National Soil Database Research Articles

Features of the application of geoinformation systems to increase the yield of agricultural land

The main problems in the effective use of land resources as an important factor of the country's development and population's food security are considered. The study was conducted in the northern region of the Republic of Kazakhstan. The object of the study was grain-growing farms in the Akmola region. The study concluded that satellite imagery and unmanned aerial vehicles should be used to monitor crops and assess yields in the Akmola region. These technologies allow for more efficient land management, prompt response to problems and informed decision-making. The experience of GIS application in the United States consists in the formation of database systems for all soil types that are of economic importance. There are 4 national soil databases, as well as several automated soil databases containing data on more than 13,400 soil varieties. The country's Soil Conservation Service has created soil geographic databases, including a geographic database on soil surveys, state soil associations, and major land resource areas. The economic efficiency of land conservation measures will be determined by the amount of net income, taking into account the prevented environmental damage in value form, using the efficiency coefficient of environmental costs relative to the total production and environmental costs. The acreage area in the Akmola region on average for 2018–2023 amounted to 26,264.32 thousand hectares, the average actual crop yield was 12.5 centner/ha. Based on the given system of formulas, the estimated crop yield on non-eroded soils (Yn) is 13.5 centner/ha, and the crop shortfall (V) due to land erosion is 871.88 thousand centners of grain, which is 69,750 thousand tenge at an average grain price of 80,000 tenge/t

Exploiting Soil and Remote Sensing Data Archives for 3D Mapping of Multiple Soil Properties at the Swiss National Scale

Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However, broad-scale DSM remains challenging because of non-purposively sampled soil data, large data volumes for processing extensive soil covariates, and high model complexities due to spatially varying soil–landscape relationships. This study presents a three-dimensional DSM framework for Switzerland, targeting the soil properties of clay content (Clay), organic carbon content (SOC), pH value (pH), and potential cation exchange capacity (CECpot). The DSM approach is based on machine learning and a comprehensive exploitation of soil and remote sensing data archives. Quantile Regression Forest was applied to link the soil sample data from a national soil data base with covariates derived from a LiDAR-based elevation model, from climate raster data, and from multispectral raster time series based on satellite imagery. The covariate set comprises spatially multiscale terrain attributes, climate patterns and their temporal variation, temporarily multiscale land use features, and spectral bare soil signatures. Soil data and predictions were evaluated with respect to different landcovers and depth intervals. All reference soil data sets were found to be spatially clustered towards croplands, showing an increasing sample density from lower to upper depth intervals. According to the R2 value derived from independent data, the overall model accuracy amounts to 0.69 for Clay, 0.64 for SOC, 0.76 for pH, and 0.72 for CECpot. Reduced model accuracies were found to be accompanied by soil data sets showing limited sample sizes (e.g., CECpot), uneven statistical distributions (e.g., SOC), and low spatial sample densities (e.g., woodland subsoils). Multiscale terrain covariates were highly influential for all models; climate covariates were particularly important for the Clay model; multiscale land use covariates showed enhanced importance for modeling pH; and bare soil reflectance was a major driver in the SOC and CECpot models.

Opposite relationships between soil organic carbon and elevation in the midlands and mountainous areas in Ireland related to different types of peat

Soil organic carbon (SOC) plays an important role in global carbon cycle which is influenced by multiple factors. Elevation, as one of these factors, has a close but complex relationship with SOC concentration. The traditional ‘global’ statistical models cannot capture the spatial variation thus are inefficient in revealing the relationships between SOC and elevation at the local scale. In this study, a ‘local’ model of geographically weighted regression (GWR) was used to explore the complex relationships between SOC and elevation in the topsoil of Ireland based on the dataset from National Soil Database of Ireland. The results indicated SOC and elevation exhibited the spatially continuously varying relationships across the study area. Positive relationships in the mountainous areas suggested SOC concentration increased with the increasing elevation. Negative relationships were observed in the midlands where SOC concentration decreased with the increasing elevation. Such varying relationships between SOC and elevation in Ireland were related to the two different main types of peat: blanket peat and raised peat. These two types of peat have different formation processes, thus are distributed at different elevations. In the mountainous areas, low temperature and high humidity create cool and anoxic environment that mitigates SOC mineralization, promoting the accumulation of blanket peat at a high elevation. In the midlands, the low-lying lakes and wetlands provide anoxic environment where raised peat is developed and located at a low elevation. The findings of the spatially varying relationships between SOC and elevation in Ireland have demonstrated the importance of modelling SOC at the ‘local’ level. Attention is required for soil mapping using a global algorithm and it is recommended that localized algorithms are considered in modelling SOC, taking the feature of continuous variation into consideration.

