Wheat is the third most important cereal crop in Nepal after rice and maize both in area and production, but its productivity of 2.3 tonne ha^-1 is very less compared to other developed countries (6 tonne ha^-1 for Switzerland and China) in the world. The main cause of low wheat yield in Nepal is the improper and inadequate use of fertilizer devoid of site specific nutrient management practices. Therefore, a farmers' field experiment was conducted during November 2015 to April 2016 to rectify the best fertilizer management options at two sites of Damak and Gauradaha in Jhapa district in eastern-Terai of Nepal using Nutrient Expert®-Wheat model. The research was accomplished in Randomized Complete Block Design with 2 treatments and 20 replications, considering farmers' field as replication. Two treatments included in the experimentation were NE® (Nutrient Expert Recommendation) and FFP (Farmer's Fertilizer Practices). The statistical result revealed the highly significant difference in terms of number of effective tiller m^-2, plant height, filled grain per spike, spike length, grain, straw and biological yields and harvest index. The highest yield (4.71 tonne ha^-1) was obtained from NE field than FFP (2.99 tonne ha^-1). On an average, NE based practices produced 58 % higher yield in comparison to FFP. NE based treatment produced significantly higher biomass yield, yield attributes and cost-benefit ratio than FFP treatments. Field experiment validation confirmed that the Nutrient Expert® Wheat model could be used as the most adoptable and practical precision decision support system tool to make a more authentic fertilizer recommendation in eastern-Terai of Nepal.

The government of Nepal has recommended blanket fertilizer application for rice cultivation, which results in lower nutrient use efficiency (NUE) particularly under rainfed conditions. With the aim of finding an appropriate nutrient management practices concerning rice production and profitability, a field experiment was conducted during rainy season of 2017 and 2018 at Kavrepalanchowk and Dang district of Nepal. Altogether, five treatments comprising various nutrient management practices viz. Nutrient Expert Model (NE), use of Leaf Color Chart (LCC), Government Recommended Fertilizer Dose (GON), Farm Yard Manure (FYM), and Farmers’ Field Practice (FFP), were laid out in RCBD with four replications in farmers’ fields. The analysis of variance showed significant difference between treatments for test weight and grain yield in Kavrepalanchowk whereas all traits except number of effective tillers were significant in Dang. The significantly higher grain yield and harvest index were obtained in NE, followed by LCC; and the overall straw yield was highest in LCC, followed by NE in both the locations. Also, yield gap analysis suggested the NE had 44.44% and 23.97% increase in yield as compared to FPP in Kavrepalanchowk and Dang, respectively. The combined analysis with Best Linear Unbiased Estimator revealed the interaction of nutrient management and location significantly effects the straw yield and harvest index across both the locations. The estimated mean straw yield and harvest index were 10.93 t/ha and 34.98%, respectively. Both correlation study and biplot of principal component analysis signaled grain yield had positive correlation with all other traits. Furthermore, the net revenue was maximum for NE, followed by LCC in both the locations. The benefit: cost ratio was highest for NE which was 1.55 in Kavrepalanchowk and 2.61 in Dang. On the basis of these findings, NE and LCC can be effectively used as nutrient management practice by the farmers to obtain maximum production and profitability in Rice.

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.

A field experiment was conducted at farmer field of Sundarbazar, Lamjung during rainy season of 2020 with an objective to determine the dynamics of weed with respect different planting methods and varieties. The experiment was laid out in split plot design with three replications where five Varieties (KabreKodo 1, KabreKodo 2, Dalle 1, Okhale 1 and local) were used as the main plot factor and three planting methods (Direct sowing;10*10cm, Conventional transplanting;10*10 cm with 15 days old seedlings and System of Crop Intensification;25*25 cm with 30 days old seedlings) as sub plot factor. Result showed that among 5 genotypes of weeds based on Importance value index, Cyperusrotundus was the most dominant during the initial stage but after the first weeding, Aegeratumhaustinianum dominated the field. Weed biomass and density was recorded highest in directly sown field than that in transplanted field. Among the transplanted crop, weed infestation was seen higher in SCI than in conventionally transplanted finger millet due to wider spacing. But after the first hand weeding, due to profuse root growth and tillering in SCI, weeds were suppressed and thus weed biomass was observed higher in CT. After one hand weeding, SCI showed higher weed control index, crop resistance index and planting method efficiency index in terms of yield. Weed infestation was observed higher in Dalle 1 among varieties.