Abstract
The assessment of soil attributes affected by land use changes or different cultivation management strategies is commonly based on a comparison between agricultural fields, neglecting the natural soil spatial variability. This study aimed to develop a methodology based on improved space series to differentiate between spatial variability of soil attributes and the effect of tillage direction when the evaluation is based on comparison between adjacent fields. The study area consists of two adjacent fields of different tillage directions, i.e. up-down tillage (UDT) and contour tillage (COT). Soil sampling was performed at 40 points in each filed at 5 m intervals along a contour line at the mid-slope position. All measured soil attributes, i.e. sand, silt, clay, MWD, GMD, bulk density; SP, CCE, OM, of UDT were significantly (P<0.05) different from those of COT compared by independent sample T test. This analysis could not differentiate between the spatial variability of the soil and the changes induced by tillage. To determine the net effect of UDT on soil attributes, we (i) performed space series analysis on COT data, (ii) used autoregressive, moving average and autoregressive-moving average models to model the space series data on COT field, and (iii) used the best model obtained for each soil attributes on COT to forecast the value of the property in ten adjacent points in the UDT field. Comparison between the forecasted and measured data in UDT showed that the evaluation of tillage direction effect on soil attributes based on comparison between adjacent fields can be over or under estimated when the sampling coordinates and the spatial correlation among adjacent observations of data are ignored. The methodology used was able to differentiate between natural and management induced differences of soil attributes. Overall, the use of this methodology will improve the prediction and understanding of the effects of different cultivation practices on soil quality.
Evaluating the tillage management direction effects on soil attributes by space series analysis (case study: a semiarid region in Iran)
