Implications of the variability in soil penetration resistance for statistical analysis

Abstract

SUMMARYSoil penetration resistance (PR) is a measure of soil compaction closely related to soil structure and plant growth. However, the variability in PR hampers the statistical analyses. This study aimed to evaluate the variability of soil PR on the efficiency of parametric and nonparametric analyses in indentifying significant effects of soil compaction and to classify the coefficient of variation of PR into low, medium, high and very high. On six dates, the PR of a typical dystrophic Red Ultisol under continuous no-tillage for 16 years was measured. Three tillage and/ or traffic conditions were established with the application of: (i) no chiseling or additional traffic, (ii) additional compaction, and (iii) chiseling. On each date, the nineteen PR data (measured at every 1.5 cm to a depth of 28.5 cm) were grouped in layers with different thickness. In each layer, the treatment effects were evaluated by variance (ANOVA) and Kruskal-Wallis analyses in a completely randomized design, and the coefficients of variation of all analyses were classified (low, intermediate, high and very high). The ANOVA performed better in discriminating the compaction effects, but the rejection rate of null hypothesis decreased from 100 to 80 % when the coefficient of variation increased from 15 to 26 %.

Estimating Sample Size of Soil Cone Index Profiles by Bootstrapping

“…These results showed that surface layers are more variable and that due to the skewness of CI data, the population mean of CI values in the surface layers would not be adequately estimated by the sample mean. Similar results were reported by Gubiani et al (2011), who concluded that the sample mean represents CI values in compacted better than in chiseled soils, due to the effect of tillage on soil structure. In this case, data transformation should be applied to meet the assumptions, or a more robust estimator, such as the sample median, should be used instead of the sample mean.…”

“…The former study reveals the soil-specific nature of the sample size recommendations, thus the greater the soil variability, the higher are the sampling requirements. The latter article describes the smoothing effect of sample support or data processing on the variability (Gubiani et al, 2011), resulting in a smaller RE for the same sample size.…”

“…On the other hand, the skewed distributions commonly observed in soil properties may be corrected by applying transformations such as the logarithmic, and then analyzed by using parametric methods, provided the assumptions are met (Sokal and Rolhf, 2012). Alternatively, data may be analyzed on the original scale by using robust estimators such as the sample median, which is less affected by departures from normality (Gubiani et al, 2011). However, the lack of an analytical method to assess the precision of a sample median limits the estimation of minimum sampling for this estimator (Efron and Tibshirani, 1993 Molin et al (2012) compared three penetrometers and studied the CI variation through the profile in depth intervals.…”

Modeling and Correlation of Soil Cone Index for Bulk Density, Moisture Content and Penetration Depth Levels in a Sandy Loam Soil

“…It can be done relatively quickly and easily, and can provide valuable data about soil conditions. Different types of penetrometers have been developed to measure soil penetrability that operates on static or dynamic principles (Lowery and Morrison 2002).The scientific literature contains a considerable number of studies which examine the dependence of soil strength on factors such as soil bulk density (BD), soil texture and soil moisture content (MC) (Wells and Treesuwan, 1978;Busscher, 1990;Mielke et al, 1994;Materechera and Mloza-Banda, 1997;Busscher et al, 2000;To and Kay, 2005;Dexter et al, 2007;Gubiani et al, 2011;Sun et al, 2012;Lina et al, 2014). Most of these studies have been carried out on idealized soils or on remolded soil samples.…”

“…Moreover, Ayers and Perumpral (1982) proposed CI as a polynomial function of BD and MC. Besides, Canarache (1990) proposed an exponential model relating BD and MC.Meanwhile, Gubiani et al (2011) developed a linear model in which CI was considered to be the dependent variable, with MC, BD and depth being the independent variables. Elbanna and Witney (1987) developed an empirical equation based on the theory of the bearing capacity of the soil under a continuous footing, in which CIwas a function of the type of soil, cohesion, angle of internal friction and MC.…”