Soil Survey Database Research Articles

Land use change and forest management effects on soil carbon stocks in the Northeast U.S.

BackgroundIn most regions and ecosystems, soils are the largest terrestrial carbon pool. Their potential vulnerability to climate and land use change, management, and other drivers, along with soils’ ability to mitigate climate change through carbon sequestration, makes them important to carbon balance and management. To date, most studies of soil carbon management have been based at either large or site-specific scales, resulting in either broad generalizations or narrow conclusions, respectively. Advancing the science and practice of soil carbon management requires scientific progress at intermediate scales. Here, we conducted the fifth in a series of ecoregional assessments of the effects of land use change and forest management on soil carbon stocks, this time addressing the Northeast U.S. We used synthesis approaches including (1) meta-analysis of published literature, (2) soil survey and (3) national forest inventory databases to examine overall effects and underlying drivers of deforestation, reforestation, and forest harvesting on soil carbon stocks. The three complementary data sources allowed us to quantify direction, magnitude, and uncertainty in trends.ResultsOur meta-analysis findings revealed regionally consistent declines in soil carbon stocks due to deforestation, whether for agriculture or urban development. Conversely, reforestation led to significant increases in soil C stocks, with variation based on specific geographic factors. Forest harvesting showed no significant effect on soil carbon stocks, regardless of place-based or practice-specific factors. Observational soil survey and national forest inventory data generally supported meta-analytic harvest trends, and provided broader context by revealing the factors that act as baseline controls on soil carbon stocks in this ecoregion of carbon-dense soils. These factors include a range of soil physical, parent material, and topographic controls, with land use and climate factors also playing a role.ConclusionsForest harvesting has limited potential to alter forest soil C stocks in either direction, in contrast to the significant changes driven by land use shifts. These findings underscore the importance of understanding soil C changes at intermediate scales, and the need for an all-lands approach to managing soil carbon for climate change mitigation in the Northeast U.S.

Addressing the effects of soil organic carbon on water retention in US Pacific Northwest wheat–soil systems

Soil organic carbon (SOC) is considered a significant contributor to soil water retention. However, generalizations about the role of SOC in available water-holding capacity (AWHC) may have inaccurately portrayed this relationship. We aim to reexamine the relationship between SOC and water retention using the National Cooperative Soil Survey (NCSS) Database. We focus on regional soil groups within the Pacific Northwest wheat production region, including Haploxerolls, Argixerolls, Haplocambids, and Durixerolls. We evaluated 77 sites based on SOC, total nitrogen (TN), pH, texture, bulk density (BD), field capacity (FC), permanent wilting point (PWP), and AWHC. Our findings indicate that texture and BD were the most significant contributors to AWHC variation, while SOC played a secondary role in explaining this variation. Mid-infrared (MIR) spectroscopy coupled with a random forest (RF) algorithm was used to evaluate the importance of spectral bands in determining changes in FC and PWP. This analysis identified mineral bands related to inner-surface hydroxyl groups in kaolinite (3700 cm −1) and Si-O-Si overtones (1870 cm −1) as the most important spectral contributors to PWP. The water retention at FC was associated with organic absorbances relevant to soil aggregation, such as polysaccharide C–O (~1035 cm −1), while mineral bands were relatively less influential. This study highlights the need to reexamine the impact of SOC as well as the interaction between soil texture and compaction on soil water retention to elucidate the underlying mechanisms responsible for AWHC, thus providing insight into future drought adaptation strategies.

Soil health assessment in the Yangtze River Delta of China: Method development and application in orchards

Soil health is universally acknowledged to be an integrative tool in revealing soil degradation and agro-ecosystem health, and it has been the focus of many national investigations to find a compromise between the increase of human demands in urbanization and the conservation of agricultural soils. Based on the soil survey database of the Yangtze River Delta Region in China, including 66 soil samples collected from 22 representative orchards and 20 soil indicators analyzed for each sample, this study made a preliminary attempt to determine a minimum data set (MDS) of 10 sensitive indicators by factor analysis, to calculate weights and establish scoring function curves respectively, and a localized comprehensive assessment methodology of soil health was successfully developed and applied in orchards with organic, green and conventional management practices in Shanghai, China. The results indicated that, the rankings of soil health index (SHI) were generally organic>green>conventional, and 95% of the orchards were rated as the grades of moderate (40−60) to better (60−80). The SHI in green orchards with different cultivation durations followed the pattern of gradual increase over 5 years. The improvement of soil fertility, soil aggregation structure and soil bio-community were crucial to improve soil health, prevent soil degradation and promote soil sustainability in this region, particularly under the circumstance of encouraging green agriculture development.

