Soil Database Research Articles

Parameter sensitivity of SWAP–PEARL models for pesticide leaching in macroporous soils

AbstractPesticide transport simulation by SWAP–PEARL (Soil–Water–Atmosphere–Plant and Pesticide Emission Assessment at Regional and Local scales) models can help to predict pesticide leaching at regional scales. For reasons of economic and time efficiency, measurement efforts should be prioritized towards critical parameters. The objective of this research is to perform a Morris screening and Sobol–Jansen sensitivity analysis to SWAP–PEARL models, using a reasonable worst‐case scenario. Three pesticide compounds were analyzed:, bentazon (zero sorption), imidacloprid (moderately sorbed), and compound I (highly sorbed). Initial macropore and pesticide parameter values were varied by ±20% to generate parameter ranges. The outputs analyzed were the concentration in drainage water, the average concentration in groundwater between 1 and 2 m, and the concentration in the soil system at 100‐cm depth. Influential parameters found through the Morris method were analyzed using the Sobol–Jansen method. The results for bentazon indicate that the degradation half‐life (DT50), the bottom depth of the internal catchment (zic), and the proportion of the internal catchment at the soil surface (pic_0) are critical parameters in all the outputs analyzed. For imidacloprid and compound I, the most relevant parameters for drainage output are the Freundlich sorption exponent (Fexp) and zic; for groundwater, the relevant parameters are Fexp, the bottom depth of static macropores (zst), and pic_0; and for soil concentrations at 100‐cm depth, the relevant parameters are Fexp, zic, and pic_0. The Morris and Sobol–Jansen methods produce the same results for the first position in the ranking. Measurement efforts should be performed to update national soil databases, including critical pesticide and macropore parameters.

Liquefaction Potential Assessment of Guwahati City Using One Dimensional Ground Response Analysis

Guwahati city which lies in the North Eastern region of India, falls in the highest seismic risk zonal level i.e. zone V in India. However, there are very few works on seismic hazard analysis of Guwahati soil considering the local site effects. The effect of large modifications in seismic waves that occur due to variation in soil properties near the surface of the earth is of great importance in geotechnical earthquake engineering. Seismic soil liquefaction, a soil seismic hazard, is evaluated in Guwahati city in terms of factor of safety against liquefaction along the soil profiles using ground response analysis. One dimensional ground response analysis has been conducted using equivalent linear and non linear method using the Deepsoil software. The input motion of 2011 Sikkim earthquake (Mw = 6.9) having bedrock PGA of 0.152 g at 30 m depth is considered. A comparative study has been made of the equivalent linear and non linear analysis in terms of surface PGA (g), maximum strain (%), maximum stress ratio and liquefaction potential using soil profiles of Guwahati city. It has been observed that stiffer soil layer results in similar PGA from both the analysis however non linear analysis generally gives a lesser surface PGA than by equivalent linear analysis. Non linear analysis generally gives a higher strain range and a lower maximum stress ratio as compared to the equivalent linear method. A slightly higher factor of safety is obtained using non linear analysis than using equivalent linear analysis. A soil database of 200 bore holes was used for the study. Spatial distribution of soil liquefaction potential is presented in the form of GIS based maps of factor of safety values.

Конструктивні та функціональні особливості провідних світових баз ґрунтових даних. Аналітичний огляд

Конструктивні та функціональні особливості провідних світових баз ґрунтових даних. Аналітичний огляд

Evaluation of pedotransfer functions to predict saturated hydraulic conductivity of Ukrainian soils

