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Water, a limited resource in most parts of the world, has always been a threat to arid and semi-arid zones. Using the sub-irrigation method with the clay capsule technique is one of the most practical strategies for conserving water and reducing water consumption in arid and semi-arid areas. A clay capsule is one of the porous pipes in a sub-irrigation system that can release water near the root zone. This paper has attempted to improve the physical and hydraulic properties of clay capsules based on changing the percentage of organic matter in the raw material (G0). The raw material used in making clay capsules is obtained from the calcareous soil of Nasr Abad village of Gorgan, Iran. The ratio of rice bran husk flour to G0 as improving hydraulic properties was 1:2, 1:5, 1: 10, 1:15, and 1:20 (kg of rice bran to kg of air-dried soil). The produced clay capsules were named G2, G5, G10, G15, and G20 respectively. The water discharge and soil water distribution of clay capsules were measured at 10, 25, 50, 80, and 100 kPa of hydrostatic pressures with a discharge-pressure automation instrument. The findings revealed a positive and significant relationship between increasing organic matter levels and the discharge of porous clay capsules. Unlike in G2 and G5, the relationship between discharges and hydrostatic pressure is linear in G10, G15, and G20. Meanwhile, the soil wetting shape followed a spherical trend due to the slow seepage of clay capsules. However, the soil-wetting shapes in G10, G15, and G20 were spherical and trended to vertical ellipsoids in G5 and G2. This method is of high importance for irrigating plants, especially in arid and semi-arid regions, and can efficiently manage the water shortage problem. [ABSTRACT FROM AUTHOR]

Salinity causes nutrient imbalances in plants, even in quinoa (Chenopodium quinoa Willd.), a halophyte with high nutritional value. Current investigation is a complementary study, carried out in continuation of the previous research on quinoa which illustrated that the Salinity Threshold Value (STV) of quinoa varies during different stages of its growth; it was performed in three cultivation periods, each period containing two irrigation methods (IM): i) Irrigating by ‎constant levels of ‎water salinity (ECi) (i.e. 0.3, 10, 15, 20, 25dSm−1) regardless of STV(P); and ii) Considering STV ‎at each ‎growth stage(T). Present study investigated the effect of abovementioned IMs (P, T) and ECis (i.e. 0.3, 10, 15, 20, 25dSm−1) on accumulation of nitrogen (N), phosphorus (P), and potassium (K) ions in shoots (end of vegetation) and seeds (end of seed-filling) of quinoa. Results indicated that an increase in ECi ‎from 0.3 to 25dSm−1, especially after STV, caused a 56.6% and 12.8% increase of N while a 44.3% and 27.8% decrease of P, respectively in shoot and seed of quinoa. K only showed 30.2% increase in shoot. The change of all three ions was sharper in shoot than seed due to halophytic properties of quinoa which reduces salinity influence on seed embryo. The seed of quinoa is the main source of N and protein while the shoot is rich in P and K. The T ‎method of irrigation was preferred for providing the plant chance to survive in more saline condition. [ABSTRACT FROM AUTHOR]

The advent of lab diffuse reflectance spectroscopy (LDRS) that exploits the fundamental vibration, overtones and combination of functional groups of soil components makes the soil study easier. The present research intends to predict sand content utilizing the proximal soil sensing (PSS) tech. Thus, in accord with the supplementary data layers and stratified randomized sampling (SRS) method, eventually, 128 samples were gathered from 20 cm of soil surface of Mazandaran Province, Iran. First of all, the sample set was subdivided into two subsets: calibration (96) and validation (32). Using the multivariate regression analysis-partial least squares regression (PLSR) algorithm with leave-one-out cross-validation (LOOCV) technique and some pre-processing algorithms, such as spectral averaging, smoothing and 1st derivative (1st-D), the definitive calibration model with two & four latent vectors (LVs/LFs) and correlation coefficient (RP), determination coefficient (R2P), root mean square error (RMSEP), ratio of performance to deviation (RPDP) and ratio of performance to interquartile distance (RPIQP) respectively: 0.83&0.82, 0.68&0.67,8.68&8.83%,1.78&1.75,2.45&2.41, were validated and spotted as the most appropriate predictive model for the sand content prediction in the study region. Last, the potentiality of the visible-near infrared diffuse reflectance spectroscopy (VNIR-DRS) for sand content estimation in Mazandaran soils was proved. Also, it is feasible to upscale the sand prediction process utilizing the principal resulted model and the key spectral domains via airborne/satellite hyperspectral data, which emphatically shows the LDRS importance as a commencement point for characterizing the informative optical wavelengths. Likewise, that will be the infrastructure for spaceborne data modeling and upscaling process. [ABSTRACT FROM AUTHOR]

