Introduction

Agriculture is the mainstay of Nepalese economy which contributes almost one third of the national economy (Anonymous, 2017a). Agricultural crop productivity in Nepal is lowest among South Asian countries (Anonymous, 2018a). The agricultural sector production during 2017-18 was increased by 2.7% which has been estimated as 5.1% in 2018-19 (Anonymous, 2019). Rice is the agricultural commodity with the third highest worldwide production (Rice, 741.5 million tones, in 2014), after sugarcane (1.9 billion tones) and maize 1.0 billion tones (Anonymous, 2017). During the year 2016-17, rice contributed 44.66% to total edible cereal grain production in the country (Anonymous, 2018b). The area increment of rice in 2016-17 over 2007-08 has been counted as only 0.21% while in production and productivity the increment is 21.66 and 21.41%, respectively. The rice in Nepal is transplanted by human labor and animal traction (Upadhyaya, 1996). Using traditional bullocks and laborers it takes 64 hr ha^-1 to prepare land, while the scale appropriate farm mechanization can prepare the same land in approximately 20 hr ha^-1 (Paudel et al., 2019). Labor migration has created acute labor shortages in the agriculture sector that have affected timely crop establishment and other crop cultivation practices (Anonymous, 2017c; Maharjan et al., 2013b, 2013a). Many studies have shown that the rising labor scarcity and/or increased labor wages as the major driver for adopting farm mechanization (Reddy et al., 2014; Wang et al., 2016; Win and Thinzar, 2016; Yang et al., 2013 and Zhang et al., 2014). Alam (2006) describes mechanization as the interjection of machinery between people and the materials handled by them. Mechanization also includes irrigation systems, food processing and related technologies and equipment (Hegazy et al., 2013). Agricultural mechanization through custom hiring of tractors services has recently been considered as an option to mitigate the impact of rising labor costs for smallholders (Takesima et al., 2016). Japan has been the strongest innovator and technology provider in terms of farm mechanization and farm machinery used in Southeast Asia. (Hegazy et al., 2013).

In 2007, India had 3.2 million agricultural tractors and 0.48 million combine harvesters and threshers. The density of tractors per 1000 ha of cropped area was about 16 compared with the world average of 19, and 27 in the US (Directorate of Economics and Statistics, 2013). The use of laser land levelers on a custom-hire basis is growing, as it saves up to 30 per cent of irrigation water and helps increase productivity. Combine harvesters operating in custom-hire business models gained popularity (Mani et al., 2008). In a study carried out in Bangladesh, Kamruzzaman et al. (2009) reported that the maximum cost in rice cultivation was incurred in transplanting, weeding, harvesting and threshing but only transplanter, weeder, reaper and thresher can reduce the big amount of production cost. Pellizzi (1992) describes The  primary  objectives  and  benefits  of  agricultural mechanization  include minimization of production costs; optimization of product quality; protection of the environment; reduction of farm  drudgery; timely provision of suitable conditions for plant  and  animal  growth. Adoption and spread of agricultural and rural mechanization technologies are increasing recently in Nepal with liberal import policies, increased connectivity and acute labor scarcity resulting from youth migration (Gauchan and Shrestha, 2017). Since rice is a labor intensive crop, and migration of youth force from rural to urban and urban to gulf and other countries in search of opportunities have created a state of labor scarcity in the country. Mechanization in rice farming is one of the best solutions to replace labor, reduce drudgery and increase income of the rice farmer.

Materials and Methods

The study was undertaken in Agricultural Machinery Testing and Research Centre (AMTRC), in Sarlahi district of Province 2, Nepal. Sarlahi district is located between 26⁰45-27⁰10 North Latitude and 85⁰20-85⁰50 East Longitude. Mechanization in rice farming has been applied with the uses of different machineries available in the country. Although, there are different machines and cultural practices for rice farming, some of them were identified at AMTRC, Nawalpur, Sarlahi. The cultivation practices for rice cultivation by using different machineries were evaluated in five treatments (Table 1) at AMTRC during 2017-18 and 2018-19.

Table 1: Treatments followed in rice experiment at AMTRC, Nawalpur, Sarlahi

The experiment was carried out into three replications of five treatments in 2800 m² plot size for each treatment. The experiment was laid out in randomized complete block design (RCBD). The variety of rice was Hardinath-1.Seeds were sown in second week of June at the rate of 30 kg ha^-1. The crop was harvested in the second week of October.