Literature overview. The parameterization of hydrological models requires knowledge of the soil filtration properties. Generally, soil profiles are characterized by properties such as sand, silt and clay content, bulk density, organic carbon fraction or humus content, and no data on filtration properties are available. Ukrainian soil database, created in Geoecophysics of soil laboratory of National Scientific Center “Institute for Soil Science and Agrochemistry Researched named after O.N. Sokolovsky” (Laktionova et al., 2012), among other properties has extensive data on texture and bulk density for more than 2000 profiles, less on organic carbon content, and almost no data on saturated hydraulic conductivity (Ksat). The most probable ranges of Ksat for most types of Ukrainian soils are given in the Atlas of natural conditions and natural resources of the Ukrainian SSR (“Pochvenno-meliorativnoye rayonirovaniye. Masshtab 1:4000000,” 1978), however, the data doesn’t present Ksat for different textures inside one soil type. To fill this gap, the best solution is the applying of pedotransfer function (PTF). The purpose of this work is to synthesize the most realistic Ksat of the main soil groups of Ukraine, corresponding to a scale map of 1:2 500 000 (Krupskiy, 1977), as well as their genetic horizons, on the basis of calculated and experimental values available in the literature. Material and methods. Ten PTFs used in the study are based on regression equations (Cosby et al., 1984; Saxton & Rawls, 2006; Weynants et al., 2009; Wösten et al., 1999), decision tree (Tóth et al., 2015), or neural network (Zhang & Schaap, 2017). Ksat was estimated for 942 horizons of 171 profiles which represented all 40 soil groups (corresponding to the legend of 1:2 500 000 map) of Ukraine according to Dokuchaev classification. Results. Wösten and Rosetta3 PTFs are determined as the most relevant by comparing the calculated Ksat values with the available data of the bottom (horizons A2, B, C) and top (A0, A1) soil layers of Ukraine. In particular, they are relevant for widespread soils such as Soddy podzolic soils (WRB – Eutric podzoluvisols), dark gray podzolized soils (Phaeozems Albic), chernozems podzolized (Chernozems Chernic), chernozems southern (Chernozems Calcic), meadow-chernozemic soils (Phaeozems Haplic), dark chestnut and chestnut soils (Kastanozems Haplic and Kastanozems Luvic), meadow soils (Umbrisols Gleic, Fluvisols Dystrict, Fluvisols Eutryc, Leptosols Umbric), mountain soils (Cambisols), and top layer of Chernozems ordinary (Chernozems Chernic). Unfortunately, all ten PTFs underestimate 2-4 times Ksat of bottom layer of ordinary and typical chernozems (Chernozems Chernic) and overestimate 2-5 times for relatively impermeable horizons (< 2 mm/h). Conclusions. Based on the calculated and experimental values, the map of Ksat of the top and bottom soil layers was obtained. Sandy soils, common in Polissia, have the highest filtration rate. Ksat of loam and clay soils of forest-steppe and steppe can differ between different types by an order. The highest Ksat have soils with high structural properties (Chernozems Luvic, Chernozems Chernic). The lowest Ksat (0.2-3 mm/h) have Phaeozems Sodic, Solonetz, Solonchaks, Planosols Albic, and bottom layer of soddy manly gley (Arenosols Protic/ Haplic) and loamy soddy podzolic soils (Albeluvisols Umbric). The estimated values should be considered as the most probable because Ksat depends on landscape location of soil profile, tillage operations, and soil temperate. The results are acceptable to use in hydrological calculations and modeling.

The critical state line of nonplastic tailings

The probability of failure of tailing dams and associated risks demand improvements in engineering practice. The critical state line provides a robust framework for the characterization of mine tailings. New experimental data for nonplastic platinum tailings and a large database for tailings and nonplastic soils (grain size between 2 and 500 μm) show that the critical state parameters for nonplastic tailings follow the same trends as nonplastic soils as a function of particle-scale characteristics and extreme void ratios. Critical state lines determined for extreme tailings gradations underestimate the range of critical state parameters that may be encountered in a tailings dam; in fact, mixtures with intermediate fines content exhibit the densest granular packing at critical state. The minimum void ratio emin captures the underlying role of particle shape and grain size distribution on granular packing and emerges as a valuable index property to inform sampling strategies for the assessment of spatial variability. Mineralogy does not significantly affect the intercept Γ100, but it does affect the slope λ. The friction coefficients M of tailings are similar to those of other nonplastic soils; while mineralogy does not have a significant effect on friction, more angular grains lead to higher friction coefficients.

The advantages of using open source GIS for soil mapping

This article analyzes opportunities, state of development, popularity and prevalence of using freelicensed geoinformation systems in the world and in Ukraine; possibilities of using free GIS for soil mapping and individual agricultural soils of Chernivtsi affiliate of DU «Derzhgruntokhorona» were evaluated. The article examines the popularity of geoinformation systems with a free GNU GPL public license in the world and Ukraine, describes some interesting facts about using this software in the world for largescale projects in science and business, reviews the achievements of scientists in creating soil maps and opportunities for updating them with the help of free code software. New techniques for soil mapping are currently being developed in Ukraine, based on the use of multispectral space scan data, geoinformation systems etc. The cost of proprietary software is quite high as for the financial capacity of state institutions in Ukraine, so in our view, a good alternative for government agencies is to use non-profit distributed GIS and build predictive soil maps using them. Such maps can be used to create a National Soil Database and integrate it into SOTER, SOVEUR or similar international systems.