Experience in creating an information system for the rational use of soil resources

The structure and functions of the “SoilGeographical Database of Russia Information System” (ISSGDBR) implemented on the platform of the Soil Science Faculty of the Lomonosov Moscow State University are considered. The current state of national soil databases and developing on that base international soil information systems are described. The created information system consists of three sections. The first section includes soil maps of different scales and maps of the natural conditions of the territory of the Russian Federation. The second section is represented by a naturalattribute database, which is based on a soil profile with associated analytical characteristics. In the third section, with the help of software tools, the collected varied information about the soil cover becomes the basis for modeling the spatial distribution of various indicators, performing calculations in applied projects for a wide range of users. The ISSGDBR is considered as an expert and analytical center, which acts as a coordinator and supports the functioning of a distributed network, as well as the development and distributing of internationally relevant standards for description, storage, presentation of soil information and data exchange through the use of specialized programs adapted to regional features. In addition, the purpose of the center is the implementation of communication between primary digital resources of natural and soil information localized in various organizations, agrochemicalservice regional centers, and federal agencies accumulating information necessary for making managerial decisions.

A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment

This study presents a framework to quantify changes in soil saturation in response to flooding caused by extreme hydrologic perturbation on coastal ecosystems at the interfaces and transition between terrestrial and aquatic systems. Subsurface heterogeneity limits the use of in situ measurements to quantify subsurface flow during flooding due to the spatial discontinuity in the measured data. While geophysical methods, including time-lapse electrical resistivity imaging (ERI), are increasingly used to monitor soil hydrological processes, their abilities to parameterize flow models have been underutilized. This study combines background ERI, ground penetrating radar (GPR), time-lapse ERI, soil characterization, and a numerical flow model developed using an Advanced Terrestrial Simulator (ATS) code to quantify the infiltration pathway and describe the hydrological dynamics during a simulated flooding experiment. We assessed the use of two conceptual models developed using [1] ERI and GPR data that described the stratigraphic distribution, and time-lapse ERI that mapped permeability contrast, and [2] information from a national soil database for capturing changes in saturation. Combining the ERI and GPR results with soil core data revealed the stratigraphic heterogeneity at the site with a silty clay layer from 1 to 2 m between an overlying loamy topsoil and an underlying saturated silty sand. This silty clay layer could restrict deep infiltration. The time-lapse ERI showed up to a 35% decrease in resistivity, which correlated with soil moisture data (R2 value > 0.53) and revealed preferential infiltration zones used to inform the flow model. Numerical simulation results from both the geophysics- and soil database-informed models quantified changes in soil saturation with calculated soil moistures that agreed with field data. The geophysics-informed model captured more of the system’s variability, reflective of shallow subsurface heterogeneities. The framework presented will serve as a precursor for a robust ecohydrological model that can describe the impacts of extreme events induced by climate change on coastal ecosystems.

