Sampling strategies for assessing lameness, injuries, and body condition score on dairy farms.

Our objective was to evaluate how sampling strategies (i.e., how many cows to sample and which animals to include) used in 4 dairy cattle welfare assessment programs affect the classification of dairy farms relative to thresholds of acceptability for animal-based measures. We predicted that classification performance would improve when more cows were sampled and when selecting from all lactating cows versus when some pens were excluded. On 38 freestall farms, we assessed all 12,375 cows for lameness, injuries on the tarsal (hock) and carpal joints, and body condition score and calculated the farm-level prevalence for each measure. Based on approaches used in the industry, we evaluated 6 sampling strategies generated using formulas with precision (d) of 15, 10, or 5% applied to either a single high-producing pen or all lactating cows; an additional sample was included with d = 10% applied to the entire herd, selecting lactating cows in proportion to their representation in the herd. For each sampling strategy, cow records were selected randomly (in 10,000 replicates) to calculate prevalence. The strategy of assessing all cows in the high-producing pen was also compared. Farms were classified as meeting (below) or failing to meet (above) thresholds of ≤15% moderate lameness; ≤20% moderate carpal or hock injuries; <10, <5, and ≤1% severe lameness; or injuries on the carpus or hock; and <5, <3, <1, or 0% thin cows. For each measure and threshold, we calculated pooled percent agreement, kappa, sensitivity, specificity, and positive and negative predictive value for each sampling strategy using true prevalence as the gold standard for herd classification. Across measures and thresholds, classification performance increased with the number of cows sampled [i.e., when narrower precision values (d = 5 vs. 10 vs. 15%) were used in the sample size calculation]. Because narrower precision values can dramatically increase sample size, assessment programs may need to consider both feasibility and the degree of misclassification they will accept. Applying the formula directly to lactating cows performed better than applying it to the entire herd and then selecting lactating cows in proportion to their representation in the herd. Farm classifications were similar whether cows in the hospital pen were included or excluded from the sample. Selecting all cows from the high-producing pen resulted in classifications similar to when including all lactating cows, suggesting that assessing cows from the high-producing pen may serve as an acceptable proxy for all lactating cows on the farm.

Risk factors associated with lameness and hoof lesions in pasture-based dairy cattle systems in southeast Brazil.

Cattle lameness is an important welfare concern that also has an economic impact on the dairy industry. It can be a significant problem among pasture-based herds. Our objectives were to identify cow- and herd-level factors related to lameness and hoof lesions in dairy cows grazing year-round in Minas Gerais, Brazil. We performed a cross-sectional study in 48 pasture-based dairy herds, visiting each farm in a single visit. We evaluated 2,262 cows for mobility score (0-3) and 392 cows for hoof lesions. We used a questionnaire and checklist to capture herd management data. All information obtained was used to build multivariable models. The factors associated with lameness were low body condition score, longer time spent in the corral, being kept in paddocks during the drought period, and poor hygiene. For hoof lesions, track features were the most significant factor in determining the likelihood of heel horn erosion, white line fissure, and sole hemorrhage-by more than 3 times. Different factors related to unhygienic conditions such as leg cleanliness, frequency of cleaning, and longer time spent in the corral were associated with infectious hoof lesions. Poor human-animal relationship was related to sole hemorrhage, but patient handling of cows on the track was a protective factor against interdigital hyperplasia. The results of this study suggest that improving hygiene conditions, track features, and cow handling can improve dairy cattle mobility scores in pasture-based farms under tropical conditions. These findings also represent a first step toward planning actions aimed at decreasing lameness and hoof lesions in the studied region.

Modeling livestock population structure: a geospatial database for Ontario swine farms.

BACKGROUND: Infectious diseases in farmed animals have economic, social, and health consequences. Foreign animal diseases (FAD) of swine are of significant concern. Mathematical and simulation models are often used to simulate FAD outbreaks and best practices for control. However, simulation outcomes are sensitive to the population structure used. Within Canada, access to individual swine farm population data with which to parameterize models is a challenge because of privacy concerns. Our objective was to develop a methodology to model the farmed swine population in Ontario, Canada that could represent the existing population structure and improve the efficacy of simulation models. RESULTS: We developed a swine population model based on the factors such as facilities supporting farm infrastructure, land availability, zoning and local regulations, and natural geographic barriers that could affect swine farming in Ontario. Assigned farm locations were equal to the swine farm density described in the 2011 Canadian Census of Agriculture. Farms were then randomly assigned to farm types proportional to the existing swine herd types. We compared the swine population models with a known database of swine farm locations in Ontario and found that the modeled population was representative of farm locations with a high accuracy (AUC: 0.91, Standard deviation: 0.02) suggesting that our algorithm generated a reasonable approximation of farm locations in Ontario. CONCLUSION: In the absence of a readily accessible dataset providing details of the relative locations of swine farms in Ontario, development of a model livestock population that captures key characteristics of the true population structure while protecting privacy concerns is an important methodological advancement. This methodology will be useful for individuals interested in modeling the spread of pathogens between farms across a landscape and using these models to evaluate disease control strategies.