Quantification of soil erosion in small watersheds on the Loess Plateau based on a modified soil loss model

The technology of slope vegetation system stability enhancement is an important part of the comprehensive ecological security improvement in small watersheds area of the Loess Plateau. The results of the comprehensive soil erosion improvement in the sub-basin are used to give an evaluation of the effectiveness of the gully slope vegetation restoration project. Soil erosion quantification distribution in the Sheep Sap Gully sub-basin of the Loess Plateau hilly gully area was simulated and explored by combining the modified universal soil loss equation RUSLE model with GIS and RS spatial information technology. The quantitative values of LS factor were extracted using DEM data, the R factor of rainfall erosion force was calculated using meteorological monitoring station data around this region, the K factor of erosion resistance was obtained based on the soil survey database, and the C factor and P factor of soil and water conservation measures were obtained by combining MODIS image data and previous research experience. The study concluded that (1) The erosion area ratio within the study area is 36.33%; (2) The quantitative grading standard of the pattern, the size of the erosion distribution area at all levels is ranked as light>moderate>very strong>strong>intense; (3) Under the conditions of land use and vegetation cover, strong erosion is mostly found in farming areas with sparse vegetation, while weak erosion is found in areas with lush vegetation such as forests and grasslands; (4) In terms of spatial distribution, erosion is greater in the south-western part of the basin than in the north-eastern part, and there is also strong erosion in the south-western part. The results of the study provide a reference for research into integration and synthesis of ecological security technologies for gully and slope management projects. The research content provides the basis and support for watershed governance and soil and water resource management and conservation.

Mapping time-to-trafficability for California agricultural soils after dormant season deep wetting

Soil compaction is a threat to agricultural soil function due to constriction of macro- and meso-pores necessary for air and water movement and crop root elongation. Soils are most vulnerable to compaction when moist. Agricultural soils saturated from winter precipitation or from intentional flooding for groundwater recharge may limit growers’ operational access to fields. The objective of this research was to develop guidance for rain-free “time-to-trafficability” (including shallow workability—when a soil is conducive to both tillage and traffic) after deep wetting, using soil survey data, pedotransfer functions, and a hydroclimatological modeling approach. Trafficability is defined as a threshold of field capacity (θfc) at the soil surface (0–10 cm), ranging from 85% of θfc (clays and silty clays) to 95% of θfc (sands and loamy sands). The θfc threshold is guided by the soil texture plasticity index, an indicator of compaction risk. 2911 soil profiles from soil survey databases were subjected to a wetting simulation, followed by drainage and evaporation using HYDRUS-1D across 11 locations representing mean annual potential evapotranspiration (PET) quantiles from 5% to 95%, four months (January-April), and three different years, assuming no precipitation. Rain-free time-to-trafficability was greatest in fine and loamy soils during cold months (January and February). However, seasonal effects on time-to-trafficability were more pronounced in loamy soils. Non-linear predictive functions were developed for each 0–10 cm textural class to enable mapping the typical time-to-trafficability across PET gradients, revealing clear regional and temporal patterns. Model derived estimates can inform agricultural managed aquifer recharge timing decisions and subsequent risk of soil compaction. Additional research is needed for validation and to better constrain time-to-trafficability estimates for loamy and fine textured soils, which show a greater degree of uncertainty amid greater risk of compaction indicated by plasticity indices.

Development and spatialization of a soil potential multifunctionality index for agriculture (Agri-SPMI) at the regional scale. Case study in the Occitanie region (France)

Europe is faced to a competing demand for areas suitable for agriculture and areas suitable for urban development. To help building the general guidelines necessary for a sustainable development at the regional scale, we developed a soil potential multifunctionality index for agriculture (Agri-SPMI), which used the concepts of soil-based ecosystem services and soil functions. It considered the provision of a physical and chemical habitat for plant growth functions, the retention and transfer of water and pollutants function, and the carbon storage function. Three agricultural land uses were considered in this index: arable lands, perennial crops, and pastures. The assessment of the Agri-SPMI was done over a large territory, using a small set of inherent soil properties available in the French soil survey database at 1:250,000 scale. The output was in the form of two maps, which can be viewed in a GIS software: a map of areas with high Agri-SPMI and a map of the service of food, biomass, and fiber production. The concept of soil multifunctionality allowed highlighting environmental issues related to agriculture and to encourage land planners to take them into account in addition to the agronomic potential of soils. The framework used is fully transferable to other scales, other soil contexts, or other ecosystem services, with adaptation of the soil properties considered.

