Predicting the soil fragmentation caused by grazing using adaptive neuro-fuzzy inference system (ANFIS)

The most current way for measuring the soil fragmentation is determination of mean weight diameter (MWD). In this study, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict of range soil fragmentation affected by different grazing intensities, distance from village and sampling depth. Present study conducted at 2015 in 3 adjacent rural areas (Alvars, Aldashin and Asbe marz) in Darvishchai watershed in Ardabil County. The studied parameters on the soil fragmentation including different grazing intensities in 3 levels (low, medium and high intensity), distance from village in 3 levels (200, 400 and 600 meters) and the soil sampling depths in 2 levels (0-15cm and 15-30cm). Obtained data were transferred to MATLAB software for the development of ANFIS models. For evaluating the models operation, mean squares error (MSE) and correlation (R2) were used. The result of best ANFIS model in prediction of soil fragmentation was compared with results of regression model. The results show that different grazing intensities, distance from village, sampling depth and their combinations had significant effect on the soil fragmentation. Increase of grazing intensity resulted in increment of soil fragmentation. With increment the distance from village from 200 to 400 meters, soil fragmentation decreased but with increment of distance, increased. Soil fragmentation in all conditions was higher at depth of 0-15 cm than depth of 15-30 cm. ANFIS model had more precision in prediction of soil fragmentation (R2=0.96) relative to regression model (R2=0.76).