AGGREGATE STABILITY AND WATER RETENTION NEAR SATURATION CHARACTERISTICS AS AFFECTED BY SOIL TEXTURE, AGGREGATE SIZE AND POLYACRYLAMIDE APPLICATION

Understanding the effects of soil intrinsic properties and extrinsic conditions on aggregate stability is essential for the development of effective soil and water conservation practices. Our objective was to evaluate the combined role of soil texture, aggregate size and application of a stabilizing agent on aggregate and structure stability indices (composite structure index [SI], the and n parameters of the VG model and the S-index) by employing the high energy (0-5.0 J kg(-1)) moisture characteristic (HEMC) method. We used aggregates of three sizes (0.25-0.5, 0.5-1.0 and 1.0-2.0 mm) from four semi-arid soils treated with polyacrylamide (PAM). An increase in SI was associated with the increase in clay content, aggregate size and PAM application. The value of increased with the increase in aggregate size and with PAM application but was not affected by soil texture. For each aggregate size, a unique exponential type relationship existed between SI and . The value of n and the S-index tended, generally, to decrease with the increase in PAM application; however, an increase in aggregate size had an inconsistent effect on these two indices. The relationship between SI and n or the S-index could not be generalized. Our results suggest that (i) the effects of PAM on aggregate stability are not trivial, and its application as a soil conservation tool should consider field soil condition, and (ii), n and S-index cannot replace the SI as a solid measure for aggregate stability and soil structure firmness when assessing soil conservation practices

Determination of Mehlich 3 extractable elements with visible and near infrared spectroscopy in a mountainous agricultural land, the caucasus mountains

Soil spectroscopy is a promising alternative to evaluate and monitor soil and water quality, particularly in mountainous agricultural lands characterized by intense degradation and limited soil tests reports; a few studies have evaluated the feasibility of VIS-NIR spectroscopy to predict Mehlich 3 (M3) extractable nutrients. This study aimed to (i) examine the potential of VIS-NIR spectroscopy in combination with partial least squares regression to predict M3-extractable elements (Ca, K, Mg, P, Fe, Cd, Cu, Mn, Pb, and Zn) and basic soil properties (clay, silt, sand, CaCO3, pH, and soil organic carbon-SOC), (ii) find optimal pre-processing techniques, and (iii) determine primary prediction mechanisms for spectrally featureless soil properties. Topsoil samples were collected from a representative area (114 samples from 525 ha) located in the mountainous region of NW Azerbaijan. A series of pre-processing steps and transformations were applied to the spectral data, and the models were calibrated and evaluated based on the coefficient of determination (R2), root mean square error (RMSE), and the residual prediction deviation (RPD). The leave-one-out cross-validated predictions showed that the first derivative spectra produce higher prediction accuracies (R2 = 0.51–0.91; RPD = 1.20–2.29) for most soil properties. The evaluation of the model performance with optimal pre-processing techniques revealed that both calibration and validation models produce considerable differences in RPD values associated with sample size and the random partition of the calibration or validation subsets. The prediction models were excellent or very good (RPD > 2.0) for CaCO3, SOC, sand, silt, Ca, and Pb, good or fair (1.4 < RPD < 2.0) for clay, K, Cd, pH, Fe, Mn, and Cu, and poor (1.0 < RPD < 1.4) for Mg, P, and Zn. Principal component and correlation, stepwise regression analysis, and variable importance in projection procedures allowed to elucidate the underlying prediction mechanisms. Unlike the previous studies, the spectral estimations of pH, Ca, Mg, P, Fe, Pb, and Cd concentrations were linked to their correlation with CaCO3 rather than soil organic matter, whereas Mg and P concentrations were also connected to Fe-oxides. Soil particle sizes contributed to predicting K concentration but confounded the prediction of P and Zn concentration. The weaker correlations of Mn, Cu or Zn with CaCO3, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. The major prediction mechanisms for M3-extractable elements relied on their relations with CaCO3, pH, clay content and mineralogy, and exchangeable cations in the context of their association with land use. The results can be used in mountain lands to evaluate and control the effect of management on soil quality indices and land degradation neutrality. Further studies are needed to develop most advantageous sampling schemes and modeling.Publikationsfonds ML

