Adoption of internet of things-enabled agricultural systems among Chinese agro-entreprises

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2024-08-22 DOI:10.1007/s11119-024-10182-5

Qing Yang, Abdullah Al Mamun, Mohammad Masukujjaman, Zafir Khan Mohamed Makhbul, Xueyun Zhong

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引用次数: 0

Abstract

Purpose

The adoption of the Internet of Things (IoT) technology in the agricultural sector has enormous potential for improving productivity, efficiency, and sustainability. Understanding the predictors affecting the acceptance of IoT-enabled agricultural systems (IAS) is crucial for policymakers, researchers, and industry practitioners.

Methods

This study adopted a cross-sectional design, collected quantitative data from 458 agro-entrepreneurs through structured interviews during July 2022, and applied partial least squares structural equation modeling for data analysis.

Results

The findings revealed that perceived need for IAS (β=0.187) and tolerance of diversity (β=0.166) positively linked with the attitude towards IAS, whereas attitude towards IAS (β=0.262), knowledge about IAS (β=0.309), industry influence (β=0.223), and IoT compatibility (β=0.274) have a positive effect on agroentrepreneurs’ intentions to adopt IAS at the 1% level of significance. Finally, the intention to adopt IAS shows a positive effect (β=0.442) on the adoption of IAS among the Chinese agro-entrepreneurs at the 1% level of significance. Using a multigroup analysis, this study also examined the associations based on the respondents’ age, gender, education level, land size, and monthly income.

Conclusion

This study establishes its originality by examining the relationship between original constructs derived from the theory of planned behavior and contextual factors, such as perceived need, industry influence, tolerance of diversity, innovativeness, knowledge, and compatibility, and investigating the relevant factors, thereby enhancing the comprehension of technology adoption processes in the agricultural sector. The results provide guidance to policymakers and professionals in formulating approaches to encourage the use of IoT in agriculture, supporting the objectives of the "Agriculture 4.0 Policy" and "Digital Rural Development Strategy" in China, and promoting sustainable development goals (SDG 13).

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中国农业企业采用物联网农业系统的情况

目的农业部门采用物联网（IoT）技术在提高生产力、效率和可持续性方面潜力巨大。本研究采用横断面设计，在 2022 年 7 月通过结构化访谈收集了 458 名农业企业家的定量数据，并采用偏最小二乘结构方程模型进行数据分析。结果研究结果显示，农业企业主对农业企业信息化系统的感知需求（β=0.187）和多样性容忍度（β=0.166）与农业企业主对农业企业信息化系统的态度呈正相关，而对农业企业信息化系统的态度（β=0.262）、对农业企业信息化系统的认知（β=0.309）、行业影响力（β=0.223）和物联网兼容性（β=0.274）对农业企业主采用农业企业信息化系统的意向有正向影响，显著性水平为1%。最后，在 1%的显著性水平上，采用 IAS 的意愿对中国农业企业家采用 IAS 有正向影响（β=0.442）。通过多组分析，本研究还考察了基于受访者年龄、性别、教育水平、土地面积和月收入的相关性。结论本研究通过考察计划行为理论的原始建构与感知需求、行业影响、多样性容忍度、创新性、知识和兼容性等背景因素之间的关系，并对相关因素进行调查，从而增强了对农业部门技术采用过程的理解，从而确立了本研究的原创性。研究结果为政策制定者和专业人士制定鼓励在农业中使用物联网的方法提供了指导，支持了中国 "农业 4.0 政策 "和 "数字农村发展战略 "的目标，并促进了可持续发展目标（SDG 13）的实现。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.