Long-Term Ecosystem Nutrient Pool Status for Aspen Forest Harvest Simulations on Glacial Till and Sandy Outwash Soils

Sandy outwash and glacial till soils compose large amounts of public forestland due to historically poor agricultural yields. The outwash soils have low fertility, poor nutrient retention and are restricted from whole-tree harvesting (WTH) in some states, whereas the glacial till has medium nutrient retention and fertility, and is unrestricted from WTH. To assess the long-term sustainability of harvesting, a nutrient budget was constructed from field measurements, the National Cooperative Soil Survey (NCSS) database, and literature values for stem-only harvesting (SOH) and WTH at a 45-year rotation length and 11 rotations were simulated. The budgets showed that SOH and WTH recovery years, or the time necessary for the inputs to match outputs through leaching and one harvest, exceeded common rotation lengths for both soil types under all weathering scenarios, and the average WTH reduced the total available rotations by one harvest. The large variation in soil nutrient pools and harvest removals complicated the ability to identify the difference between SOH and WTH early in the model, but differences became apparent with sequential harvests. The recovery years were 2–20 times the 45-year rotation length under all weathering rates. Taken together, models in this study bridge the gap between short- and long-term studies and bring into question the sustainability of WTH and SOH practices on nutrient-poor soils.

Prescribed pH for soil β-glucosidase and phosphomonoesterase do not reflect pH optima

In soil enzyme assays, buffers are most commonly employed to constrain the assay to a specific pH at which activity is assumed to be maximized (i.e., pH optima). The assumed pH optima prescribed for the assay of soil β-glucosidase and phosphomonoesterase activities are based on early studies from a limited set of soils and have subsequently been found to be invalid in broader contexts. Here, we used the USDA-NRCS national soil survey database to preselect soils with a broad range of edaphic characteristics—namely soil pH, organic matter content, and clay content—to robustly assess pH optima of β-glucosidase and phosphomonoesterase. β-Glucosidase pH optima occurred within a narrow range and were independent of soil or climatic properties, including soil pH. Conversely, phosphomonoesterase pH optima showed a broad range of values that were closely linked to soil pH. For most samples, the mismatch between the prescribed and optimal pH (maximal activity) corresponded to activity differences of <10% for β-glucosidase and <50% for phosphomonoesterase. Dichotomization of acid and alkaline phosphomonoesterase activity is likely an artifact of methodological decisions rather than an accurate representation of two distinct soil enzymes. Thus, current prescriptions of buffer pH values assumed as pH optima for assaying β-glucosidase and phosphomonoesterase are incorrect. Instead, pH optima of soil enzyme activities should be empirically determined for specific soils.

The Business Side of Ecosystem Services of Soil Systems

Current applications of the Ecosystems Services (ES) framework to soils are narrowly defined (e.g., soil-based, pedosphere-based, etc.), and focus on soil properties while treating soil as a closed system. Because soil is an open system, it receives and loses matter across its boundaries within Earth’s spheres (atmosphere, biosphere, hydrosphere, lithosphere, ecosphere, and anthroposphere), which also need to be accounted for in economic analysis. In market economies, the market transforms resources from the Earth’s pedosphere and related spheres into goods and services for societal welfare with non-market institutions mediating human and environmental interactions. These transformations and mediations can result not only in welfare but damages as well. The concept of soil ES and ecosystem disservices (ED) is a human-centered framework, which can be a useful tool in business decision-making. Soil ES (e.g., provisioning, regulation/ maintenance, and cultural) are used to produce goods and services, but the value of these ES and ED are not always accounted for as a part of business decision-making. The objective of this review is to illustrate the monetary valuation of ecosystem services of soil systems (SS) with examples based on the organizational hierarchy of soil systems. The organizational hierarchy of soil systems can be used in economic valuations of soil ES by scale (e.g., world, continent), time (e.g., soil, geologic), qualitative and quantitative degrees of computation (e.g., mental, verbal, descriptive, mathematical, deterministic, stochastic), and degree of complexity (e.g., mechanistic, empirical). Soil survey databases, soil analyses, Soil Data Systems (SDS), and Soil Business Systems (SBS) provide tools and a wide range of quantitative/qualitative data and information to evaluate goods and services for various business applications, but these sources of soil data may be limited in scope due to their static nature. Valuation of soil resources based on soil and non-soil science databases (e.g., National Atmospheric Deposition Program (NADP) databases, etc.) is critically needed to account for these ES/ED as part of business decision-making to provide more sustainable use of soil resources. Since most ecosystems on Earth have been modified by human activity, “soil systems goods and services” (SSGS) may be a more applicable term to describe soil contributions (benefits/damages) to economic activity, compared to a term such as “soil ecosystem goods and services.”

