Newcastle disease virus (NDV) live vaccines are supplied in lyophilized form and usually administered through conventional routes (drinking water, spray, or eye drop) following reconstitution in a diluent. Virus inactivation due to physico-chemical properties of the diluent at the time of administration may lead to vaccine failure. The present study aimed to evaluate the survival of NDV live vaccine strain immersed in 5 pH-amended water samples (pH 5.00, pH 6.00, pH 7.00, pH 8.00, and pH 9.00) by sequential determination of virus infectivity on Vero cells for 3 hours. Minimum reduction in virus infectivity was recorded in the water with neutral or slightly alkaline pH, while the virus was relatively less stable at extreme pH conditions. Maximum reduction of infectivity was observed in the water with pH 9.00 in which the virus was completely inactivated within 3 hours. Addition of stabilizers (Cevamune or skimmed milk) slightly altered the pH and total dissolved solids (TDS) values of the virus-charged water samples. In the stabilizer-added water samples, minimum reduction in infectivity was observed in the water with neutral pH, followed by the ones with a pH of 8.00, 6.00, 5.00, and 9.00. In all types of water samples, T-90 values (time required for 90% reduction in virus infectivity) were highest (485 minutes) at neutral pH (pH 7.00) and lowest (102 to 134 min) at an extreme alkaline condition (pH 9.00). Results of the present study indicate that water with a pH range of 7.00 to 8.00 is suitable for administration of NDV live vaccines. However, the addition of Cevamune or skimmed milk may have beneficial effects on preserving the infectivity of the virus, even at extreme pH conditions.

Due to serotype variations among different avian infectious bronchitis viruses isolated in Tunisia since 2000, protection of chicks, especially broiler flocks, with Mass H120 vaccine often fails. Therefore, association of CR88 (793B type) with H120 vaccines was used for better response. Challenge experiments were then conducted to evaluate tracheal and renal cross-protection in chickens immunized via nasal and eye drops. Conferred protection was measured by clinical signs and macroscopic lesions observed, based on scores attributed according to their severities. The results showed a low protection conferred by H120 alone, as vaccination did not reduce tracheal and kidney lesions (70% scored as 3) after TN20/00 virus challenge, which also led to 10% mortality. Conversely, the challenge results indicated that the combination of the 2 strains (H120/CR88) allow high protection. Based on the results of the challenge experiments, a vaccination protocol coupling CR88 to H120 was applied for industrial broiler flocks. Clinical observations and serological results confirmed that association of heterologous serotypes (H120 and CR88 vaccines) increased the levels of protection against infectious bronchitis viruses compared with the H120 vaccine given alone.

Several mathematical or statistical and artificial intelligence models were developed to compare egg production forecasts in commercial layers. Initial data for these models were collected from a comparative layer trial on commercial strains conducted at the Poultry Research Farms, Auburn University. Simulated data were produced to represent new scenarios by using means and SD of egg production of the 22 commercial strains. From the simulated data, random examples were generated for neural network training and testing for the weekly egg production prediction from wk 22 to 36. Three neural network architectures-back-propagation-3, Ward-5, and the general regression neural network-were compared for their efficiency to forecast egg production, along with other traditional models. The general regression neural network gave the best-fitting line, which almost overlapped with the commercial egg production data, with an R(2) of 0.71. The general regression neural network-predicted curve was compared with original egg production data, the average curves of white-shelled and brown-shelled strains, linear regression predictions, and the Gompertz nonlinear model. The general regression neural network was superior in all these comparisons and may be the model of choice if the initial overprediction is managed efficiently. In general, neural network models are efficient, are easy to use, require fewer data, and are practical under farm management conditions to forecast egg production.

The nursery industry pasteurizes soil with steam and quicklime to reduce plant pathogens. The mechanism of action for quicklime is the resulting exothermic reaction that occurs when the chemical interacts with water and its ability to increase pH levels. These treatments may also reduce pathogens in a commercial poultry house. In this study, a steam sterilization cart simulated conditions used by the nursery industry to treat litter inoculated with serovar Typhimurium. A homogenized sample of litter was exposed to steam for 0, 5, 30, or 120 min. Quicklime was used at concentrations of 0 (control), 2.5, 5.0, or 10.0%. All steam treatments, with or without quicklime, significantly reduced Typhimurium colonization by at least 3 orders of magnitude. Significant reductions were also observed in the treatments with quicklime alone. Both the steam and the quicklime treatments often reduced colonization to undetectable levels, even when samples were enriched. Therefore, we demonstrated 2 novel techniques for reducing Typhimurium in poultry litter. Soil pasteurization potentially offers an environmentally sound means of reducing the pathogens present in used poultry litter.