Soil water retention and structure stability as affected by water quality

In arid and semi-arid zones with a short water resources studying the effects of water quality on soil water retention and structure is important for the development of effective soil and water conservation and management practices. Three water qualities (electrical conductivity, EC ~ 2, 100 and 500 μS cm-1 with a low SAR representing rain, canal-runoff and irrigation water respectively) and semi-arid loam and clay soils were tested to evaluate an effect of soil texture and water quality on water retention, and aggregate and structure stability using the high energy moisture characteristic (HEMC) method. The water retention curves obtained by the HEMC method were characterized by the modified van Genuchten (1980) model that provides (i) model parameters α and n, which represent the location (of the inflection point) and the steepness of the S-shaped water retention curve respectively, and (ii) a volume of drainable pores (VDP), which is an indicator for the quantity of water released by the tested sample over the range of suction studied, and modal suction (MS), which corresponds to the most frequent pore sizes, and soil structure index, SI =VDP/MS. Generally (i) treatments significantly affected the shape of the water retention curves (α and n) and (ii) contribution of soil type, water EC, and wetting rate and their interaction had considerable effect on the stability induces and model parameters. Most of changes due to the water quality and wetting condition were in the range of matric potential (ψ), 1.2-2.4; and 2.4-5.0 J kg-1 (pore size 125-250 μm and 60-125 μm). The VDP, SI and α increased, and MS and n decreased with the increase in clay content, water EC and the decrease in rate of aggregate wetting. The SI increased exponentially with the increase in VDP, and with the decrease in MS. Contribution of water EC on stability indices and model parameters was not linear and was soil dependent, and could be more valuable at medium water EC. Effect of wetting rate was more pronounced at low water EC. Results indicate that effectiveness of water EC in the field condition has no simple outcome on water retention and soil structure, and that its application should consider and be adjusted to soil properties and condition, such as soil texture, and moisture content and solution EC. Detailed contribution of treatments on structure induces and model parameters are discussed in the paper

Soil Structure Stability under Different Land Uses in Association with Polyacrylamide Effects

Soil structural stability is a vital aspect of soil quality and functions, and of maintaining sustainable land management. The objective of this study was to compare the contribution of four long-term land-use systems (crop, bush, grass, and forest) coupled with anionic polyacrylamide (PAM = 0, 25, and 200 mg L&minus;1) application on the structural stability of soils in three watersheds of Ethiopia varying in elevation. Effect of treatments on soil structural stability indices were assessed using the high energy moisture characteristic (HEMC, 0&ndash;50 hPa) method, which provides (i) water retention model parameters &alpha; and n, and (ii) soil structure index (SI). Soil (watershed), land use and PAM treatments had significant effects on the shape of the water retention curves (&alpha;, n) and SI, with diverse changes in the macropore sizes (60&ndash;250; &gt;250 &mu;m). Soil organic carbon (SOC) content and SI were strongly related to soil pH, CaCO3 soil type-clay mineralogy, exchangeable Ca2+, and Na+ (negatively). The order of soil SI (0.013&ndash;0.064 hPa&minus;1) and SOC (1.4&ndash;8.1%) by land use was similar (forest &gt; grass &gt; bush &gt; cropland). PAM effect on increasing soil SI (1.2&ndash;2.0 times), was inversely related to SOC content, being also pronounced in soils from watersheds of low (Vertisol) and medium (Luvisol) elevation, and the cropland soil from high (Acrisol) elevation. Treating cropland soils with a high PAM rate yielded greater SI (0.028&ndash;0.042 hPa&minus;1) than untreated bush- and grassland soils (0.021&ndash;0.033 hPa&minus;1). For sustainable management and faster improvement in soil physical quality, soil properties, and land-use history should be considered together with PAM application

Allometric Model for Predicting Root Biomass of Field Crops in the Salt-Affected Clay Soil: Novel Approach

