Soil organic carbon as an indicator of environmental quality at the national scale: Inventory monitoring methods and policy relevance

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1 Soil organic carbon as an indicator of environmental quality at the national scale: Inventory monitoring methods and policy relevance Stephen M. Ogle 1 and Keith Paustian 1,2 1 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA; and 2 Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA. Received 20 May 2004, accepted 1 May Can. J. Soil. Sci. Downloaded from by on 02/16/18 Ogle, S. M. and Paustian, K Soil organic carbon as an indicator of environmental quality at the national scale: Inventory monitoring methods and policy relevance. Can. J. Soil Sci. 85: Soil organic carbon (SOC) storage is an indicator of environmental quality for mineral soils because of the influence that organic matter has on key functional properties, such as fertility, soil structure and water relations. Historically, agricultural management has caused large losses of SOC relative to native ecosystems, leading to degradation. However, new technologies and conservation practices have been developed during the past few decades that can enhance SOC storage, and thus improve environmental quality. Our objective was to describe a national inventory procedure to estimate SOC storage for purposes of monitoring environmental quality. The major steps in this procedure include: (1) model selection/development, (2) model verification, (3) identification of model input data, (4) uncertainty assessment, (5) model implementation, and (6) validation of results. Applying this approach with a simple C accounting method, the upper 30 cm of US agricultural soils were estimated to have accumulated 10.8 Tg C yr 1 between 1982 and 1997, with an uncertainty of ± 40%. A simple index was developed to relate estimated SOC stocks to the potential amounts under native conditions and conventional agricultural management. An index value of 0% on the proposed scale would be equivalent to the SOC under conventional agricultural use, while an index value of 100% would be equivalent to native levels. With an estimated 1997 stock of Tg C, the index value for US agricultural soils was about 60%. Using this inventory procedure, environmental issues related to soil, water and air quality could be informed by SOC in combination with other key indicators, in addition to using the inventory for evaluating sustainability of agricultural lands for food and fiber production. Key words: Natural resource inventory, environmental quality indicators, soil organic carbon, land use and management, national inventory, Organization for Economic Co-operation and Development Ogle, S. M. et Paustian, K Le carbone organique du sol en tant qu indicateur de la qualité de l environnement à l échelon national : méthodes de surveillance des stocks et pertinence des politiques. Can. J. Soil Sci. 85: Pour les sols minéraux, les réserves de carbone organique (CO) témoignent de la qualité de l environnement à cause de la manière dont la matière organique influe sur les propriétés fonctionnelles du sol comme sa fertilité, sa structure et ses relations avec l eau. Historiquement, la gestion des terres agricoles explique les lourdes pertes de CO accusées par les écosystèmes naturels et la détérioration subséquente du sol. Au cours des dernières décennies cependant, on a mis au point de nouvelles technologies et pratiques de conservation susceptibles d améliorer le stockage du CO dans le sol, donc la qualité de l environnement. Les auteurs décrivent une méthode d inventaire national permettant d estimer les réserves de CO du sol en vue de surveiller la qualité de l environnement. Les principales étapes de cette méthode sont les suivantes : (1) choix et développement d un modèle; (2) vérification du modèle; (3) identification des données qui serviront à l alimenter; (4) évaluation de l incertitude; (5) application et (6) validation des résultats. En combinant une simple méthode de comptabilisation du carbone à cette approche, les auteurs estiment que la couche supérieure de 30 cm des terres agricoles américaines a accumulé 10,8 Tg de carbone par année entre 1982 et 1997, à un degré d incertitude d environ 40 %. Ils ont créé un indice simple reliant les réserves estimatives de CO du sol aux stocks qui devraient exister dans des conditions naturelles et dans le cadre d une exploitation ordinaire des terres arables. Sur cette échelle, un indice de 0 % correspondrait aux réserves de CO résultant d une exploitation classique des terres et un indice de 100 %, aux réserves des terres primitives. Avec des réserves estimatives de Tg de carbone en 1997, les terres agricoles des États-Unis donnent un indice d environ 60 %. Outre l utilité de cette méthode pour évaluer la pérennité des terres agricoles