Soil Physical Quality Indicators Research Articles

Quantification of soil quality under semi-arid agriculture in the northwest of Iran

Abstract Current management practices are thought to be having adverse impacts on soil quality for semi-arid agriculture. A multidimensional quantification of soil quality was developed and tested under irrigated and rain-fed agricultural systems in the northwest of Iran. Thirty-four chemical, biological and physical soil quality indicators were quantified at two depths with mono-cropping and crop rotation (n = 154). Discriminant analysis (DA) and principal component analysis (PCA) were applied to identify a minimum data set (MDS) for developing soil quality indices (SQI). Soil organic carbon (SOC), soluble sodium (Na), geometric mean diameter of soil aggregate (GMD) and available zinc (Zn) were identified using PCA, and GMD, Zn and soil microbial respiration (SMR) were identified using DA. Six SQIs were produced using non-linear and linear scoring equations and integration approaches based on two independent MDS. SQIs were significantly different between irrigated and dry farming at both depths (P-value

Actual and Relative Soil Air Permeability as Soil Physical Quality Index

Developing indices to evaluate soil physical quality can facilitate diagnosis and decision-making. In this context, intrinsic soil air permeability (Kair) is a physical parameter very sensitive to structural changes due to soil management. However, it is important to establish a standard Kair, considering a reference soil physical state. Thus, the present study proposes to create an index called relative soil air permeability (Kairr), taking as reference the physical state of maximum bulk density, obtained by normal Proctor test. Kairr is the ratio between compacted soil air permeability (Kairc) and actual soil air permeability (Kair). Kairr was evaluated using disturbed and undisturbed samples of a Latossolo and a Planossolo. The experimental design was completely randomized, with four treatments (T1-Latossolo 0-0.1 m; T2-Latossolo 0.1-0.2 m; T3-Planossolo 0-0.1 m and T4-Planossolo 0.1-0.2 m) and eight replicates. Kairr was equal to 0.1032 and 0.3547 in the Latossolo and to 0.4115 and 0.1923 in the Planossolo, in the 0-0.1 and 0.1-0.2 m layers, respectively. These characteristic values observed in both soils and layers are due to the management adopted in the area. In Latossolo, the use of medium harrow has made the 0.1-0.2 m layer more restrictive to air movement, whereas the Planossolo showed lower values of relative soil air permeability in the 0-0.1 m layer due to animal grazing, which has greater impact on the superficial portion of the soil.

Deforestation effects on soil aggregate stability quantified by the high energy moisture characteristic method

Soil aggregate size distribution and structure stability are important soil physical quality indices. The objective of this study was to quantify soil structure stability as a result of land-use conversion from forest to tea farm within Guilan Province of northern Iran over the past 50 years. Soil aggregate or structure stability metrics, along with more common soil chemical and physical properties, were determined on triplicate soil samples (0–30 cm depth), randomly collected from six sites. Dexter's S-index (slope of soil water retention curve at its inflection point), tensile strength (TS), aggregate mean weight diameter (MWD), and water stable aggregate (WSA) were also used to assess stability. Lastly, the high energy moisture characteristic (HEMC) method measured the destructive force used to break the aggregates through controlled wetting. Structure stability using HEMC method and S-index was determined based on the soil water retention curve but using different ranges of matric potentials. The results showed that despite an insignificant decrease in TS, WSA and MWD between land use types there was significant correlation (P < 0.05) between tensile strength with HEMC method's, stability ratio (SR) and Dexter's S-index. The volume of drainable pores after fast wetting of aggregates (fast-VDP) in HEMC method reflected a significant (P < 0.01) decrease in aggregate size between forest and tea farms with means of 0.347 and 0.297 g g−1, respectively. In only one of six locations, the soil water retention curves and the aggregate stability indexes (SI and SR) of HEMC method decreased significantly after land use change. Our results indicated that macro aggregates and structure stability in HEMC method could distinguish differences between two land uses and better than previous methods like MWD, WSA, and TS. The results of S-index and stability ratio of HEMC method were different. This difference can be related to: 1) the soil initial aggregate size that is between 0.5 and 1 mm in HEMC method and about 4 mm in Dexter's S-index and 2) the range of matric potential in each method that is 0 to 50 and 0 to 15,000 hPa in HEMC and S-index, respectively. Although both methods are based on soil water retention curve to estimate soil structure stability and physical quality, but HEMC results show macrostructure stability (macro aggregates) whereas S-index is inflection point of soil water retention curve between textural and structural porosity and estimate microstructure stability.