Digital soil mapping of Italy to map derived soil profiles with neural networks

In recent years there has been an increased demand for digital soil mapping (DSM) products. DSM has become the ultimate soil spatial representation framework due to its quantitative results, replicability, and uncertainty analysis. The present study aimed to map the probability distribution of the derived soil profiles (DSPs) of soil typological units (STUs). DSPs are statistical representation of the properties of the soil profiles belonging to STUs. STUs aggregate individual profiles into a group. The criteria used for grouping were homogeneity for World Reference Base (WRB) reference soil group (WRB-RSG), qualifiers (WRB-qu), and Soil Taxonomy particle size for the family classification (USDA-PS), and the belonging to a specific Soil Region. The European Soil Regions have been suggested as the primary grouping criteria for soil mapping at the European continental scale since they define continental-scale soilscapes, distinguished mainly by their climate and geology. To map DSPs, we firstly mapped STUs. The grouping criteria of STUs were mapped at 500 m spatial resolution, using a Neural Network trained on 18,707 georeferenced and analyzed soil profiles selected from the Italian national soil database. A 10% of the soil profiles were randomly sampled using a stratified sampling approach for validation. In particular, the procedure consisted of: i) mapping the grouping criteria WRB-RSG, WRB-qu, and USDA-PS, on a 500 m national grid, through Neural Network; ii) grouping soil profiles on the base of the combinations of grouping criteria (WRB-RSG, WRB-qu, USDA-PS, and Soil Regions) as mapped with the first step at each grid node, to produce a map of Soil Typological Units (STUs); iii) calculating statistics for the soil parameters of the groups of soil profiles created, to produce a map of Derived Soil Profiles (DSPs). DSPs statistics (average, standard deviation, and sample numerosity) were elaborated for the following parameters: soil rooting depth, pH (in water), soil organic carbon, clay, silt, sand, coarse fragments, and cation exchange capacity. The maps obtained were validated against the test set. The same test set was used for the comparison with the National benchmark map (Soils Map of Italy 1:1,000,000) and with the global scale SoilGrids at 250 m spatial resolution. The overall accuracy was 45.98% for the WRB-RSG map compared with the 30.74% of WRB-RSG as mapped with the Soil Map of Italy, and 28.79% as mapped with SoilGrids; 33.07% for WRB-qu compared with the 15.69% of WRB-qu as mapped with the Soil Map of Italy, and 12.45% as mapped with SoilGrids, and 45.48% for USDA-PS, not comparable with the National and Global benchmarks. Tau statistics showed a higher accuracy Kappa of our approach than in others, due to the unbalanced classes numerosity. The predictive ability in the validation of DSPs parameters resulted in a R2 of 0.35 for clay (0.16 with SoilGrids), 0.28 for sand (0.08 with SoilGrids), 0.18 for pH in water (0.21 with SoilGrids). The proposed approach produced harmonized soil type maps with higher accuracy than the previous generation of conventional field-based soil maps for the national benchmark and the calculation of the uncertainty. The STUs express variability of soil properties between groups so their knowledge might improve our understanding of the soil distribution, the planning of their management, monitoring, and the decisions for further surveys. A future challenge will be including more dynamic parameters in the criteria used to create STU, to help monitoring soil management effects.

SOIL QUALITY OF HIGH NATURAL VALUE FARMLAND BY SUCEAVA AREA

HNV agricultural systems were first described as agricultural systems of predominantly low intensity which imply a relatively complex relation with the environment. They preserve important habitats on both cultivated and grazed land and they include diverse landscape elements such as hedgerows, fruit trees, or lakes/ponds. In the European Union Strategy framework of mitigating climate change, protecting the environment, and ensuring public health, following researches that undertook the task to build the first national soil data base from the high natural value areas (HNV). One of the eligible area for HNV compensatory payments, where was analyzed the fertility state of soils is located in the Suceava area. It was studied in six depth profiles and six adjacent agrochemical samples collected from the 0-20 cm layer. Chemical analyses showed that the soils prove to be rather poor, but within the limits frequently encountered in such areas, with an acceptable level of fertility and good conditions for characteristic plants growth. Cationic exchange capacity is generally low. Excessive nitrates quantities were determined in the upper horizon of two of the analyzed profiles. Both of these profiles belong to the HNV administered territory therefore; compliance with the set of rules regarding HNV eligibility should be more attentively monitored.

Identifying rotation and tillage practices that maintain or enhance soil carbon and its relation to soil health

Physical fractions of soil organic matter (SOM) are established indicators of management-induced change and have been used to estimate the soil carbon storage capacity and storage potential. Here, we use SOM physical fractions and soil textures to identify management practices that maintain or enhance soil health and carbon storage in agricultural soils in Ontario. Metadata from the National Soil Database were used to estimate carbon storage potentials and calculate carbon deficits. A map was created showing carbon deficits in Ontario's agricultural soils and indicates that these soils have the potential to store an additional 0 to 2 kg m−2 in the top 20 cm of the soil. Tillage system generally had no effect on the size of the carbon deficit at four long-term agricultural experiments (Delhi, Elora, Ottawa, and Ridgetown). There was only a significant tillage effect at Ridgetown and only in the maize–soybean crop rotation, where the carbon deficit was 2.95 g C kg soil−1 under conventional tillage compared to 8.97 g C kg soil−1 with no tillage. A statistically significant effect of crop rotation was detected in Elora and Ridgetown. In Elora, continuous alfalfa had the smallest carbon deficit (7.25 g C kg soil−1) and maize–soybean rotation had the largest deficit (12.07 g C kg soil−1). In Ridgetown, the maize–soybean rotation had the smallest carbon deficit (2.95 g C kg soil−1). Regression analysis showed a weak negative relationship ( R2 = 0.11; P < 0.001) between carbon storage deficits and soil health scores. This suggests that increasing SOM levels alone may not improve soil health.