pour la production d aliments et de fibres, on pourrait se servir du CO du sol et d autres grands indicateurs pour étayer les problèmes environnementaux associés à la qualité du sol, de l eau et de l air. Mots clés: Inventaire des ressources naturelles, indicateurs de la qualité de l environnement, carbone organique du sol, exploitation et gestion des terres, inventaire national, OCDE Agricultural crop and grazing lands are arguably the most important natural resource for a country, providing both food and fiber for a variety of purposes including consumption and clothing. Their sustainability is closely associated with maintaining the functionality of soils for the growth of 531 crops and forages (Doran et al. 1994). Central to this function is organic matter content, or humus, which influences a Abbreviations: OECD, Organization for Economic Cooperation and Development; SOC, soil organic carbon

2 Can. J. Soil. Sci. Downloaded from by on 02/16/ CANADIAN JOURNAL OF SOIL SCIENCE variety of soil properties that regulate plant growth. Increasing organic matter content has several positive benefits for soils, including a reduction in bulk density, improvement in soil structure, and enhancement of soil water-holding and cation exchange capacities (Carter and Stewart 1996). Moreover, it is recognized that higher amounts of soil organic matter can improve crop production through greater nutrient availability (Bauer and Black 1994). Based on this knowledge, there has been considerable research to determine the controls on SOC storage. One of the consistent findings from these studies is that land use and management has a dramatic impact on carbon storage in agricultural lands, and that there is potential for increasing SOC through adoption of conservation practices (Paustian et al. 1997a, b; Smith et al. 1997a, 1998; Lal et al. 1998; Bruce et al. 1999; Follett 2001). Through the wealth of information from studies about SOC, research has been conducted to create indices or indicators of soil quality (Doran et al. 1994; Franzleubbers 2002), but less work has been done to incorporate this information into a framework that allows for monitoring of environmental quality at the national scale. Such a framework could be used to evaluate past policy actions and consider future directives for enhancing quality and sustainability of agricultural lands for food and fiber production. With this recognition, the Organization for Economic Co-operation and Development (OECD) has recommended that countries develop national scale indicators for several soil characteristics, including SOC, soil biodiversity and erosion indices, which can be used for evaluating the sustainability of agricultural systems (Parris 2003). Hence, our objective was to describe the process for quantifying land use and management impacts on SOC storage as an indicator of environmental quality, and then provide an example of how this information can be used to support policy analysis. PRODUCING A NATIONAL INVENTORY FOR SOC IN AGRICULTURAL LANDS Monitoring national trends in SOC storage for agricultural lands can be accomplished through intensive sampling campaigns, assuming the sampling is sufficient to represent impacts for the entire region and time scale of interest (Sleutel et al. 2003; Wu et al. 2003). Alternatively, inventories can be developed using computer modeling that quantifies the effects of agricultural land use and management on SOC storage based on empirical relationships developed from field and laboratory studies, and then scaling of that knowledge across appropriate spatial extents and time periods. The latter approach is often more feasible than an inventory of SOC based solely on measurements due to the costs and resource needs associated with intensive sampling campaigns. Producing an inventory using computer modeling involves six steps: (1) model selection/development for calculating SOC storage, (2) model verification, (3) identification of model input data (i.e., spatio-temporal data on environmental characteristics as well as land use and management activity), (4) uncertainty assessment, (5) model implementation, and (6) validation of results (Fig. 1). Model Selection/Development Computer modeling techniques to estimate agricultural management impacts on SOC can take on a variety of forms, but two of the predominant approaches are carbon accounting procedures (e.g., Houghton et al. 1999; Bernoux et al. 2001; Ogle et al. 2003; West et al. 2004) and simulation modeling (e.g., Defries et al. 1999; Schimel et al. 2000; Smith et al. 2000). Experimental results provide a basis for modeling the impact of land use and management on SOC storage, but each approach uses experimental data in a different manner. For carbon accounting, data from agricultural experiments are statistically analyzed to estimate the effect of