Showing 5 results for Soil Organic Carbon
Z. Fahim, M. A. Delavar, A. Golchin,
Volume 17, Issue 63 (6-2013)
Abstract
Organic carbon is the most important component of terrestrial ecosystems and any change in its abundance can have a major impact on the processes that take place in ecosystem. The aim of this study was to estimate carbon sequestration in three different elevations (200 to 1200 m from sea level) and according to vegetation type in the Khairoodkenar forest. The highest carbon sequestration was observed in the surface layer of a soil with Fig-Carpinus betulus vegetative cover and it was estimated to be 167.4 ton/ha. But when carbon sequestration was measured in soil solum, it was found that soils with Fagus orientalis-Carpinus betulus vegetation cover had the highest amount of organic carbon (514.4 ton/ ha). The results showed that clay fraction had the highest carbon content but the highest enrichment factor (1.59) was measured for sand fraction in soils with Fagus orientalis- Carpinus betulus vegetative cover. The highest organic carbon content (7.89%) and aggregate stability (MWD= 7.79mm) and lowest bulk density (1.21 g/cm3) were measured in soils with Figs- Carpinus betulus vegetative cover.
F. Maghami Moghim, A. Karimi, Gh. Haghnia, A. Dourandish,
Volume 17, Issue 65 (12-2013)
Abstract
The quantity and variability of soil organic carbon (SOC) is one of the most important indices to determine the effect of land use changes on the soil quality. Regarding long-term changes from rangeland to dry farming in the Roin area of North Khorasan, the objectives of this study were to investigate the effect of long-term land use changes on the SOC in different slope faces and use SOC as an index to make a proper decision about the future of land use in this area. 140 soil samples were taken from 0-15 cm soil depth of back slope position of north-, south-, west- and east-facing slopes of rangeland, dry farming, alfalfa dry farming and garden in 7 points. 14 soil samples were taken from irrigated farming, too. The results showed that garden and irrigation farming with averages of 2.03 and 0.78% have the maximum and minimum SOC content. The average of SOC content in rangeland was 1.40% that decreased by land use change to 1.04 and 1.27% in dry farming and alfalfa dry farming, respectively. SOC content in southern slope aspects showed a significant difference compared to other slope aspects. The most SOC content occurred in east aspects. It seems that after long-term land use changes, the SOC content have equilibrated to environmental and land use conditions. The average SOC content in different slope aspects except south one changed from 1.4% in rangeland to 1.11% in dry farming and 1.32% in alfalfa dry farming, which are a suitable value for semiarid regions. In conclusion, to protect land from degradation and considering this fact that dry farming is the main income of the people in the study area, it is recommended to stop dry farming on south aspects and continue on east, north and west aspects with conservation practices.
N. Parsamanesh, M. Zarrinkafsh, S. S. Shahoei, Weria Wisany,
Volume 18, Issue 70 (3-2015)
Abstract
Reduction of quality and soil productivity due to organic carbon losses is one of the most important consequences of land use changes, that creates irreparable effects on the soil. To evaluate the land use effect on the amount of soil organic carbon in Vertisols, Sartip Abad series with extent of 1850 hectare in south of Bilehvar area in Kermanshah province was studied by using the completely randomized block design in factorial experiment with 10 repeats in farmland and grassland, some soil physical and chemical properties in two Lands compared with each other. The results showed that the soil organic carbon in surface horizons of grassland has been more than farmland and accordingly increase the amount of sequestrated carbon in grassland. No significant differences were found in the amount of soil organic carbon in lower horizons of two lands. Due to land use change from grassland to farmland, noticeably increase in Bulk density, Nitrogen, Acidity, soil Electrical Conductivity and decrease the organic carbon percent and the soil organic material. Pedutorbation, clay amount (higher of 50%), numerous small subsoil, and stable structure are the important factors in saving the organic carbon of vertisols that can reduce the effects of land use changes on organic carbon amount. Generally, it can be conclude that: the land use changes not only can create the severe damage on soil physical and chemical properties but with the carbon losses and more release of greenhouse gases exacerbate the pollution of environment which endangers the life in a earth planet.
Miss S. Bandak, A.r. Movhedei Naeani, Ch.b. Komaki, M. Kakooei, J. Verrlest,
Volume 27, Issue 3 (12-2023)
Abstract
Soil organic carbon (SOC) is one of the most important components of soil physical and chemical properties that have an important role in sustainable production in agriculture and preventing soil degradation and erosion. Data mining approaches and spatial modeling besides machine learning techniques to investigate the amount of soil organic carbon using remote sensing data have been widely considered. The objective of the present study was the evaluation of SOC using the remote sensing technique compared with field methods in some areas of the Gonbad Kavous and Neli forests of Azadshar. The soil samples were collected from the soil surface (0-10 cm depth) to estimate the SOC. Data were categorized into two categories: 70% for training and 30% for validation. Three machine learning algorithms including Random forest (RF), support vector machine, extra tree decision, and XGBoost were used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included bands related to Lands 8 OLI and sentinel 2 measurement images, topography, and climate. The results showed that the extraction of the components related to the bands along with the calculation of indicators such as normalized vegetation difference, wetness index, and the MrVBF index as auxiliary variables play an important role in more correct estimation of the amount of soil organic matter. Comparison of different estimation regressions showed that the Sentinel 2 random forest model and in Landsat8 with the values of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MEA) of 0.64, 0.05, and 0.17, respectively, was the best performance ratio compared to other approaches used in the study to estimate the organic carbon content of surface soil in the study area. In general, the results of this study indicated the ability of remote sensing techniques and learning models in the spatial estimation of soil organic carbon. So, this method can be used as an alternative to laboratory methods in determining soil organic carbon.
B. Attaeian, F. Teymorie Niakan, B. Fattahi, V. Zandieh,
Volume 28, Issue 3 (10-2024)
Abstract
The objective of this study was to investigate the effect of wildfire in the rangelands of the Gonbad region of Hamedan on soil organic carbon storage in two control and fire areas after three years of fire, and the feasibility of using remote sensing in indirect estimation of soil carbon. Therefore, 20 soil surface (0-10cm depth) samples were collected from the burned area and 20 samples from the control area (40 samples in total) by the systematically random method after three years of fire time. Changes in organic carbon, total nitrogen, acidity, and salinity of surface soil were tested by independent t-test between control and fire areas. Then, to investigate the linear relationship between the storage of soil organic carbon with other parameters, the Pearson correlation was used in SPSS v. 26. The results of the independent t-test showed that there was no significant difference in EC, acidity, and soil organic carbon of the control and fire areas, but the amount of total soil nitrogen showed significantly different. The results showed a significant positive correlation was observed between soil organic carbon and total nitrogen at the level of one-hundredth of 0.830 (p< 0.01) in the fire area, and the BI index showed a significant negative correlation of 0.727 (p< 0.05). In the control area, a significant positive relationship was observed between organic carbon and total nitrogen at the rate of 0.627 (p <0.05). The results of processing Landsat 8 images (OLI-TIRS sensor) in the fire area showed that there was a statistically significant relationship between soil organic carbon and light and wetness index obtained from tasseled cap (-0.726 and 0.674, respectively) and PC1 component obtained from principal component analysis and -0.724 (p <.05). These results indicate that it is possible to use tasseled cap images to predict soil organic carbon in fire areas.