Upscaling point-scale soil hydraulic properties for application in a catchment model using Bayesian calibration: An application in two agricultural regions of New Zealand

Hydrological modeling for landscape and catchment scale applications requires upscaling of soil hydraulic parameters which are generally only available at point scale. We present a case study where hourly root zone soil water content and drainage observations from nine flat, pastoral sites (Waikato and Canterbury regions in New Zealand) were used to develop an upscaling approach to parameterize the soil water balance module of the TopNet catchment model, based on scaling multi-layer soil profile information from the national soil data base, S-map, to the single-layer soil profile used in TopNet. Using a Bayesian calibration approach, the hydraulic behavioral parameters of TopNet's soil water balance module were identified. Of the eleven calibration parameters considered three were found to be insensitive to data (stress point, unsaturated hydraulic conductivity and infiltration rate); three were correlated and could be determined from specific soil water content observations (wilting point, field capacity and drainable water); and five were correlated and could be determined from combined specific soil water content and drainage observations (drainage rate, saturated hydraulic conductivity profile, effective soil depth, soil water holding capacity and wetting front suction). Based on the eight correlated parameters, upscaling functions were then developed to derive suitable model parameters from S-map-hydro for each site. The validity of the upscaling functions was verified at each site. The approach used in this research can be used to parameterize the TopNet model at other similar locations, and also provides a transferable framework to parameterize other catchment-scale hydrology models where point-scale soil hydraulic data available.

Analysis of the levels of metal(loid)s in environmental compartments in Ireland towards a screening measure for potential relative risk using open-source datasets

Several metals and metalloids (metal(loid)s) have been identified as potential pollutants. Naturally occurring background levels and anthropogenic sources (direct or indirect) contribute to the baseline concentration of metal(loid)s in the environment. Recorded metal(loid)s in various environmental media (soil, water, sediment) were evaluated from existing databases. The first database is the national soil database or Soil Geochemical Atlas of Ireland (SGAI). The second one is a higher resolution Tellus project database created by the Geological Survey Ireland. This study focussed on 16 metal(loid)s: As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sb, Sn, U, V, and Zn. A Risk Quotient (RQ) and Integrated Risk Quotient (IRQ) were used to analyse individual and combined effects of selected metal(loid)s due to the potential ingestion by humans through the food chain. The results revealed that in a few locations of Ireland, the predicted environmental concentration (PEC) of As, Cd, Pb and Zn in the soil was higher than the threshold values resulting in an RQ exceedance of 1. The RQ values of metal(loid)s corresponding to the water, and sediment samples suggest minimal exceedance of threshold values. The exceedance of the IRQ values (>2) for the soil, water, and sediment samples is 32.3 %, 14.8 %, and 1.3 %, respectively. Regions along the East coast of Ireland may pose a higher potential relative risk compared to other parts of Ireland. This research suggests the need for in-depth risk assessment studies on Cd, As and Pb, which showed elevated levels. Furthermore, it is essential to understand the fate of metal(loid)s and their risk assessment to inform regulations around metal(loid)s where intervention may be required.

Projecting the urbanization effect on soil organic carbon stocks in polar and steppe areas of European Russia by remote sensing

