Soil Depth Classes Research Articles

Cross-Correlation of Soil Moisture and Stone Content and Their Spatial Pattern Across the Different Slope Aspects and Soil Depth

The analysis of the spatial interrelationship between soil properties and slope aspect is vital for understanding the range of influence on soil depth, moisture, and stone content distribution. This study aimed to investigate the spatial interrelationship of topsoil moisture and stone content in different slope aspects and soil depth. The 53.7 km2 watershed was divided into a 500m by 500m grid using ArcGIS and 230 soil samples were collected. In each sampling point, the soil was taken at three soil depth classes (0–25cm, 25–60cm, and 60–100cm) using a cylindrical auger, then soil samples were tested to determine the percentage of topsoil moisture, and stone content. The spatial interrelationship between aspect, soil depth, topsoil moisture, and stone content was analyzed using the R and GS+ software. The study had shown non-significant effects of aspect on topsoil moisture, stone content, and soil depth. However, topsoil moisture tends to be higher on the north-facing slope, while stone content tends to be higher on the southeast-facing slope. The analysis of Local Moran’s I revealed that topsoil moisture, stone content, and soil depth were significantly autocorrelated. The cross-semivariogram analysis of soil depth with topsoil stone content depicted a negative spatial correlation. The experimental cross-semivariogram of soil depth versus topsoil moisture was positively fitted to the exponential function, whereas soil depth with topsoil stone content was best fitted to the Gaussian model. Overall, soil depth is the more influential factor than the slope aspect regarding topsoil moisture depletion and stone content distribution in the study watershed.

Determining and GIS Mapping Some Soil and Land Properties in Nigde Province of Turkey

This study was carried out within the scope of spatial evaluation of majör soil groups, land slope, erosion degrees, soil depth classes, and other soil properties by using 1/25.000 scaled digital soil maps in Niğde province. Arc GIS 10.3.1 software, which is one of the Geography Information Systems (GIS) software, was used for spatial analysis. Classification results were evaluated according to the "Soil and Land Classification Standards Technical Instruction" published by the Ministry of Agriculture and Rural Affairs in 2005. As a result of the research, it was seen that the class covering the largest area in terms of major soil groups was brown soils with 224466.1 ha. Red-brown soils cover the least area with 45.4 ha. Areas with a steep slope with a 5% slope on the land slope cover the largest area of 140325.7 ha. Areas with a very steep slope of 6%, on the other hand, cover the least area with 69064 ha. Areas with C class (50-90 cm) soil depth in the study area cover the largest area with 294054.7 ha. The areas with E class (0-20 cm) soil depth were seen in the least area with 22203.5 ha. In terms of soil erosion classes, the most common erosion class is III. These are areas with a degree of erosion risk and their total area is 269374.8 ha. It has been determined that areas with slightly salty, alkaline, weak, and adequate drainage are seen in an area of 234.1 ha in the research area. Areas with insufficient drainage have an area of 7350.9 ha. In this study, spatial analysis of some land and soil properties in Niğde province was carried out and distribution maps were produced. It is thought that the results of the research will make significant contributions to the agricultural investments operating in the region.

GIS Mapping of Land Slopes, Soil Depths, Erosian Classes, Large Soil Groups and Some Soil Properties: A Case Study of Kayseri Province in Turkey

This study was carried out to determine the spatial distribution of land slopes, soil depths, erosion classes, large soil groups and some soil properties by using Geography Information Systems (GIS) in Kayseri province (Turkey). As a result of the spatial analysis distributions; It has been observed that a large part of the province of Kayseri consists of soils with a depth of C class (50-90 m), and this area covers an area of 9443.95 km². It is seen that the areas belonging to the E class (0-20 m) soil depth group are very few and this soil depth class covers an area of 6.50 km². When the spatial analysis results of soil slope classes are examined, it is seen that the largest surface area is between 3-6% slope and this area is 8022.80 km². It has been determined that the areas in the 30-45% slope class have the smallest surface area and the total of these areas is 5.21 km². In terms of erosion classification, the lands in Kayseri province are classified as II. classified as areas of high erosion risk. When evaluated in terms of large soil groups, it has been determined that generally limeless brown soils are dominant and the total amount of these areas is 6156.54 km². It has been determined that the area covered by brown forest soils has a very small share in the whole area. Considering other soil properties, it was determined that slightly salty soils cover a very small area (0.99 km²). It has been determined that stony soils cover an area of 8600.58 km². As a result of the study, spatial distribution maps were created showing soil depth, slope and erosion classes, large soil groups and other soil characteristics of Kayseri province, which will guide the investor organizations in the region.

