No abstract

No abstract

Reproductive and lactational performances, including length of productive life, were evaluated from records for 1960 to 1988 for a Jersey herd in coastal lowland semi-humid Kenya. The herd grazed natural pastures and until the mid 1970s received supplementary feeding. Mean performance included 31 months age at first calving, lactation milk yield (MY) of 1,788 kg in 3.3 lactations and a calving interval (CI) of 408 days. Mean milk yields declined from over 2,200 kg in the 1960s to about 1,500 kg in the 1980s with concomitant changes in calving interval, length of productive life and infertility. While annual genetic components of change in MY and CI were not significantly different from zero, the environmental components were large and significant (P < 0.01) showing that the declines in performance probably resulted from decreased feed availability and less efficient disease control. Estimates of genetic parameters were consistent with those in the literature. With good management purebred Jersey cows in coastal lowland semi-humid Kenya were productive, but animal and herd productivity were highly dependent on management level which had a major effect on both milk yield and reproductive wastage.

The study is focused on the capability of artificial neural networks (ANNs) to predict next month and first lactation 305-day milk yields (FLMY305) of Kenyan Holstein-Friesian (KHF) dairy cows based on a few available test days (TD) records in early lactation. The developed model was compared with multiple linear regressions (MLR). A total of 39,034 first parity TD records of KHF dairy cows collected over 102 herds were analyzed. Different ANNs were modeled and the best performing number of hidden layers and neurons and training algorithms retained. The best ANN model had one hidden layer of logistic transfer function for all models, but hidden nodes varied from 2 to 7. The R (2) value for ANNs for training, validation, and test data were consistently high showing that the models captured the features accurately. The R (2), r, and root mean square were consistently superior for ANN than MLR but significantly different (p > 0.05). The prediction equation with four variables, i.e., first, second, third, and fourth TD milk yield, gave adequate accuracy (79.0%) in estimating the FLMY305 from TD yield. It emerges from this study that the ANN model can be an alternative for prediction of FLMY305 and monthly TD in KHF.