This paper describes the effect of urbanization on soil organic carbon (SOC) stocks by projecting the main urban land cover classes over the initial pre-urban soil maps. Two cities different in climate and soil conditions as well as in availability of SOC data were chosen as the case studies. Rostov-on-Don is the center of Russian South, where croplands and natural steppes have been conventionally thoroughly studied by soil scientists. In contrast, soils of Murmansk located in Russian Arctics have always been overlooked due to low suitability for agriculture. Global, national and regional soil maps and databases were used to estimate pre-urban SOC stocks in the areas. The outcomes based on Harmonized World Soil Database were highly uncertain, underestimating 0-100 cm SOC stocks in the polar region and overestimating them in the steppe region, whereas the results based on Digital Soil Map of Russia and regional maps were comparable. Land cover structures of Rostov-on-Don and Murmansk were mapped based on the stepwise per-pixel and sub-pixel classification algorithms applied to Sentinel-2 and included the following classes: sealed soils, green lawns, trees and shrubs, bare soils and water. Murmansk was dominated by trees and shrubs (58.1%) with the proportion of area 17.5% covered by sealed soils. In Rostov-on-Don, less than 30% of the total area was covered by trees and shrubs which is also comparable with bare soils (19.6%)_and lawns (23.4%), whereas almost one third of the territory was sealed (27.6%). These land cover structures had a different impact on the topsoil SOC stocks: a 30-50% increase in Murmansk compared to the 18% decrease in Rostov-on-Don. An increase of the 0-100 cm SOC stocks was shown for both regions, however in the polar conditions it was two times higher compared to the steppe. In polar conditions, conversion of natural soils into urban non-sealed soils increased SOC stocks from 30% to more than 4 times in 0-10 cm layer and from 47% to almost 3 times in the 0-100 cm layer. The highest increase was reported for the lawns, whereas SOC under trees and shrublands were considerably lower. In Rostov-on-Don , sealed and bare soils stored less SOC compared to the initial natural soils. The conversion of natural areas into urban green infrastructure increased SOC up to 50-70%. Although the absolute SOC values based on the global and national legacy data are highly uncertain, especially for the polar areas, the research outcomes clearly reveal possible patterns in SOC changes induced by different urbanization pathways in contrast climatic conditions and highlight the complexity of the urbanization effect on soils

How important is the description of soil unsaturated hydraulic conductivity values for simulating soil saturation level, drainage and pasture yield?

Accurate simulation of soil water dynamics is a key factor when using agricultural models for guiding management decisions. However, the determination of soil hydraulic properties, especially unsaturated hydraulic conductivity, is challenging and measured data are scarce. We investigated the use of APSIM (Agricultural Production Simulation Model) with SWIM3 as the water module, based on Richards equation and a bimodal pore system, to determine likely ranges of the hydraulic conductivity at field capacity (K-10; assumed at a matric potential of −10 kPa) for soils representing different drainage characteristics. Hydraulic conductivity measurements of soils with contrasting soil drainage characteristics and values for K-10 were extracted from New Zealand’s national soil database. The K-10 values were then varied in a sensitivity analysis from 0.02 to 5 mm d−1 for well-drained soils, from 0.02 to 1 mm d−1 for moderately well-drained soils, and from 0.008 to 0.25 mm d−1 for poorly drained soils. The value of K-10 had a large effect on the time it took for the soil to drain from saturation to field capacity. In contrast, the saturated hydraulic conductivity value had little effect.Simulations were then run over 20 years using two climatic conditions, either a general climate station for all seven different soils, or site-specific climate stations. Two values for K-10 were used, either the APSIM default value, or the soil-specific measured K-10. The monthly average soil saturation level simulated with the latter has a better correspondence with the morphology of the seven soils. Finally, the effect of K-10 on drainage and pasture yield was investigated. Total annual drainage was only slightly affected by the choice of K-10, but pasture yield varied substantially.

Harmonization of a large-scale national soil database with the World Reference Base for Soil Resources 2014

Legacy soil data currently represents an irreplaceable source of data for many national and international applications. Their full use at the international level largely depends on adequate harmonization. In this study, we adopted different methods of taxonomic distance and reclassification of soil properties to convert a large-scale database originating from an extensive national mapping campaign of agricultural soils in Czechoslovakia (1961–1971), according to the criteria of the World Reference Base for Soil Resources (WRB), version 2014. The harmonization was effectuated at two levels of detail represented by two different types of soil mapping units [soil type and general soil representative (GSR)] and resulted in the conversion of original soil units into their counterparts in the WRB (Reference Soil Groups). The results showed a good potential of the adopted methods for soil data harmonization. The conceptual differences of the two harmonised classifications decreased the applicability of the taxonomic distance in the case of broadly defined and less developed soil classes lacking specific diagnostic features. The replacement of a simple conversion by soil association was a relevant approach, especially at the soil type level, where the simple one-to-one conversion showed only poor accuracy (39.5%). The accuracy at the GSR level was satisfactory (reaching ca 60%) and increased with soil association (72%). The conversion accuracy of different soil classes significantly differed from very high (e.g. 92% in Luvisols) to completely mismatched (Technosols and Histosols). The latter can be attributed to a questionable determination of soil classes in legacy maps. The study showed the potential of taxonomic distance methods in soil data harmonization and the limits of historical databases resulting from the way data were collected and recorded.

