Determinants of farm mechanization among arable crop farmers in Ibarapa zone, Oyo State, Nigeria.

Adewuyi, SA; Ashaolu, O. F.; Ayinde, I. A.; Ogundele, SO

doi:10.4314/mjar.v7i1.31840

Cited by 4 publications

(5 citation statements)

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“…From Table 4, it can be deduced that the firm size, participation in promotional programmes and the average education of the labour are positively influenced the MI (coincides with the results of Adewuyi et al, 2006;Gosh, 2010;Koundouri et al, 2006;Okello et al, 2014;Oladeji et al, 2015), whereas the presence of labour union inversely influence the MI. It can also be noted that the other variables like awareness about available machines for manufacturing, education of the manufacturer and average age of the labour are not significant to influence MI.…”

Section: Resultssupporting

confidence: 73%

“…From the studies of Atanu et al 1994), Gale (1998), Burton et al (1999), Daberkow et al (2003), Adewuyi et al (2006), Lippert and Davis (2006), Varukolu (2007), Katungi (2007), Lin and Zhang (2009), Gosh (2010), Kuriakose et al (2011), Okello et al (2014), Narayanamoorthy et al (2014) and Oladeji et al (2015), it can be inferred that the speed of diffusion and adoption of technology is determined by human capital endowment, type of government, proximity to city, degree of openness to trade, competition from foreign goods and the demands of the world market. Training and organisational innovations are the main ‘non environmental’ significant drivers of technology adoption in Ministry of Small and Medium Enterprises (MSMEs).…”

Section: Review Of Literature and Research Gap Identifiedmentioning

confidence: 99%

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Firm-Specific Determinants of Mechanisation: The Case of Coir Industry in Kerala

Raseena¹

2022

SEDME (Small Enterprises Development, Management & Extensio

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This article is an attempt to measure the degree of mechanisation in the coir industry of Kerala for eliciting the firm-specific determinants of mechanisation. The study uses primary data collected from 115 coir manufacturers of Alappuzha district by using simple random sampling. It starts with measuring the degree of mechanisation of the selected coir firms in Kerala using two indices: extent of mechanisation (EM) and mechanisation index (MI). After that, the coir industry of Kerala is categorised into four levels of mechanisation, namely, traditional firms, low mechanised firms, medium mechanised firms and high mechanised firms using MI. The study found that, majority of the coir producing firms of Kerala belongs to traditional and low mechanised levels. The firm-specific determinants of the mechanisation are identified using a Tobit regression analysis and the results reveal that, firm size, participation in promotional programmes and the average education of the labour positively influences the MI whereas, the presence of labour union inversely affects it.

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Section: Resultssupporting

confidence: 73%

Section: Review Of Literature and Research Gap Identifiedmentioning

confidence: 99%

Firm-Specific Determinants of Mechanisation: The Case of Coir Industry in Kerala

Raseena¹

2022

SEDME (Small Enterprises Development, Management & Extensio

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“…High levels of energy is required to cut and invert the soil, and the draft force required to plough also needs relatively high weight to give traction. (Adewuyi et al, 2006). The depth of ploughing depends on the crop to be cultivated, soil characteristics and also on the source of power available (Pandey, 2004).…”

Section: Introductionmentioning

confidence: 99%

Fuel Consumption Evaluation of Some Commonly Used Farm Tractors for Ploughing Operations on the Sandy-loam soil of Oyo State, Nigeria

Adewoyin¹

2013

RJASET

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Farm Tractors acquired in Oyo State-Nigeria are primarily utilized for ploughing operations which represents the most costly single item in the budget of an arable farmer. Tractor fuel consumption constitutes a significant parameter that affects ploughing operations performances. Ploughing operations, using ASABE standard D 497.5 were carried out using Massey Ferguson ( MF 435, 100 hp), Fiat ( F130D, 100 hp) and Steyr ( CVT170, 100 hp) tractors that were purchased in 2009. Field experiments were conducted at 5.5, 6.5 and 7.5 km/h ploughing speeds for ploughing depths of 20, 25 and 30 cm. Mean fuel consumption for Fiat, MF and Steyr models were 23.35, 23.58 and 24.55 l/ha while average of 16.78, 22.02 and 32.67 l/ha of diesel were used to plough 20, 25 and 30 cm depths, respectively. Fuel consumption increased by 31% when ploughing depth increased from 20 to 25 cm and 48% when increased from 25 to 30 cm. Mean fuel consumption at 5.5, 6.5 and 7.5 km/h ploughing speeds were 20.0, 24.25 and 27.23 l/ha, respectively. Fuel consumption varies significantly with ploughing depth and speed (p<0.05, 0.87≤r 2 ≤0.99). Fuel consumption values increases with ploughing dept and speed significantly, the depth of crop roots should determine the appropriate ploughing depth in order to minimize expenses on fuel.

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“…There are also plenty of studies analyzing the influence of demographic characteristics such as age, gender, and level of education on the adoption of agricultural mechanization [7][8][9][10][11]. Meanwhile, GC et al [7] studied the impact of internet use on agricultural mechanization adoption in Nepal.…”

Section: Introductionmentioning

confidence: 99%

Adoption of Agriculture Mechanization on Paddy Farmers in Indonesia: Demographic Determinants, Internet Access Influence, and The Impact of Adoption on The Yield

Kadir¹,

Prasetyo²

2022

asks

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This study aims to identify the demographic determinants of the agriculture mechanization adoption on paddy farmers in Indonesia and the impact of internet use by farmers on the probability of being adopters. Besides, it also analyses the difference in the average paddy yield cultivated by adopters and non-adopters to determine the adoption impact on agricultural productivity. We found that farmers' level of education and age significantly impacts the probability of being adopters. However, the magnitude of the age impact tends to be diminishing with the increase in age. The probability of being adopters is affected significantly by gender, region, and the farm scale. Male farmers tend to be more likely of being adopters than their female counterparts. Farmers in Java have a slightly higher probability of being adopters than farmers outside Java. Adopting agriculture mechanization also has a positive association with the farm scale, where the larger the farm scale, the more likely the farmers are to be adopters. Our study also found that internet use positively and significantly impacts the farmers' probability of being adopters. Moreover, our study also confirmed a strong indication that the mechanization adoption affects the paddy yield positively indicated by the higher paddy yield average of adopters than non-adopters. Therefore, boosting the adoption of mechanization must be done, for instance, by attracting young people with high education to get involved in agriculture and up-scaling the implementation of tools and agricultural machinery assistance facilitated by the government.

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Determinants of farm mechanization among arable crop farmers in Ibarapa zone, Oyo State, Nigeria.

Cited by 4 publications

References 0 publications

Firm-Specific Determinants of Mechanisation: The Case of Coir Industry in Kerala

Firm-Specific Determinants of Mechanisation: The Case of Coir Industry in Kerala

Fuel Consumption Evaluation of Some Commonly Used Farm Tractors for Ploughing Operations on the Sandy-loam soil of Oyo State, Nigeria

Adoption of Agriculture Mechanization on Paddy Farmers in Indonesia: Demographic Determinants, Internet Access Influence, and The Impact of Adoption on The Yield

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