The Business Side of Ecosystem Services of Soil Systems

Current applications of the Ecosystems Services (ES) framework to soils are narrowly defined (e.g., soil-based, pedosphere-based, etc.), and focus on soil properties while treating soil as a closed system. Because soil is an open system, it receives and loses matter across its boundaries within Earth’s spheres (atmosphere, biosphere, hydrosphere, lithosphere, ecosphere, and anthroposphere), which also need to be accounted for in economic analysis. In market economies, the market transforms resources from the Earth’s pedosphere and related spheres into goods and services for societal welfare with non-market institutions mediating human and environmental interactions. These transformations and mediations can result not only in welfare but damages as well. The concept of soil ES and ecosystem disservices (ED) is a human-centered framework, which can be a useful tool in business decision-making. Soil ES (e.g., provisioning, regulation/ maintenance, and cultural) are used to produce goods and services, but the value of these ES and ED are not always accounted for as a part of business decision-making. The objective of this review is to illustrate the monetary valuation of ecosystem services of soil systems (SS) with examples based on the organizational hierarchy of soil systems. The organizational hierarchy of soil systems can be used in economic valuations of soil ES by scale (e.g., world, continent), time (e.g., soil, geologic), qualitative and quantitative degrees of computation (e.g., mental, verbal, descriptive, mathematical, deterministic, stochastic), and degree of complexity (e.g., mechanistic, empirical). Soil survey databases, soil analyses, Soil Data Systems (SDS), and Soil Business Systems (SBS) provide tools and a wide range of quantitative/qualitative data and information to evaluate goods and services for various business applications, but these sources of soil data may be limited in scope due to their static nature. Valuation of soil resources based on soil and non-soil science databases (e.g., National Atmospheric Deposition Program (NADP) databases, etc.) is critically needed to account for these ES/ED as part of business decision-making to provide more sustainable use of soil resources. Since most ecosystems on Earth have been modified by human activity, “soil systems goods and services” (SSGS) may be a more applicable term to describe soil contributions (benefits/damages) to economic activity, compared to a term such as “soil ecosystem goods and services.”

Ecological Stoichiometry of Carbon, Nitrogen, and Phosphorus in Subtropical Paddy Soils

This study aims to understand the existence of stable soil organic carbon (C), nitrogen (N), and phosphorus (P) ratios in paddy soil. Based on a field soil survey database, the ecological stoichiometry of the C:N:P ratio of 110 subtropical paddy soil profiles and 587 genetic horizons were analyzed at a regional scale. Relevant analysis and redundancy analysis (RDA) are used to study the relationships between C:N:P ratios and soil-environmental factors (topography, parent materials, soil genetic horizons, soil groups, soil physical, and chemical properties). The results showed that the weighted averages of C:N, C:P, and N:P in paddy soils of subtropical regions were 12.6, 49, and 3.9, respectively, and C:N:P was 38:3.2:1. The C:N of paddy soil did not vary significantly with parent materials, soil groups, or genetic horizons. However, the C:P and N:P variations were significantly different, and the mean values of the two were much lower than global ratios (186 and 13.1) and average levels of C:P and N:P in Chinese soils (136 and 9.3). Although the C:N:P ratio in the paddy soil profile was relatively unstable, the topsoil C:N (14.2) was relatively stable due to the strong interaction between the topsoil and the environment. This reflects the close coupling of C and N in the topsoil of paddy fields under long-term anthrostagnic maturation. However, in the paddy soil profile, C:P and N:P were not stable, and there was no significant correlation between soil organic carbon (SOC) and total P content, total N, or total P content, which suggests that environmental changes may lead to soil C:N:P decoupling. It was found that topography, soil texture, iron oxide, and bulk density are all key soil-environmental factors that regulate the soil profile of rice paddy C:N:P.

