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Soil management practices can have negative or positive effects on soil quality. Our objective was to assess the effect of long-term agricultural practices by evaluating selected soil physical and chemical properties. Soil samples were collected from two depths (0 to 15 and 15 to 30 cm) within a native pasture and an adjacent agricultural field that was being used for three different crop rotations. Soil quality was quantified using aggregate stability, bulk density, soil texture and available water content as physical properties and pH, electrical conductivity, organic matter and available phosphorus as chemical properties. The farmland soils were functioning at 71 and 70 per cent of their full potential at the 0-to 15-and 15 to 30-cm-depth increments, respectively, whereas those from the pasture were functioning at 73 and 69 per cent, respectively. The assessment showed substantial loss in soil organic carbon following 50 years of farmland cultivation. Tillage and fertilizer applications were presumably the primary reasons for weaker spatial dependence within farmland at the 0-to 15-cm depth. Grazing was postulated as the main reason for weaker spatial dependence within the pasture soils at the 15-to 30-cm depth. Overall, we conclude that 50 years of cultivation has not caused soil quality to decline to a point that threatens sustainability of the agricultural fields.Notes: CV, coefficient of variation; ns, not significant; TOC, total organic carbon; BD, bulk density; EC, electrical conductivity; SQI, soil quality index. a Significant at the 0·01 level. b Significant at the 0·05 level.

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Current agricultural practices and their impacts on the sustainability of crop production can be evaluated by simple and reliable soil structure assessment tools. The study was conducted to determine the effects of long‐term (2006–2017) tillage systems on structural quality of a clayey soil using the visual evaluation of soil structure (VESS) and classical field and laboratory measurements. A field experiment with seven tillage systems, representing both traditional and conservation tillage methods, was conducted on a clayey soil in the Cukurova region, Turkey. Soil samples from 0–10, 10–20 and 20–25 cm depths were analysed for mean weight diameter (MWD), porosity and organic carbon. Penetration resistance (PR) was determined in each treatment plot. The VESS scores (<2) of upper 0–5 cm indicated a good structural quality for all tillage systems. The VESS scores were positively related to PR and MWD and negatively to macroporosity (MaP) and total porosity. In reduced and no‐till systems, poorer soil structures were observed in subsurface layers where firm platy and angular blocky structures were defined. Mean VESS score (3.29) in 20–25 cm depth where PR was 3.01 MPa under no‐till indicated a deterioration of soil structural quality; thus, immediate physical interventions would be needed. Lower VESS scores and PR values under strategic tillage which was created by ploughing half of no‐till plots in November 2015 indicated successful correction of compaction caused by long‐term no‐till. The results suggest that the VESS approach is sensitive and useful in distinguishing compacted layers within the topsoil.

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.