Varietal characterization of different hybrid variety of maize in Lamjung, Nepal

Varietal characterization of different hybrid variety of maize in Lamjung, Nepal | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Varietal characterization of different hybrid variety of maize in Lamjung, Nepal Pappu Kumar Sah, Pankaj Kumar yadav, Ankit Ojha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3642546/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lamjung is one of major maize producing region with limited availability of spring hybrid maize varieties. This research examine the performances of ten different hybrid varieties using Randomized Complete Block Design in spring season. The highest days to emergence of seedlings was in RAMPUR HYBRID-16 (9.67 days) and the lowest days to emergence was in RML145/RL197 (6.67 days). The highest plant germination percentage was in RML145/RL197 and PVAEQH-1 (50%) and the lowest plant germination percentage was in RAMPUR HYBRID-14 (27.22%). The plant height was significantly higher in RAMPUR HYBRID-12 (245.33 cm) followed by RML83/RML146 (136 cm) and lowest in PVAEQH-1(191.80 cm) whereas the leaf area index were significantly higher in RML145/RL197 (4.86). Phenological behavior like days to 50% tasseling (61 days) and silking (67 days) were significantly earlier in PVAEQH-1 and silking tasseling interval lower in RAMPUR HYBRID-14 (4.33 cm). Yield attributing characteristics like cob length higher in RAMPUR HYBRID-10 (19.267cm) and PVAEQH-1 (19.167 cm), cob diameter higher in CML491/CLWQHZN51 (5.148 cm) and RML145/RML2 (5.318 cm), number of rows/cobs was higher in RAMPUR HYBRID-16, CML491/CLWQHZN51, number of kernels/rows higher in CML491/CLWQHZN51, RML83/RML146. Shelling % was higher in RAMPUR HYBRID-10(69.64) and lower in RML83/RML146(57.81). Thousand grain weight were found significantly higher in CAH1511 (250.23 gm) followed by RAMPUR HYBRID-10 (231.77 gm), RML145/RL197(230.17 gm). Grain yield was significantly higher in RML145/RL197 (7.20 mt/ha) followed by RML83/RML146 (7.13 mt/ha), RML145/RML2(6.58mt/ha), PVAEQH-1(6.25mt/ha), RAMPUR HYBRID-10(6.11 mt/ha), CAH1511 (5.76 mt/ha), CML491/CLWQHZN51(5.68mt/ha), RAMPUR HYBRID-16(5.39mt/ha), RAMPUR HYBRID-12 (5.07 mt/ha)and lower in RAMPUR HYBRID-14 (3.95 mt/ha). The results indicated the among ten hybrid varieties RML145/RL197 and RML83/RML146 were high yielding so the farmers of mid hill specially in Lamjung can cultivate these varieties. Agronomy Characterization Maize Production Spring Varieties Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Maize ( Zea mays L.) is the world’s widely grown cereal and is ranked third most important crop after paddy and wheat. Globally, maize covers 193.7 million ha area with production of 1147.7 million Mt and productivity of 5.75 ton/ha (FAO, 2020 ). Maize is second most important crop in Nepalese agriculture after rice in terms of area (979,776 ha) and production (2,997,733t) with productivity of 3.06 t ha − 1 (MoALD, 2021 ). Agriculture contributes 22.18% of total GDP of Nepal. Maize’s contribution in GDP and AGDP is about 3.15% and 9.5% respectively (MoALD, 2020 ). The production of maize in Gandaki province is 617760 Mt. The area, production and productivity of maize in Lamjung district is 10098ha, 26397 Mt and 2.61Mt/ha respectively (MoALD, 2021 ). Feed demand is increasing at the rate of 11% per annum while maize production has been increased by 4.52% in last three years (KC et al., 2015 ). About 80% of produced maize is being fed to livestock & poultry and our production can fulfill only 30% of the current demand (FAO, 2020 ). This circumstance have created utmost need for producing higher maize yield per unit area to fulfill the national demand. Hybrid maize with high yield potential is being introduced as our local and open pollinated varieties cannot meet the demand. Those hybrid varieties could be best alternative to boost the production of maize in the given ecology (NMRP, 2005 ). However, hybrid maize covers only about 12–15 % of the totl maize area (Tripathi, Shrestha, & Gurung, 2016). Lack of high yielding genotypes suitable for agro-climatic condition, inadequate variety in the existing system, lack of improved seeds and lack of agricultural inputs have always been associated with low productivity of maize in mid-hill of Nepal. The annual seed replacement rate (SRR) of maize is about 17.83% (Memoire, 2017 ). Nearly, 12–15% area is covered by hybrids, and 85–88% by open-pollinated varieties (improved, or farmers’ varieties). It is believed that there is less scope for increasing maize production and productivity through area expansion, thus aggressive intervention of hybrid maize technology in Terai and potential pockets of mid- hills could be one of the potential options to tackle with the situation. Hybrids can give 25–30% higher grain yield as compared to better OPVs whereas there is huge demand of maize for food grain as well as feed ingredients. There is still lack of varietal selection option of farmers preferred varieties. For the widespread of modern varieties; it needs to promote a range of crop varieties to suit specific crop production niches end Socio- cultural preferences as there is diversity in agro-ecological and socio economic conditions in the country (Joshi, 2002). In order to tackle these problems Researches is carried out in different agro-ecological zone. The main objective of the varietal research is to develop/identify farmer's preferred high yielding, better performing varieties that fit into existing cropping patterns. Maize production scenario of Nepal In Nepal, maize is produced and consumed in significant amount. The area, production and productivity of maize in Nepal is 979,776 ha, 2,997,733ton and 3.06 t ha -1 respectively (MoALD, 2021 ). In Lamjung Area, production and productivity of maize 10098ha, 26397 Mt and 2.61Mt/ha respectively (MoALD, 2021 ). Feed industry has tremendous demand of maize. Out of total maize that was used in feed production, 87% of the maize was imported from India each year by feed industries (Timsina et.al. 2016 ). At least 1.5 million tons of maize is required only to the feed industries affiliated with national feed industry association of Nepal (MOALD, 2014). Maize is mainly consumed in the form of grits like as rice, bread as chapatti prepared from the flour and processed products like confectionaries (Gurung, 2011 ). The application of high dose of N (180 kg N /ha) could help increase hybrid maize grain production in the acidic soils of Chitwan valley (Adhikary, B., & Adhikary, R., 2013 ).Hybrid maize is suitable for higher production and has higher potential than OPVs in the Terai. (Ghimire S.Sherchan DP, 2016). It is estimated that for the next two decades the overall demand of maize will be increased by 4–8% per annum resulting from the increased demand for food. Such increase in demand must be met by increasing the productivity of maize per unit of land (Poudyal, 2001). The genetic diversity was observed in inbred lines differences for grain yield and anthesis silking interval, SPAD reading and leaf senescence, tassel blast and leaf firing percentage, plant and ear height, leaf area index, ear per plant, cob length and diameter, number kernel per ear, number of kernel row per ear, number kernel row, silk receptivity, shelling percentage, thousand kernel weight under heat stress condition (S.K.G., 2017). The Early maize genotypes showed considerable variation in grain yield (Dhakal,B. 2017). Maize varieties developed in Nepal Nepal Agricultural Research Council (NARC) has developed 32 maize varieties (NMRP, 2015 ; NMRP, 2017 ); seven hybrids (Gaurav, Khumal Hybrid-2, Rampur Hybrid-2, Rampur Hybrid-4, Rampur Hybrid-6, Rampur Hybrid-8 and Rampur Hybrid-10) until 2017 and six were de-notified (Makalu-2, Janaki, Sarlahi Seto, Hetauda Composite, Kakani Pahelo and Rampur Pahelo) (NMRP, 2015 ). Thirty-four imported hybrids of maize were registered in Nepal (NMRP, 2013 ). In 2016, based on two years’ multi-location trials conducted by NARC, 13 multinational company hybrids were registered by the National Seed Board (NMRP, 2017 ). In FY 2016/2017, 43 tons of source seed of maize were produced. Only eight varieties (Rampur Composit, Arun2, Arun 3, Arun 4, Arun 6, Deuti, Manakamana 3, Poshilo makai 1) were used for maize seed production (NMRP, 2017 ). For different special projects like the Agriculture and Food Security Project (3000 kg breeder seed and 2600 kg foundation seed) and Kisan ka lagi Unnat Biu bijan Karyakram (3000 Kg of breeder seed) for seed multiplication, NARC has been producing source seed. Seed maintenance has been carried out for released maize varieties. In 2016/2017, grid selection was applied in Rampur Composite and Deuti. Half sib family selection was used in Arun-2, Manakamana-3 and Poshilo Makai-1, and 3 kg, 4 kg and 3.5 kg nucleus seed of these varieties, respectively, was produced (NMRP, 2017 ). In the case of Rampur Composite, 7.37 tons of breeder seed and 13.02 tons of foundation seed were produced. In case of Arun-2, 1.34 tons of breeder seed and 3.93 tons of foundation seed were produced. For Manakamana-3, 3.19 tons of breeder seed and 11.10 tons of foundation seed, while for Deuti, 1.24 tons of breeder seed and 2.60 tons of foundation seed were produced. Finally, 0.06 tons of breeder seed and 0.11 tons of foundation seed of Poshilo Makai-1 were produced. However, Ghimire et al (2003) reported that seed cycle was not maintained, leading to low and slow adoption of newer maize varieties in Nepal. Gairhe., (2021) reported that the number indicated after any maize variety refers to Kernel Color. Yellow varieties have even numbers (e.g. Arun-2, 4, 6; Rampur Hybrid-2, Rampur Hybrid-4, Rampur Hybrid-6, Khumal Hybrid-2, Rampur Hybrid-8 and Rampur Hybrid-10 etc.) and White maize varieties have odd numbers (e.g. Manakamana-1, 3, 5; Poshilo-1; Arun-1, 3; Ganesh-1 etc). It is common understanding in Nepal that white varieties are for food and yellow varieties are for feed, but farmers in Kavre and Lamjung do not produce different varieties based on their utilization such as food, feed and seed purpose (Timsina et al., 2016 ). These varieties have been scaled up in different parts of the country, leading to increased maize production. Effect of Variety : Nearly half the area under maize is planted with traditional varieties home saved seeds, which are continuously at the risk of degenerating (due to open pollination) (Koirala G., 2002). The statistically analyzed results revealed that the effect of cultivation practice and their interaction effect on grain yield were found non-significant but the responses of the variety were found highly significant difference on grain yield. (Dawadi and Sah, 2012 ).Hybrid maize is suitable for higher production and has higher potential than OPVs in the Terai (Ghimire S., 2016). Maize yield attributing traits such as number of cob /plant, number of kernel/row, number of kernel row/ear and thousand kernel weight have important role for yield (Haydar et al., 2013). Thus, indirect selection can be done through identifying improved yield components. Yield can be estimated knowing only components character, so the contribution of each component is essential to know (Kumar et al., 2011 ). The time between sowing to flowering exhibit highly heritable traits, selection of those traits is important for selection of variety. The efficiency of maize breeding program can be improved through understanding the interrelationships existed between yield and its contributing components (Raut., Ghimire., & Kharel,. 2017). There is still lack of varietal selection option of farmers preferred varieties. Seed replacement rate is less than l % (Koirala G., 2002). For the widespread of modern varieties; it needs to promote a range of crop varieties to suit specific crop production niches end socio cultural preferences as there is diversity in agro-ecological and socio economic conditions in the country (Joshi, 2002). Out of the total maize area in the country, hybrids and open-pollinated varieties (OPVs) occupy 12–15, and 85–88%, respectively (Adhikari, et al., 2021 ). Our local landraces and OPVs cannot meet the ever-rising demand (Adhikari, et al., 2021 ). To date five and two maize hybrids have been released and registered, respectively. Among them, only Khumal Hybrid-2 has been released for mid-hills and rest four along with this Khumal Hybrid-2 for Terai, inner Terai, and river basin areas. Gaurav, Rampur Hybrid-2, Khumal Hybrid-2, Rampur Hybrid-4, Rampur Hybrid-6 are the released, and Rampur Hybrid-8 and Rampur Hybrid-10 are registered single cross yellow kernel hybrids (Koirala, 2017 ).Therefore, hybrid maize is one of the options of breaking the present scenario of maize import. Adoption of modern maize hybrids, characterized by higher yielding genetic potential with good quality and profitability, is the utmost necessity in current Nepali agriculture. In Nepal, the hybrid maize of multinational seed companies is progressively being popular among farmers (Tripathi & Shrestha, 2016 ). Although few hybrids are developed from national research system of Nepal, they are not competitive with hybrids of multinational seed companies (Tripathi & Shrestha, 2016 ). Large numbers of multinational companies’ hybrids have been registered in National Seed Board of Nepal. The commercial seed companies are the main source of seed as the acreage of hybrid maize has expanded extensively in Terai (i.e., plain area) and partly in mid hill of Nepal. Farmers and breeders need to select suitable maize hybrids with high yield and other essential agronomic characteristics. Most of the released maize varieties in Nepal are trialed at research stations only, lacking field evaluations most likely due to lack of proper extension service. Even some released varieties are not accepted by farmers due to lower than expected levels of production at the farmer’s field (Gurung, 2011 ). Consequently, there is dominance of the multinational varieties over locally released varieties on the market (Gurung, 2011 ). Hybrid maize seed marketing is flourishing every year. However limited commercial hybrids are suitable for cultivation due to the country’s diverse agro ecological regions. Therefore, it is necessary to identify superior maize hybrids that are suitable for different agro-ecological regions. Farmers of Lamjung do not have scientific knowledge about the site specific, better performing hybrid varieties. This has led to crop failure, lower benefit cost ratio, low germination and low yield. The use of site specific varieties gives better performance and higher yield. Despite the great potential of maize farming, production is low and substantial amount of maize is imported every year. Information regarding site specific variety during spring season with recently introduced hybrids are lacking in Nepal particularly in Lamjung (MoALD, 2021 ). The low production and productivity of maize in Nepal are mainly due to limited hybrid choices (Subedi, 2015 ). The main objectives of present research was to compare and characterized the different hybrid variety of maize and providing information to the farmers of Lamjung about the high yielding site specific hybrid varieties of spring maize which will help to increase the productivity of the spring maize. 