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BACKGROUND: Microbiomes that can serve as an indicator of gut, intestinal, and general health of humans and animals are largely influenced by food consumed and contaminant bioagents. Microbiome studies usually focus on estimating the alpha (within sample) and beta (similarity/dissimilarity among samples) diversities. This study took a combinatorial approach and applied machine learning to microbiome data to predict the presence of disease-causing pathogens and their association with known/potential probiotic taxa. Probiotics are beneficial living microorganisms capable of improving the host organism's digestive system, immune function and ultimately overall health. Here, 16 S rRNA gene high-throughput Illumina sequencing of temporal pre-harvest (feces, soil) samples of 42 pastured poultry flocks (poultry in this entire work solely refers to chickens) from southeastern U.S. farms was used to generate the relative abundance of operational taxonomic units (OTUs) as machine learning input. Unique genera from the OTUs were used as predictors of the prevalence of foodborne pathogens (Salmonella, Campylobacter and Listeria) at different stages of poultry growth (START (2-4 weeks old), MID (5-7 weeks old), END (8-11 weeks old)), association with farm management practices and physicochemical properties. RESULT: While we did not see any significant associations between known probiotics and Salmonella or Listeria, we observed significant negative correlations between known probiotics (Bacillus and Clostridium) and Campylobacter at the mid-time point of sample collection. Our data indicates a negative correlation between potential probiotics and Campylobacter at both early and end-time points of sample collection. Furthermore, our model prediction shows that changes in farm operations such as how often the houses are moved on the pasture, age at which chickens are introduced to the pasture, diet composition and presence of other animals on the farm could favorably increase the abundance and activity of probiotics that could reduce Campylobacter prevalence. CONCLUSION: Integration of microbiome data with farm management practices using machine learning provided insights on how to reduce Campylobacter prevalence and transmission along the farm-to-fork continuum. Altering management practices to support proliferation of beneficial probiotics to reduce pathogen prevalence identified here could constitute a complementary method to the existing but ineffective interventions such as vaccination and bacteriophage cocktails usage. Study findings also corroborate the presence of bacterial genera such as Caloramator, DA101, Parabacteroides and Faecalibacterium as potential probiotics.

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This study examined the effect of Medicago sativa (A), Trifolium repens (WC), Lolium perenne (PR), and their mixture (Mix) on growth performance, carcass characteristics, internal organ weights, and meat quality of slower-growing broiler chickens in a free-range production system. The animal materials comprised of mixed-sex Hubbard ISA Red JA, which were reared in the deep litter system for the first 3 weeks, and thereafter, the pop hole in each of the indoor pens was opened to allow birds access to the range containing one of the above pasture treatments. The range availability was ensured from 08:30 a.m. to 4:30 p.m. The results indicated no significant difference in broiler live body weight, feed conversion ratio, and livability between 28 and 77 days among the pasture treatments (P > 0.05). No significant variations were observed in the carcass and internal organ weights among pasture types (P > 0.05). Additionally, while the dry matter content (P < 0.01), TBA value (P < 0.05), and fatty acid profiles differed (P < 0.01), the protein, ash, water holding capacity, and oxidation stability of broiler breast meat did not differ among pasture groups (P > 0.05). Moreover, while the values of breast meat lightness (L*) and brightness (b*) differed (P < 0.05), redness and pH did not differ between the sexes (P > 0.05). Furthermore, sex significantly affected ash, protein content (P < 0.05), and TBA value (P < 0.01) of breast meat but did not affect dry matter, water holding capacity, and peroxide value of breast meat. Also, there was a significant difference in the weight of the neck, breast, thigh, live weight, hot carcass yield, heart, liver, abdominal fat, and digestive system between sexes (P < 0.05). However, breast meat fat content and fatty acid profiles were not influenced by the sex of the broilers (P > 0.05). It was concluded that access to the studied pasture species has no effect on the growth performance traits but results in notable variations in the fatty acid profiles in broiler breast meat.

