A B S T R A C TCation exchange capacity (CEC), as an important indicator for soil quality, represents soil's ability to hold positively charged ions. We attempted to predict CEC using different statistical methods including monotone analysis of variance (MONANOVA), artificial neural networks (ANNs), principal components regressions (PCR), and particle swarm optimization (PSO) in order to compare the utility of these approaches and identify the best predictor. We analyzed 170 soil samples from four different nations (USA, Spain, Iran and Iraq) under three land uses (agriculture, pasture, and forest). Seventy percent of the samples (120 samples) were selected as the calibration set and the remaining 50 samples (30%) were used as the prediction set. The results indicated that the MONANOVA (R 2 = 0.82 and Root Mean Squared Error (RMSE) = 6.32) and ANNs (R 2 = 0.82 and RMSE = 5.53) were the best models to estimate CEC, PSO (R 2 = 0.80 and RMSE = 5.54) and PCR (R 2 = 0.70 and RMSE = 6.48) also worked well and the results were very similar to each other. While the most influential variables for the various countries and land uses were different and CEC was affected by different variables in different situations, clay (positively correlated) and sand (negatively correlated) were the most influential variables for predicting CEC for the entire data set. Although the MANOVA and ANNs provided good predictions of the entire dataset, PSO gives a formula to estimate soil CEC using commonly tested soil properties. Therefore, PSO shows promise as a technique to estimate soil CEC. Establishing effective pedotransfer functions to predict CEC would be productive where there are limitations of time and money, and other commonly analyzed soil properties are available.
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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