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Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0–20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.

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Soil salinization is the widespread problem seriously affecting the agricultural sustainability and causing income losses in arid regions. The major objective of the study was to quantify and map the spatial variability of soil salinity and sodicity. Determining salinity and sodicity variability in different soil layers was the second objective. Finally, proposing an approach for delineating different salinity and sodicity zones was the third objective. The study was carried out in 871.1 ha farmland in Southeast of Dushak town of Ahal Province, Turkmenistan. Soil properties, including electrical conductivity (EC), soil reaction (pH), sodium adsorption ratio (SAR), calcium carbonate and particle size distribution (clay, silt and sand fractions) in 0–30, 30–60, 60–90 and 90–120 cm soil layers were recorded. The EC values in different soil layers indicated serious soil salinization problem in the study area. The mean EC values in 0–90 cm depth were high (8 dS m-1), classifying the soils as moderate to strongly saline. Spatial dependence calculated by the nugget to sill ratio indicated a strong spatial autocorrelation. The elevation was the primary factor affecting spatial variation of soil salinity in the study area. The reclamation of the field can be planned based on three distinct areas, i.e., high (≥12 dS m-1), moderate (12–8 dS m-1) and low (<8 dS m-1) EC values. The spatial trend analyses of SAR values revealed similar patterns for EC and pH; both of which gradually decreased from north to the south-west. The amount of water needed to leach down the salts from 60 cm of soil profile is between 56.4–150.0 ton ha-1 and the average leaching water was 89.8 tons ha-1. The application of leaching water based on the amount of average leaching water will result in higher or lower leaching water application to most locations and the efficiency of the reclamation efforts will be low. Similar results were recorded for sulfur, sulfuric acid and gypsum requirements to remediate sodicity. The results concluded that the best management strategy in planning land development and reclamation schemes for saline and sodic soils require accurate information about the spatial distribution of salinity and sodicity across the target area.

Soil salinity is a major land degradation process reducing biological productivity in arid and semi-arid regions. Therefore, its effective monitoring and management is inevitable. Recent developments in remote sensing technology have made it possible to accurately identify and effectively monitor soil salinity. Hence, this study determined salinity levels of surface soils in 2650 ha agricultural and natural pastureland located in an arid region of central Anatolia, Turkey. The relationship between electrical conductivity (EC) values of 145 soil samples and the dataset created using Landsat 5 TM satellite image was investigated. Remote sensing dataset for 23 variables, including visible, near infrared (NIR) and short-wave infrared (SWIR) spectral ranges, salinity, and vegetation indices were created. The highest correlation between EC values and remote sensing dataset was obtained in SWIR1 band (r = -0.43). Linear regression analysis was used to reveal the relationship between six bands and indices selected from the variables with the highest correlations. Coefficient of determination (R2 = 0.19) results indicated that models obtained using satellite image did not provide reliable results in determining soil salinity. Microtopography is the major factor affecting spatial distribution of soil salinity and caused heterogeneous distribution of salts on surface soils. Differences in salt content of soils caused heterogeneous distribution of halophytes and led to spectral complexity. The dark colored slickpots in small-scale depressions are common features of sodic soils, which are responsible for spectral complexity. In addition, low spatial resolution of Landsat 5 TM images is another reason decreasing the reliability of models in determining soil salinity.

Feedstock type is the most dominant factor influencing the physical characteristics and chemical composition of biochar. The main purpose of this study was to characterize and compare some of the physical and chemical properties of biochars produced by slow pyrolysis of 18 feedstocks, which are locally available agricultural residues. Moreover, elucidating the potential agronomic benefits of these biochars was the other objective of the study. Biochars were produced at 500 o C in an ingeniously developed reactor. The biochars were characterized for specific surface area (SSA), field capacity (FC), wilting point (WP), plant available water content (AW), pH, electrical conductivity (EC), cation exchange capacity (CEC), total carbon (C) and nitrogen (N), plant available phosphorus (P) and potassium (K) concentrations. Considerable variation of characteristics among biochars indicates the dominant impact of feedstock type on physical properties and chemical composition of biochars. Total C contents were highly variable with values up to 91.9% for pine sawdust. Phosphorus and K in feedstocks were concentrated in the biochars and were two to four times higher in the biochars. The CEC of biochars varied from 79.5 cmol kg-1 (pepper residues) to 5.77 cmol kg-1 (poplar sawdust). The CEC and SSA had a significant negative correlation (P<0.01, r=-0.70) that probably be attributed to the loss of functional groups during pyrolysis. The results revealed that depending on the feedstock, some biochars have potential to serve as nutrient sources as well as an additive to improve soil quality.