Data value creation in agriculture: A review

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-11-05 DOI:10.1016/j.compag.2024.109602
Havva Uyar , Ioannis Karvelas , Stamatia Rizou , Spyros Fountas
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Abstract

Agricultural data have great potential to improve decision-making, enhance operational efficiency, and drive innovation. Despite the growing acknowledgment of their value, there remains a gap in understanding how data value creation is perceived and implemented in agriculture. This study addresses this gap by investigating data value creation mechanisms, targets, and impacts through a structured literature review of 80 articles, including 13 core articles retrieved via targeted database searches and 67 additional articles identified through cross-reference snowballing. Key “value creation mechanisms” are categorized as transparency and access, discovery and experimentation, prediction and optimization, customization and targeting, learning and crowdsourcing, and monitoring and adaptation. The value creation mechanisms aim to enhance key “targets”, namely organizational performance, business process improvement, product and service innovation, and consumer and market experience. Organization performance was the most frequently addressed value target, appearing in approximately 85% of the core articles, followed by business process improvement, highlighted in approximately 77% of the articles. Together, the mechanisms and targets create “impact”, constructing the value of data. The findings reveal that all core articles (100%) emphasize the functional value of agricultural data, while 54% also explore their symbolic value, which enhances reputation and market positioning. A key takeaway is that, unlike many other assets, the value of agricultural data increases with reuse, which calls for a shift in focus from data ownership to ownership of the value derived from them. This study highlights the need for robust frameworks to fully realize the potential of agricultural data and calls for future research to further characterize and assess this value. These insights are essential for developing tools and methodologies that enhance productivity, sustainability, and profitability in agriculture.
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农业数据价值创造:综述
农业数据在改善决策、提高运营效率和推动创新方面具有巨大潜力。尽管人们日益认识到数据的价值,但在了解农业领域如何看待和实施数据价值创造方面仍存在差距。本研究针对这一空白,通过对 80 篇文章进行结构化文献综述,调查数据价值创造机制、目标和影响,其中包括通过定向数据库搜索检索到的 13 篇核心文章,以及通过交叉引用滚雪球法确定的 67 篇其他文章。主要的 "价值创造机制 "分为透明度与获取、发现与实验、预测与优化、定制与目标定位、学习与众包,以及监测与适应。价值创造机制旨在提高关键 "目标",即组织绩效、业务流程改进、产品和服务创新以及消费者和市场体验。组织绩效是最常涉及的价值目标,出现在约 85% 的核心文章中,其次是业务流程改进,在约 77% 的文章中得到强调。这些机制和目标共同产生了 "影响",构建了数据的价值。研究结果显示,所有核心文章(100%)都强调了农业数据的功能价值,54%的文章还探讨了其象征价值,即提高声誉和市场定位。一项重要启示是,与许多其他资产不同,农业数据的价值会随着重复使用而增加,这就要求将重点从数据所有权转移到数据衍生价值的所有权上。这项研究强调,要充分发挥农业数据的潜力,就必须建立健全的框架,并呼吁今后开展研究,进一步描述和评估这种价值。这些见解对于开发提高农业生产力、可持续性和盈利能力的工具和方法至关重要。
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
期刊最新文献
Optimization and testing of a mechanical roller seeder based on DEM-MBD rice potting tray Development of plant phenotyping system using Pan Tilt Zoom camera and verification of its validity Human robot interaction for agricultural Tele-Operation, using virtual Reality: A feasibility study Corrigendum to “A chlorophyll-constrained semi-empirical model for estimating leaf area index using a red-edge vegetation index” [Comput. Electron. Agric. 220 (2024) 108891] Design and experiment of monitoring system for feed rate on sugarcane chopper harvester
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