Multivariate approach to evaluate the penetrometer resistance in different tillage systems

At present, the penetrometer is the most widely used instrument for assessing in situ soil strength, one of the extrinsic factors affecting plant growth and crop productivity. In this paper we propose a method that discriminates penetrometer resistance due to different soil treatments, by means of Principal Component (PC) analysis. We hypothesized and demonstrated that penetrometer resistance values measured at different soil depths are correlated among themselves (multicollinearity). Considering measurements at each depth as different variables, PC analysis restructured data sets containing these correlated variables into a smaller number of components, whose scores were utilized in univariate analysis of variance (ANOVA) to test differences among imposed soil treatments. We applied the procedure to penetrometer resistance values measured by means of a hand-held cone penetrometer in two long-term experiments conducted in southern Italy, on durum wheat (Triticum durum Desf.) under continuous cropping. In the first trial, four different soil tillage treatments were compared; in the second, two different tillages and two residue management systems were examined. In both trials, PC analysis reduced the original 14 depths of measurements into only 4 PC's, based on correlations of their resistance values, explaining more than 80% of the total variation. Furthermore, ANOVA applied to the scores of each PC, clearly indicated treatment effects on soil strength.

The proposed method has thus allowed assemblage a posteriori of penetration resistance data into only a few significative intervals, using correlations among the measurements made at the different depths. This way, the possible resistance differences due to tillage and/or management treatments have been more easily and more unambiguously showed.