A thorough quantification of soil chemical properties is essential for assessing the engineering properties of forest soils for road design, construction, and maintenance. Here, we investigate the applicability of visible–near‐infrared (Vis–NIR) spectroscopy in conjunction with advanced statistical analysis for estimation of soil chemical properties. Sixty forest soil samples were collected and analyzed for pH, electrical conductivity (EC), CaCO3, organic matter (OM), and cation exchange capacity (CEC) with established laboratory methods. The spectral measurements were performed with a Vis–NIR spectrometer within a range of 350–2,500 nm. To estimate abovementioned soil properties from reflectance spectra, advanced statistical techniques including partial least squares regression (PLSR), hybrid partial least squares and artificial neural networks (PLS–DI–ANN) models, hybrid partial least squares and adaptive neural fuzzy inference system (PLS–DI–ANFIS) models, as well as narrow band spectral indices were applied. The obtained results ​​indicate that the PLS–DI–ANFIS models show great potential for the estimation of pH, EC, OM, and CEC from reflectance spectra and their first derivatives, exhibiting higher R2 values and lower RMSE than the other investigated models. The estimation accuracy for CaCO3, however, was low for all applied methods. The results confirm that Vis–NIR spectroscopy may be applied as a rapid and cost‐efficient alternative to standard chemical soil analysis techniques, aiding forest road design, construction, and maintenance. Core Ideas: Vis–NIR spectroscopy and machine learning improve soil chemical property estimation.The combined PLS–DI–ANFIS analysis provides the most robust estimation model.Vis–NIR spectroscopy aids the design, construction, and maintenance of forest roads. [ABSTRACT FROM AUTHOR]

Understanding the physical and especially mechanical properties of forest soils is very important in forest engineering operations including road construction and exploitation. Unconfined compressive strength (UCS) is one of the most important mechanical properties for evaluating the strength of adhesive soils in many engineering projects including forest roads. Measuring the USC of soil in the laboratory and in situ is difficult, time-consuming, and costly, so it is essential to explore a rapid, low-cost, and non-destructive method. The aim of this study was to estimate the UCS of forest roads using spectroscopy. The applied support vector machine (SVM) method determined the soil moisture classes (i.e., 14%, 25%, 31%, and 36%) with R2 of 0.98. Two methods of partial least squares regression (PLSR) and the normal difference index (NDI) were used to estimate the UCS at different moisture classes. The results showed that the NDI performed better than the PLSR to estimate the USC at four different moisture classes of 14% (R2 = 0.8, RMSE = 19.36, RPD = 2.61), 25% (R2 = 0.78, RMSE = 16.06, RPD = 1.64), 31% (R2 = 0.82, RMSE = 11.2, RPD = 2.28), and 36% (R2 = 0.83, RMSE = 7.49, RPD = 2.41). Also, a sampling interval method was applied to reduce spectral dimensionality and processing time. The sampling interval of 16 nm was selected using a genetic algorithm. Finally, the UCS was estimated at maximum soil moisture using the simple regression model (SRM) based on the results of NDI. Based on this current study, it has been found that using spectroscopy to estimate the UCS of soil could be considered an alternative method for developmental operations in identifying the UCS of soil due to the costly and time-consuming conventional laboratory methods. [ABSTRACT FROM AUTHOR]

Quinoa (Chenopodium quinoa Willd.) is a high-nutrient halophyte suitable for ‎cultivating in semi-arid climates and saline soils. The current study investigated the ‎effect of various water salinities (ECi) (i.e., 0.3, 10, 15, 20, and 25 dS m−1) and different ‎irrigation methods (IMs) on accumulation of calcium (Ca), magnesium (Mg), ‎sodium (Na), and chloride (Cl) ions in shoot (stem+leaves) of quinoa at the end of vegetation (onset of flowering) and seed at the end of seed-filling. Therefore, 30 pots (five ECi and two IM in three replications) were prepared with similar conditions. Considering that the salinity threshold value (STV) of quinoa varies during growth and is 8, 20 and 15 dSm−1 at each of ‎establishment, flowering, and seed-filling growth stages, the two IMs consisted of considering STV at each growth stage (T) and permanent irrigation by ‎constant levels of ECi(P). Results indicated that by increasing the ECi from 0.3 to 25 dSm−1 the amount of Na, Cl, and Mg in shoot increased 82.2%, 75.8%, and 8.7%, respectively, while Ca decreased 37.2%. In seeds, Na and Cl increased 43.3% and 50%, respectively, while Mg increased 8% and Ca did not change significantly. An increase in ECi ‎significantly changed ion accumulation content, especially at ECi higher than STV, ‎because it is the onset of damage due to salinity stress and particularly in‏ ‏‎shoot compared to seed due to the halophytic properties of quinoa. The T ‎method of irrigation was preferred due to less accumulation of Na and Cl in ‎shoot and seed, and therefore less damage and loss, especially at higher ‎salinities.‎ [ABSTRACT FROM AUTHOR]

