Spatial variability of soil phosphorus in the Fribourg canton, Switzerland

Spatial variability of potassium in agricultural soils of the canton of Fribourg, Switzerland

Spatial prediction of soil organic carbon is a crucial proxy to manage and conserve natural resources, monitoring CO2 and preventing soil erosion strategies within the landscape, regional, and global scale. The objectives of this study was to evaluate capability of artificial neural network and multivariate linear regression models in order to predict soil organic carbon using terrain attributes. A study area of 24 km2 in hilly regions of Zargham Ababd in south of Semirom under natural rangeland uses, was selected and then 125 soil samples (0-10 cm depth) were collected. Soil organic carbon was measured for the collected soil samples. Topographic attributes were calculated by a digital elevation model with 10 m spacing. Finally, multiple linear regression (MLR) analysis and ANN models were developed for soil organic carbon estimation in the study area and then the developed modeless were validated by additional samples (25 points). The results showed that the MLR and ANN models explained 60 and 89 % of the total variability of SOC, respectively, in the study area using terrain attributes. Sensitivity analysis based upon the ANN models, revealed that the profile curvature, stream power index, slope, sediment transport index, wetness index, plan curvature and aspect were identified as the important topographic attributes influencing the SOC distribution within the selected hillslope. The overall results indicated that topographic attributes and hydrological process control a significant variability of SOC. Prediction of the statistical studied models in the study area resulted in mean error and root mean square error values of 0.25, 0.3 in MLR equation and 0.006, 0.027 in ANN, respectively. Therefore, the ANN model could provide superior predictive performance when compared with developed MLR model. Keywords: Soil organic carbon, Terrain attributes, Linear regression, Artificial neural network

Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: The case study of Denmark

Real-time kinematic (RTK) global positioning system (GPS) receivers are being marketed for applications where accurate estimates of elevation are required. Because this technology is fairly expensive, the quality of these data should be considered with respect to that of USGS digital elevation DEMs (digital elevation models) that are freely available for most of the United States at the 30-m grid scale and for some areas on 10-m grids. The objective of this study was to compare terrain attributes calculated from USGS DEMs with those calculated from DEMs developed with precision RTK GPS measurements. At two locations in central Kentucky, intensive elevation measurements were collected with RTK GPS. The USGS and RTK data for these areas were interpolated to create 4-m DEMs for comparing terrain attributes. Correlations between the attributes developed from different DEMs were substantially larger for slope and aspect than for curvature, topographic wetness index, and stream power index values. Although there were large quantitative differences in terrain attribute values, the general spatial patterns were similar for the attributes calculated from the RTK and USGS DEMs. However, landscape features defined using DEMs developed with RTK data were more similar to those observed with 10- than 30-m USGS DEMs. For some course-scale applications, 30-m USGS DEMs may be adequate for identifying important landscape characteristics. However, the resolution of many of these features will be substantially better with 10-m USGS DEMs. However, quantitative estimates of terrain attributes at a specific location may be grossly incorrect. For situations where accuracy is essential, precision elevation surveys will be required.

<p>Human land use and occupation often lead to a high heterogeneity of soil stratigraphy and properties in landscapes within small, clearly delimited areas. Legacy effects of past land use also are also abundant in recent forest areas. Although such land use legacies can occur on considerable fractions of the soil surface, they are hardly considered in soil mapping and inventories. The heterogenous spatial distribution of land use legacy soils challenges the quantification of their impacts on the landscape scale. Relict charcoal hearths (RCH) are a widespread example for the long-lasting effect of historical land use on soil landscapes in forests of many European countries and also northeastern USA. Soils on RCH clearly differ from surrounding forest soils in their stratigraphy and properties, and are most prominently characterized by a technogenic substrate layer with high contents of charcoal. The properties of RCH soils have recently been studied for several regions, but their relevance on the landscape scale has hardly been quantified.</p><p>We analyse and discuss the distribution and ecological relevance of land use legacy soils across scales for RCH in the state of Brandenburg, Germany, with a focus on soil organic matter (SOM) stocks. Our analysis is based on a large-scale mapping of RCH from digital elevation models (DEM), combined with modelled SOM stocks in RCH soils. The distribution of RCH soils in the study region shows heterogeneity at different scales. The large-scale variation is related to the concentration of charcoal production to specific forest areas and the small-scale accumulation pattern is related to the irregular distribution of single RCH within the charcoal production fields. Considerable fractions of the surface area are covered by RCH soils in the major charcoal production areas within the study region. The results also show that RCH can significantly contribute to the soil organic matter stocks of forests, even for areas where they cover only a small fraction of the soil surface. The study highlights that considering land use legacy effects can be relevant for the results of soil mapping and inventories; and that prospecting and mapping land use legacies from DEM can contribute to improving such approaches.</p>

