Tidsskrift : Journal of Animal Science , vol. 81 , p. E187–E195 , 2003
Trykt : 0021-8812
Elektronisk : 1525-3163
Publikasjonstype : Vitenskapelig artikkel
Sak : 14
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The models dealt with herein are driven by descriptors of pig growth potential and environment, predicting growth from their interaction. Growth potential parameters relate to resource intake and partitioning to maintenance, protein (P) deposition (PD), and lipid (L) deposition (LD); these parameters quantify genotype (breed, etc.). Simulation of a pig’s growth requires characterization of its potential in terms of the associated model parameters. This requires a set of parameters that fully describe the potential, measurement of resource input, and partitioning in a genotype, and using these measurements to quantify those parameters for that genotype. Resource partitioning is commonly covered by potential PD, required LD, and MEm. Description of the first two features commonly requires three parameters. The MEm here is restricted to a neutral environment without functions for coping with stressors, which would require extra parameters. Nutrient intake is best modeled as resulting from nutrient requirements and from constraints to physical uptake, be they external or genetic. Intake and partitioning observations must reflect potential; environmental load must be minimized. Repeatedly measuring whole-body P and L and ad libitum ME intake over a sufficiently wide maturity range (for example from 10 to 175 kg of BW) requires serial slaughter trials with chemical analysis or in vivo techniques such as ultrasound. The latter allow for the description of individual growth patterns and for quantification of variation in addition to mean levels. Parameters can be estimated in three ways. First, P and L observations can be fitted to P and L growth functions. Then, MEm comes out as the remainder of the ME budget, given valid assumptions about PD and LD efficiency. Second, observed feed intake, growth, and body composition can be fitted to their simulations (parameter calibration, inverted modeling) to avoid P or L measurement. This requires serial data and iteration to match resource requirements to allowance. Third, differential nutrient restriction techniques can be used.