Predicting the growth and economic performance of goats using livestock feeding simulation model

This study was conducted to test and validate the applicability of Livestock Feeding Simulation Model 2004 for Goat in predicting the growth and economic performance of goats under different feeding scenarios. The model was tested by simulating the data of the study on feeding urea-treated rice straw with different levels of fine rice bran supplementation to growing goats. The validation on the predictable ability of the model was done by comparing the output of the model with the actual growth and economic performance of goats fed with: Napier-based ration supplemented with Leucaena and concentrate feed (all female); Napier-based ration supplemented with Leucaena and concentrate feed (all male). Napier-based ration supplemented with Rensonii and concentrate feed; and under semi-intensive production system in lowland rainfed condition. The actual and predicted results in the four feeding strategies were compared using T-test. Likewise, feeding alternatives were formulated for goats raised under semi-intensive production system using results from the feeding scenario 4. Problems associated with the use of the model were identified. Testing the model demonstrated that the predicted DMI, %DMI and gain in weight performance (final body weight, total gain in weight and ADG) of the goats fed with UTRS and fine rice bran were not significantly different with the observed values. The predicted total income was also found similar with the actual value. In terms of the total production cost, gross income, cost of production, income/cost ratio and gain per kilogram body weight, the difference between the predicted and actual were significant (P0.05). The predicted and actual DMI and %DMI of goats in Feeding Scenarios 1, 2 and 3, were not significantly different. However, the differences in the predicted and actual intake for individual feeds, i.e. Napier, Lucaena or Rensonii and concentrate by the goats were significant (P.001). In Feeding Scenario 4 (semi-intensive), the difference in total DMI was significant (P0.01) while for individual feeds and %DMI differences were not significant. On the other hand, the predicted and actual final BW and ADG of goats in Feeding Scenarios 1, 3 and 4 were not significantly different. This was not the case in Feeding Scenario 2 wherein the predicted total gain in weight and ADG were significantly lower (P0.01) than the observed values. Results of the analysis of economic performance of the four feeding scenarios show that the model was successful in predicting most of the economic parameters. The predicted and actual total income, total production cost, cost of producing one-kilogram live weight, income/cost ratio and gain per kilogram gain weight were not significantly different. However, the differences between the predicted and observed gross margin was found to be significantly different (P0.05). The best feeding alternatives that could be adopted under semi-intensive goat production system for higher gain in weight, more income and lower methane gas emission include upgrading of the existing goats to above _u -¾%, and increasing the level of Leucaena supplementation to about 50% or adding around 100 g of UMMB to the regular feeding rate. This condition is applicable if upgrading of the animals is not a limiting factor. If it is a limiting factor, increased the amount of cut and carry could be increased to around 67% or the goats may be given increased Leucaena supplement. Among the problems observed in the use of the LIFE-SIM 2000 for Goat were that some cells tend to require input values; the model over-predicts performance when shifting from one bloodline to higher level; there are texts or values that remain fixed in the cells, and some values intended to specific feed(s) in the summary table for DMI interchange with other feeds. Overall, the model is an effective tool in predicting the total feed intake and body weight changes in goats for a given feeding regime. Too some degree, the model was also found effective in predicting the economic performance of goats. The model, likewise, has the potential of predicting the performance of goats under complex feeding scenario such as the semi-intensive production system.