Sequential Path Analysis of Yield Components in Potato

Abstract

In order to determine proper selection criteria for tuber yield in potato clones obtained from true potato seed (TPS) and to evaluate relationships between tuber yield and its components, an investigation was conducted using the five check cultivars Agria, Marfona, Draga, Agata, and Arinda, and 120 potato clones obtained from TPS. The study was conducted in an augmented randomized complete block design with three replications. Yield and its components were recorded. Correlation coefficient analysis showed that tuber yield had significant positive associations with number of tubers per plant, average weight per tuber (tuber size), plant height, diameter of main stem, and number of main and secondary stems per plant, whereas its correlation with tuber dry-matter concentration was significantly negative. Number of tubers per plant, tuber size, and plant height had significant standardized partial regression coefficients with very small tolerance and large variance inflation factor (VIF) values. These were considered as the first-order variables in sequential path analysis.

Interrelationships between seed yield and 20 related traits of 49 canola (Brassica napus L.) genotypes in non-stressed and water-stressed environments

“…On the other hand, the basic assumption while carrying out multiple regression is that the traits used as predictor variables are independent of each other, In reality yield-related traits are intricately interrelated, often leading to high multicollinearity. Thus a novel approach of organizing the variables into a different order path, based on trait relationships indicated by earlier studies, was adopted and used in different crops by Samonte et al (1998), Mohammadi et al (2003) and Asghari-Zakaria et al (2007). Sadaqat et al (2003) reached similar inferences regarding the correlation between HI and NPB but the other traits did not show significant correlations.…”

“…However, collinearity of predictor variables was not tested before organization of the variables into different path orders. Recently sequential path analysis was used in maize (Zea mays L.) by Mohammadi et al (2003) and in potato (Solanum tubersum L.) by Asghari-Zakaria et al (2007). Although, there are several reports on correlation and path coeff icient analysis in canola (Degenhart and Kondra, 1984;Engqvist and Becker, 1993;Ozer et al, 1999;Ali et al, 2003;Khan et al, 2006Khan et al, , 2008Ivanovska et al, 2007;Marjanovic-Jeromela et al, 2007;Basalma, 2008), detailed cause and effect relationships using sequential path analysis have not been examined in canola.…”

Sequential Path Model for Grain Yield in Soybean

“…A path coefficient is a standardized partial regression coefficient, and measures the direct influence of a predictor variable on the response variable (Mohammadi et al, 2003). This method has been studied in soybean (Akhter and Sneller, 1996;Ball et al, 2001;Barbaro et al, 2006;Kau and Modhova, 1972;Santos et al, 1995), rice (Kumar et al, 1999), green gram (Singh and Singh, 1973), corn (Mohammadi et al, 2003) and potato (Asghari-zakaria et al, 2007). Scientists in path analysis, consider the predictor characters as first-order variables to analyze their effects over a dependent variable such as yield (Kumar et al, 1999;Mohammadi et al, 2003).…”

Does using stepwise variable selection to build sequential path analysis models make sense?

“…This paper aims to point out a methodological flaw that can be found in some recent causal studies based on path analysis (Asghari‐Zakaria et al 2007, Feyzian et al 2009, Mohammadi et al 2003, Nemati et al 2009, Rabiei et al 2004, Sabaghnia et al 2010). In these analyses, which are all based on sequential path models, the sequentiality does not refer to directions of causal associations among traits (as discussed earlier), although this is how the sequentiality is normally understood and assumed in the methods of causal inference (Pearl 2000, Shipley 2002).…”