Identification of Soil Properties Influencing Some Soil Physical Quality Indicators Using Hybrid PSO-ICA-SVR Algorithm in Some Agricultural Land Uses of Kerman Province, Iran

ABSTRACTThis research was conducted in order to determine the importance of the effect of some soil properties on some soil physical quality indicators (SPQIs) in southeast Iran. To this end, 169 points from different locations in Kerman province were selected which had different agricultural land uses, and disturbed and undisturbed soil samples were taken from a depth of 0–20 cm. Soil properties such as soil pH, soil texture, field capacity (FC), permanent wilting point (PWP), soil bulk density (BD), electrical conductivity (ECe), calcium carbonate equivalent (CCE), and soil organic matter (SOM), and soil physical quality indicators (SPQIs) such as field capacity (FC), mean weight diameter (MWD), air capacity (AC), and relative field capacity (RFC) were determined. Subsequently, soil properties affecting SPQIs were ascertained using Particle Swarm Optimization-Imperialist Competitive Algorithm-Support Vector Regression (PSO-ICA-SVR) hybrid algorithm, and after the sensitivity analysis, the importance of each selected property in terms of its impact on SPQIs was recognized. The results indicated OM, BD, clay content, and CaCO3 in general, affect soil physical quality and this selection was carefully made by the hybrid algorithm. Furthermore, after modeling using the features selected by the SVR method and performing the sensitivity analysis, clay content and BD, among the selected properties, had the most significant effects on SPQIs. The highest value of the coefficient of determination was associated with MWD index (R2 = 83.33) and the lowest error was related to AC and RFC indicators (RMSE = 0.031).

Effects of soil wetness and tyre pressure on soil physical quality and maize growth by a slurry spreader system

Slurry spreaders are among the heaviest machines used for farming operations. The effects of soil wetness and tyre pressure on physical soil properties were evaluated by using a ˜5 Mg wheel load slurry spreader mounted on a tractor. The impacts were also compared with Terranimo® model predictions and X-ray micro-computed tomography (X-ray micro-CT) parameters. In the consecutive maize growing season, soil physical properties, total mineral nitrogen content and maize above biomass were evaluated additionally between in and out track positions. Immediately after traffic, penetration resistance (PR) was significantly higher (P < 0.05) under moist conditions and at in-track positions compared with dry conditions and out-track positions for the top 10 cm. Tyre pressure did not affect PR at in- or out-track position. At 10 cm depth, bulk density and macro-porosity (d> 30 μm) of soil trafficked under moist conditions was different (P ≤ 0.10) from those under dry conditions. Macro-porosity showed a clear response (P < 0.10) to tyre pressure and all the trafficked treatments. Deeper in the profile, there were no significant differences in these soil physical quality indicators. X-ray micro-CT results agreed well with the soil physical quality indicators, and detected slight changes in degree of compaction more precisely. Terranimo® well predicted the contact area and mean ground pressure (RMSE=0.06 m2). It indicated considerable compaction risk from the tractor’s rear wheels, which seemed to contradict with the relatively minor changes in soil physical properties observed. In the maize growing season, soil physical properties and nitrogen content showed no significant differences (P > 0.05) between in- and out-track positions while above ground dry biomass of maize reduced with ˜7% at in-track positions.