2. Materials and Methods A field experiment entitled “Characterization of different hybrid Varieties of Spring Maize in Lamjung, Nepal" was conducted during 2022. The details of the methods and methodology used in the study are described under the following headings: Site selection Research is to be conducted at Siundibar, Sundarbazar Municipality-09 Lamjung lies on the geographical coordinates of 28.136˚0r 28 \(^\circ\) 8'10''N, 84.4331˚or 84 \(^\circ\) 25'59''E. which is 857m above sea level. It lies in mid-hill district of Nepal. The district consists of sub-tropical to alpine climate. The research site lies in upper tropical zone of the district. Physio-chemical characteristics of the experimental soil Soil samples were collected randomly from each four corners and a center experimental-plot at 0–20 cm depth from the surface using Shovel to analyze the initial soil physio-chemical properties. The sub-samples were mixed, air-dried under shade, grounded and sieved through 2 mm sieve, and through 0.5 mm sieve for the purpose of organic matter determination. The samples were then collected in plastic and sent to Soil Testing Laboratory for the test. From the analysis, the pH of the soil was acidic in nature. The total nitrogen content of the soil was low, while available phosphorus and available potassium were high and medium respectively. The organic matter content of the soil was low. Table 1 Physio-chemical Characteristics of soil of the experimental field as per the reports of soil test. S.N. Soil Properties Values Rating 1 Soil PH 4.8 Acidic 2 Nitrogen (%) 0.06 Low 3 Phosphorus(kg/ha) 48.3 Medium 4 Potassium(kg/ha) 244.92 Medium 5 Organic Matter (%) 0.60 Low Climatic condition during the experiment The experiment site falls under the upper sub-tropical humid climatic belt of Nepal where summer is hot and winter is cool. It receives most of the rainfall during monsoon season in summer. The weather condition of the experiment site for the research plot was taken from secondary source. The maximum temperature was recorded within the research period of five month from Falgun to Asar was 31 o C while the minimum temperature was 8 o C. The highest rainfall was recorded in the month of Asar. Experimental Setup : The research design was Randomized Complete Block Design (RCBD) with total ten treatments including RDF: 180:60:40 kg NPK/ha, with three replication, distance between replication/block was 1m, and with total number of plot: 30, gap of outer periphery was 50cm, net plot size 3×3 m (9m 2 )with spacing 75×20 cm 2 with total experimental area 9m×30m (270m 2 ) Treatment Details (varieties) : T1 = RAMPUR HYBRID-14, T2 = RML145/RL197, T3 = RAMPUR HYBRID-10, T4 = CAH1511, T5 = RAMPUR HYBRID-16, T6 = CML491/CLWQHZN51, T7 = RML83/RML146, T8 = RAMPUR HYBRID-12, T9 = RML145/RML2, T10 = PVAEQH-1 Layout of experiment : General cultural practices Field preparation : The field was ploughed 15 days prior of seed sowing by using rotavator to bring the soil under good tilth. Again, ploughing was done at the time of sowing and planking was done after ploughing for leveling the land. After leveling, the clods were broken and weeds and stubbles of the previous crop were removed. Manure and fertilizer application FYM was applied as main source of organic fertilizer in the field. The FYM@10kg/plot area was applied in all experimental plots and it was uniformly incorporated into the soil during the first land preparation. The sources of fertilizers were Urea, DAP and MOP. RDF for nitrogen for hybrid maize is 180 kg/ha (Adhikary, B., & Adhikary, R., 2013 ). The recommended dose of 60 kg P 2 O 5 /ha and 40 kg K 2 O/ha was applied as basal in all plots at the time of seed sowing (MoALD, 2021 ) Fertilizer time application About 20% dose of N was applied as basal dose from DAP, 30% N is top dressed at first weeding, 30% N during earthling up, 20% before flowering. Seed sowing : Line sowing of fungicide treated seed was done manually. In each hill, two seed were sown at a depth of 5 cm. Irrigation schedule : First irrigation was given at knee high stage, Second irrigation was given at tasseling stage Weeding and earthling up : First weeding was done after 40 days of seed sowing, Earthling up and second weeding was done at 30 days after first weeding Plant protection : During the seedling and young stage fall armyworm infestation was seen. For control of this insect All kill (Chlorpyriphos + Cypermethrin) @ 1.5ml/lit of water was sprayed also (Emamectin Benzoate)@5gm per 16 liter water was used. Harvesting and threshing : Harvesting was carried out manually. After removing the cobs, the cobs are sun dried for few days. De-husking of cobs was done separately on the threshing floor. After shelling of grains, seeds were carefully and separately dried by maintaining 12% moisture for further study. Phenological Observation : Five plants will be tagged for taking phonological observations. The Phenological data will be taken when 50% observation occurred and ended when 75% observation completed. The Phenological observations will be recorded as, Emergence : Seed emergence will be recorded when about 50% of the seedling will have emerged out of the soil. Plant population/m 2 : The plant population/m 2 will be counted about 20 days after sowing. Days of tasseling : The date of tasseling will be recorded from tassel emergence to 50% of plant will have tasseled in each plot. The mid 2 rows will be taken for each Phenological observation. Days of silking : The date will be recorded from the initiation of silk to 50% silking in each plot. The silk exposed 1cm from closed ear will be considered as emerged silk. The same rows as that of tasseling records will be taken for days of silking. Days to anthesis : The date will be recorded when 50% plants have shedding pollen. The same rows are taken for the data of days to anthesis. Days of physiological maturity : The appearance of black layer between ear surface and ear grains and occurrence of senescence of ear husks will be considered as an indication to physiological maturity. Biometric observation Number of Leaf : Number of leaves per plant was counted from the 5 randomly selected plants from each plot at 30,45,60,75 and 90 DAP. Leaf area index (LAI) : Linear dimensions (length and breadth) of fully opened leaf were recorded manually. And then, leaf area was computed using the equation suggested by Montegory (1911). A = b*L*W Where, b = Leaf Shape Coefficient L = length A = Area W = width. The coefficient was conventionally assumed 0.75 for maize leaf. Leaf Area Index (LAI) was computed by finding the ratio of leaf area to ground area. Leaf Area Index is an indicator of primary photosynthetic productivity and rate of evapotranspiration in crop field. Leaf area Index (LAI) = Area of leaves (canopy)/ Ground area under canopy Plant height : Plant height will be measured from the ground level to the top most visible due lap of five randomly selected plants from each plot at 30 DAS, 45 DAS, 60 DAS, 75 DAS, 90 DAS, 105 DAS, and at maturity. Yield attributing characters : Number of harvested ears : Total number of ears harvested from net harvestable area will be recorded as harvested ears per plot and it is converted to hectare basis. Ear length and circumference : Ten dehusked ears will be selected from each plot randomly and length from the base up to top grain bearing portion of each ear will measure. The average of five ears will be calculated and expressed as ear length. The circumference of five randomly selected ears from each plot will be measured and average value will express as ear circumference. Number of kernels (grains) rows per ear : Five randomly selected ears from each plot will be shelled and the entire kernels/grains row will count. And will be reported as number of kernels row per ear. Numbers of grain per row : Numbers of grain counted in a row. Thousand Grains Weight (TGW) or Test weight : One thousand shelled maize grains from each plot will randomly be taken, weighed and recorded as test weight and expressed in gram (g). The kernels used for test weight will be corrected to 15% moisture content. Shelling percentage : It is the ratio of grain to ear (grain: ear) and expressed in percentage. Five randomly selected ears will be weighed with grains. All grains will be shelled out and the weight of grain will be taken and the shelling percentage will calculated as: Grain moisture content (%) : Five cobs will be selected randomly and central two kernel rows will be shelled out and will bulk the kernels from all ears and moisture will be measured by multigrain moisture meter. Grain yield : Calculate production per plot then convert on hector basis at 13% moisture. Grain yield will be also calculated on hectare basis by using following formulae: Where, FEW = filled ears weight (Kg) SP = shelling percentage (%) GMC = grain moisture content at harvest (%) NHA = net harvested area (m 2 ) Data analysis techniques The data recorded on different parameters from field and laboratory was first tabulated in Microsoft Excel (MS- Excel), then Analysis of variance (ANOVA) for all data was computed using R-studio software. All the analyzed data were subjected to Duncan's Multiple Range Test (DMRT) for mean comparison at 5% dose of significance. 3. Results and discussion The results pertaining to the field experiment were analyzed and presented in this chapter with figures and tables where necessary. An attempt has been made to evaluate the results so obtained to give explanation with available evidences as far as possible for observed variations in mentioned parameters. Biometrical observation Plant height : There was significant difference in plant height at different days after sowing (DAS) except at 30DAS. The average plant height varied from 32.76 cm (30DAS) to 222.48 cm (90 DAS). At 30 DAS, no significant difference was found among different varieties. At 45 DAS, plant height was highest in RML145/RML2 followed by RML145/RL197 and minimum in RAMPUR HYBRID-14. At 60 DAS, maximum plant height was observed in PVAEQH-1 which was statistically similar with CML491/CLWQHZN51, RML83/RML146, RML145/RML2 and RML145/RL197 whereas minimum was in RAMPUR HYBRID-14. At 75 DAS, maximum plant height was in RAMPUR HYBRID-12(241.67cm) and minimum in PVAEQH-1(189.80 cm).At 90 DAS, maximum plant height was observed in RAMPUR HYBRID-12 i.e. 245.33 cm and minimum was observed in PVAEQH-1 i.e. 191.80 cm. This result was in line with Neupane, B., Paudel, A., &Wagle P. (2020) who reported that significant variation is observed in plant height due to hybrid maize variety. In addition, Radma and Dagash ( 2013 ) also reported that significant variation in plant height is observed due to varieties. This is probably due to genetic variation in plant height. Difference of plant height was found in different varieties which was due to the fact that the plant height is a genetically as well as environmentally controlled factor so the height of different varieties remain different, according to (Kunwar & Shrestha, 2014 ). Table 2 Effect of different varieties of maize on plant height at different days after sowing Treatment 30DAS 45DAS 60DAS 75DAS 90DAS RAMPUR HYBRID-14 RML145/RL197 RAMPUR HYBRID-10 CAH1511 RAMPUR HYBRID-16 CML491/CLWQHZN51 RML83/RML146 RAMPUR HYBRID-12 RML145/RML2 PVAEQH-1 26.43 b 35.86 ab 35.33 ab 27.33 b 30.73 ab 38.33 a 36.67 ab 27.26 b 36.26 ab 33.27 ab 79.06 f 103.67 b 91.67 cde 84.60 def 93.40 cd 96.60 bc 82.46 ef 82.20 ef 114.26 a 85.33 def 123.33 c 157.67 a 138 b 132.53 bc 141 b 158.60 a 162.67 a 137.33 b 164 a 166.46 a 216.067 de 218 de 224.40 bcd 230.40 abc 212.93 e 217.73 de 234.33 ab 241.67 a 222.33 cde 189.80 f 216.067 de 220.33 de 226.067 bcd 232.40 bc 214.80 e 218.67 de 236 ab 245.33 a 223.33 cde 191.80 f LSD(0.05) SEm (±) C.V% F-test Grand mean 10.191 3.43 18.135 NS 32.76 10.262 3.45 6.550 *** 91.326 11.76 3.95 4.627 *** 148.16 11.354 3.82 2.998 *** 220.76 11.184 3.76 2.930 *** 222.48 Means followed by common letter(s) within column are non-significantly different based on DMRT at P = 0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance. Number of leaves : In this experiment, the mean value of number of leaves indicated that it increased from 30 DAS to 90 DAS. There was non-significance difference among number of leaves at 30 DAP, 45 DAP and 60 DAP. The mean value of number of leaves at 30 DAS was 5.45 which ranged from 4.73 in RAMPUR HYBRID-14 to 6.13 in RML145/RML2. The average number of leaves at 45 DAP was found to be 8.32 and ranged from 7.40 in Rampur Hybrid-14 to 9.26 in Rampur Hybrid-10. At 60 DAP it increased to 9.28 and ranged from 7.67 in Rampur Hybrid-16 to 10.60 in PVAEQH-1.The mean value of number of leaves at 75 DAS was found to be 11.89 in this experiment and ranged from 9.33 in PVAEQH-1 to 13 in CML491/CLWQHZN51. The average number of leaves increased to 12.08 in 90 DAS and ranged from 9.067 in PVAEQH-1 to 13.867 in Rampur Hybrid-12. This result was similar with Neupane, B., Paudel, A., &Wagle P. (2020) who reported that significant variation is observed in number of leaves due to hybrid maize variety. Similar result was obtained in(Devkota, 2020) and (Neupane, B., Paudel, A., &Wagle P. 2020).Bastola, et al., ( 2021 ) also observed the significant divergences among deferent genotypes of maize in number of leaf per plant. Table 3: Effect of different varieties of maize on number of leaves at different days after sowing Treatment 30 DAS 45 DAS 60 DAS 75 DAS 90 DAS RAMPUR HYBRID-14 RML145/RL197 RAMPUR HYBRID-10 CAH1511 RAMPUR HYBRID-16 CML491/CLWQHZN51 RML83/RML146 RAMPUR HYBRID-12 RML145/RML2 PVAEQH-1 4.73 c 5.60 abc 6.067 ab 5.20 abc 5.40 abc 5.13 bc 6.067 ab 5.20 abc 6.13 a 5.0 c 7.40 d 9.13 ab 9.267 a 8.53 abcd 7.73 cd 7.67 cd 8.73 abc 7.93 bcd 8.93 abc 7.93 bcd 8.267 bc 9.667 abc 10.13 ab 7.73 c 7.67 c 9.60 abc 10.20 ab 8.400 abc 10.53 a 10.60 a 12.67 ab 11.53 c 12.13 bc 11.867 bc 11.267 c 13.0 a 11.93 bc 12.80 ab 12.067 bc 9.33 d 12.467 ab 11.867 b 11.93 b 12.0 b 11.867 b 13.0 ab 12.4 ab 13.867 a 12.33 ab 9.067 c LSD(0.05) SEm(±) C.V% F-test Grand mean 0.977308 0.4228 10.44734 NS 5.45 1.293 0.435 9.057 NS 8.32 2.202 0.741 13.835 NS 9.28 1.121 0.3774 5.496 *** 11.893 1.59 0.535 7.679 ** 12.08 Means followed by common letter(s) within column are non-significantly different based on DMRT at P=0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance Leaf area index: The experiments showed that leaf area index increased from 30 DAS to 90 DAS. The leaf area index was statistically non-significance at 30 and 60 DAS while significance at 45, 75 and 90 DAS. The mean LAI was found to be 0.192 at 30 DAS and ranged from 0.126 in Rampur Hybrid-14 and 0.265 in RML145/RML2. At 45 DAS mean value of leaf area index was 1.022 and ranged from 0.652 in Rampur Hybrid-14 to 1.447 in RML145/RML2. The average LAI at 60 DAS was found to be 3.216 and ranged from 2.55 in CAH1511 to 3.69 in RML145/RML2.The mean average value of Leaf area index at 75 DAS was found to be 4.10 and ranged from 3.009 in PVAEQH-1 to 4.897 in Rampur Hybrid-14.The mean value of LAI at 90 DAS was same as 75 DAS i.e.4.10. This result was similar with Neupane, B., Paudel, A., &Wagle P. (2020) who reported that significant variation is observed in leaf area index due to hybrid maize variety. Also reported by Ahmed et al. ( 2012 ) this might be due to the difference in their genetic makeup. Table 4: Effect of different hybrid varieties of maize on Leaf area index at different days after sowing Treatment 30 DAS 45 DAS 60 DAS 75 DAS 90 DAS RAMPUR HYBRID-14 RML145/RL197 RAMPUR HYBRID-10 CAH1511 RAMPUR HYBRID-16 CML491/CLWQHZN51 RML83/RML146 RAMPUR HYBRID-12 RML145/RML2 PVAEQH-1 0.126 b 0.193 ab 0.228 ab 0.140 b 0.191 ab 0.226 ab 0.188 ab 0.173 ab 0.265 a 0.195 ab 0.652 d 1.391 ab 0.821 cd 0.821 cd 0.893 cd 1.162 abc 1.233 abc 0.840 cd 1.447 a 0.962 bcd 2.848a 3.442a 3.334a 2.551a 3.122a 3.619a 3.451a 3.128a 3.697a 2.966a 4.897a 3.647de 4.077bcd 4.090bcd 3.852cd 4.810ab 4.016cd 4.27abcd 4.416abc 3.009e 4.897a 3.647de 4.077bcd 4.090bcd 3.852cd 4.810ab 4.016cd 4.272abcd 4.416abc 3.009e LSD(0.05) SEm(±) C.V% F-test Grand mean 0.1033 0.0347 31.20 NS 0.192 0.4380 0.1474 24.96 * 1.022 1.1606 0.390 21.036 NS 3.216 0.7480 0.2517 10.613 ** 4.10 0.7480 0.2517 10.613 ** 4.10 Means followed by common letter(s) within column are non-significantly different based on DMRT at P=0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance. Phenological observation Germination The germination percentage of different maize varieties was found to be significant. It was recorded that the highest germination percentage was found in RML145/RL197 (50%) and PVAEQH-1(50%) followed by RML83/RML146 (49.44%) and RML145/RML2 (49.167%) and lowest germination percentage in Rampur