Management of people in swine production; what data says

Introduction

During the last decades, the use of data by farmers has been limited. Most of the systems used were simple and mainly focused on the management of farm tasks, with limited or no capacity of analysis. Integration of data from different devices or farms was also difficult, and there was little applied knowledge on the value of data in the strategic decision-making. Another weak point, not solved so far, is the lack of support services in use of data promoting digital transformation and the implementation of systems of information management.

The use of data in many agricultural crops has exploded in recent years; however, its use in livestock is still limited. In pigs, data collection has not changed for many years and analysis is still focused on the main reproductive key performance indicators (KPIs) such as farrowing rate, the number of repeat services, total born, born alive, stillborn, mummifies, weaning to first service interval and preweaning mortality. Other types of data, such as environmental or slaughterhouse data, or data from feeding stations or other automatic sources have not been used in practice except to create simple alerts like detection of temperatures out of range or sows that have not eaten. Among the reasons for this lack of progress are the low added value perceived by producers, the good margins that for years prevented the need for improvement based on production data analysis, the lack of professionals with solid education on-farm data management or the lack of tools to facilitate the process of extracting value, benchmarking and monitoring. On top of these issues, companies manufacturing farm equipment and software that generate data did not facilitate its extraction and use, rather the opposite to protect their equipment and systems.

The Five Steps in New Swine Management System

Data must be transformed into information to generate knowledge. It is not the same despite many times is confused. Companies of any size must set their own information system to solidly support their decision-making process. A swine management information system can be defined as ‘A system made up of tools (software and devices) that together with a working protocol and procedures, including the roles of users, can generate the necessary information to diminish the risk and uncertainties in decision-making’. Such a system always has five steps (Fig. 1), independently of the size and characteristics of the company that uses it:

− Step 1, Data collection. Data is the raw material of the system. Must be of quantity and quality enough and can come from human inputs or sensor-robots. Until now, data is just numbers, but the sector is coming closer to the use of images (disease detection based on altered movements patterns, organs and tissue lesions for presumptive disease diagnostics, smaRt Suite™ Ro-main Inc., Quèbec, Canada) and sounds (respiratory distress detected by Sound talksTM, Sound talks, Leuven, Belgium).

− Step 2, Data processing. Includes several tasks like management of outliers, missing data and use of different formats from different sources. The objective should be the adequate set-up of database structures that allow proper use and interoperability of data (data sharing across systems).

− Step 3, Reporting. Deciding and producing the type of reports of interest for the farm or company at every level is not a minor task. From sow cards or working lists (i.e. sows to be mated or vaccinated) up to multivariate regression analysis to define the optimum value for a certain KPI (i.e. age at first mating considering several variables), every farm or company must decide the reports needed by every work level (farm staff, farm manager, veterinarian, technical manager, board of directors or chief executive officers), not forgetting that could be just technical, economical or mixed.

− Step 4, Distribution of the information. The objective of this step would be sending the right information to the right person at the right time and using the right channel. This is not properly done in many cases and is an overlooked reason for underuse of data. Sometimes information arrives a bit late and is useless (i.e. hypoproductive sows to be culled if report arrives once mated) or is too complex for farm staff or too simple for veterinarians or managers. User preferences to receive it must be considered as well and can include from classical PDF files, text messages at the smartphone or web applications. Every user will be more comfortable and will make better use of the communication channel is the most adequate.

− Step 5, Analytics and decision-making. Information received must be read, understood and used by a person with the right education and with time enough to make a decision to be implemented. Until now, analytics were aimed to be mainly explanatory, but predictive analytics is becoming a key step in most industries due to the amount of quality data available using artificial intelligence techniques such as machine learning (an application that provides systems the ability to automatically learn and improve from experience without being explicitly programmed) or artificial neural networks (an information processing paradigm that is inspired by the way biological nervous systems, like the brain, process information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems).

Figure 1. The five steps of an information system.

Every farm or company must follow these five steps to establish a robust information system that supports its production efficiency and required quality standards.

The need for new technologies

In the last decade, the productive global framework is changing. New technologies have been developed in all sectors and are finally reaching livestock farming. Moreover, producers are becoming aware that their competitiveness depends on using their data appropriately to support their decision-making, both for daily decisions as well as strategic ones to improve their competitiveness. The current productive demands are forcing producers to optimise all aspect of the productive chain.

Modern swine genetics demand a higher degree of understanding of their capacities to optimise their performance under commercial conditions. For instance, consider and extend the knowledge in terms of gilts’ adaptation period, ages for the first mating, optimization of lifetime performance (Iida et al., 2017), causes of early culling, quality of piglets (small or intrauterine growth-retarded ones), mortalities and mortality patterns (Tani et al. 2017), as well as feeding and feeding patterns (Koketsu et al., 1996a,b) are paramount to get the most of its potential. This information could be fed back automatically to genetic companies and change the current selection structure. It would finally include disease status in the genetic pyramid in an efficient way.

