The majority of veterinarians consider it important to classify sow herd PRRS status.Our survey showed that 8/21 follow AASV guidelines, with the others using alternative criteria.
Half of the surveyed veterinarians use processing fluids as part of their testing protocol for determining sow herd PRRS status.
Most of the respondents mentioned that AASV PRRS classification guidelines should be re-visited.
Twenty-one veterinarians from 12 participant systems and 1 non-participant group completed the questionnaire accounting approximately for 1.5 million sows.
When asked how important it was to classify sow farm PRRS status, 12/21 (57%) answered very important, 8/21 (38%) answered important. Among the most important reasons requiring PRRS status were:
Commingling of pigs downstream,
Timing the Depopulation/Re-population of growing sites with continuous flow, and
Defining gilt acclimation and introduction procedures.
The testing protocol to classify a farm as stable varied across and within systems. However, the most frequent sample collected was due-to-wean blood sampling. Other samples are shown in the figure below.
Today on the blog, we are sharing a study by our colleagues: Dr. Lee Johnston from the College of Food, Agricultural, and Natural resources Sciences (CFANS) and Sara Schieck from the swine extension team, regarding floor space allowance and its impact on growth on finishing pigs.
Most floor space allowance studies were conducted 20 years ago when pigs were sent to market when they reached 113kg (around 248 lb) whereas pigs are currently sent at 128kg (281 lb). Therefore, guidelines need to be updated.
Experiment 1: Pigs from 27 to 138 kg (59 to 304 lb) were housed providing either 0.71, 0.80, 0.89, 0.98, or 1.07 m2/pig of floor space (respectively 7.64, 8.61, 9.58, 10.55, 11.52 square ft/pig). Growth rate, cortisol concentration and lesion scores were measured for each pig.
Experiment 2: Pigs around 130 kg (286 lb) were housed providing either 0.71, 0.80, 0.89, 0.98, or 1.07 m2/pig of floor space (respectively 7.64, 8.61, 9.58, 10.55, 11.52 square ft/pig).
Initial body weight of pigs was not different across floor space allowances; however, increasing floor space allowance increased final body weight (linear, P = 0.04) and tended to increase ADG (linear, P = 0.06) and ADFI (linear, P = 0.06). Gain efficiency was not influenced by increasing floor space allowance. There were no differences in initial salivary cortisol concentrations across floor space treatments. Similarly, there were no differences in salivary cortisol among floor space allowances 2 and 1 wk before the final weight, when pigs should have experienced the greatest differences in crowding among treatments.
Based on the growth performance and pig welfare data collected in Exp. 1, a clearly optimal floor space recommendation is not apparent. The equation from previously published studies estimates that 138-kg pigs require 0.91 m2 of floor space; therefore, the present study provided 2 treatments below and 2 treatments above the predicted requirement. Our data are clear that pigs in the present study did not respond to floor space allowances greater than the predicted need of 0.91 m2 with improved growth performance or welfare.
In Exp. 2, the floor space needs of heavy market pigs could be studied isolated from the diluting effects of the early growth period that were present in Exp. 1. Results of Exp. 2 indicate that 0.98 m2/pig optimized growth performance of pigs between the weights of 133 and 148 kg.
Pigs marketed at 138 kg BW optimize growth performance when provided 0.89 to 0.98 m2 of floor space per pig. However, the negative effects of low space allocations were mostly observed in heavy pigs. Therefore, the use of a pig removal strategy near the end of the finishing period may be an effective strategy to diminish the negative effects of crowding when pigs are near market weight.