Carbon isotope discrimination as a surrogate for soil available water capacity in rainfed areas: A study in the Languedoc vineyard plain

Soil available water capacity (SAWC) is a key soil function for plant growth. Classical SAWC characterization requires time consuming determinations of bulk density and specific soil moisture contents. Consequently, these data are extremely sparse in existing soil databases. Using surrogates of the vegetal response to characterize SAWC across a great number of sites constitutes a promising perspective. The carbon isotope ratio (δ13C) measured in a plant organ is largely known as an indicator of plant water status. The aim of the paper is to test δ13C as an indicator of SAWC in rainfed vineyard.δ13C values of grapes at harvest time were measured at 21 sites on the Languedoc vineyard plain with contrasting SAWC (33 to 308 mm) for four years (2015 to 2018) with contrasting annual precipitation (from 390 to 715 mm). The relationships between δ13C and SAWC determined using a classical approach (soil description, soil sampling and laboratory methods) were satisfactory for all years (RMSEs from cross validation were between 35 and 61 mm). Better relationships were obtained between δ13C and SAWC for years that showed a full winter recharge of SAWC (2015, 2017 and 2018). Averaging the δ13C measurements over such years provided an even better relationship (R2 = 0.85; RMSE 32 mm), which was successfully validated in distant sites on the Languedoc vineyard plain.This work demonstrated that δ13C can be considered as a simple and inexpensive surrogate for estimating SAWC. In addition to considerably increasing the density of SAWC measurements, the use of δ13C would lead to better consideration of the contribution of deep horizons in the case of perennial plants. Application of this isotopic technique to other agro-systems is required to better define the potential areas of use of δ13C.

Exploring the effect of varying soil organic matter contents on current and future moisture supply capacities of six Italian soils

The Available Soil Water Capacity (AWC) is standard data in most soil databases and expresses soil water contents in the rootzone between field capacity (FC; –33 kPa) and permanent wilting point (WP; −1500 kPa). Literature suggests that increasing the content of soil organic matter (SOM) of a given soil does not significantly increase AWC and this has important implications when estimating soil moisture supply to crops and evaluating the potential for climate mitigation. For most crops, the real FC values vary between −10 and −50 kPa in different soils and WP values between −800 and −1500 kPa. Thus standard values for AWC of FC and WP do not represent field conditions in many soils. When exploring AWC for six Italian soil series, ranging from clay to sandy, AWC values at increasing %SOM were lower in clay soils and higher in sand as compared with actual conditions, which could be explained by considering the shape of the corresponding moisture retention curves. Rather than focus on static AWC values to define moisture supply to plants, real or actual soil moisture supply capacities (MSC) can be obtained by dynamic modeling of the soil-water-atmosphere-plant system, including a “sink-term” indicating a continuous relation between water uptake and negative pressure head of soil water and evaporative demand. Also, only models allow exploration of the effects of future severe IPCC climate scenario RCP 8.5. Thus, studying MSC for the six Italian soil series showed that MSC values were: (i) on average 30% higher than the corresponding AWC; (ii) distinctly different for the six soils; (iii) affected by declines of 1–9% as a result of the effects of future climate scenarios.; (iv) not significantly affected by increases of %SOM when considering climate change, except for the sand. Generalizations as to the effect of future climate scenarios and %SOM on MSC can only be realistic when modeling is performed for soil series in different climate zones.

Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods.

