预测智能化农业早期创新技术的未来采用情况:一项案例研究,调查影响使用智能饲喂器技术在牧草放牧奶牛系统中输送甲烷抑制剂的关键因素

IF 6.3 Q1 AGRICULTURAL ENGINEERING Smart agricultural technology Pub Date : 2024-08-27 DOI:10.1016/j.atech.2024.100549
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引用次数: 0

摘要

在全球范围内,畜牧业者面临着减少温室气体排放以减缓气候变化的挑战。牧场奶农的一个潜在选择是在饲料中添加甲烷抑制化合物。智能饲喂器是一种新颖的方法,可按要求的频率和精度提供这些化合物,这是一种现有的智能农业技术,用于自动向围场内的动物喂食补充剂。然而,这项创新要想取得成功,就必须与农场系统相结合,并为农民提供积极的价值主张。本研究旨在探讨影响牧场内智能技术在牧草种植系统中输送甲烷抑制剂的潜在采用率的农场系统和技术因素。我们利用采用预测工具(ADOPT)来模拟智能饲喂器作为甲烷抑制剂输送机制在奶牛场的采用结果,并通过焦点小组听取行业专家和牧场主的意见。结果表明,在以牧场为基础的系统中,智能饲喂器的采用率较低。我们通过对七个关键的 ADOPT 因素进行敏感性分析,进一步探讨了这一问题。我们模拟了七个关键 ADOPT 因素对两种智能饲喂器概念的影响,以评估其相对采用潜力。采用模型显示,技术成本和功能等因素固然重要,但未来的监管环境、创新的不确定性以及是否符合农民对其农场系统的价值观和世界观也会对采用产生很大影响。这项研究强调,草场内施肥技术及其在农场的使用过程是一项早期创新,因此农民和其他利益相关者必须参与到进一步的开发中,以确保采用因素得到解决。
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Predicting future adoption of early-stage innovations for smart farming: A case study investigating critical factors influencing use of smart feeder technology for potential delivery of methane inhibitors in pasture-grazed dairy systems

Globally, livestock farmers are challenged with reducing greenhouse gas emissions to mitigate climate change. A potential option for pasture-based dairy farmers involves including methane-inhibiting compounds in the diet. A novel approach to deliver these compounds with the required frequency and precision is via smart-feeders, an existing smart farming technology used to feed supplements automatically to animals in-paddock. For this innovation to be successful, however, it must integrate with farm systems and provide farmers with a positive value proposition. The aim of this study was to examine the farm system and technology factors influencing potential uptake of in-paddock smart technologies for delivering methane inhibitors in pasture-grazed systems. We utilized an adoption prediction tool (ADOPT) to model the adoption outcomes of smart-feeders as methane inhibitor delivery mechanisms on dairy farms, with input from industry experts and farmers via focus groups. The results indicated low adoption of smart-feeders in a pasture-based system context. This was further explored with a sensitivity analysis of seven critical ADOPT factors which were identified as influential through the farmer focus groups. We modelled the impact of the seven critical ADOPT factors for two smart-feeder concepts to evaluate their relative adoption potential. The adoption modelling showed that while factors such as technology cost and function were important, adoption would also be highly influenced by future regulation settings, innovation uncertainty, and the alignment with farmer values and worldviews about their farm system. This research highlighted that in-paddock delivery technology, and processes for its use on-farm, represents an early-stage innovation and therefore is vital that farmers and other stakeholders are involved in further development to ensure adoption factors are addressed.

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