management on SOC. For example, using a method developed by the Intergovernmental on Climate Change (IPCC 1997), management factors can be derived from a meta-analysis of experimental results, and then used in a national inventory assessment to apply carbon credits or debits for changing land use and management (Ogle et al. 2003, 2004). For simulation approaches, data from agricultural experiments combined with laboratory findings and theoretical relationships are integrated into a dynamic model to formulate mechanistic relationships representing the processes that affect SOC storage. These processes include plant production, microbial decomposition, water flows through the agricultural system and nutrient cycling. One notable variant to this general simulation model approach incorporates remote sensing measurements to quantify net primary production in crop and grazing lands (Tate et al. 2000). Several models exist that can be used for simulating land use and management impacts, including Century (Parton et al. 1987), DNDC (Li et al. 1994), and Roth C (Coleman and Jenkinson 1996), and each has been used to simulate management effects across large regions (Smith et al. 2000; Falloon and Smith 2002b; Paustian et al. 2002; Li et al. 2003). While carbon accounting approaches only account for the direct influence of land use and management, simulation models are capable of providing more complete accounting of the changes in SOC storage by representing a broader suite of the driving variables and their interactions (Elliott et al. 1994), including changing climatic conditions, nitrogen deposition and elevated CO 2 effects on plant production. Also, simulation models typically are better at capturing the dynamic changes in SOC through time. Simulation approaches are more complex, however, and much more difficult to implement for purposes of conducting a national inventory. Verification Analysis For the simulation approach, model results are compared with measurements from the field experiments to verify if the application is appropriate (e.g., Falloon and Smith 2002a). Verification is considered adequate if the model results are reasonably similar to the measured patterns in the experiments. Various statistically based measures of model performance can be used in judging the adequacy of a particular model and/or choosing among potential alternative models (e.g., Smith et al. 1997b). This step does not occur with carbon accounting methods because the SOC stock changes that are measured in the experiments are used to derive the management factors, and thus are consistent with

3 OGLE AND PAUSTIAN SOC: ENVIRONMENTAL QUALITY INDICATOR AT THE NATIONAL SCALE 533 Can. J. Soil. Sci. Downloaded from by on 02/16/18 Fig. 1. Process to produce a national inventory for monitoring trends in SOC as an indicator of environmental quality. a best fit to the field measurements. If model verification is not successful, current model algorithms or parameters can be modified in an attempt to improve the relationship between measured and modeled trends, or another model may be selected. Model Inputs: Environmental Characteristics and Land Use/Management Activity Data In addition to experimental data needed to develop a model, there must be sufficient information representing environmental characteristics as well as land use and management activity at the national scale. The completeness and precision of an inventory in capturing management impacts will be highly dependent on the amount of detail that can be provided for model inputs. Soil and climate data are essential environmental characteristics for modeling land use and management impacts on SOC storage, and can be further supplemented with topographical data to model erosion (Harden et al. 1999). Many countries have soil maps with at least a modest amount of information that can be used for modeling purposes. Soil characteristics can range from broad categories based on general soil types, such as USDA taxonomic order, to more detailed information on soil texture, bulk density, and ph. Similarly, most if not all countries have data on climatic characteristics for modeling, which can range from simple categories describing the climate in qualitative terms (e.g., warm temperate, cool temperate, dry tropical, and wet tropical; IPCC 1997) to more detailed information such as daily temperatures and precipitation. In general, more detail on environmental characteristics is needed for the simulation modeling approaches compared with the carbon accounting procedures. In addition to environmental characteristics, national statistics on agricultural land use and management activity are

