Factors influencing average milk yield of herds at two levels of production.
An experiment was started in 1950 designed to find out what proportion of the difference in milk yield between herds is genetic in origin. Sixty-five pairs of twins were split at birth equally between farms having high-yielding herds and farms having low-yielding herds. A previous paper on this experiment reported on the growth of the twins, the present paper on milk yield and on management practices in the herds. Twenty-four pairs of twins survived to complete one lactation and these provide an estimate of 24%±18% for the genetic part of the difference between the herds. Aspects of rearing of the twins and of management of the herds were examined. Twins in the high-yielding herds were served and calved earlier than those in the low-yielding group but they had a longer interval between service and calving. Differences between average heifer yields and average cow yields were from one-and-a-half to two times greater in the high- than in the low-yielding herd group. There were no obvious quantitative differences in feeding practice between the two groups although differences in body size suggest that twins in the high group were done better. For the age at first service there was a positive rank correlation of 0·30 between body size differences and subsequent differences in first lactation milk yield. There was no single factor of management or environment which appeared to be the cause of the yield difference between the herds.
Genetic and Environmental Portions of the Variation among Herds in Butterfat Production
Summary The genetic part of the variation between herds has been estimated. In Method A the intra-sire regression of heifer production on herd average is used; whereas, in Method B, the variance components for herd differences between related and between unrelated cows in different herds are compared. Both methods imply that the genetic part of the differences between herd averages is caused entirely by additive gene effects. Evidence from the literature, though scanty, indicates that this is a good approximation. Method A was applied to records of 1,072 Jersey HIR cows and of 880 Holstein DHIA cows. The results indicated that 14 and 10%, respectively, of the differences between herd averages in the two populations are heritable. Prediction equations which utilize both individual performance and herd average are developed. Selection according to these indexes should be 1.19 and 1.35 times as effective as selection based on individual performance alone, without reference to herd averages, in the Jersey and the Holstein population, respectively. Method B was used on records of 2,903 artificially sired Holstein DHIA heifers and of 735 of their dams. When the variance components were derived from an analysis within years, the heritability of herd differences was 6.5%, both for milk and butterfat production. When the analysis was within year-seasons, the resulting heritability estimates were much higher (28 and 24%). It is suspected that the large size of the latter estimates results from sampling variation and, therefore, that the latter values are too large.
Instrumentation and Automated Photometric Titration Procedure for Total Acidity Determination in Red Wine Employing a Multicommuted Flow System
An automated procedure for photometric titration of red wine and associated instrumentation is described. The procedure was based on the flow-batch approach implemented employing multicommutation. The photometric detection was carried out using a homemade LED-based photometer. The mixing device, LED, and photodetector were attached to the titration chamber in order to form a compact and small-sized unit. The flow system comprised an automatic injector and three-way solenoid valves, which were controlled by a microcomputer through an electronic interface card. The software, written in Quick BASIC 4.5, was designed with abilities to accomplish all steps of the titration procedure including data acquisition and real-time processing to decide about the course of titration in the following step and so forth, until the titration endpoint was reached. The usefulness of the proposed titration system was demonstrated by analyzing red wine samples. When results were compared with those obtained using the AOAC reference method, no significant difference was observed at the 95% confidence level. A relative standard deviation of ca  2% (n=9 ) was obtained when processing a typical red wine sample containing 7.3 gl -1 total acidity expressed as tartaric acid.