Hybrid-14 i.e.27.22%.This result was similar with Neupane, B., Paudel, A., &Wagle P. (2020) who reported that significant variation is observed in germination percentage due to hybrid maize variety. Also this might be due to the difference in their genetic makeup and different variety requires different environmental condition to germinate. Table 5 Effect of varieties on Germination percentage S.N. Treatment Germination percentage 1 2 3 4 5 6 7 8 9 10 RAMPUR HYBRID-14 RML145/RL197 RAMPUR HYBRID-10 CAH1511 RAMPUR HYBRID-16 CML491/CLWQHZN51 RML83/RML146 RAMPUR HYBRID-12 RML145/RML2 PVAEQH-1 27.222 d 50.000 a 40.556 bc 43.611 abc 45.000 abc 48.333 ab 49.444 a 37.222 c 49.167 a 50.000 a LSD(0.05) SEm (±) C.V% F-test Grand mean 7.859 2.645 10.399 *** 44.056 Means followed by common letter(s) within column are non-significantly different based on DMRT at P = 0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance. According to (JICA, 2016 ), a range of 21–27˚C temperature is suitable for the better growth of maize plant while 20 ˚C is required for germination. Tasseling and silking The mean value of number of days for 50% tasseling was found to be 66 and it varied from 61 in PVAEQH-1 to 69 in Rampur Hybrid-14. Analysis of variance revealed significant differences among genotype for the number of days for 50% tasseling. The number of days for 50% silking was varied from 67 in PVAEQH-1 to 69 in RML145/RL197 with a mean value of 71.166. Analysis of variance revealed significant differences among genotype for the number of days for 50% silking. There was non-significance difference of the anthesis-silking interval. The mean value of anthesis silking interval was found to be 5.167 which were varied from 4.33 in Rampur Hybrid-14 to 6 in PVAEQH-1. This result was similar with Neupane, B., Paudel, A., &Wagle P. (2020) who reported that significant variation is observed in days to 50% tasseling, days to 50% silking due to different maize variety. Table 6 Effect of different hybrid variety of maize on days to tasseling, silking and tasseling-silking interval Treatments Tasseling Silking ASI(Anthesis-Silking interval) RAMPUR HYBRID-14 RML145/RL197 RAMPUR HYBRID-10 CAH1511 RAMPUR HYBRID-16 CML491/CLWQHZN51 RML83/RML146 RAMPUR HYBRID-12 RML145/RML2 PVAEQH-1 69.00 a 64.00 f 65.667 def 66.333 cde 66.00 cd 67.66 abc 67.00 bcd 68.33 ab 65.00 ef 61.00 g 73.33333 ab 69.00 de 70.333 cd 72.00 abc 71.00 cd 73.33 ab 71.666 bc 74.00 a 70.00 cd 67.00 e 4.333333 c 5.00 abc 4.667 bc 5.667 ab 5.00 abc 5.667 ab 4.667 bc 5.667 ab 5.00 abc 6.00 a LSD(0.05) SEm (±) C.V% F-test Grand mean 1.737 0.584 1.53 *** 66 2.162 0.727 1.77 *** 71.166 1.124 0.3784 12.68 NS 5.167 Means followed by common letter(s) within column are non-significantly different based on DMRT at P = 0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance. Grain yield and yield attributing characters Number of kernel row/ ear, Number of Kernels/row, length of ear, diameter of ear Number of kernel row/ ear, Number of Kernels/row and diameter of ear was found to be significance while length of ear was non-significance. The mean value of number of kernel row per ear was found to be 14.43 and varied from 13.026 in RML145/RL197 to 15.60 in CML491/CLWQHZN51.The mean value of number of kernels per row was found to be 37.18, the highest number of kernels per row was 41.66667 in CML491/CLWQHZN51 followed by RML83/RML146 (40.18) and PVAEQH-1(40.133) and lowest number of kernels per row was 31.84 in RAMPUR HYBRID-16. Average ear length was found to be 18.08 cm in the experiment... Cob length was found higher in PVAEQH-1 with 19.16 cm and lowest in Rampur Hybrid-14 i.e. 15.94 cm. Average value of cob diameter was 5.031 cm in the experiment. It was significantly different among the varieties. RML145/RML2 recorded significantly higher cob diameter of 5.318 cm which was statistically similar with CML491/CLWQHZN51 (5.148 cm) while Rampur Hybrid-14 produced significantly lower cob diameter of 4.63 cm. This result was similar with Neupane, B., Paudel, A., &Wagle P. (2020) who reported that significant variation is observed in Number of kernel row/ ear, Number of Kernels/row, diameter of ear this could be due to genetic variation of maize varieties. Cob length, cob diameter and number of kernel rows per cob play an important role in determining yield of grain (Nemati et al., 2009 ). Table 7 Effect of different varieties of maize on Number of kernels/ear, length of ear (cm) and diameter of ear (cm) Treatment No. of kernel row per ear No. of kernels per row Ear length(cm) Ear Diameter(cm) RAMPUR HYBRID-14 RML145/RL197 RAMPUR HYBRID-10 CAH1511 RAMPUR HYBRID-16 CML491/CLWQHZN51 RML83/RML146 RAMPUR HYBRID-12 RML145/RML2 PVAEQH-1 14.34667 ab 13.02667 c 14.26667 bc 14.00000 bc 15.56619 a 15.60000 a 14.43667 ab 14.21667 bc 14.40000 ab 14.53333 ab 34.47000 de 34.72333 cd 37.20000 bc 36.73333 cd 31.84667 e 41.66667 a 40.18000 a 39.59167 ab 35.31333 cd 40.13333 a 15.94815 c 18.7666 ab 19.26667 a 18.73333 ab 16.51825 bc 18.30000 ab 17.3047 abc 18.65833 ab 18.16667 abc 19.16667 a 4.639066 d 4.831665 cd 5.127389 ab 5.074310 abc 5.113487 abc 5.148620 a 5.127389 ab 5.094214 abc 5.31847 a 4.840764 bcd LSD(0.05) SEm(±) C.V% F-test(0.05) Grand mean 1.2978 0.4368 5.239947 * 14.43 2.6551 0.8936 4.162438 *** 37.1858 2.308 0.776 7.44 NS 18.08 0.28799 0.09692 3.336691 ** 5.031 Means followed by common letter(s) within column are non-significantly different based on DMRT at P = 0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance. Shelling percentage : Shelling percentage of different maize varieties are presented in Table and was significant. The mean value of shelling percentage was 63.45 and ranged from 69.64% in Rampur Hybrid-10 to 57.81% in RML83/RML146. Similar findings was observed in (Adhikari, et al., 2021 ) and (Bista & Gaire, 2021). Shelling percentage had the vital role for determining the grain yield of maize which is the reason for better yield in hybrids. Kandel, (2017) also observed divergences among deferent maize genotypes for shelling percent. Table 8 Effect of varieties on shelling percentage S.N. Treatment Shelling % 1 2 3 4 5 6 7 8 9 10 RAMPUR HYBRID-14 RML145/RL197 RAMPUR HYBRID-10 CAH1511 RAMPUR HYBRID-16 CML491/CLWQHZN51 RML83/RML146 RAMPUR HYBRID-12 RML145/RML2 PVAEQH-1 60.02638 def 68.62947 ab 69.64786 a 68.10157 ab 59.35534 ef 59.57752 ef 57.81997 f 63.75954 cd 62.87127 cde 64.7863 bc LSD(0.05) SEm (±) C.V% F-test(0.05) Grand mean 3.906058 5.185 18 3.58832 *** 63.45 Means followed by common letter(s) within column are non-significantly different based on DMRT at P = 0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance. Thousand grain weight and grain yield : The grand mean value of thousand grain weight was 199.164 gm and significantly different among the maize varieties. Thousand grain weight was found significantly higher in CAH1511which had 250.23 gm and lower in RML83/RML146 i.e.154.71gm. The average grain yield of different maize hybrids was found to be 5.198 Mt/ha. Grain yield was found to be significantly influenced by the maize varieties. The highest grain yield was seen in RML145/RL197 (7.20 Mt/ha) which was statistically similar to RML83/RML146 (7.130) Rampur Hybrid-14 recorded significantly lower yield i.e. 3.95Mt/ha. This result was in line with Neupane, B., Paudel, A.,&Wagle P.(2020) who reported that significant variation is observed in thousands grain weight and yield due to different maize variety. (Raut, Ghimire, & Kharel, 2017) Reported that there were highly significant differences for grain yield and yield attributing traits among genotypes which strongly support our findings. According to (Bista & Gaire, 2021), yield attributing characteristics like cob length, cob diameter, number of rows/cobs, number of kernels/rows, shelling%, thousand grain weight (TGW) were found significantly higher in hybrid maize varieties as compared to open pollinated varieties. Bastola, (2021)study reported open pollinated varieties had shown poor performance in many parameters whereas hybrid varieties had shown better performance. Development and use of hybrid seeds can enhance crop yields and performance in ways that are different from and not necessarily dependent on heterosis by itself (Duvick, 1999 ). Hybrid maize technology has made significantly yield advances and increased productivity in both developed and developing countries (Katuwal, 2012 ) Table 9 Effect of different varieties of maize on thousand grain weight (gm) and yield (Mt/ha) Treatment TGW(g) Yield (Kg ha − 1 ) RAMPUR HYBRID-14 RML145/RL197 RAMPUR HYBRID-10 CAH1511 RAMPUR HYBRID-16 CML491/CLWQHZN51 RML83/RML146 RAMPUR HYBRID-12 RML145/RML2 PVAEQH-1 185.6421 cd 230.1772 ab 231.7738 ab 250.2384 a 176.6991 cd 166.1162 cd 154.7120 d 197.8200 bc 202.0215 bc 196.4484 bc 3.958841 c 7.207264 a 6.115917 ab 5.768865 ab 5.390339 bc 5.689674 ab 7.130970 a 5.074280 bc 6.586450 ab 6.25755 ab LSD(0.05) SEm(±) C.V% F-test(0.05) Grand mean 40.139 2.136 11.748 ** 199.164 1.551604 0.522 15.28412 * 5.198 Means followed by common letter(s) within column are non-significantly different based on DMRT at P = 0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance. Table 10 the rank of treatments Rank Variety 1 RML145/RL197, RML83/RML146 2 RML145/RML2, PVAEQH-1, RAMPUR HYBRID-10, CML491/CLWQHZN51, CAH1511 3 RAMPUR HYBRID-16, RAMPUR HYBRID-12 4 RAMPUR HYBRID-14 4. Conclusion Among ten different maize varieties cultivated in spring season at Sundarbazar, Lamjung, RML145/RL197 (7.20 Mt/ha) has higher yield followed by RML83/RML146 (7.130 Mt//ha) among treatments. Higher germination %, shelling %, thousands grain weight was observed higher in RML145/RL197. Thus, this research suggested the farmers of Lamjung district to cultivate RML145/RL197 variety as hybrid in spring season to increase the productivity of maize. However, this research was carried out in only one season and at only one location. So, this experiment should be further verified by conducting similar research at different location. Further studies with other varieties can be conducted to see their effect on the yield. Declarations Conflict of Interest The authors declare that there is no conflict of interest regarding the publication of this paper. Funding statement No funding was given Data availability statement Data will be made available on request. Declaration of interests statement The authors declare no conflict of interest. Additional information Additional information will be made available on request for this paper. References Adhikary, B. H., & Adhikary, R. (2013). Enhancing effect of nitrogen on grain production of hybrid maize in Chitwan valley. Agronomy Journal of Nepal , 3 , 33-41. Adhikari, K., Bhandari, S., Aryal, K., Mahato, M., & Shrestha, J. (2021). Effect of different levels of nitrogen on growth and yield of hybrid maize (Zea mays L.) varieties. Journal of Agriculture and Natural Resources , 4 (2), 48-62. Ahmed, K., Saqib, M., Akhtar, J., & Ahmad, R. (2012). Evaluation and characterization of genetic variation in maize (Zea mays L.) for salinity tolerance. 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Analysis of genetic diversity among the maize inbred lines. Journal of maize research and developement , 86-97. Subedi, S. (2015). A review on important maize diseases and their management in Nepal. . Journal of Maize Research and Developement 1 , 28-52. Timsina, K. P., Ghimire, Y. N., & Lamichhane, J. (2016). Maize production in mid hills of Nepal: from food to feed security. Journal of maize research and development , 2 (1), 20-29. Tripathi, M. P., & Shrestha, J. (2016). Performance evaluation of commercial maize hybrids across diverse Terai environments during the winter season in Nepal. Journal of Maize Research and Development , 2 (1), 1-12. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3642546","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":251596588,"identity":"233bdf1d-878b-4f34-955d-d92fbe7b9dd9","order_by":0,"name":"Pappu Kumar Sah","email":"","orcid":"https://orcid.org/0009-0002-4996-0427","institution":"Agriculture and Forestry University, Rampur Chitwan, 44209, Nepal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pappu","middleName":"Kumar","lastName":"Sah","suffix":""},{"id":251596589,"identity":"9e3ef578-de29-45b6-ba5c-217913aa393b","order_by":1,"name":"Pankaj Kumar yadav","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYPACCSBmPvgASPLwkaCFLdkApIWNBJt4zEAaGQhqMZc+fHTjjxqLaH7pBrPKrzl2MmwMzA8f3cCjxbIvLe02zzGJ3JlzDqTdlt2WDHQYm7FxDh4tBmd4zG4zsEnkbriRcOy25DZmoBYeNmn8Wvi/3fzxD6Qlsa1Ycls9MVp42G7wtoG0JLMxftx2mLAWyx42s9u8fUC/zEhjlmbcdpyHjZmAX8x5mJ/d/PGtLrdfIv/jx5/bqu352ZsfPsbrMGQOMw+YxKMcQwvjDwKqR8EoGAWjYGQCAMUGRc3bppWeAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4725-5153","institution":"Agriculture and Forestry University, Rampur Chitwan, 44209, Nepal","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Pankaj","middleName":"Kumar","lastName":"yadav","suffix":""},{"id":251596590,"identity":"1a3544a8-5d95-4622-89d9-5bd4f4d10792","order_by":2,"name":"Ankit Ojha","email":"","orcid":"https://orcid.org/0000-0001-9556-8667","institution":"Agriculture and Forestry University, Rampur Chitwan, 44209, Nepal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ankit","middleName":"","lastName":"Ojha","suffix":""}],"badges":[],"createdAt":"2023-11-21 07:09:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3642546/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3642546/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46992254,"identity":"4bdc9923-ee4b-41f3-a0e5-b2d514698ad3","added_by":"auto","created_at":"2023-11-23 21:54:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31798,"visible":true,"origin":"","legend":"\u003cp\u003eHigh yielding countries of maize with their production\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003eSource: FAO, 2020)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/090a934cae0a5477c6475a71.png"},{"id":46993174,"identity":"92d135d0-10a8-4fcc-a23e-353d9d9cf369","added_by":"auto","created_at":"2023-11-23 22:02:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39575,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of maize production in Nepal\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/1d35ca88ff1ec2896aed1579.png"},{"id":46992262,"identity":"cc843aaa-f9bc-4dec-98f4-136e1351c520","added_by":"auto","created_at":"2023-11-23 21:54:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":485008,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing experimental site\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/3188c6cde0edb28fd290c01b.png"},{"id":46992261,"identity":"732a55d8-91db-4fc1-884d-7d6ff6723b86","added_by":"auto","created_at":"2023-11-23 21:54:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68936,"visible":true,"origin":"","legend":"\u003cp\u003eAgro climatic condition during research Period in Sundarbazar, Lamjung.\u003c/p\u003e\n\u003cp\u003eSource: Climate Data \u0026amp; Network Section, Department of Hydrology and Meteorology, Ministry of Energy, Water Resources and Irrigation, Babarmahal, Kathmandu, Nepal.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/6f616ec86c08abca18266e43.png"},{"id":46992255,"identity":"876b9343-268e-46f2-88a6-f445b318ba85","added_by":"auto","created_at":"2023-11-23 21:54:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":29980,"visible":true,"origin":"","legend":"\u003cp\u003eLayout of experimental field for maize research in Sundarbazar, Lamjung, 2022\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/00dfbbca6ca69ef2a115fadf.png"},{"id":46993176,"identity":"a9790e02-17e9-43ec-95f3-64b679bf4c3a","added_by":"auto","created_at":"2023-11-23 22:02:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1986274,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent activities during research\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/73dc31e6dfb7636afc8184ae.png"},{"id":46992257,"identity":"396d4785-c6ec-4f56-8730-9a5b8bcd6e97","added_by":"auto","created_at":"2023-11-23 21:54:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":37939,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of varieties on Germination percentage\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/d500ca5a35551a11a0f46095.png"},{"id":46992259,"identity":"8ea8e387-5a27-45cd-9118-b2225ac5b6c4","added_by":"auto","created_at":"2023-11-23 21:54:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38749,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of varieties on shelling percentage.