Structural equation modeling for indicators of sustainable agriculture: Prospective of a developing country’s agriculture

Sarkar, Apurbo; Azim, Jony Abdul; Asif, Abdullah Al; Lu, Qian; Peau, Anamika Kor

doi:10.1016/j.landusepol.2021.105638

Cited by 58 publications

(52 citation statements)

References 65 publications

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“…At the same time, the younger and older generations of farmers have different social relations and information sources, allowing them to obtain additional social support. Therefore, there were significant intergenerational differences in the impacts of risk expectation and social support on farmers' confidence in recovery, which is consistent with the findings of Sarkar et al [102].…”

Section: Discussionsupporting

confidence: 90%

Determinants of Farmers’ Confidence in Agricultural Production Recovery during the Early Phases of the COVID-19 Pandemic in China

et al. 2021

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The COVID-19 pandemic has adversely impacted the agricultural supply chain, export of agricultural products, and overall food security. However, minimal exploration has been attempted of farmers’ confidence in agricultural production recovery after the COVID-19 pandemic. Therefore, this study intends to explore the determinants of farmers’ confidence in agricultural production recovery in China during the early stages of the COVID-19 pandemic. More specifically, we analyzed the relationship between risk expectation and social support on the farmers’ confidence in agricultural production recovery by using the ordered probit model. Cross-sectional survey data were collected from February to March 2020 from 458 farm households in the 7 provinces of China to produce the findings. We found that the risk expectation of farmers had a significant negative impact on farmers’ confidence in agricultural production recovery. Social support seemingly had a significant positive impact on the farmers’ confidence in agricultural production recovery, and could play a supportive role in moderating the relationship between risk expectation and farmers’ confidence in recovery. However, social support alleviates the adverse effect of risk expectation on farmers’ confidence in agricultural production recovery to a certain extent. In addition, there were intergenerational differences in the effects of risk expectation and social support on farmers’ confidence in agricultural production recovery. These results imply that policies establishing the risk early warning mechanisms for agricultural production and strengthening the social support from governments and financial institutions are likely to significantly impact agricultural development in the post-COVID-19 era. The formal and informal risk minimization mechanisms should extend their support to vulnerable sectors such as agribusiness.

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Section: Discussionsupporting

confidence: 90%

Determinants of Farmers’ Confidence in Agricultural Production Recovery during the Early Phases of the COVID-19 Pandemic in China

et al. 2021

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“…SS generates a high motivation and morale that translates into a higher EnS by reducing costs associated with new products, production processes with less waste, and orders without rejection by the client. Likewise, administrative sanctions related to high levels of risk at work are avoided, as indicated by Apaydin et al ( 2018 ) and Sarkar et al ( 2021 ) in the agriculture sector.…”

Section: Discussion Of Results and Conclusionmentioning

confidence: 99%

“…The relationship between the different types of sustainability (social, environmental, and economic) has been studied by other authors. For example, Sarkar et al ( 2021 ), using a SEM consider that social indicators are a consequence of environmental indicators and these, in turn, explain economic indicators.…”

Section: Literature and Hypothesis Reviewmentioning

confidence: 99%

Effect of lean manufacturing tools on sustainability: the case of Mexican maquiladoras

Alcaráz

García

Díaz-Reza

et al. 2022

Environ Sci Pollut Res

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The Mexican maquiladora industry is applying Lean Manufacturing Tools (LMT) in its production lines; however, few studies have investigated its relationship with sustainability (social, economic, and environmental). This paper presents a second-order structural equation model (SEM) relating 8 LMT integrated into three independent latent variables: continuous improvement (Kaizen and Gemba), supporting tools (Andon, visual management, and Poka-yoke), and machinery and equipment (total productive maintenance, overall equipment effectiveness, and Jidoka) that are related to social, economic, and environmental sustainability as dependent variables. The model is validated with information obtained from 249 companies using partial least squares. Findings show that the application of LMT in the Mexican maquiladora industry avoids the generation of waste and reprocessing. Likewise, the improvement of production processes reduces the waste emitted into the environment and reduces energy consumption. Also, when companies have environmental programs, the work environment is safe, and labor relations are improved, increasing morale and the commitment to work for the company, gaining economic and ecological benefits. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-18978-6.

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“…Traditional statistical methods need to assume that the independent variables are error free and cannot deal with latent variables effectively. Structural equation models can analyze the complex relationship between multiple dependent variables and independent variables on the basis of dealing with latent variables which are difficult to observe [56]. Therefore, this article will use a structural equation model to analyze the mechanism and effect of rural population aging on farmers' cleaner production behavior.…”

Section: Modelmentioning

confidence: 99%

The Impact of Rural Population Aging on Farmers’ Cleaner Production Behavior: Evidence from Five Provinces of the North China Plain

Liu

2021

Sustainability

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The purpose of this article is to examine the impact of rural population aging on farmers’ cleaner production behavior through the intervening effects of three aspects (learning capacity, factor substitution and behavior imitation). The data used in this article were collected from a questionnaire survey of 916 farmers in the main grain producing provinces of the North China Plain. First, confirmatory factor analysis was carried out, and then data were analyzed through structural equation modeling using the bootstrap methodology in AMOS 24. The findings indicate that rural population aging inhibits farmers’ cleaner production behavior through learning capacity. In contrast, rural population aging promotes farmers’ cleaner production behavior through factor substitution and behavior imitation. From the perspective of both challenges and opportunities, this article investigates the impact mechanism and comprehensive effect of rural population aging on farmers’ cleaner production behavior, so as to explore new ways to promote cleaner agricultural production under the background of an accelerated aging the of rural labor force and provide reference for the formulation of relevant policies.

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Structural equation modeling for indicators of sustainable agriculture: Prospective of a developing country’s agriculture

Cited by 58 publications

References 65 publications

Determinants of Farmers’ Confidence in Agricultural Production Recovery during the Early Phases of the COVID-19 Pandemic in China

Determinants of Farmers’ Confidence in Agricultural Production Recovery during the Early Phases of the COVID-19 Pandemic in China

Effect of lean manufacturing tools on sustainability: the case of Mexican maquiladoras

The Impact of Rural Population Aging on Farmers’ Cleaner Production Behavior: Evidence from Five Provinces of the North China Plain

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