Effect of the physical fractions of organic matter on soil aggregate stabilities in three various land uses of forest, range, and agricultural lands

Soil structure stability refers to the ability of a soil to hold up the solid particle arrangement and the spaces among them when face to different stresses. According to the important role of organic matters in formation and stability of soil aggregates; it appears that the different fractions of them may also have significant effects on soil aggregate stability. Furthermore, separating the different fractions of organic matter helps the identification of the sensitive and sustainable parts and their locations in aggregate structure. The purpose of this study is to evaluate the effect of different fractions of organic matters on the soil macro (>0.25 mm) and micro (
Fifteen soil samples were taken from the surface (0-10 cm) using a supervised random method in three different land uses including forest, range, and agricultural lands (totally, 45 points) in Rabor region, Kerman province. After air-drying the samples and passing them through a 4 mm sieve, mean weight diameter (MWD) of soil aggregates was measured using the wet sieve method for the macro and micro aggregates. Then, different fractionations of organic matters in the macro and micro aggregates were determined using the density method. Finally, the amount of organic carbon in different fractions of organic materials and also the total amount of soil organic carbon were identified using the Walkley-Black method and then the percentage of total organic matter existed in each fraction was calculated by considering the weight and percentage of organic matter in each fraction,. Afterward, the organic matter data were used as input to the artificial neural network model. Besides, the regression relationships among the variables and soil aggregate stability were investigated.
The results showed that the free light fraction of organic matter (F1) in the macro-aggregates was greater than in the micro-aggregates. Also, the F1-amount in the forest was greater than the other two investigated land uses due to the higher organic matter content. The amounts of occluded light fraction of organic matter (F2) in the agricultural lands were lower than other land uses which might be due to tillage operation, soil aggregate destruction, and release of the trapped organic matter inside of them. The parts of organic matter which were associated with mineral fractions (F3) were allocated with the largest percentage of the total soil organic matter as compared to the other two fractions. The amount of last mentioned fraction was higher in the micro-aggregates than the macro-aggregates in all three investigated land uses. Results also revealed that the linear regression was not able to identify the interrelationship between the studied variables and aggregates stability index. In contrast, the diagrams related to artificial neural network model showed that all input variables to the model have influenced the MWD, although the important coefficients of the input variables were different.
Different fractions of soil organic matter were more sensitive to the land use type than the total organic matter. The macro and micro aggregate percentages were different depending to the land use type; however, the micro aggregates with greater organic matter contents were more stable against entered stresses than the macro aggregates. Artificial neural network model had more efficiency in estimating the soil aggregate stability than the linear regression method indicating that there is a non-linear relationship between different fractions of organic matter and MWD. By considering the accuracy and efficiency of artificial network model, it appears that this method can be used to determine the linear and non-linear relationship among different soil properties, with higher precision and lower cost and time.