Evaluation of phytase concentration needed for growing–finishing commercial turkey toms

1. Growth performance, serum bone markers, and bone strength and mineralisation were determined in tom turkeys grown from 9 to 17 weeks of age. 2. Dietary non-phytate phosphorus was formulated to be reduced by 1·0 g/kg in the low phosphorus diet compared to a control diet and phytase was added to provide 0, 150, 300, 450 or 600 units/kg activity to the low phosphorus diet. 3. From 9 to 12 weeks of age, body weight and gain:food were reduced by the low phosphorus diet without added phytase, compared to the adequate phosphorus diet. Increasing the concentration of phytase linearly increased these growth parameters. There were no significant growth responses at 17 weeks of age. 4. Serum osteocalcin was reduced by increasing dietary phosphorus at 12 weeks of age when growth was affected, but not at later ages. Serum pyridinoline was reduced by higher dietary phosphorus and decreased linearly with increasing phytase activity at 17 weeks of age. 5. Fracture force of the ulna and femur increased linearly with increasing phytase activity but bone strength was not affected when corrected for bone cross-sectional area. Bone strength of the ulna and ash concentration of the ulna and tibia were increased by higher dietary phosphorus. Humerus and ulna ash increased linearly with increasing phytase activity. 6. Water-soluble phosphorus content of the litter was increased by higher dietary phosphorus and addition of phytase to the low phosphorus diet. The increase in water-soluble phosphorus content of the litter when phytase was fed may indicate that phosphorus could be fed at a lower concentration than used in this trial, at least in the finisher diet when phytase is added to the food. 7. Bone fracture force, strength and ash were generally optimised when 450 units/kg phytase activity was added to the low phosphorus diet. However, growth performance was best in the grower I (9 to 12 weeks) phase when 600 units/kg phytase was added to the diet

Effects of dietary calcium and phosphorus regimen on growth performance, bone strength and carcass quality and yield of large white tom turkeys

An experiment was conducted to estimate the calcium (Ca) and nonphytate phosphorus (npP) levels needed for toms in the starter (ST) (3-9 wk of age) and the grower/finisher (G/F) (9-15/15-17 wk of age) periods to support growth performance, bone breaking strength and carcass parameters. After 3 wk of group brooding, poults (B.U.T.) were divided into treatment (trt) pens and fed pellets containing Ca and npP at approximately NRC requirements (3 wk interval basis) or at typical industry (IND) levels (breeder recommendations). At 9 wk of age, birds from each ST trt were fed either a low npP (75% of NRC requirement) diet, the NRC recommended level, or an IND level of npP (Ca:npP=2:1 for all trts) until marketed at 17 wk of age. The birds were weighed every 3 wks and at 17 wk of age. Feed intake was estimated by feed disappearance to calculate feed efficiency. There were 15 pens of 31 birds/pen for each trt in the ST period and 5 pens for each of the 6 trt combinations during the G/F period. Three toms/pen were selected at 15 and 17 wk for bone and component yield measurements. All birds from 3 pens/trt were judged for a walking score (range 1-5, 5 best) during the 17th wk. There was no difference in body weight or feed intake in the ST period. Body weight was decreased when the NRC ST-low npP G/F trt was fed relative to the products in this experiment, especially, for FTM. There was an average moisture reduction of 2.4% for both PBMs and FTMs used in this experiment

Automatic classification of strike techniques using limb trajectory data

The classification of trajectory data is required in a wide variety of movement tracking experiments. Automatic classification using machine learning techniques has the potential to greatly increase efficiency and reliability of these studies. Here, we apply supervised classification algorithms on a dataset obtained through a kickboxing experiment to classify the limb and technique that was used for each strike as well as the expertise of the person performing the strike. Beginner and expert kickboxers were asked to strike a boxing bag from several distances, producing a dataset of approximately 4000 strike trajectories. These trajectories were classified using the K-nearest neighbours (KNN) and multi-class linear support vector classification (SVC). We show that both of these algorithms are capable of correctly classifying the limb used for the strike with ∼99% prediction accuracy. Both algorithms could classify the techniques used with ∼86% accuracy. The accuracy of technique classification was improved even further by applying hierarchical classification, classifying techniques separately for each limb. Only 10% of the dataset was required as training set to approach the observed prediction accuracy. Finally, KNN was capable of classifying the strikes by skill level with 73.3% accuracy. These findings demonstrate the potential of using supervised classification on complex limb trajectory datasets