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/79da154bf07504ed5a92f925.png"},{"id":46993175,"identity":"a82b4e3c-fd93-49fa-8900-5b2a0dc93ffd","added_by":"auto","created_at":"2023-11-23 22:02:50","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":40308,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of different varieties of maize on thousand grain weight (gm) and yield (Mt/ha)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/d91de3e9bf92611c9d22e3b2.png"},{"id":46993320,"identity":"b93e7966-da7b-46d8-9b62-7b4d4b922c1a","added_by":"auto","created_at":"2023-11-23 22:10:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3376187,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3642546/v1/6f98d632-018d-4505-891f-8b9ba09aa65e.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eVarietal characterization of different hybrid variety of maize in Lamjung, Nepal\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMaize (\u003cem\u003eZea mays\u003c/em\u003e L.) is the world\u0026rsquo;s widely grown cereal and is ranked third most important crop after paddy and wheat. Globally, maize covers 193.7\u0026nbsp;million ha area with production of 1147.7\u0026nbsp;million Mt and productivity of 5.75 ton/ha (FAO, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Maize is second most important crop in Nepalese agriculture after rice in terms of area (979,776 ha) and production (2,997,733t) with productivity of 3.06 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (MoALD, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Agriculture contributes 22.18% of total GDP of Nepal. Maize\u0026rsquo;s contribution in GDP and AGDP is about 3.15% and 9.5% respectively (MoALD, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The production of maize in Gandaki province is 617760 Mt. The area, production and productivity of maize in Lamjung district is 10098ha, 26397 Mt and 2.61Mt/ha respectively (MoALD, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Feed demand is increasing at the rate of 11% per annum while maize production has been increased by 4.52% in last three years (KC et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). About 80% of produced maize is being fed to livestock \u0026amp; poultry and our production can fulfill only 30% of the current demand (FAO, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This circumstance have created utmost need for producing higher maize yield per unit area to fulfill the national demand. Hybrid maize with high yield potential is being introduced as our local and open pollinated varieties cannot meet the demand. Those hybrid varieties could be best alternative to boost the production of maize in the given ecology (NMRP, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, hybrid maize covers only about 12\u0026ndash;15 % of the totl maize area (Tripathi, Shrestha, \u0026amp; Gurung, 2016). Lack of high yielding genotypes suitable for agro-climatic condition, inadequate variety in the existing system, lack of improved seeds and lack of agricultural inputs have always been associated with low productivity of maize in mid-hill of Nepal.\u003c/p\u003e \u003cp\u003eThe annual seed replacement rate (SRR) of maize is about 17.83% (Memoire, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Nearly, 12\u0026ndash;15% area is covered by hybrids, and 85\u0026ndash;88% by open-pollinated varieties (improved, or farmers\u0026rsquo; varieties). It is believed that there is less scope for increasing maize production and productivity through area expansion, thus aggressive intervention of hybrid maize technology in Terai and potential pockets of mid- hills could be one of the potential options to tackle with the situation. Hybrids can give 25\u0026ndash;30% higher grain yield as compared to better OPVs whereas there is huge demand of maize for food grain as well as feed ingredients. There is still lack of varietal selection option of farmers preferred varieties. For the widespread of modern varieties; it needs to promote a range of crop varieties to suit specific crop production niches end Socio- cultural preferences as there is diversity in agro-ecological and socio economic conditions in the country (Joshi, 2002). In order to tackle these problems Researches is carried out in different agro-ecological zone. The main objective of the varietal research is to develop/identify farmer's preferred high yielding, better performing varieties that fit into existing cropping patterns.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMaize production scenario of Nepal\u003c/b\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn Nepal, maize is produced and consumed in significant amount. The area, production and productivity of maize in Nepal is 979,776 ha, 2,997,733ton and 3.06 t ha\u003csup\u003e-1\u003c/sup\u003e respectively (MoALD, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Lamjung Area, production and productivity of maize 10098ha, 26397 Mt and 2.61Mt/ha respectively (MoALD, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Feed industry has tremendous demand of maize. Out of total maize that was used in feed production, 87% of the maize was imported from India each year by feed industries (Timsina et.al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). At least 1.5\u0026nbsp;million tons of maize is required only to the feed industries affiliated with national feed industry association of Nepal (MOALD, 2014). Maize is mainly consumed in the form of grits like as rice, bread as chapatti prepared from the flour and processed products like confectionaries (Gurung, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The application of high dose of N (180 kg N /ha) could help increase hybrid maize grain production in the acidic soils of Chitwan valley (Adhikary, B., \u0026amp; Adhikary, R., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).Hybrid maize is suitable for higher production and has higher potential than OPVs in the Terai. (Ghimire S.Sherchan DP, 2016).\u003c/p\u003e \u003cp\u003eIt is estimated that for the next two decades the overall demand of maize will be increased by 4\u0026ndash;8% per annum resulting from the increased demand for food. Such increase in demand must be met by increasing the productivity of maize per unit of land (Poudyal, 2001). The genetic diversity was observed in inbred lines differences for grain yield and anthesis silking interval, SPAD reading and leaf senescence, tassel blast and leaf firing percentage, plant and ear height, leaf area index, ear per plant, cob length and diameter, number kernel per ear, number of kernel row per ear, number kernel row, silk receptivity, shelling percentage, thousand kernel weight under heat stress condition (S.K.G., 2017). The Early maize genotypes showed considerable variation in grain yield (Dhakal,B. 2017).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMaize varieties developed in Nepal\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNepal Agricultural Research Council (NARC) has developed 32 maize varieties (NMRP, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; NMRP, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); seven hybrids (Gaurav, Khumal Hybrid-2, Rampur Hybrid-2, Rampur Hybrid-4, Rampur Hybrid-6, Rampur Hybrid-8 and Rampur Hybrid-10) until 2017 and six were de-notified (Makalu-2, Janaki, Sarlahi Seto, Hetauda Composite, Kakani Pahelo and Rampur Pahelo) (NMRP, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Thirty-four imported hybrids of maize were registered in Nepal (NMRP, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In 2016, based on two years\u0026rsquo; multi-location trials conducted by NARC, 13 multinational company hybrids were registered by the National Seed Board (NMRP, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn FY 2016/2017, 43 tons of source seed of maize were produced. Only eight varieties (Rampur Composit, Arun2, Arun 3, Arun 4, Arun 6, Deuti, Manakamana 3, Poshilo makai 1) were used for maize seed production (NMRP, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For different special projects like the Agriculture and Food Security Project (3000 kg breeder seed and 2600 kg foundation seed) and Kisan ka lagi Unnat Biu bijan Karyakram (3000 Kg of breeder seed) for seed multiplication, NARC has been producing source seed. Seed maintenance has been carried out for released maize varieties. In 2016/2017, grid selection was applied in Rampur Composite and Deuti. Half sib family selection was used in Arun-2, Manakamana-3 and Poshilo Makai-1, and 3 kg, 4 kg and 3.5 kg nucleus seed of these varieties, respectively, was produced (NMRP, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the case of Rampur Composite, 7.37 tons of breeder seed and 13.02 tons of foundation seed were produced. In case of Arun-2, 1.34 tons of breeder seed and 3.93 tons of foundation seed were produced. For Manakamana-3, 3.19 tons of breeder seed and 11.10 tons of foundation seed, while for Deuti, 1.24 tons of breeder seed and 2.60 tons of foundation seed were produced. Finally, 0.06 tons of breeder seed and 0.11 tons of foundation seed of Poshilo Makai-1 were produced. However, Ghimire et al (2003) reported that seed cycle was not maintained, leading to low and slow adoption of newer maize varieties in Nepal.\u003c/p\u003e \u003cp\u003eGairhe., (2021) reported that the number indicated after any maize variety refers to Kernel Color. Yellow varieties have even numbers (e.g. Arun-2, 4, 6; Rampur Hybrid-2, Rampur Hybrid-4, Rampur Hybrid-6, Khumal Hybrid-2, Rampur Hybrid-8 and Rampur Hybrid-10 etc.) and White maize varieties have odd numbers (e.g. Manakamana-1, 3, 5; Poshilo-1; Arun-1, 3; Ganesh-1 etc). It is common understanding in Nepal that white varieties are for food and yellow varieties are for feed, but farmers in Kavre and Lamjung do not produce different varieties based on their utilization such as food, feed and seed purpose (Timsina et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These varieties have been scaled up in different parts of the country, leading to increased maize production.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffect of Variety\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eNearly half the area under maize is planted with traditional varieties home saved seeds, which are continuously at the risk of degenerating (due to open pollination) (Koirala G., 2002). The statistically analyzed results revealed that the effect of cultivation practice and their interaction effect on grain yield were found non-significant but the responses of the variety were found highly significant difference on grain yield. (Dawadi and Sah, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).Hybrid maize is suitable for higher production and has higher potential than OPVs in the Terai (Ghimire S., 2016). Maize yield attributing traits such as number of cob /plant, number of kernel/row, number of kernel row/ear and thousand kernel weight have important role for yield (Haydar et al., 2013). Thus, indirect selection can be done through identifying improved yield components. Yield can be estimated knowing only components character, so the contribution of each component is essential to know (Kumar et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The time between sowing to flowering exhibit highly heritable traits, selection of those traits is important for selection of variety. The efficiency of maize breeding program can be improved through understanding the interrelationships existed between yield and its contributing components (Raut., Ghimire., \u0026amp; Kharel,. 2017). There is still lack of varietal selection option of farmers preferred varieties. Seed replacement rate is less than l % (Koirala G., 2002). For the widespread of modern varieties; it needs to promote a range of crop varieties to suit specific crop production niches end socio cultural preferences as there is diversity in agro-ecological and socio economic conditions in the country (Joshi, 2002). Out of the total maize area in the country, hybrids and open-pollinated varieties (OPVs) occupy 12\u0026ndash;15, and 85\u0026ndash;88%, respectively (Adhikari, et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our local landraces and OPVs cannot meet the ever-rising demand (Adhikari, et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To date five and two maize hybrids have been released and registered, respectively. Among them, only Khumal Hybrid-2 has been released for mid-hills and rest four along with this Khumal Hybrid-2 for Terai, inner Terai, and river basin areas. Gaurav, Rampur Hybrid-2, Khumal Hybrid-2, Rampur Hybrid-4, Rampur Hybrid-6 are the released, and Rampur Hybrid-8 and Rampur Hybrid-10 are registered single cross yellow kernel hybrids (Koirala, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).Therefore, hybrid maize is one of the options of breaking the present scenario of maize import. Adoption of modern maize hybrids, characterized by higher yielding genetic potential with good quality and profitability, is the utmost necessity in current Nepali agriculture.\u003c/p\u003e \u003cp\u003eIn Nepal, the hybrid maize of multinational seed companies is progressively being popular among farmers (Tripathi \u0026amp; Shrestha, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although few hybrids are developed from national research system of Nepal, they are not competitive with hybrids of multinational seed companies (Tripathi \u0026amp; Shrestha, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Large numbers of multinational companies\u0026rsquo; hybrids have been registered in National Seed Board of Nepal. The commercial seed companies are the main source of seed as the acreage of hybrid maize has expanded extensively in Terai (i.e., plain area) and partly in mid hill of Nepal. Farmers and breeders need to select suitable maize hybrids with high yield and other essential agronomic characteristics. Most of the released maize varieties in Nepal are trialed at research stations only, lacking field evaluations most likely due to lack of proper extension service. Even some released varieties are not accepted by farmers due to lower than expected levels of production at the farmer\u0026rsquo;s field (Gurung, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Consequently, there is dominance of the multinational varieties over locally released varieties on the market (Gurung, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Hybrid maize seed marketing is flourishing every year. However limited commercial hybrids are suitable for cultivation due to the country\u0026rsquo;s diverse agro ecological regions. Therefore, it is necessary to identify superior maize hybrids that are suitable for different agro-ecological regions. Farmers of Lamjung do not have scientific knowledge about the site specific, better performing hybrid varieties. This has led to crop failure, lower benefit cost ratio, low germination and low yield. The use of site specific varieties gives better performance and higher yield. Despite the great potential of maize farming, production is low and substantial amount of maize is imported every year. Information regarding site specific variety during spring season with recently introduced hybrids are lacking in Nepal particularly in Lamjung (MoALD, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe low production and productivity of maize in Nepal are mainly due to limited hybrid choices (Subedi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The main objectives of present research was to compare and characterized the different hybrid variety of maize and providing information to the farmers of Lamjung about the high yielding site specific hybrid varieties of spring maize which will help to increase the productivity of the spring maize.