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In order to reduce antimicrobial use in pig production, the consequences of insufficient biosecurity and welfare problems need to be known. This study aimed to investigate associations between the number of antimicrobial treatments per fattening pig, and biosecurity, indicators for animal welfare as well as the prevalence of lesions at slaughter. The data used in this study were extracted from the pig health and welfare classification system (Sikava), which gathers data on medicine usage, meat inspection, animal welfare and the condition of farm buildings from over 95% of pig production in Finland. The data were registered during years from 2011 to 2013. Upon antimicrobial prescription, information on the number of fattening pigs treated and the main reason for treatment was recorded. In addition, at least 4 times per year, pig farms registered in Sikava were visited by the farm veterinarian who assessed, among other things, biosecurity and indicators for animal welfare (air quality, condition of facilities, cleanliness, enrichment and stocking density). Finally, data from slaughterhouse inspections were collected (number of carcasses with joint infection, abscesses, lung lesions, pleurisy and liver lesions). For analysis, these datasets were aggregated at the farm level to a quarter of a year. During the studied period, the mean number of antimicrobial treatments per fattening pig per 3 months was equal to 0.09. The main reasons for antimicrobial treatments were musculoskeletal diseases, tail biting and respiratory disorders (42, 33 and 12% of diagnoses, respectively). The meat inspection scoring indicated that as much as 14.7% of all pigs had pleurisy, 5.3% liver lesions and 4.1% abscesses. A standard zero-inflated negative binomial model was used to identify factors associated with the number of antimicrobial treatments per pig. The count of antimicrobial treatments per pig increased with the size of a farm. Regardless of prevalence of lesions, farms with poor drinking equipment, insufficient enrichment and a combination of poor condition of pens and high stocking density were associated with an increased number of antimicrobial treatments for musculoskeletal diseases per pig. Problems with stocking density and enrichment were associated with the number of antimicrobial treatments for tail biting, although these results depended on prevalence of joint infections. Problems with air quality and the combination of poor cleanliness and poor condition of facilities were associated with increased number of antimicrobial treatments due to respiratory diseases. This study suggests that by improving biosecurity and welfare at pig farms, antimicrobial use can be reduced.

This paper forms a part of a series of studies aiming to estimate the costs of abortion in Iranian dairy herds. In previous studies we have determined mastitis as a significant risk factor for abortion. In order to provide a more reliable estimation of the costs associated with abortion in Iranian dairy herds, the risk of a cow getting infected with mastitis needs to be included. Data from 6 commercial herds and 32,191 cows was assigned to 3-weeks in milk (3-WIM) records from 1 to 567 d after calving (1st–27th 3-WIM). The effect of herd, parity, calving season, past incidence of abortion, cumulative FCM yield level (CFCML), past incidence of mastitis in previous 3-WIM periods (EMAS), days in milk (DIM) and their significant 2-way interactions on mastitis in current 3-WIM period (MAS) were evaluated using a logistic regression model. Mastitis rate (MR) in studied herds was on average 28.3%. Results show that herd, parity, EMAS, CFCML, calving season, and lactation stage significantly (P It can be concluded that a risk factor analysis with all significant interactions is more informative than a model without the interactions, especially when making the optimal decision for a cow with given characteristics. Moreover, knowledge on the effect of influential factors on mastitis will be useful when designing mastitis control programs at herd or national level.

In order to estimate costs of abortion, influencing factors on conception rate (CR) need to be identified. Data from 6 commercial herds were used to determine influencing factors on CR in Iranian dairy herds. Insemination observations were assigned to 3-weeks in milk (3-WIM) records from 21 to 294 days after calving (2nd – 14th estrus cycle (3-WIM period)). After eliminating outliers and periods with missing records, the dataset included 200,580 insemination records of 31,830 cows observed between 2004 and 2014. A logistic regression model was developed to evaluate the effects of herd, parity, calving season, cumulative fat corrected milk yield level (CFCML), mastitis in current 3-weeks in milk period (MAS), experience of mastitis in previous 3-WIM periods (EMAS), days in milk for insemination (IDIM) and all significant 2-way interactions on CR. Overall, the CR (total cows pregnant/total number of inseminations) until 294 days in milk was 35.1% ranging from 31.7% to 40.1% across studied herds. The results of logistic regression analysis showed that the CR differs at the herd level. Furthermore, the herd effect significantly interacted with other factors, except EMAS, illustrating that the effects of the investigated factors on CR also differed between herds. The odds of conception decreased with higher parity, MAS incidence, and calving during warm seasons. The CFCML significantly interacted with other factors. Therefore the odds of conception for high or low yielding cows will depend on the herd, parity, IDIM, abortion and EMAS history. There was a general decrease in CR with abortion incidence. However, the probability of conception for aborted cow depended on other parameters in the model. The trend of CR during the lactation was season dependent. For example, for cows calving in winter the CR decreased with lactation stage, while for cows calving in summer the CR increased with lactation stage. Moreover, the predicted probability of conception for different parities, calving seasons, CFCML and EMAS varied across the lactation stages. An experience of mastitis was just an indirect influencing factor because of a significant interaction with IDIM and milk yield level. In conclusion, this study revealed that difference in CR between as well as within studied herds was remarkable. Moreover, calving season (due to for example heat stress in the warmer season) is a more important factor influencing CR than milk production.

