Characterization of Heavy Metal Fractions in the Soil Developed on Volcanic Rocks of Karadag Mountain, Turkey by Sequential Extraction.

In this work, soil samples were taken from 15 different sites and the contents of Cd, Cr, Cu, Ni, Pb, and Zn in the mobile and residual fractions of the soils formed from the volcanic materials were determined by the sequential extraction procedure. The mobility of each metal was revealed by analysing fractions. The order of heavy metals in each fraction of Karadag samples was:Cd: Acid and Water Soluble > Reducible > Oxidizable > Residual; Cr: Residual > Oxidizable > Reducible > Acid and Water Soluble; Cu: Residual > Oxidizable > Reducible > Acid and Water Soluble; Ni: Residual > Reducible > Oxidizable > Acid and Water Soluble; Pb: Reducible > Residual > Oxidizable > Acid and Water Soluble; Zn: Residual > Reducible > Oxidizable > Acid and Water Soluble.According to the results, while the concentrations of Cd and Pb in the mobile fraction were higher than those in the residual fraction, Cr, Cu, Ni and Zn were higher in the immobile fraction. When the higher mobility levels of Cd and Pb are evaluated in terms of environmental pollution and toxicity in soil, these metals have been found to have a higher potential risk than other metals.Cd and Pb are likely to be in close contact with plant roots and thus could potentially affect soil fertility. To avoid threats to productivity and food security in the long term, further trace metal inputs to soils in these areas should be avoided by agricultural management or other means.

Assessment of the vegetation indices on Sentinel-2A images for predicting the soil productivity potential in Bursa, Turkey.

Although field surveys represent an essential method for determining soil productivity, the use of remote sensing techniques has become a popular option over recent years due to its economic and practical applications. The fundamental basis of this approach is the estimation of soil productivity by using the vegetation indices as an indicator, with reference to the yield. In this study, it is aimed to estimate the productivity potential of the agriculture areas from biomass density in case of limited pedological and parcel-based data. For this purpose, relationships between the FAO Soil Productivity Rating (SPR) and different vegetation indices were investigated. The indices NDVI, RE-OSAVI, and REMCARI were used with Sentinel-2A images. Wheat was selected as an indicator plant to estimate the yield because it is the most occupied (27.47%) cultigen in the field. The study was conducted at the Karacabey State Farm with an area of 87 km2 and is located in Bursa province, Turkey. The research showed a positive relationship between SPR and 2018 yield values (r2 = 0.616). During the tillering period, the r2 for RE-OSAVI was 0.629. In the heading stage, the r2 for NDVI was 0.577. The index REMCARI provided yield estimations with low accuracy coefficient (0.216 ≤ r2 ≤ 0.258) during all vegetation periods. These findings can be interpreted as the monitoring of the land quality with multispectral satellite images via NDVI and RE-OSAVI. In this way, we could decide the time to re-definition of soil properties with land surveys for determination of soil productivity when the detection of a decrease using the indices during some vegetation periods. However, further investigations are needed in controlled trial patterns with differential reference plants, although the findings obtained from the study are promising for the use of spectral vegetation indices to prediction and/or monitoring of soil productivity. Thus, the possibilities of using spectral indices in different ecologies and different plant species can be evaluated from a broad perspective. It was also suggested that Sentinel-2A images may be used for similar studies due to their spectral capabilities with the ESA-SNAP tool.

Assessment of soil quality index for wheat and sugar beet cropping systems on an entisol in Central Anatolia.

The sustainable use of agricultural lands is significantly affected by the implemented management and land processing methods. In sugar beet and wheat cropping, because the agronomic characteristics of plants are different, the tillage methods applied also exhibit significant variability. Soil quality concept is used, as a holistic approach to determining the effects of these applications on the sustainable use of soil. Agricultural soil quality evaluation is essential for economic success and environmental stability in rapidly developing regions. At present, a variety of methods are used to evaluate soil quality using different indicators. This study was conducted in one of the most important irrigated agriculture areas of Çumra plain in Central Anatolia, Turkey. In the soil under sugar beet and wheat cultivation, 12 soil quality indicators (aggregate stability (AS), available water capacity (AWC), surface penetration resistance (PR0-20), subsurface penetration resistance (PR20-40), organic matter (OM), active carbon (AC), potentially mineralizable nitrogen (PMN), root health value (RHV), pH, available phosphorus (AP), potassium (K), and macro-micro elements (ME) (Mg, Fe, Mn, and Zn)) were measured and scored according to the Cornell Soil Health Assessment (CSHA) and the Soil Management Assessment Framework (SMAF). The differences among 8 (AS, AWC, PR0-20, PR20-40, AC, PMN, AP, and ME) of these 12 soil quality characteristics measured in two different plant cultivation were found statistically significant. The result of the soil quality evaluation with scoring function in the examined area revealed a soil quality score of 61.46 in the wheat area and of 51.20 in the sugar beet area, which can be classified as medium and low, respectively. Low soil quality scores especially depend on physical and biological soil properties. Therefore, improvement of soil physical and biological properties with sustainable management is necessary to enhance the soil quality in the study area soils.

Investigation of N Deficiency in Cherry Trees Using Visible and Near-Infrared Spectra Part of the Spectrum in Field Condition.

This objective of the study was to develop a model for the determination of N deficiency in cherry trees using a combination of visible near infrared methods and spectro-radiometric measurement. In our experimental design, cherry seedlings were grown under various N deficiency conditions in nutrient-controlled containers. The reflectance values of plant leaves were measured using a spectro-radiometer. Plant leaves samples were simultaneously collected. Their nutrient contents were determined in the laboratory. Afterwards, we performed a statistical comparison of the reflectance values. Sample analysis results established the significant wavelengths. Moreover, we received accurate regression models for predicting N deficiency in cherry leaves that were grown in nutrient solutions. Next, we verified the model validity by measuring the reflectance of the leaves collected from cherry orchards at various locations using a spectroradiometer. Nutrient deficiencies were calculated using the developed model, and then, the predicted and measured data were compared to evaluate model validity. From these results, we determined the wavelengths that yielded the most accurate results for N prediction, selected from the blue and green regions of the spectrum. We established that for N prediction in cherry trees, the simplest model can be created using 560 and 570 nm wavelengths. However, the evaluated model can be applicable only under certain conditions. We concluded that in order to develop a prediction method with sufficient application capacity, as well as the ability to assess nutritional and physiological characteristics, the ecology condition of the plant should be properly considered based on the model.

Determining the Zn content of cherry in field using VNIR spectroscopy.

Visible and near-infrared (VNIR) spectroscopy is an eco-friendly method used for estimating plant nutrient deficiencies. The aim of this study was to investigate the possibility of using VNIR method for estimating Zn content in cherry orchard leaves under field conditions. The study was conducted in 3 different locations in Isparta region of Turkey. Fifteen cherry orchards containing normal and Zn deficient plants were chosen, and 60 leaf samples were collected from each location. The reflectance spectra of the leaves were measured with an ASD FieldSpec HandHeld spectroradiometer and a plant probe. The Zn contents of leaf samples were predicted through laboratory analysis. The spectral reflectance measurements were used to estimate the Zn levels using stepwise multiple linear regression analysis method. Prediction models were created using the highest coefficient of determination value. The results show that Zn content of cherry trees can be estimated using the VNIR spectroscopic method (87.5