Current floor space allowances were determined in research studies conducted 10 to 20 yr ago using pigs that were marketed at a BW of about 113 kg or less. Currently, pork producers are regularly marketing pigs that weigh over 128 kg. Given this precipitous increase in market weight, we conducted 2 experiments to determine if floor space allowances previously determined apply to pigs marketed at greater than 128 kg. Experiment 1 was conducted at 5 university research stations throughout the Upper Midwest region. In this experiment, we evaluated the growth performance, salivary cortisol concentrations, and lesion scores of pigs weighing between 27 and 138 kg provided 0.71, 0.80, 0.89, 0.98, or 1.07 m2/pig of floor space. Within each station, group size (range = 6 to 19 pigs) remained constant across floor space treatments but pen size was altered to achieve the desired space allocations. There were 14 replicate pens for each treatment. Overall, increasing floor space allowance increased final BW (linear, P = 0.04) and tended (linear, P < 0.06) to increase ADG and ADFI. There were no improvements in final BW or ADG beyond 0.89 m2/pig. The G:F was not influenced by increasing floor space allocation. Salivary cortisol concentrations and lesion scores were not affected by floor space allowances. Experiment 2 focused on floor space needs of pigs nearing market weight and was conducted at 4 research stations. Pigs weighing about 130 kg were assigned to pens that provided 0.71, 0.80, 0.89, 0.98, or 1.07 m2/pig of floor space. Group size ranged from 4 to 11 pigs per pen but was constant across floor space treatments within station. The study lasted 2 wk and there were 8 replicate pens per treatment. As floor space allowance increased, ADG (0.86, 0.95, 0.95, 1.10, and 1.06 kg; linear, P < 0.01), ADFI (3.03, 3.26, 3.22, 3.49, and 3.25 kg; quadratic, P < 0.05), and final BW (145.6, 145.7, 146.4, 148.3, and 147.9 kg; linear, P < 0.01) increased. Based on the results of these 2 experiments, pigs marketed at about 138 kg require at least 0.89 m2/pig to support optimal growth performance. However, heavier pigs (about 148 kg) at the end of the finishing period require 0.98 m2/pig.
The EWMA chart is a smoothed chart of the percentage of farms that are breaking.
Newly added farms to MSHMP increase the denominator therefore diluting the estimate which affects the EWMA chart giving the impression that PRRS season has changed.
Reminder: What is the EWMA?
The Exponential Weighted Moving Average (EMWA) is a statistical method that averages data over time, continually decreasing the weight of data as it moves further back in time. An EWMA chart is particularly good at monitoring processes that drift over time and is used to detect small shifts in a trend.
In our project, EWMA is used to follow the evolution of the % of farms at risk that broke with PRRSV every week. EWMA incorporates all the weekly percentages recorded since the beginning of the project and gives less and less weight to the results as they are more removed in time. Therefore, the % of farms at risk that broke with PRRSV last week will have much more influence on the EMWA than the % of farms at risk that broke with PRRSV during the same week last year.
MSHMP report chart 4 depicts: 1)the number of new cases (green dots – secondary Y axis) during a specific week and 2)the percentage of farms that broke during that week of the total in the MSHMP project in a smoothed way (blue line/Y axis). The red horizontal line indicates the threshold (upper confidence limit – UCL). This UCL is calculated based on the average of cases during the lowest PRRS months in the year, June, July and August and is recalculated every two years.
When there are more cases than expected, the blue line crosses the threshold (red line) indicating there is an epidemic.
The formula used in the EWMA chart is the following:
where E is the smoothed % of infected herds, lambda the constant smoothing the curve, I the % of infected herds during that week and Et-1 is the smoothed % of infected herds during the previous week.
If different smoothing factors are applied to the MSHMP data this would generate different trends and then we would place the threshold based on the sensitivity
that we consider that signals an epidemic.
Has the incidence of PRRS changed?
One possible reason the EWMA % of cases decreasing might be that the number of farms that are breaking expressed as a percentage is less. This can be due to the fact that the total number of farms sharing PRRS status has been increasing and these new farms might have a lower underlying incidence.
Earlier this year, Dr. Fabian Chamba Pardo successfully defended his PhD under the supervision of Drs. Montse Torremorell and Marie Culhane. The focus of his thesis is influenza epidemiology with an emphasis on sow farms and nurseries. We share with you today a summary of his work.
Influenza is an economically important disease in pigs and a public health threat. Breed-to-wean (BTW) farms play a central role in influenza epidemiology and control because piglets maintain and disseminate influenza A virus (IAV) to other farms. Despite the importance of piglets in influenza epidemiology, there is limited information on IAV infection parameters in piglets, risk factors that impact IAV prevalence in piglets at weaning, and how strategies that are implemented in BTW farms affect IAV infections in weaned pigs.
