Retrospective Broiler Health Survey: Scope, Parameters, and Overall Trends.

A retrospective analysis encompassing 18 yr (1999-2016) of broiler chicken health surveys from broiler production complexes throughout the United States was conducted to identify trends and areas of opportunity. The analysis used necropsy data from 19,577 broiler chickens considered to be "healthy" or "clinically normal" by visual assessment. The very low incidence of dehydrated (0.01%) or undersized (0.02%) broilers is evidence that the objective of examining "healthy" birds was achieved. The results of the correlation analysis were what was expected for "healthy" broilers, with these birds having positive correlations with skin color and size of the bursa of Fabricius. The average age of broilers included in this analysis ranged from 26.2 days in 2008 to 33.7 days in 2013. The percentage of "normal" broilers (those without visible lesions or abnormalities) varied from 5.63% in 2012 to 28.33% in 1999. The results of this study identified four areas of opportunity for improving poultry health: 1) coccidiosis, 2) oral lesions and gizzard erosions, 3) retained yolks, and 4) pododermatitis.

Encuesta retrospectiva sobre la salud de los pollos de engorde: alcance, parámetros y tendencias generales. Se realizó un análisis retrospectivo que abarcó 18 años (1999-2016) de encuestas de salud de pollo de engorde de complejos de producción en los Estados Unidos para identificar tendencias y áreas de oportunidad. El análisis utilizó datos de necropsia de 19,577 pollos de engorde considerados "saludables" o "clínicamente normales" mediante evaluación visual. La incidencia muy baja de pollos de engorde deshidratados (0.01%) o de tamaño pequeño (0.02%) es evidencia de que se logró el objetivo de examinar aves "sanas". Los resultados del análisis de correlación fueron los esperados para pollos de engorde "sanos", ya que estas aves tuvieron correlaciones positivas con el color de la piel y el tamaño de la bolsa de Fabricio. La edad promedio de los pollos de engorde incluidos en este análisis osciló entre 26.2 días en 2008 y 33.7 días en 2013. El porcentaje de pollos de engorde "normales" (aquellos sin lesiones o anomalías visibles) varió del 5.63% en 2012 al 28.33% en 1999. Los resultados de este estudio identificaron cuatro áreas de oportunidad para mejorar la salud de las aves: 1) coccidiosis, 2) lesiones orales y erosiones de la molleja, 3) retención de saco vitelino y 4) pododermatitis.

The future of feed formulation for poultry: Toward more sustainable production of meat and eggs.

Current methods for feed formulation are based on minimizing costs, not maximizing profits. Complex models of bird growth and reproduction as functions of genetic, feed and other environmental variables are being developed, but their adaptation has been slow. The development of profit maximizing models will evolve to center on the production functions of broilers and layers. The production functions are the relationship between the value of products (mainly meat and eggs) and the cost of feed. The production function is the tool used to maximize profits subject to all the various inputs, not just feed or nutrition. The production function is subject to the law of diminishing returns. The most profitable output levels are those where the marginal value (price) of the meat or eggs is just equal to the marginal cost of the inputs including feed, housing, processing and all other costs. Anything that affects the production function, bird genetics, feed quality, housing and environment, will be considered to maximize profits for the poultry firm. The profit maximizing models of poultry firms will improve as various technical improvements are made: metabolizable energy to describe ingredients will evolve to net energy systems that consider that the heat production (and therefore energetic efficiency) of broilers is different depending on the ingredients used to formulate the feed and the environmental temperatures under which they are reared. Amino acid needs will include a method to find the birds' needs for the non-essential amino acids. "Digestible" amino acid assays will differentiate between digestion and absorption to best balance various sources. The carbohydrate fractions of feed ingredients will be determined to optimize the use of exogenous enzymes. The value of meat and egg co-products will reduce overall costs (e.g., organic fertilizer for crop enhancement). Future profit maximizing production models will be ever evolving processes where field conditions and results are continually being utilized to re-calibrate the technical models so that the management team can use them with cost and return projections to decide on the best choices of inputs and outputs.

Total amino acid level affects the results of standardized ileal digestibility assays for feed ingredients for swine.