Soil carbon stock and stability under Eucalyptus-based silvopasture and other land-use systems in the Cerrado biodiversity hotspot

During the past few decades, commercial silvopastoral systems (SPS) with exotic Eucalyptus (hybrid) trees have become popular in the Brazilian Cerrado (savanna). With the increasing awareness about the role of carbon (C) storage in soils as a climate-change mitigation strategy and the relationship between the nature of soil aggregates and the soil's carbon sequestration potential, it is important to understand the influence of such SPS systems on soil organic carbon (SOC) storage. We studied C content in three aggregate size classes in six land-use systems on Oxisols in Minas Gerais, Brazil. The systems were planted forest, native secondary forest, managed pasture, and three 8-year-old SPS, differing in their tree-planting configurations. Eucalyptus hybrid was the tree in SPS and planted forest treatments, and Urochloa decumbens was the grass in SPS and pasture treatments. From each treatment, replicated soil samples were collected from four depth-classes (0–10, 10–30, 30–60, and 60–100 cm), fractionated by wet sieving into the three aggregate-size classes, 2000 to 250 μm, 250 to 53 μm, and <53 μm size classes representing macroaggregates, microaggregates, and silt + clay, respectively, and their C contents determined. Down to 1 m, total SOC stock values ranged from 260 Mg ha−1 under pasture to 167 Mg ha−1 under native forest, with 174 Mg ha−1 for Eucalyptus plantation and about 195 Mg ha−1 for the three SPS. Compared to the degraded native forest, the pasture system had significantly higher SOC in the whole soil and the aggregate size fractions, especially in the lower soil-depth classes. The lower SOC stock of Eucalyptus hybrid SPS compared to open pasture differs from the general trend of SPS having higher stock. Given that the Cerrado biome is a biodiversity hotspot, the use of native nitrogen-fixing trees, of which there are several, is worth investigating. In addition, the conversion from Eucalyptus monocultures to SPS could be considered as a strategy to increase the SOC stock.

Regional ensemble modeling reduces uncertainty for digital soil mapping

Recent country and continental-scale digital soil mapping efforts have used a single model to predict soil properties across large regions. However, different ecophysiographic regions within large-extent areas are likely to have different soil-landscape relationships so models built specifically for these regions may more accurately capture these relationships relative to a ‘global’ model. We ask the question: Is a single ‘global’ model sufficient or are regionally-specific models useful for accurate digital soil mapping? We test this question by modeling soil depth classes across the 432,000 km2 upper Colorado River Basin in the Western USA using a single global model, multiple ecophysiographic models, and ensembles of the ecophysiographic models.Effective soil depth class observations (n = 12,194) were derived from multiple soil databases. Fifty-seven environmental covariates were derived from a 30 m digital elevation model, climate data, satellite imagery, and aeroradiometric data. Three independent land classifications were used to stratify the area. Two expert-derived land classifications, USDA Major Land Resource Areas (MLRA) and US-EPA Level III ecoregions, divided the study area into multiple ecophysiographic regions based on vegetation and broad-scale physiographic differences. The third land classification divided the study area into broad landforms.Soil depth observations were split into separate training (n = 10,470) and validation (n = 1,724) datasets. First, a ‘global’ random forest model was used to model soil depth classes using all training observations and covariates. ‘Global’ denotes a model built with all training data across the extent of the area, not a model at world extent. Second, the land classifications were used to subset the observations into ecophysiographic sub-datasets and random forest models were refit for each region. Models fit by ecophysiographic region are referred to as regional models. Thirdly, predictions from each regional model were fused into regional-ensemble models. Accuracy, Brier scores, and Shannon’s entropy were used to compare model accuracy and uncertainty. Regional ecophysiographic models were also compared to models built for geographic areas that were defined solely to be approximately equal in area. Training dataset density and the imbalance ratio were investigated to determine if data characteristics influenced regional accuracy/uncertainty metrics.Accuracy for the global model using the validation set was 62.8%. Regional model accuracies ranged between 56.1% and 75.0%. We found: 1) useful inter-regional differences in global model accuracy were revealed when the global model was validated by region, 2) no consistent relationship between training observation density and accuracy/uncertainty metrics, 3) no meaningful differences in accuracy and uncertainty metrics between physiographic and geographic regions, 4) ensembles of regionally-specific models were approximately as accurate as global models, and 5) both region-specific models and ensembles of regional models were less uncertain than the global model. Overall, we recommend the use of soil depth class predictions made from MLRA regional ensemble models because this prediction had higher accuracy than the ecoregion ensemble model prediction, but lower uncertainty than both the global model and the landform ensemble model predictions. We answer our question: Ensembles of regionally-specific models are approximately as accurate as global models, but result in less uncertainty.

