BREEDING AND GENETICS - American Society of Animal Science

244 Assessment of estrus detection by observation and
an electronic detection method in beef heifers. D. O. Rae* 1 ,
P. J. Chenoweth 2 , M. A. Giangreco 1 , P. W. Dixon 1 , and F. L. Bennett 1 ,
1 University of Florida, Gainesville and 2 Kansas State University, Manhattan.
One hundred sixty-five-beef heifers (Angus, AN, Brahman, BH, Angus
times Brahman, AB) were estrus synchronized following evaluation
of weight, body condition score, and reproductive tract. Heifers
were randomly assigned to one of two methods of estrous detection,
either traditional observation for signs of standing estrus or a rumpmounted
pressure-sensitive-detection- device. All heifers were bred by
artificial insemination potentially three opportunities and subsequently
by a bull). The effectiveness of estrus detection and timely insemination
were evaluated by detection method, heifer breed-type and effective
breeding event (that event leading to conception). At the end of
three insemination opportunities, 60.5% of heifers observed were pregnant
while only 45.8% of those detected by the mount detection device
(p=0.04). Heifers categorized by effective breeding event, were different
with respect to duration of estrus and time of insemination compared
with the end of standing estrus. Heifers pregnant to the first service had
a duration of estrus of 8 hours 58 minutes while at that same event the
heifers that later became pregnant to the second or third estrus event
and insemination were 11 hours 38 minutes and 19 hours 3 minutes, respectively
(p=0.007) and the time of insemination relative to the end of
estrus was 3 hours 8 minutes (-4 hours 34 minutes, -21 hours 13 minutes,
respectively, p=0.03). Based on this data, the reduced first service conception
rate in the detection device group suggests that insemination of
detected heifers may have been earlier than was optimal for pregnancy.
Breed differences were observed in estrus durations (AN 8 hr 31 min, BH
6 hr 44 min, AB 11 hr 51 min, p=0.03), number of mounts (AN 19, BH
26, AB 37, p=0.02) and gestation length (281, 291, 286 d, respectively,
p=0.001).
Key Words: Estrus Detection, Estrus Synchronization, Estrus Detection
Aids
246 Dairy Herd Improvement records as replacements
of technician breeding receipt database for routine estimates
of non-return rates for AI bulls. R. A. Baron*, J. E.
Chandler, and R. W. Adkinson, LSU Agricultural Center, Baton Rouge.
The objective of this study was to find whether DHI data could be
used to estimate sire non-return rates to replace current technician data
estimates. Bull weighted least squares means for non-return rates were
calculated separately for five overlapping 60–90 day service periods from
each data source. Models included stud, sire, service number, and linear
and quadratic form of breeding month for both data sources, service unit
for technician and lactation for DHI data. Sire and lactation were not
significant (P > .10). Technician differences (P < .05) were in service
unit, stud, service number, and linear and quadratic service month in
all but one service period. DHI differed (P < .05) for service number,
month (linear and quadratic), and stud in two service periods. Technician
R-square values were 0.23 to 0.28 versus 0.94 to 0.96 for DHI. Sire
estimated non-return rates were weighted using the inverted estimator
standard error squared and compared. Sire, stud, data source, service
period, and appropriate interactions were modeled. Weighted bull nonreturns
differed (P < .01)in magnitude across data sources. Stud, data
source by stud, and sire within stud by data source were significant (P
< .01). Services per bull, service period and its interactions did not
differ (P > .10). Four fertility categories based on mean and standard
deviation of the weighted estimates were formed within the data sources
across service periods. These categories were correlated (.5 > r > .9)
and 52.9 to 87.4% congruent within data source for adjacent service periods
and across data sources within service periods. With declining
availability of technician data, DHI data was shown to be a reasonable
substitute. Correlations and congruency of fertility categories suggest
sire choices would be very similar.
Key Words: DHI, non-return rate, fertility estimate
247 A comparison of electronic management methods
with conventional methods for managing sows. R. L.
Korthals 1 and R. O. Bates* 2 , 1 Osborne Industries, Inc., Osborne, KS,
and 2 Michigan State University, East Lansing.
245 The application of EDI in commercial pig breeding
programmes. J. W. M. Merks* and H. Bruggink, IPG, Institute
for Pig Genetics B.V., The Netherlands.
To enable a regular and standardised exchange of test data and derived
breeding values between sow management systems and the databases of
pig breeding organisations, a standard has been developed, introduced
and experienced for already 10 years. By means of this standard EDI-
PIGS, weekly exchange of test data, pedigree information and breeding
values is currently in use by more than 500 breeding herds (80,000 purebred
sows) and 4 breeding/A.I. organisations. The standard EDI-PIGS
is based on the ADIS protocol (ISO 11787). Next to a description of data
elements in a dynamic data dictionary, standard events and message decriptions
are included. In addition to the standard for data exchange,
an error recovery procedure is set up. Data exchange between herds
and central computers is performed via electronic mail boxes and/or
internet. The implementation of the standard for data exchange has
decreased the costs of data entry enormously and moreover enabled a
direct exchange of the latest data and breeding values between the different
sow management systems of breeders and the computers of the
breeding organisations.
Key Words: Electronic Data Exchange, Computers, Breeding Programmes
Osborne Industries Inc. (OII) operates a 300-sow Demonstration Farm
for research, development, and demonstration of electronic animal management
(EAM) methods. This facility demonstrates automatic data
collection, analysis, and real-time control as part of the Electronic Animal
Recognition Systems (EARS TM ) program at OII. Production evaluation
during the first three years of operation compares sow performance
under EAM with conventional management. Treatments include electronic
gestation (EG), conventional gestation (CG), electronic farrowing
(EF), and conventional farrowing (CF). Performance analyses for a
three-year period show few significant differences between treatments.
A significant difference was the time for return to estrus was less for
sows under EG than CG for parities three through five (P 0.10). Other
results may require further study. For example, the number of pigs born
alive is higher in CG than EG on parity-3 sows, but lower in EG than
CG for parity-four sows (P 0.10).
The reactions and adjustments of researchers, managers, and operations
personnel to management differences between conventional and EAM
methods also were observed. All sows are individually identified using
radio frequency identification (RFID) EarButton TM transponders.
Porcode r and Hunday r electronic sow feeding (ESF) stations automatically
collect daily production data. Farm personnel use hand-held Osborne
ID Loggers r to collect of data in a working database, which is later
transferred directly to a PC farm management program. Data entry errors
and tedium are eliminated, permitting more time for husbandry
tasks with better at-hand information. EG and EF are compared with
CG and CF on the basis of ability for individual feed control and for recognizing
on-set of sow health problems. The success of automatic spray
marking and automatic sorting combined with ESF was evaluated. The
differences in observed behavior between EG and EF managed animals
and CG and CF managed animals suggest lower stress in the electronically
managed animals owing to disassociation of the care-giver from
feed delivery.
Key Words: Electronic Feeding, Electronic ID System, Dataloggers
J. Anim. Sci. Vol. 76, Suppl. 1/J. Dairy Sci. Vol. 81, Suppl. 1/1998 63