Extent of farm mechanization and technical efficiency of rice production in some selected areas of Bangladesh

Vortia, Pooja; Nasrin, Mahmuda; Bipasha, Shahana Khatun; Islam, MN

doi:10.1007/s10708-019-10095-1

Cited by 22 publications

(16 citation statements)

References 16 publications

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“…The farm assets index shows the ownership of farm implements; farmers who rank high on the higher farm assets index can carry out agricultural operations in time, particularly at critical growth stages, which in turn positively affects efficiency. Our results are in line with Vortia et al (2019), which reports a positive relationship between farm mechanization and efficiency. Access to credit has a positive and statistically significant effect on efficiency as it enables farmers to utilize improved or new technologies and a better mix of quality inputs for crop production (Laha 2013).…”

Section: Factors Affecting Technical Inefficiencysupporting

confidence: 92%

Impact of natural resource management technologies on technical efficiency in sorghum cultivation: Application of meta-frontier and endogenous switching regression model

Kumar¹,

Singh²,

Jha³

et al. 2020

Agricultural Economics Research Review

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This study assesses the impact of the adoption of natural resource management (NRM) technologies on sorghum production in the drought-prone areas of Indian state of Karnataka, using plotlevel data. The key factors affecting adoption are access to credit, extension services, and social networks. The bias-corrected technical efficiency scores and the meta-technology ratio indicate that efficiency can be improved by 30%. The result of endogenous switching regression shows that the average treatment effect on treated is -0.24, suggesting a 13%. reduction in production efficiency. Adopting NRM technologies could enhance production and farmer livelihoods in drought-prone areas.

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Section: Factors Affecting Technical Inefficiencysupporting

confidence: 92%

Impact of natural resource management technologies on technical efficiency in sorghum cultivation: Application of meta-frontier and endogenous switching regression model

Kumar¹,

Singh²,

Jha³

et al. 2020

Agricultural Economics Research Review

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“…The average technical efficiency of mechanized Journal of Agriculture and Natural Resources (2020) 3(2): 82-91 ISSN: 2661-6270 (Print), ISSN: 2661-6289 (Online) DOI: https://doi.org/10.3126/janr.v3i2.32484 rice farm (0.8056) was higher than that of traditional rice farm (0.7011) and the difference was statistically significant at 1% level of significant. This was in line with Vortia (2019) who concluded that rice farms with higher level of mechanization are technically more efficient than the others although both farm groups are technically inefficient in rice production. The current study revealed that there was still scope of increasing output level of rice by increasing the efficiency of farm through wise mobilization and use of existing resources.…”

Section: T-test Of Technical Efficiency Of Traditional and Mechanizedsupporting

confidence: 89%

“…Mechanization is important option to ensure profitability in agriculture (Vortia et al, 2019). According to Asefa (2012), if existing inputs and technologies are not efficiently utilized, trying to introduce new technologies will not be cost-effective.…”

Section: Introductionmentioning

confidence: 99%

Comparative study on technical efficiency of mechanized and traditional rice farm in Nepal

Acharya

Regmi

Gauchan³

et al. 2020

J. Agric. Nat. Res.

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The study was conducted among 274 mechanized and 220 traditional rice farms using multistage sampling technique to assess the technical efficiency in rice production among mechanized and traditional farmers in Jhapa, Sunsari and Bardiya districts. The Cobb-Douglas functional form of the stochastic production frontier was employed to obtain the technical efficiency in mechanized and traditional rice farms. The overall technical efficiency of the mechanized and traditional rice farm ranged from 40.31 to 92.23 and 31.21 to 85.02%t with the mean technical efficiency of 80.56 and 70.11% respectively. The scope of increasing output by adopting the technology adopted by the best performer was 19.44% in mechanized and 29.89% in traditional rice farm respectively. Majority of the farmers were operating at an efficiency level 70-80% and 60-70% in mechanized and traditional farms respectively. The average technical efficiency of mechanized rice farm was higher than that of traditional rice farm and the difference was significant. There was scope of increasing output through rational use of existing resources in both farm categories. Manures, chemical fertilizers had significant and positive effect to total yield of rice kg/ha. The effect of machine use to total yield of rice was positive and significant. Rice farms adopting machines were more technically efficient compared to traditional rice farm.

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“…Ren et al (2019) presented efficiency assessments and explained the size of the farm to impact the agricultural production by affecting the socio-economic and environmental behavior. Vortia et al (2019) performed DEA on paddy farmers of Bangladesh and suggested that extent of mechanization and farm experience were exerting significant effect on farm performance. A recent study by Ullah et al (2019) recommended the need of agricultural extension services among the farmers to adopt new technologies and enhance the managerial skills.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of fuzzy DEA and machine learning algorithms in efficiency estimation of paddy producers of rural Eastern India

Nandy

Singh

2020

BIJ

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PurposeData envelopment analysis (DEA) has wide applications in the agricultural sector to evaluate the efficiency with crisp input and output data. However, in agricultural production, impreciseness and uncertainty in data are common. As a result, the data obtained from farmers vary. This impreciseness in crisp data can be represented in fuzzy sets. This paper aims to employ a combination of fuzzy data envelopment analysis (FDEA) approach to yield crisp DEA efficiency values by converting the fuzzy DEA model into a linear programming problem and machine learning algorithms for better evaluation and prediction of the variables affecting the farm efficiency.Design/methodology/approachDEA applications are focused on the use of a common two-step approach to find crucial factors that affect efficiency. It is important to identify impactful variables for minimizing production adversities. In this study, first, FDEA was applied for efficiency estimation and ranking of the paddy growers. Second, the support vector machine (SVM) and random forest (RF) were used for identifying the key leading factors in efficiency prediction.FindingsThe proposed research was conducted with 450 paddy growers. In comparison to the general DEA approach, the FDEA model evaluates fuzzy DEA efficiency giving the user the flexibility to measure the performance at different possibility levels.Originality/valueThe use of machine learning applications introduces advanced strategies and important factors influencing agricultural production, which may help future research in farms' performance.

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Extent of farm mechanization and technical efficiency of rice production in some selected areas of Bangladesh

Cited by 22 publications

References 16 publications

Impact of natural resource management technologies on technical efficiency in sorghum cultivation: Application of meta-frontier and endogenous switching regression model

Impact of natural resource management technologies on technical efficiency in sorghum cultivation: Application of meta-frontier and endogenous switching regression model

Comparative study on technical efficiency of mechanized and traditional rice farm in Nepal

Application of fuzzy DEA and machine learning algorithms in efficiency estimation of paddy producers of rural Eastern India

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