Field calibration of DFM capacitance probes for continuous soil moisture monitoring

This study was undertaken to derive textural and lumped site-specific calibration equations for Dirk Friedhelm Mercker (DFM) capacitance probes and evaluate the accuracy levels of the developed calibration equations for continuous soil moisture monitoring in three selected soil types. At each site, 9 probes (3 per plot) were installed in 2 m2 plots, for continuous soil moisture measurements at 5 different depths (viz. 10, 20, 30, 40 and 60 cm) under dry, moist and wet field conditions. Textural site-specific calibration equations were derived by grouping the same soil textural classes of each site regardless of soil depth, while lumped site-specific calibration equations were derived by grouping all datasets from each site, regardless of soil depth and textural classes. Sensor readings were plotted against gravimetrically measured volumetric soil moisture (θv) for different textural classes as a reference. The coefficient of determination (r2) was used to select the best fit of the regression function. The developed calibration equations were evaluated using an independent dataset. The results indicated that all developed textural and lumped site-specific calibration equations were linear functions, withr2 values ranging from 0.96 to 0.99. Relationships between the measured and estimated θv from calibration equations were reasonable at all sites, with r2 values greater than 0.91 and root mean square error (RMSE) values ranging from 0.010 to 0.020 m3∙m-3. The results also indicated that textural site-specific calibration equations (RMSE &lt; 0.018 m3∙m-3) should be given preference over lumped site-specific calibrations (RMSE &lt; 0.020 m3∙m-3) to attain more accurate θv measurements. The findings of this study suggest that once DFM capacitance probes are calibrated per site, they can be reliably used for accurate in-situ soil moisture measurements. The developed calibration equations can be applied with caution in other sites with similar soil types to attained reliable in-situ soil moisture measurements.

Validation of digital maps derived from spatial disaggregation of legacy soil maps

Spatial disaggregation of soil map units involves downscaling existing information to produce new information at a finer scale than that of the original source. Currently, it is becoming a powerful tool to address the spatial distribution of soil information over large areas, where legacy soil polygon maps are the only source of soil information. Because of the high expense of additional resampling, few studies have sought to validate disaggregated soil maps using independent sampling. This study implemented spatial disaggregation approach to measure the quality of soil property predictions derived from disaggregated soil maps, using stratified simple random sampling of a study area of 6848 km2 (11 strata and 135 soil profiles). In a previous study, the existing legacy soil polygon map of Brittany (France) at 1:250,000 scale was spatially disaggregated at 50 m resolution using an algorithm called Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART), which uses soil-landscape expert rules of soil distribution in space. By fitting equal-area spline functions, soil properties were then estimated at six depth intervals according to GlobalSoilMap specifications. To validate disaggregated soil maps, two approaches were developed according to the soil attribute nature (continuous or categorical). For categorical soil properties (soil parent material, soil drainage class, soil type and soil depth class), the overall strict purity (the degree to which all classification criterion are respected) by the most probable STU (Soil Typological Unit) map was estimated at 34%, while the overall average purity reached 70%. The overall partial soil-type purity reached 60%, the overall partial parent material purity reached 78% and the overall partial soil drainage class as well as soil depth class purities reached 65% and 78%, respectively. Continuous soil properties (clay content, fine silt content, coarse silt content, total silt content, fine sand content, coarse sand content, coarse fragments, Cation Exchange Capacity (CEC) and pH) were validated at two soil depth intervals (5–15 and 30–60 cm) using 260 soil samples. In general, soil property predictions were unbiased except for coarse fragments and CEC in the 5–15 cm layer. Validation statistics (R2, RMSE, RRMSE and ME) were better for the 30–60 cm layer except for soil particle-size distribution. Thus, differences in prediction accuracies among strata (the validation support) denote areas where more soil data or better soil prediction models are needed to improve the disaggregation process.