4 Can. J. Soil. Sci. Downloaded from by on 02/16/ CANADIAN JOURNAL OF SOIL SCIENCE needed to conduct an inventory and assess environmental quality and sustainability of current practices. This is particularly important for policy because legislation arguably will likely have its largest impact on environmental quality through influences on management. There are three general activity categories of activity data that are needed for an agricultural SOC inventory, including land use change, cropland management and grazing land management. For the first category, it is widely known that the SOC budget is altered through land use change between cultivated croplands and native lands or non-cultivated uses, such as continuous grazing and hay (Davidson and Ackerman 1993; Post and Kwon 2000; Guo and Gifford 2002; Ogle et al. 2005). To maintain a consistent land base, an inventory will also need to account for non-agricultural uses, such as open water, forest, and urban uses, that are converted into or out of agricultural production during an inventory time period. For the second activity category, croplands can be managed in a variety of ways that affect the SOC budget including differences in tillage practices, crop type or rotation, fertilizer usage and organic amendments, planting winter cover crops, residue management, as well as usage of bare-summer fallow (Kern and Johnson 1993; Paustian et al. 1997a, b, 2000; Smith et al. 1997a, 1998; Bruce et al. 1999; West and Post 2002; VandenBygaart et al. 2003; Ogle et al. 2005). The third category addresses the influence of grazing land management on SOC storage, including the impact of grazing intensity, seeding legumes, fertilization, organic amendments, liming, planting more productive varieties of grasses, irrigation and manipulating fire regimes (Conant et al. 2001; Follett et al. 2001; Ogle et al. 2004). While some data are typically available on these activities because of their economic importance, this information is often highly aggregated and incomplete, which makes it difficult to account for all relevant agricultural activities that impact SOC storage [see Conant and Paustian (2004) for discussion of grazing lands]. Regardless, a national inventory assessment should have a consistent land base and account for the same land use and management activities through time. If data are not available for all practices, the inventory will be incomplete, but still impart useful information that provides a greater understanding of management impacts. In contrast, an inventory may be confounded for monitoring purposes if it does not account for the same practices each year or if the land base is changing through time. Both complications will create false trends in SOC storage that are unrelated to the land use and management activity. If data availability and quality change at some point, possibly due to new technologies or survey programs, it may be desirable to replace older sources with the newer, higher quality data. Inventories using a mixture of newer and older sources will need careful evaluation, however, ensuring that the patterns are due to land use and management activity and not the result of changing data sources. Data availability will also have consequences for the modeling approach. In some cases, the activity or environmental data may not support the desired model, particularly more complex simulation approaches. Under those circumstances, the model selection/development phase will have to be repeated to find an appropriate model for the level of input information that is available at the national scale (Fig. 1). Uncertainty Assessment Uncertainties exist in any inventory regardless of the use of models or measurements. For modeling, sources of uncertainties include initial values for SOC storage, model parameters, input data (e.g., land use and management history, weather and soil characteristics), model formulation, and verification/validation data (Kros et al. 1993; Klepper 1997). These uncertainties are caused by measurement/sampling error, classification error, model error and interpolation/extrapolation error (IPCC 2004). By evaluating uncertainties, knowledge gaps can be identified and used to set research agendas and inventory needs for improving the precision and accuracy of future estimates. There are several approaches for dealing with uncertainty such as error propagation equations and Monte Carlo analyses (IPCC 2000, 2004). Monte Carlo or similar analyses [e.g., Ensemble Approach, Reiners et al. (2002)] are likely to be more appropriate for assessing uncertainties in SOC inventories due to the complexity in the number of inputs and parameters in the models. Error propagation equations are not as effective because they are less accurate for approximating uncertainties when there are many inputs and strong dependencies among them (IPCC 2000, 2004). Even the more simplistic carbon accounting procedures often have multiple inputs and important dependencies that are likely to be more effectively addressed in a