The fertilizer doses supplied were at the rate of 100:30:30 kg NPK ha^-1. The full dose of phosphorous, potash and half dose of nitrogen were applied as basal dose during the time of land preparation while remaining half dose of nitrogen was top dressed. The source of phosphorous was Dia-ammonium phosphate (DAP) and that of potassium was muriate of potash and of nitrogen was DAP and urea.

All intercultural practices were followed as per need and recommendation for this crop. Data were recorded on date of sowing, date of harvesting, plant height, Panicle length, number of plant per square meter area and average number of grain per panicle. Similarly, average number of tiller per hill, thousand grains weight, grain yield and straw yield ha^-1 were also recorded.

The data were fed into computer and analyzed using ms-excel and Mstat package. The data recorded were analyzed for individual parameters separately for each year. Similarly, the combined analysis was performed for two years data of 2017-18 and 2018-19.

Results and Discussion

Data were analyzed using mstat and excel. The results obtained after statistical analysis are discussed according to the headings of different parameters. All results are presented in Table 2.

Table 2: Performance of different parameters of rice at AMTRC, Nawalpur, Sarlahi, Nepal (2017-18 and 2018-19)

Table 2: Continue...

3.1.  Plant height

Plant height of the rice in experiment was found significant at 5% level in 2017-18 and non-significant during 2018-19. It was significant at 1% level in pooled analysis (Table 2). The highest plant height of 104.77 cm was obtained in T₂ where the rice seeds were directly seeded with power tiller drill followed by T₁ which was 100.69 cm in the plot where the rice was directly seeded with zero till drill machine. The lowest height of 93.56 cm was recorded in T₄. During the year 2018-19, the treatment T₂ obtained highest plant height of 105.43 cm and the lowest was 98 cm in T₄.

In pooled analysis, the effect of year in treatments and interaction between year and treatment was found non-significant, while the treatment itself was significant at 1% level. The average of two years plant height was obtained highest in T₂ which was 105.10 cm followed by 100.90 cam recorded in T₁. The treatment T₄ attained lowest plant height of 95.78 cm in the experiment (Table 2).

3.2.  Panicle length

The panicle length was non-significant in both of the years of 2017-18 and 2018-19. It was also non-significant in pooled analysis (Table 2). The highest panicle length in 2017-18 and in 2018-19 was 24.89 cm and 26.01 cm in T₁ and T₅, respectively. As an average of two years, the highest panicle length was attained by T₂ which was 25.78 cm followed by 25.23 cm in T₁. The lowest panicle length was recorded in T₅ which was 24.78 cm (Table 2).

3.3.  Plant m^-2

The number of plant per meter square area was significant at 1 per cent level in both of the years and in combined analysis too (Table 2). The treatment T₁ recorded the highest number of plant per meter square in both of the years of 2017-18 and 2018-19 recording a figure of 281.89 and 285.33 cm, respectively.

3.4.  Number of grain panicle^-1

The number of grain panicle^-1 was found non-significant in 2017-18 but significant at 5% level in 2018-19. In combined analysis it was found non-significant (Table 2). Despite non-significant result in 2017-18, the treatment T₃ recorded highest number of grain panicle^-1 (61.66) and T₄ recorded the lowest number of 59.33. Similarly in 2018-19, the highest number was observed in T₁ (64.66) followed by T₃ (63.89) and the lowest number was recorded in T₂ (56.55) in the experiment. The average of two years was recorded highest in T₁ which was 63.11 followed by T₃ (62.78) and the lowest number of 56.50 was found in T₄. The effect of year and interaction between year and treatment was found non-significant (Table 2).

3.5.  Number of tillers hill^-1

The number of tiller hill^-1 was significant at 1% level in 2017-18, 2018-19 and also in combined analysis of these two years (Table 2). The highest number of tiller in 2017-18 was found in T₃ which was 32.00 followed by T₁ (24.89) and the lowest number of 12.77 was observed in T₄. Similarly, in 2018-19 the treatment T₃ recorded the highest number of tiller as 34.33 followed by T­₁ (26.11) and the lowest number (14.00) was found in T₄.

In combined analysis, the treatment was found significant at 1% level while the effect of year in treatments and the interaction between year and treatment was found non-significant. The average of two years data revealed the highest number of tiller hill^-1 in T₃  (33.16) followed by T₁ (25.50) and the lowest number of tiller per hill was recorded in T­₄ which was found to be 13.38 in the experiment (Table 2).