PEDOFUNCTIONS APPLIED TO THE LEAST LIMITING WATER RANGE TO ESTIMATE SOIL WATER CONTENT AT SPECIFIC POTENTIALS

ABSTRACT The least limiting water range (LLWR) is a soil physical quality indicator that receives much attention. It has been criticized and put to the test regarding mathematical models that compose it since they describe the behavior of soil physical attributes in a simplified way. This study aimed to assess the efficiency of some pedofunctions proposed in the literature and artificial neural networks on the accuracy in predicting soil water retention at potentials equivalent to field capacity (θFC) and permanent wilting point (θPWP). In other words, to apply the best models to LLWR of two soil types (Oxisol and Ultisol) and verify changes in their structure. The results indicated that pedofunctions using sand, silt, clay, bulk density, and soil organic matter contents are more efficient in estimating θFC and θPWP. However, the use of multiple linear regression models to predict θFC values below 0.20 m3 m−3 may present a slight tendency to overestimate it, which is not observed in the neural networks. As in R2, equations from neural networks were more efficient in estimating θFC and θPWP. Pedofunctions used to calculate LLWR differ in the establishment of the critical soil bulk density, exposing the limitations of the model.

Changes in physicochemical properties and quality index of an Alfisol after three years of rice husk biochar amendment in rainfed rice – Maize cropping sequence

Biochar's potential in improving crop performance, soil quality and functioning has been extensively studied with promising results reported. This study addressed research gaps and updated existing knowledge on long term effect of single biochar application on soil physicochemical properties. A three year field trial was set up on an acidic Alfisol testing the effect of rice husk biochar on select soil quality indicators viz.; soil pH, electrical conductivity, bulk density, water stable aggregates, water holding capacity, soil organic carbon, total nitrogen, CEC and C:N ratio sampled at two soil depth (0–10 cm and 10–20 cm). A soil quality index was estimated to provide a quantitative tool for assessing the long term effects of biochar amendment on Alfisol productivity. Biochar amendment at 6–12 t ha−1 proved more efficient at improving soil quality index (SQI = 1.00) on a long term basis. Further, biochar amendment significantly enhanced all determined soil quality indicators after one year of biochar amendment at sampled soil depths. However, the effect of biochar on some soil quality indicators such as electrical conductivity, organic carbon, total nitrogen and C:N ratio diminished after three years, while soil pH, CEC, bulk density – (decreased), water stable aggregates and water holding capacity remained consistently enhanced. Biochar showed more positive long term effect on improving soil physical quality indicators than chemical indicators. Particularly, decreasing soil electrical conductivity, organic carbon, total nitrogen and C/N ratio after three years suggest increased uptake of soluble ions by plants, carbon mineralization and nitrogen flux with plausible soil organic carbon and nitrogen losses. To mitigate these accelerated carbon and nitrogen flux and increase the efficiency of aged biochar in sequestering carbon and nitrogen, re-application of biochar after three years is recommended. Overall, biochar amendment at the dosage of 6–12 t ha−1 is recommended for long term soil quality improvement in the study area and regions of the world with similar soil type and climatic conditions.

