International Journal of Horticulture, 2025, Vol.15, No.6, 299-311 http://hortherbpublisher.com/index.php/ijh 308 Table 8 Ranking of mixed agricultural drawback top of form bottom of form Drawbacks Importance rating sale Weightage Score Index Rank 1 0.8 0.6 0.4 0.2 1 2 3 4 5 Exhaustive nutrient 52 35 3 0 0 90 81.8 0.90 I Shading effect 36 47 5 0 2 90 77 0.85 II Pest and Disease 0 2 57 15 16 90 45 0.50 III Water resource stress 2 1 20 66 1 90 41.4 0.46 IV More labour requirement for harvesting 0 5 5 9 71 90 24.8 0.27 V 3.8 Ranking of marketing problems The ranking for marketing problems provided a thorough assessment of the challenges encountered in agricultural marketing, focusing on their severity (Table 9). The most severe issue identified was "seasonal price variation", which emerged as a critical concern due to its substantial impact on farmers’ profitability and market stability, reflected in its high index of 0.99. Table 9 Ranking of marketing problems Marketing problem Importance rating scale Weightage Score Index Rank 1 0.8 0.6 0.4 0.2 1 2 3 4 5 Seasonal price variation 89 1 0 0 0 90 89.8 0.99 I Monopoly in trade 0 0 78 10 2 90 72.2 0.80 II Distant market 0 0 78 10 2 90 51.2 0.56 III Inadequate market infrastructure 0 0 3 52 35 90 29.6 0.32 IV Postharvest loss 0 0 9 28 53 90 27.2 0.30 V Following this, "monopoly in trade" represented a significant challenge, as market control by a few entities could undermine competition and adversely affect pricing structures. The third most severe issue was "distant market", which posed a notable challenge for farmers trying to access larger or more lucrative markets due to geographical barriers. The fourth issue, "inadequate market infrastructure", was acknowledged as a relevant problem, though its impact was deemed less severe compared to the top three challenges, indicating a clear need for improvements in logistics and market access. Lastly, "postharvest loss" ranked as the least severe problem, highlighting the ongoing issue of food waste after harvest, but its impact was recognized as minimal compared to the other marketing challenges. Overall, the analysis underscored the importance of addressing seasonal price fluctuations and monopolistic practices while also acknowledging the need for enhanced market access and infrastructure to mitigate postharvest losses. 3.9 Regression analysis based on solo cropping and mixed cropping costs The regression analysis on solo cropping costs revealed significant findings regarding various cost factors (Table 10). The estimated coefficient for chemical fertilizer cost was 0.334, indicating a strong positive relationship with the dependent variable, supported by a t-value of 4.61 and a p-value of 0.000, which highlighted its statistical significance. Similarly, potato tuber cost also showed a significant positive effect, with an estimated coefficient of 0.467, a t-value of 4.31, and a p-value of 0.000. In contrast, other variables such as tillage cost, organic manure cost, irrigation cost, pesticide cost, and labour cost exhibited no significant impact, as indicated by their higher p-values. The overall model was robust, as evidenced by an F-value of 40.95 and an R2 of 0.777, suggesting that approximately 77.7% of the variability in solo cropping costs was explained by the included variables, with an adjusted R2 of 0.758 indicating a good fit of the model. The regression analysis on mixed cropping costs yielded several significant insights (Table 11). Notably, the estimated coefficient for mixed chemical fertilizer cost was
RkJQdWJsaXNoZXIy MjQ4ODYzNA==