It has been demonstrated that the fiber content of oilseed meals and total amino acids (AA) in distillers' grains products affects standardized ileal digestibility (SID) values from swine assays and that total protein concentration affects the AA digestibility in assays using chickens. This analysis was conducted to test the hypothesis that the total AA content of test samples affects the SID assay results. Databases containing total AA profiles and SID values of 20 feedstuffs commonly fed to pigs from 2 sources, AMINODat 5.0 (Evonik Industries, 2015) and the Nutrient Requirements of Swine, 12th edition (NRC, 2012) were used to compare AA concentration effects on standardized ileal digestibility coefficients. Databases were compared with AA and SID available in both data sets. The total AA values were similar for the 2 databases with an R 2 of 0.979 (P < 0.001). The linear relationship between digestibility coefficients from the 2 databases was highly significant, an R 2 of 0.810 (P < 0.001). Both databases had increased SID values with increasing AA contents within and across feed ingredients. The SID = f(AA concentration) relationship was confirmed with an individual paper. Since SID, as typically measured, is a function of both digestion and absorption, both processes following Michaelis-Menten kinetics, SID = f(AA concentration) may simply be a natural phenomenon. Other reasons for the relationship were explored. Methods of estimating endogenous AA losses and misapplication statistical procedures may contribute to variation in results and at least partially explain why SID = f(AA concentration).

Sex proportion as a covariate increases the statistical test power in growth performance based experiments using as-hatched broilers.

The availability of sexed day-old broiler chicks is becoming an issue as feather sexing is no longer possible. This has great implications for broiler researchers as the use of randomly distributed mixed-sex birds may result in a greater between-pen variation and thus less statistical power than the use of single-sex birds. The objective of this study was to evaluate the effect of including sex proportion as a covariate in an analysis of covariance (ANCOVA) on the statistical power compared to analysis of variance (ANOVA) where sex was not considered. The statistical parameters examined include mean square error (MSE), the F-statistic, model fit, model significance and observed power. A total of 4 separate experiments that used mixed-sex broilers with unequal numbers of male and female birds per pen were conducted during which performance of the birds was measured. The male % in each pen was recorded during each experiment and corrected for mortality. The performance results were analysed by ANOVA and the statistical parameters were then compared to ANCOVA where sex proportion was included as a covariate. The results showed that a set of assumptions first needed to be met to run ANCOVA. In addition, if the ANOVA results show a high level of model significance and power, then ANCOVA may not be necessary. In other circumstances where the assumptions are met and model significance and observed power are low, the inclusion of sex proportion as a covariate in the analysis will help to reduce MSE, increase the F-statistic value and improve the model significance, model fit and observed power. Therefore, it is suggested that sex proportion should be considered as a covariate in ANCOVA to improve statistical power in nutritional experiments when male and female broilers are unequally and randomly distributed in pens.

Abductive statistical methods improve the results of calibration curve bioassays: An example of determining zinc bioavailability in broiler chickens.

In this paper, we discuss the theory behind calibration curve experiments and their application to a zinc (Zn) bioavailability study with broiler chickens. Seven replicates of 16 male commercial broiler chicks were fed starter diets for 14 days. Six diets had different levels of a potential Zn source and one was a positive control with standard industry levels of Zn for comparison. Four commonly used methods of calculating bioavailability means and confidence intervals (CI) from a calibration curve (standard curve) experiment to estimate the bioavailability of a new zinc source in broiler chickens were compared. The methods compared were the following: 1) the Counter-Intuitive Method uses a multiple-range test to compare unknown test and standard samples; 2) the Intuitive Method uses standard linear regression and inverts the equation to predict Zn bioavailability for each replicate of test samples; 3) the Abductive Method uses Graybill's Equation, based on theory and observation, to estimate CI's; and 4) the Sophistic Method uses reverse regression, and calculates Zn bioavailability values directly from the equation. The Counter-Intuitive Method only gives information about which standards the test samples are, or are not, significantly different from respectively (average available Zn not predicted). The Intuitive Method ignores error about the standard curve and theoretically cannot estimate the CI directly ( X ¯ ± SEM  = 107.5 ± 15.8 mg Zn/kg). The Sophistic Method underestimates and overestimates the test sample mean values above and below the mean of the standards, respectively ( X ¯  = 96.6 mg Zn/kg). The Abductive Method has an advantage over the other methods: The mean prediction estimation is consistent with theory (107.5 ± 6.1 mg Zn/kg; X ¯ ± SEM ). When test or "unknown" samples are near the mean of the standard samples, the CI is smaller than when near the extremes of the calibration curve. When calibration curve error is small (R 2 > approximately 0.95), there is little advantage to using the Abductive Method, but when calibration curve error is larger, as in many bioassays with growing animals, the Abductive Method improves the accuracy of the CI calculations. The Abductive Method was used to demonstrate the influence of the number of replicate samples on experimental power and cost.