Semi-automatic disaggregation of a national resource inventory into a farm-scale soil depth class map

Knowledge of soil depth spatial variability is important for land use management especially in dryland agriculture regions, which rely on climate and soils to provide adequate water and nutrients during the growing season. Soil spatial variability can be predicted from legacy soil data through machine learning techniques producing quantitative soil maps requiring minimal resources. South Africa has a country wide 1:250,000 scale resource map known as the Land Type Survey (LTS) which includes soil properties such as soil depth, soil class, root limiting layer, clay content, and texture. Each LTS polygon (land type), is comprised of unique soil – terrain patterns and is therefore, not a true soil map. This study aims to disaggregate the LTS into a farm-scale soil depth class map through a two-step disaggregation approach. First, landform elements were predicted through a pattern recognition algorithm known as geomorphons. Geomorphons, together with the original LTS were overlaid to produce polygons with unique distributions of soil. The polygons were disaggregated further to produce a raster map of soil depth classes through a soil map disaggregation algorithm known as DSMART. The first most probable class raster achieved an accuracy of 68% and for the two most probable class rasters, an accuracy of 91% was achieved. The two-step approach proved necessary for producing a farm-scale soil map. The result of this study is significant as it produced a soil depth class map from a national resource map at a scale and resolution (10 m) suitable for farm management.

Seed bank and growth comparisons of native (Virgilia divaricata) and invasive alien (Acacia mearnsii and A.melanoxylon) plants: implications for conservation.

BackgroundInvasive alien plants with long-lived dormant seed banks and fast growth rates are difficult to manage. Acacia mearnsii and Acacia melanoxylon are two such invaders in the southern Cape of South Africa which occasionally co-occur with a native, ecologically analogous species, Virgilia divaricata. We compared the performance of these three species to determine potential for the native species to be used in management of the invasives.MethodsWe compared the study species in terms of (i) soil seed bank densities, their vertical distribution, and the viability of seeds underneath the canopies of mature trees; (ii) seedling growth from planted seeds over a period of three months; and (iii) growth rates of saplings over a period of 10 months in stands that have naturally regenerated in the field (these stands were dominated by A. mearnsii) and where saplings have been exposed to varying levels of competition from surrounding saplings.ResultsSeed bank densities differed significantly among species but not among soil depth classes. Acacia mearnsii had the highest seed bank densities (mean of 7,596 seeds m−2), followed by V. divaricata (938 seeds m−2) and A. melanoxylon (274 seeds m−2). Seed viability was high (87–91%) in all three study species and did not differ significantly among species or soil depth classes. As seedlings, V. divaricata significantly outgrew A. mearnsii in terms of height, root and shoot dry mass, and root:shoot ratio. Relative growth (the relationship between growth in height and initial height) was negative in the seedlings of both species. Trends during the sapling stage were opposite to those during the seedling stage; A. mearnsii (but not A. melanoxylon) saplings significantly outgrew V. divaricata saplings in height, while relative growth rates were positive in all species. Sapling growth of all species was furthermore uninfluenced by the collective biomass of surrounding competitors.DiscussionOur findings suggest that amongst the measures considered, A. mearnsii’s success as an invader is primarily attributable to its large seed banks, and secondly to its vigorous growth in height as saplings. However, the superior growth performance of V. divaricata seedlings and no apparent negative effect of competition from the acacias on sapling growth show promise for its use in integrated management of the acacias.

Changes in soil carbon stocks across the Forest-Agroforest-Agriculture/Pasture continuum in various agroecological regions: A meta-analysis