Online Fertilizer Recommendation System (OFRS): A Step Towards Precision Agriculture And Optimized Fertilizer Usage By Smallholder Farmers In Bangladesh

The recent progression and Green Revolution (approx. between the 1990s-2010s) in agriculture of Bangladesh resulted in an increase of total production despite yield-gap to ensure food security. But agriculture in Bangladesh is still backed-up by higher use of inputs (agrochemicals-fertilizers, pesticides; modern varieties, irrigation etc.) and inversion tillage. This conventional agrochemical-based smallholder agriculture may lead to soil and environmental degradation, soil acidification, and a decline in soil fertility. Therefore, it is significant to optimize input application in intensive agriculture, especially fertilizers. This paper introduces the potential online facilities of generating online fertilizer recommendations for smallholder farmers in Bangladesh to ensure proper usage of fertilizers and enable sustainable agricultural production. We also highlighted how the usage of fertilizers increased with an increase in total production over time. But the sustainability of production in the years to come still remain challenging. With the aim of sustainable crop production, reduction in the misuse of fertilizers and reduction of input cost by optimizing the present pattern of excessive fertilizer application, the Soil Resource Development Institute (SRDI) provides location-specific fertilizer recommendation through both the manual and soil test based interpretation of plant nutrients: soil database in Upzazila Nirdeshika and static laboratory soil analysis. Recently, SRDI developed web-based software named Online Fertilizer Recommendation System (OFRS). The system is capable of generating location-specific fertilizer recommendations for selected crops by analyzing the national soil database developed by this governmental institute. The software requires farmer field location, respective soil and land type, and crop type and variety information to generate crop-specific instant fertilizer recommendation. It was observed that by using fertilizer according to the recommended dose calculated on the basis of soil test values, farmers could harvest approx. 7-22% higher yield of different crops over usual farmers practice. If this system can be popularized and disseminated by effective agricultural extension, this would immensely contribute to the promotion of precision agriculture, input cost reduction and it would certainly enable us to optimize fertilizer application by the smallholder farmers in Bangladesh.

Catchment-scale 3D mapping of depth to soil sodicity constraints through combining public and on-farm soil databases – A potential tool for on-farm management

There has been much recent effort in creating digital soil maps across large areas, but these products are rarely used by farmers and those managing the land. This is due to a variety of reasons; few maps represent soil properties of agronomic relevance, the spatial resolution and depth intervals are often too coarse, and there is limited confidence in the quality of the predicted maps. This study attempted to overcome these pitfalls by creating maps of the depth to a soil sodicity constraint across the Namoi River Catchment, a 42,000 km2 area in eastern Australia. The maps were produced at a fine vertical support (1 cm) and horizontal resolution (30 m) using a single random forest model to a depth of 100 cm. Different exchangeable sodium percentage (ESP) thresholds of 6%, 10% and 15% were explored to define a constraint. Various publicly available spatial datasets were used as covariates, including satellite imagery, terrain attributes, climate, and geophysical data. The soil data used for modelling were collated from a national soil database, various soil surveys, as well as data collected on case study farms. The model could predict soil ESP across the catchment with a Lin’s concordance correlation coefficient (LCCC) of 0.56 when tested with 10-fold cross-validation based on whole-profiles. The value of including soil data collected on-farm with regional databases was clearly demonstrated to produce maps for an individual farm, resulting in considerable improvements in model prediction accuracy. The utility of the maps for farm management was assessed by comparing with cotton and grain crop yield maps for individual paddocks on case study farms. The depth to soil sodicity constraint maps proved valuable for understanding yield variability, with constraints shallow in the soil profile generally resulting in lower yields, however, this fluctuated by crop type, and seasonal conditions. Future work should create depth-to-constraint maps for several important soil constraints, which may lead to a better understanding of variation in crop yields. An operational framework where farmers are able to add their own soil data to neighbouring and regional soil databases could have considerable benefits for broadacre agricultural industries in Australia.