Physical assessment of a Haplohumox soil under integrated crop-livestock system

Soil structure characterization in both macro and micro scales is very complex. The soil porous system (SPS) is responsible for numerous functions in soil, such as water infiltration, retention, redistribution, gas exchange, root growth and other processes related to the SPS. Indices, hydraulic functions and micromorphological measures of soil physical quality (SPQ) were carried out to evaluate the structure of a Haplohumox under integrated crop-livestock system (ICLS). Two soil depths were analyzed (0–10 and 10–20 cm) in four tillage systems: conventional tillage (CT), minimum tillage (MT), no-tillage (NT) and chiseled no-tillage (CNT), taking into account the annual ryegrass cropped during the winter for grazing (G) and silage (S) use. Soil water retention curve (SWRC), pore size distribution function (PSD), SPQ indices and the aeration hydraulic-energy function were associated together with other parameters obtained via micromorphological analysis of soil structure. Areal porosity (AP) was determined to classify the shape of pores as rounded (R), elongated (E) and complex (C). The size of pores was chosen based on their function and classified as storage pores (0.5–50 μm), transmission pores (50–500 μm) and fissures (>500 μm). The number of pores (NPo) was also estimated considering the disconnected pores. Results indicated that the top layer is most affected by tillage. Rounded pores were predominant for all evaluated systems, whereas complex pores such as fissures presented the largest contribution to AP at the deeper surface layer for all treatments. The plowing operation affected soil infiltration and water retention processes in MT and CT as well as AP and NPo in the NT system. NT and CNT required less energy to drain water in both surface layers evaluated. In the top layer MT/S yielded the largest energy to release water, exhibiting small values of AP and a homogeneous SPS. Strong correlation was found between absolute aeration index and some shape parameters of PSD, evidencing the physical relation between these indices. The micromorphological analysis supported the understanding of the other SPQ indices and aeration function for the tillage systems evaluated here.

Least limiting water as a soil indicator in an integrated crop-livestock systems of the Cerrado, Brazil

The least limiting water range (LLWR) is widely used as an agronomic indicator of soil physical quality, however few studies have reported its spatial variability, mainly in integrated crop-livestock systems (ICL). This study aimed to evaluate the spatial dependence of LLWR and its limits that establish the minimum physical and water restrictions for plant growth in an Oxisol under ICL systems. In addition, to verify correlations between LLWR limits and soil attributes that are commonly used to evaluate the soil compaction state. A georeferenced sampling mesh of 12 ha and 170 randomly distributed points was installed at an experimental station, which specifically studies five ICL systems and a no-tillage system with soybean and maize successions. The soil was sampled in 0.00–0.10 and 0.10–0.20 m layers for determinations of porosity, penetration resistance (PR), bulk density and LLWR. The results showed that LLWR presented spatial dependence and adjusted to the exponential model of semivariance, was positively correlated to macroporosity and total porosity and negatively correlated to bulk density, PR and microporosity. Among the parameters that compose it, the PR curve presented spatial dependence and explained the main changes in soil caused by management systems.

Modelling of some soil physical quality indicators using hybrid algorithm principal component analysis - artificial neural network

One of the important issues in the analysis of soils is to evaluate their features. In estimation of the hardly available properties, it seems the using of Data mining is appropriate. Therefore, the modelling of some soil quality indicators, using some of the early features of soil which have been proved by some researchers, have been considered. For this purpose, 140 disturbed and 140 undisturbed soil samples were collected from Jiroft, southern Kerman, Iran. Some physical and chemical properties of soil, for example, sand, silt and clay percentage, organic matter (OM), calcium carbonate (CaCO3), electrical conductivity at saturation (ECe), porosity (F), and bulk density (BD) were measured using standard methods. Some soil physical property indicators, including plant available water (PAW), relative field capacity (RFC), air capacity (AC) and saturated hydraulic conductivity (Ks) were also calculated. Using the hybrid algorithm of principle component analysis-artificial neural network (PCA-ANN), the calculated indicators were predicted by the easily available properties. The results showed that PCA-ANN had an acceptable accuracy in the modelling of soilphysical quality. The coefficient of determination (R2) of training and testing data for PAW, RFC and AC were 0.82 and 0.81, 0.90 and 0.79, 0.99 and 0.99, respectively. The optimization of Ks did not have the desired results. In other words, the R2 values of the training and testing data for this indicator were equal to 0.25 and 0.13, respectively.

