A review on beef cattle supplementation technologies

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY
Guilherme Defalque, Ricardo Santos, Marcio Pache, Cristiane Defalque
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引用次数: 0

Abstract

The increase in the worldwide population reflects the expansion of beef cattle production and exportation. Although pasture is the world’s primary feed source of cattle food, failures in pasture management can endanger the productivity of beef cattle. An option for reducing the issues brought on by a shortage of nutritional resources and maintaining the fodder pasture is to perform the supplementation process on the livestock, even being one of the most costly activities in animal management. To decrease expenses and the need for labor to supplement the herd and improve animal performance, many parameters directly associated with supplementation must be monitored, such as environmental climate, soil and pasture characteristics, animal welfare, weight, and health. With so many parameters that impacts the decision on the quality and quantity of supplement to be supplied to the herd, sensors, remote sensing, and agricultural machinery are essential. The joint usage of these technologies in the supplementation process is complex, and there is a gap in decision-making systems for dynamic supplementation. Therefore, this work aims to carry out a comprehensive literature review that characterizes the main technologies related to the bovine supplementation process, mapping the main processes that involve the use of technological tools in the most diverse application domains. Finally, we propose a new Internet of Things architecture focused on the cattle supplementation process that combines technologies to compose a dynamic supplementation decision-making system capable of estimating the quantity and quality of the supplement that the herd needs in the presence of changes in the environment, pasture, and animals’ conditions parameters to reach production targets.
肉牛补充饲料技术研究进展
世界人口的增加反映了肉牛生产和出口的扩大。虽然牧场是世界上牛的主要饲料来源,但牧场管理的失败可能危及肉牛的生产力。减少营养资源短缺和维持饲料牧场所带来的问题的一个选择是对牲畜进行补充过程,即使是动物管理中最昂贵的活动之一。为了减少费用和对劳动力的需求,以补充畜群和提高动物的生产性能,必须监测许多与补充直接相关的参数,如环境气候、土壤和牧场特征、动物福利、体重和健康。由于有如此多的参数影响着决定向畜群提供补品的质量和数量,传感器、遥感和农业机械是必不可少的。这些技术在补充过程中的联合使用是复杂的,并且在动态补充的决策系统方面存在空白。因此,这项工作旨在进行全面的文献综述,描述与牛补充过程相关的主要技术,绘制涉及在最多样化的应用领域使用技术工具的主要过程。最后,我们提出了一种新的物联网架构,以牛的补充过程为重点,结合技术组成一个动态补充决策系统,能够在环境、牧场和动物条件参数发生变化的情况下,估计牛群所需的补充数量和质量,以达到生产目标。
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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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