Abortions, especially those occurring during late pregnancy, lead to considerable economic losses. To estimate the financial losses related to pregnancy loss, at first the influencing factors on abortion need to be identified. Thus, the objective of this study was to determine and quantify the risk factors and their interactions for abortion in Iranian dairy herds. Based on data from 6 commercial herds, logistic regression was used to identify the risk factors for abortion. The basic time unit used in the study was a 3-week period corresponding to an estrus cycle. Thus, stage of lactation is measured as number of 3-week periods in milk (3-WIM) and stage of pregnancy accordingly as number of 3-week periods in pregnancy. After removing the records with missing information, the analysis included 482,071 3-WIM records for 26,289 pregnant cows collected between 2005 and 2014. The investigated factors were herd effect, pregnancy stage, previous abortion, calving month, cumulative fat corrected milk (FCM) yield level, mastitis in current 3-weeks in milk, accumulated number of mastitis and all 2-way interactions. Pregnancy tests were performed between 35 and 50 days after insemination. Abortion was defined as fetal death or return to estrus after confirmed pregnancy between 63 and 252 days in pregnancy. The overall rate of abortion, calculated as the number of aborted cows divided by the number of pregnant cows, was 15.4% ranging from 13.6% to 17.4% at herd level. The results of the logistic regression analysis showed that the risk of abortion differs between herds. Furthermore, all other investigated factors interacted significantly with herd thus illustrating that the effects of risk factors also differ between herds. Other significant risk factors included parity (interacting with pregnancy stage, mastitis, lactation stage and previous abortion), calving month, mastitis (interacting with pregnancy stage), pregnancy stage (interacting with previous abortion and mastitis), lactation stage (interacting with mastitis) and previous abortion. Milk yield was not a significant risk factor for abortion, but due to significant interaction with mastitis it was kept in the final model. In general, it is concluded that inclusion of significant interactions in a risk factor analysis as the present is of paramount importance for a correct quantification of the risk factors for a cow with given characteristics.

Actinobacillus pleuropneumoniae infections (APP) can cause severe economic losses in pig production. Swine producers can prevent the disease from occurring and spreading. However, to be profitable for the producer, the anticipated benefits of a mitigation policy should exceed its cost. In this study, a dynamic programming model and multivariate normal approximation of general stochastic epidemic model (SIR) was developed, and policies for managing fattening pigs under the risk of APP infection were evaluated. The following disease mitigation options were considered: 1) cleaning including washing, drying and disinfection procedures (for efficient and inefficient cleaning), 2) cleaning and vaccination (with high, medium and low efficacy of vaccine), and 3) cleaning and medication (antibiotics use). The results were calculated for five different levels of APP prevalence, where the disease can occur at ten levels of severity. The economic value of mitigation policy depended on the prevalence and severity of disease as well as on the efficiency of the implemented mitigation protocol. The results suggest that inefficient cleaning caused substantial economic losses which can reach up to €20.2 per pig space unit per year. The cleaning and vaccination policy was economically superior over the policy of only cleaning when the prevalence of disease was high (> 0.3), there was a severe clinical disease and the medium or high efficacy vaccine was available. Moreover, even when vaccination costs were decreased the vaccination policy was not economically favored in situations with low disease prevalence among herds (0.1 and 0.3). In cases of severe disease, the cleaning and medication policy resulted in higher economic benefits than cleaning or cleaning and vaccination with low and medium efficacy vaccine. With a high prevalence (> 0.3) and substantial decrease in the average daily gain (ADG), the joint application of cleaning and medication was economically less favorable than cleaning combined with the use of the high efficacy vaccine. Therefore, the availability of high efficacy vaccine could benefit farms facing the severe forms of APP and help to reduce the use of antibiotics. To avoid substantial economic losses, pig producers should not ignore cleaning procedures, which due to low efficacy of available vaccines and concerns regarding excessive use of antibiotics and antibiotic resistance in animal production should be the primary policy to mitigate APP.