3.6.  Thousand grain weight

The weight of thousand grains was non-significant in 2017-18 and 2018-19. However, T₁ in 2017-18 and in 2018-19 obtained highest weight of 18.55 and 18.17 gram while the lowest weight of 17.88 gram in 074/075 and 17.72 gram in 2018-19 was found in T₂ and T₃ respectively (Table 2). In combined analysis, T₁ recorded highest mean grin yield of 18.36 gram and lowest of 17.85 gram in T₃ during the experiment.

3.7.  Grain yield

The mean grain yield was significant at 1% level in both of the years. It was also significant at 1% level in pooled analysis (Table 2). The highest mean grain yield was obtained in T₃ (3641.67 kg ha^-1) followed by T₄ (3191.67 kg ha^-1) in 2017-18 and the lowest yield of 2473.67 kg ha^-1 was found in T₂. Similarly in 2018-19, the treatment T₃ recorded the highest mean grain yield of 3558.33 kg ha^-1 followed by T₄ (3227.50 kg ha^-1) and the lowest yield of 2709.67 kg ha^-1 was obtained in T₁. In combined analysis, the treatment T₃ obtained highest mean grain yield of 3558.33 kg ha^-1 followed by T₄ (3227.50 kg ha^-1). The lowest mean grain yield of 2709.67 kg ha^-1 was found in T₁. The effect of year in treatment was found non-significant while the interaction between year and treatment was significant at 5% level (Table 2).

3.8.  Straw yield

The mean straw yield was found significant at 1 per cent level in 2017-18 and at 5% level in 2018-19. In pooled analysis it was also found significant at 1% level (Table 10). The treatment T₁ recorded the highest straw yield of 5436.67 and 4521.67 kg ha^-1 in 2017-18 and 2018-19, respectively. The lowest mean straw yield of 3299.67 kg was found in T₄ during 2017-18 while in 2018-19 the lowest yield of 2930.00 kg ha^-1 was found in the same treatment of T₄.

In combined analysis, the highest mean straw yield of two years was observed in T₁ which recorded 4979.17 kg ha^-1 followed by 4311.33 kg ha^-1 in T₃. The lowest mean straw yield was recorded in T₄ which was 3114.83 kg ha^-1. The effect of year in treatments was found non-significant and the interaction between year and treatment was also the same in experiment (Table 2).

3.9.  Gross margin

The difference between revenue and variable costs incurred in input expenditures is termed as gross margin. The gross margin is also calculated in percentage terms by dividing the gross margin amount by revenue. Gross margin=(Total revenue–Variable costs)/Total revenue. Thus it can be expressed in percentage too. Gross margin supports to measure the production costs related to the revenue of the farm. If gross margin is low, it may look for the processes that allow the farm to cut in use of the variable cost which seem less productive in generating farm income.

Gross margin in this experiment was calculated based on the expenses incurred in different inputs and farm works related to the farm operations. The different methods of cultivation practices obtained varying quantity of production and thus gross margin was also different for different treatments followed in the trials.

The highest amount of revenue as an average of two years (2017-18 and 2018-19) was found in T₃ where the rice was transplanted with mechanical transplanter which was Rs. 79786.60 ha^-1 followed by T₄ in which the rice was directly seeded with the use of drum seeder. The check treatment counting the total revenue of Rs. 71709.60 ha^-1 (Table 3) was third among the treatments. The total variable cost was highest in check (Farmer’s practices) which was Rs. 58779.25 followed by T₃ (Rs. 49245.75 ha^-1). A gross margin of Rs. 30540.85 ha^-1 was found highest in T₃ followed by T₄ (Rs. 23733.75 ha^-1). The lowest gross margin of Rs. 17016.70 ha^-1 was calculated in T₁.

Table 3: Average gross margin of two years data from different cultivation practices of rice at AMRTC, Sarlahi

Conclusion

The result showed that the mechanization in rice cultivation could be one of the best solutions to overcome labor scarcity problem which has caused to pay more for labor causing comparatively high variable costs in rice farming. It has ultimately affected the gross margin of the farmers with less return than cultivating rice with different machines. In this experiment, the rice transplanted by mechanical transplanter has been found efficient in production among the practices included in the trial.

Acknowledgment

Authors express their sincere gratefulness to Nepal Agricultural Research Council (NARC) managements for their encouragements and supports during the experiments. Authors also wish to thank Mr. Ram Bahadur Bhujel (Retired Senior Scientist, NARC) for his valuable suggestion in research paper preparation. Authors are also thankful to all those who directly and indirectly supported to carry out all experiments smoothly.

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