Intake, growth and meat quality of steers given diets based on varying proportions of maize silage and grass silage

Simmental × Holstein-Friesian steers were offered four forage diets. These comprised grass silage (G); proportionately 0·67 grass silage, proportionately 0·33 maize silage (GGM); 0·33 grass silage, 0·67 maize silage ( MMG); maize silage ( M) from 424 (s.d. = 11·5) kg to slaughter at a minimum weight of 560 kg. Forages were mixed and offered ad libitum. Steers were offered 2 kg of a concentrate daily, the concentrate being formulated such that all steers had similar crude protein intakes across dietary treatments. A sample of steers was slaughtered at the beginning of the experimental period to allow the calculation of the rate of gain of the carcass and its components. Carcass dissection of a sample of steers allowed the development of a prediction equation of carcass composition based on thoracic limb dissection of all carcasses. Forage dry matter intake and live-weight gain increased linearly as maize silage replaced grass silage in the forage mixture, resulting in improvements in food conversion ratio (all P = 0·001). Killing-out proportion increased with maize silage inclusion ( P < 0·001) but fat and conformation scores did not differ significantly between diets. However, increasing maize inclusion in the diet resulted in a greater weight ( P = 0·05) and proportion ( P = 0·008) of fat in the carcass, and significant increases in internal fat deposition. The inclusion of maize led to a progressive increase in the daily gains of carcass ( P < 0·001), and significant increases in the daily gains of both fat ( P < 0·001) and lean tissue ( P < 0·001). Fat colour was more yellow in cattle given diets G and GGM than diets MMG and M ( P < 0·001) and colour intensity was lower on diet M than the other three diets ( P < 0·001). There were no significant differences in any aspects of eating quality between diets. Therefore, maize silage has the potential to reduce the time taken for finishing beef animals to achieve slaughter weight with no apparent detrimental effects on subsequent meat quality.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.