Summative Model For Prediction Of Energy Value In Ruminant Feeds

Author : Brandi Marie Bourg
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (37 download)

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Download or read book Evaluation of a Mathematical Model in Predicting Intake of Growing and Finishing Cattle written by Brandi Marie Bourg and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Cattle Value Discovery System (CVDS) was developed to predict growth and feed requirements of individual cattle fed in groups based on animal, diet, and environment information (Tedeschi et al., 2006). Evaluations of the CVDS using several databases of finishing cattle were conducted to determine the accuracy and precision of the model in predicted dry matter required (DMR) of pen-fed cattle. As well, the sensitivity of the model's predictions to deviations from actual ration metabolizable energy (ME) value was conducted. A meta-analysis of growing and finishing steers evaluated to model's accuracy in predicting DMR of individually fed steers, and the relationships between several model-predicted variables and actual performance and efficiency measures. Results for the first CVDS model evaluation involving pen-fed Santa Gertrudis cattle fed finishing diets revealed that accurate predictions of DMR are possible. The average mean bias for both steers and heifers was 2.43%. The sensitivity analysis of dietary ME values revealed that the model tends to consistently over- and under-predict DMR when the ME values are under- and over-estimated, respectively. However the ranking of pens was not affected by this mis-estimation of diet ME. In the second evaluations, both methods (mean body weight; MBW, dynamic iterative model; DIM) of CVDS were highly accurate and precise in allocating feed to pens of steers fed diverse types of diets and environmental conditions, with both models having a mean bias under 4%. The DIM model was slightly more accurate than the MBW model in predicting DMR. An evaluation of sources of variation revealed that for both models a large portion of the error was random, indicating that further work is needed to account for this variation. The meta-analysis study revealed that the model was able to account for 64% and 67% of the variation in observed dry matter intake (DMI) for growing and finishing steers, respectively. The two model-predicted efficiency measures, the ratio of DMR to average daily gain (ADG) and predicted intake difference (PID), were strongly to moderately correlated with their observed efficiency counterparts. In growing and finishing steers, DMR: ADG was able to account for 76% and 64% of the variation in observed feed conversion ratio (FCR) in growing and finishing studies, respectively. Strong correlations were also found between residual feed intake (RFI) and PID, suggesting that there may also be some similarity on these two measurements.