Social network data of Swiss farmers related to agricultural climate change mitigation

Kreft, Cordelia Sophie; Angst, Mario; Huber, Robert; Finger, Robert

doi:10.1016/j.dib.2021.106898

Cited by 9 publications

(13 citation statements)

References 3 publications

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“…➁ Social networks. The communication frequency with villagers in other villages is used to represent the weak ties, and the communication frequency with villagers in this village is used to represent the strong ties ( Kreft et al, 2021 ), and the average of them is taken as the final value of this index. ➂ Social norms.…”

Section: Methodsmentioning

confidence: 99%

The influence of social capital on farmers’ green control technology adoption behavior

Ren

Zhong

2022

Front. Psychol.

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Relying on social capital to promote farmers’ adoption of green control technology is of great significance for the governance of rural environment and the realization of sustainable agricultural development. Based on the survey data of 754 farmers in Shandong Province, this paper uses the Probit model and the instrumental variable method to empirically analyze the impact of social capital on farmers’ green control technology adoption behavior. The results show that: social capital has a promoting influence on farmers’ green control technology adoption behavior; the influence of the three dimensions of social capital on farmers’ green control technology adoption behavior is in turn social norms, social networks, and social trust; social networks play an enhanced moderating role in the process of social trust and social norms promoting farmers’ green control technology adoption behavior; education level, the number of family labor force and annual family income level have a significant positive impact on farmers’ green control technology adoption behavior, while age has a significant negative impact. Therefore, the government should make full use of social capital to promote farmers to adopt green control technology.

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Section: Methodsmentioning

confidence: 99%

The influence of social capital on farmers’ green control technology adoption behavior

Ren

Zhong

2022

Front. Psychol.

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“…The complete questionnaires, all resulting data sets plus the codebooks explaining the variables, are available in Kreft et al (2021b) and freely accessible on the ETH Research Collection: https:// www. resea rch-colle ction.…”

Section: Data Collectionmentioning

confidence: 99%

“…More specifically, we use a Bayesian approach which allows us to model multiple influence processes and compare them simultaneously (Dittrich et al 2020). Control variables such as age, education, farm type, or perceived self-efficacy are used at the individual farmer level to account for possible correlated effects which do not reflect social interactions (Kreft et al 2021a(Kreft et al , 2021bWuepper et al 2019). Our analysis is based on a combination of census, survey, and detailed network data.…”

Section: Introductionmentioning

confidence: 99%

Farmers’ social networks and regional spillover effects in agricultural climate change mitigation

et al. 2023

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Climate change poses a severe threat to global agricultural production and rural livelihoods, and since agriculture itself is a significant source of greenhouse gas (GHG) emissions, it can also play an important role in climate change mitigation. This article investigates how farmers’ social networks influence the adoption of on-farm mitigation strategies. More precisely, we use a network autocorrelation model to explore the relationship between a farmer’s own mitigation behavior and the mitigation behavior and knowledge of his fellow farmers. The analysis is based on a regional case study in Switzerland and uses data obtained from personal network interviews combined with survey and census data of 50 farmers. Half of them are members of a local collective action initiative for agricultural climate change mitigation, while the others do not participate in the initiative. We find that, on average, farmers with a larger network adopt more mitigation measures, and furthermore, mitigation adoption is linked with the level of knowledge within farmers’ networks. Indeed, the likelihood that non-members will adopt mitigation measures increases if they are closely associated with members of the collective action, suggesting a local spillover effect. It follows that strengthening knowledge exchange among farmers and supporting local farmers’ initiatives can potentially contribute to the diffusion of agricultural climate change mitigation practices.

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“…In this work, we propose a new approach based on the social network analysis for analysing the chemical space of metal organic frameworks. Social network analysis was originally established in the field of social sciences [12,13], but has been expanded to virtual learning [14], health informatics [15][16][17][18], life sciences [19][20][21][22], agriculture [23][24][25], economy [25,26], and others.…”

Section: Introductionmentioning

confidence: 99%

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

Jalali¹,

Tsotsalas²,

Wöll³

2022

Nanomaterials

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The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks.

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Social network data of Swiss farmers related to agricultural climate change mitigation

Cited by 9 publications

References 3 publications

The influence of social capital on farmers’ green control technology adoption behavior

The influence of social capital on farmers’ green control technology adoption behavior

Farmers’ social networks and regional spillover effects in agricultural climate change mitigation

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

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