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eA field experiment entitled \u0026ldquo;Characterization of different hybrid Varieties of Spring Maize in Lamjung, Nepal\u0026quot; was conducted during 2022. The details of the methods and methodology used in the study are described under the following headings:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSite selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch is to be conducted at Siundibar, Sundarbazar Municipality-09 Lamjung lies on the geographical coordinates of 28.136˚0r 28\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003e8\u0026apos;10\u0026apos;\u0026apos;N, 84.4331˚or 84\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(^\\circ\\)\u003c/span\u003e\u003c/span\u003e25\u0026apos;59\u0026apos;\u0026apos;E. which is 857m above sea level. It lies in mid-hill district of Nepal. The district consists of sub-tropical to alpine climate. The research site lies in upper tropical zone of the district.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysio-chemical characteristics of the experimental soil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil samples were collected randomly from each four corners and a center experimental-plot at 0\u0026ndash;20 cm depth from the surface using Shovel to analyze the initial soil physio-chemical properties. The sub-samples were mixed, air-dried under shade, grounded and sieved through 2 mm sieve, and through 0.5 mm sieve for the purpose of organic matter determination. The samples were then collected in plastic and sent to Soil Testing Laboratory for the test. From the analysis, the pH of the soil was acidic in nature. The total nitrogen content of the soil was low, while available phosphorus and available potassium were high and medium respectively. The organic matter content of the soil was low.\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePhysio-chemical Characteristics of soil of the experimental field as per the reports of soil test.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.N.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSoil Properties\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValues\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRating\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil PH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcidic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus(kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium(kg/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e244.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrganic Matter (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClimatic condition during the experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment site falls under the upper sub-tropical humid climatic belt of Nepal where summer is hot and winter is cool. It receives most of the rainfall during monsoon season in summer. The weather condition of the experiment site for the research plot was taken from secondary source.\u003c/p\u003e\n\u003cp\u003eThe maximum temperature was recorded within the research period of five month from Falgun to Asar was 31\u003csup\u003eo\u003c/sup\u003eC while the minimum temperature was 8\u003csup\u003eo\u003c/sup\u003eC. The highest rainfall was recorded in the month of Asar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental Setup\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe research design was Randomized Complete Block Design (RCBD) with total ten treatments including RDF: 180:60:40 kg NPK/ha, with three replication, distance between replication/block was 1m, and with total number of plot: 30, gap of outer periphery was 50cm, net plot size 3\u0026times;3 m (9m\u003csup\u003e2\u003c/sup\u003e)with spacing 75\u0026times;20 cm\u003csup\u003e2\u003c/sup\u003e with total experimental area 9m\u0026times;30m (270m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment Details (varieties)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eT1\u0026thinsp;=\u0026thinsp;RAMPUR HYBRID-14, T2\u0026thinsp;=\u0026thinsp;RML145/RL197, T3\u0026thinsp;=\u0026thinsp;RAMPUR HYBRID-10, T4\u0026thinsp;=\u0026thinsp;CAH1511, T5\u0026thinsp;=\u0026thinsp;RAMPUR HYBRID-16, T6\u0026thinsp;=\u0026thinsp;CML491/CLWQHZN51, T7\u0026thinsp;=\u0026thinsp;RML83/RML146, T8\u0026thinsp;=\u0026thinsp;RAMPUR HYBRID-12, T9\u0026thinsp;=\u0026thinsp;RML145/RML2, T10\u0026thinsp;=\u0026thinsp;PVAEQH-1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLayout of experiment\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral cultural practices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eField preparation\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe field was ploughed 15 days prior of seed sowing by using rotavator to bring the soil under good tilth. Again, ploughing was done at the time of sowing and planking was done after ploughing for leveling the land. After leveling, the clods were broken and weeds and stubbles of the previous crop were removed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManure and fertilizer application\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFYM was applied as main source of organic fertilizer in the field. The FYM@10kg/plot area was applied in all experimental plots and it was uniformly incorporated into the soil during the first land preparation. The sources of fertilizers were Urea, DAP and MOP. RDF for nitrogen for hybrid maize is 180 kg/ha (Adhikary, B., \u0026amp; Adhikary, R., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The recommended dose of 60 kg P 2 O 5 /ha and 40 kg K 2 O/ha was applied as basal in all plots at the time of seed sowing (MoALD, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFertilizer time application\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout 20% dose of N was applied as basal dose from DAP, 30% N is top dressed at first weeding, 30% N during earthling up, 20% before flowering.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSeed sowing\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eLine sowing of fungicide treated seed was done manually. In each hill, two seed were sown at a depth of 5 cm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIrrigation schedule\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eFirst irrigation was given at knee high stage, Second irrigation was given at tasseling stage\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeeding and earthling up\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eFirst weeding was done after 40 days of seed sowing, Earthling up and second weeding was done at 30 days after first weeding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant protection\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eDuring the seedling and young stage fall armyworm infestation was seen. For control of this insect All kill (Chlorpyriphos\u0026thinsp;+\u0026thinsp;Cypermethrin) @ 1.5ml/lit of water was sprayed also (Emamectin Benzoate)@5gm per 16 liter water was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHarvesting and threshing\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eHarvesting was carried out manually. After removing the cobs, the cobs are sun dried for few days. De-husking of cobs was done separately on the threshing floor. After shelling of grains, seeds were carefully and separately dried by maintaining 12% moisture for further study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenological Observation\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eFive plants will be tagged for taking phonological observations. The Phenological data will be taken when 50% observation occurred and ended when 75% observation completed. The Phenological observations will be recorded as,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmergence\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eSeed emergence will be recorded when about 50% of the seedling will have emerged out of the soil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant population/m\u003c/strong\u003e \u003csup\u003e\u0026nbsp;\u003cstrong\u003e2\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e:\u003c/p\u003e\n\u003cp\u003eThe plant population/m\u003csup\u003e2\u003c/sup\u003e will be counted about 20 days after sowing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDays of tasseling\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe date of tasseling will be recorded from tassel emergence to 50% of plant will have tasseled in each plot. The mid 2 rows will be taken for each Phenological observation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDays of silking\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe date will be recorded from the initiation of silk to 50% silking in each plot. The silk exposed 1cm from closed ear will be considered as emerged silk. The same rows as that of tasseling records will be taken for days of silking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDays to anthesis\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe date will be recorded when 50% plants have shedding pollen. The same rows are taken for the data of days to anthesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDays of physiological maturity\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe appearance of black layer between ear surface and ear grains and occurrence of senescence of ear husks will be considered as an indication to physiological maturity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiometric observation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNumber of Leaf\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eNumber of leaves per plant was counted from the 5 randomly selected plants from each plot at 30,45,60,75 and 90 DAP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeaf area index (LAI)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eLinear dimensions (length and breadth) of fully opened leaf were recorded manually. And then, leaf area was computed using the equation suggested by Montegory (1911).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u0026thinsp;=\u0026thinsp;b*L*W\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhere, b\u0026thinsp;=\u0026thinsp;Leaf Shape Coefficient L\u0026thinsp;=\u0026thinsp;length\u003c/p\u003e\n\u003cp\u003eA\u0026thinsp;=\u0026thinsp;Area W\u0026thinsp;=\u0026thinsp;width.\u003c/p\u003e\n\u003cp\u003eThe coefficient was conventionally assumed 0.75 for maize leaf. Leaf Area Index (LAI) was computed by finding the ratio of leaf area to ground area. Leaf Area Index is an indicator of primary photosynthetic productivity and rate of evapotranspiration in crop field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeaf area Index (LAI)\u0026thinsp;=\u0026thinsp;Area of leaves (canopy)/ Ground area under canopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant height\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003ePlant height will be measured from the ground level to the top most visible due lap of five randomly selected plants from each plot at 30 DAS, 45 DAS, 60 DAS, 75 DAS, 90 DAS, 105 DAS, and at maturity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYield attributing characters\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNumber of harvested ears\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eTotal number of ears harvested from net harvestable area will be recorded as harvested ears per plot and it is converted to hectare basis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEar length and circumference\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eTen dehusked ears will be selected from each plot randomly and length from the base up to top grain bearing portion of each ear will measure. The average of five ears will be calculated and expressed as ear length. The circumference of five randomly selected ears from each plot will be measured and average value will express as ear circumference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNumber of kernels (grains) rows per ear\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eFive randomly selected ears from each plot will be shelled and the entire kernels/grains row will count. And will be reported as number of kernels row per ear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNumbers of grain per row\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eNumbers of grain counted in a row.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThousand Grains Weight (TGW) or Test weight\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eOne thousand shelled maize grains from each plot will randomly be taken, weighed and recorded as test weight and expressed in gram (g). The kernels used for test weight will be corrected to 15% moisture content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShelling percentage\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eIt is the ratio of grain to ear (grain: ear) and expressed in percentage. Five randomly selected ears will be weighed with grains. All grains will be shelled out and the weight of grain will be taken and the shelling percentage will calculated as:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"371\" height=\"62\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrain moisture content (%)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eFive cobs will be selected randomly and central two kernel rows will be shelled out and will bulk the kernels from all ears and moisture will be measured by multigrain moisture meter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrain yield\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eCalculate production per plot then convert on hector basis at 13% moisture.\u003c/p\u003e\n\u003cp\u003eGrain yield will be also calculated on hectare basis by using following formulae:\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"425\" height=\"55\"\u003e\u003c/span\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWhere,\u003c/p\u003e\n\u003cp\u003eFEW\u0026thinsp;=\u0026thinsp;filled ears weight (Kg)\u0026nbsp; \u0026nbsp; SP\u0026thinsp;=\u0026thinsp;shelling percentage (%)\u003c/p\u003e\n\u003cp\u003eGMC\u0026thinsp;=\u0026thinsp;grain moisture content at harvest (%)\u0026nbsp; \u0026nbsp; NHA\u0026thinsp;=\u0026thinsp;net harvested area (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis techniques\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data recorded on different parameters from field and laboratory was first tabulated in Microsoft Excel (MS- Excel), then Analysis of variance (ANOVA) for all data was computed using R-studio software. All the analyzed data were subjected to Duncan\u0026apos;s Multiple Range Test (DMRT) for mean comparison at 5% dose of significance.