Assessment of Yield and Yield Attributing Characters of Hybrid Maize using Nutrient Expert® Maize Model in Eastern Terai of Nepal

Indiscriminate use of fertilizer and lack of site specific nutrient management technology is the main cause of low maize productivity in Nepal. Thus, field experiments on farmer\u27s field were conducted on maize to assess the productivity at two sites of Jhapa district viz. Damak and Gauradaha using Nutrient Expert® Maize model from November 2015 to May 2016. The experiment was laid out in Randomized Completely Block Design consisting two treatments viz. NE (Nutrient Expert recommendation) and FFP (Farmer\u27s Fertilizer Practice) with twenty replications. The result revealed significant differences in terms of grain yield, stover yield, biological yield, and yield attributing characters. NE based practices produced higher grain yield (9.22 t ha-1), which was 86.6 percent higher than FFP (4.94 t ha-1). Similarly, higher average cob number m 2 (8.2), average kernel rows cob-1 (14.2), average kernels number row-1 (589.9) and test weight (361.4 g) were recorded in NE based practice. Thus, NE based practice can be adopted for obtaining higher productivity in eastern terai region of Nepal

Factors Affecting the Productivity of Coffee in Gulmi and Arghakhanchi Districts of Nepal

Coffee is one of the major potential cash crops with lucrative export value grown in mid-hills of Nepal. Nepalese coffee production has suffered long by low productivity. Research was conducted from February to May, 2019 to analyze the factors affecting the productivity of coffee in Arghakhanchi and Gulmi districts of Nepal. These two districts were, purposively selected for this study taking account of comparative advantage and past studies recommendations for coffee sector. Altogether, 100 coffee growing households 50 from each, Arghakhanchi and Gulmi, were sampled by using multistage sampling technique. A pre-tested semi-structured interview schedule was used to collect the primary information while secondary information was collected reviewing the relevant publications. Ordinary Least Square (OLS) regression model was used to determine the factors affecting the productivity of coffee. The study revealed that the number of active family members involved in coffee production (0.000), adoption of income diversification through intercropping (0.005), training (0.072) and technical assistance (0.021) had positive and significant effect on coffee productivity. Encouraging the household to have coffee production as their primary occupation, providing technical assistance on rational land utilization and intercropping and strengthening the skill and knowledge of farmers through trainings could significantly support in increasing the productivity of coffee