Texture Bulk Research Articles

Effects of extrusion screw speed, feed moisture content, and barrel temperature on the physical, techno-functional, and microstructural quality of texturized lentil protein.

Utilizing lentil protein as a novel ingredient for producing texturized vegetable proteins (TVPs) can provide new opportunities for the production of next-generation hybrid meat products. TVPs from lentil protein isolate were manufactured using low-moisture extrusion cooking at different combinations of screw speed (SS), feed moisture content (MC), and barrel temperature (BT) profile. In total, seven different combinations of processing treatments were tested, and the resulting TVPs were characterized for their physical (rehydration ratio, texture profile analysis, color, and bulk density), techno-functional (oil and water holding capacities), and microstructural properties. The processing conditions of higher SS and lower MC resulted in increased values of several textural profile attributes (springiness, cohesiveness, and resilience), increased water holding capacity (WHC), and decreased bulk density. Compared to raw lentil protein, TVPs showed enhanced oil holding capacity, though WHC either decreased or remained constant. The extrusion response parameters (die pressure, torque, and specific mechanical energy) showed positive correlations with several physical properties (texture, WHC, and total color change), revealing their potential for serving as important TVP quality indicators. TVPs produced at SS, MC, and BT of 450rpm, 30%,and 140°C, respectively, showed relatively better overall physical and techno-functional quality and can be used as meat extenders in hybrid meat patties. Overall, this research evidenced the viability of lentil protein as a potential ingredient for producing low-moisture TVPs.

Impact of soil structure and texture on occurrence of microplastics in agricultural soils of karst areas

The impact of microplastics (MPs) on soil ecosystems has attracted widespread attention; however, the effects of soil structure and texture on the occurrence of MPs are not fully understood. In this study, we investigated the effects of soil structure and texture on the abundance of MPs and their potential mechanisms in agricultural soils of karst areas in Guizhou, China. The results showed the average abundance of MPs was 2948 items/kg. The soil texture in the study area can be categorized into seven types such as powdered-light clay, the range of total soil porosity was 39.05–69.22 % and the range of soil bulk density was 0.66–1.51 g/cm3. Soils with a powdered-light clay, low soil porosity, and low soil bulk density showed higher MPs pollution. The percentage of pellet MPs in agricultural soils with a powdered-light clay was 84 %, which was higher than that of the other soil textures. The direct effects of soil texture, soil porosity, and soil bulk density on MPs abundance were much lower than the indirect effects, with soil texture having the highest effect on MPs abundance. We speculated that karst geology may affect the accumulation and distribution of MPs in soil by affecting soil texture and structure, which, in turn, affects the fragmentation and migration of MPs. These findings will help to better understand the mechanisms of soil MPs pollution and provide a scientific basis for the development of relevant control strategies.

Modelling and mapping soil nutrient depletion in humid highlands of East Africa using ensemble machine learning: A case study from Rwanda

Soil nutrient depletion is one of the major causes of high yield gaps and nutrient deficiencies in East Africa highlands, including Rwanda. This research sought to determine the current soil nutrient balance and its spatial variation in 10 Rwandan agro-ecological zones. Soil nitrogen (N), phosphorus (P) and potassium (K) depletion in croplands were calculated using data from 455 field trials of the Optimizing Fertilizer Recommendations in Africa (OFRA) project in Rwanda. Calculated soil nutrient balances (NPK) and 15 environmental covariates were used to calibrate soil nutrient depletion models using ensemble machine learning (EML) and 10-fold cross-validation. In the 2019–2020 growing season, annual N and K depletions were 33.6 kg N ha−1 yr−1 and 71.0 kg K ha−1 yr−1, with a positive P balance of 2.30 kg P ha−1 yr−1. High soil nutrient uptake and high soil nutrient loss due to erosion and leaching were two main causes of NPK depletion. Spatial variations of NPK balance were influenced by soil nutrient stocks, soil erosion, elevation, rainfall, soil texture, and soil bulk density. The 10-fold cross-validation showed that coefficients of determination (R2) of NPK models were 62%, 58%, and 58%, respectively. Compared to single models, ensemble machine learning improved NPK model accuracy up to 5%. Our research revealed that soil nutrient depletion was highest in the northwest and lowest in the southeast of the study area. We conclude that increasing soil nutrient inputs without reducing soil nutrient loss due to soil degradation will not decrease soil nutrient depletion in Rwanda and ensemble machine learning outperforms single models in predicting soil nutrient balance. Thesolutiontoreducehighsoilnutrientdepletionin all agro-ecological zones of Rwanda would be to prioritize soil and water conservation measures and increase soil nutrient inputs.