The effect of duration of feeding (continuous or discontinued after d 14) and form (granular vs. powder) of spray-dried plasma (SDP) on performance and mortality of broilers using used litter was evaluated with 240 Ross × Ross 308 male broilers (6 broilers per pen, 8 pens per treatment). Dietary treatments were control (no SDP) or SDP as powder or granular included in the pellet and fed continuously (d 0 to 35) or discontinued after d 14. During the experiment, broilers developed necrotic enteritis, and tissue cultures were positive for and , resulting in 50% mortality on control broilers. Addition of SDP to the feed improved ( < 0.05) average daily gain, feed intake, and feed efficiency for each period of the study (d 0 to 14, 15 to 28, 29 to 35, and 0 to 35). Continuous feeding of SDP improved ( < 0.05) average daily gain, feed intake, and feed efficiency from d 15 to 35 compared with broilers fed SDP to d 14. Liveability was improved ( < 0.05) in broilers consuming SDP either for 14 d or continuously throughout the experiment compared with control broilers. Spray-dried granular plasma was more effective than spray-dried powder plasma from d 0 to 14. The results of this experiment confirmed that SDP improved broiler growth rate, feed intake, feed efficiency, and minimized enteric challenge associated with necrotic enteritis with maximal protection afforded by continuous feeding. The response to SDP was independent of age of the broiler.

Darkling beetle homogenates (DBH) were prepared from beetles collected from seven premises (farms). DBH were shown to contain myriad infectious organisms including bacteria (, ), viruses (, reovirus), and (the causative agents of intestinal coccidiosis). The present study establishes the fact that darkling beetles serve as vectors for common avian pathogens. Darkling beetles must be considered on a list of other vectors known to transmit common poultry pathogens. The risk posed by beetles with respect to dissemination of diseases is of immense importance to the poultry industry. The possibility of severe adverse economic impact as a result of these diseases should not be overlooked or casually dismissed.

A study of broiler litter re-utilization potential was conducted with the goal of determining if storage of litter significantly reduced potential pathogens to levels safe for re-utilization. Litter from four broiler houses was separated into a fine fraction for fertilizer use and a coarse fraction for use as a supplement to wood shavings in growing subsequent flocks of birds. Fractions and whole litter were stored in indoor piles for four months with periodic analysis for culturable pathogenic and indicator bacteria. Significant reductions in microbial concentrations occurred in a majority of samples tested during four months of storage (in most cases to below detection limits of approximately 30 CFU/g dry weight). Poultry feed was found to be one possible source of litter contamination.

Poultry growth is usually modeled with the Gompertz model or another nonlinear statistical model using average BW data over certain periods of time for a given strain of birds under specific farm management conditions. Constant selection in the genetic pool, nutritional factors, and environmental concerns, however, make such models limited in their utility because of the difficulty of fitting the growth curve across time, bird strains, and other determining variables. Moreover, generating data for every strain of birds under continually changing variables is difficult, expensive, and time consuming. The current model addresses 2 objectives: to simulate data using published literature for different growth periods, and to develop artificial intelligence models with various neural network architectures. By breaking down the actual broiler growth data into 5-d intervals, with known means and SD, normal distributions were generated for broiler growth using @Risk software. These simulated data were then used to recognize data patterns and model growth curves by using various neural networks. Three neural networks, namely, BackPropagation-3 (3 layers of back propagation, with each layer connected to the previous layer), BackPropagation-5 (5 layers of back propagation, with each layer connected to the previous layer), and Ward-5 (5 hidden slabs with various activation functions, using NeuroShell 2 Ward software) were used in this research. Once the networks were sufficiently trained, they were exposed to actual growth data to predict broiler growth over the next 50 d. The Back-Propagation-3 neural network gave the best fitting line, with predictions fitting tightly to the actual data points. The R was 0.998, and nearly perfect. The R for the BackPropagation-5 and Ward-5 neural networks were 0.967 and 0.973, respectively. To test the approach further, the same methodology was applied in guinea fowl growth prediction, resulting in R of 0.96 both for the general regression and Ward-5 neural networks.