In this thesis, my goal was to address some of the questions that are central to the transmission and control of influenza in BTW farms, especially infection in piglets ready to wean. The questions addressed are also critical to guide control strategies to mitigate IAV infections in the post weaning period. More specifically, I aimed to: 1) estimate herd-level prevalence and seasonality of influenza in BTW farms, 2) evaluate farm factors associated with IAV infection in piglets at weaning, 3) assess transmission patterns and parameters of influenza in nursery pigs based on IAV prevalence at weaning, and 4) evaluate the impact of maternally-derived antibodies (MDA) at weaning on IAV infection parameters in nursery pigs.
Research Chapter 1
Influenza herd-level prevalence and seasonality in breed-to-wean pig farms in the Midwestern United States
Results showed that IAV herd-level prevalence in piglets at weaning from Midwestern BTW farms is seasonal with higher infection rates in winter (December) and spring (May) than those in summer and fall. Additionally, influenza seasonality was partially explained by the seasonal variations of outdoor air absolute humidity and temperature. Finally, there was significant genetic diversity of influenza strains circulating in those farms and that, co-circulation of more than one genetically distinct clade over time was very common in the studied farms. This is critical knowledge that may help to identify high risk periods where influenza control measures can be placed. It may also help to create research opportunities on absolute humidity and influenza transmission in pigs and finally, it supports other studies that have shown that genetic diversity and circulation is wide and common and that new vaccines and vaccination strategies should take that into consideration.
Research Chapter 2
Breed-to-wean farm factors associated with influenza A virus infection in piglets at weaning
In this chapter, there were 24 farm factors evaluated for their association with influenza at weaning and among those, only IAV sow vaccination and the IAV-negative status of replacement breeding females (gilts) at entry to the herd were significantly associated with less IAV infected piglets at weaning. This is critical information that veterinarians and producers may use to manage IAV levels at weaning. In addition, there was also a lack of significant association with factors such as air filtration and farm density which may be indicative that endemic influenza infections are more important than airborne lateral transmissions between farms. Finally, disease control strategies such as herd closure, early weaning, batch farrowing, gilt isolation and gilt influenza vaccination were not fully evaluated in this study. Hence, more work is needed to further understand how to use these strategies to decrease influenza infections in pigs.
Research Chapter 3
Influenza A virus transmission patterns and parameters in growing pigs
Results indicate that groups of piglets with different prevalence at weaning had different transmission patterns and parameters after weaning and these patterns were characterized by 1, 2 or no peaks of infection after weaning. Piglets with low prevalence at weaning had less influenza infections in the nursery. This information may help producers and veterinarians to make informed decisions when it comes to use control strategies such as sow vaccination aimed to reduce influenza infections in the nursery.
Research Chapter 4
Effect of maternally-derived antibodies on influenza A virus infection in growing pigs
In my last chapter, I reported that if pigs had high levels of strain-specific maternally-derived antibodies at weaning, IAV infection occurred later and it was of shorter duration after weaning. Piglets with hemagglutination inhibition (HI) titers of 1:40 or higher were less likely to test IAV positive at weaning and during the nursery. These results indicate that strain-specific maternally-derived antibodies generated with sow vaccination pre-farrow significantly reduce influenza infections at weaning and in the nursery.
Knowledge of influenza seasonality and what factors are significantly associated with influenza in breed-to-wean farms can help producers and veterinarians to better use and allocate influenza control strategies such as sow vaccination. In addition, lower prevalence of influenza at weaning due to high strain-specific maternally-derived antibodies levels may help decrease influenza spread from wean-to-finish farms. Reducing the burden of influenza in growing pigs should decrease influenza-associated economic losses and the generation of novel strains, including strains with pandemic potential. More studies are needed to further elucidate control strategies to limit influenza infections and spread in pigs.
Microbiome refers to all of the microbes present in an area. For example, gut microbiome is the entire population of microorganisms (most of the time bacteria) present in the intestinal tract.
