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.
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 reports are available at: https://z.umn.edu/SwineDiseaseSurveillance
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