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Animal sourced foods including contaminated poultry meat and eggs contribute to human non-typhoidal salmonellosis, a foodborne zoonosis. Prevalence of Salmonella in pastured poultry production systems can lead to contamination of the final product. Identification of farm practices that affect Salmonella prevalence is critical for implementing control measures to ensure the safety of these products. In this study, we developed predictive models based predominantly on deep learning approaches to identify key pre-harvest management variables (using soil and feces samples) in pastured poultry farms that contribute to Salmonella prevalence. Our ensemble approach utilizing five different machine learning techniques predicts that physicochemical parameters of the soil and feces (elements such as sodium (Na), zinc (Zn), potassium (K), copper (Cu)), electrical conductivity (EC), the number of years that the farms have been in use, and flock size significantly influence pre-harvest Salmonella prevalence. Egg source, feed type, breed, and manganese (Mn) levels in the soil/feces are other important variables identified to contribute to Salmonella prevalence on larger (≥3 flocks reared per year) farms, while pasture feed and soil carbon-to-nitrogen ratio are predicted to be important for smaller/hobby (<3 flocks reared per year) farms. Predictive models such as the ones described here are important for developing science-based control measures for Salmonella to reduce the environmental, animal, and public health impacts from these types of poultry production systems.

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Though most strains of E. coli are non-pathogenic components of the intestinal microbiome, certain pathogenic E. coli strains are the cause of diseases and outbreaks. Poultry is identified as a common reservoir for pathogenic E. coli. It is important to identify farm practice factors associated with E. coli in the pastured poultry environment. The objective of this study is to develop models that can predict E. coli levels and to select farm practice factors contributing to E. coli concentration in pastured poultry farms. Five kinds of samples: feces, soil, whole carcass rinse after processing (WCR-P), final product after chilling and storage (WCR-F), and ceca samples were collected for E. coli counts from 11 pastured poultry farms in the southeastern US. The regression tree (RT) and least absolute shrinkage and selection operator (LASSO) methods were applied to data from each sample type. The farm management practices and processing factors such as source of eggs, type of feed used, appearance of other animals on farm, chilling method, and storage time and temperature were all considered as possible explanatory factors in the models. Models were developed to predict the levels of E. coli and to select the most important factors used in predicting E. coli population. Model performances were compared using root mean square error (RMSE). For feces samples, average number of birds and animal source were the two most important variables affecting E. coli population by LASSO. The RT selected animal source, brood feed, day of year, flock age in days, and flock size as the most important variables in predicting E. coli concentration. The RMSE (in log10 scale) under LASSO was 0.974, while under RT it was 1.032 for feces samples. The predictive models provide practical and effective tools to predict E. coli population and to identify farm practice factors that affect E. coli levels.

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Escherichia coli shows the potential of indicating foodborne pathogens. The purpose of this study is to investigate the association between E. coli and foodborne pathogens such as Campylobacter, Salmonella, and Listeria in pastured poultry farms, as well as in related processing facilities. Five different sample types: (i) feces, (ii) soil, (iii) whole carcass rinse during processing (WCR-P), (iv) whole carcass rinse of final product after chilling and storage (WCR-F), and (v) ceca were measured for E. coli populations. A logistic regression model for pathogen presence was developed for each sample type. The E. coli population significantly increased the predicted probability of Salmonella presence for soil and WCR-P samples (p = 0.0011 and p = 0.0157 respectively). For Campylobacter, the initial prevalence in feces and ceca were high and a decreasing trend of detecting Campylobacter was observed as E. coli concentration increased. In soil and WCR-P models, the probability of the presence of Campylobacter significantly increased as E. coli population increased. These models provide a practical and effective way of evaluating the relationship between E. coli and foodborne pathogens and enable prediction of foodborne pathogen presence based on E. coli prevalence within the pastured poultry farm-to-fork continuum.

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Consumer demand has increased for pastured poultry products as the drive for sustainable farming practices and ethical treatments of livestock have become popular in the press. It is necessary to identify the important meteorological factors associated with the prevalence of Salmonella in the pastured poultry settings since the presence of Salmonella in the environment could lead to contamination of the final product. The objective of this study was to develop a model to describe the relationship between meteorological factors and the presence of Salmonella on the pastured poultry farms. The random forest method was used to develop a model where 83 meteorological factors were included as the predicting variables. The soil model identified humidity as the most important variable associated with Salmonella prevalence, while high wind gust speed and average temperature were identified as important meteorological variables in the feces model. The developed models were robust in predicting the prevalence of Salmonella in pastured poultry farms with the area under receiver operating characteristic (ROC) curve values of 0.884 and 0.872 for the soil model and feces model, respectively. The predictive models developed in this study can provide users with practical and effective tools to make informed decisions with scientific evidence regarding the meteorological parameters that are important to monitor for increased on-farm Salmonella prevalence.

