Palm kernel meal in broiler diets: effect on chicken performance and health

An increase in the production of palm kernel meal (PKM) coupled with the concern for continued availability of conventional feedstuffs in some parts of the world has led to research to establish the maximum inclusion level of palm kernel meal in broiler diets. The results suggested that palm kernel meal has no anti-nutritional properties and thus its inclusion is safe up to at least 40% in the diet, provided the diet is balanced in amino acids and metabolisable energy. Although feed digestibility is decreased due to high dietary fibre when PKM is included in the diet, the feed intake is increased. This makes total digestible nutrient intake relatively high. beta-mannan is the main component of palm kernel meal non-starch polysaccharide (NSP). Both mannose and manno-oligosaccharides have been reported to act as prebiotics. The inclusion of palm kernel meal in the diet improves the immune system of birds and reduces pathogenic bacteria and increases the population of nonpathogenic bacteria in the intestine. These two benefits should be considered as strong recommendations for using palm kernel meal in broiler diets, particularly in palm kernel meal producing countries, not only for increasing bird productivity but also to improve chicken health. Selective enzyme addition increases feed efficiency and digestibility as well as decreasing the moisture content of faeces.

Performance analysis using equivariant kernel density estimator in nonlinear mixture

This paper investigates the performance analysis of separation of mutually independent sources in nonlinear models. The nonlinear mapping constituted by an unsupervised linear mixture is followed by an unknown and invertible nonlinear distortion, are found in many signal processing cases. Generally, blind separation of sources from their nonlinear mixtures is rather difficult. We propose using a kernel density estimator incorporated with equivariant gradient analysis to separate the sources with nonlinear distortion. The kernel density estimator parameters of which are iteratively updated to minimize the output independence expressed as a mutual information criterion. The equivariant gradient algorithm has the form of nonlinear decorrelation to perform the convergence analysis. Experiments are proposed to illustrate these results.