Ileal digestibility of amino acids in feed ingredients for broilers.

To more precisely formulate feed and predict animal performance, it is important to base both the recommendations and feed formulations on digestible rather than total amino acid contents. Most published data on the digestibility of amino acids in feed ingredients for poultry are based on excreta digestibility. Ileal digestibility is an alternative and preferred approach to estimate amino acid availability in feed ingredients. Both methodologies are described and assessed. In addition, the differences between apparent and standardised (in which corrections are made for basal endogenous losses) digestible amino acid systems are discussed. The concept of a standardised digestibility system as a mean of overcoming the limitations of apparent digestibility estimates is proposed. In this context, different methodologies for the determination of basal endogenous amino acid losses are discussed. Although each methodology suffers from some limitations and published data on endogenous losses at the ileal level in growing poultry are limited, averaged data from repeated experiments using the 'enzymatically hydrolysed casein' method are considered as the best measure of basal losses. Standardised ileal amino acid digestibility values of 17 feed ingredients commonly used in broiler nutrition are presented including grains (barley, corn, sorghum, triticale, wheat), grain by-products (wheat middlings, rice pollard), plant protein sources (soybean meal, canola meal, corn gluten meal, cottonseed meal, lupins, peas/beans, sunflower meal), and animal by-products (feather meal, fish meal, meat and bone meal). This comprehensive set of the ileal amino acid digestibility of feed ingredients in broiler nutrition may serve as a basis for the establishment of the system in broiler feeding and for further research.

Water balance in goats subjected to feed restriction

The effect of feed restriction on water balance and nutrient utilization was investigated in individually penned Boer x Saanen kids. Twenty-two male Boer x Saanen kids with an initial average live weight (LW) of 15 kg were used. Seven kids were slaughtered at the beginning of the experiment (reference animals) and the remainders were allocated to one of the three treatments (0, 30 and 60% restriction) and therefore there were five kids per treatment. The feed intake for the 0% restriction treatment animals determined the intake for the animals in the 30 and 60% restriction treatment. When the animals in the 0% restriction treatment group reached 25 kg LW, the animals in the 30 and 60% restriction treatment groups were also slaughtered. There was a negative relationship between DMI and water intake. The digestibility coefficients for DM, OM, carbohydrates, ash, ether extract, energy, NDF, ADF and lignin did not differ between treatments, whereas the digestibility coefficient for CP was different between treatment groups. The highest metabolic water production was in animals in the 0% restriction treatment group. No significant differences were observed in the composition of gastro-intestinal tract contents of the goats in the different treatments. Lower water retention was found in the animals in the 60% restriction treatment group. The study showed that feed restriction affected water intake, CP digestibility and water retention in the body of the kid goats. This experiment demonstrated that DM:water intake ratio changed when severe feed restriction was applied (60% restriction) and water was freely available. It shows a different pattern of behaviour of penned goats, particularly if feed intake is restricted and perhaps caution is needed to extrapolate results from nutritional and physiological trials in pens to goats at pasture. (c) 2005 Elsevier BX All rights reserved.

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.