MORPHO-PRODUCTIVE AND NUTRITIONAL EVALUATION OF GREEN LEAVES IN FODDER BEET LINES OBTAINED THROUGH SELECTION PUBLISHED
Christianna-Maria ISTRATE-SCHILLER, M.N. HORABLAGA, Cristian BOSTAN, Luminița COJOCARIU University of Life Sciences „King Mihai I” from Timisoara luminitacojocariu@yahoo.comFodder beet (Beta vulgaris L. var. crassa) is a crop of great agricultural importance due to its ability to produce a significant amount of biomass with high nutritional value, widely used in animal feeding. The green leaves, often underestimated compared to the roots, represent a valuable source of crude protein, minerals, and bioactive pigments, contributing to the balance of forage rations. The aim of this study was to evaluate the morpho-productive performance and nutritional value of green leaves in four experimental fodder beet lines compared to the control cultivar C6/24, in order to identify genotypes with superior yield potential and forage quality. The experiment was conducted in 2024 at the Agricultural Research and Development Station (ARDS) Lovrin. The analyzed parameters included green leaf weight, number of leaves, dry matter content (%), and crude protein (%). The results showed values ranging from 12.98–16.17% for dry matter and 3.24–3.91% for crude protein, with superior performance recorded in lines V104/4/24 and V105/1/24. Green leaf weight varied between 0.73 and 1.33 kg, and the average number of leaves ranged from 36 to 84, confirming significant biological variability among genotypes. Correlation analysis revealed a strong association between the number and weight of green leaves (r = 0.82) and a positive correlation between dry matter and crude protein (r = 0.70). Principal Component Analysis (PCA) explained 88.3% of the total variability, highlighting the clear separation of lines V104/4/24 and V105/1/24 from the control. These findings confirm the potential of these genotypes for improving the productivity and nutritional value of fodder beet
fodder beet, green leaves, nutritional value, morpho-productive traits, multivariate analysis
agronomy
Presentation: poster
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