Comparing Field Sampling and Soil Survey Database for Spatial Heterogeneity in Surface Soil Granulometry: Implications for Ecosystem Services Assessment

Lithospheric-derived resources such as soil texture and coarse fragments are key soil physical properties that contribute to ecosystem services (ES), which can be valued based on “soil” or “mineral” stocks. Soil survey data provides an inexpensive alternative to detailed field measurements which are often labor-intensive, time-consuming, and costly to obtain. However, both field and soil survey data contain heterogeneous information with a certain level of variability and uncertainty in data. This study compares the potential of using field measurements and information from the Soil Survey Geographic database (SSURGO) for coarse fragments (CF), sand (S), silt (Si), clay (C) and texture class (TC) in the surface soil (Ap horizon) for the 147-hectare Cornell University Willsboro Research Farm, NY. Maps were created based on following methods: a) utilizing data from the SSURGO database for individual soil map unit (SMU) at the field site and using representative or reported values across individual SMU; b) averaging the field data within a specific SMU boundary and using the averaged value across the SMU; and c) interpolating field data within the farm boundaries based on the individual soil cores. This study demonstrates the important distinction between mapping using the “crisp” boundary soil SSURGO databases compared to the actual spatial heterogeneity of field interpolated data. Maps of CF, S, Si, C, and TC values derived from interpolated field core samples were dissimilar to maps derived by using averaged core results or SSURGO values over the SMUs. Dissimilarities in the maps of CF, S, Si, C, and TC can be attributed to several factors (e.g., official soil series data being collected from “type locations” outside of the study areas). Correlation plot of clay estimates for each SMU showed statistically significant correlations between SSURGO and field-averaged (r = 0.823, p = 0.003) and field-interpolated clay (r = 0.584, p = 0.028) estimates, but no correlation was found for CF, S, and Si.

Development of a spatially-varying Statistical Soil Moisture Profile model by coupling memory and forcing using hydrologic soil groups

Information on vertical Soil Moisture Content (SMC) profile is important for several hydro-meteorological processes. This study borrows the idea of coupling the memory and forcing from a previous study and develops a spatially-varying Statistical Soil Moisture Profile (SSMP) model to estimate the vertical SMC profile. It uses only surface soil moisture (0–5 cm) values and Hydrological Soil Groups (HSGs) information of the location. The focus of the study is incorporation of the HSG information to ensure the spatial transferability of the proposed model by capturing the spatial variations of soil moisture profile with the change in soil hydraulic properties. The wide range of soil moisture data for model development as well as for spatial validation is obtained from 171 stations from different networks of International Soil Moisture Network (ISMN) at five different depths, i.e., 5, 10, 20, 51 and 102 cm. The HSG information at the locations are extracted from the Web Soil Survey (WSS) database. The potential of spatial transferability of the SSMP model is assessed by applying it to the new stations within the corresponding HSG. Model performances are promising for all four depth pairs (5–10, 10–20, 20–51 and 51–102 cm) of all four HSGs during both model development and spatial validation given the model complexity. Hence, the spatially-varying SSMP model is suitable at the ungauged locations by incorporating the HSG information. The potential application of the proposed model shows the future scope to assimilate the satellite based surface SMC data into the model to develop a vertical soil moisture profile map over a large area.

Using soil survey data as a predictive tool for locating deeply buried archaeological deposits in stream valleys of the Midwest, United States

AbstractIn stream valleys of the US Midwest, most of the existing record of prehistoric cultures is deeply buried. Therefore, there is a need for understanding the age and distribution of different landform sediment assemblages (LSAs) in these settings to adequately evaluate the landscape for buried archaeological materials. In this study, we show the applicability of US Department of Agriculture Natural Resources Conservation Service soil survey data to (a) provide information on the spatial distribution and characteristics of different LSAs and (b) determine the associated geologic potential for buried cultural deposits. We considered four factors in our assessment, including the age of sedimentary deposits, soil stratigraphy, depositional environment, and drainage conditions. These data were used to develop a GIS‐based predictive model that was verified with data from a large volume of geoarchaeological research conducted throughout the region. The spatial scale of soil survey data, the uncertain nature of soil mapping, and the ability to detect the presence or absence of buried soils were found to be important limitations. Nevertheless, our results show that soil survey data can be useful as each of the four factors is considered to discriminate particular LSAs and appropriately evaluate buried‐site potential. Our approach can be broadly applied to fluvial systems where cultural deposits are deeply buried, particularly in other countries where soil survey databases exist.