Application of Multivariate Analysis Techniques for Selecting Soil Physical Quality Indicators: A Case Study in Long‐Term Field Experiments in Apulia (Southern Italy)

Core Ideas Soil physical quality (SPQ) on two long‐term experiments was evaluated. Relationships among five SPQ indicators (BD, PMAC, AC, PAWC and RFC) were evaluated. Two multivariate analysis techniques (PCA, SDA) were applied to select key indicators. PCA and SDA generally identified RFC as a key soil physical indicator. An optimal AC range was suggested to assess the air capacity of agricultural soils Long‐term field experiments and multivariate analysis techniques represent research tools that may improve our knowledge on soil physical quality (SPQ) assessment. These techniques allow us to measure relatively stable soil conditions and to improve soil quality judgment, thereby reducing uncertainties. A monitoring of SPQ under long‐term experiments, aimed at comparing crop residue management strategies (burning vs. incorporation of straw, FE1) and soil management (minimum tillage vs. no tillage, FE2), was established during the crop growing season of durum wheat. The relationships between five SPQ indicators (bulk density [BD], macroporosity [PMAC], air capacity [AC], plant available water capacity [PAWC], and relative field capacity [RFC]) were evaluated, and two techniques of multivariate analysis (principal component analysis and stepwise discriminant analysis) were applied to select key indicators for SPQ assessment. According to the used indicators, an SPQ from optimal to intermediate (i.e., not definitely poor) was detected in 65% of the observations in FE1 and in 54% in FE2. The main results showed a significant negative relationship between RFC and AC, and multivariate analysis identified RFC as a key SPQ indicator, mainly in FE2. Plant available water capacity and BD showed the highest discriminating capability in the FE1 dataset. The highest scores of RFC assessment were highlighted for burning and minimum tillage treatments (+1 and +2). An optimal AC range, derived from optimal RFC limits, was obtained and was suggested to better assess the AC of agricultural soils (0.10 ≤ AC ≤ 0.26 cm3 cm–3).

Evaluation of reclamation success in an open-pit coal mine using integrated soil physical, chemical and biological quality indicators

The evaluation and success of ecological reclamation can be assessed by measuring soil physical, chemical and biological variables, either in isolation or combined into composite indicators. In this study we tested the suitability of biological, chemical and physical quality indicators–and their combination in a General Indicator of Soil Quality (GISQ)—to monitor soil quality in restored areas of an open cast coal mine. These composite indicators were computed with principal component analyses (PCA) and co-inertia multivariate analyses (CoIA). Our biological indicator showed a significant recovery of soil invertebrate communities along the chronosequence. Taxonomic richness increased from 7 taxa the 1st year to 13–17 taxa in 6–20-y sites, far less than in forests (19–20). Soil pH, bulk density and proportion of physical and macro aggregates were highest in the 1-y site, while soil organic matter (SOM), total N and proportion of biogenic aggregates were highest in older sites. In general, the three sub-indicators and the GISQ yielded the lowest values in the 1-y site (from 0.1 to 0.3 on a scale of 0.1 to 1.0), intermediate in the 16- and 20-y sites, (0.4–0.7) and reached highest values in the two forests (0.4–1.0). The GISQ methodology proved efficient in assessing progress in the reclamation process which should not be achieved only by monitoring vegetation cover changes.

