Relationship between sire predicted transmitting ability for daughter pregnancy rate and daughter's reproductive performance and milk production in Japanese dairy herds

Modern genetic improvement in dairy cattle is directed towards improvement of fertility; however, reproduction traits generally exhibit a genetic antagonism with milk yield. Herein, we aimed to clarify the effects of sire predicted transmitting ability (PTA) for daughter pregnancy rate (DPR) on the reproductive performance and milk yield of daughters in Japanese dairy herds. We conducted a retrospective cohort study on four dairy herds in eastern Hokkaido, Japan, using 1,612 records from 1,018 cows with first, second, or third calvings between March 2015 and September 2018. First, we classified sires into three groups based on the tertile value of their DPR estimate: ≤ −2.2 (low), −2.1 to −0.4 (intermediate), and ≥ −0.3 (high). Subsequently, we compared the sire PTA estimates, reproductive performance, and milk production among DPR groups for each parity of the daughters. In the first and second parity, the hazard of pregnancy by 200 days postpartum was highest in cows from the high-DPR group (P < 0.05); in the third parity, it was unaffected by DPR group. Although sire PTA for milk production in cows from the low-DPR group was highest, actual milk production was unaffected by DPR group regardless of parity. Our findings demonstrate that using sires with PTA for high fertility can enable farmers to improve reproductive performance without decreasing milk yield in Japanese dairy herds. However, it should be noted that sires with PTA for high fertility are at risk for reducing the genetic merit for milk production.

Model-Based Real-Time Non-Rigid Tracking

This paper presents a sequential non-rigid reconstruction method that recovers the 3D shape and the camera pose of a deforming object from a video sequence and a previous shape model of the object. We take PTAM (Parallel Mapping and Tracking), a state-of-the-art sequential real-time SfM (Structure-from-Motion) engine, and we upgrade it to solve non-rigid reconstruction. Our method provides a good trade-off between processing time and reconstruction error without the need for specific processing hardware, such as GPUs. We improve the original PTAM matching by using descriptor-based features, as well as smoothness priors to better constrain the 3D error. This paper works with perspective projection and deals with outliers and missing data. We evaluate the tracking algorithm performance through different tests over several datasets of non-rigid deforming objects. Our method achieves state-of-the-art accuracy and can be used as a real-time method suitable for being embedded in portable devices.