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

Using a Crop Modeling Framework for Precision Cost-Benefit Analysis of Variable Seeding and Nitrogen Application Rates

A key goal of precision agriculture is to achieve the maximum crop yield while minimizing inputs and loses from cropping systems. The challenge of precision agriculture is these factors interact with one another on a subfield scale. The seeding density and the nitrogen (N) fertilizer application rate are two of the most important inputs influencing agronomic, economic and environmental outcomes including yield, return on investment (ROI) and nitrate (NO3-) leaching. A cropping system simulation framework is used to predict site-specific subfield optimum seeding density and (N) fertilizer application rates for the economic optimum (maximum ROI) versus agronomic optimum (maximum yield). The framework couples the process-based APSIM cropping system model with the SSURGO soils database, Daymet weather data service, land grant university estimates of crop production costs and commodity price estimates, and the R statistics software. The framework performance was evaluated using multiple years of precision yield monitor data obtained from a continuous maize (Zea mays L.) cropping system field experiment with varying N-fertilizer rates. Subfield model estimates of crop yield were sensitive to initial conditions related to historical management of the field and had an r2 = 0.65 and a root mean square error of 2356.0 kg ha-1. A site-specific application of the framework comparing economic optimum seeding density and N-fertilizer rates with agronomic optimum values estimated the average ROI benefit to be up to 12.1% with a NO3- leaching reduction of up to 15.2 kg ha-1 at the economic optimum. However, in a minority of cases NO3- leaching was greater at the economic optimum, indicating that managing to maximize ROI rather than yield may not always reduce environmental impacts. Our results suggest that managing cropping systems for the economic optimum is plausible using publicly available data with our framework and may likely lead to improved environmental outcomes.

Prediction of soil properties with machine learning models based on the spectral response of soil samples in the near infrared range

Abstract One of the basic methods for soil analysis time and cost reduction is using soil sample spectral response in laboratory conditions. The problem with this method lies in determining the relationship between the shape of the soil spectral response and soil physical or chemical properties. The LUCAS soil database collected by the EU’s ESDAC research centre is good material to analyse the relationship between the soil properties and the near infrared (NIR) spectral response. The modelling described in the paper is based on these data. The analysis of the impact of soil properties configuration on absorbance levels in various NIR spectrum ranges was conducted using the stepwise regression models with the properties, properties squared and products of properties being explanatory variables. The analysis of partial correlation of soil properties values with absorbance values and absorbance derivative in the entire spectral range was conducted in order to evaluate the impact of the absorbance transformation (the first derivative of absorbance vector) on the change of significance of relationship with properties values. The Multi Layer Perceptron (MLP) models were used to estimate the absorbance relationship with single soil features. Soil property modelling based on the selection and transformation algorithm of raw values and first and second absorbance derivatives was also conducted along with the suitability evaluation of such models in building digital soil maps. The absorbance is affected by a limited number of tested soil features like pH, texture, content of carbonates, SOC, N, and CEC; P and K contents have, in case of this research, a negligible impact. The NIR methodology can be suitable in conditions of limited soil variation and particularly in development of thematic soil maps.

Increased Lateral Transfer of Soil Organic Carbon Induced by Climate and Vegetation Changes Over the Southeast Coastal Region of China

AbstractSoil erosion and the subsequent lateral movement of soil carbon profoundly affect the spatial pattern of carbon sources and sinks in terrestrial ecosystems. The eight river basins of Fujian Province considered in this study are an important part of the southeastern coastal river basin of the ninth largest river system in China. In recent years, the erosion of soil and soil organic carbon in the southeast coastal region of China is not ideal owing to the high and concentrated rainfall, natural vegetation destruction, and other reasons. In this study, the soil organic carbon erosion and deposition rates related to erosion in the eight river basins of Fujian Province are quantified, and their spatial distribution characteristics are extracted. Based on the soil erosion rate from two national soil erosion surveys and topsoil carbon content from the Harmonized World Soil Database, we estimated that the lateral soil organic carbon erosion increased from 7.53 to 26.75 g C m−2 yr−1 in eight river basins of Fujian Province from 1995–1996 to 2010–2012. The soil organic carbon erosion rate in the eight river basins increased by 2.6 times, while the soil organic carbon erosion rate for the whole of China decreased by 44% during the same period. The increase in torrential rain and decrease in vegetation cover in the southeastern coastal areas largely contributed to the erosion of soil and soil organic carbon. The lateral transfer rate of soil organic carbon may be further exacerbated by the increase of torrential rain in the southeastern coastal areas of China.

