Determinants of Income Diversification of Farm Households’ in Uttar Pradesh, India

Khan, Waseem; Jamshed, Mohammed; Fatima, Sana; Dhamija, Aruna

doi:10.1080/07360932.2019.1666728

Cited by 14 publications

(12 citation statements)

References 32 publications

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“…Findings of the study showed that farm size and education have negative relationship with participation in off-farm labour activities. In addition, low income from agriculture was the prime causes for participating in non-farm activities.A research study conducted by Khan et al [8] analyzed the effect of farmers' characteristics, farm characteristics, institutional factors, and perceived climate risk on income sources diversification adopted by farm households in Uttar Pradesh, India. Data were collected from 220 respondents through structured questionnaires.…”

Section: Discussion Of Previous Literaturesmentioning

confidence: 99%

An Analysis of the Pattern of Farmer’s Income in Gopalganj District: An Empirical Investigation

Mina

Rahman²,

Mahmuda³

et al. 2022

AJEBA

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Aims: Although the income of the people of other sectors in the economy is relatively stable but the income of farmers are comparatively unstable. In addition, most of the previous studies analyzed partially the determinants of farmer’s income.Thus, the aim of the current study is to investigate the pattern of farmers’ income in Gopalganj district of Bangladesh. Place and Duration of Study: The present study was conducted in Gopalganj district of Bangladesh where 10 unions, 20 villages and 400 respondents were selected randomly. Data were collected during the period from January to May in 2022. Methodology: A multistage sampling technique was applied for the study where district and upazilas were selected purposively while unions, villages and respondents were selected using simple random sampling techniques. Primary data were collected from 400 farmers using a well-structured questionnaire. Descriptive statistics and the log linear regression model were used as the analytical tools. Results: Result found from the descriptive statistics indicates that mean age of the respondents was 49.02 years whereas average educational attainment of the farmers in the study area was 5.72. In regards to the farming experience, it is found that average farming experience of the farmer was 5.07 years with maximum and minimum values were 25 years and 3 years, respectively. Regarding the household size of the respondents, the study showed that the mean family member was 4.82 with a standard deviation of 1.02. The study also indicates that 35.12% average income of the farmer obtained from non-farm sources while 64.88% average income obtained from farm sources.In regards to the farm income, it is found from the regression analysis that farm income is positively related with age, household size, education, farm size, agricultural training, access to credit facilities, membership of agricultural cooperative and distance to nearest market whereas the same is influenced by non-farm income. The findings suggest that variables like household size, education, household expenditure, access to internet and access to technical and vocational training has positive impact on non-farm income. In contrast, variables like age, farm size, distance from town, farm income have negative impact on non-farm income of the respondents in the study area. Finally, some policy recommendations are made based on the findings obtained from the study.

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Section: Discussion Of Previous Literaturesmentioning

confidence: 99%

An Analysis of the Pattern of Farmer’s Income in Gopalganj District: An Empirical Investigation

Mina

Rahman²,

Mahmuda³

et al. 2022

AJEBA

View full text Add to dashboard Cite

show abstract

“…Therefore, the dependent variable for multinomial logit is hypothesised to have the following values: Y = 1 if the choice lies in on-farm; Y = 2 if the choice is on-farm + offfarm; Y = 3 if the choice lies in on-farm + non-farm; and Y = 4 if the choice is on-farm + off-farm + non-farm. Some of the key individual and household variables that may influence the probability of farmers adopting a specific livelihood strategy identified in the literature are caste status of a household, age and years of schooling of household head, percentage of dependent members in the household, household's access to formal credit, household's possession of own source of irrigation, land area owned, area under tenancy, ownership of livestock, debt-asset ratio, per capita crop output and per capita non-land income-generating assets, and distance of the village from district headquarters (Barretta et al, 2001;Ellis, 1998Ellis, , 2000Ersado, 2006;Gecho et al, 2014;Khan et al, 2020;Reardon, 1997). Integrating these explanatory variables, a multinomial logistic regression model was employed to estimate the probability of farmers adopting a livelihood strategy of on-farm, on-farm + off-farm, on-farm + non-farm, or on-farm + off-farm + non-farm.…”

Section: Description Of the Variables Used In The Modelmentioning

confidence: 99%

Agricultural Indebtedness and Livelihood Diversification in Uttar Pradesh: A Study of the Eastern and Bundelkhand Regions

Varkey¹

2022

Review of Development and Change

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The study aims to address the issue of indebtedness among agrarian households and the associated livelihood diversification using the cases of the Eastern and Bundelkhand regions of Uttar Pradesh. It raises the following broad questions: What is the nature and extent of the debt burden experienced by the farmer households of the study areas? How do these households diversify their livelihood strategies? And what explains the livelihood outcomes of these households? It is argued that the skewed distribution of productive resources in the study regions leads to various kinds of interlinked transactions among agrarian classes in the context of production and exchange. Such interlinkages are essentially exploitative, resulting in lower crop productivity and lower returns from cultivation. In the face of lower returns from cultivation, farmers are compelled to take recourse to borrowing. The majority of these borrowings, especially by the poorer classes, are from informal sources, exposing them to usurious extortions at the hands of money lenders, traders and rich peasants/farmers, leading to difficulties in meeting payment obligations and, finally, to indebtedness. The higher debt burden has resulted in an increased livelihood diversification by agrarian households in recent years, mostly to the informal sector. We term it forced livelihood diversification, because such diversification is not the natural outcome of a development process but is forced upon the peasantry because of the non-viability of cultivation.

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“…LM's growth theory was increasingly led by TPS.TPS gradually led to LM's theory of development. LM is a set of techniques aimed at defining and eliminating all forms of wasteseven 'wastes'or 'Muda' in Japanare recognized -1) overproduction waste, 2) waste of waiting for machines or operators, 3) waste in transport, 4) waste of processing itself, 5) Inventory waste, 6) waste of movement of operators and 7) waste of making defective products [27][28][29][30].…”

Section: Various Kind Of Waste Elimination With Help Of Lean Manufacturingmentioning

confidence: 99%

“…Many businesses understand that professional employees are one of the toughest strategic capabilities. [4] Workers must be able to adjust themselves to the complexity of sudden changes in workplaces, consumer orders, creation of goods and the rapid speed of technological development and sustain the same output level without stopping production or decreasing efficiency [28][29][30][31][32][33]. The flexibility required in lean environment can be only achieved through developing multiskilled workers.…”

Section: Flexibility and Multi-skilledmentioning

confidence: 99%

Lean Companies Employed Assembly Workers Train with New Approches

Dhamija¹

2021

TURCOMAT

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LM has proven itself the production system that enhances shop floor efficiency. Furthermore, the current environment for production firms is accelerating the pace at which LM is implemented. The manufacture of lean is not easy to introduce. It is constant and complex activity. Assembly workers in production processes are the core of lean manufacturing activity. Training is known as vehicle to aid the implementation process. While the importance of training is known so far, there are only a few options for organizing effective training. The results of the survey of questions conducted inside UK manufacturing companies are examined. This article illustrates the definition of lean production and worker requirements in lean environment.

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Determinants of Income Diversification of Farm Households’ in Uttar Pradesh, India

Cited by 14 publications

References 32 publications

An Analysis of the Pattern of Farmer’s Income in Gopalganj District: An Empirical Investigation

An Analysis of the Pattern of Farmer’s Income in Gopalganj District: An Empirical Investigation

Agricultural Indebtedness and Livelihood Diversification in Uttar Pradesh: A Study of the Eastern and Bundelkhand Regions

Lean Companies Employed Assembly Workers Train with New Approches

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