Monte Carlo analysis (e.g., Ogle et al. 2003). Simulation models typically have a large number of parameters (> 20), and it can be difficult to construct their probability distribution functions, much less determine their dependencies. Consequently, a traditional Monte Carlo analysis can be difficult to implement with simulation model-based inventories. An empirically based analysis provides another alternative for dealing with these uncertainties (Monte et al. 1996). This approach relies on comparisons between simulation model results and measurements from benchmark sites in agricultural lands. For example, Falloon and Smith (2003) used this technique to calculate the root mean square Error (RMSE) in RothC and Century model predictions based on comparisons with field measurements from agricultural experiments. Then they evaluated the relative uncertainty for modeling impacts in regional applications across European agricultural lands using the estimated RMSE. This method does not attempt to propagate uncertainties through the model calculations or construct probability distribution functions, a priori, which form the basis for a Monte Carlo analysis. Rather, an empirically based analysis uses the model s prediction error to assess uncertainties in resulting estimates after the model has been fully implemented. Model Implementation and Validation of Results Model implementation and validation are the final steps in the inventory process. The key to implementation is that there is consistency in the model application over time

5 OGLE AND PAUSTIAN SOC: ENVIRONMENTAL QUALITY INDICATOR AT THE NATIONAL SCALE 535 Can. J. Soil. Sci. Downloaded from by on 02/16/18 (IPCC 2004). Optimally this means that the same modeling framework is used for the entire time series. If the modeling framework is adjusted for different time spans during the inventory period, trends in SOC storage are also likely to change. Consequently, the resulting indicator will suggest that environmental quality has been altered, when those trends are merely an artifact of the modeling and not the land use and management activity. If a change in the modeling application is necessary, the interpretation must account for its effect on resulting trends before evaluating the influence of land use and management activity; re-computing trends for the entire time series using the new methodology is the most direct way to eliminate the potential artifacts from changing methods. Model validation is a critical and often overlooked step, largely because of the additional resources needed for validation. For example, results can be validated periodically based on measurements of soil organic carbon stocks from a monitoring network of benchmark sites (e.g., Brejda et al. 2001), but this requires resources for the maintenance and sampling of the sites. Also, it is important to realize that validation data do not serve the same purpose as verification data. Verification data provide a basis for evaluating the adequacy of a model for estimating land use and management impacts on SOC storage, and are often used for model tuning and algorithm development. In contrast, validation data are used for evaluating inventory results and should remain separate from model tuning and other methods development. Few if any current inventories are validated, but this is a much needed improvement that should be incorporated into future analyses. Poor validation will require a diagnosis of the problem with a thorough investigation of the assessment procedure and underlying data, which may uncover a variety of errors, such as poor model implementation, inappropriate model inputs or in the worst-case scenario an ineffective model for estimating land use and management impacts on SOC storage. With identification of problems, modifications are made and then successive steps in the modeling process are repeated (Fig. 1). In addition, validation data could provide the basis for an empirically based uncertainty analysis. After successful completion of the modeling application, the trends in SOC storage can be used to evaluate past policy actions and future options to improve environmental quality. A CASE EXAMPLE: US AGRICULTURAL SOC INVENTORY Overview An inventory of agricultural land use and management impacts on SOC storage has been developed for the United States of America and is described in detail elsewhere (Eve et al. 2002; Ogle et al. 2003). In summary, this inventory was conducted using a carbon accounting method developed by the Intergovernmental Panel on Climate Change (IPCC 1997), and addressed management effects on SOC storage in the top 30 cm of soil profiles, during the first 20 yr after a management change. The inventory was based on land use and management activity data from the US National Resources Inventory (USDA-NRCS 2000) and the Conservation