Managing variability in decision making in swine growing-finishing units

Development of a pig wean-quality score using machine-learning algorithms to characterize and classify groups with high mortality risk under field conditions

Mortality during the post-weaning phase is a critical indicator of swine production system performance, influenced by a complex interaction of multiple factors of the epidemiological triad. This study leveraged retrospective data from 1723 groups of pigs marketed within a US swine production system to develop a Wean-Quality Score (WQS) using machine learning techniques. The study evaluated three machine learning models, Random Forest, Support Vector Machine, and Gradient Boosting Machine, to classify groups having high or low 60-day mortality, where high mortality groups represented 25 % of the groups among the study population with the highest mortality values (n=431; 60-day mortality=9.98 %), and the remaining 75 % of the groups were of low mortality (n=1292; 60-day mortality=2.75 %). The best-performing model, Random Forest (RF), outperformed the other ML models in terms of accuracy (0.90), sensitivity (0.84), and specificity (0.92) metrics, and was then selected for further analysis, which consisted of creating the WQS and ranking the most important factors for classifying groups as high or low mortality. The most important factors ranked through the RF model to classify groups with high mortality were pre-weaning mortality, weaning age, average parity of litters in sow farms, and PRRS status. Additionally, stocking conditions such as stocking density and time to fill the barn were important predictors of high mortality. The WQS was developed and correlated (r = 0.74) with the actual 60-day mortality of the groups, offering a valuable tool for assessing post-weaning survivability in swine production systems before weaning. This study highlights the potential of machine learning and comprehensive data utilization to improve the assessment and management of weaned pig quality in commercial swine production, which producers can utilize to identify and intervene in groups, according to the WQS.

Effects of parity, season of birth, and sex on within-litter variation and pre-weaning performance of F1 Large White × Landrace pigs

Background and Aim

A piglet's pre-weaning performance significantly influences both animal welfare and profitability in pig production. Understanding piglet pre-weaning performance influencing factors is key to enhancing animal welfare, reducing losses, and boosting profitability. The study aimed to evaluate the impact of parity, season of birth, and sex on within-litter variation and pre-weaning performance of F1 Large White × Landrace pigs.

Materials and Methods

Information regarding total litter size, number of born alive, number of stillbirths, piglet weight at birth, mortality, and count of weaned F1 Large White × Landrace piglets was acquired from the farm database (April 2022-February 2023). 2602 females and 2882 males, a total of 5484 piglets were utilized, with records from 360 sows. The coefficient of variation (CV) of birth weights among piglets within a litter was calculated. The general linear model analysis in MiniTab 17 was used to evaluate the data, with Fisher's least significant difference test (p < 0.05) used for mean separation and Pearson's moment correlation coefficient calculated to assess relationships between survival rates, mortality rates, litter size, birth weight, and birth weight CV.

Results

Parity had a statistically significant impact on litter size, birth weight, and survival rate (p < 0.05). The sow's parity did not significantly (p > 0.05) impact the number of piglets born alive or weaned. Multiparous sows had a significantly larger litter size (p < 0.05) than primiparous sows at birth. The litter weights for parities 2, 4, and 5 did not significantly differ (p > 0.05), with averages of 20.95, 20.74, and 20.03 kg, respectively. About 91.29% was the highest survival rate recorded in parity 2 (p < 0.05). The 1st week of life recorded an 8.02% mortality rate. The mortality rate in parity 3-5 group was significantly (p < 0.05) higher (11.90%) in week 1 than in the other groups (parity 1: 6.79%, parity 2: 5.74%, parity 3-5: 8.54 and 9.21%). The litter sizes in autumn (17.34) and spring (17.72) were significantly larger (p < 0.05) than those in summer (16.47) and winter (16.83). In autumn and spring, the survival rate (83.15 and 85.84%, respectively) was significantly lower (p < 0.05) compared to summer (88.40%) and winter (89.07%). In all seasons, the litter weights did not significantly differ (p > 0.05). The birth weight CV was significantly (p < 0.05) lower during summer (20.11%) than during spring (22.43%), autumn (23.71%), and winter (21.69%). The season of birth had no significant effect (p > 0.05) on the number of live piglets. Males (1.34 kg) were heavier (p < 0.05) than females (1.30 kg) at birth. Notably, the birth weight CV was similar between males (22.43%) and females (22.52%). Litter size was positively correlated with average litter weight (rp = 0.576, p < 0.001), birth weight CV (rp = 0.244, p < 0.001), and mortality rate (rp = 0.378, p < 0.001). An insignificant relationship was observed between average litter weight and birth weight CV (rp = -0.028, p > 0.05) and survival rate (rp = -0.032, p > 0.05).