The Potential of Palm Oil Mill Effluent for Improving the Water Infiltration Rate in Lateritic Soil

Poor soil drainage of the lateritic soil is due to its clayey texture and high bulk density. Palm oil mill effluent (POME) may be administrated to increase the water infiltration rate and improve the drainage of the lateritic soil. The share farm of the Faculty of Plantation and Agrotechnology, UiTM Jasin was treated with various volume of POME for 16 weeks by using Completely Randomized Design (CRD). Overall, treatment of POME did not increased water infiltration rate. The bulk density of the lateritic soil that was treated with POME is suitable for agriculture. Treatment 1 (2L) gave 68 mm/hour of water infiltration rate and 1.25 g/cm3 of bulk density with 0.570 and 0.451 of p-values respectively which is acceptable with the consideration of cost benefits, cost inputs, and profit to the industry. The results shown were not significant might be due to types and soil textures, duration for the research to be completed, and also climate change factors.

Investigating the capability of estimating soil thermal conductivity using topographical attributes for the Southern Great Plains, USA

Soil thermal conductivity is one of the main factors influencing the soil temperature regime. Over the recent years, by developing remote sensing technology, topographic attributes such as elevation, slope and aspect have become accessible from digital elevation models (DEMs). Based on the interactions between soil properties and topographically heterogeneous landscapes, this research investigates the application of topographical attributes (geographical and satellite information), along with soil physical properties, in order to create pedotransfer functions (PTFs) and estimate soil thermal conductivity, not reported so far. The hydrology experiment database of the Southern Great Plains 1997 (SGP97) available online at (http://www.cei.psu.edu/nasa_lsh/), was used to obtain these functions through applying linear regression, artificial neural networks (ANNs) and support vector machine methods. Twelve PTFs (PTF1-12) were developed to predict the thermal conductivity using different combinations of soil physical properties and topographical attributes. Improvement was attained via a combination of soil physical properties and topographical attributes (PTF5-11) rather than soil physical properties alone (PTF2-4). Thermal conductivity was better estimated by use of east-west slope degree, north-south slope degree and saturated water content along with texture fractions and bulk density as predictors in PTF7. The root mean square error decreased from 0.266 and 0.217 in PTF1 to 0.217 and 0.157 in PTF7 in the training and testing steps, respectively. ANNs were the best performing method with the relative improvement ranging from 3.52 to 19.35% and from 14.00 to 28.00% in the training and testing steps, respectively. The results displayed the successful estimation of the thermal conductivity in the training and testing steps, applying a combination of soil physical and topographical attributes.

Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area

This study evaluated the performance of deep learning (DL), artificial neural network (ANN) and k-nearest neighbour (kNN) models to estimate field capacity (FC) and permanent wilting point (PWP) using four combinations of soil data. The DL, ANN and kNN models are compared with the previous published pedotransfer functions (PTF). The data consist of 256 calcareous soil samples collected from Konya-Çumra plain, Turkey. The results demonstrated that the DL_a with inputs of soil texture components, bulk density, organic matter and lime contents, particle density and aggregate stability showed the best performances with coefficient of determination (R2) of 0.829, correlation coefficient (r) of 0.911, mean absolute error (MAE) of 0.027 and relative root mean square error (RRMSE) 9.397 % in FC estimation for calcareous soil samples. For the PWP estimation of calcareous soil samples, the kNN_b with soil texture components, bulk density, organic matter and lime content and particle density indicated the best performance with the value of R2 to 0.800, of r to 0.894, of MAE to 0.021 and RRMSE to 12.043 %. Lastly, the results showed that the DL, ANN and the kNN models perform better than the previously applied PTF for calcareous soils. Therefore, the DL model could be recommended for the estimation of FC when full soil data are available and the kNN model could be recommended for estimation of PWP with all combinations of soil data.

Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty

Soil available water capacity is an important soil property for land use planning, drought risk assessment, and modelling crop production or carbon cycling. Measurements of soil moisture at several soil water potentials are expensive and time-consuming, hence it is common to estimate available water capacity with pedotransfer functions (PTFs). Available PTFs for France rarely provide uncertainty estimates for the model coefficients and predictions, which are often required for error propagation analysis and modelling ecological processes. The objectives of this study were: 1) to develop class-PTFs and 2) continuous-PTFs with associated uncertainties, and 3) to assess the domain of applicability of the PTFs across metropolitan France. We used the SOLHYDRO database for calibrating continuous and class PTFs. For continuous PTFs we used linear regression models using sand, clay, organic carbon (SOC), and bulk density (BD) as predictor variables, whereas class-PTFs were defined by texture class, bulk density, and horizon type (all horizons, topsoils, subsoils). The models were validated with an independent validation dataset. The domain of applicability of the PTFs was evaluated calculating the Mahalanobis distance between the calibration dataset and horizon data from the French soil monitoring network (RMQS). At independent validation, texture class-PTFs had a RMSE between 0.047 cm3 cm–3 and 0.058 cm3 cm–3 and continuous-PTFs had a RMSE between 0.040 cm3 cm–3 and 0.053 cm3 cm–3. Texture class-PTFs had similar or better predictive performance than texture-structural class-PTFs. The prediction performance of continuous-PTFs improved slightly when including SOC and BD in the models. The variance of the predictions of continuous-PTFs associated to error in the model coefficients was rather small, and increased as the values of the predictor variables were more distant to the centroid of the calibration data. We provide the PTF users with tools for classifying new samples as within or outside the applicability domain of the PTFs. When applied to the RMQS dataset, >50% of the horizons outside the domain of applicability were located in forests and natural areas or managed pastures. The spatial distribution of RMQS horizons outside the domain of applicability of the PTF can inform future sampling locations for increasing the diversity of the soil properties and site conditions represented by the SOLHYDRO dataset.

A Field‐Scale Sensor Network Data Set for Monitoring and Modeling the Spatial and Temporal Variation of Soil Water Content in a Dryland Agricultural Field

AbstractWe describe a soil water content monitoring data set and auxiliary data collected at a 37 ha experimental no‐till farm in the Northwestern United States. Water content measurements have been compiled hourly since 2007 by ECH2O‐TE and 5TE sensors installed at 42 locations and five depths (0.3, 0.6, 0.9, 1.2, and 1.5 m, 210 sensors total) across the R.J. Cook Agronomy Farm, a Long‐Term Agro‐Ecosystem Research Site stationed on complex terrain in a Mediterranean climate. In addition to soil water content readings, the data set includes hourly and daily soil temperature readings, annual crop histories, a digital elevation model, Bt horizon maps, seasonal apparent electrical conductivity, soil texture, and soil bulk density. Meteorological records are also available for this location. We discuss the unique challenges of maintaining the network on an operating farm and demonstrate the nature and complexity of the soil water content data. This data set is accessible online through the National Agriculture Library, has been assigned a DOI, and will be maintained for the long term.

How do root and soil characteristics affect the erosion-reducing potential of plant species?