Using Soil Survey Database to Assess Soil Quality in the Heterogeneous Taihang Mountains, North China

Soil quality evaluation is an effective pathway to understanding the status of soil function and ecosystem productivity. Numerous studies have been made in managed ecosystems and land cover to quantify its effects on soil quality. However, little is coincident regarding soil quality assessment methods and its compatibility in highly heterogeneous soil. This paper used the soil survey database of Taihang Mountains as a case study to: (i) Examine the feasibility of soil quality evaluation with two different indicator methods: Total data set (TDS) and minimum data set (MDS); and (ii) analyze the controlling factors of regional soil quality. Principal component analysis (PCA) and the entropy method were used to calculate soil quality index (SQI). SQI values assessed from the TDS and MDS methods were both significantly correlated with normalized difference vegetation index (p &lt; 0.001), suggesting that both indices were effective to describe soil quality and reflect vegetation growth status. However, the TDS method represented a slightly more accurate assessment than MDS in terms of variance explanation. Boosted regression trees (BRT) models and path analysis showed that soil type and land cover were the most important controlling factors of soil quality, within which soil type had the greatest direct effect and land cover had the most indirect effect. Compared to MDS, TDS is a more sensitive method for assessing regional soil quality, especially in heterogeneous mountains. Soil type is the fundamental factor to determining soil quality. Vegetation and land cover indirectly modulate soil properties and soil quality.

Soil Survey

Soil survey is an ongoing process from initial soil mapping through soil survey updates. A national model of linear extensibility percent (LEP) is needed to improve this process. The objective of this work was to develop and validate models for estimating LEP using general linear models and readily available soil survey properties. Measured data from the Kellogg National Soil Survey Laboratory database (Lincoln, Nebraska) were used to develop the prediction models. Twenty LEP prediction equations were developed based on pH and major mineralogy classes. Noncarbonate clay and, depending on the soil pH, either cation-exchange capacity or effective cation-exchange capacity explained between 42% and 86% of the total variation in LEP. Model equations using cation-exchange capacity as a predictive variable collectively produced a prediction root mean square error (RMSEP) of 1.44% and mean error (ME) of −0.16%. For low pH soils, the model equations using effective cation-exchange capacity as a predictive variable collectively produced an RMSEP of 1.29% and ME of −0.034%. The small negative MEs indicate an overall underestimation of LEP. Breaking down the validation results further among the different mineralogy groups produced a range of RMSEp from 0.42% to 1.80%. The smectitic group had the largest and the siliceous group had the lowest RMSEp. The prediction accuracy is considered adequate for soil survey purposes, and it is expected that LEP estimates will ultimately enhance soil survey interpretations. The models will be added to the soil survey database for soil scientists to use when measured data are not available.

Quantification of primary salinity, sodicity and alkalinity in South African soils using climate regions

No reliable primarily salinity, sodicity and alkalinity information is available for South Africa, nor are there monitoring programs in place to track the salt-affected status of South African soils. Such information is, however, needed for various agricultural and environmental studies on a provincial and national scale. Natural geological, hydrological, geomorphological and pedological processes lead to the development of most salt-affected soils. The aim of the study was therefore to determine the baseline salinity, sodicity and alkalinity of South African soils as related to selected climatic parameters. Soil analyses from the Agricultural Research Council–Institute for Soil, Climate and Water (ARC-ISCW) soil survey database were used in this study. Data from the ARC-ISCW and the South African Weather Service weather stations with a recording period of five years or more were used to determine temperature, rainfall and evaporation statistics. The aridity index (precipitation/evaporation) was used to express aridity. No clear relationship was observed between rainfall, evaporation, and aridity and salt accumulation on a national scale and other factors, such as geology, position in the landscape and previous climatic conditions, should also be accounted for. The relationship between climate and salt-affected soils was difficult to determine, probably because of the paleoclimate differing from the present climate. Thus, not all salt-affected soils showed predictable associations with present-day climate.