Salinity is one of the major agricultural problems in arid and ‎semi-arid regions. Considering the variation of plant’s ‎sensitivity to salinity during growth, a greenhouse study with completely randomized design‎ was conducted to determine the relative salinity tolerance of Quinoa (Chenopodium quinoa Willd.) at different growth stages from seedling establishment to maturity (establishment, flowering and seed filling) by evaluating the Salinity Threshold Value (STV). Eight levels of ECi (i.e., Non-saline, 2, 4, 8, 12, 15, 20, 25 dSm−1) with four replications and five levels (i.e., Non-saline, 10, 15, 20, 25 dSm−1) with three triplications were applied at first and two last growth stages, respectively. A ‎comparison was performed on some growth and yield parameters of plants irrigated by considering STV (T) and ‎plants irrigated permanently by mentioned salinity levels regardless of STV (P) to choose ‎which method (P or T) is better at each salinity level. The STV was ‎evaluated 8, 20 and 15 dSm−1 at each growth stage, respectively. Seedling of Quinoa was more sensitive to salinity than the mature plant. Therefore, after establishment Quinoa has the ‎feasibility of irrigation by high-saline waters. The (P) ‎method was suitable only if the freshwater was available during all growth period of the plant; otherwise at higher salinities irrigation should be performed by considering STV (T method) to minimize the intensity of growth and yield reduction and to prevent yield loss at very high salinities. To achieve this, if high-saline water is available it’s possible to ‎use plant propagation techniques or cultivating Quinoa simultaneously with seasonal rainfall.‎ [ABSTRACT FROM AUTHOR]

The use of bio-mulches to stabilize dust has gained worldwide attention during the last five decades. We report herein on a study of the application of 20 new types of bio-mulches as stabilizing agents. To understand the effect of new bio-mulches on dust stabilization, several tests have been applied for strength, structural stability and wind erosion. Fourier Transforms Infrared and Scanning Electron Microscopy have been applied on untreated and treated soil samples. Bio-mulch modified mechanisms have been analysed. Wind erosion and aggregate stability test results clearly indicate that certain biomulch specimens were useful in controlling dust in relatively arid and semiarid areas. The Scanning Electron Microscopy images show that when bio-mulches were applied to sandy soil, some voids in the soil were filled, while other parts stayed on the surface of soil aggregates. The molecular structure of polyelectrolyte groups reacted chemically with positive ions of the clay grain and created physicochemical bonds between molecules and soil aggregates with ionic, hydrogen or Vander Waals bonds. Untreated samples had no bonds between molecules and soil aggregate, and the strength and erosion resistance were weak. [ABSTRACT FROM AUTHOR]

Accumulation of trace elements in arable soils is an important global hazard worldwide. In this research, the available content of Zn, Fe, B, Co, Cu, Mn, Mo and other soil parameters (pH, organic carbon content, carbonates and electrical conductivity) were analysed in northwest Iran. Concentration levels of trace elements were relatively low in areas with high pH values and low organic matter content, and only the Mo value exceeded the reference threshold. Based on the correlation among the elements, two datasets were produced. The first consists of Fe and Mn data, while the second contains Zn, B, Co, Cu and Mo data. Two fuzzy clustering approaches, Fuzzy C-means (FCM) and Gustafson–Kessel (GK), were applied for clustering both datasets. Multiple accumulation of trace elements was investigated from the clustering results and then visualized in spatial regionalization maps. The fuzzy clustering evaluating indices showed that the GK method was more appropriate than FCM for clustering datasets. The results revealed that the first and second datasets were divided into seven and six clusters, respectively. Fuzzy clustering analyses combined with geostatistical methods were used to map the spatial variability of each cluster. This method enabled the monitoring of multiple metal accumulation in large agricultural soils. [ABSTRACT FROM PUBLISHER]