Conclusion

In F1 Large White × Landrace pigs, birth uniformity among piglets declines as litter size grows larger. In parity 3-5, multiparous sows yield litters with reduced uniformity. With an increase in litter size, uniformity among piglets at birth worsens. A larger litter size and greater piglet birth weight variation are linked to a higher pre-weaning mortality rate. Producers need a balanced selection approach to boost litter size and must cull aging sows carefully to introduce younger, more productive females.

Utilizing productivity and health breeding-to-market information along with disease diagnostic data to identify pig mortality risk factors in a U.S. swine production system

Aggregated diagnostic data collected over time from swine production systems is an important data source to investigate swine productivity and health, especially when combined with records concerning the pre-weaning and post-weaning phases of production. The combination of multiple data streams collected over the lifetime of the pigs is the essence of the whole-herd epidemiological investigation. This approach is particularly valuable for investigating the multifaceted and ever-changing factors contributing to wean-to-finish (W2F) swine mortality. The objective of this study was to use a retrospective dataset ("master table") containing information on 1,742 groups of pigs marketed over time to identify the major risk factors associated with W2F mortality. The master table was built by combining historical breed-to-market performance and health data with disease diagnostic records (Dx Codes) from marketed groups of growing pigs. After building the master table, univariate analyses were conducted to screen for risk factors to be included in the initial multivariable model. After a stepwise backward model selection approach, 5 variables and 2 interactions remained in the final model. Notably, the diagnosis variable significantly associated with W2F mortality was porcine reproductive and respiratory syndrome virus (PRRSV). Closeouts with clinical signs suggestive of Salmonella spp. or Escherichia coli infection were also associated with higher W2F mortality. Source sow farm factors that remained significantly associated with W2F mortality were the sow farm PRRS status, average weaning age, and the average pre-weaning mortality. After testing for the possible interactions in the final model, two interactions were significantly associated with wean-to-finish pig mortality: (1) sow farm PRRS status and a laboratory diagnosis of PRRSV and (2) average weaning age and a laboratory diagnosis of PRRS. Closeouts originating from PRRS epidemic or PRRS negative sow farms, when diagnosed with PRRS in the growing phase, had the highest W2F mortality rates. Likewise, PRRS diagnosis in the growing phase was an important factor in mortality, regardless of the average weaning age of the closeouts. Overall, this study demonstrated the utility of a whole-herd approach when analyzing diagnostic information along with breeding-to-market productivity and health information, to measure the major risk factors associated with W2F mortality in specified time frames and pig populations.

Multi-Criteria Evaluation Model of Management for Weaned Piglets and Its Relations with Farm Performance and Veterinary Medicine Consumption

Weaned piglets, being immature, demand careful handling to mitigate post-weaning stress in order to avoid immunosuppression and the use of antimicrobials to palliate the effects of disease outbreaks due to poor management. The objective of this work is to design a quick scan calculator or multi-criteria evaluation model of management for weaned piglets, founded on 10 critical indices covering post-weaning management aspects based on hygienic measures and management of facilities and animals. These include pre-weaning handling, batch management, biosecurity, water management, feed management, health program, stockmen training, temperature, ventilation, and floor conditions and density to relate handling and hygiene practices with farm performance and the consumption of veterinary medication. Each index carries a maximum score of ten, with evaluations derived from different management factors that make up each index (from three to eight factors were evaluated per index). Their cumulative score reflects the degree of adequacy of on-farm management. Therefore, a perfectly managed farm would achieve 100 points. The calculator underwent testing on 23 intensive farms with a total population of close to 16,000 sows and more than 400,000 weaned piglets, revealing the highest mean scores in floor conditions and density (8.03 out of 10) and pre-weaning handling and health programs (6.87 and 6.28, respectively). Conversely, the lowest scores corresponded to temperature, ventilation, water management, and stockmen training (4.08, 4.32, 4.81, and 4.93, respectively). The assessed farms averaged a global score of 56.12 out of 100 (from 37.65 to 76.76). The calculator's global score correlated with key post-weaning productivity and piglet health indicators, such as the feed conversion ratio, mortality rate, and piglet production cost, with r values of -0.442, -0.437, and -0.435, respectively (p < 0.05). Additionally, it negatively correlated with medication costs per piglet (r = -0.414; p < 0.05) and positively with annual farm productivity (r = 0.592; p < 0.01). To enhance management, hygiene, and prevention, farms should prioritize addressing indices with the lowest scores, thereby reducing medication consumption and enhancing productivity and health outcomes. Additionally, this quick scan calculator can be used for benchmarking purposes.

Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System

The performance of five forecasting models was investigated for predicting nursery mortality using the master table built for 3242 groups of pigs (~13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model's performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE = 0.406), Mean Absolute Error (MAE = 0.284), and Coefficient of Determination (R2 = 0.731). Subsequently, the forecasting performance of the SVM model was tested on a new dataset containing 72 new groups, simulating ongoing and near real-time forecasting analysis. Despite a decrease in R2 values on the new dataset (R2 = 0.554), the model demonstrated high accuracy (77.78%) for predicting groups with high (>5%) or low (<5%) nursery mortality. This study demonstrated the capability of forecasting models to predict the nursery mortality of commercial groups of pigs using pre-weaning information and stocking condition variables collected post-placement in nursery sites.

Development and validation of a porcine artificial colonic mucus model reflecting the properties of native colonic mucus in pigs

Colonic mucus plays a key role in colonic drug absorption. Mucus permeation assays could therefore provide useful insights and support rational formulation development in the early stages of drug development. However, the collection of native colonic mucus from animal sources is labor-intensive, does not yield amounts that allow for routine experimentation, and raises ethical concerns. In the present study, we developed an in vitro porcine artificial colonic mucus model based on the characterization of native colonic mucus. The structural properties of the artificial colonic mucus were validated against the native secretion for their ability to capture key diffusion patterns of macromolecules in native mucus. Moreover, the artificial colonic mucus could be stored under common laboratory conditions, without compromising its barrier properties. In conclusion, the porcine artificial colonic mucus model can be considered a biorelevant way to study the diffusion behavior of drug candidates in colonic mucus. It is a cost-efficient screening tool easily incorporated into the early stages of drug development and it contributes to the implementation of the 3Rs (refinement, reduction, and replacement of animals) in the drug development process.

Whole-herd risk factors associated with wean-to-finish mortality under the conditions of a Midwestern USA swine production system

Swine wean-to-finish (W2F) mortality is a multifactorial, dynamic process and a key performance indicator of commercial swine production. Although swine producers typically capture the relevant data, analysis of W2F mortality risk factors is often hindered by the fact that, even if data is available, they are typically in different formats, non-uniform, and dispersed among multiple unconnected databases. In this study, an automated framework was created to link multiple data streams to specific cohorts of market animals, including sow farm productivity parameters, sow farm and growing pig health factors, facilities, management factors, and closeout data from a Midwestern USA production system. The final dataset (master-table) contained breeding-to-market data for 1,316 cohorts of pigs marketed between July 2018 and June 2019. Following integration into a master-table, continuous explanatory variables were categorized into quartiles averages, and the W2F mortality was log-transformed, reporting geometric mean mortality of 8.69 % for the study population. Further, univariate analyses were performed to identify individual variables associated with W2F mortality (p < 0.10) for further inclusion in a multivariable model, where model selection was applied. The final multivariable model consisted of 13 risk factors and accounted for 68.2 % (R²) of the variability of the W2F mortality, demonstrating that sow farm health and performance are closely linked to downstream W2F mortality. Higher sow farm productivity was associated with lower subsequent W2F mortality and, conversely, lower sow farm productivity with higher W2F mortality e.g., groups weaned in the highest quartiles for pre-weaning mortality and abortion rate had 13.5 %, and 12.5 %, respectively, which was statistically lower than the lowest quartiles for the same variables (10.5 %, and 10.6 %). Moreover, better sow farm health status was also associated with lower subsequent W2F mortality. A significant difference was detected in W2F mortality between epidemic versus negative groups for porcine reproductive and respiratory syndrome virus (15.4 % vs 8.7 %), and Mycoplasma hyopneumoniae epidemic versus negative groups (13.7 % vs 9.9 %). Overall, this study demonstrated the application of a whole-herd analysis by aggregating information of the pre-weaning phase with the post-weaning phase (breeding-to-market) to identify and measure the major risk factors of W2F mortality.