The contribution of agroforestry systems (AFS) to enhance soil organic carbon (SOC) storage in soil layers due to the presence of deep tree roots are of interest in the context of promoting carbon sinks and greenhouse gas mitigation. To quantify the relative soil C contribution from trees in agroforestry systems (AFS), this study assessed the reported differences in SOC stocks under agroforestry systems in comparison with other land-use systems (Agriculture, Forestry, Pasture, or Uncultivated Land) in various soil-depth classes in four agroecological regions (arid and semiarid, ASA; lowland humid tropics, LHT; Mediterranean, MED; and temperate, TEM) around the world. Using mixed-effect models and a meta-analytical approach, we synthesized data from 78 peer-reviewed studies that generated 858 data points (sites) on SOC stock under various AFS practiced globally. Comparing Agroforest vs. Agriculture or Agroforest vs. Pasture, SOC stocks under AFS were higher by +27% in the ASA region, +26% in LHT, and +5.8% in TEM, but –5.3% in the TEM in the 0–100 cm soil depth. The Agroforest aged between 10–20 years had higher SOC stock than newly established, as well as <10-year-old systems across all soil-depth classes and agroecological regions. Overall, Agroforest vs. Agriculture land management practices resulted in positive SOC stock changes within AFS up to 100 cm soil depth, whereas SOC stock under AFS was lower than under Forest. The results suggest that a general pattern of Forest – Agroforest – Agriculture – Pasture continuum could be expected in SOC stock decline during land-use changes. Improvement of SOC stocks under AFS varied across agroecological regions, the highest being under arid and semiarid region, closely followed by the low humid tropics. The important outcome of this meta-analysis is worthy of emphasizing of the role of AFS in climate change adaptation and greenhouse gas mitigation strategies by promoting carbon sinks.

Integrating soil analyses with frameworks for ecosystem services and organizational hierarchy of soil systems

ABSTRACTEcosystems provide various goods and services (provisioning, regulating, cultural, and supporting) that benefit humans both directly and indirectly. Soil plays an important role in ecosystem services therefore soil analyses can provide quantitative and qualitative data to evaluate ecosystem goods and services. Soil analyses must be integrated with the frameworks for ecosystem services and existing organizational hierarchy of soil systems to provide missing links to scale, time, degree of computation and complexity. This case study demonstrates the importance of evaluating soil organic carbon (SOC) within this newly proposed context using glaciated soils at the Cornell University Willsboro Research Farm in upstate New York as the example. The vertical distribu tion of SOC was analyzed quantitatively by soil depth class (topsoil versus subsoil), soil order, and other relevant variables that relate to the organizational hierarchy of soil systems.

GIS-fuzzy logic technique in modeling soil depth classes: Using parts of the Clay Belt and Hornepayne region in Ontario, Canada as a case study

Globally, there is a growing desire to predict and map the spatial distribution of soil depth on the landscape due to its numerous roles in areas such as agriculture, forestry, hydrology and ecological land classification. The aim of this study was to develop a technique to model and map soil depth classes based on GIS-fuzzy logic modeling approach and to examine the mapping accuracy of this technique with the accuracy of soil depth classes' derived using randomForest modeling approach. This was carried out for a portion of the Clay Belt and Hornepayne region in Ontario, Canada as a case study. The GIS-fuzzy logic approach was performed based on the soil-environment model (case-based reasoning and rule-based reasoning) using a 10m LiDAR-derived Digital Elevation Model (DEM) and derivatives such as curvature, slope, aspect, slope position classification, smooth multi-path wetness index and surface roughness in combination with mode of deposition and spatial observations of soil depth information. A digital soil depth map with classes of deep (>120cm), moderately deep (>60 and ≤120cm), shallow (>15 and ≤60cm) and very shallow (≤15cm) was predicted across 430,076ha. An overall mapping accuracy of 94% in validation was obtained from the GIS-fuzzy logic modeling approach relative to 88% derived from the randomForest modeling technique. The GIS-fuzzy logic modeling approach could be readily implemented to predict and map the spatial distribution of soil depth classes on various landscapes.

Soil strength assessment using threshold probability approach on soils from three agro-ecological zones in Eritrea

Soils in many agro-ecological zones in Eritrea, a country in sub-Saharan Africa, are low in productivity due to erosion, low organic matter and poor soil management. Recently, mechanised farming has been intensively practiced to increase agricultural food production. However, the use of heavy machinery and vehicles can cause soil compaction that reduces soil productivity and crop yield. A study was conducted to determine the in-situ soil compaction and the probability of exceeding a root restricting threshold penetration resistance (PR) value of 2 MPa in three agro-ecological zones that varied in soil type, topography and climate. Soil cone penetration resistance measurements were taken up to a depth of 600 mm in 30 m by 30 m field plots with three replicates located in the three agro-ecological zones. Simultaneously, core samples were collected to determine soil moisture and bulk density from surface and subsurface soil layers. In all the soils, moisture contents during soil penetration resistance measurements were nearly the same as the field capacity soil moisture content (−10 kPa soil moisture suction). Penetration resistance values measured from 0 to 600 mm were averaged in depth increments of 100 mm. For clay loam (Adiguadad) and loam (Sheeb) soils, the penetration resistance values up to a soil depth class of 0–300 mm were statistically insignificant and categorised as minimum mechanical impedance to root growth (<1.0 MPa). In the sandy loam soils (Western lowland soil) the penetration resistance reached 2 MPa at 250 mm depth implying strong mechanical impedance to roots. Logistic regression analysis indicated that the probability of exceeding 2 MPa was significantly affected by soil moisture, bulk density and soil type. The probability increased with bulk density and decreased with soil moisture for clay loam, loam and sandy loam soils.