Agroecosystem resilience in response to extreme winter flooding

Evidence suggests that climate change is increasing the frequency of extreme weather events (e.g. excessive rainfall, heat, wind). The winter of 2013-14 saw exceptional levels of rainfall across the UK leading to extreme and prolonged flooding (up to 3 months with floodwater depths up to 3 m) in several low-lying agricultural areas (e.g. Somerset Levels, Thames Valley). The impact of extreme flooding and the speed of ecosystem recovery at the field-scale, however, remain poorly understood. The main objectives of this study were therefore to: (1) assess the effect of this extreme winter flooding event on a range of soil physical, chemical and biological quality indicators at 15 flood-affected sites (arable and grassland), (2) determine if these changes in soil health were reversible in the short term (< 1 year), and (3) to evaluate the effectiveness of different mechanical interventions (sward-lifting, subsoiling, slot-seeding and aerating) to accelerate the amelioration of the damage caused by winter flooding at 2 of the 15 sites. Once the floodwater had receded (April 2014), we found that several of the measured soil quality indicators were negatively affected in the flooded areas in comparison with non-flooded areas. This included a decrease in soil bulk density (by 19%), soil pH (by 0.4 units), and available P (by up to 42%). Flooding increased soil microbial biomass (60%), induced a shift in soil microbial community structure and reduced earthworm numbers. After 8 months of recovery, only soil pH remained significantly reduced (by 0.3 units) in the flooded areas in comparison to the unflooded areas. Flooding had a negative impact on the overlying vegetation at the arable sites (biomass production was reduced by between 19 and 34%) but had no major impact at the grassland sites in the long-term. In the flood amelioration experiment, the subsoiled plots produced grass with a higher nutrient content (e.g. N - up to 35%, Ca - up to 19% and Mg - up to 58%). However, the four different interventions appeared to have little positive impact on most of the soil quality indicators measured. In conclusion, extreme winter flooding was found to induce short-term alterations in key soil quality indicators and to destroy winter crops, although these effects did not persist in the longer term. Our results therefore indicate that the temperate agroecosystems evaluated here were highly resilient to winter flood stress and that recovery to a pre-flood state could be achieved within 1 year. Improved management strategies are still needed to speed up the rate of recovery after flood events to facilitate a faster return to agricultural production.

Developing a Soil Physical Quality Index (SPQi) for lowlands under different deployment times of no-tillage

ABSTRACT: Soil physical quality in lowlands from the Pampa biome under no-tillage (NT) plays an important role; therefore, this study aimed to establish a soil physical quality index (SPQi) from a minimum data set to detect the effects of different deployment times of NT in an Albaqualf. The comparison of areas with one (NT1), three (NT3), five (NT5) and seven (NT7) years of notillage was established using as reference a non-cultivated field plot (NC) for at least thirty years, nearby the sites under NT. Soil samples with undisturbed and disturbed structure were collected to determine the physical quality indicators and soil organic matter (SOM) fractions. The factor analysis (FA) was used to identify and select a minimum data set. The SPQi was elaborated by using the deviations of the measured indicators at different deployment times of NT in relation to NC. The SPQi showed sensibility to identify and explain soil physical quality changes with different deployment times of NT. In well-drained lands, higher deployment times of no-tillage promote the physical quality of lowlands.

Water retention and penetration resistance equations for the least limiting water range

ABSTRACT: The least limiting water range is a soil physical quality indicator, which is useful to predict the optimum water range for plant growth in a given soil and to study the effects of soil use and management over this optimum water range by integrating the effects of available water, penetration resistance and air filled porosity. This study tested six equations to fit water retention and penetration resistance surface responses used to determine the least limiting water range and present a simple algorithm written in the open source software R for fitting, calculation and visualization of the least limiting water range. Five soils from Brazil and Canada, under different use and management conditions were used to test the functions. The results show that the three water retention surface responses had good statistical properties for fitting water retention and that two of the penetration resistance surface responses were adequate to fit the data, while one failed to achieve convergence in two instances. The open source code performed as well as the commercial statistical package SAS for fitting the penetration resistance and water retention equations.

Comparison of Soil Physical Quality Indicators Using Direct and Indirect Data Inputs Derived from a Combination of In‐Situ and Ex‐Situ Methods