Technology Information Center Database (CTIC 1998), which provided information on land use, cropping, and tillage practices. Management factors were derived from a statistical analysis of results from agricultural experiments, and uncertainties were assessed using a Monte Carlo analysis. The inventory addresses changes in SOC storage for both mineral and organic soils (i.e., Histosols), but here we will focus on mineral soils, which constitute the majority of agricultural land in the United States of America. Land use and management activity between 1982 and 1997 led to a net increase in SOC storage for mineral soils, estimated at 10.8 Tg C yr 1, with a 95% confidence interval ranging from 6.5 to 15.3 Tg C yr -1, or ± 40%. The uncertainties in this inventory are high, and further analysis has shown that over 90% of that uncertainty was associated with the estimated management factors, which determine the relative change in SOC stocks for the different land use and management activities. Reducing uncertainty will depend on new experiments and better scaling of those results to represent variation in management effects across different regions of the country. Alternatively, simulation modeling may be used to estimate management effects on SOC storage, and it is anticipated that this approach could considerably improve the accuracy and precision of the inventory estimates (Paustian and Ogle, unpublished data). As with any indicator, SOC stocks need to be placed in an appropriate context so that they are meaningful for policy analysis. For agricultural systems, one possible context is to consider the relationship between an estimated stock in a particular year and the amount of SOC stored under native conditions. In addition, stocks could be compared with the amount of SOC that would occur under a baseline of conventional agricultural management in a country, which for the United States of America, would likely be considered annual row cropping rotations or small grains with conventional tillage (i.e., tillage practices that fully invert the soil). A simple index referenced to both conditions can be developed by equating an index value of 100% to the SOC storage under native conditions, and equating an index value of 0% to the storage under conventional agricultural management. Using the IPCC method, the native and conventional management stocks for the US agricultural land base would be and Tg C, respectively (Table 1). Therefore, with an estimated total stock of Tg C in 1997, the index value for US agricultural soils would be about 60% on the proposed scale [i.e., (estimated stock conventional stock) / (native stock conventional stock)]. Of course, this is not the only context for evaluating stock estimates, and research is needed to investigate this issue more fully, taking into consideration other possibilities that may be more appropriate depending on how the information is used to assess environmental quality and sustainability. Policy Relevance Natural resource inventories provide information about the state of a particular resource that can be used in a variety of ways, including an evaluation of the impact of past policy

6 Can. J. Soil. Sci. Downloaded from by on 02/16/ CANADIAN JOURNAL OF SOIL SCIENCE Table 1. Recent trends in SOC storage for US agricultural lands. The relative capacity is an index value for the estimated stock based on the difference between the native stock and the baseline stock for conventionally managed agricultural land Per area basis z Relative capacity y SOC (Tg) (Mg C ha 1 ) (%) z Total agricultural land base = 385 Mha. y Relative capacity compared with uncultivated reference condition; [(estimated stock conventional tock) / (native stock conventional stock)] 100; conventional management = Tg C; native stock = Tg C. on a resource, its sustainability under business as usual scenarios, and potential effect of alternative policy options intended to enhance its sustainability (Fig. 3). In the United States of America, the agricultural SOC inventory has been used to assess air quality issues, specifically the effect of agricultural land use and management on greenhouse gas emissions (US-EPA 2003). Results have demonstrated that recent management has created a modest sink for atmospheric CO 2 due to setting aside cropland from production, adoption of conservation tillage management, and cropping intensification through a reduction in the usage of bare-summer fallow (Eve et al. 2002). Of these activities, past policy had the most direct role in promoting the setting aside of cropland through the Conservation Reserve Program, which was established through US congressional legislation in the 1985 Food and Security Act. This program was designed to reduce food surpluses in combination with improving soil conservation, by encouraging producers to set aside highly erodible