But it is not just about quantity anymore; quality criteria are a key component of competitiveness. Quality must be guaranteed within the production chain of live animals including requirements like piglets with adequate weight, homogeneity suitable for fattening, free of certain diseases or from antibiotics treatments, ensuring welfare status or certain feeding practices, like only vegetables based).

This high demand for good production under high-quality standards cannot be achieved in a production model as complex and sophisticated as the current swine production without adequate use of the information generated.

Biosecurity data

Biosecurity is known as the implementation of measures to reduce the risk of introduction and spread of disease agents (FAO, 2010) and can thus be divided into two aspects. External biosecurity relates to the prevention of pathogens entering a herd, while internal biosecurity prevents the spread of disease within a herd, mainly from older to younger animals (FAO, 2010). In this regard, biosecurity is an important aspect of preventing the transmission of diseases, thus improving health and reducing the need for antimicrobials (Laanen et al., 2013). Moreover, most diseases have a negative impact on the well-being of the animals and consequently, on their productivity. Thereby higher levels of biosecurity lead to also improved the economy of the farmer.

Under our experience performing biosecurity audits in western/eastern Europe and Asia it's quite frequent to get the same answer when we find mistakes ‘we always do well, but just today…’. And this is one of the crucial points, the exceptions. It is very common that farm staff knows, in general, the theory or the working principles they should follow, but they don’t comply in every case. Following are presented some personal examples:

− Responsibilities in biosecurity not clearly assigned to anyone. Some farms have a comprehensive written protocol that is not put in practice because there is no one with the responsibility of its implementation and lack special training on biosecurity.

− Mixing of personnel in common areas (canteen) without changing boots or clothes.

− Shower partially compulsory because ‘you are not going to take them a shower 4 times a day’.

− ‘I am special’. Farm arranged by colours in clothes for every zone but manager wearing special and different ones without respecting rules just because ‘I am the manager’.

− Public and private roads used shared with no preventive measures at all.

− ‘Creativity’ in complying with the rules. ‘I only shower when I arrive in the morning’

Data were there wasn`t any

The human factor is known as of paramount importance in swine health and production since most of the tasks are performed by people. This includes how animals are managed in terms of animal care, feeding administration, application in practice of medicines and vaccines, follow-up of biosecurity protocols and accomplishment of farms’ tasks and duties plans.

In this scenario of great progress in data management using different sources, our understanding of real human behaviour in swine farms is lacking. Among others, this affects biosecurity and general farms’ operations.

The assessment of the biosecurity level of a farm/group of farms is very complex due to the cross-sectional characteristic of the farm. Biosecurity affects all the processes that take place on a farm, animals, people, supplies, environment, etc. For this reason, it is essential to have an evaluation method that is as ordered and standardized as possible, only in this way details that can be of great importance will not be overlooked.

The first and most traditional method are biosecurity audits. These are based on surveys together with a farm visit and a final analysis of data collected. The limitations of this are clearly related to the subjectivity of data reported, sometimes even with the best will, but answers could be more related with a guess than with a fact. When visiting the farm this corresponds normally with the picture of what happens at that moment, which could match totally o partially with a fair image of farm facts and actions performed. Finally, follow-up is mostly based on the repetition of this process rather than in getting objective information about the recommendations agreed. Altogether, biosecurity audits appear at the best possible solution until now to understand and improve biosecurity standards but with a clear margin of improvement.

Despite many diseases share common facts and approach related to the biosecurity principles required, I will focus on one of the most common in the porcine industry; the porcine reproductive and respiratory syndrome (PRRS). This disease impairs swine health and is responsible for huge economic losses in the swine industry worldwide (Neumann et al., 2005). Infection with PRRS virus (PRRSv) is characterized by reproductive failures in pregnant sows, high pre-weaning mortality in piglets infected in utero and respiratory signs in both growers and finishers pigs (Done et al., 1996; Kranker et al., 1998; Rossow, 1998).

Numerous studies (Baysinger et al., 1997; Weigel et al., 2000; Mortensen et al. 2002, Otake et al., 2002a,b, 2004; Firkins & Weigel, 2004; Evans et al., 2008; Dee et al., 2009; Lambert et al., 2012) have described the routes which are involved in PRRSv transmission between and within herds, including the introduction of positive animals or semen, management of the quarantine for the newly introduced animals, as well as vehicles, aerosols, insects or contaminated fomites. Moreover, the marked genetic and antigenic heterogeneity of the virus, combined with its immune evasion strategies, inhibit the full efficacy of current commercial PRRS vaccines (Hu & Zhang, 2014). Therefore, PRRS control based only on the use of vaccination has often provided limited efficacy under field conditions (Geldhof et al., 2013). Hence, it is of paramount importance the implementation of good biosecurity measures to prevent the introduction of the virus into a farm but also to slow down its transmission within a herd once infected.

