A new study on precision farming has demonstrated the importance of collecting big data on swine farms, having those transformed into decision making tools and thus maximising profitability. Essentially, such a management system needs to be developed in 5 steps.
一项关于精准养殖的新研究表明了，收集猪场大数据、将其转化为决策工具、从而实现盈利最大化的重要性。从本质上讲，这样一个管理系统需要通过5个步骤来开发。
The study was recently published in the scientific publication Animal Frontiers, a journal of the American Society of Animal Science. The article was authored by a range of pig experts from Spain, Ireland and Japan.
这项研究最近发表在美国动物科学学会的期刊《动物前沿》上，文章由来自西班牙、爱尔兰及日本的养猪专家撰写。
In the article, the authors explain that key in these developments are new technologies such as electronic feeders and artificial intelligence systems capturing big data will provide a better understanding of animal requirements and behaviour, increasing efficiency and sustainability.
在该文章中，作者解释成，这些发展的关键是新技术，如电子饲喂器以及捕捉大数据的人工智能系统，这些将更好地理解动物的需求和行为，从而提高效率和可持续性。
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In addition, biosecurity can be improved using tracking devices for farm staff, recording movements real-time to decrease disease risks and consequently, improve health and productive performance.
再者，通过让猪场工作人员使用跟踪设备、实时记录行动轨迹，可以改进生物安全，因为减少了疾病风险，改善了体况和生产性能。
No strong farm analysis purpose
没有很强的分析目的
The authors explain that most of the data used by farmers have been related to the management of farm tasks, but not too much for analysis purposes. Most of them related to sow reproductive data or basic production summaries. Integration of data from different sources, think of slaughterhouse, lab, reproduction, health, or medicine use, was difficult and rare, according to the authors. And on top of that, there were few data support services assisting swine producers to get any further.
作者解释说，养猪户使用的大部分数据都与猪场任务的管理有关，但没有太多用于分析目的。其中大多数与母猪繁殖数据或基本生产总结有关。作者认为，整合来自不同来源的数据，比如屠宰场、实验室、生殖、健康或药物使用，既困难又罕见。最重要的是，几乎没有数据支持服务来帮助养猪户取得任何进展。
Examples of using data in arable farming has shown that it is possible, the authors wrote. Ideally, such a management system should consist of 5 steps:
作者写道，在可耕地上使用数据的例子表明，这是可行的。理想情况下，这样的管理系统应该包括5个步骤:
1 Data collection
The authors wrote that data are the raw material of the system and can come from human inputs or sensor-robots. Until now, data consisted only of numbers, but the sector is coming closer to the use of images and sounds.
1 数据采集
作者写道，数据是系统的原材料，可以来自人类输入或传感器机器人。到目前为止，数据仅由数字组成，但未来越来与有可能使用图像和声音。
2 Data processing
Data processing is related to the manipulation of data, including several tasks such as validation, sorting or aggregation, management of outliers and missing data. The objective is the correct set-up of databases that allows proper information generation, overcoming interoperability problems.
2 数据处理
数据处理与数据操作相关，包括验证、排序或聚合、管理异常值和丢失的数据等几个任务。其目标是正确地设置数据库，以便正确地生成信息，克服互操作性问题。
3 Reporting
From sow cards or working lists up to multivariate regression analysis to define the optimum value for a certain key performance indicator, every farm or company must decide the information needed from every work level, not forgetting that this could be either technical, economical, or a combination of the 2.
3 数据报告
从母猪卡或工作单到定义某一关键绩效指标的最优值的多元回归分析，每个农场或公司都必须确定每个工作级别所需的信息，别忘了可以是技术层面或经济层面，也可以是两者的结合。
4 Distribution
The objective of this step is sending the right information to the right person at the right time. User preferences must also be considered and can include various types.
4 数据分配
这一步的目标是在正确的时间向正确的人发送正确的信息。还必须考虑用户首选项，可以包括各种类型。
5 Analytics and decision making
Information must be readable and understood by the recipient, and the recipient must have sufficient time to make key decisions. Until now, analytics were aimed at being mainly explanatory, but due to the amount of quality data available, predictive analytics is becoming a key step.