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While conventionally grown poultry continues to dominate the U. S. poultry industry, there is an increasing demand for locally-grown, "all natural" alternatives. The use of next generation sequencing allows for not only the gross (e.g., community structure) but also fine-scale (e.g., taxa abundances) examination of these complex microbial communities. This data provides a better understanding of how a pasture flock's microbiome changes throughout the production life cycle and how that change in microbial ecology changes foodborne pathogens in alternative poultry production systems. In order to understand this ecology better, pooled broiler samples were taken during the entire flock life cycle, from pre-hatch gastrointestinal samples (N = 12) to fecal samples from the brood (N = 5), and pasture (N = 10) periods. Additional samples were taken during processing, including skin and feather rinsates (N = 12), ceca (N = 12), and whole carcass rinses (N = 12), and finally whole carcasss rinsates of final products (N = 3). Genomic DNA was extracted, 16S rDNA microbiome sequencing was conducted (Illumina MiSeq), and microbiomes were analyzed and compared using QIIME 1.9.1 to determine how microbiomes shifted throughout production continuum, as well as what environmental factors may be influencing these shifts. Significant microbiome shifts occurred during the life cycle of the pasture broiler flock, with the brood and pasture fecal samples and cecal samples being very distinct from the other pre-hatch, processing, and final product samples. Throughout these varied microbiomes, there was a stable core microbiome containing 13 taxa. Within this core microbiome, five taxa represented known foodborne pathogens (Salmonella, Campylobacter) or potential/emerging pathogens (Pseudomonas, Enterococcus, Acinetobacter) whose relative abundances varied throughout the farm-to-fork continuum, although all were more prevalent in the fecal samples. Additionally, of the 25 physiochemical and nutrient variables measured from the fecal samples, the carbon to nitrogen ratio was one of the most significant variables to warrant further investigations because it impacted both general fecal microbial ecology and Campylobacter and Enterococcus taxa within the core fecal microbiomes. These findings demonstrate the need for further longitudinal, farm-to-fork studies to understand the ecology of the microbial ecology of pasture production flocks to improve animal, environmental, and public health.

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As commercial free-range and pastured poultry production has gained increased popularity in the United States in recent years, there is a greater need to understand basic husbandry practices including feeding and lighting practices. Because husbandry practices vary greatly between individual commercial pastured poultry and free-range operations, gaining knowledge across a wide cross-section of producers is necessary to better understand current practices. Specifically, because feed is considered the most expensive part of poultry production with respect to operating cost, in collaboration with the American Pastured Poultry Producers Association, an on-line survey of commercial free-range and pastured poultry producers was conducted aimed primarily at evaluating feeding and lighting practices. Data were collected from 14 commercial pastured (n = 13) and free-range (n = 1) producers (6 broiler, 1 layer, and 7 broiler/layer facilities) across 9 U.S. states. Results showed that these operations most commonly use Cornish Cross for broiler production and ISA Brown and Australorp for egg production. Only 1 of the 14 farms calculates feed conversion ratio on a monthly basis. Estimates of Hen Month Egg Production (HMEP) resulted in HMEP percentages that were over 100% in 6 of the 7 farms that provided data. 5 of the 7 farms that have layers used supplemental lighting but do not use a lux meter. These lack of data reflect an important challenge with this segment of the poultry industry. Extension based outreach focused on enabling data collection and record-keeping and analysis are necessary. Despite its small sample size, this study's results provide some valuable insights with respect to feeding equipment and the current state of data collection for this segment of the poultry industry.

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: Nowadays there is an increasing demand for poultry products from alternative rearing systems. These systems, commonly named pastured poultry production (PPP), are more expensive than intensive rearing system but sustain biodiversity, local economies and farm multi-functionality besides providing meat to which consumers attribute high ethical value and quality. PPP generally uses large outdoor runs, small number of animals and requires chickens adapted to natural environment. One of the most relevant obstacles to further development of PPP systems is related to the slaughtering of animals economically and at the same time complying with the sanitary regulations to maintain food safety standards. A possible solution could be represented by a Mobile Poultry Processing Unit (MPPU), which directly reaches the poultry farms. MPPU can consider a good compromise for the niche production providing an opportunity to small farmers to exploit the full potential of their production system. The aim of this review is to analyse the essential requisites and MPPU economic viability in an Italian system. Qualitative, societal aspects are discussed together with bird welfare and hygiene implications. The case study indicates the viability of MPPUs but notes that up scaling to medium sized operations would not be permissible under current EU regulations.

