Carcass composition of Venda indigenous scavenging chickens under village management

Four Venda indigenous scavenging (VIS) chickens (one young male and one young female of 10-16 weeks of age, a mature cockerel and a mature hen) were randomly purchased from each of six adjacent rural villages during three different seasons (autumn, winter and spring) to determine the meat yield and carcass chemical composition. A total of 72 chickens were slaughtered and feathers, head, neck, viscera, feet and lungs were removed. The live body weight, dressed carcass weight and also the mass of the breast without wings, thighs and drumsticks were recorded with bones and skin. The muscle tissues of the breast and both legs without tendons and fat were sampled for chemical analysis and were analysed for dry matter, ether extract, crude protein and ash. The carcass weight, dressing %, mass of the breast, mass of the thighs, mass of the drumsticks, breast yield, thighs yield and drumsticks yield of both grower and adult VIS chickens were not influenced by season. The crude protein of the grower chickens breast muscles and fat content of the adult chicken leg muscles differed with season. The meat from VIS chickens provided a constant nutrient (crude protein) supply throughout the year to the rural communities.

The Effect of Climate Variability on Main Components of Cow Milk in Iran

The main purpose of this study is to assess the relationship between six bioclimatic indices for cattle (temperature humidity (THI), environmental stress (ESI), equivalent temperature (ESI), heat load (HLI), modified heat load (HLInew) and respiratory rate predictor(RRP)) and fundamental milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when cows use natural pasture, with possibility for cows to choose to stay in the barn or to graze on the pasture in the pasturing system. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty estimation through resampling in the confidence intervals. To find the relationships between climate indices (THI, ETI, HLI, HLInew, ESI and RRP) and main components of cow milk (fat, protein and yield), multiple liner regression is applied. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Cross validation is used to avoid over-fitting. Based on results of investigation the effect of heat stress indices on milk compounds separately, we suggest the use of ESI and RRP in the summer and ESI in the spring. THI and HLInew are suggested for fat content and HLInew also is suggested for protein content in the spring season. The best linear models are found in spring between milk yield as predictands and THI, ESI,HLI, ETI and RRP as predictors with p-value < 0.001 and R2 0.50, 0.49. In summer, milk yield with independent variables of THI, ETI and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. It is strongly suggested that new and significant indices are needed to control critical heat stress conditions that consider more predictors of the effect of climate variability on animal products, such as sunshine duration, quality of pasture, the number of days of stress (NDS), the color of skin with attention to large black spots, and categorical predictors such as breed, welfare facility, and management system. This methodology is suggested for studies investigating the impacts of climate variability/change on food quality/security, animal science and agriculture using short term data considering uncertainty or data collection is expensive, difficult, or data with gaps.