Application of BW monitoring methods for the whole batch of pigs is not common in commercial herds. Instead, farm managers may regularly weigh a chosen subset of pigs (observed group) and use the obtained information for monitoring, forecasting, and decision support. The objective of this study was to construct a model for growth monitoring and forecasting in pig fattening herds and use the developed model framework to quantify the value of information on BW. The dynamic process of pig growing was described by means of a dynamic linear model (DLM) with Kalman filtering. For this study, data from 9 fattening cycles with the total registration for 9,800 pigs were used. The variance components were estimated by fitting a mixed-effects linear model on selected BW measurements. The obtained model was evaluated on its performance in forecasting the number of pigs ready to deliver from the whole batch and from a particular pen given the level of information on a reference data set consisting of 2 batches (Batch 3 [B1] and Batch 4 [B2]). Scenarios with a different frequency of observations (only 1 selected week, every second week, or weekly) on individual and aggregated levels for an observed group comprising 1 pen (36 pigs, which constitute 7.5% of pigs in a batch) or 2 pens (15.5% of pigs) were analyzed. Moreover, results with only initial herd information and insertion BW at the batch, pen, and pig level were presented. The model can be used for growth monitoring of the batch and for prediction of the number of pigs ready for slaughter in a given week (i.e., with a BW exceeding a threshold, which, in this study, is set to 105 kg). With an increased level of information, both accuracy (measured by the mean absolute deviation [MAD] of actual number of pigs above 105 kg from predicted number) and precision (measured by CV) of the model continue to improve. When monitoring all pigs at insertion and the observed groups every week (15.5% of pigs) compared with predictions based on only initial herd information, the MAD between the observed and predicted number of pigs above 105 kg in a single pen decreased by 1.4 and 2 pigs whereas CV was reduced by 147 and 78% for B1 and B2, respectively. The DLM was able to detect variation between pens already at insertion; therefore, data on initial BW had high value for the prediction procedure. Moreover, the aggregation had a marginal effect on model performance.

The selection of animals for improved performance affects the profitability of pig fattening and has environmental consequences. The goal of this paper was to examine how changes in genetic and market parameters impact the biophysical (feeding patterns, timing of slaughter, nitrogen excretion) and economic (return per pig space unit) results describing pig fattening in a Finnish farm. The analysis can be viewed as focusing on terminal line breeding goals. An integrated model using recursive stochastic dynamic programming and a biological pig growth model was used to estimate biophysical results and economic values. Combining these models allowed us to provide more accurate estimates for the value of genetic improvement and, thus, provide better feedback to animal breeding programs than the traditional approach, which is based on fixed management patterns. Besides the benchmark scenario, the results were simulated for 5 other scenarios. In each scenario, genotype was improved regarding daily growth potential, carcass lean meat content, or the parameters of the Gompertz growth curve (maturing rate [], adult weight of protein [α], and adult weight of lipid mass []). The change in each parameter was equal to approximately 1 SD genetic improvement (ceteris paribus). Increasing , , daily growth potential, or carcass lean meat content increased the return on pig space unit by €12.60, €7.60, €4.10, or €2.90 per year, respectively, whereas an increase in decreased the return by €3.10. The genetic improvement in and resulted in the highest decrease in nitrogen excretion calculated in total or per kilogram of carcass gain but only under the optimal feeding pattern. Simulated changes in the Gompertz growth function parameters imply greater changes in ADG and lean meat content than changes in scenarios focusing on improving ADG and lean meat content directly. The economic value of genetic improvements as well as the quantity of nitrogen excreted during the fattening period largely depends on feeding. Improved genotypes can require changes in pig management pattern. Estimating the influence of the genotype on the nitrogen excretion without considering changes in the management pattern can result in flawed conclusions. To improve overall economic performance and to decrease the environmental footprint of fattening pig production, the pig producer can adjust the herd management pattern according to the pigs' genetics.

Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs’ growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity=400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.

The aim of this study was to provide farmers an efficient tool for supporting optimal decisions in the beef heifer rearing process. The complexity of beef heifer management prompted the development of a model including decisions on the feeding level during prepuberty (age

Evolutionary operations is a method to exploit the association of often small changes in process variables, planned during systematic experimentation and occurring during the normal production flow, to production characteristics to find a way to alter the production process to be more efficient. The objective of this study was to construct a tool to assess the intervention effect on milk production in an evolutionary operations setup. The method used for this purpose was a dynamic linear model (DLM) with Kalman filtering. The DLM consisted of parameters describing milk yield in a herd, individual cows from a herd, and an intervention effect on a given day. The model was constructed to handle any number of cows, experimental interventions, different data sources, or presence of control groups. In this study, data from 2 commercial Danish herds were used. In herd 1, data on 98,046 and 12,133 milkings registered from an automatic milking system (AMS) were used for model building and testing, respectively. In herd 2, data on 3,689 milkings on test days were used for estimating the initial model parameters. For model testing, data from both bulk tank milk yield (85 observations) and test-day milkings (1,471) were used. In herd 1, the manager wanted to explore the possibility of reducing the amount of concentrate provided to the cows in an AMS. In herd 2, the manager wanted to know if the milk yield could be increased by elevating the energy level provided to the cows in a total mixed ration. The experiment conducted in herd 1 was designed with a treatment and a control group, whereas in herd 2 it was a pretest/posttest design. The constructed tool provided estimates (mean and confidence intervals) for each of 3 interventions carried out in both herds. In herd 1, it was concluded that the reduction in concentrate amount provided in the AMS had no negative influence on milk yield. For herd 2, the increased level of energy had a significant positive effect on milk yield but only for the first intervention. In this herd, the effect of intervention was also evaluated for cows in the first lactation and without bulk tank records. The presented model proved to be a flexible and dynamic tool, and it was successfully applied for systematic experimentation in dairy herds. The model can serve as a decision support tool for on-farm process optimization exploiting planned changes in process variables and the response of production characteristics.