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As pastured poultry production has gained increased popularity in the United States in recent years, there is a growing need for research and outreach efforts aimed at this sector of poultry production. In order to get familiarized with American pastured poultry producers, we conducted an online questionnaire aimed primarily at evaluating what operational challenges are faced by producers and what educational opportunities should be initiated by researchers. Results showed that pastured poultry farms largely vary in total number of acres farmed and total number of birds kept. The vast majority of farms (96%) rotate their flocks on pasture and include livestock species (78%) in their rotation systems. Mobile coops are the preferred housing option provided by producers (88%). The most common source of mortality listed by respondents was predation (52%), followed by "other" (32%). However, predation was not selected as the most important challenge by the majority of respondents. Sixty-four percent of participants instead mentioned providing adequate feed at reasonable cost as the major challenge in raising poultry on pasture, followed again by "other" (52%) and lack of processing facilities for small numbers of birds (40%). Finally, the topics considered by respondents as the most helpful to learn more about were how to improve egg production rate and/or feed conversion ratio (67%), followed by how to improve pasture condition and optimum vegetative cover all year round (62%). Despite its small sample size, this study's results provide some valuable insights of challenges encountered and information needed on pastured poultry farms.

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The occurrence of Listeria monocytogenes has been widely investigated in the poultry production chain from the processing plant to the final product. However, limited data are available on Listeria species, including Listeria monocytogenes, in the poultry farm environment. Therefore, fecal and soil samples from 37 pastured poultry flocks from 10 all-natural farms over 3 years were assessed to determine the prevalence and diversity of Listeria within these alternative poultry farm environments using standard cultural and molecular methods. Listeria species were isolated in 15% of poultry farm samples and included Listeria innocua (65.7%), L. monocytogenes (17.4%), and Listeria welshimeri (15.1%). Additional multiplex PCR serotyping showed group 1/2a-3a to be the most dominant L. monocytogenes serovar group. Based on these results, monoculture growth experiments were conducted on four Listeria soil isolates (three L. monocytogenes isolates representing the three recovered serovar groups and one L. innocua isolate) to determine if culture medium [tripticase soy broth (TSB) and University of Vermont modified Listeria enrichment broth (UVM)], inoculum concentration (102 or 105 CFU/ml), or incubation temperature (20, 30, and 42°C) differentially affected these Listeria species. Overall, very few significant growth differences were observed between the behavior of the three L. monocytogenes isolates (representing the three recovered serovar groups) under the growth conditions tested. Alternatively, at 30°C in UVM with the lower inoculum concentration, the L. innocua isolate had a significantly shorter lag phase than the L. monocytogenes isolates. In coculture growth studies under these same incubation conditions, the lag phase of L. innocua and L. monocytogenes was similar, but the final concentration of L. innocua was significantly higher than L. monocytogenes. However, cocultures in UVM for high inoculum concentration did not show preferential growth of L. innocua over L. monocytogenes. These results indicate that the use of UVM as an enrichment medium may preferentially allow L. innocua to outcompete L. monocytogenes at low concentrations, biasing the Listeria prevalence from these farm samples toward L. innocua and potentially underreporting the presence of L. monocytogenes in these environments.

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While pasture-raised poultry comprises a small portion of the commercial poultry industry in North America, these alternative rearing systems have become increasingly popular. As such, it is critical to improve our understanding of husbandry practices and prevalence of zoonotic and epizoonotic diseases in these systems. This research reviews the results of a survey sent to 82 commercial pastured poultry farms in California. While the survey response was low (13.4%), it was enhanced by detailed in-person interviews and farm visits. In addition, we conducted drag swabs for Salmonella Enteritidis. On average, farms utilized 12.3% of their total farmland for pastured poultry operations, which often coexisted with other livestock (45%), touch crops (27%), and non-touch crops (45%). While the mean (44.6 sq. ft./hen) and median (22.2 sq. ft./hen) pasture stocking densities were within auditing guidelines, the mean (1.2 sq. ft./hen) and median (0.5 sq. ft./hen) coop stocking densities were below the pending USDA (2016) guidelines recommended in 7 CFR Part 205. Drag swab results showed the presence of Salmonella Enteritidis (SE) in the environment of one of the 11 farms (9.1%). In addition, Salmonella Pullorum (SP) whole blood agglutination tests were used to understand the prevalence of Salmonella spp. in laying hens within the studied farms. Results showed the presence of antibodies in flocks at six of the seven non-SE vaccinated farms, with a mean on-farm prevalence of 25.6% in laying hens. Logistic regression was used to determine risk factors for Group D Salmonella exposure in non-vaccinated flocks, using the SP blood agglutination data as the dependent variable and the survey questions as the independent variables. Statistically significant (P < 0.05) risk factors included exposed wire floors and flock size. These results improve our understanding of Salmonella prevalence and husbandry practices on commercial pastured poultry farms in California.

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