Plant roots can be very effective in stabilizing the soil against concentrated flow erosion. So far, most research on the erosion-reducing potential of plant roots was conducted on loamy soils. However susceptible to incisive erosion processes, at present, no research exists on the effectiveness of plant roots in reducing concentrated flow erosion rates in sandy soils. Therefore, the prime objective of this study was to assess the erosion-reducing potential of both fibrous and tap roots in sandy soils. Furthermore, we investigated potential effects of root diameter, soil texture and dry soil bulk density on the erosion-reducing potential of plant roots. Therefore, flume experiments conducted on sandy soils (this study) were compared with those on sandy loam and silt loam soils (using the same experimental set up). Results showed that plant roots were very efficient in reducing concentrated flow erosion rates in sandy soils compared to root-free bare soils. Furthermore, our results confirmed that fibrous roots were more effective compared to (thick) tap roots. Dry soil bulk density and soil texture also played a significant role. As they were both related to soil cohesion, the results of this study suggested that the effectiveness of plant roots in controlling concentrated flow erosion rates depended on the apparent soil cohesion. The nature of this soil type effect depended on the root-system type: fine root systems were most effective in non-cohesive soils while tap root systems were most effective in cohesive soils. For soils permeated with a given amount of fibrous roots, an increase of soil bulk density seemed to hamper the effectiveness of roots to further increase soil cohesion and reduce erosion rates. In soils reinforced by tap root systems, the erosion-reducing power of the roots depended on sand content: the higher the percentage of sand, the smaller the erosion-reducing effect for a given amount of roots. This was attributed to more pronounced vortex erosion around the thicker tap roots in non-cohesive soils, increasing soil erosion rates. The results presented in this study could support practitioners to assess the likely erosion-reducing effects of plant root systems based on both root and soil characteristics.

Investigating the correlation between soil tensile strength curve and soil water retention curve via modeling

Soil water retention curve (SWRC) is a crucial soil property required for solving many soil and water management problems. But, its direct measurement needs a lot of time, effort and money. The aim of this study was to develop pedotransfer functions (PTFs) for estimating water content through the van Genuchten (1980) model by employing tensile strength (TS) models. One hundred forty eight samples were gathered from five provinces in Iran. Bulk density, TS curve, SWRC and particle size distribution were measured. Four empirical TS models were fitted to the experimental soil mechanical data. Also, three physically based equations were used to estimate soil water content. In order to develop PTFs to estimate the parameters of van Genuchten (1980) model, artificial neural networks (ANNs) and regression (MLR) methods were used. In nine PTFs, the parameters of the empirical TS models and other soil properties were used as predictors for estimating SWRC. In developing the PTFs, ANNs were superior to MLR. Using the parameters of the TS models as predictors improved the estimation of water content between 2.8 and 6.9%. The SWRC was estimated better by using the parameters of the developed model of TS along with texture fractions and bulk density as predictors. The result showed the high capability of three physically based equations in the estimation of water content. Lu et al. equation had the highest accuracy in the SWRC estimation, in comparison with other physically based equations. The results showed the significance of TS in the estimation of SWRC.

Predicting soil moisture condition in arbitrary agricultural lands using the digital soil map and soil physical properties database

To predict soil moisture condition in arbitral agricultural lands by taking spatial distribution of soil type in a watershed into account, we investigated the possible proper methods of determining the soil hydrological parameters using available soil physical properties database of Japanese soils, “SolphyJ”, and the digital soil map. In this study, simulation of soil moisture was conducted at three neighboring locations, including monitoring sites each in Toyama and Fukui cities. The simulated results of soil moisture appeared to be improved when hydraulic parameters were obtained by fitting water retention data of SolphyJ compared to the parameters estimated by neural network with soil texture and dry bulk density data. It is probably because peculiar soil structure in each field could affect the hydraulic properties more than the soil texture. Simulation results also indicated that soil moistures are much different even if they are located in neighbors (<5 km). We concluded that combination of these two databases is useful for estimating soil hydraulic parameters and to predict soil moisture condition in arbitrary agricultural lands.

