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    Wyświetlanie 1-2 z 2
    Tytuł:
    Soil organic carbon physical fractions and aggregate stability influenced by land use in humid region of northern Iran
    Autorzy:
    Ayoubi, S.
    Mirbagheri, Z.
    Mosaddeghi, M.R.
    Powiązania:
    https://bibliotekanauki.pl/articles/2082913.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Instytut Agrofizyki PAN
    Tematy:
    Hyrcanian forest
    tea plantation
    paddy rice
    complexed organic carbon
    Opis:
    The present study was executed in order to examine the influences of land use change on aggregate stability and soil organic carbon fractions in the humid region of the north of Iran. The study area featured three land uses which included natural Hyrcanian forest, tea plantation and paddy rice cultivation. One hundred soil samples were taken from the 0-10 cm layer in a grid pattern to allow for variations in the study area as much as possible in summer 2016. The results revealed that land use change significantly altered the physical and chemical characteristics of the soil, as the highest values of soil organic carbon and complexed organic carbon, and the lowest values of pH, calcium carbonate equivalent and bulk density, were observed in the natural forest. The greatest percentage of macro-aggregates was found in the natural forest followed by the tea plantation. Particulate organic carbon and soil organic carbon associated with clay and silt particles as well as soil organic carbon associated with all aggregate fractions showed the following trend: natural forest > tea plantation > rice cultivation. Overall, our results confirmed the importance of forest soils in C sequestration and the vital role played by soil organic carbon in soils to improve soil quality indicators and aggregation.
    Źródło:
    International Agrophysics; 2020, 34, 3; 343-353
    0236-8722
    Pojawia się w:
    International Agrophysics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Prediction of soil physical properties by optimized support vector machines
    Autorzy:
    Besalatpour, A.
    Hajabbasi, M.A.
    Ayoubi, S.
    Gharipour, A.
    Jazi, A.Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/24338.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Instytut Agrofizyki PAN
    Opis:
    The potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression. The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model. Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model. The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model. The mean square error values for soil aggregate stability prediction using the multiplelinear regression and support vector machine models were 0.001 and 0.012, respectively. It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods.
    Źródło:
    International Agrophysics; 2012, 26, 2
    0236-8722
    Pojawia się w:
    International Agrophysics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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