Root biomass and phenotyping are vital parameters for studies on crop performance and response to environmental change, as well as abiotic stresses, crop water uptake, nutrient supply, and soil C sequestration and quality. However, root sampling and measurement, including biomass estimation, are laborious and time-consuming tasks. This study developed a novel allometric model to predict the root biomass of annual crop species using root collar diameter, an easy aboveground field measure. The root samples of alfalfa, sorghum and maize were collected (45 from each) at the harvesting stage from the irrigated agricultural field of the semi-arid region (clay soil, salinity: EC = 2&ndash;12 dS m&minus;1, 70% of full irrigation). Crops collar diameter (CD) and root biomass (RM) increased in the following order: alfalfa &lt; sorghum &lt; maize. For each crop species, strong power (RM = aCDb) relations (R2 &ge; 0.90) were found between RM and CD (analogous to tree species). The coefficient (a) and exponent (b) of the relations and the soil quality indices (e.g., soil organic carbon, aggregate stability) in the root zone were concomitant with the crop (root) traits. The use of the allometric model was crucial for the fast assessment of the root biomass of the crop species, such as estimating biomass allocation. The approach could be used for evaluation of soil&ndash;root&ndash;plant interaction under abiotic stresses in the context of the sustainable agriculture (e.g., soil C deposition and respiration, crop transpiration and photosynthesis rate, and selecting the best genotypes-cultivars)

Predicting Soil Properties for Agricultural Land in the Caucasus Mountains Using Mid-Infrared Spectroscopy

Visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy are increasingly being used for the fast determination of soil properties. The aim of this study was (i) to test the use of MIR spectra (Agilent 4300 FTIR Handheld spectrometer) for the prediction of soil properties and (ii) to compare the prediction performances of MIR spectra and Vis-NIR (ASD FieldSpecPro) spectra; the Vis-NIR data were adopted from a previous study. Both the MIR and Vis-NIR spectra were coupled with partial least squares regression, different pre-processing techniques, and the same 114 soil samples, collected from the agricultural land located between boreal forests and semi-arid steppe belts (Kastanozems). The prediction accuracy (R2 = 0.70–0.99) of both techniques was similar for most of the soil properties assessed. However, (i) the MIR spectra were superior for estimating CaCO3, pH, SOC, sand, Ca, Mg, Cd, Fe, Mn, and Pb. (ii) The Vis-NIR spectra provided better results for silt, clay, and K, and (iii) the hygroscopic water content, Cu, P, and Zn were poorly predicted by both methods. The importance of the applied pre-processing techniques was evident, and among others, the first derivative spectra produced more reliable predictions for 11 of the 17 soil properties analyzed. The spectrally active CaCO3 had a dominant contribution in the MIR predictions of spectrally inactive soil properties, followed by SOC and Fe, whereas particle sizes and hygroscopic water content appeared as confounding factors. The estimation of spectrally inactive soil properties was carried out by considering their secondary correlation with carbonates, clay minerals, and organic matter. The soil information covered by the MIR spectra was more meaningful than that covered by the Vis-NIR spectra, while both displayed similar capturing mechanisms. Both the MIR and Vis-NIR spectra seized the same soil information, which may appear as a limiting factor for combining both spectral ranges. The interpretation of MIR spectra allowed us to differentiate non-carbonated and carbonated samples corresponding to carbonate leaching and accumulation zones associated with topography and land use. The prediction capability of the MIR spectra and the content of nutrient elements was highly related to soil-forming factors in the study area, which highlights the importance of local (site-specific) prediction models

Soil Salinity Type Effects on the Relationship between the Electrical Conductivity and Salt Content for 1:5 Soil-to-Water Extract

