Precision Nutrient Management Techniques to Bridge Maize and Rice Yield Gaps in Tikapur, Nepal

Purpose: The main objective was to evaluate the effectiveness of site-specific nutrient management (SSNM) practices to enhance productivity and reduce yield gaps in spring maize and rice in the Rani Jamara Kulariya Irrigation Project Command Area (RJKIPCA), Kailali, Nepal. Additionally, it sought to validate the NE®-Hybrid maize and rice models for predicting yields and profitability compared to traditional farmers’ fertilizer practices (FFP).
Research Method: Nutrient Expert ®-Hybrid maize and Rice models embedded the household survey form of the NE-based questionnaire and the soil analysis of the farmers’ field, the fertilizer rates and application timings were formulated by Nutrient Expert® Rice and maize before plantation. The sowing of spring maize for various SSNM practices at 42 fields (18 from both Tikapur and Janaki and 6 for Lamkichuha)] and Nutrient Omission Plot Technique (NOPT) trials at 21 fields in maize 9 from both Tikapur and Janaki and 3 from Lamkichuha. The nursery raising and transplanting of spring rice using SSNM approaches in 6 farmers field and NOPT approaches in three farmers field of Tikapur-7, Janaki-4 and Lamkichuha-7 was accomplished with a Randomized Block Design using diamond trials and farmers’ fields as replications. R software was used for data analysis, including yield gap analysis and ANOVA.
Findings and Values: The results show a small variation (10-15%) between both observed grain production and farmers’ income and the NE® model predictions, which supports the validity of the rice and maize models. Results show that majority of soil at the experiment site was deficient in N and K. Absence of P resulted in the least yield loss for both spring maize and rice. Farmer’s fertilizer practices (FFP) led to declining yields, particularly among hybrid maize growers. The soils in Janaki and Tikapur were more likely to N and K deficiencies than those in Lamkichuha. The NE®-Hybrid maize and rice models effectively predicted yields and profitability, outperforming traditional farmer practices.