The resolution of digital elevation model (DEM) used for watershed delineation and data extracted from the delineated watershed relevant to hydrography plays an important role in the calibration of hydrological models. The effect of DEM resolution on the drainage length, watershed area and average slope are studied in many earlier works. DEM resolution can have impact on hydrologic modeling parameters, water quality simulation results. The impacts of DEM resolution on results of the watershed response and water quality modeling are studied using HSPF (Hydrologic Simulation Program Fortran) model applied to a watershed in Kentucky in this study. Three major streams in this watershed are impaired due to pathogen pollution according to the current state of Kentucky water quality standard set for recreation. The effects of DEM resolution on water quality modeling and calibration for fecal coliform bacteria are studied using a HSPF model. Results from this study indicate that the DEM resolution affects hydrography parameters of the delineated watershed, influence the calibration process, and the non-point and point source loadings to the streams in the watershed.

Phosphorus (P) is essential for plant growth, but excessive accumulation in soils can pose environmental risks, particularly through losses into water bodies. However, despite large spatial variability in P concentration and availability, no nationwide map of soil P exists for Switzerland. Moreover, knowledge of the relative importance of management-related factors compared to soil chemical and pedoclimatic drivers on the distribution of total and available P remains limited.This study aims to predict and explain the distribution of total and available P pools in Swiss topsoils. For this purpose, we combined machine-learning approaches (ML) using Random Forests with model interpretation based on Shapley values to identify the main drivers controlling the P distribution. As model input, we used total P data measured at 960 sites of the Geochemical Soil Atlas of Switzerland in combination with predictor variables representing land use, soil properties, as well as environmental and geological conditions. To further investigate P dynamics in agricultural soils, we integrated a dataset from the Swiss Proof of Ecological Performance (PEP) subsidy scheme, which comprises 14 years of soil analyses since 2010. Available P pools were operationally defined using CO₂ saturated water extraction as a proxy for immediately plant available P, and ammonium acetate - EDTA extraction (AAE10) representing a larger pool of exchangeable soil P. This dataset includes approximately 150’000 observations, allowing differentiation between arable land and grassland.As expected, total P concentrations are significantly higher in arable land and in pastures/grasslands compared with forests and alpine areas. Accordingly, interpretable ML measures, including Shapley values, indicate that land use is the most important predictor, followed by the presence of nutrients such as nitrogen (N), potassium (K), and sulfur (S). In contrast, soil chemical properties (e.g. pH, soil organic carbon) and proxies for pedoclimatic conditions, such as temperature or lithology, are less important for the prediction on a national scale.Across Switzerland, the lowest available P concentrations are observed in north-western and southern regions, whereas the highest concentrations were measured along the Swiss Plateau and in central and north-eastern Switzerland. While P concentrations extracted with CO₂ saturated water are similar between arable crops and grassland, arable soils exhibit systematically higher AAE10 extractable P. Further work will focus on identifying the main drivers of available P pools and their temporal changes across Switzerland, including data from remote sensing and other monitoring programmes.By combining spatially resolved geochemical data, interpretable machine learning approaches, and long-term agricultural monitoring data, this study provides a framework for identifying key drivers of the distribution of P pools in Swiss soils, thereby supporting targeted and sustainable nutrient management strategies.