5 数据分析及决策
信息必须可为接收者阅读和理解，并且，接收者必须有足够的时间做出关键决定。到目前为止，分析的目的主要是解释，但受限于可用的高质量数据的数量，预测分析正成为一个关键步骤。
The authors mentioned much technology which can help acquire and interpret data. They mentioned for instance:
作者提到了许多有助于获取和解读数据的技术。例如，他们提到:
Data collection performed by robots and sensors
由机器人和传感器完成数据收集
This technology includes a range of technology that can measure, observe and interpret pig and sow behaviour. Some are well-known, others are brand new. Think of:
这项技术包括一系列可以测量、观察和解读猪及母猪行为的技术。有些是我们熟知的，有些是全新的。比如:
Oestrus behaviour: PigWatch, by Canadian company Ro-Main is a computerised artificial insemination management system designed to predict the best time to inseminate recently weaned sows. It consists of motion sensors installed on the top of every stall in the breeding area, a data analysis module and a software user interface.
发情行为:加拿大公司Ro-Main开发的PigWatch是一个计算机化的人工授精管理系统，旨在预测近期断奶母猪的最佳授精时间。由安装在繁殖区每一个畜栏顶部的运动传感器、一个数据分析模块和一个软件用户界面组成。
Eating behaviour in gestating sows: This behaviour is usually monitored using an ear transponder with radio frequency identification (RFID) that identifies the individual animal at each visit to the feeder.
妊娠母猪的进食行为:这种行为通常通过带有射频识别(RFID)的耳朵应答器进行监控，该设备在每次这头猪前去进食时都能识别出来。
Eating behaviour in lactating sows: Similar electronic sow feeder systems are also available for lactating sows, which are individually housed. New options allowing the sow to choose how much and when to eat have recently arrived at the market (Gestal Solo, Jyga Technologies), thus enabling the farmer to know the lactation intake pattern. ESF systems are also available for grow-finishing pigs.
哺乳期母猪的进食行为:类似的电子喂猪系统也可用于单独饲养的哺乳期母猪。最近市场上出现了允许母猪选择吃多少和什么时候吃的新产品(Gestal Solo, Jyga Technologies)，养猪户因而有机会了解哺乳期的饲喂模式。电子母猪饲喂（ESF）系统也可用于生长肥育猪。
Early disease detection based on image analytics: A motion-based video system for early disease detection has recently been described.
基于图像分析的早期疾病检测:这是一种通过记录生猪行动，进行早期疾病检测的视频系统。
Environment-oriented data
以环境为导向的数据
Thanks to the development of new technologies, farmers are now able to continuously monitor, air quality, temperature, and humidity in real time via sensors. Recently, EU-funded ProHealth project showed how big data could be used to fight diseases.
得益于新技术的发展，养猪户现在可以持续通过传感器实时监测空气质量、温度和湿度。最近，欧盟资助的ProHealth项目展示了如何利用大数据对抗疾病。
Real-time biosecurity control
实时生物安全控制
Models to assess biosecurity are based on scoring systems or survey forms. A new approach addresses biosecurity is the use of real-time devices (B-eSecure System) to control the internal movement of farm staff.
评估生物安全的模型基于评分系统或调查表格。一种解决生物安全问题的新方法是使用实时设备(B-eSecure系统)来控制工作人员在场内的移动。
The article was written by Carlos Piñeiro, Maria Aparicio, Joaquín Morales and María Rodríguez, PigChamp Pro Europa, Spain; Edgar García Manzanilla, Teagasc, Ireland; Yuzo Koketsu, Meiji University, Japan.
这篇文章是由西班牙的Carlos Pineiro、Maria Aparicio、Joaquin Morales、Maria Rodriguez、PigChamp Pro Europa；爱尔兰的Edgar García Manzanilla,、Teagasc，以及日本明治大学的Yuzo Koketsu共同撰写而成。
