Productivity and Profitability Assessment of Drought Tolerant Rice Cultivars under Different Crop Management Practices in Central Terai of Nepal

Reduction in productivity has led to lower profitability of rice production in Nepal. Proper selections of resource conservation technologies and drought tolerant cultivars are being potential strategies determining productivity of rice in drought prone areas. Thus, a field experiment was accomplished in central-terai of Nepal during 2014 to assess the productivity and profitability of drought tolerant rice cultivars under different crop management practices. The experiment was carried out in strip-plot design with three replications consisting four drought tolerant rice cultivars and three crop management practices. The analyzed data revealed that SRI (System of Rice Intensification) produced significantly higher grain yield (5.28 t ha-1) than other management practices. The straw yield of SRI (5.12 t ha-1) was also significantly higher than other management practices. The cultivars had no influence on grain yield, but the straw yield was significantly influenced by cultivars, with the highest straw yield in Sukkha-3 (5.21 t ha-1). Similarly, SRI management practice also had significantly higher gross returns (NRs. 144652 ha-1), net return (NRs. 56647 ha-1) and B:C ratio (1.64:1). Thus, SRI management practice can be adopted as adaptation approach for obtaining higher productivity and profitability in central terai and similar agro-climatic regions of Nepal

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

Simulation of Growth and Yield of Rainfed Maize Under Varied Agronomic Management and Changing Climatic Scenario in Nawalparasi, Nepal

Correction: Figure 3 was corrupted and so the PDF was replaced on 29th December 2016 with the corrected Figure 3.A field experiment and simulation modeling study in combination for different maize cultivars planted at different sowing dates were accomplished at Kawasoti-5, Nawalparasi during spring season of 2013 to assess the impact of climate change scenario as predicted by IPCC in rainfed spring maize by using CSM-CERES-Maize model. Result showed that RML-4/RML-17 produced higher kernel rows/ ear (13.77), kernel per row (30.42) and test weight (244.9 g). Significantly higher grain yield was also found for RML-4/RML-17 (6.03 t/ha) compared to Poshilo makai-1 (4.73 t/ha), Arun-2 (3.55 t/ha) and Local (2.92 t/ha). Earlier sowing date (7th April) actually produced higher kernel/row (27.97), kernel rows/ear (12.89) and 1000 grain weight (230 g). Significantly higher grain yield (5.13t/ha) was obtained in earlier sowing date (7th April). The CSM-CERES-Maize model was calibrated and found well validated with days to anthesis (RMSE= 0.426 day and D-index= 0.998), days to physiological maturity (RMSE=0.674 day and D-index= 0.999), number of grain/m2 at maturity (RMSE= 85.287 grain /m2 and D-index= 0.993), unit weight at maturity (RMSE=0.012 g/kernel and D-index= 0.854) and grain yield (RMSE=54.94 kg/ha and D-index= 1.00). The model was found sensitive to climate change parameters. The sensitivity for various climate change parameter indicated that there was severely decreased trend in simulated rainfed spring maize yield with the increment of maximum and minimum temperature, decrease in solar radiation and decrease carbondioxide concentration. Even 2°C rise in temperature can decrease around 15-20% yield of spring maize and this negative effect was even more pronounced in hybrid than other cultivars.Journal of Maize Research and Development (2015) 1(1):123-133DOI: http://dx.doi.org/10.5281/zenodo.3428

Multi-year Prediction of Rice Yield Under the Changing Climatic Scenarios in Nepal Central Terai Using DSSAT Crop Model

NASA-POWER derived weather data of Dumkauli in Nawalparasi (27.68˚ N, 84.13˚E) district in the Nepal central Terai for the past 33-years (1984/85-2017/18) were purposively downloaded and validated with recorded weather data of Department of Hydrology and Meteorology (DHM). The trend analysis for grain yield of rice in Nawalparasi was drawn with the historical data of the maximum and minimum temperatures and rainfall. Positive correlations between grain yields and minimum temperature and rainfall each showed an acceptable coefficient of determination (R2). The CSM-CERES-Rice embedded in DSSAT ver 4.7 was used for multi-year prediction of rice yield using both historically recorded and simulated climatic scenarios. The model simulated results closely agreed with the observed rice yield recorded by the Ministry of Agriculture and Livestock Development (MoALD) in Nepal. The correlation between precipitation and observed rice yield was 0.71 and the correlation between precipitation and observed and DSSAT simulated yield was 0.379. The multi-year predicted rice yield using historical weather data and the DSSAT rice model showed that rice yield could be sustained with the use of the current crop cultivars only for the upcoming few years. The climate index, mainly the rainfall index, was found to be more sensitive to rice production in the Nepal central Terai region. This study suggests for the development of new climate change ready rice cultivars to feed the increasingly growing Nepalese population

Response of Varying Levels of Phyto-hormones and Micro-nutrients on Growth and Yield of Brinjal (Solanum Melongena L.) in Sub-tropical Terai Region of India

A field study was conducted at Horticultural Research Farm, Institute of Agricultural Sciences of Banaras Hindu University, Varanasi, India during summer season of 2017-2018 to test the sole effect of phyto-hormones and micro-nutrients on growth of brinjal (Solanum melongena, L.). The thirteen treatments having six different concentration of phytohoromnes viz., T1 (20 ppm NAA), T2 (40 NAA), T3 (60 ppm NAA), T4 (25 ppm GA3), T5 (50 ppm GA3), T6 (75 ppm GA3), and six different concentrations of micronutrients viz., T7 (Boron 0.1%),T8 (Boron 0.2%), T9 (Boron 0.3%), T10 (Zinc 0.1%), T11 (Zinc 0.2%), T12 (Zinc 0.3%) and T13 (control-water spray) for a “Kashi Uttam” cultivar of brinjal were grown under randomized complete block design (RCBD) having three replications. The results findings indicated that treatment T4 (25 ppm GA3) had significant effect on growth parameters, mainly plant height, number of leaves, leaf length, leaf width, crop canopy, number of side roots and main root length. Similarly, yield parameters like number of fruits per plant, fruit weight, fruit yield per plant were found to be significantly superior under treatment T4 (25 ppm GA3). Number of branches per plant, stem diameter, main root length and fruit weight were found superior under treatment T1 (20 ppm NAA). Among the different concentrations of micronutrients treatment T9 (Boron 0.3%) and T12 (Zinc 0.3%) were found to be significant over control. It can be concluded that the phyto-hormones and micro-nutrients can be judiciously used for increasing the growth and yield of brinjal