Refining benchmarks for soil organic carbon in Australia’s temperate forests

Soil organic carbon (SOC) stocks in Australia’s temperate forests have been overlooked in national soil databases and in global SOC analyses of natural ecosystems despite the importance of temperate forests to the global terrestrial carbon balance. This limits the potential to both predict change in SOC stocks in temperate Australia and to identify where and how SOC stocks can be managed to mitigate climate change. Based on data from 707 sites, we examine variations in SOC concentrations and stocks across a range of natural temperate broadleaf forests in south-eastern Australia. Comparisons with current Australia-wide databases highlight previous under-estimation of forest SOC concentrations, leading to substantial underprediction of SOC stocks in the most productive forests (e.g. this study’s mean of 207 Mg C ha−1 compared with a database mean estimate of 141 Mg C ha−1 for Tall open-forests to 30-cm soil depth). Random Forest models involving 27 environmental variables (representing climate, terrain, parent material, soil attributes, vegetation, and fire history) explained up to 79% of the variation in SOC concentrations and 77% of the variation in SOC stocks to 30-cm depth. Climate variables (precipitation, temperature) were of greatest importance to the prediction of both SOC concentrations and SOC stocks, tending to override the importance of terrain and fire-history variables at this study’s regional scale. While patterns in SOC concentrations and stocks were correlated, SOC concentrations were not a reliable proxy for SOC stocks to 10-cm depth, reiterating the importance of mass equivalent measures (i.e. soil bulk density) to assessing changes in soil carbon storage. Our study provides a timely check of the model-based estimates of SOC concentrations and stocks in Australia’s temperate forests that are currently available in nation-wide databases and improves the available information for defining benchmarks, and for identifying potential areas of SOC loss and gain, in programs that aim to mitigate climate change.

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.

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.

Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra

Sustainable land management requires reliable information about soil hydraulic properties. Among these properties, available water-holding capacity (AWC) is a key attribute, as it quantifies the amount of water available for plants that the soil can hold. Since direct measurements of AWC are costly, pedotransfer functions (PTF) are often used to estimate AWC, leveraging statistical relationships with properties that are easier to measure, such as texture, bulk density, and organic carbon content. This study evaluates visible near-infrared spectroscopy (vis-NIR) as an alternative approach to predict volumetric water content at field capacity (FC) and permanent wilting point (PWP) — AWC being the difference between PWP and FC. A suite of 970 vis-NIR soil spectra, recorded from air-dried, 2-mm, sieved soil samples, were associated with FC and PWP analytical data obtained from New Zealand’s National Soils Database. Partial least squares (PLS) regression and support vector machines on PLS latent variables (PLS-SVM) were used for spectroscopic modelling. With root mean squared errors below 7% and 5% for FC and PWP, respectively, our results indicate that vis-NIR spectroscopy can be used to quantitatively predict volumetric water content at FC and PWP.

Estimation of soil subsurface hydraulic conductivity based on inverse modelling and soil morphology

Soil hydraulic properties play a primary role in water infiltration and movement, and transport of nutrients and contaminants through the soil profile. However, the determination of soil hydraulic properties, especially the hydraulic conductivity at lower depths within the soil profile is very challenging. We investigated the use of inverse modelling based on APSIM (Agricultural Production Simulation Model) to determine likely ranges of the hydraulic conductivity at saturation (Ksat) and field capacity (KDUL) for a slowly permeable subsurface horizon. Simulations were run for the poorly drained Otokia silt loam, in the Otago region of New Zealand. APSIM simulations were set up either according to the site specific soil profile description or based on the national soils database (S-map) pedotransfer functions. Simulated temporal soil moisture data were compared to measurements from a field experiment. Within a sensitivity analysis Ksat values of the subsurface soil were varied from 2.4 to 192 mm/d, and values for KDUL from 0.05 to 1 mm/d. Finally simulations were run over 45 years using three climatic conditions, with annual rainfall amounts of 582, 628 and 730 mm. The effect of varying the hydraulic conductivity of the subsurface fragipan horizon (Cx) on the wetness status of the overlying B2g horizon was determined by calculating the average annual duration of soil moisture above field capacity. These were then used to estimate ranges of the hydraulic conductivity that contributes to a soil being imperfectly or poorly drained. The results highlight the significant influence that KDUL has on the reliability of modelling the hydraulic behaviour of soils with slowly permeable subsoils.