Evaluating the performance of DRAINMOD using soil hydraulic parameters derived by various methods

The objective of this study was to determine which level of soil information would be sufficient to use with DRAINMOD in predicting subsurface drainage. The model was evaluated by predicting and comparing with observed monthly and annual subsurface drainage for 14 years (1990–2003) from Webster silty clay loam soil experimental plots located near Gilmore City, in Pocahontas County, Iowa, USA. Three groups of input soil hydraulic parameters were obtained by: (1) determining the soil texture and bulk density (BD) from the ISPAID7.1 Soil Survey Database, then inputting them into a pedotransfer function model (ROSETTA) to determine soil hydraulic parameters (denoted as SP_1); (2) analyzing the soil texture and organic matter (OM) content in laboratory and deriving the BD, field capacity (θ−33kPa) and wilting point (θ−1500kPa) from literature, then inputting them into ROSETTA to determine soil hydraulic parameters (SP_2); and (3) calibrated soil hydraulic parameters based on initial inputs from the Soil Survey Database plus ROSETTA (SP_3). All methods resulted in an acceptable level of accuracy in predicting monthly and annual subsurface drainage volume from the study site and the differences in model performance using all these methods were subtle. This suggests that ROSETTA in combination with the Soil Survey Database offers a quick and easy way to derive the soil hydraulic parameters for running DRAINMOD to simulate subsurface drainage systems in Iowa's subsurface drained landscape where site-specific soil hydraulic properties may not be available. This warrants further evaluation of the model performance at other sites to support the findings in this study.

Soil natural capital quantification by the stock adequacy method

A method is presented for assessing soil natural capital based on the principles of land evaluation. Policymakers are adopting concepts of flows of ecosystem services, and the natural capital stocks that support them, to provide more integrated analyses of the trade-offs between environmental, economic, social and cultural outcomes from land use. Soil is frequently overlooked in these analyses. Techniques are needed to quantify and map soil natural capital and their potential to provide ecosystem services to enable the soil science community to more effectively engage with decision-makers. To support this engagement, these techniques need to use available soil survey maps and databases to provide extensive geographic coverage of soil natural capital estimates. The method presented estimates the adequacy of soil natural capital stocks to support the soil processes behind the provision of ecosystem services under a specific land use. A stock adequacy index estimates the degree to which the provision of services is limited by soil natural capital stocks or advantaged by a stock surplus under a given land use. Reference values are derived from a curve of the response of the provision of the service to key soil stocks for a specified land use. These curves are determined from land evaluation and soil quality literature, or by modelling. The method is essentially an extension of land evaluation in which the evaluations are calibrated using an ecosystem approach. The output indices provide information about potential ecosystem services provision, land-use suitability, soil resource use efficiency, and environmental performance. Outputs from the method are demonstrated for a range of soils under pastoral dairy land use in Wairarapa, New Zealand.

Modeling the dynamics of natural forest ecosystems in the northeast of European Russia under climate change and forest fires

The individual-based EFIMOD simulation model was used for regional-scale assessments of the dynamics of basic characteristics of the carbon and nitrogen balance in the forest ecosystems of north central Russia. Two forest strict nature reserves were chosen as case studies. Data from the National Forest Inventory were used for model initialization. Initial soil data were taken from a soil survey database containing data on soil organic matter and nitrogen content in the organic layer and mineral soil for different forest types and regions of European Russia. Standard meteorological data were used as climatic inputs. Two simulation scenarios (without disturbances and with forest fires) were coupled with 2 climatic ones (actual climate and the scenario of climate change). The main sources of uncertainty were analyzed and the model parameters were evaluated. A Monte Carlo procedure was applied for evaluation of the robustness of coefficients. Simulation results showed that the greatest carbon accumulation occurred in the scenario without disturbances. Fires resulted in significant losses in soil organic matter and tree biomass through direct and indirect carbon dioxide emissions. Simulated climate change led to an increased decomposition rate of soil organic matter and a related increase in the productivity of vegetation; however, for this region, the carbon balance was positive. This was primarily because young and middle-aged stands are prevalent in the region modeled. A full analysis would require analytical data on the possible dynamics of mature and over-mature forests in the same scenarios of climate change and forest fires.