates of uncertainty and prescribe how to combine different sources of uncertainty (13). Tier 1 methods usually have large uncertainty; e.g., the 95% confidence interval (CI) for direct N2O emissions from N additions to soils ranges from 0.3 to 3.0 kg N2O-N for every 100 kg of N fertilizer added. Tier 2 methods have lower uncertainty because country specific emissions factors that better reflect region specific cropping practices and environmental conditions are used. Tier 3 methods should provide the most certain estimates because they use complex models that account for more of the variables that influence emissions and how they interact. Although Tier 3 methods should yield more accurate and precise estimates, most nations use Tier 1 and 2 methods, because Tier 3 methods require extensive resources to develop and validate model outputs, acquire model input data, execute simulations, process model results, and verify quality control.

Figure 2. IPCC default Tier 1 methodology for soil Nitrous oxide (N2O) emissions. PRP N refers to nitrogen deposited onto pasture, range,and paddock soils by grazing animals (unmanaged livestock waste). In contrast, manure N refers to N in managed livestock waste that was applied to cropped or grazed fields by humans. 336 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


IPCC Tier Comparison for Soil Carbon Stock Changes Soil organic carbon stock changes in U.S.A. croplands are estimated annually as part of the National Greenhouse Gas Inventory (16). The methods have been refined over time from the simplest Tier 1 method to the most complex Tier 3 method, providing an example of how estimates and uncertainties change with application of different methodological tiers. For this comparison, the Tier 1 method used the default factors and equations from the 2006 IPCC Guidelines (13), the Tier 2 method was developed using the same equations with country specific factors (17, 18), and the Tier 3 method was based on estimating the carbon stock changes with a process-based simulation model (14). All three methods were implemented with activity data from the 1997 USDA National Resource Inventory (19, 20). The Tier 3 method has been customized for the crop management conditions in the US, and thus incorporated additional activity data about the production systems compared to the Tier 1 and 2 methods. For example, the Tier 3 method requires additional data on fertilization rates and management practices such as planting and harvesting dates (16). The estimated changes in soil organic C stocks in 1997 were not statistically different among the three approaches (Figure 3), suggesting that the methods provide comparable estimates for the change in soil organic C stocks. The major difference between the methods was level of precision in the result. The confidence intervals had ranges of ±59%, ±40% and ±16%, for the Tier 1, 2 and 3 methods, respectively. The Tier 2 method increases the precision by 19%, while the Tier 3 increases the precision by another 24%. Note that this comparison does not include complete coverage of all the cropped land in the US because the Tier 3 method was not applied to all cropped soils, as explained in the next section.

Figure 3. Soil organic C stock change (Tg CO2 yr-1) in 1997 for US croplands using IPCC Tier 1, 2 and 3 methods. 337 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


One of the key goals for developing higher Tier methods within the IPCC guidance (13) is to reduce uncertainties in the estimated greenhouse gas emissions. The example for US croplands is consistent with this goal in terms of increasing the precision in the estimated soil organic C stock change for US croplands. It is not surprising that the estimates are relatively consistent among the methods, particularly the Tier 1 and 2 methods because a large portion of the data used to derive IPCC default factors is from experiments conducted in the US (21). The Tier 3 method addressed practices with more specificity which led to a higher estimated change in soil organic C stocks, but again this result has a large overlap with lower tier results. Therefore, developing country specific-factors with Tier 2 and addressing crop management with greater specificity in the Tier 3 method appears to have a larger influence on the precision of the estimate than the accuracy, assuming that the confidence intervals contain the true value of soil organic C stock change. More comparative analyses among estimates from different tiers will be needed to determine if this result is generalizable.

U.S.A. Soil GHG Inventory The U.S.A. uses a combination of Tier 1, Tier 2, and Tier 3 approaches to estimate nationwide GHG emissions from agricultural soils (16). A Tier 3 approach is used to estimate soil C stock changes for major cropping systems (corn, soybean, wheat, hay, sorghum, cotton) and non-federally managed grasslands used for livestock grazing (14). Specifically, the CENTURY ecosystem model simulates cropped and grazed systems across the U.S.A. at small spatial scales (sub county) using data from the National Resources Inventory (19, 20). A Tier Two approach is used for soil C stock changes for minor crops, organic soils, and federal grasslands (17, 18). Similarly, a Tier 3 method using the DayCent ecosystem model is used to calculate N2O emissions for major cropping systems and non-federally managed grasslands used for livestock grazing (15). A Tier 1 approach is used to estimate N2O emissions from minor crops, cropped and grazed organic soils, and federal grasslands, as well as CH4 emissions from flooded rice paddies (16). For both soil C stock changes and N2O emissions calculated using the Tier 3 approaches based on CENTURY and DayCent model simulations, a Monte Carlo method is used to quantify uncertainty in model outputs (14, 15). Repeated simulations, using random draws from probability distribution functions, quantify uncertainty for key model inputs that are not precisely known, and empirical based estimators derived from comparing model outputs with measured values are used to quantify uncertainty due to model algorithms and parameterizations being imperfect representations of reality. IPCC (13) Guidelines are used to quantify uncertainty for the Tier 1 and 2 estimates and to combine uncertainties into an overall uncertainty range for each GHG source category. Nitrous oxide emissions account for the vast majority of soil emissions in the U.S.A. because other key soil emissions, such as CH4 from rice paddies, are small because rice paddies are a small portion of total agricultural land and agricultural soils are a net CO2 sink (16). In 2008, cropped soils were an N2O source of 154 338 Guo et al.; Understanding Greenhouse Gas Emissions from Agricultural Management ACS Symposium Series; American Chemical Society: Washington, DC, 2011.


Tg CO2 eq. with a 95% CI of -26% to +57%; grazed soils were an N2O source of 62 Tg CO2 eq. with a 95% CI of -37% to +153% (22). Agricultural soils were estimated to be a small source of CH4 (8 Tg CO2 eq. with a 95% CI of -57% to +127%) and a sink for CO2 of 40 Tg CO2 eq. with a 95% CI of -53% to +42%. In aggregate, agricultural soils are estimated to be a GHG source of 184 Tg CO2-C eq. yr-1 with a 95% confidence interval of -19 to +37% (22).

Conclusion Soil organic matter cycling results in both release and uptake of the three important biogenic GHG’s (CO2, CH4, N2O). Because such vast amounts of C and N are cycled through soils, small changes in the amounts of these elements cycled, or in the portions of cycled C and N that are converted to GHG’s, can result in substantial changes in the atmospheric concentrations of these gases. In recent years, both measuring and modeling methods used to quantify SOM cycling and the associated GHG fluxes have improved, leading to narrowing of confidence intervals for estimates of the rates of these processes and the associated gas fluxes. Still, the uncertainties remain large compared to other sources of anthropogenic GHG’s (e.g. fossil fuel combustion). Availability of more observational data collected at various spatial and temporal scales and continued improvement of modeling methods should result in further increases in the accuracy and precision of GHG flux estimates resulting from SOM transformations.

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Soil Organic Matter Cycling and Greenhouse Gas Accounting

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