Water quality in turkey farms in Khemisset (Morocco) and potential risk factors. / Qualité des eaux en élevage de dinde à Khémisset (Maroc) et hypothèses sur les facteurs de risques.

The objectives of this study were to assess the microbiological and physical/chemical quality of water in broiler turkey farms in the province of Khemisset (north-western Morocco) and, based on a questionnaire, to ascertain potential risk factors for contamination of drinking water with faecal coliforms. A total of 80 samples were collected and analysed in 20 farms (four from each farm). At the main inlet to the water line at the entrance to each turkey house, 100% of the samples were of unacceptable quality in terms of faecal coliforms, Escherichia coli, faecal streptococci, sulphitereducing anaerobes and enterococci. A significant reduction in microbiological contamination of the water line (p < 0.05) was observed on Day 60. While more than 90% of the samples were of satisfactory quality in terms of pH, nitrites, conductivity, nitrates and iron, only 35% were satisfactory in terms of total hardness and only 20% met quality standards for ammonium content. The factors affecting levels of contamination with faecal coliforms were water chlorination (p = 0.065; odds ratio = 14; 90% confidence interval [CI] = 1.14-71), cleaning and disinfection (p = 0.028; odds ratio = 14; 95% CI = 1.25-156.6) and antibiotic treatment (p = 0.001; odds ratio = 6; 95% CI = 2.1-35.2). To improve water quality in poultry farms, farmers are advised to protect wells from contamination and to install water purification units (pre-oxidation, coagulation, flocculation, disinfection). In addition, turkey houses and rearing equipment should be rigorously cleaned and disinfected between each batch of birds.

Cette étude a pour objectifs d'évaluer la qualité microbiologique et physico-chimique de l'eau des élevages de dinde de chair dans la province de Khémisset (nord-ouest du Maroc) et de formuler, au moyen d'un questionnaire, certaines hypothèses sur les facteurs de risque potentiels associés à la contamination de l'eau d'abreuvement par les coliformes fécaux. Au total, 80 échantillons ont été prélevés et analysés dans 20 élevages (quatre de chaque élevage). Au sas de la ligne d'abreuvement, 100 % des échantillons étaient de qualité inacceptable en ce qui concerne les coliformes fécaux, Escherichia coli, les streptocoques fécaux, les anaérobies sulfito-réducteurs et les entérocoques. Une réduction significative de la contamination microbiologique a été observée en bout de la ligne d'abreuvement (p < 0,05) au jour 60. Plus de 90 % des échantillons étaient de qualité satisfaisante pour ce qui concerne le pH, les nitrites, la conductivité, les nitrates et le fer ; en revanche, seulement 35 % et 20 % d'échantillons étaient conformes pour ce qui concerne la dureté totale et la présence d'ammonium, respectivement. Les facteurs associés à la contamination par les coliformes fécaux étaient la chloration de l'eau (p = 0,065 ; rapport des cotes = 14 ; intervalle de confiance [IC] à 90 % = 1,14-71), le nettoyage et la désinfection (p = 0,028 ; rapport des cotes = 14 ; IC à 95 % = 1,25-156,6) et le traitement par antibiotiques (p = 0,001 ; rapport des cotes = 6 ; IC à 95 % = 2,1-35,2). Afin d'améliorer la qualité de l'eau dans les élevages avicoles, il est recommandé aux éleveurs de protéger les puits contre la contamination et d'installer des stations de potabilisation de l'eau (pré-oxydation, coagulation, floculation, désinfection). Il convient également de procéder à un nettoyage et une désinfection rigoureux des bâtiments et du matériel d'élevage à la fin de chaque bande.

