Performance Comparison of Different Particle Size Distribution Models in the Prediction of Soil Particle Size Characteristics

Abstract

Particle size distribution (PSD) is a rich source of information about soil properties, including soil gradation and soil particle size characteristics. This paper compared the PSD prediction ability of three types of mathematical model. We selected nine models that have been proven to accurately predict sample points in previous studies, and we fit 144 pieces of experimental data on 12 texture classes of soil samples from the UNSODA database. We compared the models’ capability for predicting non-sample points, which is important for predicting soil particle size characteristics. Each model’s ability to predict non-sample points of different texture classes of soil was studied using a comprehensive ranking method. The relative differences in the models’ prediction of non-sample points of different texture classes of soil were analyzed using the relative error method. The results showed no considerable correlation between the number of model parameters and the prediction accuracy. For the various texture classes of soil, the Skaggs model and Weipeng model had the highest accuracy in predicting non-sample points, and the Skaggs model had the widest range of application. The Zhongling model and the Weibull model were better in predicting only one texture class of soil, respectively. The Fredlund model, Kolve model, Rosin model, Van Genuchten model and Best model were not as successful as other models. The Weipeng model overestimated the solid particle mass proportion, while the Skaggs model underestimated it when the clay particle content was greater than 20%. Both the Weipeng model and the Skaggs model demonstrated good prediction accuracy when the particle size was within the silt particle size range. The Skaggs model overestimated the particle mass proportion, while the Weipeng model underestimated it when the particle size was within the sand particle size range.

Changes in soil carbon, nitrogen, and phosphorus in Pinus massoniana forest along altitudinal gradients of subtropical karst mountains

“…After the soil was dried in an oven for 12 h at 105 °C, the SBD was determined as the dry weight per unit volume of soil core, and SMC was determined gravimetrically by weighing. All the 28 soil samples were analyzed using the laser diffraction method for their soil particle size distribution using a Bettersizer 2,600 laser particle size analyzer (Bettersize Instruments Ltd, Liaoning, China), and the percentages of clay (<0.002 mm), silt (0.002–0.05 mm) and sand (0.05–2 mm) were calculated according to the US soil taxonomy classification system ( Zhang et al, 2022a ). The pre-treatment was done by adding 10 mL of 10% hydrogen peroxide (H 2 O 2 ) and 10% hydrochloric acid (HCl) to remove organic matter and carbonates.…”

“…The effects of altitudinal changes on the biodiversity and environmental factors of mountain ecosystems are a hot topic in ecological research ( Körner, 2007 ; Zhang et al, 2022a ; Zhang et al, 2022b ). Altitudinal gradients have been used as “natural experiments” to understand the influence of temperature, light, rainfall, and other environmental factors on soil physicochemical properties in mountain ecosystems ( Maja, Ranko & Bishal, 2011 ; Wu et al, 2013 ).…”

“…Altitudinal gradients have been used as “natural experiments” to understand the influence of temperature, light, rainfall, and other environmental factors on soil physicochemical properties in mountain ecosystems ( Maja, Ranko & Bishal, 2011 ; Wu et al, 2013 ). In particular, altitude is one of the most important factors that determines micro-climatic conditions (temperature and precipitation) that impact plant/microbe distribution and growth ( Pan et al, 2009 ; Zhang et al, 2022a ; Zhang et al, 2022b ), which eventually impacts soil carbon, nitrogen, and phosphorus contents (SCNPC) in mountain forests ( Körner, 2007 ). It is of great importance to understand the changes in and factors influencing SCNPC in mountain forest soils at different altitudes to reveal the interaction between biogeochemical cycles and altitudinal gradients ( Njeru et al, 2017 ; Zhang et al, 2019a ; Zhang et al, 2019b ).…”

“…Pinus massoniana , as a pioneer tree species that is currently involved in the vegetation succession in this region, has rapid organic matter accumulation and superb adaptability to harsh environments. The P. massoniana forest constitutes the main forest vegetation type in southern China and plays an important role in the karst mountain forest ecosystem of this region ( Huang et al, 2020 ; Lei et al, 2018 ; Zhang et al, 2022a ; Zhang et al, 2022b ). Research on the characteristics of vertical variation of soil nutrients and their influencing factors in subtropical mountain horsetail forests with different altitudinal gradients is important to enhance the scientific management of regional forests in this region.…”

Correlation and Variability of Soil Properties of Arable Сhernozems in the South of the Central Russian Upland

“…В работе [Rasmussen et al, 2018] говорится о связи между актуальной кислотностью и органическим углеродом, содержанием углекислоты карбонатов. Содержание различных гранулометрических фракций зачастую обладает высокой степенью корреляции друг с другом [Шеин, 2009;Zhang et al, 2022].…”