Modeling multivariate distribution of multiple soil parameters using vine copula model

This study introduces the vine copula model to model the multivariate distribution of multiple soil parameters. First, the conventional bivariate and multivariate copulas are presented to model the joint probability distribution of soil parameters. Then, the procedure for modeling the multivariate distribution of soil parameters using the vine copula model is explained. Finally, two soil databases (CLAY/5/345 and CLAY/6/535) containing complete multivariate data of multiple soil parameters are studied to demonstrate the validity of the vine copula model. The results indicate that there exist different levels of correlation between all pairs of soil parameters. The dependence structure among multiple soil parameters shows obvious diversity and non-Gaussianity, which cannot be adequately characterized by the commonly-used multivariate normal distribution. The vine copula model performs well in modeling the multivariate distribution of multiple soil parameters. It can effectively consider the diversity and non-Gaussianity in the dependence structure among multiple soil parameters. In comparison with the original distributions, the coefficients of variation (COVs) for the conditional distributions of soil parameters may be significantly reduced. The incorporation of more information may produce smaller COVs for the conditional distributions. The reduced COVs for soil parameters can be eventually converted to cost savings in the reliability-based design of geotechnical structures, which provides an incentive for geotechnical engineers to collect more information of soil parameters from various sources.

Updated information on soil salinity in a typical oasis agroecosystem and desert-oasis ecotone: Case study conducted along the Tarim River, China.

Precise and spatially explicit regional estimates of soil salinity are necessary to efficiently management and utilise limited land and water resources. Despite advances achieved in remote sensing over the past century, knowledge about the distribution and severity of soil salinization in economically important areas, such as oasis agroecosystems and desert-oasis ecotones (OADoE), is currently limited. An example of an area is southern Xinjiang, where the OADoE has a high anthropogenic influence. This study was conducted with the aim of mapping soil salinity in typical OADoE using remote sensing and machine learning techniques (Cubist and Random Forest, RF). A range of covariates was obtained from the multi-temporal Landsat-8 operational land imager (OLI) satellite for the period from 2013 to 2018. The values of coefficients of determination (R2), Lin's concordance correlation coefficient, root mean square error, and relative root mean squared error values, were 0.78, 0.87, 9.59, and 0.76, respectively, for the Cubist and 0.78, 0.86, 9.79, and 0.78, respectively, for RF models. The slope of the linear fitting equation was higher for the Cubist model (0.75) than for RF (0.69). The explanatory power of Cubist and RF for soil salinity variation were 33.22% and 31.41% in the agroecosystem, and 72.25% and 71.66% in desert-oasis ecotone, respectively. For the agroecosystem, the range of the predicted values for 89.13% (Cubist) and 84.78% (RF) of sample was controlled within the same observational range at an interval of 0-5 dS m-1. Compared to single-year data (from 2013 to 2018), the ability to account for model spatial variability in soil salinity based on multi-year Landsat images was increased by 16%-35%. According to the variable importance evaluation, soil-related indices are the most important predictor variables, followed by vegetation, topography, landform, and land use, with relative importance values of 60%, 21%, 16%, and 3%, respectively. The predicted map was also broadly consistent with those obtained for Xinjiang in the Harmonized World Soil Database (HWSD) from the second national soil survey of China conducted from 1984 to 1997. The results also showed that the average value of the study area is 8.10 dS m-1 based on the Cubist-based map whereas that of the HWSD is 10.60 dS m-1, this implied that the overall salinity level has reduced by 23.58%. The methodological framework presented covers all prediction process steps and has considerable potential to be used in future soil salinity mapping at large scales for other similar region as OADoEs. The map derived from the Cubist/RF model revealed more detailed variation information about spatial distribution of the soil salinity compared to HWSD, and can further assist with decision-making when planning and utilising on existing soil and water resources in OADoEs.

Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications

Large-scale distributed watershed models are data-intensive, and preparing them consumes most of the research resources. We prepared high-resolution global databases of soil, landuse, actual evapotranspiration (AET), and historical and future weather databases that could serve as standard inputs in Soil and Water Assessment Tool (SWAT) models. The data include two global soil maps and their associated databases calculated with a large number of pedotransfer functions, two landuse maps and their correspondence with SWAT’s database, historical and future daily temperature and precipitation data from five IPCC models with four scenarios; and finally, global monthly AET data. Weather data are 0.5° global grids text-formatted for direct use in SWAT models. The AET data is formatted for use in SWAT-CUP (SWAT Calibration Uncertainty Procedures) for calibration of SWAT models. The use of these global databases for SWAT models can speed up the model building by 75–80% and are extremely valuable in areas with limited or no physical data. Furthermore, they can facilitate the comparison of model results in different parts of the world.

