Development and magnetic properties of loess-derived forest soils along a precipitation gradient in northern Iran

Pourmasoumi, Masoumeh; Khormali, Farhad; Ayoubi, Shamsollah; Kehl, Martin; Kiani, Farshad

doi:10.1007/s11629-018-5288-4

Cited by 12 publications

(5 citation statements)

References 68 publications

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“…Most Ultisols and Mollisols were found in the dense forest in Mazandaran province showing the higher C inputs into the soils. The high precipitation with a relatively low MAT in the center of the Mazandaran province leads to higher SOC accumulation at the soil surface and in turn higher clay content in a Bt horizon [6,113] and the deeper Bk horizon [6,113,114]. Entisols had the highest SOC variability (CV = 40.66%) followed by the Inceptisols (33.94%), Alfisols (CV = 33.64%), and Mollisols (30.55%).…”

Section: Soc Contents In Soil Classes and Geological Erasmentioning

confidence: 99%

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

et al. 2020

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Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines (SVM), artificial neural networks (ANN), regression tree, random forest (RF), extreme gradient boosting (XGBoost), and conventional deep neural network (DNN) for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 14.9% of SOC spatial variability followed by the normalized difference vegetation index (12.5%), day temperature index of moderate resolution imaging spectroradiometer (10.6%), multiresolution valley bottom flatness (8.7%) and land use (8.2%), respectively. Based on 10-fold cross-validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 0.59%, a root mean squared error of 0.75%, a coefficient of determination of 0.65, and Lin’s concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 3.71%, followed by the aquic (2.45%) and xeric (2.10%) classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN (hidden layers = 7, and size = 50) is a promising algorithm for handling large numbers of auxiliary data at a province-scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC base-line map and minimal uncertainty.

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Section: Soc Contents In Soil Classes and Geological Erasmentioning

confidence: 99%

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

et al. 2020

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“…These deposits represent outstanding archives of past climate in a region highly sensitive to changes in hydroclimate and potentially important in past human migrations (Shoaee et al, 2021) but otherwise poorly represented by past climate records. The loess units in the area are separated by paleosols showing various development degrees, providing valuable sedimentary archives of loess accumulation, soil, and paleosol formation within the loess units (Kehl et al, 2005;Frechen et al, 2009;Kehl, 2010;Khormali and Kehl, 2011;Ghafarpour et al, 2016Ghafarpour et al, , 2021Vlaminck et al, 2016Vlaminck et al, , 2018Lauer et al, 2017a;Lauer et al, 2017b;Shahriari et al, 2017;Pourmasoumi et al, 2019;Sharifigarmdareh et al, 2020;Kehl et al, 2021). These previous studies of loess-paleosol sequences in northern Iran mainly focused on dating of the deposits, particle size distributions, and paleosol formation in response to paleoprecipitation and paleopedogenic process, with little emphasis on phases of eolian deposition, meaning that additional detailed analyses of the loess units in particular are essential.…”

Section: Introductionmentioning

confidence: 99%

Late Pleistocene Climate and Dust Source From the Mobarakabad Loess–Paleosol Sequence, Northern Foothills of the Alborz Mountains, Northern Iran

et al. 2021

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Paleoclimatic investigation of loess-paleosol sequences from northern Iran is important for understanding past changes in a region highly sensitive to shifts in precipitation, and along potential routes of past human migration. Here, we present carbon and oxygen isotopic compositions of bulk carbonate (δ13Cbc and δ18Obc, respectively) coupled with particle size distributions of samples from the Mobarakabad section, northern Iran, to study past wind dynamics and hydroclimate. We also present new initial clay-sized Hf-Nd isotope results from key horizons in order to assess general dust sources. Variations of δ13Cbc and δ18Obc values of modern soils compared to paleosols allow reconstruction of late Pleistocene–Holocene climate change in the area. Our results show severe drought during a major eolian deposition phase (EDP) after 34 ka. The thickness and PSD of the C horizon of unit 5 suggest significant shifts in loess sources and depositional environments during this EDP after 34 ka. Indeed, based on our new clay-sized Hf-Nd data, we hypothesize that the loess unit 5 might originate from the young crustal source of the Alborz and Kopet Dagh mountains. In general, the PSD of C horizons in the section is bimodal in the silt fraction and the very small, very fine clay fraction, with a mode at c. 1 μm in the modern soil and paleosols possibly produced by weathering and pedogenic processes. There also appears to be a good correlation between δ13Cbc and δ18Obc values, differentiating phases of loess accumulation and paleosol formation and hence providing quantitative data for reconstructing paleoclimatic conditions in the study area.

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“…In contrast to the spatial distribution of sorghum, there is a certain dependence in the distribution of MS values: growth in the south-eastern direction, which roughly corresponds to the growth of НТК. We have not confirmed the known relationship between soil MS and the amount of annual precipitation [17,18]. The main statistical characteristics of the distribution of MS values and Sorghum content are given in Table.…”

Section: Fig 2 the Value Of The Ms Of The Soil Of The Study Areamentioning

confidence: 57%

Magnetic susceptibility of soils of the chernozem zone of the left bank of Ukraine and its informativity in the agrochemical aspect

Miroshnychenko¹,

Круглов²,

Panasenko³

et al. 2020

Visnyk agrarnoi nauky

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Goal. To study the distribution of values of magnetic susceptibility (MS) of soils of agricultural lands of the chernozem zone of the Left Bank of Ukraine and to evaluate their informativeness for control of organic carbon (Corg) of soils at the regional level. Methods. Methods of soil sampling according to DSTU 4287:2004 were used and the humus content was determined according to DSTU 4289:2004. The standard software product Statistica® was used to establish the statistical indicators, and the results of the research were visualized in the environment of MapInfo and Surfer. Results. 90 samples were taken from the upper soil horizon (0-30 cm) of lands of different types (arable land, pastures, forest), their ratio roughly corresponded to the current structure of agricultural lands. For all samples, the specific MS was determined using a kappa-containing KLY2 according to the Evans method. Conclusions. The degree of relationship between the content of sorghum in the soils of the study area did not allow making unambiguous recommendations for the use of MS by interpolation or extrapolation of values at the regional level. This is due to differences in pedogenesis and soil use. The scattering graph of MS and Corg can identify areas that meet different conditions of soil formation: steppe, meadow, gray forest. They did not find regularities in the spatial distribution of soil MS (except for the growth of values with increasing hydrothermal coefficient) and Corg content.

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Development and magnetic properties of loess-derived forest soils along a precipitation gradient in northern Iran

Cited by 12 publications

References 68 publications

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Late Pleistocene Climate and Dust Source From the Mobarakabad Loess–Paleosol Sequence, Northern Foothills of the Alborz Mountains, Northern Iran

Magnetic susceptibility of soils of the chernozem zone of the left bank of Ukraine and its informativity in the agrochemical aspect

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