Updating Soil Depth Class Information in North Dakota with Ground-Penetrating Radar

Updating Soil Depth Class Information in North Dakota with Ground-Penetrating Radar

Soil information system of Arunachal Pradesh in a GIS environment for land use planning

Arunachal Pradesh, the largest mountainous state of India, is situated in the northeastern part of the Himalayan region and characterized by high annual rainfall, forest vegetation and diversity in soils. Information on the soils of the state is essential for scientific land use planning and sustainable production. A soil resource inventory and subsequent database creation for thematic mapping using a Geographical Information System (GIS) is presented in this paper. Physiographically, Arunachal Pradesh can be divided into four distinct zones: snow-capped mountains (5500 m amsl); lower Himalayan ranges (3500 m amsl); the sub-Himalayan Siwalik hills (700 m amsl); and the eastern Assam plains. Soils occurring in these physiographic zones are Inceptisols (37 percent), Entisols (35 percent), Ultisols (14 percent) and Alfisols (0.5 percent). The remaining soils can be classed as miscellaneous. Soil resource inventory studies show that the soils of the warm perhumid eastern Himalayan ecosystem, with a 'thermic’ temperature regime, are Inceptisols and Entisols; and that they are highly acidic in nature. Soils of the warm perhumid Siwalik hill ecosystem, with a 'hyperthermic’ temperature regime, are also Entisols and Inceptisols with a high to moderate acidic condition. The dominant soils of the northeastern Purvachal hill ecosystem, with 'hyperthermic’ and 'thermic’ temperature regimes, are Ultisols and Inceptisols. Inceptisols and Entisols are the dominant soils in the hot and humid plain ecosystem. Steeply sloping landform and high rainfall are mainly responsible for a high erosion hazard in the state. The soil erosion map indicates that very severe (20 percent of TGA) to severe (25 percent of TGA) soil erosion takes place in the warm per-humid zone, whereas, moderate erosion takes place in the Siwalik hills and hot, humid plain areas. This is evident from the soil depth class distribution of Arunachal Pradesh, which shows that shallow soils cover 20 percent of the TGA of the state. Most of the state is covered by hills and agricultural practices are limited to valley regions. However, the soils of other physiographic zones (lower altitudinal, moderately hilly terrain) provide scope for plantations, such as orange, banana and tea plantations.

Nondestructive assessment of rangeland soil depth to petrocalcic horizon using electromagnetic induction

The distribution of soil depth to cemented subsoil horizons is important for land evaluation and management, particularly in arid and semiarid rangelands, where plant growth is limited by soil depth. Traditional methods of quantifying soil depth distribution are destructive, time‐consuming, and costly. Therefore nondestructive, efficient, and inexpensive methods for assessing soil depth are needed. We investigated the effectiveness of electromagnetic induction for nondestructively assessing the distribution of soil depth to petrocalcic horizon in a semiarid rangeland landscape in central Utah. Apparent conductivity was measured with the EM31 and EM38 electromagnetic induction meters in an 11.5‐ha survey area and in four 0.1‐ha plots. Soil depth was measured in hand‐excavated pits or in hand‐augered holes. Rangeland soil depth was positively and significantly correlated with apparent conductivity. Apparent conductivity measurements made by different operators were not significantly different, indicating that different operators should not affect precision. Apparent conductivity measurements made on different days about 3 weeks apart were significantly different, indicating that regressions of apparent conductivity and soil depth should be recalibrated if data are collected over extended time periods. Plant cover measured in 1‐m‐diameter subplots was not significantly correlated with apparent conductivity and therefore does not affect the ability of the EM38 to measure apparent conductivity. In a validation subset, apparent conductivity did not accurately predict actual soil depth, nor did it predict the corred distribution of soil depth classes, but it provided rudimentary information about soil depth distribution (<50 versus ≥50 cm) that could be useful to land managers.