Core Ideas Soil quality indicators such as S and PAWC are considered as static indicators with low sensitivity. The soil quality indicator AWr is considered a dynamic indicator with high sensitivity. Indirect pedotransfer function based SWRCs are appropriate for static baseline indicators. Soil monitoring programs should use AWr as a sensitive and reliable soil quality indicator. The quality of a soil is its ability to deliver functions providing ecosystem services, human health and well‐being. Soil physical quality (SPQ) values use different parts of the soil water retention curve (SWRC) to calculate SPQ. For example, the plant available water capacity (PAWC) method is the difference in water content between permanent wilting point and field capacity. The S‐index uses the slope of the SWRC at its inflection point and the relative air‐water energy (AWr) is the integral of “dry” divided by the “wet” area of the SWRC. Increasing demand for soil monitoring policies calls for reliable and sensitive soil quality indicators (SQIs). The objectives of the study were to assess the sensitivity and applicability of SPQ indicators using direct and indirect data inputs. The indirect approach provided sufficient data complexity for the PAWC and S‐index values, but the more complex AWr required the direct approach. PAWC and S‐index values were identified as static SPQ indicators. The values obtained from these approaches should be used to form baseline static datasets and therefore have an indicative role only. The AWr value was identified as a dynamic SPQ indicator and provided required sensitivity to pick up temporal changes in SPQ. This indicator could be used at multiple scales and could even guide grassland management in terms of SPQ. Higher SWRC data resolution will require more complex hydraulic models to fit and will ultimately improve the accuracy of soil hydraulic data and improve the sensitivity of AWr as a SPQ indicator.

Effects of tillage intensity on pore system and physical quality of silt-textured soils detected by multiple methods

Understanding the effects of agricultural management practices on soil functionality is an ongoing challenge in environmental science and agricultural practice. In the present study we quantified the effects of changes in tillage intensity on soil physical quality and pore size distribution after 6, 10 and 23 years. At three long-term tillage experimental sites in central Europe we analysed soils under four different soil management systems: conventional mouldboard tillage; chiselling + rotary harrow; rotary harrow; and no till. These treatments differed in mechanical intensity and depth. Pore size distributions were calculated from soil water retention curves based on high-resolution measurements. Subsequently, fractions of functional pore size classes and indicators of soil physical quality were determined and compared between the treatments. In addition, we evaluated the performance of two calculation approaches for pore size distribution: (1) fitting of a smoothing cubic spline; and (2) a bimodal van Genuchten function. The parametric function yielded a higher proportion of storage pores by approximately 3–5%. The combination of multiple measurement and evaluation methods enabled detailed comparison of soil physical characteristics between different tillage treatments. No-till soils showed a distinct lack of transmissive pores and higher bulk density, but similar plant-available water capacity, compared with the other treatments. Under all soil management systems, aeration deficits were observed, emphasising the high vulnerability for compaction of silt-dominated arable soils with a low organic matter content. Hence, the design of agricultural soil management strategies on such soils needs to consider the risks of compaction as thoroughly as erosion or chemical degradation.

Prediction of soil hard-setting and physical quality using water retention data

Hard-setting soils become hard and compact during drying, and are therefore difficult or impossible to cultivate unless they are re-wetted. This study was done to quantify the hard-setting behavior on nine soils with a wide range of texture (from sandy loam to silty clay). Repacked soil cores were prepared with least disturbance of soil aggregates (05‒4 mm). Water retention curves of the soils were measured in the matric suction range 10–15,000 hPa in different bulk densities (1.20–1.70 Mg m−3). Single-porosity van Genuchten-Mualem and dual-porosity Dexter models were fitted to the water retention data. Dexter's indices of soil physical quality (S) and hard-setting (HDexter) were calculated using the van Genuchten-Mualem parameters. Soil physical quality indices S1 and S2 (slopes of water retention curve at first and second inflection points, related to matrix and structural pore spaces, respectively) and new hard-setting indices (H1 and H2 related to matrix and structural pore spaces, respectively) were also calculated from the parameters of dual-porosity model. Tensile strength (Y) of the soil samples prepared at different bulk densities was also measured from near-saturation to oven-dry. Slopes of relationships between Y and water content were used as soil strength-based hard-setting indices. A larger slope means that the soil is more susceptible to hard-setting. The HDexter, H1 and H2 increased with an increment in the bulk density, whereas the S, S1 and S2 decreased. Significant positive relationships between hard-setting indices (HDexter and H1) and calcium carbonate content were observed. Negative linear relations were derived between S, S1 and S2, and bulk density and positive linear relations were obtained between HDexter, H1 and H2, and bulk density. Soil hard-setting and physical quality indices were significantly correlated with relative bulk density. Positive relationships were observed between soil strength-based hard-setting indices and relative bulk density, and the correlations between these indices and H2 and S1 were significantly positive and negative, respectively. These findings confirmed that the new proposed indices enable prediction of soil hard-setting behavior using only water retention data with no need to measure soil mechanical strength.