Nonetheless, most of the developed programs of biosecurity measures are based on scoring systems or survey forms. For instance, researchers from Ghent University developed a scoring system called Biocheck. UGent™ (Laanen et al., 2013; Postma et al., 2016) as a risk-based scoring tool to evaluate the biosecurity quality of pig herds. Another scoring system has been developed by the University of California-Davis (Disease Bioportal®) for the dynamic risk assessment and farms benchmarking also based on surveys (Holtkamp et al., 2013). In this line, Sternberg-Lewerin et al. (2015) developed a risk assessment tool for B. hyodysenteriae and M. hyopneumoniae considering the frequency of contacts, but it was only focused on external biosecurity. All these tools are based on values obtained through expert opinion panels; however, the perception of experts may vary depending on different circumstances, therefore, scoring systems based on perceptions should be adapted to each situation.

However, the development of objective systems to assess biosecurity is constantly evolving. In this regard, new solutions for digital biosecurity control are appearing in the market as objectives tools for the evaluation of internal biosecurity based on a system of control of the flow of internal movement of personnel on pig farms.

The new digital biosecurity system was based on two on-farm hardware pieces including beacons and readers. Each farm worker was given small Bluetooth^TM transmitters called beacons, which were required to wear all the time while they were within the farm facilities. Readers were installed and fixed at every access of every barn, including lockers and showers. These devices can detect beacon signals by proximity. Whenever a beacon was within a device’s detection range, the device registered the beacon identity as well as the detection time and uploads the record to a database.

Data collection and processing records from readers were sent to the cloud and processed, so the movements and routes of the farm’s workers were computed. Each movement represented a route made by a farm worker from an origin zone to a destination zone. Thus, the system allowed the real-time monitoring of the farms’ staff movements patterns. Figure 2 shows the map of readers set in a farm:

Figure 2. Map of readers to track farm staff movements set in a farm.

Díaz et al. (2020) have recently proved that the internal movements of farm staff are related to PRRVs incidence. First, personal farm movements were classified into Safe, Unsafe and Risk depending on which farm areas are being produced. Results showed that neither the percentage nor the total amount of both Safe and Unsafe movements were significantly different between the PCR Positive and Negative PRRS status groups, being Safe movements was always above 80% in the Negative PCR PRRS status group. However, both the percentage and the total amount of Risky movements were significantly smaller in the PCR negative PRRS status group. These results show a clear relationship between the total amount of Risky movements and the probability of a PRRSv outbreak on the farms (Fig. 3).

Figure 3. Average of percentages of Safe (green), Unsafe (orange) and Risky (red) movements before (■) and after (□) the training session considering the eight farms in which the system control movement was installed.

Average of percentages of Safe (green), Unsafe (orange) and Risky (red) movements before (■) and after (□) the training session considering the eight farms in which the system control movement was installed.

Moreover, proper training can decrease risky movements and increase the safe one (Fig. 4).

Figure 4. Comparison of percentages and totals of Safe, Unsafe and Risky movements between the PCR analytics positive and negative groups. The boxes extend between the first and third quartiles of the data for each group, and the whiskers extend between the minimum and maximum values. Group average is shown by the red triangles, while median lines are shown with solid green lines.

Comparison of percentages and totals of Safe, Unsafe and Risky movements between the PCR analytics positive and negative groups. The boxes extend between the first and third quartiles of the data for each group, and the whiskers extend between the minimum and maximum values. Group average is shown by the red triangles, while median lines are shown with solid green lines.

Operational data

Farms’ operations must be performed every day more with a higher degree of accuracy to ensure expected results. Generating the expected number of pigs of a certain quality is a consequence of a good number of ordered actions adequately performed. Most of those tasks are performed by people and, generally, little is known about how it is really performed beyond of reports or checklists normally filled by farm staff or managers despite is not common yet in pig production, some companies are starting to use some techniques coming from other sectors to meet these objectives, including LEAN methodology, Six Sigma, Kaizen, Hoshin Planning or Balanced Scorecard in order to meet operational excellence. This can be described as a philosophy that embraces problem-solving and leadership as the key to continuous improvement. People are often unsure of how to approach the subject of operational excellence. It is a difficult term to define and most people either find the topic to be too ambiguous or too broad to talk about. Operational excellence, however, is not a set of activities that you perform. It is more of a mindset that should be present within you and your employees. Now, you’re probably thinking, “that sounds nice in theory, but how do I translate this into actionable steps?”.