During the last decades, agriculture has transformed into highly effective and economically optimized production systems with extended cultivation areas and increased usage of fertilizers and pesticides. This development is accompanied by the loss and fragmentation of remaining semi-natural habitats, thereby negatively affecting farmland biodiversity, resulting in species loss and declining ecosystem services such as biological pest control, and pollination. Agri-environmental schemes such as organic farming practices and habitat management can help to mitigate these negative effects, while it is increasingly recognised that attention should be paid to both local and landscape scales. Improving habitat diversity on the landscape scale can enhance biodiversity at the local scale, compensating for intensified local land use. In this thesis, we analysed the effects of local habitat management (sown flower strips, forest edges and hedges) and farming practices (extensive vs. intensive management) on syrphid fly guilds. In addition, we focused on the influence of landscape scale parameters such as the proportion of arable land as well as the proportion of mass-flowering oilseed rape in the surrounding of study sites. Syrphid flies represent one of the biggest groups of the order Diptera and occur in a wide range of habitats in agricultural landscapes. While adult syrphids flies are pollen and nectar feeders, their larvae show different feeding strategies ranging from predators of aphids, bacteria feeders and phytophagous to fungivorous species. Aphidophagous species such as Episyrphus balteatus or Sphaerophoria scripta represent the most frequently occurring syrphid fly species in agricultural landscapes, preying on a wide range of aphids species (e.g. Sitobion avenae, Rhopalosiphum padi, Metopolophium dirhodum), and can play an important role in the suppression of cereal aphid outbreaks. Here, we studied the effect of naturally occurring grassy strips and sown flower strips on syrphid flies in winter wheat fields, which were located along a gradient of landscape complexity (ranging from 30 to 100 % arable land and at multiple spatial scales ranging from 0.5 to 4 km radii). Analyses on the impact of extensive vs. intensive farming practices on syrphid flies were carried out in two European countries (South-Sweden and North-Germany), in each region by comparing four fields at low and four fields at high levels of agricultural intensification. In addition, syrphid flies were analysed by comparing forest edges, forest-connected hedges and isolated hedges adjacent to crop fields (winter wheat and oilseed rape) with respect to the influence of varying proportions of oilseed rape at the landscape scale. The results showed that sown flower strips increased syrphid fly abundance. Syrphid species richness was increased in wheat fields adjacent to sown flower strips. Furthermore, species richness and abundance of syrphid flies in sown flower strips increased as the proportion of arable land in the surrounding landscape increased, indicating a local concentration effect to highly rewarding pollen and nectar resources. Total and aphidophagous syrphid abundances were generally higher in the German than the Swedish study region. Aphidophagous syrphid abundance was higher in high intensity managed fields being located in landscapes with high proportions of arable land, while non-aphidophagous syrphid flies showed higher abundances in low intensity managed fields being located in landscapes with low proportions of arable land. Furthermore, syrphids in the German region appeared earlier in the season, therewith possibly allowing for a better predator-prey synchronization. Syrphid flies appeared to profit from high amounts of pollen and nectar resources in oilseed rape fields. Aphidophagous syrphid abundance in hedges and forest edges showed contrasting responses to the neighbouring crop, with low abundance when neighboured by oilseed rape fields (dilution) and higher abundance when neighboured by winter wheat fields (concentration) at high proportions of oilseed rape fields at the landscape scale. Aphidophagous syrphid fly abundance was increased in forest-connected hedges as well as in crop fields which lay adjacent to forest-connected hedges, indicating noncrop-crop spillover with potentially positive effects on local bicontrol potential. In conclusion, results showed that local habitat management can enhance diversity and abundance of syrphid flies, thereby potentially improving local biological control of cereal aphids. On the landscape scale, results support the idea that environmental schemes are more effective in structurally simple compared to complex landscapes owing to the concentration of highly dispersive organisms such as syrphid flies in resource-rich habitats. The relative abundance of aphidophagous syrphids varied largely between German and Swedish study regions indicating a changing role of species identity along latitudes. Syrphid fly guilds (aphidophagous vs. non-aphidophagous) were contrastingly affected by management type (extensive vs. intensive farming). Aphidophagous syrphid fly abundance in semi-natural habitats is mediated by the percentage of oilseed rape at the landscape scale, depending on local crop identity. The positive influence of forest-connected hedges on syrphids calls for group-specific habitat management practices in order to enhance biodiversity and related important ecosystem services such as biological pest control and pollination in the agricultural landscape.

<div> <p>The Hydrologic Simulation Program-FORTRAN (HSPF) model is widely used to develop management strategies for water resources and to evaluate the hydrologic effect of various management scenarios. The spatial resolution of the input data used to parameterize HSPF model may induce uncertainty in model outputs. In this study, the impact of spatial resolutions of Digital Elevation Model (DEM) and land use map on the uncertainty of HSPF predicted flow and sediment were evaluated. DEM resolution can affect stream length, watershed area and average slope, whereas land use data resolution can lead to redistribution of land use information. Results showed that finer resolution DEM and land use maps can generate higher flow volumes and sediment loads compared to modelling scenarios using inputs of coarse resolution. The relative change in model performance between the baseline scenario (high-resolution) and scenarios of coarser resolution described uncertainties due to DEM and land use spatial information, and the probability density function of these uncertainties was used to estimate these uncertainties. Modelled flow and sediment uncertainty due to DEM resolution seems to follow a log-normal and a general extreme value distribution respectively, whereas modelled flow and sediment uncertainty due to land use resolution seems to both follow a general extreme value distribution. Overall, results highlight the need for a high-resolution DEM and land use maps in the application of the HSPF model while they provide useful information for reducing the model uncertainties.</p> </div> <p> </p>

