High-performance simulation of disease outbreaks in growing-finishing pig herds raised by the precision feeding method

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-08-18 DOI:10.1016/j.compag.2024.109335
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Abstract

Perturbations always affect livestock during the breeding process, including harmful diseases. Researching the impact of disease outbreaks on pig herds is extremely important so that disease control measures can be applied early. However, conducting practical experiments on disease outbreaks is extremely expensive. Precision feeding systems (PFS) for pigs use data on the animal’s own feed intake to calculate the appropriate amount of feed for each individual. This helps increase productivity and product quality while contributing to reducing waste generation in the environment. Daily feed intake (DFI) and cumulative feed intake (CFI) data can be automatically collected and estimated from the PFS, which can help detect or predict disease outbreaks. In this article, we introduce an advanced simulation model of the PFS for pigs and the integration of disease outbreak models into this system. A disease outbreak simulation application within the pig herd raised by the precision feeding method is also developed for running high-performance experimental simulations. The results of the simulation scenarios are analyzed and compared with data from a real-world experiment to assess the accuracy of the application. The correlation coefficient values of DFI in all scenarios fall within the range of 0.25 to 0.5, suggesting almost no correlation between simulated DFI and actual DFI. The overall average correlation coefficient of CFI for all scenarios is 0.764, falling within the strong correlation range. It can be concluded that the simulation accurately represents CFI values compared to reality.

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高性能模拟精确饲养法饲养的生长育肥猪群的疾病爆发
牲畜在繁殖过程中总会受到各种干扰,其中包括有害疾病。研究疾病爆发对猪群的影响极为重要,这样才能及早采取疾病控制措施。然而,对疾病爆发进行实际实验的成本极其昂贵。猪的精确饲喂系统(PFS)利用动物自身的采食量数据来计算每个个体的适当饲料量。这有助于提高生产率和产品质量,同时有助于减少环境中产生的废物。日采食量(DFI)和累计采食量(CFI)数据可从 PFS 中自动收集和估算,这有助于检测或预测疾病的爆发。在本文中,我们将介绍一种先进的猪场采食量模拟模型,并将疾病爆发模型集成到该系统中。此外,还开发了在采用精准饲养法饲养的猪群中进行疾病爆发模拟的应用程序,以运行高性能的实验模拟。对模拟场景的结果进行了分析,并与真实世界的实验数据进行了比较,以评估应用的准确性。所有情景中 DFI 的相关系数值都在 0.25 至 0.5 之间,表明模拟 DFI 与实际 DFI 几乎没有相关性。所有方案中 CFI 的总体平均相关系数为 0.764,属于强相关范围。由此可以得出结论,与现实相比,模拟准确地反映了 CFI 值。
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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.
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