A Comprehensive Study of Three Different Portable XRF Scanners to Assess the Soil Geochemistry of An Extensive Sample Dataset

The assessment of soil elemental concentrations nowadays mainly occurs through conventional laboratory analyses. However, proximal soil sensing (PSS) techniques such as X-ray fluorescence (XRF) spectrometry are proving to reduce analysis time and costs, and thus offer a worthy alternative to laboratory analyses. Moreover, XRF scanners are non-destructive and can be directly employed in the field. Although the use of XRF for soil elemental analysis is becoming widely accepted, most previous studies were limited to one scanner, a few samples, a few elements, or a non-diverse sample database. Here, an extensive and diverse soil database was used to compare the performance of three different XRF scanners with results obtained through conventional laboratory analyses. Scanners were used in benchtop mode with built-in soil calibrations to measure the concentrations of 15 elements. Although in many samples Cu, S, P, and Mg concentrations were up to 6, 12, 13, and 5 times overestimated by XRF, and empirical recalibration is recommended, all scanners produced acceptable results, even for lighter elements. Unexpectedly, XRF performance did not seem to depend on soil characteristics such as CaCO3 content. While performances will be worse when expanding to the field, our results show that XRF can easily be applied by non-experts to measure soil elemental concentrations reliably in widely different environments.

Effective Friction Angle Of Deltaic Soils In The Vistula Marshlands

Abstract This article presents the results of laboratory tests on soft, normally consolidated soils from the Vistula Marshlands. Samples of high-plasticity organic soils (muds) taken from 3.2–4.0 m and 9.5–10.0 m depth, as well as peat deposit at 14.0 m, are analysed. Presented case study confirms the applicability of the Norwegian Institute of Technology (NTH) method based on Cone Penetration Tests (CPTU) and allows for a conservative estimation of effective friction angle for muds. The plastification angle equal to 14.5° for organic silt, applied in the modified NTH method, fits well the triaxial test (TX) results. Moreover, the dilative-contractive behaviour according to the CPTU soil classification based on the Robertson’s proposal from 2016 corresponds well with volumetric changes observed in the consolidated drained triaxial compression tests. The internal friction angles of the Vistula Marshlands’ muds and peats are lower in comparison with the database of similar soft soils.

Yarı-humid ekolojik koşullar altında oluşmuş toprakların bazı fiziko-kimyasal özellikleri belirlenmesi ile seri düzeyinde haritalanması ve sınıflandırması

Bu çalışma ile Samsun ilinin güneyinde bulunan Kavak ilçesi sınırları içerisinde yer alan Seyitali, Kaya, İdrisli, Muhsinli, Beyköy ve Çayırlı mahallelerine ait toplam 1523 parselden oluşan ve 397.28 ha alan kaplayan arazilerin temel fiziko-kimyasal toprak özelliklerinin belirlenmesi, sınıflandırılması, toprak veri tabanının oluşturulması ve sayısal toprak haritasının hazırlanması amaçlanmıştır. Yıllık ortalama sıcaklık 10.2 O C ve yıllık ortalama yağış miktarı da 512.5 mm’dir. Newhall simülasyon modeline göre, toprak sıcaklık rejimi Mesic ve toprak nem rejimi ise Typic Xeric olarak belirlenmiştir. Çalışma sahasında yapılan arazi gözlemleri; topografik, jeolojik ve jeomorfolojik gibi veriler ışığında değerlendirilerek altı profil çukuru açılmıştır. Detaylı arazi gözlemleri, grid yöntemi ve burgu yoklamaları ile gerçekleştirilmiştir. Açılan profillerden genetik horizon esasına dayanarak toprak örnekleri alınmış ve laboratuvar analizleri yapılmıştır. Arazi çalışması ve analiz sonuçlarına göre altı farklı toprak serisi tanımlanmıştır. Tanımlanan topraklardan üç tanesi pedolojik sürecin yetersiz olması nedeniyle Entisol ordosuna, iki tanesi Inceptisol ve bir tanesi de Vertisol ordosuna dahil edilmiştir. Ordolar içerisinde %67.5 ile Entisoller en fazla alan kaplarken, bunu sırasıyla %21.8 ile Inceptisoller ve %10.6 ile Vertisoller izlemektedir. FAO-WRB göre ise topraklar, Vertisol, Cambisol ve Regosol olarak sınıflandırılmıştır. Tespit edilen serilerden Çayırlı serisi %24.08 ile en fazla büyüklüğe sahip toprak serisi iken, İdrisli serisi ise %8.17’lik oran ile en az alana sahip seri olarak belirlenmiştir.