A statistical analysis of nonlinear regression models for different treatments for layers.

As the cost of research increases, mathematical models become valuable tools to answer research questions. A major application of mathematical modeling is accurate estimation of production performance, growth, and feed consumption for poultry research and production. There are many ways that a given data set can be analyzed, and different models have been proposed to fit those curves. To explore the models available, data were investigated from a study on the effects of a series of balanced dietary protein levels on egg production and egg quality parameters in lying hens from 18 to 74 wk of age. Forty eight pullets were assigned to each of 3 different protein levels. The results clearly demonstrated that balanced dietary protein level was the limiting factor for body weight (BW), average daily feed intake (ADFI), egg weight, and egg production. To test differences of fitted curves, the sum of squared reduction test is used. Using a unique data set with data from individual hens, 6 commonly used models were fitted to hen performance technical data. The resulting statistical inferences from using individual and pooled data were compared. There are only differences in using individual or grouped data in fitting nonlinear models to laying hen response data. For the most important response variables, hen-day egg production, and feed intake, predicted responses are within 0.12 and 0.65%, respectively, throughout the production cycle.

Historical flaws in bioassays used to generate metabolizable energy values for poultry feed formulation: a critical review.

Dietary energy available to animals is key for formulating feed as it is required for all aspects of the animal's life. In poultry, apparent (AME) and true (TME) metabolizable energy (ME) values have been used for feed formulation with (AMEn or TMEn) or without correction for nitrogen balance. For the past 50 yr, the accuracy of ME has been an ongoing debate, and the comparability of data produced using different bioassay systems is often questionable. Overall, the ingredient matric ME values used in feed formulation are not consistent, and to some extent, confusing. This review was to examine ME data published in the past century to elucidate the accuracy of different bioassay systems and examine the values for accuracy and useability. A variety of flaws are identified in the literature, suggesting a thorough re-thinking of feedstuff ME values currently used in feed formulation and in developing prediction equations. Two protocols, namely multiple linear regression and basal diet substitution methods, are proposed as more accurate bioassays for feedstuff ME values. AME aligns more closely with the actual energy levels of feed ingredients likely available to growing birds, which should be used for poultry feed formulations instead of AMEn. It is suggested that nutritionists need to carefully apply any reported AME values and only use those in formulation practice after careful scrutinizing. Any in vitro, NIR or table values must be calibrated or computed based on the values produced from flawless bioassays so as to apply the derived values accurately. Flaws identified in this literature review can be avoided with care to achieve more accurate AME. However, the assumption that the energy of individual ingredients is additive in a complete diet is still untrue at least under some circumstances. This may require efforts from industry and researchers to investigate relations among the main ingredients in a complete diet so that more accurate formulation can be performed based on the outcomes that may fine-tune the additivity assumption.

Relationship Between Broiler Body Weights, Eimeria maxima Gross Lesion Scores, and Microscores in Three Anticoccidial Sensitivity Tests.

