The experiment was carried out to investigate the effect of ensiling on the several feed characteristics of a total mixed ration (TMR) prepared for dairy dry cows. The TMR was composed of 36% (on a dry matter [DM] basis) whole plant corn silage (late yellow ripe stage), 32% grass silage (first cut at heading stage), 25% grass hay (first cut at blooming stage) and 7% compound feed. The contents of total digestible nutrient and crude protein were set at 63% and 12% on a DM basis, respectively. The TMR was ensiled using a roll baler for chopped material and opened after 4 months. The chemical compositions, fermentative characteristic, aerobic stability and palatability by pre-parturition animals were compared with those of TMR prepared using the same lot of ingredients. We found that TMR silage contained significantly but slightly more DM (by approximately one percentage point). No significant differences were observed in the lactate and ethanol contents between TMR silage and TMR. Although the TMR silage contained more acetate, propionate and volatile basic nitrogen, the fermentative quality was classified as fairy good based on V-score value (92 points). The TMR silage showed higher aerobic stability after exposed to air. When the TMR silage and TMR were offered simultaneously to 8 animals (4 dry cows and 4 pregnant heifers) and the daily intake of each diet was recorded, the palatability index of TMR silage (the proportion of TMR silage intake to total intake) was 61% which was significantly higher than 50%. These results indicate that, although several characteristics seem to differ from those of TMR silage for lactating cows which were demonstrated previously by several authors, the ensiling of TMR for dry cows which contains more roughage and less concentrate can improve the aerobic stability and palatability as known for TMR silage for lactating cows.

Four genera of tree leaves (Robinia, Quercus, Ligustrum, and Morella) are popularly fed to giraffe and other herbivores in Japanese zoos. However, rational feeding of the tree leaves is not feasible because the amount of tree leaves offered is difficult to quantify and also little is known about the nutrient contents in the leaves. In this study, we estimated the weight of the leaves by using allometric relationship to the branch size and determined seasonal change of nutrient contents in the leaves. In all the species, the weight of leaves on the branch (Y) was well estimated from the area of cutting surface of the branch (X) (all species combined ; Y＝0.819X^2.028, R²＝0.784, P<0.001). The nutrient contents every three months showed species-specific seasonal changes. This may be caused not only by maturity and regeneration of leaves but also by the changes of atmospheric condition before and after overwintering. However, binding protein content of the leaves was consistently high (23.5-79.8%CP) in all the species, suggesting that proteins in the tree leaves do not digested well in the reticulo-rumen of giraffes and other ruminants. The results of this study provide the fundamental information for rational feeding of tree leaves to zoo herbivores.

In this study, we developed the models to predict the total digestible nutrients (TDN) content of ear corn silage (ECS) based on the contents of acid detergent lignin (ADL) and starch. The 51 samples of ECS were collected for the analysis of the chemical composition and the TDN content. The content of TDN was determined by the total feces collection method using wethers. The TDN content of the ECS ranged from 72.2 to 88.9% on the DM basis and averaged 79.5%. To explain the TDN content by the contents of both ADL and starch, we applied a multiple regression model or a neural network model (NNM). The multiple linear regression model showed low correlation between predicted and actual TDN contents (R²＝0.09). On the other hand, the NNM showed relatively high R² of 0.58 between predicted and actual TDN contents. These results indicated that the relationship among the contents of ADL, starch and TDN of the ECS should be nonlinear and the TDN content can be estimated more precisely by the NNM compared with the multiple regression model.