Sensitivity of labile carbon fractions to tillage and organic matter management and their potential as comprehensive soil quality indicators across pedoclimatic conditions in Europe

Soil quality is defined as the capacity of the soil to perform multiple functions, and can be assessed by measuring soil chemical, physical and biological parameters. Among soil parameters, labile organic carbon is considered to have a primary role in many soil functions related to productivity and environmental resilience. Our study aimed at assessing the suitability of different labile carbon fractions, namely dissolved organic carbon (DOC), hydrophilic DOC (Hy-DOC), permanganate oxidizable carbon (POXC, also referred to as Active Carbon), hot water extractable carbon (HWEC) and particulate organic matter carbon (POMC) as soil quality indicators in agricultural systems. To do so, we tested their sensitivity to two agricultural management factors (tillage and organic matter input) in 10 European long-term field experiments (LTEs), and we assessed the correlation of the different labile carbon fractions with physical, chemical and biological soil quality indicators linked to soil functions. We found that reduced tillage and high organic matter input increase concentrations of labile carbon fractions in soil compared to conventional tillage and low organic matter addition, respectively. POXC and POMC were the most sensitive fractions to both tillage and fertilization across the 10 European LTEs. In addition, POXC was the labile carbon fraction most positively correlated with soil chemical (total organic carbon, total nitrogen, and cation exchange capacity), physical (water stable aggregates, water holding capacity, bulk density) and biological soil quality indicators (microbial biomass carbon and nitrogen, and soil respiration).We conclude that POXC represents a labile carbon fraction sensitive to soil management and that is the most informative about total soil organic matter, nutrients, soil structure, and microbial pools and activity, parameters commonly used as indicators of various soil functions, such as C sequestration, nutrient cycling, soil structure formation and soil as a habitat for biodiversity. Moreover, POXC measurement is relatively cheap, fast and easy. Therefore, we suggest measuring POXC as the labile carbon fraction in soil quality assessment schemes in addition to other valuable soil quality indicators.

Soil Quality Assessment for Wheat Cultivation in El–Menoufia Governorate, Nile Delta, Egypt

Sustainable agriculture is one of the most important strategies to overcome world hunger and food shortage. Soil deterioration due to mismanagement seems to be a major obstacle against achieving these goals. The present study aimed at assessing the physical and chemical quality of the soils in El-Menoufia Governorate for the cultivation of wheat which is commonly practiced. Seventeen soil profiles were selected to represent El-Menoufia Governorate soils in addition to 120 surface soil samples. Twelve physical and chemical soil parameters were chosen to evaluate soil quality including; soil salinity, exchangeable sodium, calcium carbonates content, pH, drainage, soil texture, depth, topography, surface stoniness, hardpan depth, hydraulic conductivity, and water holding capacity . Results showed variations among values of different parameters and localities. Interpolated thematic maps were produced for quantitative variables. Soil chemical quality index (CQI) and physical quality index (PQI) were calculated from the twelve investigated soil parameters. Results showed that El-Menoufia Governorate could be classified into two classes according to the physical quality measures (high and moderate), while results of chemical quality index revealed three categories (high, moderate and low). Over 85% of the soil of Menoufia governorate is of high physical quality while more than 89% is of moderate chemical quality for wheat cultivation according to both physical and chemical parameters. The results of this study are useful in planning land use management.