How does Internet use affect farmers’ low-carbon agricultural technologies in southern China?

Zhou, Wenfeng; Chen, Qing; Deng, Xin; Song, Jiahao; Xu, Dingde

doi:10.1007/s11356-022-23380-3

Cited by 40 publications

(20 citation statements)

References 44 publications

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“…The stronger the subjective will of the cotton farmers, the more they will take the initiative to obtain information in various ways and thus adopt it more quickly. In addition to being available in villages or missions, there are also online sources that can be accessed 51 . For example, tools such as the government’s online agricultural extension service platform, the ShakeOut platform or public numbers can be used to provide online training, browse agricultural policies or explain agricultural knowledge directly through mobile phones 52 , 53 .…”

Section: Research Theory and Hypothesismentioning

confidence: 99%

Study on the influence mechanism of adoption of smart agriculture technology behavior

Liu

Chen

et al. 2023

Sci Rep

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Smart agricultural (SA) technology has become a technological support for modern agriculture. By exploring the decision-making process and psychological motivation of farmers in adopting SA technology, it is conducive to achieving the popularisation of SA technology and promoting the modernisation of agriculture. Based on microscopic research data, a Structural Equation Model (SEM) is used to analyse the influencing factors and extent of cotton farmers’ adoption of SA technologies, using Deconstructive Theory of Planned Behavior (DTPB) as the analytical framework. This was combined with in-depth interviews to further reveal the motivations and influencing mechanisms of cotton farmers’ adoption of SA technologies. The results show that under the behavioural belief dimension, cotton farmers value the positive effect of perceived usefulness even though the risk of the technology itself has a dampening effect on adoption intentions. Under the normative belief dimension, superior influence influenced the willingness to adopt SA technologies to a greater extent than peer influence. Under the control belief dimension, factors such as self-efficacy and information channels influence willingness to adopt technology and behaviour. In addition, behavioural attitudes, subjective norms, and perceived behavioural control all contribute to cotton farmers’ willingness to adopt SA technologies, and can also influence behaviour directly or indirectly through willingness to adopt. Policy and technology satisfaction positively moderate the transition from willingness to behaviour. Therefore, preferential policies are proposed to reduce the cost of adopting SA technologies; to continuously improve the level of SA technologies; to establish SA technology test plots to provide a reference base; and to increase knowledge training on SA and expand access to information.

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Section: Research Theory and Hypothesismentioning

confidence: 99%

Study on the influence mechanism of adoption of smart agriculture technology behavior

Liu

Chen

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The stronger the subjective will of the cotton farmers, the more they will take the initiative to obtain information in various ways and thus adopt it more quickly. In addition to being available in villages or missions, there are also online sources that can be accessed 54 . For example, tools such as the government's online agricultural extension service platform, the ShakeOut platform or public numbers can be used to provide online training, browse agricultural policies or explain agricultural knowledge directly through mobile phones 55,56 .…”

Section: Research Theory and Hypothesismentioning

confidence: 99%

Study on the Influence Mechanism of Adoption of Smart Agriculture Technology Behavior

Liu

Chen

2023

Preprint

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Smart agricultural (SA) technology has become an inevitable trend in the modernisation of China's agriculture and a realistic demand for the transformation and upgrading of traditional agriculture. Based on microscopic research data, a structural equation model (SEM) is constructed using deconstructive planning behaviour theory (DPBT) as the analytical framework to empirically analyse the factors and degrees of influence on cotton farmers' adoption of SA technologies. In-depth interviews were then combined to further reveal the mechanisms influencing cotton farmers' adoption of SA technology. The results show that: (1) the risk of the technology itself has a dampening effect on willingness, but cotton farmers value the positive role played by perceived usefulness more. (2) superior influences such as trial field demonstrations and government publicity have a greater impact on willingness to adopt SA technologies than the group effect of peer influence. (3) factors such as self-efficacy and information channels have an impact on willingness and behaviour to adopt. (4) Willingness influences technology adoption behaviour through mediating effects. (5) Policy and technology satisfaction have a positive moderating role in the occurrence of willingness and adoption behaviour.

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“…The empirical evidence indicated that digitalization positively contributes to promoting low-carbon agricultural production. From a micro perspective, Zhou et al (2022) pointed out that the internet substantially promotes farmers' low-carbon tillage technology adoption and low-carbon fertilization technology adoption after surveying 1080 farmers in Sichuan Province in China. Meanwhile, Huang et al (2022) further found that digital technology applications can indirectly promote the adoption of low-carbon technologies by influencing farmers' risk perceptions in an empirical test using the field survey data of 571 farm households in Jiangsu Province, China.…”

Section: Digitalization and Low-carbon Agricultural Productionmentioning

confidence: 99%

Digitalization, resource misallocation and low-carbon agricultural production: evidence from China

Wang

et al. 2023

Front. Environ. Sci.

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With the rapid development of digital technologies such as artificial intelligence, big data and cloud computing, China’s agricultural production is entering a new era characterized by digitalization. Based on provincial panel data of China from 2013 to 2020, this paper adopts the system GMM and mediating effects model to systematically examine the impact of digitalization on low-carbon agricultural production from the perspective of resource misallocation. The results indicate that digitalization can significantly curb agricultural carbon emissions and thus promote low-carbon agricultural production, and this finding still holds after the robustness test. The heterogeneity analysis indicates that the inhibiting effect of digitalization on agricultural carbon emissions is most pronounced in the eastern region relative to the central and western regions (the regression coefficients are −0.400 and −0.126 respectively). Further mechanism analysis suggests that digitalization can reduce agricultural carbon emissions by correcting the widespread capital and labor misallocation in agricultural factor markets. The findings of this study provide significant policy implications for low-carbon agricultural production in China.

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How does Internet use affect farmers’ low-carbon agricultural technologies in southern China?

Cited by 40 publications

References 44 publications

Study on the influence mechanism of adoption of smart agriculture technology behavior

Study on the influence mechanism of adoption of smart agriculture technology behavior

Study on the Influence Mechanism of Adoption of Smart Agriculture Technology Behavior

Digitalization, resource misallocation and low-carbon agricultural production: evidence from China

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