Thermal properties of boring core samples from the Kanto area, Japan: Development of predictive models for thermal conductivity and diffusivity

The subsurface of the Earth is facing evermore thermal impact due to global warming, urban heat islands, and the widespread use of ground source heat pump (GSHP) systems. This potentially causes changes in its physical, mechanical, microbiological, and chemical properties, and in the subsurface water quality. To predict and evaluate this thermal impact (or thermal pollution), a better understanding and improved models for the thermal properties governing heat transport in subsurface sediments are needed. Also, data acquisition in high spatial resolution for the thermal properties and basic physical properties of the subsurface sediments are essential. In this study, the main thermal properties (the thermal conductivity, heat capacity, and thermal diffusivity) together with the basic physical properties (the soil texture, water content, and dry bulk density) were measured on boring core samples representing depths from 0 to 50 or 80m, at three study sites in the Kanto area of Japan. Based on the measured data, models for thermal conductivity as functions of gravimetric water content, dry bulk density, and volumetric sand content were developed. The new models performed markedly better than presently available models from the literature and, in combination with a modified de Vries type model for heat capacity, the resulting model for thermal diffusivity was capable of describing the measured data well. The usefulness of the newly developed models were validated and illustrated by using data from a two-day thermal response test (TRT) performed at one of the three study sites. The new predictive models for the thermal properties used in a numerical heat transport simulation accurately predicted subsurface (5–50m) temperature changes during the TRT.

Assessment of water and air permeability of chernozem supported by image analysis

We aimed at the assessment of water and air permeability of a Haplic Chernozem developed from loess. The laboratory permeability measurements were presented against a number of morphometric indices that characterize the pore system in this soil, and were obtained by computer-aided image analysis on the basis of large resin-impregnated soil opaque blocks. Two indices of soil pore connectivity are proposed, i.e. the index of soil pore network growth rate and the percolation number. Basic soil properties were evaluated (soil texture, TOC, carbonates, pH, particle and soil bulk density, total porosity). The saturated hydraulic conductivity, and soil water content, air capacity and permeability at −15.54kPa and −9.81kPa were measured. From the samples with preserved structure, resin-impregnated soil opaque blocks 8cm×9cm in size were prepared and then used for morphological and morphometric structure analysis. The preliminary image analysis was made to find the best representation of the actual chernozem pore system. The images were modified by applying the morphological closing with or without the spike noise, which modelled tiny pores missed during scanning. Consequently, the macroporosity of the images approximated the air capacity at potentials −15.54 and −9.81kPa. During the extended image analysis, we calculated: the index of soil pore network growth rate; the percolation number; the average cross-sectional size of the pore; the total length of pore path; the relative volume of pores overlapping the left and right, and the top and bottom image edge; the relative volume of pores connecting the left and right and the top and bottom edge of the image. The correlation coefficients between the parameters’ values obtained from image analysis and from laboratory permeability measurements were calculated.The water and air permeability and the air capacity of the chernozem decreased with depth into the soil pedon. With decrease in the saturated hydraulic conductivity, measured in the laboratory, there was a decrease in the relative volumes of pores overlapping the left and right and upper and lower edges of the image, obtained from image analysis. The air permeability was positively correlated with the index of pore network growth rate. Morphological and morphometric image analysis confirmed that the most important parameters determining the transport of fluids in the soil are continuity of the pore system and pore volume. Based on the results obtained from image analysis one can formulate qualitative conclusions concerning the water and air permeability of soil.

Effects of the height and geographical aspects on potential of soil carbon sequestration in Kermanshah, Iran

Main phenomenon of climate change that is harmful for human being, increased concentration of carbon dioxide in the atmosphere. Regarding to the potential of carbon storage in soil and plant tissues, this approach has a serious interest in recent decades. Due to the different climate zones in IRAN, share and amount of carbon sequestration in any of these areas need more contemplation. Therefore, the ability of soil carbon sequestration of mountain pastures in Kermanshah Province was studied in 3 height and 4 geographical aspects. Then, soil samples were taken from the areas that Astragalus Gossypinus and Astragalus Parrowianus grew at two depths of 0-30cm and 30-60cm. The amount of organic carbon, bulk density, electrical conductivity, pH, moisture content and soil texture in both depths of each soil profile were measured. Stepwise regression results also showed that texture parameters, bulk density and pH, respectively, were the most important factors affecting on soil organic carbon. Based on the results of data analysis using a completely randomized factorial design, significant differences were observed at 1% significance level between the two species and also in height between the classes and geographical aspect on soil carbon sequestration rates.Comparison of results based on SNK tests also indicates that, biggest amount of carbon sequestration taking place in the maximum height in the geographic north aspect. Also according to the results can be stated that capability of Soil Carbon Sequestration in Astragalus Parrowianus is more than Astragalus Gossypinus.

