Correlation, path-coefficient and principal component analysis association among quantitative traits in strawberry to unlock potential of vertical farming system

Abstract

The study explores the complex relationship between quantitative and qualitative characteristics in strawberries grown using a vertical farming system and artificial full spectrum light (AFSL). The research reveals the interdependencies between traits, identifies direct, and indirect effects on yield and quality, and highlights critical factors influencing overall variation in strawberry characteristics using correlation analysis, path-coefficient analysis, and principal component analysis (PCA). These discoveries open the door for specialized breeding and cultivation techniques that optimize vertical farming procedures and increase the sustainability and productivity of strawberries. This study confirmed the suitability of a four-layered vertical farming system for strawberry cultivation; however, an additional supply of AFSL at lower levels of verticals ensures a higher yield. The number of fruits per plant and average berry weight had a high (p_ij > 0.3) degree of association with yield per plant at genotypic and phenotypic levels. The principal component analysis revealed a close association of T₁ (top level of vertical with natural light only), T₂ (third level from the top of vertical with natural light only), and T₅ (third level from the top of vertical with natural light and AFSL for 2 h) with yield contributing traits (number of bud formation, number of flowers, number of fruits per plant, fruit setting (%), fruit volume, average berry weight, average yield, and estimated yield per hectare).