Impact of land use change on erodibility and soil quality indicators (case study: Sidasht, Guilan Province)

Soil is one of the important parts of ecosystem. Land use change and developed agriculture can lead to soil loss and land degradation because they have damaging effects on soil properties including soil organic carbon, aggregate stability and soil erodibility factor. Soil erodibility factor can be measured by different methods including experimental plots. It shows that the problem should be dealt directly and it demands high amount of cost and time. The factor can be calculated by soil properties such as soil organic matter and particle size distribution. They play a crucial role for sustainable ecosystem and decreased soil erosion. Since a few decades ago, deforestation has caused increased soil degradation and it has had devastative effects on soil surface and subsurface properties. This study investigated soil erodibility factor by different methods in three land uses including forest, grassland, and cropland at two depths in Sidasht of Guilan province. Soil quality index was calculated for evaluation of effects of land use on soil quality degradation.

The study area is located in Tootkabon in Guilan province (latitude 36º 50' 10" N, longitude 49º 39' 15" E). Parent material is limestone and geomorphologic units that are comprised of hill land and plateau. The soil moisture and temperature regimes are xeric and thermic, respectively. In order to reach the goals of the study, samples were collected from three land uses of forest, grassland, and cropland at two depths of 0 to 10 and 10 to 20 cm in regards to parent material, slope class, and equal slope aspect. Soil samples were prepared in two categories: the disturbed soil and the undisturbed ones. After becoming air drying, the disturbed samples were sieved by a 2 mm sieve. Soil properties such as soil texture, bulk density, soil organic carbon, CaCO3, and soil stability were measured. Soil erodibility factor is calculated by nomograph, Vaezi and Ostovari methods. Also sensitivity index and stratification ratio were taken into account. Soil quality index was determined using linear and nonlinear scoring methods based on minimum data set. All soil parameters were tested using one-way analysis of variance and the differences among means were analyzed using Duncan's significant difference test at the probability level of 0.05.

Results showed that the effects of land use and soil depth on bulk density, soil texture and soil erodibility factor using nomograph method were non-significant (p ≤ 0.05). The amount of organic matter, soil structure stability index and soil erodibility factor of Vaezi method were significantly decreased by increasing the depth. MWD and GMD at forest were higher than cropland, and CaCO3 and soil erodibility factor of Ostovari method at forest were lower than cropland. In comparison with other methods, soil erodibility factor of Ostovari method demonstrated that the effect of land use was significant (p ≤ 0.05). Soil properties including bulk density, MWD, organic matter, and soil erodibility factor of Ostovari method were selected as the minimum data set. Results of nonlinear scoring method were better than linear scoring method because the linear scoring method just showed the effects of soil depth on soil quality index (p ≤ 0.05). The soil quality index using linear scoring method was decreased by increasing the depth. However, soil quality index using nonlinear scoring method in forest was higher than cropland, and it was decreased by increasing the soil depth. It was found that non linear scoring methods are superior to linear and soil quality index using the nonlinear scoring method showed better the soil quality among different land uses.

Sensitivity index and stratification ratio values showed that land use change and soil depth effect on soil properties including CaCO3, organic matter, structure stability index and MWD. The stratification ratio values more than 1.5 for organic matter and soil structure stability index can be stated that these properties can show the degradation of soil quality due to land use change. Soil quality evaluation showed that in relation to the effect of land use on soil quality index, nonlinear scoring method is superior to linear scoring, so that forest and agricultural land use had the highest and lowest soil quality index by nonlinear scoring method, respectively. Therefore, due to the high sensitivity of soil quality to land use change, preventing land use change is one of the necessary measures for sustainable soil management in the study area.