\u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eThe results pertaining to the field experiment were analyzed and presented in this chapter with figures and tables where necessary. An attempt has been made to evaluate the results so obtained to give explanation with available evidences as far as possible for observed variations in mentioned parameters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBiometrical observation\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePlant height\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThere was significant difference in plant height at different days after sowing (DAS) except at 30DAS. The average plant height varied from 32.76 cm (30DAS) to 222.48 cm (90 DAS). At 30 DAS, no significant difference was found among different varieties. At 45 DAS, plant height was highest in RML145/RML2 followed by RML145/RL197 and minimum in RAMPUR HYBRID-14. At 60 DAS, maximum plant height was observed in PVAEQH-1 which was statistically similar with CML491/CLWQHZN51, RML83/RML146, RML145/RML2 and RML145/RL197 whereas minimum was in RAMPUR HYBRID-14. At 75 DAS, maximum plant height was in RAMPUR HYBRID-12(241.67cm) and minimum in PVAEQH-1(189.80 cm).At 90 DAS, maximum plant height was observed in RAMPUR HYBRID-12 i.e. 245.33 cm and minimum was observed in PVAEQH-1 i.e. 191.80 cm. This result was in line with Neupane, B., Paudel, A., \u0026amp;Wagle P. (2020) who reported that significant variation is observed in plant height due to hybrid maize variety. In addition, Radma and Dagash (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) also reported that significant variation in plant height is observed due to varieties. This is probably due to genetic variation in plant height. Difference of plant height was found in different varieties which was due to the fact that the plant height is a genetically as well as environmentally controlled factor so the height of different varieties remain different, according to (Kunwar \u0026amp; Shrestha, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of different varieties of maize on plant height at different days after sowing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e30DAS 45DAS 60DAS 75DAS 90DAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRAMPUR HYBRID-14\u003c/p\u003e \u003cp\u003eRML145/RL197\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-10\u003c/p\u003e \u003cp\u003eCAH1511\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-16\u003c/p\u003e \u003cp\u003eCML491/CLWQHZN51\u003c/p\u003e \u003cp\u003eRML83/RML146\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-12\u003c/p\u003e \u003cp\u003eRML145/RML2\u003c/p\u003e \u003cp\u003ePVAEQH-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.43\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e35.86\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e35.33\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e27.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e30.73\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e38.33\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e36.67\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e27.26\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e36.26\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e33.27\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.06\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e103.67\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e91.67\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e84.60\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e93.40\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e96.60\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e82.46\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e82.20\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e114.26\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e85.33\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123.33\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e157.67\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e138\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e132.53\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e141\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e158.60\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e162.67\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e137.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e164\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e166.46\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e216.067\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e218\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e224.40\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e230.40\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e212.93\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e217.73\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e234.33\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e241.67\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e222.33\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e189.80\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e216.067\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e220.33\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e226.067\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e232.40\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e214.80\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e218.67\u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e236\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e245.33\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e223.33\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e191.80 \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD(0.05)\u003c/p\u003e \u003cp\u003eSEm\u003cb\u003e(\u0026plusmn;)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eC.V%\u003c/p\u003e \u003cp\u003eF-test\u003c/p\u003e \u003cp\u003eGrand mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10.191\u003c/p\u003e \u003cp\u003e3.43\u003c/p\u003e \u003cp\u003e18.135\u003c/p\u003e \u003cp\u003eNS\u003c/p\u003e \u003cp\u003e32.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.262\u003c/p\u003e \u003cp\u003e3.45\u003c/p\u003e \u003cp\u003e6.550\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003cp\u003e91.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003cp\u003e3.95\u003c/p\u003e \u003cp\u003e4.627\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003cp\u003e148.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.354\u003c/p\u003e \u003cp\u003e3.82\u003c/p\u003e \u003cp\u003e2.998\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003cp\u003e220.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.184\u003c/p\u003e \u003cp\u003e3.76\u003c/p\u003e \u003cp\u003e2.930\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003cp\u003e222.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeans followed by common letter(s) within column are non-significantly different based on DMRT at P\u0026thinsp;=\u0026thinsp;0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNumber of leaves\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eIn this experiment, the mean value of number of leaves indicated that it increased from 30 DAS to 90 DAS. There was non-significance difference among number of leaves at 30 DAP, 45 DAP and 60 DAP. The mean value of number of leaves at 30 DAS was 5.45 which ranged from 4.73 in RAMPUR HYBRID-14 to 6.13 in RML145/RML2. The average number of leaves at 45 DAP was found to be 8.32 and ranged from 7.40 in Rampur Hybrid-14 to 9.26 in Rampur Hybrid-10. At 60 DAP it increased to 9.28 and ranged from 7.67 in Rampur Hybrid-16 to 10.60 in PVAEQH-1.The mean value of number of leaves at 75 DAS was found to be 11.89 in this experiment and ranged from 9.33 in PVAEQH-1 to 13 in CML491/CLWQHZN51. The average number of leaves increased to 12.08 in 90 DAS and ranged from 9.067 in PVAEQH-1 to 13.867 in Rampur Hybrid-12. This result was similar with Neupane, B., Paudel, A., \u0026amp;Wagle P. (2020) who reported that significant variation is observed in number of leaves due to hybrid maize variety. Similar result was obtained in(Devkota, 2020) and (Neupane, B., Paudel, A., \u0026amp;Wagle P. 2020).Bastola, et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also observed the significant divergences among deferent genotypes of maize in number of leaf per plant.\u003c/p\u003e \n\u003cp\u003eTable 3: Effect of different varieties of maize on number of leaves at different days after sowing\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.04498269896194%\" valign=\"top\"\u003e\n \u003cp\u003eTreatment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.95501730103807%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"66.95501730103807%\" valign=\"top\"\u003e30 DAS\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"66.95501730103807%\" valign=\"top\" style=\"width: 10.8931%;\"\u003e45 DAS\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"66.95501730103807%\" valign=\"top\" style=\"width: 7.9325%;\"\u003e60 DAS\u003c/td\u003e\n \u003ctd width=\"66.95501730103807%\" valign=\"top\"\u003e75 DAS\u003c/td\u003e\n \u003ctd width=\"66.95501730103807%\" valign=\"top\"\u003e\n \u003cdiv align=\"center\"\u003e90 DAS\u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.85146804835924%\" colspan=\"2\" valign=\"top\" style=\"width: 22.6629%;\"\u003e\n \u003cp\u003eRAMPUR HYBRID-14\u003c/p\u003e\n \u003cp\u003eRML145/RL197\u003c/p\u003e\n \u003cp\u003eRAMPUR HYBRID-10\u003c/p\u003e\n \u003cp\u003eCAH1511\u003c/p\u003e\n \u003cp\u003eRAMPUR HYBRID-16\u003c/p\u003e\n \u003cp\u003eCML491/CLWQHZN51\u003c/p\u003e\n \u003cp\u003eRML83/RML146\u003c/p\u003e\n \u003cp\u003eRAMPUR HYBRID-12\u003c/p\u003e\n \u003cp\u003eRML145/RML2\u003c/p\u003e\n \u003cp\u003ePVAEQH-1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.953367875647668%\" valign=\"top\" style=\"width: 16.0057%;\"\u003e\n \u003cp\u003e4.73\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e5.60\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e6.067\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e5.20\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e5.40\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e5.13\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e6.067\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e5.20\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e6.13\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e5.0\u003csup\u003ec\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12607944732297%\" valign=\"top\" style=\"width: 10.8931%;\"\u003e\n \u003cp\u003e7.40\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e9.13\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003e9.267\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e8.53\u003csup\u003eabcd\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e7.73\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e7.67\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e8.73\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e7.93\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e8.93\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e7.93\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398963730569948%\" valign=\"top\" style=\"width: 7.9325%;\"\u003e\n \u003cp\u003e\u0026nbsp;8.267\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e9.667\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;10.13\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;7.73\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;7.67\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e9.60\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e10.20\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e8.400\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e10.53\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e10.60\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12607944732297%\" valign=\"top\"\u003e\n \u003cp\u003e12.67\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e11.53\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e12.13\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e11.867\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e11.267\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e13.0\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e11.93\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e12.80\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e12.067\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e9.33\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.544041450777202%\" valign=\"top\"\u003e\n \u003cp\u003e12.467\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e11.867\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e11.93\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e12.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e11.867\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e13.0\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e12.4\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e13.867\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e12.33\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e9.067\u003csup\u003ec\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.85146804835924%\" colspan=\"2\" valign=\"top\" style=\"width: 22.6629%;\"\u003e\n \u003cp\u003eLSD(0.05)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSEm(\u0026plusmn;)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC.V%\u003c/p\u003e\n \u003cp\u003eF-test\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGrand mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.953367875647668%\" valign=\"top\" style=\"width: 16.0057%;\"\u003e\n \u003cp\u003e0.977308\u003c/p\u003e\n \u003cp\u003e0.4228\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.44734\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003cp\u003e5.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12607944732297%\" valign=\"top\" style=\"width: 10.8931%;\"\u003e\n \u003cp\u003e1.293\u003c/p\u003e\n \u003cp\u003e0.435\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.057\u003c/p\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003cp\u003e8.32\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.398963730569948%\" valign=\"top\" style=\"width: 7.9325%;\"\u003e\n \u003cp\u003e2.202\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;0.741\u003c/p\u003e\n \u003cp\u003e13.835\u003c/p\u003e\n \u003cp\u003eNS\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12607944732297%\" valign=\"top\"\u003e\n \u003cp\u003e1.121\u003c/p\u003e\n \u003cp\u003e0.3774\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;5.496\u003c/p\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003cp\u003e11.893\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.544041450777202%\" valign=\"top\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003cp\u003e7.679\u003c/p\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003cp\u003e12.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMeans followed by common letter(s) within column are non-significantly different based on DMRT at P=0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eLeaf area index:\u003c/b\u003e\u003c/p\u003e\n\u003cp\u003eThe experiments showed that leaf area index increased from 30 DAS to 90 DAS. The leaf area index was statistically non-significance at 30 and 60 DAS while significance at 45, 75 and 90 DAS. The mean LAI was found to be 0.192 at 30 DAS and ranged from 0.126 in Rampur Hybrid-14 and 0.265 in RML145/RML2. At 45 DAS mean value of leaf area index was 1.022 and ranged from 0.652 in Rampur Hybrid-14 to 1.447 in RML145/RML2. The average LAI at 60 DAS was found to be 3.216 and ranged from 2.55 in CAH1511 to 3.69 in RML145/RML2.The mean average value of Leaf area index at 75 DAS was found to be 4.10 and ranged from 3.009 in PVAEQH-1 to 4.897 in Rampur Hybrid-14.The mean value of LAI at 90 DAS was same as 75 DAS i.e.4.10. This result was similar with Neupane, B., Paudel, A., \u0026amp;Wagle P. (2020) who reported that significant variation is observed in leaf area index due to hybrid maize variety. Also reported by Ahmed et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) this might be due to the difference in their genetic makeup.