Development of class pedotransfer functions for integrating water retention properties into Portuguese soil maps

Hydrological modellers have recently been challenged to improve watershed models by better integrating soil information into model applications. Reliable soil hydraulic information is thus necessary for better describing the water balance components at the catchment scale. Frequently, that information does not exist. This study presents a set of class-pedotransfer functions (PTFs) for estimating the water retention properties of Portuguese soils. The class-PTFs were established from a dataset containing 697 soil horizons/layers, by averaging values of total porosity and volumetric water contents at –0.25, –1, –3.2, –6.3, –10, –33, –100, –250, and –1500 kPa matric potentials after grouping data by soil texture class, soil horizon, and bulk density. Fitted retention curves using the van Genuchten model were also obtained for every class-PTF. The root mean square error varied between 0.039 and 0.057 cm3/cm3, with smaller values found when using the 12 texture classes of the International Soil Science Society (ISSS) system rather than the five texture classes of FAO, and when bulk density was also considered. The class-PTFs were then integrated into Portuguese soil maps and its usage was demonstrated by deriving maps of available water capacity to be used for modelling the water balance in a small catchment area with the SWAT model. The model successfully simulated the reservoir inflow when using the derived maps, but the results did not vary much whether using coarser or finer description of the catchment soils. Nonetheless, the class-PTFs contributed to a better soil characterisation than when using coarse-scaled information. The approach followed here was simple, inexpensive, and feasible for modellers with few resources but interested in considering the spatial variability of soil retention properties at large scales and in advancing hydrologic modelling in Portugal.

Prelamination of Radial Forearm Free Flap with Buccal Mucosa

Reconstruction of extripative defects in the head and neck with vascularized free tissue transfer has become the standard of care. In order to overcome disadvantages of the traditional radial forearm free flap (RFFF), e.g., color and texture difference, bulk, and donor site morbidity, the concept of prelamination of the RFFF with mucosa was developed. The term prelamination, introduced in 1994, is a process in which tissue is implanted into a vascular flap prior to transfer [1]. Prelamination with oral mucosa has been implemented in the reconstruction of a variety of intraoral and facial defects [2–4]. With their high cell renewal rate, morcelized buccal mucosal grafts can spread over a vascularized fascial bed and become functional in 3 weeks [2]. The resultant mucosa resembles the native oral mucosa both macroscopically and histologically [5], providing lubrication. Fasicomucosal flaps are thinner than fasciocutanous flaps since no subcutaneous tissue is included. This allows for better tongue mobility and rehabilitation [2]. We studied patients who underwent reconstruction with a prelaminated radial forearm fasciomucosal flap (RFFMF) to describe surgical technique and report multi-institutional outcomes.

Thermoelectric conversion via laser-induced voltage in highly textured polycrystalline NaxCoO2 ceramic

We have studied and analyzed the laser-induced voltage effect in highly c-axis-oriented polycrystalline NaxCoO2. The textured and layered stacking NaxCoO2 (x ∼ 0.7) bulks were prepared by a solid-state reaction process. Under the irradiation on Na0.67CoO2 bulk surface with pulsed laser (λ = 248 nm), the induced voltage signals were observed on the inclined surface with rise time 30 ns–43 ns and peak voltage 200 mV–500 mV; the voltage peak values show a linear dependence of laser energy densities. The crystal grains orientation plays a critical role in voltage peak value whether in film or texture bulk. The transverse voltage signal brings the information of thermoelectric anisotropy. In NaxCoO2 band structure, the Fermi surface is different in the ab plane and along the c axis, leading to anisotropy of Seebeck coefficient. Additionally, the artificial structure of the inclined surface for highly textured bulk enables us to obtain a transverse voltage on inclined surface. These results demonstrated the layered textured bulk has potential applications in waste-heat conversion via transverse thermoelectric effect.

