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Abstract
The effect of commercial selection on the growth, efficiency, and yield of broilers was studied using 2 University of Alberta Meat Control strains unselected since 1957 and 1978, and a commercial Ross 308 strain (2005). Mixed-sex chicks (n = 180 per strain) were placed into 4 replicate pens per strain, and grown on a current nutritional program to 56 d of age. Weekly front and side profile photographs of 8 birds per strain were collected. Growth rate, feed intake, and measures of feed efficiency including feed conversion ratio, residual feed intake, and residual maintenance energy requirements were characterized. A nonlinear mixed Gompertz growth model was used to predict BW and BW variation, useful for subsequent stochastic growth simulation. Dissections were conducted on 8 birds per strain semiweekly from 21 to 56 d of age to characterize allometric growth of pectoralis muscles, leg meat, abdominal fat pad, liver, gut, and heart. A novel nonlinear analysis of covariance was used to test the hypothesis that allometric growth patterns have changed as a result of commercial selection pressure. From 1957 to 2005, broiler growth increased by over 400%, with a concurrent 50% reduction in feed conversion ratio, corresponding to a compound annual rate of increase in 42 d live BW of 3.30%. Forty-two-day FCR decreased by 2.55% each year over the same 48-yr period. Pectoralis major growth potential increased, whereas abdominal fat decreased due to genetic selection pressure over the same time period. From 1957 to 2005, pectoralis minor yield at 42 d of age was 30% higher in males and 37% higher in females; pectoralis major yield increased by 79% in males and 85% in females. Over almost 50 yr of commercial quantitative genetic selection pressure, intended beneficial changes have been achieved. Unintended changes such as enhanced sexual dimorphism are likely inconsequential, though musculoskeletal, immune function, and parent stock management challenges may require additional attention in future selection programs.
The yield of carcass parts as well as levels of carcass fat, moisture, and ash were measured in the 1957 Athens-Canadian Randombred Control (ACRBC) and in the Ross 308 commercial broiler, when fed diets that were representative of those being fed during 1957 and 2001. The Ross 308 was used to represent 2001 commercial broilers. Comparisons of carcass weights of the Ross 308 on the 2001 diet versus the ACRBC on the 1957 diet showed they were 6.0, 5.9, 5.2, and 4.6 times heavier than the ACRBC at 43, 57, 71, and 85 d of age, respectively. Yields of hot carcass without giblets (fat pad included) were 12.3, 13.6, 12.2, and 11.1 percentage points higher for the Ross 308 than for the ACRBC at those ages. The yields of total breast meat for the Ross 308 were 20.0, 21.3, 21.9, and 22.2% and were 8.4, 9.9, 10.3, and 9.8 percentage points higher than for the ACRBC at those ages. Yields of saddle and legs for the Ross 308 broiler were approximately 31 to 32% over the four ages and were about 1.5 to 2% higher than for the ACRBC at the different ages. The Ross 308 averaged 13.7, 15.0, 18.6, and 18.5% whole carcass fat versus 8.5, 10.6, 12.7, and 14.0% for the ACRBC at the four ages. In conjunction with previous studies, the current data show that yield of broiler carcass parts has continued to increase over time and that genetics has been the major contributor to changes in yield.
Adolescent meat-type poultry and cage layers exhibit a high incidence of bone problems that include bone weakness, deformity, breakage, and infection and osteoporosis-related mortalities. These problems include economic and welfare issues. To improve bone quality in poultry, it is essential to understand the physiological basis of bone maturity and strength in poultry. A complex array of factors that include structural, architectural, compositional, physiological, and nutritional factors interactively determine bone quality and strength. Bone is approximately 70% mineral, 20% organic, and 10% water. Collagen is the major organic matrix that confers tensile strength to the bone, whereas hydroxyapatite provides compressional strength. In recent years, the roles of different collagen crosslinks have been shown to be important in the increase of bone mechanical strength. Similarly, age-related glyco-oxidative modifications of collagen have been shown to increase the stiffness of collagen. These posttranslational modifications of matrix can affect bone quality as it would be affected by the changes in the mineralization process. Our studies show that the growth in the tibia continued until 25 wk of age, which correlated with the increase in the content of hydroxylysylpridinoline (HP) and lysylpyridinoline (LP), the collagen crosslinks. The tibia from 5-wk-old chicks were strong but brittle because of low collagen crosslinks and high mineral content. Bone maturity may relate to its crosslink content. Compared to crosslink content, bone density and ash content showed moderate increases during growth. The bones from younger turkeys were more susceptible to corticosteroid-induced stunting of growth, which also resulted in decreased bone strength. This review discusses how different factors can compromise bone strength by reducing growth, altering shape, affecting mineralization, and affecting collagen crosslinking.
Many types of data are best analyzed by fitting a curve using nonlinear regression, and computer programs that perform these calculations are readily available. Like every scientific technique, however, a nonlinear regression program can produce misleading results when used inappropriately. This article reviews the use of nonlinear regression in a practical and nonmathematical manner to answer the following questions: Why is nonlinear regression superior to linear regression of transformed data? How does nonlinear regression differ from polynomial regression and cubic spline? How do nonlinear regression programs work? What choices must an investigator make before performing nonlinear regression? What do the final results mean? How can two sets of data or two fits to one set of data be compared? What problems can cause the results to be wrong? This review is designed to demystify nonlinear regression so that both its power and its limitations will be appreciated.
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