精准农业技术和数字化采用的驱动因素和障碍:决策选择模型的元分析

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Zdeňka Žáková Kroupová, Renata Aulová, Lenka Rumánková, Bartłomiej Bajan, Lukáš Čechura, Pavel Šimek, Jan Jarolímek
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引用次数: 0

摘要

本文定义了采用精准农业技术和数字化的关键决定因素。根据Web of Science和Scopus数据库中的PRISMA协议,通过对相关研究的系统综述和荟萃分析来完成研究目标。研究结果强调了社会经济因素的重要性,如教育、年龄和农场规模。高技术素养和有关新技术的充分信息(包括其预期盈利能力)对于评估精准农业和数字化的效益至关重要,这些技术在农业实体实践中的更大规模扩展依赖于此。大型和资本密集的企业更有可能在生产实践中实施新技术,特别是如果它们由更年轻和受过更多教育的管理人员领导,这些管理人员对现代技术更开放,更愿意承担风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drivers and barriers to precision agriculture technology and digitalisation adoption: Meta-analysis of decision choice models

The article defines the key determinants of adopting precision agriculture technologies and digitalisation. The research objectives are fulfilled by the systematic review and meta-analysis of relevant studies, identified and selected in accordance with the PRISMA protocol in the Web of Science and Scopus databases. The findings emphasize the importance of socio-economic factors, such as education, age, and farm size. High technical literacy and adequate information about new technologies—including their expected profitability—are crucial for assessing the benefits of precision agriculture and digitalisation, on which a more considerable expansion of these technologies into the practice of agricultural entities depends. Large and capital-intensive enterprises are more likely to implement new technologies in production practices, especially if they are led by younger and more educated managers who are more open to modern technologies and are more willing to take risks.

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来源期刊
Precision Agriculture
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.
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