Capillary pressure-dependent anisotropy of layered unsaturated soils

This paper presents an approach based on a conceptualization of combining the neural network based pedo-transfer function (PTF) results with the thin layer concept to explore capillary-pressure-dependent anisotropy in relation to soil texture and soil bulk density. The effects of capillary pressure (or saturation degree) on the hydraulic conductivity anisotropy of unsaturated soils are still poorly understood. The main objective is to examine how anisotropy characteristics are related to the relationships between hydraulic parameters and the basic soil attributes such as texture and bulk density. The hydraulic parameters are correlated with the texture and bulk density based on the pedo-transfer function (PTF) results. It is demonstrated that non-monotonic behavior of the unsaturated soil anisotropy in relation to the capillary pressure is only observed when the saturated hydraulic conductivity and the shape parameter are both related to the particle diameter. Therefore, it is suggested that this behavior is mainly due to the coupled dependence of the layer saturated hydraulic conductivities and the shape factors on the texture and bulk density. The results illustrate that the inter-relationships of soil texture, bulk density, and hydraulic properties may produce vastly different characteristics of anisotropic unsaturated soils.Key words: Anisotropy, unsaturated soils, capillary pressure-dependent

Effect of compaction on pore functions of soils in a Saalean moraine landscape in North Germany

AbstractSoil compaction and related changes of soil physical parameters are of growing importance in agricultural production. Different stresses (70, 230, 500, and 1000 kPa) were applied to undisturbed soil core samples of eight typical soils of a Saalean moraine landscape in N Germany by means of a confined compression device to determine the effect on (1) total porosity/pore‐size distribution, (2) saturated hydraulic conductivity, and (3) air conductivity to assess the susceptibility towards compaction. Different deformation behaviors after exceeding the mechanical strength particularly resulted from a combination of soil characteristics like texture and initial bulk density. The saturated hydraulic conductivity, as an indicator for pore continuity, was largely affected by the volume of coarse pores (r² = 0.82), whereas there was no relationship between bulk density and saturated hydraulic conductivity. Since coarsely textured soils primarily possess a higher coarse‐pore fraction compared to more finely textured soils, which remains at a high level even after compaction, only minor decreases of saturated hydraulic conductivity were evident. The declines in air conductivity exceeded those in hydraulic conductivity, as gas exchange in soils is, besides the connectivity of coarse pores, a function of water content, which increases after loading in dependence of susceptibility to compaction. A soil‐protection strategy should be focused on more finely textured soils, as stresses of 70 kPa may already lead to a harmful compaction regarding critical values of pore functions such as saturated hydraulic conductivity or air capacity.

Simplifying the evaporation method for quantifying soil hydraulic properties

AbstractSince about 25 years, we have measured the unsaturated hydraulic conductivity function and water retention curve with the evaporation method of more than 1500 mineral and organic soils samples. From this data base, 104 representative samples of varying texture and dry bulk density were selected and the temporal dynamics of the basic measured values (mass or water loss, respectively, and tension change over time) was analyzed. With the exception of sand, water loss per time interval was constant in all other mineral and organic soils during the measuring time in the tension range between 0 and about 60 kPa. In sands, the nonlinear water loss over time by evaporation can be described by a quadratic function with high accuracy (r2 &gt; 0.99). For all other soils, a linear function is sufficient (r2 &gt; 0.99). The use of evaporation functions enables extending weighing intervals. This reduces costs for the measuring equipment and increases the effectiveness of the method while maintaining the same quality of unsaturated hydraulic conductivity and water retention functions. It was confirmed that measuring with two tensiometers is sufficient for accurate hydraulic conductivity and water retention function. Reducing evaporation by screening the sample surface helps to decrease hydraulic gradients and keeps tension distributions approximately linear with depth. This is recommended in particular for clayey soils.