International Journal of Horticulture, 2025, Vol.15, No.4, 201-207 http://hortherbpublisher.com/index.php/ijh 205 al., 2021). Interestingly, secondary and higher education did not show statistically significant differences in yield, suggesting that targeted agricultural training may be more impactful than formal higher education in this context. Similar trends were observed by Gayak et al. (2020) in Mustang District, where practical knowledge and training influenced production outcomes more than academic qualifications. 4.2 Role of tree age in apple yield Tree age was found to have a profound impact on apple productivity, with trees older than 15 years producing the highest yields. This is consistent with findings by (Dhakal et al. 2016), who reported that mature apple trees in Nepal’s hill regions yield more fruit, given appropriate management. However, productivity in older trees can decline if pruning and soil fertility are not maintained, emphasizing the importance of rejuvenation practices. Bajgain et al. (2024) further highlighted that accurate knowledge of orchard planting ages is vital for predicting yield and making effective orchard management decisions. 4.3 Effects of varietal selection and planting methods The adoption of modern apple varieties such as Gala, particularly when planted using high-density planting (HDP) techniques, was associated with significantly higher yields. This finding is in line with international research showing that HDP systems using dwarfing rootstocks and spur-type cultivars enhance productivity through improved canopy management and efficient resource utilization (Thakur et al., 2024). Moreover, such systems are suitable for areas like Jumla, where land availability is limited, and can offer farmers quicker returns on investment due to earlier fruiting. Diversification into varieties such as Gala and Fuji also helps reduce the risks associated with monoculture farming. Studies have noted that varietal diversification improves resilience to climate variability and market fluctuations (Shrestha et al., 2020). This is particularly relevant in Jumla, where traditional varieties dominate and may not perform uniformly across changing microclimatic conditions. 4.4 Geographical variation and environmental influence Geographic differences across municipalities, especially the high productivity in Sinja and Kanakasundari, suggest that local agro-climatic conditions and management practices vary substantially within Jumla. These differences highlight the need for location-specific extension strategies, soil and climate analysis, and varietal recommendations tailored to each municipality (Dahal and Karki, 2018). Areas with lower productivity, like Chandannath, may benefit from increased technical support and training to close the yield gap. 4.5 Limitations of the study Despite its valuable insights, this study has several limitations. First, the sample size was limited to 150 farming households, which may not fully capture the diversity of apple farming practices in Jumla. Second, certain variables, such as varietal diversification and HDP adoption were not uniformly distributed across the sample, limiting the generalizability of those specific findings. Third, the analysis relied primarily on descriptive statistics and chi-square tests, without incorporating more advanced statistical models that could examine interaction effects between variables (e.g., between education level and planting method). Furthermore, the study used cross-sectional data, which does not allow for causal inference or temporal assessment of changes in orchard productivity. These constraints may limit the robustness of some conclusions, especially regarding long-term yield trends and the effectiveness of newer techniques such as HDP in different micro-environments. 4.6 Recommendations for future research Future studies should aim to overcome these limitations by employing larger and more representative samples across different ecological zones. The adoption of multivariate statistical techniques—such as linear regression, logistic regression, or multilevel modeling—would allow for a more nuanced analysis of how factors such as education, varietal selection, and orchard management interact to influence productivity. Additionally,
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