land from crop production through financial incentives. Although it was not an intended effect, this policy did increase SOC storage, and presumably lands set aside in this program will be of higher quality after they are returned to production. However, the longer-term sustainability of this enhanced quality will depend on their subsequent management, particularly in regard to decisions that can maintain low rates of soil erosion (Gilley et al. 1997). Sperow et al. (2003) conducted an analysis to consider how future management could enhance SOC stocks in US agricultural lands. They evaluated the net change in SOC stocks with widespread adoption of conservation tillage management, elimination of fallow in rotations, increasing usage of winter cover crops, and setting aside additional highly erodible cropland from cultivation. Using the IPCC carbon accounting method, they estimated an additional annual increase in SOC stocks of 66 Tg C yr 1 over the current rate of 10.8 Tg C yr 1 (Table 1). In another analysis, Lal et al. (1998) estimated annual increases in SOC from 75 to 208 Tg C yr 1, taking into account a broader suite of land use and management options. This information can be used to consider future policy options for mitigating greenhouse gas emissions, and then monitoring agricultural SOC would allow for an evaluation of the resulting policy action. For example, policies based on the analysis by Sperow et al. (2003) would be expected to increase SOC to Tg C over a 20-yr period at a rate of 66 Tg C yr 1 (taking into account that the current rate of 10.8 Tg C yr 1 will continue for 5 more years), and raise the index value for US agricultural soils to 85% (Fig. 2). Although trends in the US inventory have not been evaluated for other purposes, this information could be used to assess other quality-related issues, such as relationships to agricultural production and the usage of materials and energy in these operations at the national scale (Fig. 3). For example, improving soil fertility by increasing SOC should reduce the amount of fertilizer needed for optimal crop production, and this could be investigated by comparing trends in SOC storage with cropping practice surveys and fertilizer sales. Moreover, increasing SOC stocks is often linked with reduced erosion due to increased aggregate stability (Carter and Stewart 1996), and this could be evaluated through erosion data such as records of sediment content of rivers. Conservation of nutrients and soil would make agricultural operations more sustainable and efficient, as well as potentially more profitable. In a case study for southwestern Australia, Lefroy and Rydberg (2003) analyzed the energetic costs for different practices, and found that agroforestry was more sustainable and profitable than other cropping systems due to reduced erosion and its benefit for the agricultural operation. In addition, SOC could be used as an indicator of water quality to the extent that increasing soil organic matter is associated with reduced losses of nutrients to groundwater and overland flow, which is transferred into streams and aquifers. ADDITIONAL CONSIDERATIONS IN USING SOC AS AN INDICATOR Increasing SOC may not uniformly enhance soil quality as noted by Loveland and Webb (2003) in a review of the importance of soil organic matter for agricultural production systems. The reason for this conclusion is attributable to the fact that soil organic matter is composed of a wide variety of organic compounds that have different reactive qualities. In a simplistic categorization used by Loveland and Webb (2003), soil organic matter is divided into active and inert fractions. Inert C often refers to organic matter that is virtually free from biological attack with residence times in the range of millennia (Jenkinson 1990), but the definition is expanded in their paper to include organic matter with intermediate residence times in the range of decades. In contrast to the inert fraction, C in the active fraction typically has a residence time of less than a year, and is composed of labile forms of organic matter and microbial biomass. It is the active fraction that Loveland and Webb (2003) suggest contributes most to improved soil quality, particularly nutrient availability, and therefore total soil organic C may not always be an indicator of overall quality unless there is an increase in the active fraction. This conclusion does not necessarily imply that more inert forms of soil organic matter do not have an influence on soil quality, but that the active fraction has a more critical role. Moreover, it is worth noting that increasing total soil organic C in agricultural systems is often associated with more microbial biomass and labile organic matter (e.g., Franzleubbers et al. 1995; Salinas-Garcia et al. 1997; Doran et al. 1998; Janzen et al. 1998).