\u003c/p\u003e \n\u003cp\u003eTable 4: Effect of different hybrid varieties of maize on Leaf area index at different days after sowing\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92894280762565%\" valign=\"top\"\u003e\n \u003cp\u003eTreatment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07105719237435%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"67.07105719237435%\" valign=\"top\"\u003e30 DAS\u003c/td\u003e\n \u003ctd width=\"67.07105719237435%\" valign=\"top\"\u003e45 DAS\u003c/td\u003e\n \u003ctd width=\"67.07105719237435%\" valign=\"top\"\u003e60 DAS\u003c/td\u003e\n \u003ctd width=\"67.07105719237435%\" valign=\"top\"\u003e75 DAS\u003c/td\u003e\n \u003ctd width=\"67.07105719237435%\" valign=\"top\"\u003e90 DAS\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73702422145329%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRAMPUR HYBRID-14\u003c/p\u003e\n \u003cp\u003eRML145/RL197\u003c/p\u003e\n \u003cp\u003eRAMPUR HYBRID-10\u003c/p\u003e\n \u003cp\u003eCAH1511\u003c/p\u003e\n \u003cp\u003eRAMPUR HYBRID-16\u003c/p\u003e\n \u003cp\u003eCML491/CLWQHZN51\u003c/p\u003e\n \u003cp\u003eRML83/RML146\u003c/p\u003e\n \u003cp\u003eRAMPUR HYBRID-12\u003c/p\u003e\n \u003cp\u003eRML145/RML2\u003c/p\u003e\n \u003cp\u003ePVAEQH-1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.975778546712803%\" valign=\"top\"\u003e\n \u003cp\u003e0.126\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.193\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.228\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.140\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.191\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.226\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.188\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.173\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.265\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.195\u003csup\u003eab\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14878892733564%\" valign=\"top\"\u003e\n \u003cp\u003e0.652\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e1.391\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.821\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e0.821\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;0.893\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e1.162\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e1.233\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;0.840\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003cstrong\u003e1.447\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.962\u003csup\u003ebcd\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.418685121107266%\" valign=\"top\"\u003e\n \u003cp\u003e2.848a\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;3.442a\u003c/p\u003e\n \u003cp\u003e3.334a\u003c/p\u003e\n \u003cp\u003e2.551a\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;3.122a\u003c/p\u003e\n \u003cp\u003e3.619a\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.451a\u003c/p\u003e\n \u003cp\u003e3.128a\u003c/p\u003e\n \u003cp\u003e3.697a\u003c/p\u003e\n \u003cp\u003e2.966a\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14878892733564%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.897a\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e3.647de\u003c/p\u003e\n \u003cp\u003e4.077bcd\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.090bcd\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.852cd\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.810ab\u003c/p\u003e\n \u003cp\u003e4.016cd\u003c/p\u003e\n \u003cp\u003e4.27abcd\u003c/p\u003e\n \u003cp\u003e4.416abc\u003c/p\u003e\n \u003cp\u003e3.009e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.570934256055363%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.897a\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e3.647de\u003c/p\u003e\n \u003cp\u003e4.077bcd\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.090bcd\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;3.852cd\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.810ab\u003c/p\u003e\n \u003cp\u003e4.016cd\u003c/p\u003e\n \u003cp\u003e4.272abcd\u003c/p\u003e\n \u003cp\u003e4.416abc\u003c/p\u003e\n \u003cp\u003e3.009e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73702422145329%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLSD(0.05)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSEm(\u0026plusmn;)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC.V%\u003c/p\u003e\n \u003cp\u003eF-test\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGrand mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.975778546712803%\" valign=\"top\"\u003e\n \u003cp\u003e0.1033\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;0.0347\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e31.20\u003c/p\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14878892733564%\" valign=\"top\"\u003e\n \u003cp\u003e0.4380\u003c/p\u003e\n \u003cp\u003e0.1474\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;24.96\u003c/p\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003cp\u003e1.022 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.418685121107266%\" valign=\"top\"\u003e\n \u003cp\u003e1.1606\u003c/p\u003e\n \u003cp\u003e0.390\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;21.036\u003c/p\u003e\n \u003cp\u003eNS\u003c/p\u003e\n \u003cp\u003e3.216\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14878892733564%\" valign=\"top\"\u003e\n \u003cp\u003e0.7480\u003c/p\u003e\n \u003cp\u003e0.2517\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;10.613\u003c/p\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.570934256055363%\" valign=\"top\"\u003e\n \u003cp\u003e0.7480\u003c/p\u003e\n \u003cp\u003e0.2517\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;10.613\u003c/p\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMeans followed by common letter(s) within column are non-significantly different based on DMRT at P=0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance.\u003c/p\u003e\n\u003cp\u003e \u003cb\u003ePhenological observation\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGermination\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe germination percentage of different maize varieties was found to be significant. It was recorded that the highest germination percentage was found in RML145/RL197 (50%) and PVAEQH-1(50%) followed by RML83/RML146 (49.44%) and RML145/RML2 (49.167%) and lowest germination percentage in Rampur Hybrid-14 i.e.27.22%.This result was similar with Neupane, B., Paudel, A., \u0026amp;Wagle P. (2020) who reported that significant variation is observed in germination percentage due to hybrid maize variety. Also this might be due to the difference in their genetic makeup and different variety requires different environmental condition to germinate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of varieties on Germination percentage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.N.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermination percentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAMPUR HYBRID-14\u003c/p\u003e \u003cp\u003e\u003cb\u003eRML145/RL197\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-10\u003c/p\u003e \u003cp\u003eCAH1511\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-16\u003c/p\u003e \u003cp\u003eCML491/CLWQHZN51\u003c/p\u003e \u003cp\u003e\u003cb\u003eRML83/RML146\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-12\u003c/p\u003e \u003cp\u003e\u003cb\u003eRML145/RML2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePVAEQH-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.222\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e50.000\u003c/b\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e40.556\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e43.611\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e45.000 \u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e48.333\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e49.444\u003c/b\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e37.222 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e49.167\u003c/b\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e50.000\u003c/b\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSD(0.05)\u003c/p\u003e \u003cp\u003eSEm\u003cb\u003e(\u0026plusmn;)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eC.V%\u003c/p\u003e \u003cp\u003eF-test\u003c/p\u003e \u003cp\u003eGrand mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.859\u003c/p\u003e \u003cp\u003e2.645\u003c/p\u003e \u003cp\u003e10.399\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003cp\u003e44.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeans followed by common letter(s) within column are non-significantly different based on DMRT at P\u0026thinsp;=\u0026thinsp;0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance.\u003c/p\u003e \u003cp\u003eAccording to (JICA, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), a range of 21\u0026ndash;27˚C temperature is suitable for the better growth of maize plant while 20 ˚C is required for germination.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTasseling and silking\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe mean value of number of days for 50% tasseling was found to be 66 and it varied from 61 in PVAEQH-1 to 69 in Rampur Hybrid-14. Analysis of variance revealed significant differences among genotype for the number of days for 50% tasseling. The number of days for 50% silking was varied from 67 in PVAEQH-1 to 69 in RML145/RL197 with a mean value of 71.166. Analysis of variance revealed significant differences among genotype for the number of days for 50% silking. There was non-significance difference of the anthesis-silking interval. The mean value of anthesis silking interval was found to be 5.167 which were varied from 4.33 in Rampur Hybrid-14 to 6 in PVAEQH-1. This result was similar with Neupane, B., Paudel, A., \u0026amp;Wagle P. (2020) who reported that significant variation is observed in days to 50% tasseling, days to 50% silking due to different maize variety.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of different hybrid variety of maize on days to tasseling, silking and tasseling-silking interval\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTasseling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSilking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASI(Anthesis-Silking interval)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAMPUR HYBRID-14\u003c/p\u003e \u003cp\u003eRML145/RL197\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-10\u003c/p\u003e \u003cp\u003eCAH1511\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-16\u003c/p\u003e \u003cp\u003eCML491/CLWQHZN51\u003c/p\u003e \u003cp\u003eRML83/RML146\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-12\u003c/p\u003e \u003cp\u003eRML145/RML2\u003c/p\u003e \u003cp\u003ePVAEQH-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e69.00\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e64.00\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e65.667\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e66.333\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e66.00\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e67.66\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e67.00\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e68.33\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e65.00\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e61.00\u003c/b\u003e\u003csup\u003e\u003cb\u003eg\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.33333 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e69.00\u003c/b\u003e\u003csup\u003e\u003cb\u003ede\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e70.333\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e72.00\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e71.00\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e73.33\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e71.666\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e74.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e70.00\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e67.00\u003c/b\u003e\u003csup\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.333333\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.00\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e4.667\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.667\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.00\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.667\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e4.667\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.667\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.00\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e6.00\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD(0.05)\u003c/p\u003e \u003cp\u003eSEm\u003cb\u003e(\u0026plusmn;)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eC.V%\u003c/p\u003e \u003cp\u003eF-test\u003c/p\u003e \u003cp\u003eGrand mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.737\u003c/p\u003e \u003cp\u003e0.584\u003c/p\u003e \u003cp\u003e1.53\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.162\u003c/p\u003e \u003cp\u003e0.727\u003c/p\u003e \u003cp\u003e1.77\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003cp\u003e71.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003cp\u003e0.3784\u003c/p\u003e \u003cp\u003e12.68\u003c/p\u003e \u003cp\u003eNS\u003c/p\u003e \u003cp\u003e5.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeans followed by common letter(s) within column are non-significantly different based on DMRT at P\u0026thinsp;=\u0026thinsp;0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGrain yield and yield attributing characters\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNumber of kernel row/ ear, Number of Kernels/row, length of ear, diameter of ear\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNumber of kernel row/ ear, Number of Kernels/row and diameter of ear was found to be significance while length of ear was non-significance. The mean value of number of kernel row per ear was found to be 14.43 and varied from 13.026 in RML145/RL197 to 15.60 in CML491/CLWQHZN51.The mean value of number of kernels per row was found to be 37.18, the highest number of kernels per row was 41.66667 in CML491/CLWQHZN51 followed by RML83/RML146 (40.18) and PVAEQH-1(40.133) and lowest number of kernels per row was 31.84 in RAMPUR HYBRID-16. Average ear length was found to be 18.08 cm in the experiment... Cob length was found higher in PVAEQH-1 with 19.16 cm and lowest in Rampur Hybrid-14 i.e. 15.94 cm. Average value of cob diameter was 5.031 cm in the experiment. It was significantly different among the varieties. RML145/RML2 recorded significantly higher cob diameter of 5.318 cm which was statistically similar with CML491/CLWQHZN51 (5.148 cm) while Rampur Hybrid-14 produced significantly lower cob diameter of 4.63 cm. This result was similar with Neupane, B., Paudel, A., \u0026amp;Wagle P. (2020) who reported that significant variation is observed in Number of kernel row/ ear, Number of Kernels/row, diameter of ear this could be due to genetic variation of maize varieties. Cob length, cob diameter and number of kernel rows per cob play an important role in determining yield of grain (Nemati et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of different varieties of maize on Number of kernels/ear, length of ear (cm) and diameter of ear (cm)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of kernel row per ear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of kernels per row\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEar length(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEar Diameter(cm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAMPUR HYBRID-14\u003c/p\u003e \u003cp\u003eRML145/RL197\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-10\u003c/p\u003e \u003cp\u003eCAH1511\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-16\u003c/p\u003e \u003cp\u003eCML491/CLWQHZN51\u003c/p\u003e \u003cp\u003eRML83/RML146\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-12\u003c/p\u003e \u003cp\u003eRML145/RML2\u003c/p\u003e \u003cp\u003ePVAEQH-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.34667 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e13.02667 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e14.26667 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e14.00000 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e15.56619\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e15.60000\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e14.43667\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e14.21667 