Meta-analyses of oil yield in Cuphea PSR23

Abstract

Oil content and composition of Cuphea seed are of special economic value as raw materials for industrial and food applications. The inherent unpredictability in determining and predicting Cuphea’s oil yield is attributed, in part, to the indeterminate growth habit and the persistence of the domestication syndrome of this semi-domesticated potential oilseed crop. Meta-analysis using multivariate statistical modeling, computer simulations, and custom profiling was carried out on a database collated from several studies carried out in growth chamber, greenhouse and field experiments. Meta-analyses identified the importance of, and quantified direct and indirect relationships and tradeoffs between and within functional traits classified within five interrelated plant modules. Several multivariate statistical analyses procedures were employed in predicting oil content and oil yield, as performance measures of Cuphea at the plant and population levels of integration. The most parsimonious partial least squares regression model identified plant-, capsule-, and seed-based traits that can be used in reconstructing the best configuration needed for high agronomic performance at the individual plant and population levels. Variance-based structural equation modeling suggested that the variation in relative growth rate was strongly linked to differences in specific leaf area and leaf mass ratio; both traits expressed large positive direct and indirect effects on oil yield, but not oil content. Results of custom profiling suggested that seed yield, oil% and oil yield can be optimized by trait adjustments within the phenotypic and metabolic modules. Adjustments to thousand-seed weight and protein content would influence seed yield, oil yield and oil%, in a decreasing order. Improvements in eco-physiological traits, nutrient ratios and structural traits would lead to a slightly higher oil% and eventually higher oil yield.