7 OGLE AND PAUSTIAN SOC: ENVIRONMENTAL QUALITY INDICATOR AT THE NATIONAL SCALE 537 Can. J. Soil. Sci. Downloaded from by on 02/16/18 Fig. 2. Potential carbon stock increases for policies that encourage widespread adoption of conservation practices based on analysis by Sperow et al. (2003). Index values are given in parentheses for the current SOC storage relative to the difference between the native and conventional management stocks. Fig. 3. Relationships among SOC as an indicator of environmental quality, agricultural production, sustainability, other indicators and policy. SOC in combination with other indicators of environmental quality provides information for policy that is used to make decisions about programs and regulations. After policy actions are implemented, the effects on environmental quality are monitored through the indicators, as well as information on agricultural production and sustainability. In turn, this affects future policy based on the trends in the indicators and their relationship with past policy. Increasing SOC stocks for the sole purpose of having more soil organic matter is not always advisable when attempting to meet objectives for improving environmental quality. For example, while it is clear that organic amendments can increase SOC (Ogle et al. 2005), excessive additions can lead to increased nutrient leaching to groundwater and streams, thus reducing water quality. There are also cases in which increasing SOC can have un-intended impacts on air quality by enhancing greenhouse gas emissions. For example, organic and mineral fertilizers increase crop production and thus have a positive effect on SOC. However, fertilization also stimulates the production of nitrous oxide from agricultural soils, which is another major greenhouse gas, and thus can counteract the benefit of soil carbon sequestration for mitigation of greenhouse gases (Mosier et al. 1998). In a similar example, Six et al. (2004)

8 Can. J. Soil. Sci. Downloaded from by on 02/16/ CANADIAN JOURNAL OF SOIL SCIENCE found that changing from conventional tillage management to no-till increased nitrous oxide emissions during the first 10 yr after adoption, and thus counteracted some of the benefit of carbon sequestration for mitigation purposes over this time frame. Longer-term maintenance of no-till (>10 yr), however, showed an overall decrease in N 2 O emissions relative to conventional tillage, and thus a greater benefit for mitigation of greenhouse gas emissions. Due to these considerations and to provide a more complete assessment, a set of indicators is needed for evaluating environmental quality of agricultural lands that incorporates other features than just SOC storage, such as soil erosion, water quality, non-co 2 greenhouse gas emissions, soil biodiversity and biomass, in addition to agricultural effects on environmental quality beyond those related to soil processes (Fig. 3). Furthermore, other indicators specific to agricultural production and rural economies must be considered (Fig. 3). In the long-term, improving environmental quality is important, but it must be done taking into consideration the supply of food and fiber products, as well as the viability of rural economies in agricultural regions. Policies improving environmental quality will not be very beneficial if imposing unreasonable constraints on agricultural operations that lack sufficient government support or alternatives for producing a viable product. With information on a variety of indicators, relationships can be identified from past trends and used for refining policy to minimize deleterious side-effects of land use and management that would not be apparent based solely on SOC as an indicator for policy assessment and development. CONCLUSIONS SOC storage is a valuable indicator of environmental quality and through a combination of field measurements and modeling, a national inventory can be conducted on a periodic basis to monitor trends and evaluate quality-related issues. While the US agricultural SOC inventory has not been used to assess other issues related to environmental quality beyond greenhouse gas emissions, it is likely that the broader perspective gained through such an evaluation would be a tremendous benefit for policy. By monitoring SOC, in combination with other indicators, there could be assessments of past policy actions, and evaluations of the sustainability of agricultural lands in terms of the material and energy needed to maintain food and fiber production, with the goal of protecting this natural resource for the long-term needs of society. ACKNOWLEDGMENTS We would like to thank those who have provided insights and worked on the development of the national agricultural SOC inventory for the US, particularly Marlen Eve, Mark Sperow, Ron Follett, John Kimble, and F. Jay Breidt, as well as many others who have provided technical assistance. We are also grateful for the opportunity provided by the OECD organizers to participate in the expert meeting on Soil Organic Carbon and Agriculture. Preparation of this manuscript was supported by the Environmental Protection Agency (Agreement No. 3W-2310-NAEX), in addition to USDA/CSREES (Agreement No ) through funding for the Consortium for Agricultural Soils Mitigation of Greenhouse Gases (CASMGS). Bauer, A. and Black, A. L Quantification of the effect of soil organic matter content on soil productivity. Soil Sci. Soc. Am. J. 58: Bernoux, M., Carvalho, M. D. C. S., Volkoff, B. and Cerri, C. C CO 2 emission from mineral soils following land-cover change Brazil. 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