Los autores describen un estudio destinado a evaluar la calidad microbiológica y fisicoquímica del agua de explotaciones productoras de pavos de engorde de la provincia de Khemisset (noroeste de Marruecos) y a formular, mediante un cuestionario, ciertas hipótesis sobre los posibles factores de riesgo asociados a la contaminación del agua de bebida por coliformes fecales. En total se tomaron y analizaron 80 muestras de 20 explotaciones. En la esclusa de entrada del agua en la línea de bebederos, el 100% de las muestras era de calidad inaceptable por lo que respecta a la presencia de coliformes fecales, Escherichia coli, estreptococos fecales, bacterias anaerobias sulfitorreductoras y enterococos. A la salida de la línea de bebederos se observaba una reducción significativa de la contaminación microbiológica (p < 0,05) en el día 60. Más del 90% de las muestras eran de calidad satisfactoria por lo que respecta a pH, nitritos, conductividad, nitratos y hierro. En cambio, solo un 35% y un 20% de las muestras satisfacían los criterios relativos a la dureza total y la presencia de amonio, respectivamente. Los factores asociados a la contaminación por coliformes fecales eran la cloración del agua (p = 0,065; razón de probabilidades = 14; intervalo de confianza [IC] al 90% = 1,14-71), la limpieza y desinfección (p = 0,028; razón de probabilidades = 14; IC al 95% = 1,25-156,6) y el tratamiento con antibióticos (p = 0,001; razón de probabilidades = 6; IC al 95% = 2,1-35,2). Con el fin de mejorar la calidad del agua en las explotaciones avícolas se recomienda a los productores que protejan los pozos de la contaminación e instalen potabilizadoras de agua (preoxidación, coagulación, floculación y desinfección). Asimismo, conviene proceder a una limpieza y una desinfección rigurosas de los locales y el material de cría al final de cada banda.

The role of farm business type on biosecurity practices in West Java broiler farms.

Biosecurity is important to prevent introduction and spread of infectious diseases, including Highly Pathogenic Avian Influenza (HPAI). HPAI is currently endemic in Indonesia and it has triggered the adoption of a biosecurity policy. Although there is anecdotal evidence about the level of biosecurity on Indonesian broiler farms, formal evidence is lacking, as well as knowledge about determinants of low biosecurity levels. This paper has two main objectives: 1) to assess the levels of biosecurity measures across different farm business types, and (2) to analyze the determining factors, with a special reference to farm business type, for the level of biosecurity and the availability of biosecurity infrastructure in the West Java poultry farms. A multi-stage sampling procedure was applied to collect data from approximately 400 broiler farmers in four districts in West Java province, namely Ciamis, Tasikmalaya, Subang and Sukabumi. Data were collected through farmer interviews and direct observation at the farms. The Biosecurity Control Score (BCS), consisting of 16 practice indicators, and the Farm Infrastructures for Biosecurity (FIB) list consisting of 16 building or physical set-ups as biosecurity facilities were used to assess the level of biosecurity. Determinants of biosecurity measures were analyzed via tobit regression analysis. The results show that the BCS was associated with the total number of birds per cycle, and risk perception (P < 0.05). The BCS was significantly (P < 0.05) higher for the farm business types 'independent farming', 'price contract farming' and 'company' compared to the 'makloon contract farming' business type (farming for a fee). Physical set-ups as farm infrastructure for biosecurity (FIB) were associated with the total number of birds per cycle, and with risk perception (P < 0.05). FIB was significantly (P < 0.05) higher for the farm business types 'independent farming', 'price contract farming' and 'company' compared to makloon contract farming. The results of this study suggest that business types play a significant role in determining biosecurity practices; a makloon contract farm (farming for a fee) tends to have a lower level of biosecurity than other business types due to lack of incentives. Consequently, in developing incentives for improved biosecurity measures. policymakers should encourage farm registration, and at the same time develop incentives that support the independent and price contract farming.

Volatile terpenoids, norisoprenoids and benzenoids as markers of fine scale vineyard segmentation for Corvina grapes and wines.

In this study, the diversity existing at the very small scale of single vineyard parcels in volatile composition of grapes and wines from a single estate in the Valpolicella wine region has been studied. Corvina grapes from eight contiguous vineyards were used for the study and vinified with the same protocol. The compounds analyzed by GC-MS were representative of the terpenoid, norisoprenoid and benzenoid chemical families. Free and bound compounds analysis showed that differences between parcels were relatively small on grapes samples, whereas after fermentation larger differences between wine samples were highlighted. Multivariate statistical analysis of wine volatiles highlighted the existence of similarities between wine volatile profiles, which reflected to a good extent the geographical location of the corresponding vineyard parcels. The main drivers of this diversity were the monoterpene alcohols linalool, α-terpineol, linalool oxide; the benzenoids vanillin, ethyl vanillate and methyl vanillate; and the norisoprenoid ß-damascenone. Wine from one vineyard parcel was not correctly classified, possibly due to the influence of the peculiar training system applied to this parcel. With aging the vineyard parcel geographical diversity was still reflected by the chemical diversity of wines, even if the separation was less fine. As many reactions occurred, some drivers of the diversity were changed after aging. They were benzenoids: ethyl vanillate, methyl vanillate and vanillin; the norisoprenoid 3-oxo-α-ionol; the terpenes linalool oxide, linalool, p-methane-1,8-diol, α-terpineol, and the precursors of nerol, geraniol, linalool.