Microbes have been traditionally viewed through a lens of distrust, as pathogens affecting health. However, molecular and computational breakthroughs to study microbial diversity and function by sorting DNA sequences have presented a novel concept of an animal “flora” that acts as a friend as opposed to a foe.
Characterizing the microbiome to improve nutrition
Characterizing the specific microbes that increase or decrease in abundance upon pharmaceutical or dietary interventions is critical to determine precise dose-response relationships and to potentially reduce feed costs while achieving desired improvements in pig health and productivity.
Defining “healthy” microbiomes to identify poor-doing pigs
Regular “microbiome snapshots” along the most critical stages of pig growth (e.g., pre- and post-weaning), can be used to predict health and potential pathogen threats for disease by early identification of bacteria in slow-growing pigs or those that are at most risk of infection. This would allow producers to make early decisions on therapeutic or dietary interventions to enhance performance and health.
Enhancing the protective microbiome
The microbiome in the gut or respiratory tract is a protective layer against infectious diseases. Thus, with microbiome research, we can determine how novel feed additives and management interventions work, by either enhancing the abundance of microbes that promote health and/or displacing those that cause disease.
Microbiome beyond pork production
For instance, specialized bacteria and fungi can degrade otherwise underutilized natural resources to maximize pig productivity, while decreasing the environmental footprint. Additionally, specialized microbial communities can also mitigate the production of dangerous gases produced in manure pits.
It is a public, private and academic partnership to implement a system for near real time global surveillance of swine diseases.
The output of the system is a report of hazards identified and subsequently scored that may represent a risk for the US pork industry.
Developing systems to provide situational awareness to stakeholders in near-real time can facilitate the coordination between government agencies and the industry with the ultimate objective of preventing or mitigating the impact of diseases epidemics.
The system of near real time global surveillance of swine diseases is based on an online application. Initially focused on three main potential
threats: Classical Swine Fever (CSF), African Swine Fever (ASF), and Foot and Mouth Disease (FMD), it will expand to other exotic swine diseases in the US in the near future. A report, distributed on a monthly basis by SHIC, includes a list of identified hazards that may represent a risk for the US.
Several steps are needed to build the Swine Global Surveillance report as shown in the figure above.
Screening/Filtering phase: Multiple official data sources and soft data sources are systematically screened to build a raw repository. After that, an Include/exclude process is undertaken under a crowdsourcing model.
Scoring phase: A multi-criteria rubric was built based on: credibility, scale and speed of the outbreak, connectedness, local capacity to respond and potential financial impact on the US market. Each event is score independently by a group of experts.
Quality assurance (QA)/building: Its aim being to ensure that the design, operation, and monitoring of processes/systems will comply with the principles of data integrity including control over intentional and unintentional changes to information. The monthly report is put into a PDF document automatically from the app after the scoring process is finalized. At last, assembly of figures and proofreading is done before sending it to SHIC for monthly publication.
Complete automation of event capture into the database
Expansion of the list of diseases in the report
Shrinking the gap between Search/Filter phase and Final Publication – (1 week)
Expanding scoring experts panel
Process documentation – Quality assurance compliance
Influenza is endemic and seasonal in piglets from sow farms in the Midwest with higher infections in winter and spring.
Influenza seasonality was partially explained by outdoor air absolute humidity and temperature trends.
Influenza genetic diversity was high and co-circulation of more than one genetically distinct virus was common.
To study influenza levels over time and its seasonality, monthly testing data of piglets at weaning from 34 sow farms during ~5 years were analyzed.
There were 28% of positive submissions with a median influenza herd-level prevalence of 28%. Genetic diversity was significant with 10 genetically distinct clades of contemporary US swine influenza viruses as shown below. Furthermore, 21% of farms had 3 genetically distinct viruses circulating over time; 18% had 2, 41% had 1 and 20% had no isolates available.
In summary, influenza herd-level prevalence in Midwestern sow farms had a seasonal pattern with higher levels in winter and spring. This is important to better allocate influenza control strategies such as vaccination in sow farms. Influenza seasonality was partially explained by outdoor air absolute humidity and temperature although other factors such as immunity and new introductions may play a role in the observed seasonality.