International Journal of Horticulture, 2025, Vol.15, No.4, 143-161 http://hortherbpublisher.com/index.php/ijh 146 The cells were filled halfway with the respective growing media, followed by seed placement, and then covered with the remaining media. Each treatment unit received 20 seeds, and five seedlings were randomly selected from each for observation. The trays were arranged randomly inside the plastic tunnel as per the CRD layout. Light irrigation was applied based on the moisture condition of the growing media to ensure proper germination and uniform seedling development throughout the experimental period. 2.3 Observation and data collection Throughout the experiment, various germination and growth parameters were systematically recorded. Germination was monitored daily for seven days following showing to assess germination characteristics and trends. The number of germinated seeds was counted continuously during this period to evaluate germination performance. For growth analysis, five seedlings were randomly selected from each replication at 12, 18, 24, and 30 days after sowing (DAS). These selected seedlings were carefully uprooted and examined to measure key growth parameters. This approach enabled a detailed assessment of seedling development over time, providing valuable insights into their growth dynamics under different growing media conditions. 2.4 Germination parameters This study aimed to evaluate essential germination parameters, including germination percentage, germination energy, germination speed, vigor index, and the germination rate index. Germination percentage represents the proportion of seeds that successfully sprout under optimal conditions, serving as an indicator of seed viability. Germination energy measures the percentage of seeds that germinate within a specific period, providing insight into seedling uniformity and early vigor. Germination speed reflects the rate at which seeds emerge over time, emphasizing differences in efficiency. The vigor index integrates various seed traits that influence seedling development, making it a critical measure of seedling strength. Meanwhile, the germination rate index offers a cumulative assessment of germination speed by considering the number of seeds germinated at different time points. These parameters were determined using the formulas established by Mehata et al. (2023), ensuring a methodical and comprehensive analysis of seed germination behavior. This systematic approach enabled a thorough examination of seed performance across treatments, enhancing the understanding of seedling establishment and initial growth. (1) 퐺푒푟푚푖� 푖 � 푃푒푟푐푒� 푒 퐺% =푁푄 푢푢 푚 � 푒푖 푟 푠 푠푒 푒푒 푒 푠 푠푝 푙 ℎ � 푒푠 푝 푟푖 � 푢 푒 푙 × 100 (2) 퐺푒푟푚푖� 푖 � 푆푝푒푒 퐺푆 = 퐶 푢� 푠푒푒 푠 ℎ 푠푝푟 푢 푒 푤푖 ℎ푖� 72ℎ 푢푟푠 푄푢 � 푖 푠푒푒 푠 ℎ 푠푝푟 푢 푒 푤푖 ℎ푖�168ℎ 푢푟푠×100 (3) 퐺푒푟푚푖� 푖 � 퐸�푒푟 퐺퐸 =푝푟 푝 푟 푖 � 푠푒푒 푠 ℎ 푠푝푟 푢 푒 푤푖 ℎ푖� 72ℎ 푢푟푠 (4) 푆푒푒 푉푖 푟 퐼� 푒 푉퐼 =퐺푒푟푚푖� 푖 � 푝푒푟푐푒� 푒 %× 푆푒푒 푙푖� 푙푒� ℎ (푐푚) 2.5 Vegetative growth metrics The evaluation of capsicum seedling growth parameters involved precise measurements of root and shoot lengths using a graduated scale. At 25, 30, 35, and 40 days after sowing (DAS), five randomly selected seedlings were carefully uprooted, and their root and shoot lengths were recorded. Additionally, an electronic weighing machine was employed to determine the fresh weight of these seedlings. To assess dry weight, a separate set of five seedlings was subjected to an air-drying process. This integrative methodology facilitated a comprehensive analysis of capsicum seedling development, capturing both morphological dimensions and biomass accumulation across distinct growth stages. 2.6 Statistical analysis The collected raw data was systematically arranged in chronological sequence for both treatment and replication blocks using MS Excel 2021 (Microsoft Corporation, Washington, USA). Subsequent statistical analysis was performed in R Studio (Version 4.2.2, Boston, Massachusetts, USA) by conducting ANOVA. Mean comparisons among different treatments were carried out using Duncan’s Multiple Range Test (DMRT) at a 5% significance level. Furthermore, R Studio facilitated an in-depth examination of interactive effects between treatments and
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