Utilizing multispectral satellite data and digital elevation models (DEMs) has emerged as the primary approach for cartographically representing landforms. By using high-resolution satellite photos that capture spatial, temporal, spectral, and radiometric data, one may get a fresh comprehension of the geomorphology of a particular area by recognizing its landforms. In addition, a synergistic method is used by using data produced from digital elevation models (DEMs) such as Slope, Aspect, Hillshade, Curvature, Contour Patterns, and 3-D Flythrough Visuals. The increasing use of UAV (drone) technology for obtaining high-resolution digital images and elevation models has become an essential element in developing complete topographic models in landslide scars that are very unstable and prone to erosion. Comparison (differences in values) of seven (7) different DEMs between two algorithms used, i.e., QGIS and White Box Tool (WBT), were successfully attempted in the present research. The TLS, UAV and Satellite data of the study area—Kshetrapal Landslide, Chamoli (District), Uttarakhand (State), India was subjected to two different algorithms (QGIS and WBT) to evaluate and differentiate seven different DEMs (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, MERIT, and FabDEM/FATHOM) taking into consideration various parameters viz. Aspect, Hillshade, Slope, Mean Curvature, Plan Curvature, Profile Curvature and Total Curvature. The different values of aforesaid parameters of various DEMs evaluated (using algorithms QIGS and WBT) reveal that only three parameters, i.e., Aspect, Hillshade, and Slope, show results. In contrast, the remaining ones do not show any meaningful results, and therefore, the comparison was possible only with regard to these three parameters. The comparison is drawn by comparing minimum, maximum, and elevation values (by subtracting WBT values from QGIS values) regarding Aspect, Hillshade, and Slope, arranging the differences in values as per their importance. (Increasing or decreasing order), assigning merit scores individually, and then cumulatively, and ascertaining the order of application suitability of various Dems, which stand in the order of (CARTOSAT, ASTER, SRTM, Alos 3D, TanDEM, and MERIT, and FabDEM/FATHOM).

The digital elevation model (DEM) matrix allows to reveal the relationship of the soil cover with morphometric parameters. Therefore, in the absence of the possibility of a large-scale field survey of soils, for territories with a high degree of erosion hazard, the data on the relief make it possible to carry out predictive large-scale soil maps. The aim of the work is to create a cartographic model of the soil cover of agricultural land based on the extrapolation of the results of DEM processing and to compare it with the existing large-scale soil map in similar natural conditions. The object of research is the territory of LLC “Selskoe” located in the Solikamsk urban district of the Perm region. Agricultural land use belongs to the northernmost agricultural lands in the region. The total area of research was 429 hectares of arable land. The plot includes 8 fields. For soil mapping, a digital elevation model ALOS 30 and a large-scale soil map of the key site were used, which characterizes part of the land use of the subsidiary farm “Voskhod”. Using the results of the classification of the relief according to the GIS SAGA TPI based landform classification algorithm as a contour base and the existing soil map of the key site, a soil map of LLC “Selskoe” was made by the extrapolation method. The steepness of the slopes and the topographic moisture index were used as auxiliary data. In conditions of complex relief, a clear dependence of the location of soils on relief elements has been established. By extrapolating data from a large-scale soil survey, 10 soil cartographic units were identified. According to the relief elements, podzolic, sod-podzolic, bog-podzolic and alluvial soils were identified. In terms of granulometric composition, light soils prevail, a small area is occupied by medium loamy soils.

Terrain attributes are commonly used to explain the spatial variability of agronomic, pedologic, and hydrologic variables. The terrain attributes studied here (elevation, slope, aspect, and curvature) are estimated readily from digital elevation models (DEMs), but questions remain about how the accuracy and sample spacing of the elevation data affect the estimated attributes. The main objective of this study was to quantify differences in each terrain attribute due to factors affecting DEM accuracy and grid cell size. Three data sources were compared: (i) real‐time kinematic global positioning system (RTKGPS); (ii) satellite‐differentially corrected global positioning system (DGPS); and (iii) U.S. Geological Survey (USGS) 30‐m DEMs. The GPS data from three undulating agricultural fields in northeastern Colorado were interpolated onto 5‐, 10‐, 20‐, and 30‐m grid DEMs. The DGPS and USGS DEMs produced similar elevation differences relative to RTKGPS DEMs, but elevation differences in USGS DEMs were more spatially correlated. Estimates of curvature were highly sensitive to DEM differences and the sensitivity of slope, aspect, and curvature estimates decreased as grid cell size increased. The impacts of DEM accuracy and grid cell size were investigated using correlations between wheat ( Triticum aestivum L.) grain yields and estimated terrain attributes. The highest correlation coefficients were obtained using RTKGPS data, and decreasing the sample spacing or grid cell size below 30 m did not consistently improve the correlations. These analyses on agricultural lands indicate the importance of accurate elevation data for detailed terrain analyses on grid cell sizes of 30 m or less.

Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation is not well understood. To assess the effectiveness of TWI, we examined spatial correlations between in situ soil volumetric water content (VWC) and TWI values over 5 years in soils at 42 locations in an agroforestry catena in Fayetteville, Arkansas, USA. We calculated TWI 546 ways using different flow algorithms and digital elevation model (DEM) preparations. We found that most TWI algorithms performed poorly on DEMs that were not first filtered or resampled, but DEM filtration and resampling (collectively called generalization) greatly improved the TWI performance. Seasonal variation of soil moisture influenced TWI performance which was best when conditions were not saturated and not dry. Pearson correlation coefficients between TWI and grand mean VWC for the 5-year measurement period ranged from 0.18 to 0.64 on generalized DEMs and 0.15 to 0.59 for on DEMs that were not generalized. These results aid management of crop fields with variable moisture characteristics.

Growing crop production with ignoring the quality of arable land leads to lower crop yields and unreasonable costs. There are areas of waterlogged, marshy, eroded soils or soil with low humus content and low available form for plant of nutrients in intensive use of land, that is leased or owned by modern agricultural companys. These lands have different area and configuration of soil contours and it is the cause of complicated planning of the fields’ boundaries. The purpose of representing data in this article is the justification of the rational use of arable land on condition detailed analysis of soil properties. Because the land management is the basis for optimization of agricultural production in a certain area. In order to obtain indicative results, the territory with complex structure of soil cover and with soils, which differ greatly in their physical and chemical properties and conditions of soil formation, was chosen as the object of research. Soil conditions were analyzed by natural indicators such us groundwater levels, granulometric composition of soil, nutrients concentration that are available for plants, acidity of soils. The explored area has flat relief with moderate climate and insufficient humidification in separate periods. Soils’ parent material: alluvial deposits, loess loam, deluvial deposits. There are 11 groups of soils on the research area of 1272.4789 ha. Among them Haplic Chernozem, Albic Luvisol, Gleyic Chernozem, Mollic Gleysol, Luvic Greyic Phaeozem, Endogleyic Arenosol and Histic Gleysol. The Gleysols occupy a large area of surveyed land (106 hectares). These soils have a very poor water-air mode and suffer from drought or waterlogging in some periods. In order to achieve the purpose of the work an existing methodology for the soils grouping into classes for the suitability of growing agricultural crops was used. Also the scale of soil evaluation for the Ruzhinsky agricultural region was applied. The article represents data of soil classification into classes of suitability by ten classification items. It is established that not the whole area of arable land is suitable for growing wheat. The estimated yield of winter wheat was calculated on the basis of soil's properties. Winter wheat is the demanding plant to soil and climate condition. This is the cause there are low productivity of this plant on clay soil types and soils that have sandy granulometric composition. The estimated yield of winter wheat on Histic Gleysol and Mollic Gleysol is 6.8 centner/ha and on Endogleyic Arenosol is 12.5 centner/ha. Inclusion such tapys of soils in fields area leads to a decrease the yield of winter wheat crops. This is difficult for land use in case low fertility soils are situated in higher quality soils’ area. This situation is inherent territory of Polisya and south part of Ukrainian Lisostep zone. As a result of researchi it was realized that Haplic Gleysol occupy small areas (between 0.1-0.3 ha). These soils are located near Endogleyic Arenosol that don’t suffer from excessive moisture and need another approach of land use. The importance of taking into account the properties of soils is proved. The approach of land use of arable land is proposed. Keywords . S uitability of soils, groups of soils, wheat crop, land use .

The accuracy of topographic indices derived from digital elevation models (DEMs) is of critical importance for eco-environment simulating and modeling based on DEMs. The objective of this study was to evaluate the accuracy of topographic indices derived from DEMs with different scales. Maps with scales of 1:10000, 1:50000, 1:100000, and 1:250000 were digitized and corresponding DEMs were produced using Arclnfo. Topographic indices including slope, aspect, profile curvature, and plan curvature were determined using the DEMs. Overall, certain topographic features were less perceptible on coarser DEMs than finer DEMs. Slope, profile and plan curvatures varied with the scales of the studied DEMs. Decreasing the scale of the studied DEMs produced lower slope gradients, narrower ranges in profile and plan curvatures. Further work will focus on the extraction and self-information of more other topographic indices and the scale impacts on different indices.