These technologies allow moving forward into that direction in an objective way, understanding better how human behavior influences farms’ results. Recent work from Arruda demonstrated a relationship between the movements and the efficiency in production in US farms (data not published). In her study, a five-hour increase in time spent in rooms by the manager was associated with an increase in one weaned piglet for every 10 litters (P = 0.01). In a second farm, an increase in time spent working in the farrowing rooms of approximately two hours per worker per week tended to increase the number of weaned piglets by one piglet for every 4 litters (P = 0.087). These findings could be related to an increase in pig care, which would likely lead to a higher number of weaned animals. Having control of this at a glance will be of great help to monitor that operations are performed as expected and easen early personalized corrections (Fig. 5).

Figure 5. Key biosecurity indicators monitored real-time in a farm, including lab results.

IPVS - Management of people in swine production; what data says - Image 1

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Under our own experience, we have observed in farms worldwide some interesting facts that relate human behaviour and farm performance, both from visitors and farm staff:

1. Football World Cup 2018. In some farms, it was observed how on the days of national team games, the time spent by the workers in the mating area was reduced up to 35%.

2. Repeats of gilts. In a Central America farm, we detected an unusually high percentage of repeats in gilts during the weekend. The time people spent in the mating area during the weekend was 40% lower compared to the rest of the days and the multiparous area.

3. Gilts development unit. Wrong and forbidden farm staff entries during the quarantine period to gilts development units barn were detected which lead to immediate preventive or corrective actions. Remarkably, nothing was informed from farm staff about these facts.

4. Lactation feeding management. When feeding sows manually in lactation it is recommended to space out the feed delivery to promote higher intake and less wastage. We have found evidence that the third feed delivery, expected in the afternoon, was brought backwards considerably during the weekends, to finish the job earlier.

5. Holidays. In Spain, we analyzed the movement and entry-exit patterns during the stay in certain areas of the farm. When one of the workers were on holiday and another covered the position, the pattern changed and the stays in that particular area were shorter and the entry-exit was more frequent. We believe that, due to the lack of habit of working in the area, the performance of the task was less efficient.

6. Facilities. In Spain, we also evidenced that the workers of the farrowing area spent more time in old barns to wean the same number of piglets that in new barns.

Metrics focused on hours spent by farm zone or by farm staff can be very useful to really understand the work performed and its pattern (Fig. 6).

Finally, it must be mentioned the use of these digital technologies to control external biosecurity, mainly related to access control of visitors, trucks and company staff. Some companies in Latino America has set a system to control the access of the staff to farms, since in large production systems, along with the transport of animals, it is considered one of the greatest health risks of entry of diseases. The first company that implemented the system controlled almost 400,000 accesses of their workers and contractors in the first year (Figures 7 and 8).

Figure 6. Charts showing hours spent by zone or by farm staff in a farrow to wean farm monitored real-time.

IPVS - Management of people in swine production; what data says - Image 3

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Figure 7. Number of visitors controlled in the system. October 2018 – June 2019.

Figure 8. Overtime entries and failures detected by the system. October- December 2018.

If every entry is a risk, it can be said that large companies, including this, are exposed to extreme risk. The system allowed to effectively control the flow of workers in farms responding to the restrictions of biosecurity established according to the company criteria, providing at the same time absolute traceability of all movements made and the possibility of immediate response. In this way, and in addition to the entry control, objective risk indicators are generated to manage and ensure biosecurity standards set. These new indicators are called "key biosecurity indicators" (KBI) and allow to keep an online and instantly updated record in the cloud (unlike traditional paper systems which are difficult to understand and generates slow reactions). Figures 9 and 10 show some risks of entry:

Figure 9. Total number of entries per farm.

Figure 10. Traceability of movements among farms.

The system allows generating:

− Instant alerts to minimize damages, early control of its extent and prompt corrective actions.

− Easy monitoring of compliance with company standards.

− Detect those factors that generate more risk to the system and work specifically in its correction.

− Puts pressure on respect for rules since behaviour improves where users are aware of being controlled.

− Perform biosecurity audits more objective, effective and focused since they are based in data and not in checklists

− Design tailor-made training programs based on errors and not using the generic ones.

Conclusions

Swine production sector is becoming more professional than ever since it will be the only way to meet the objectives of efficiency and quality that support its competitiveness. Among other challenges, ensure high biosecurity standards to keep farms free of disease and therefore improve productive efficiency and quality standards, mainly related with a low or minimum use of antibiotics will be one of the key most important challenges in the next decade. Besides of this, promoting good or excellent operations will greatly help to achieve the objectives mentioned.

Digital technologies, are very reliable and cost effective will be a great new tool for the sector and in particular for veterinarians that can do more and better consultancy work, without the mandatory need of visiting the farm, being able to understand better many factors that influence health and farm performance based on staff behavior, developing better solutions and deliver customized training to specific workers based on their behavior ad performance.

Published in the proceedings of the International Pig Veterinary Society Congress – IPVS2020. For information on the event, past and future editions, check out https://ipvs2022.com/en.

Management of people in swine production; what data says

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