Abstract
Estimates of phenotypic correlations and the direct and indirect effects of grain yield and protein content components on grain and protein yields per plant were studied as source of information on eight soybean populations. Four BC1F4 and four F4 populations from crosses of high protein content materials and commercial varieties were studied. Protein yield and grain yield per plant followed an analogous tendency. The CD201-BC population presented positive correlations of protein content with protein and grain yields. All yield components presented positive direct effects for both grain and protei n yields. The number of pods per plant, amongst the yield components, presented the largest direct effects on grain and protein yields, except in the OC13-CR population. The direct effect of the protein content on protein yield was positive, yet small. Gr ain yield per plant was the main determining factor for protein yield per plant.
Highlights
Plant breeding demands continuous phenotypic evaluation of the genetic material that is being generated by the breeding program in measurements of several variables that characterize the individuals
Results of correlations obtained in the present work and the bibliographical citations both refer to the phenotypic correlation coefficients, unless otherwise stated
The correlation estimates between the grain yield components were variable among the populations, presenting a tendency of the number of pods per plant and of the number of seeds per pod to correlate negatively with the weight of one hundred seeds
Summary
Plant breeding demands continuous phenotypic evaluation of the genetic material that is being generated by the breeding program in measurements of several variables that characterize the individuals. Knowledge about correlation coefficients is a basic requirement to quantify the magnitude and the direction of the influences of certain traits on others, or when several traits are to be improved simultaneously (Cruz and Regazzi 1997). According to Wright (1921), the ideal scientific method is the investigation of the direct effect of one condition on another in experiments free from any other possible variation causes. One often has to deal with a group of traits or conditions correlated in a complex of interacting, uncontrollable, and often obscure causes. The correlation degree between two variables can be calculated by the common methods, but it merely presents the result of all connecting paths of influence
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
