评估一种数据驱动的工具,用于监测奶牛群的幼畜饲养:用户的看法和改进方案的验证。

IF 2.4 2区 农林科学 Q1 VETERINARY SCIENCES
Preventive veterinary medicine Pub Date : 2025-11-01 Epub Date: 2025-07-31 DOI:10.1016/j.prevetmed.2025.106637
Anouk M B Veldhuis, Henriëtte Brouwer-Middelesch, Nienke Paarlberg, Angela Deterink, Thomas Dijkstra, Lourens Heres, Debora Smits, Thijs Poll, Sabine Stoelinga, Inge Santman-Berends
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引用次数: 0

摘要

自2018年以来,荷兰的奶农可以使用人口普查数据驱动的工具来监测小牛的饲养质量,称为“KalfOK”。参与者(N ≈ 12000;95% (占奶牛场人口的%)每季收到一份评分在0到100分之间的农场报告。该分数是根据12个幼畜饲养质量指标(如:“活产百分比”)。本研究旨在通过独立兽医对205个农场的访问评估,验证KalfOK的表现,以区分小牛健康状况较差或良好的农场。在研究的第二部分,通过随机选择324名奶农进行问卷调查来评估KalfOK。他们被调查了使用KalfOK的经验和改进建议。可行的建议被纳入情景分析,以量化这些拟议的变化对KalfOK绩效的影响。结果表明,KalfOK识别犊牛质量非常高的养殖场的敏感性为83.3 %(95 % CI: 36-100),特异性为63.8 %(95 % CI: 55-72)。检测小牛饲养质量低的农场的灵敏度估计为83.3 %(95 % CI: 36-100),相应的特异性为75.6% %(95 % CI: 67-83)。被调查的农民提出了一些关于改善KalfOK的建议,这些建议涉及健康指标的替代计算,例如将用于计算指标的时间单位从季度改为每年,以及在双胞胎/三胞胎小牛围产期死亡的情况下予以宽大处理。情景分析显示,这些被农民视为改进方案的替代方案并不总是符合他们的利益,因为它们要么导致较低的分数,要么降低了KalfOK在区分高绩效和低绩效农场方面的表现。结果还显示,约50% %的奶农在小牛饲养管理中不积极使用该工具。这意味着,为了提高农民对卡尔福克的吸收,应该通过强调工具的可能性、局限性和附加价值,做出实质性的沟通努力,改变农民对卡尔福克的看法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of a data-driven tool to monitor youngstock rearing in dairy herds: Perception by its users and validation of improvement options.

Since 2018, dairy farmers in the Netherlands have access to a census data-driven tool to monitor calf rearing quality, called "KalfOK". Participants (N ≈ 12,000; 95 % of the population of dairy farms) receive a quarterly farm report with a score ranging between 0 and 100 points. The score is built on points graded for the value of 12 indicators for youngstock rearing quality (e.g. 'percentage of live births'). This study aimed to validate KalfOK's performance to distinguish farms with either reduced or excellent calf health, by the assessment of 205 farm visits by independent veterinarians. In a second part of the study, KalfOK was evaluated by means of a questionnaire amongst 324 randomly selected dairy farmers. They were surveyed on their experiences with KalfOK and suggestions for improvement. Feasible suggestions were included in a scenario analysis to quantify the impact of these proposed changes on the performance of KalfOK. Results showed that KalfOK's sensitivity to distinguish farms with very high calf rearing quality is 83.3 % (95 % CI: 36-100), with a corresponding specificity of 63.8 % (95 % CI: 55-72). The sensitivity to detect farms with supposedly low calf rearing quality was estimated to be 83.3 % (95 % CI: 36-100), with corresponding specificity of 75.6 % (95 % CI: 67-83). Surveyed farmers made a number of suggestions for improvement of KalfOK related to alternative calculation of health indicators, such as adapting the temporal unit used for calculating indicators from quarterly to annually, and clemency in the event of perinatal mortality of twin/triplet calves. Scenario analyses revealed that such alternatives, perceived by farmers as improvement options, are not always in their interest as they either lead to lower scores or they reduce KalfOK's performance in distinguishing high and low performing farms. Results also revealed that about 50 % of the dairy farmers do not actively use the tool in their calf rearing management. This means that in order to improve uptake of KalfOK by farmers, substantial communication efforts should be made to change farmer's perceptions of KalfOK by emphasizing the tool's possibilities, limitations and added value.

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来源期刊
Preventive veterinary medicine
Preventive veterinary medicine 农林科学-兽医学
CiteScore
5.60
自引率
7.70%
发文量
184
审稿时长
3 months
期刊介绍: Preventive Veterinary Medicine is one of the leading international resources for scientific reports on animal health programs and preventive veterinary medicine. The journal follows the guidelines for standardizing and strengthening the reporting of biomedical research which are available from the CONSORT, MOOSE, PRISMA, REFLECT, STARD, and STROBE statements. The journal focuses on: Epidemiology of health events relevant to domestic and wild animals; Economic impacts of epidemic and endemic animal and zoonotic diseases; Latest methods and approaches in veterinary epidemiology; Disease and infection control or eradication measures; The "One Health" concept and the relationships between veterinary medicine, human health, animal-production systems, and the environment; Development of new techniques in surveillance systems and diagnosis; Evaluation and control of diseases in animal populations.
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