Testing the Assumption of Normality for pH and Electrical Conductivity of Substrate Extract Obtained Using the Pour-through Method

Abstract

The pour-through method is a simple and useful technique for on-site monitoring of pH and electrical conductivity (EC) in container nurseries, and has also been used in numerous research studies focused on substrates, plant nutrition, and plant production. Linear models, including the special cases of analysis of variance and linear regression analysis, are often used for statistical analysis of extract data and are readily available as procedures in statistical software packages. Certain assumptions, including normality of the data values or model residuals, are required to develop valid statistical inferences using linear models. This study evaluated the normality of pH and EC variables using data obtained from 100 extract samples collected weekly over 12 weeks using the pour-through method from a uniform containerized substrate (25 pine bark : 18 peatmoss : 7 sand blend amended with calcium sulfate and top-dressed with Polyon 17N–2.1P–9.1K + micros, a 365-day controlled-release fertilizer, at 10 g/container) in 2.8-L containers. Graphical techniques (histograms and QQ plots) and formal goodness-of-fit tests (tests based on the empirical distribution function, moment tests, and the Shapiro-Wilk regression test) were used to demonstrate methods for assessing normality. The variables pH and EC both exhibited relatively normal distributions. For comparative purposes, the transformed variables ln(pH), 10–pH, and ln(EC) were also evaluated. The latter two variables exhibited significant departures from normality, whereas ln(pH) did not. Average weekly EC exhibited positive correlations with time-lagged, average weekly substrate temperature, suggesting that nutrient release from the controlled-release fertilizer could be more dependent on temperature in the second to fourth weeks preceding extraction than on temperature in the week immediately preceding extraction.