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e14.40000 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e14.53333 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.47000 \u003csup\u003ede\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e34.72333 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e37.20000 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e36.73333 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e31.84667 \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e41.66667\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e40.18000\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e39.59167 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e35.31333\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e40.13333\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.94815\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e18.7666 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e19.26667\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e18.73333\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e16.51825\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e18.30000\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e17.3047\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e18.65833\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e18.16667\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e19.16667\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.639066 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e4.831665 \u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.127389\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.074310\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.113487\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e5.148620\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.127389\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.094214\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e5.31847\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e4.840764\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD(0.05)\u003c/p\u003e \u003cp\u003eSEm(\u0026plusmn;)\u003c/p\u003e \u003cp\u003eC.V%\u003c/p\u003e \u003cp\u003eF-test(0.05)\u003c/p\u003e \u003cp\u003eGrand mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2978\u003c/p\u003e \u003cp\u003e0.4368\u003c/p\u003e \u003cp\u003e5.239947\u003c/p\u003e \u003cp\u003e*\u003c/p\u003e \u003cp\u003e14.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6551\u003c/p\u003e \u003cp\u003e0.8936\u003c/p\u003e \u003cp\u003e4.162438\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003cp\u003e37.1858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.308\u003c/p\u003e \u003cp\u003e0.776\u003c/p\u003e \u003cp\u003e7.44\u003c/p\u003e \u003cp\u003eNS\u003c/p\u003e \u003cp\u003e18.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28799\u003c/p\u003e \u003cp\u003e0.09692\u003c/p\u003e \u003cp\u003e3.336691\u003c/p\u003e \u003cp\u003e**\u003c/p\u003e \u003cp\u003e5.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeans followed by common letter(s) within column are non-significantly different based on DMRT at P\u0026thinsp;=\u0026thinsp;0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eShelling percentage\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eShelling percentage of different maize varieties are presented in Table and was significant. The mean value of shelling percentage was 63.45 and ranged from 69.64% in Rampur Hybrid-10 to 57.81% in RML83/RML146. Similar findings was observed in (Adhikari, et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and (Bista \u0026amp; Gaire, 2021). Shelling percentage had the vital role for determining the grain yield of maize which is the reason for better yield in hybrids. Kandel, (2017) also observed divergences among deferent maize genotypes for shelling percent.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of varieties on shelling percentage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.N.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShelling %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAMPUR HYBRID-14\u003c/p\u003e \u003cp\u003eRML145/RL197\u003c/p\u003e \u003cp\u003e\u003cb\u003eRAMPUR HYBRID-10\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCAH1511\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-16\u003c/p\u003e \u003cp\u003eCML491/CLWQHZN51\u003c/p\u003e \u003cp\u003eRML83/RML146\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-12\u003c/p\u003e \u003cp\u003eRML145/RML2\u003c/p\u003e \u003cp\u003ePVAEQH-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.02638\u003csup\u003edef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e68.62947\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e69.64786\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e68.10157\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e59.35534\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e59.57752\u003csup\u003eef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e57.81997\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e63.75954\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e62.87127\u003csup\u003ecde\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e64.7863 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSD(0.05)\u003c/p\u003e \u003cp\u003eSEm\u003cb\u003e(\u0026plusmn;)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eC.V%\u003c/p\u003e \u003cp\u003eF-test(0.05)\u003c/p\u003e \u003cp\u003eGrand mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.906058\u003c/p\u003e \u003cp\u003e5.185 18\u003c/p\u003e \u003cp\u003e3.58832\u003c/p\u003e \u003cp\u003e***\u003c/p\u003e \u003cp\u003e63.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeans followed by common letter(s) within column are non-significantly different based on DMRT at P\u0026thinsp;=\u0026thinsp;0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThousand grain weight and grain yield\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe grand mean value of thousand grain weight was 199.164 gm and significantly different among the maize varieties. Thousand grain weight was found significantly higher in CAH1511which had 250.23 gm and lower in RML83/RML146 i.e.154.71gm. The average grain yield of different maize hybrids was found to be 5.198 Mt/ha. Grain yield was found to be significantly influenced by the maize varieties. The highest grain yield was seen in RML145/RL197 (7.20 Mt/ha) which was statistically similar to RML83/RML146 (7.130) Rampur Hybrid-14 recorded significantly lower yield i.e. 3.95Mt/ha. This result was in line with Neupane, B., Paudel, A.,\u0026amp;Wagle P.(2020) who reported that significant variation is observed in thousands grain weight and yield due to different maize variety. (Raut, Ghimire, \u0026amp; Kharel, 2017) Reported that there were highly significant differences for grain yield and yield attributing traits among genotypes which strongly support our findings. According to (Bista \u0026amp; Gaire, 2021), yield attributing characteristics like cob length, cob diameter, number of rows/cobs, number of kernels/rows, shelling%, thousand grain weight (TGW) were found significantly higher in hybrid maize varieties as compared to open pollinated varieties. Bastola, (2021)study reported open pollinated varieties had shown poor performance in many parameters whereas hybrid varieties had shown better performance. Development and use of hybrid seeds can enhance crop yields and performance in ways that are different from and not necessarily dependent on heterosis by itself (Duvick, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Hybrid maize technology has made significantly yield advances and increased productivity in both developed and developing countries (Katuwal, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of different varieties of maize on thousand grain weight (gm) and yield (Mt/ha)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGW(g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYield (Kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAMPUR HYBRID-14\u003c/p\u003e \u003cp\u003eRML145/RL197\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-10\u003c/p\u003e \u003cp\u003eCAH1511\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-16\u003c/p\u003e \u003cp\u003eCML491/CLWQHZN51\u003c/p\u003e \u003cp\u003eRML83/RML146\u003c/p\u003e \u003cp\u003eRAMPUR HYBRID-12\u003c/p\u003e \u003cp\u003eRML145/RML2\u003c/p\u003e \u003cp\u003ePVAEQH-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185.6421\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e230.1772 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e231.7738 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e250.2384\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e176.6991\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e166.1162\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e154.7120 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e197.8200 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e202.0215\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e196.4484 \u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.958841 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e7.207264\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e6.115917\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.768865 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.390339\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.689674\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e7.130970\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e5.074280\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e6.586450\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e6.25755 \u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD(0.05)\u003c/p\u003e \u003cp\u003eSEm(\u0026plusmn;)\u003c/p\u003e \u003cp\u003eC.V%\u003c/p\u003e \u003cp\u003eF-test(0.05)\u003c/p\u003e \u003cp\u003eGrand mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.139\u003c/p\u003e \u003cp\u003e2.136\u003c/p\u003e \u003cp\u003e11.748\u003c/p\u003e \u003cp\u003e**\u003c/p\u003e \u003cp\u003e199.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.551604\u003c/p\u003e \u003cp\u003e0.522\u003c/p\u003e \u003cp\u003e15.28412\u003c/p\u003e \u003cp\u003e*\u003c/p\u003e \u003cp\u003e5.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeans followed by common letter(s) within column are non-significantly different based on DMRT at P\u0026thinsp;=\u0026thinsp;0.05; LSD, Least Significant Difference; SEM, Standard Error of Mean; CV, Coefficient of Variation; DAS, Days After Sowing; ; NS, Non-Significant ,* significant at 5% level of significance,** significant at 1% level of significance, *** significant at 0.1% level of significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ethe rank of treatments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariety\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRML145/RL197, RML83/RML146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRML145/RML2, PVAEQH-1, RAMPUR HYBRID-10, CML491/CLWQHZN51, CAH1511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAMPUR HYBRID-16, RAMPUR HYBRID-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAMPUR HYBRID-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eAmong ten different maize varieties cultivated in spring season at Sundarbazar, Lamjung, RML145/RL197 (7.20 Mt/ha) has higher yield followed by RML83/RML146 (7.130 Mt//ha) among treatments. Higher germination %, shelling %, thousands grain weight was observed higher in RML145/RL197. Thus, this research suggested the farmers of Lamjung district to cultivate RML145/RL197 variety as hybrid in spring season to increase the productivity of maize. However, this research was carried out in only one season and at only one location. So, this experiment should be further verified by conducting similar research at different location. Further studies with other varieties can be conducted to see their effect on the yield.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo funding was given\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional information will be made available \u0026nbsp;on request for this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdhikary, B. 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A review on important maize diseases and their management in Nepal. . \u003cem\u003eJournal of Maize Research and Developement 1\u003c/em\u003e , 28-52.\u003c/li\u003e\n\u003cli\u003eTimsina, K. P., Ghimire, Y. N., \u0026amp; Lamichhane, J. (2016). Maize production in mid hills of Nepal: from food to feed security. \u003cem\u003eJournal of maize research and development\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 20-29.\u003c/li\u003e\n\u003cli\u003eTripathi, M. P., \u0026amp; Shrestha, J. (2016). Performance evaluation of commercial maize hybrids across diverse Terai environments during the winter season in Nepal. \u003cem\u003eJournal of Maize Research and Development\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 1-12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Characterization, Maize, Production, Spring, Varieties","lastPublishedDoi":"10.21203/rs.3.rs-3642546/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3642546/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLamjung is one of major maize producing region with limited availability of spring hybrid maize varieties. This research examine the performances of ten different hybrid varieties using Randomized Complete Block Design in spring season. The highest days to emergence of seedlings was in RAMPUR HYBRID-16 (9.67 days) and the lowest days to emergence was in RML145/RL197 (6.67 days). The highest plant germination percentage was in RML145/RL197 and PVAEQH-1 (50%) and the lowest plant germination percentage was in RAMPUR HYBRID-14 (27.22%). The plant height was significantly higher in RAMPUR HYBRID-12 (245.33 cm) followed by RML83/RML146 (136 cm) and lowest in PVAEQH-1(191.80 cm) whereas the leaf area index were significantly higher in RML145/RL197 (4.86). Phenological behavior like days to 50% tasseling (61 days) and silking (67 days) were significantly earlier in PVAEQH-1 and silking tasseling interval lower in RAMPUR HYBRID-14 (4.33 cm). Yield attributing characteristics like cob length higher in RAMPUR HYBRID-10 (19.267cm) and PVAEQH-1 (19.167 cm), cob diameter higher in CML491/CLWQHZN51 (5.148 cm) and RML145/RML2 (5.318 cm), number of rows/cobs was higher in RAMPUR HYBRID-16, CML491/CLWQHZN51, number of kernels/rows higher in CML491/CLWQHZN51, RML83/RML146. Shelling % was higher in RAMPUR HYBRID-10(69.64) and lower in RML83/RML146(57.81). Thousand grain weight were found significantly higher in CAH1511 (250.23 gm) followed by RAMPUR HYBRID-10 (231.77 gm), RML145/RL197(230.17 gm). Grain yield was significantly higher in RML145/RL197 (7.20 mt/ha) followed by RML83/RML146 (7.13 mt/ha), RML145/RML2(6.58mt/ha), PVAEQH-1(6.25mt/ha), RAMPUR HYBRID-10(6.11 mt/ha), CAH1511 (5.76 mt/ha), CML491/CLWQHZN51(5.68mt/ha), RAMPUR HYBRID-16(5.39mt/ha), RAMPUR HYBRID-12 (5.07 mt/ha)and lower in RAMPUR HYBRID-14 (3.95 mt/ha). The results indicated the among ten hybrid varieties RML145/RL197 and RML83/RML146 were high yielding so the farmers of mid hill specially in Lamjung can cultivate these varieties.\u003c/p\u003e","manuscriptTitle":"Varietal characterization of different hybrid variety of maize in Lamjung, Nepal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-23 21:54:45","doi":"10.21203/rs.3.rs-3642546/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9fd2d921-2039-443f-b8dc-9023c8111391","owner":[],"postedDate":"November 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":26559786,"name":"Agronomy"}],"tags":[],"updatedAt":"2023-11-23T21:54:46+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-23 21:54:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3642546","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3642546","identity":"rs-3642546","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}