پژوهش‌های آبخیزداری (Dec 2021)

The Role of Land Use and Physical Properties on Soil Organic Carbon in the Flood Spreading Fields of Kowsar Station

  • Mohammad Javad Rousta,
  • Mojtaba Pakparvar,
  • Seyed Masoud Soleimanpour,
  • Maryam Enayati

DOI
https://doi.org/10.22092/wmrj.2021.355443.1426
Journal volume & issue
Vol. 34, no. 4
pp. 135 – 149

Abstract

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Soil properties and land use affect the soil carbon content and reduce the adverse effects of climate change. This study aims to assess the effect of some physical properties of soil and land use on the amount of soil organic carbon content (SOC) and model development to estimate the amount of SOC. This investigation was carried out in 2020 in the areas of flood spreading in of Fasa (Kowsar station). Land uses included acacia (Acacia salicina Lindl.), eucalyptus (Eucalyptus camaldulensis Dehnh.), atriplex (Atriplex lentiformis (Torr.) Wats.) plantation, and natural rangeland, all of which are irrigated by flood spreading. By sampling in three replications of the soil of different land uses from the depth of 0-30 cm (15 composite samples), percentage of sand, silt, clay, silt+clay, percentage of soil saturation moisture (SP), bulk density (BD), particle density (PD), porosity percentage (PS), void ratio (VR) and SOC were determined. Obtained data were statistically analyzed in a complete randomized design and the means were compared with the Duncan test at P<0.05. The analysis of variance showed that the effect of land use (PT) on the percentage of sand, silt, silt + clay, SP, BD, PS, VR, and SOC has been significant at P<0.01, and the clay, at P<0.05. Comparison of the means of SOC in different land uses showed that the Eucalyptus forest, with 1.68%, has the highest value and the control with 0.14% organic carbon has the lowest value. There was no statistically significant difference between the SOC in Acacia forest, Atriplex, and rangeland. Stepwise regression analysis was used to present the model. Soil physical properties and land use were considered as independent variables and SOC was considered as a dependent variable. The results showed that the variable of silt explains 77.00% of the changes in organic carbon. Based on the principal component analysis (PCA) method, according to the specific values, considering the first two axes, about 91.70% of the changes can be explained. Considering the first axis, 69.71% and considering the second axis, 21.99% of the changes are justifiable. The PT, with 91.40%, sand with 84.30%, BD, 82.70%, showed a negative correlation with SOC. While the SP had 90.30%, percentage of clay+silt had 84.80%, PS, 80.70%, VR, 79.10%, silt, 78.50%, clay, 78.50%, had a positive correlation with SOC.

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