A decision support system to improve individual cattle management. 1. A mechanistic, dynamic model for animal growth

摘要: Abstract A deterministic and mechanistic growth model was developed to dynamically predict rate, accumulated weight, days required reach target body composition, carcass weight (CW) composition of individual beef cattle for use in management systems. The can either average daily gain (ADG) when dry matter intake (DMI) is known or (DMR) ADG known. For both scenarios, the following parameters are required: metabolizable energy diet length feeding period, animal characteristics [age, gender, breed, initial (BW), condition score, adjusted final BW at 28% empty fat (EBF)] environmental information (temperature, humidity, hours sunlight, wind speed, mud, hair depth, coat). Two iterative methods based on were derived compute efficiency net (NE g ). This evaluated with data from 362 individually fed steers measured feed values predicted NRC (2000). method that used a decay equation adjust NE proportion retained as protein showed best prediction BW. When known, accounted 89% variation bias −2.6% predicting explained 83% −1% estimating observed actual total feed. only 2% r 2 74%. sub-model (FAT) quality yield grades (YG) during growth. With unadjusted method, this 84% had −14.3% Additionally, an 407 animals YG EBF (%) 0.49. Equations CW stage 3 kg bias. In conclusion, dynamic performance within acceptable degree accuracy.