Soil salinity severely affects soil ecosystem quality and crop production in semi-arid and arid regions. A vast quantity of data on soil salinity has been collected by research organizations of the Commonwealth of Independent States (CIS, formerly USSR) and many other countries over the last 70 years, but using them in the current international network (irrigation and reclamation strategy) is complicated. This is because in the CIS countries salinity was expressed by total soluble salts as a percentage on a dry-weight basis (total soluble salts, TSS, %) and eight salinity types (chemistry) determined by the ratios of the anions and cations (Cl−, SO42−, HCO3−, and Na+, Ca2+, Mg2+) in diluted soil water extract (soil/water = 1:5) without assessing electrical conductivity (EC). Measuring the EC (1:5) is more convenient, yet EC is not only affected by the concentration but also characteristics of the ions and the salinity chemistry. The objective of this study was to examine the relationship between EC and TSS of soils in a diluted extract (1:5) for eight classic salinity types. We analyzed extracts (1:5) of 1100 samples of a clayey soil (0–20 cm) collected from cultivated semi-arid and arid regions for EC, TSS, soluble cations (Na+, Ca2+, Mg2+), and anions (HCO3−, Cl−, SO42−). Results revealed that (i) the variation in the proportional relationships (R2 ≥ 0.91–0.98) between EC (0.12–5.6 dS m−1) and TSS (0.05–2.5%) could be related to salinity type, and (ii) the proportionality coefficient of the relationships (2.2 2–3.16) decreased in the following order of salinity type: SO4 &lt; Cl(SO4)–HCO3 &lt; Cl(HCO3)–SO4 &lt; SO4 (HCO3)–Cl &lt; Cl. The findings suggest that once the salinity type of the soil is established, EC (1:5) values can be safely used for the evaluation of the soil salinity degree in the irrigated land in the context of sustainable soil and crop management

Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

Soil spectroscopy is a promising alternative to evaluate and monitor soil and water quality, particularly in mountainous agricultural lands characterized by intense degradation and limited soil tests reports; a few studies have evaluated the feasibility of VIS-NIR spectroscopy to predict Mehlich 3 (M3) extractable nutrients. This study aimed to (i) examine the potential of VIS-NIR spectroscopy in combination with partial least squares regression to predict M3-extractable elements (Ca, K, Mg, P, Fe, Cd, Cu, Mn, Pb, and Zn) and basic soil properties (clay, silt, sand, CaCO3, pH, and soil organic carbon-SOC), (ii) find optimal pre-processing techniques, and (iii) determine primary prediction mechanisms for spectrally featureless soil properties. Topsoil samples were collected from a representative area (114 samples from 525 ha) located in the mountainous region of NW Azerbaijan. A series of pre-processing steps and transformations were applied to the spectral data, and the models were calibrated and evaluated based on the coefficient of determination (R2), root mean square error (RMSE), and the residual prediction deviation (RPD). The leave-one-out cross-validated predictions showed that the first derivative spectra produce higher prediction accuracies (R2 = 0.51–0.91; RPD = 1.20–2.29) for most soil properties. The evaluation of the model performance with optimal pre-processing techniques revealed that both calibration and validation models produce considerable differences in RPD values associated with sample size and the random partition of the calibration or validation subsets. The prediction models were excellent or very good (RPD > 2.0) for CaCO3, SOC, sand, silt, Ca, and Pb, good or fair (1.4 3 rather than soil organic matter, whereas Mg and P concentrations were also connected to Fe-oxides. Soil particle sizes contributed to predicting K concentration but confounded the prediction of P and Zn concentration. The weaker correlations of Mn, Cu or Zn with CaCO3, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. The major prediction mechanisms for M3-extractable elements relied on their relations with CaCO3, pH, clay content and mineralogy, and exchangeable cations in the context of their association with land use. The results can be used in mountain lands to evaluate and control the effect of management on soil quality indices and land degradation neutrality. Further studies are needed to develop most advantageous sampling schemes and modeling

Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

Soil spectroscopy is a promising alternative to evaluate and monitor soil and water quality, particularly in mountainous agricultural lands characterized by intense degradation and limited soil tests reports; a few studies have evaluated the feasibility of VIS-NIR spectroscopy to predict Mehlich 3 (M3) extractable nutrients. This study aimed to (i) examine the potential of VIS-NIR spectroscopy in combination with partial least squares regression to predict M3-extractable elements (Ca, K, Mg, P, Fe, Cd, Cu, Mn, Pb, and Zn) and basic soil properties (clay, silt, sand, CaCO3, pH, and soil organic carbon-SOC), (ii) find optimal pre-processing techniques, and (iii) determine primary prediction mechanisms for spectrally featureless soil properties. Topsoil samples were collected from a representative area (114 samples from 525 ha) located in the mountainous region of NW Azerbaijan. A series of pre-processing steps and transformations were applied to the spectral data, and the models were calibrated and evaluated based on the coefficient of determination (R2), root mean square error (RMSE), and the residual prediction deviation (RPD). The leave-one-out cross-validated predictions showed that the first derivative spectra produce higher prediction accuracies (R2 = 0.51&ndash;0.91; RPD = 1.20&ndash;2.29) for most soil properties. The evaluation of the model performance with optimal pre-processing techniques revealed that both calibration and validation models produce considerable differences in RPD values associated with sample size and the random partition of the calibration or validation subsets. The prediction models were excellent or very good (RPD &gt; 2.0) for CaCO3, SOC, sand, silt, Ca, and Pb, good or fair (1.4 &lt; RPD &lt; 2.0) for clay, K, Cd, pH, Fe, Mn, and Cu, and poor (1.0 &lt; RPD &lt; 1.4) for Mg, P, and Zn. Principal component and correlation, stepwise regression analysis, and variable importance in projection procedures allowed to elucidate the underlying prediction mechanisms. Unlike the previous studies, the spectral estimations of pH, Ca, Mg, P, Fe, Pb, and Cd concentrations were linked to their correlation with CaCO3 rather than soil organic matter, whereas Mg and P concentrations were also connected to Fe-oxides. Soil particle sizes contributed to predicting K concentration but confounded the prediction of P and Zn concentration. The weaker correlations of Mn, Cu or Zn with CaCO3, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. The major prediction mechanisms for M3-extractable elements relied on their relations with CaCO3, pH, clay content and mineralogy, and exchangeable cations in the context of their association with land use. The results can be used in mountain lands to evaluate and control the effect of management on soil quality indices and land degradation neutrality. Further studies are needed to develop most advantageous sampling schemes and modeling

Structure Stability of Cultivated Soils from Semi-Arid Region: Comparing the Effects of Land Use and Anionic Polyacrylamide Application

The Sustainable Development Goals of the United Nations call for applying soil management practices that contribute land degradation neutrality. Our objectives were to investigate the effect of (i) soil management&mdash;conventional tillage (CT under crop) and no-tillage (NT under grass)&mdash;and (ii) an amendment (polyacrylamide (PAM)) application on the structure stability indices of soils from a semi-arid region. Two sets of experiments were conducted using the high-energy moisture characteristic (HEMC) method for the assessment of (i) land-use type (CT vs. NT) in soils (30 samples) varying in texture, and (ii) the effect of six PAM concentrations (0, 10, 25, 50, 100, and 200 mg L&minus;1) on three typical soils (sandy clay loam, clay loam, and clay) under CT management; then, the contributions of PAM concentration (CT) and NT were compared. Water retention curves of samples were obtained at a matric potential from 0 to &minus;5.0 J kg&minus;1 and characterized by a modified van Genuchten model that yields (i) model parameters &alpha; and n, and (ii) a soil structure stability index (SI). The treatments affected the shape of the water retention curves. Change of land use from CT to NT and PAM application to CT soil increased the SI and ɑ, and decreased n compared to CT-managed soils. The magnitude of the NT and PAM effect was inversely related to soil clay content. CT-managed soils treated with a low PAM rate (10&ndash;25 mg L&minus;1) gave SI comparable to that obtained for the NT-managed soils, while CT-managed soils treated with a high PAM rate (50&ndash;200 mg L&minus;1) yielded 1.3&ndash;2.0 and 2&ndash;4 times higher SI than that for NT and CT-managed soils, respectively. Our findings suggest that both the change of land use to NT or the addition of small amounts of PAM are viable alternatives for stabilizing CT-managed weakly alkaline semi-arid soils, whose soil structure stability is a priori limited