Anticoccidial sensitivity tests (ASTs) serve to determine the efficacy of anticoccidial drugs against Eimeria field isolates in a controlled laboratory setting. The most commonly measured parameters are body weight gain, feed conversion ratio, gross intestinal lesion scores, and mortality. Due to the difficulty in reliably scoring gross lesion scores of Eimeria maxima , microscopic analysis of intestinal scrapings (microscores) can be used in the field to indicate the presence of this particular Eimeria. The goal of this study was to determine the relationship between E. maxima microscores and broiler body weights and gross E. maxima lesion scores in three ASTs. Day-old broiler chicks were raised for 12 days on a standard corn-soy diet. On Day 12, chicks were placed in Petersime batteries and treatment diets were provided. There were six birds per pen, four pens per treatment, and 12 treatments, for a total of 288 chicks per AST. The treatments were as follows: 1) nonmedicated, noninfected; 2) nonmedicated, infected; 3) lasalocid, infected; 4) salinomycin, infected; 5) diclazuril, infected; 6) monensin, infected; 7) decoquinate, infected; 8) narasin + nicarbazin, infected; 9) narasin, infected; 10) nicarbazin, infected; 11) robenidine, infected; and 12) zoalene, infected. On Day 14, chicks were challenged with an Eimeria field isolate by oral gavage. On Day 20, broilers were weighed, and gross lesion scores and microscores were classified from 0 to 4 depending on the severity of the gross lesion scores and E. maxima microscores. Data from three trials using different field isolates were statistically analyzed using a logarithmic regression model. There was no relationship (P = 0.1224) between microscores and body weight gain. There was a positive relationship between microscores and gross lesion scores (P = 0.004). However, there was also an interaction between isolate and treatment (P < 0.0001). Lastly, the interaction between isolate and gross lesion scores (P = 0.0041) demonstrates that the significance of the relationship between microscores and gross lesion scores may be dependent on pathogenicity of the challenge Eimeria or the amount of E. maxima in the inoculum.

Methods for simulating nutritional requirement and response studies with all organisms to increase research efficiency.

Nutritional requirements and responses of all organisms are estimated using various models representing the response to different dietary levels of the nutrient in question. To help nutritionists design experiments for estimating responses and requirements, we developed a simulation workbook using Microsoft Excel. The objective of the present study was to demonstrate the influence of different numbers of nutrient levels, ranges of nutrient levels and replications per nutrient level on the estimates of requirements based on common nutritional response models. The user provides estimates of the shape of the response curve, requirements and other parameters and observation to observation variation. The Excel workbook then produces 1-1000 randomly simulated responses based on the given response curve and estimates the standard errors of the requirement (and other parameters) from different models as an indication of the expected power of the experiment. Interpretations are based on the assumption that the smaller the standard error of the requirement, the more powerful the experiment. The user can see the potential effects of using one or more subjects, different nutrient levels, etc., on the expected outcome of future experiments. From a theoretical perspective, each organism should have some enzyme-catalysed reaction whose rate is limited by the availability of some limiting nutrient. The response to the limiting nutrient should therefore be similar to enzyme kinetics. In conclusion, the workbook eliminates some of the guesswork involved in designing experiments and determining the minimum number of subjects needed to achieve desired outcomes.

Symposium: experimental design for poultry production and genomics research.

This symposium dealt with the theoretical and practical aspects of choosing and evaluating experimental designs, and how experimental results may be related to poultry production through modeling. Additionally, recent advances in techniques for generating high-throughput genomic sequencing data, genomic breeding values, genomics selection, and genome-wide association studies have provided unique computational challenges to the poultry industry. Such challenges were presented and discussed.

Evaluation of the fixed nitrogen-to-protein (N:P) conversion factor (6.25) versus ingredient specific N:P conversion factors in feedstuffs.

BACKGROUND: The crude protein (CP) of feedstuffs is important as an indicator of essential and non-essential amino acids for livestock. The protein (P) level needs to be known accurately, to minimize the feeding of excess nitrogen (N) and to reduce N pollution. Laboratory methods for determining N content report N from amino acids, but also N from ammonia and from non-amino acid sources. The determined CP based on 6.25 × N level typically overestimates the true protein of feedstuffs. RESULTS: Determined ingredient-specific N:P conversion factors k(A) , k(P) and k were not equal to the standard 6.25 factor. The k(A) had the highest value in all ingredients, which leads to the estimation of specific crude protein (SCP), which is closer to true protein (the summation of the total amino acid residues from amino acid analyses). The SCP(k(A) ) was lower than CP and true protein in all ingredients, demonstrating that CP might overestimate the actual protein in feedstuffs. CONCLUSION: Based on data from 677 feedstuff samples from 2009, it is concluded that the mean k(A) should be 5.68 for corn, 5.64 for soybean meal, 5.74 for corn DDGS, 5.45 for poultry by-product meal and 5.37 for meat and bone meal.