Land Fragmentation, Technology Adoption and Chemical Fertilizer Application: Evidence from China

Chi, Liang; Han, Sunyoung; Huan, Meili; Li, Yajuan; Liu, Jifang

doi:10.3390/ijerph19138147

Cited by 20 publications

(11 citation statements)

References 43 publications

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“…Furthermore, economies of scale help to achieve increased agricultural productivity and reduce agricultural carbon emissions. In terms of environmental factor inputs, AMS can improve farmers’ expectations of crop yields, which in turn effectively reduces excessive inputs of environmental factors such as pesticides and fertilizers through factor substitution mechanisms [ 45 , 61 ]. Additionally, AMS can help promote the formation of pro-environmental behaviors among farmers, which in turn improves the AGTFP at the micro level [ 44 ].…”

Section: Literature Reviews and Theoretical Frameworkmentioning

confidence: 99%

How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?

Guan

2023

IJERPH

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Agricultural mechanization service (AMS) is a critical path to achieving agricultural green transformation with smallholders as the mainstay of agricultural production. Based on the panel data of 30 Chinese provinces from 2011 to 2020, this paper measures the AGTFP using the Super-SBM model and examines the effects of different AMS supply agents on AGTFP and spatial spillover effects through the spatial Durbin model. The main conclusions are as follows: First, China’s AGTFP showed a stable growth trend, with the mean value increasing from 0.1990 in 2011 to 0.5590 in 2020. Second, the specialization (SPO) and large-scale (LSO) of AMS supply organizations have significantly positive effect on the AGTFP of the local province. However, SPO has a significantly positive effect on the AGTFP of the neighboring provinces, while LSO has the opposite effect. Third, the specialization of AMS supply individuals (SPI) has significantly negative effect on the AGTFP of the local province. In contrast, the large-scale AMS supply individuals (LSI) has the opposite effect. Furthermore, the spatial spillover effects of both are insignificant. Fourth, the spatial spillover effect of AGTFP shows asymmetry among different regions and indicates that AMS resources flow from non-main grain production and economically developed regions to main grain production and less developed regions. These findings provide helpful policy references for constructing and improving the agricultural mechanization service system and realizing the agricultural green transformation in economies as the mainstay of agricultural production.

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Section: Literature Reviews and Theoretical Frameworkmentioning

confidence: 99%

How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?

Guan

2023

IJERPH

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“…Previous studies showed that there is a negative or U-shaped correlation between planting size and fertilizer use ( Guo et al, 2022), and that a moderate increase in planting size will increase the FUE (Zhu et al, 2022). Chi et al (2022) believed that the plots fragmentation will lead to more fertilizer inputs, and then lower FUE (Zhu et al, 2022). Besides, the risk attitude of the farmers will affect the fertilizer use and then the FUE.…”

Section: Introductionmentioning

confidence: 98%

Impact of social networks on fertilizer use efficiency in China: Evidence from kiwifruit production in Shaanxi province

Chang,

Liu,

Wang

et al. 2023

Environ Sci Pollut Res

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Improving fertilizer use efficiency (FUE) is an effective way to reduce the fertilizer use and non-point source pollution in agriculture production in China. Existing research related to fertilizer use efficiency ignores the impact of famers' social networks, which can significantly affect the fertilizer use and its efficiency. This study examined how farmers' social networks and adoption of green fertilization technology mediate the impact FUE in kiwifruit production in Shaanxi province, China. The results show that the kiwifruit production had a low FUE, with an average value of 0.333. Social networks positively influenced FUE, with network trust and learning having the greatest impact. Adoption of green fertilization technology mediated the social network's influence on FUE, mainly promoted by network trust and learning. Farmers' experience in agricultural production, joining cooperative society, the quantity of village agricultural supply points had a significant positive impact on FUE, while age and gentle of the head of household had a significant negative impact of FUE. Planting scale had a U-shape impact on FUE. Policies aimed at improving FUE and reducing non-point source pollution should, therefore, focus on strengthening social networks and encouraging green fertilization technology adoption, are essential for reducing non-point source pollution and promoting sustainable agricultural production.

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“…Behaviour is also influenced by a combination of other individual characteristics such as literacy, part-time employment, access to information, risk perception, social networks and other household characteristics 29 – 31 . Resource endowment constraints, in the form of land, labour and capital, are one of the key factors influencing the technology adoption behaviour of farm households 19 , 32 – 34 . The larger the size of the land, the more efficient production can be achieved when new technologies are adopted, and the more motivated farmers will be to adopt them 35 , 36 .…”

Section: Introductionmentioning

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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Land Fragmentation, Technology Adoption and Chemical Fertilizer Application: Evidence from China

Cited by 20 publications

References 43 publications

How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?

How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?

Impact of social networks on fertilizer use efficiency in China: Evidence from kiwifruit production in Shaanxi province

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

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