Medicinal Plant Research 2024, Vol.14, No.2, 97-106 http://hortherbpublisher.com/index.php/mpr 99 3 Traditional Breeding Methods 3.1 Selection of superior genotypes The selection of superior genotypes in Lonicera japonica involves identifying varieties that exhibit high yield and enhanced medicinal properties. Key criteria include the concentration of bioactive compounds, resistance to environmental stressors, and overall plant vigor. For instance, research has shown that certain varieties of Lonicera japonica, such as ‘Yujin2’, have been bred for their strong aroma and high content of bioactive compounds like terpenoids and phenolic acids, which are crucial for their medicinal efficacy (Li et al., 2022). Additionally, the ability to withstand salt stress while maintaining high levels of bioactive constituents is another important criterion, as demonstrated by studies on the impact of salt stress on the quality of Lonicera japonica (Cai et al., 2021). Once superior genotypes are identified, effective propagation techniques are essential to maintain and multiply these desirable traits. Traditional methods such as cuttings and layering are commonly used. For example, propagation through cuttings ensures that the genetic makeup of high-yielding varieties is preserved, allowing for consistent medicinal quality across generations. Advanced techniques like tissue culture can also be employed to propagate selected genotypes rapidly and in large quantities, ensuring a steady supply of high-quality medicinal plants. 3.2 Crossbreeding strategies Crossbreeding strategies aim to combine desirable traits from different parent plants to produce hybrids with superior medicinal properties. Hybridization efforts focus on enhancing the content of bioactive compounds such as terpenoids, phenolic acids, and flavonoids. For instance, integrating volatile metabolomic and transcriptomic analyses has identified key biosynthetic pathways and genes involved in the production of these compounds, which can be targeted in hybridization programs to develop varieties with enhanced medicinal properties (Li et al., 2022). Evaluating hybrid vigor, or heterosis, is crucial in determining the success of crossbreeding efforts. This involves assessing the growth, yield, and medicinal quality of hybrid plants compared to their parent varieties. Studies have shown that hybrids can exhibit improved resistance to environmental stressors and higher concentrations of bioactive compounds, making them more effective for medicinal use. For example, hybrids developed through polyploidy breeding have shown significant improvements in both yield and quality of Lonicera japonica. 3.3 Field trials and performance evaluation Field trials are essential for evaluating the performance of selected and hybrid genotypes under real-world conditions. Experimental plots should be established in diverse environments to assess the adaptability and stability of the plants. Proper plot design, including randomized block designs, ensures that the data collected is statistically valid and reliable. These trials help in understanding how different genotypes perform in terms of growth, yield, and medicinal quality under various environmental conditions (Li et al., 2023). The analysis of growth and medicinal quality in field trials involves measuring various parameters such as plant height, biomass, and the concentration of bioactive compounds. Advanced analytical techniques like UFLC-QTRAP-MS/MS can be used to quantify the levels of multiple bioactive constituents, providing a comprehensive assessment of the medicinal quality of the plants. Additionally, statistical analyses such as partial least squares discrimination analysis and gray relational analysis can be employed to systematically evaluate and distinguish the performance of different genotypes, ensuring that the best-performing varieties are selected for further development (Cai et al., 2021). 4 Advanced Breeding Techniques 4.1 Genomic selection and its application Genomic selection (GS) represents a significant advancement in plant breeding, leveraging the power of high-density DNA markers to predict the performance of breeding lines. Unlike traditional marker-assisted selection, which focuses on identifying individual loci associated with traits, GS uses all available marker data to
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