Valuation of Total Soil Carbon Stocks in the Contiguous United States Based on the Avoided Social Cost of Carbon Emissions

Total soil carbon (TSC) is a composite (total) stock, which is the sum of soil organic carbon (SOC) and soil inorganic carbon (SIC). Total soil carbon, and its individual two components, are all important criteria for assessing ecosytems services (ES) and for achieving United Nations (UN) Sustainable Development Goals (SDGs). The objective of this study was to assess the value of TSC stocks, based on the concept of the avoided social cost of carbon dioxide emissions, for the contiguous United States (U.S.) by soil order, soil depth (0–20, 20–100, 100–200 cm), land resource region (LRR), state, and region using information from the State Soil Geographic (STATSGO) database. The total calculated monetary value for TSC storage in the contiguous U.S. was between $8.13T (i.e., $8.13 trillion U.S. dollars, where T = trillion = 1012) and $37.5T, with a midpoint value of $21.1T. Soil orders with the highest TSC storage midpoint values were Mollisols ($7.78T) and Aridisols ($2.49T). Based on area, however, the soil orders with highest midpoint TSC values were Histosols ($21.95 m−2) and Vertisols ($5.84 m−2). Soil depth was important, with the highest values of TSC storage being found in the interval 20–100 cm ($9.87T—total midpoint value, and $1.34 m−2—midpoint area density). The soil depth interval 0–20 cm had the lowest TSC storage ($4.30T) and lowest area-density ($0.58 m−2) value, which exemplifies the prominence of TSC in the deeper subsurface layers of soil. The LRRs with the highest midpoint TSC storage values were: M—Central Feed Grains and Livestock Region ($2.82T) and D—Western Range and Irrigated Region ($2.64T), whereas on an area basis the LRRs with the highest values were I—Southwest Plateaus and Plains Range and Cotton Region ($6.90 m−2) and J—Southwestern Prairies Cotton and Forage Region ($6.38 m−2). Among the U.S. states, the highest midpoint TSC storage values were Texas ($4.03T) and Minnesota ($1.29T), while based on area this order was reversed (i.e., Minnesota: $6.16 m−2; Texas: $6.10 m−2). Comprehensive assessment of regulating ES requires TSC, which is an important measure in achieving the UN SDGs. Despite the known shortcomings of soil databases, such as their static nature and the wide ranges of uncertainty reported for various soil properties, they provide the most comprehensive information available at this time for making systematic assessments of ecosystem services at large spatial scales.

The landscape of soil carbon data: Emerging questions, synergies and databases

Soil carbon has been measured for over a century in applications ranging from understanding biogeochemical processes in natural ecosystems to quantifying the productivity and health of managed systems. Consolidating diverse soil carbon datasets is increasingly important to maximize their value, particularly with growing anthropogenic and climate change pressures. In this progress report, we describe recent advances in soil carbon data led by the International Soil Carbon Network and other networks. We highlight priority areas of research requiring soil carbon data, including (a) quantifying boreal, arctic and wetland carbon stocks, (b) understanding the timescales of soil carbon persistence using radiocarbon and chronosequence studies, (c) synthesizing long-term and experimental data to inform carbon stock vulnerability to global change, (d) quantifying root influences on soil carbon and (e) identifying gaps in model–data integration. We also describe the landscape of soil datasets currently available, highlighting their strengths, weaknesses and synergies. Now more than ever, integrated soil data are needed to inform climate mitigation, land management and agricultural practices. This report will aid new data users in navigating various soil databases and encourage scientists to make their measurements publicly available and to join forces to find soil-related solutions.