Postweaning mortality in commercial swine production. I: review of non-infectious contributing factors

Postweaning mortality is a complex causal matrix involving animal, environment, and infectious etiologic factors. Despite advances in swine productivity such as total pigs born, growth rate, feed intake, and efficiency, there have been modest to no improvements in postweaning mortality rates over the last several years. Industry averages for postweaning mortality range from four to eight percent for each the nursery, grow-finish, or wean-finish stages. Retrospective mortality causal analyses of individual databases have been performed. However, little information derived from meta-analysis, systematic review, or comprehensive literature reviews are available. In order to develop and evaluate strategies to comprehensively manage and reduce postweaning mortality, addressing the complexity and range of impact that factors have on mortality is necessary to identify and prioritize such contributing factors. Our objective is to describe the current state of knowledge regarding non-infectious causes of postweaning mortality, focusing on estimates of frequency and magnitude of effect where available. Postweaning mortality can be generalized into non-infectious and infectious causes, with non-infectious factors further classified into anatomic abnormalities, toxicity, animal factors, facility factors, nutritional inadequacies, season, and management factors. Important non-infectious factors that have been identified through review of literature include birth weight, pre-weaning management, weaning age and weight, and season. Additionally, reasons for mortality with a low incidence but a high magnitude include abdominal organ torsion/volvulus, sodium ion or ionophore toxicosis, or dietary imbalance due to feed formulation or manufacture error. Many interactive effects are present between and among infectious and non-infectious factors, but an important trend is the impact that non-infectious factors have on the incidence, severity, and resolution of infectious disease. Strategies to reduce postweaning mortality must consider the dynamic, complex state that forms the causal web. Control of postweaning mortality through understanding of the complexity, evaluation of mortality reduction strategies through rigorous scientific evaluation, and implementation remains an area of opportunity for continued growth and development in the global swine industry.

Factors affecting the daily feed intake and feed conversion ratio of pigs in grow-finishing units: the case of a company

BACKGROUND

The aim of this study was to use mathematical modeling to identify and quantify the main factors that affect daily feed intake (DFI) and feed conversion ratio (FCR) in grow-finishing (GF) pig units. We evaluated the production records of 93 GF farms between 2010 and 2013, linked to a company, working in a cooperative system, located in western Paraná State, Brazil. A total of 683 batches, consisting of approximately 495,000 animals, were used. Forty production factors related to the management, health, plant and equipment, nutrition, genetics and environment were considered. The number of pigs per pen, type of feeder, origin and sex (the last two variables were combined in the models) of the animals and initial and final body weights were included in the final models to predict DFI and FCR (dependent variables). Additionally, the duration of the GF phase was included for the parameter FCR. All factors included in the final models had significant effects for both dependent variables.

RESULTS

There was a reduction in DFI (0.04 kg) (P < 0.001) and an improvement in FCR (6.0 points) (P < 0.001) in batches from pens with less than 20 animals compared with batches from pens with more than 20 animals. In barns with "other" feeder types (mostly the linear dump type) different of conical semiautomatic feeder, a reduction of DFI (0.03 kg) (P < 0.05) and improved FCR (3.0 points) (P < 0.05) were observed. Batches of barrows from units specialized for producing piglets (SPU) had higher DFI (approximately 0.02 kg) (P < 0.01) than batches of females and batches of mixed animals from SPU, and batches of mixed animals from farms not specialized for piglet production (farrow-to-finish farms). Batches of females from SPU and mixed batches from SPU had better FCR (5.0 and 3.0 points respectively) (P < 0.001 and P < 0.001, respectively) than batches of piglets originating from farrow-to-finish farms. The variables selected for the final models explained approximately 50 and 64 % of the total variance in DFI and FCR, respectively.

CONCLUSIONS

The models are tools for the interpretation of the factors related to the evaluated parameters, aiding in the identification of critical aspects of production. The main parameters affecting DFI and FCR in this company during the GF period were the number of pigs per pen, the type of feeder used and the combination origin-sex of the animals.