Mapping soil depth classes in dry Mediterranean areas using terrain attributes derived from a digital elevation model

Modern land management increasingly demands quantitative information on spatially variable soil properties. Traditional soil survey maps do not provide this information. In this paper we report on the application of terrain attributes to mapping soil depth classes at high spatial resolution over large areas under dry Mediterranean conditions. Soil data were collected in 111 georeferenced field plots of 30 m × 30 m, more or less equally distributed over three lithological units, phyllites, shales and limestones. Topographic attributes were computed from a digital elevation model at 30 m resolution. A principal components analysis was carried out on the map overlays of the terrain attributes in order to obtain uncorrelated topographic factors that enabled us to apply a probability approach. Using the maximum likelihood classifier with the field plots as a dispersed training area, predictions were made of mean soil depth class and the probability of occurrence of shallow or deep soils. A cross-validation revealed a 65%, 81% and 61% accuracy for the three maps of the shale area, a 50%, 55% and 40% for the maps of the phyllite area, and a 78%, 72% and 75% accuracy for the maps of the limestone area. Explanations for both the good results in the shale and limestone areas, and the poor results in the phyllite area, focus on the effects of the spatial scale of topographic variation, sediment transport mechanisms, and the impact of land use.

Towards identifying the fate of15N applied in spring as ammonium nitrate fertiliser to a kikuyu pasture

Abstract Nitrogen was applied as ammonium nitrate in solution to kikuyu pasture at 225 kg N ha‐1, split into three equal dressings and applied in October, December and February. The first, at 75 kg N ha‐1 was labelled in the ammonium ion and the latter two dressings were not labelled. Herbage, soil and roots were sampled monthly to determine the proportions of the labelled N in the herbage, root and soil over the season. Total 15N recovered over the season, as a percentage of that applied as 15NH4NO3, was 99.0%. A total of 52.1% of the labelled N was recovered in the herbage over the season, the highest recovery being in November. Recovery rates declined from November through to March. However, the accumulated recovery of 15N was curvilinear over the season. The recovery rate of 15N in the roots was lower than in the herbage, and the highest percentage recovered was in December. The 15N recovered, as a percentage of that applied, in the soil depth classes showed similar trends. The highest percentage of I...

Analysis of Long‐Term Water Table Depth Records from a Hydrosequence of Soils in Central Ohio

AbstractWater table depths and precipitation were measured over a 10‐yr period on four adjacent forested soils in central Ohio. The soils included a hydrosequence of well, moderately well, poorly, and very poorly drained fine‐textured soils. Graphs describing the probability of observing water tables during consecutive 2‐week (semi‐monthly) intervals, by soil depth class, were compared with estimates reported on USDA‐Soil Conservation Service forms SOIL‐SOI‐5 and a method proposed in the Soil Survey Manual. All soils had a probability of an observed water table within 50 cm of the soil surface. Water tables were observed in all soils, with the exception of the well drained soil, within 25 cm of the surface, 10% of the time or more, over a 12‐ or 14‐week period in the late winter and spring.

Crop Tree Thinning Increases Availability of Soil Water to Small Yellow Birch Poles

AbstractMeasurements of soil‐moisture depletion with a neutron probe on five forest sites supporting yellow birch poles (Betula alleghaniensis Britton) in Vermont indicate moisture is depleted at slower rates around individual trees that have had “partial release” and “full release” than around trees with “no release.” The effects of thinning on depletion are cumulative over a growing season; by late August moisture depletion curves have diverged substantially. Significant differences in depletion rates due to treatment effects occurred for 20 to 40% of the measurement intervals within a growing season. Most of the significant differences are judged to occur because of oddities in the data. Measures of net soil depletion rate are extremely variable because of microsite differences. Apparent depletion rates and theoretical depletion patterns for short intervals are examined. Moisture depletion rates between soil depth classes (0–15 cm, 15–46 cm, 46–76 cm) are significantly different during most intervals within a growing season. The data obtained in this study portray only general trends, and it is questionable whether more intensive studies of this nature should be planned to obtain significant differences in results.