• Asian-Australasian Journal of Animal Sciences
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• v.24 no.4
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• pp.449-456
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• 2011

Eighteen different biometric traits in 407 Kankrej cows from their breeding zone, i.e. Palanpur district of Gujarat, India, were recorded and analyzed by factor analysis to explain body conformation. The averages of body length, height at withers, height at shoulder, height at knee, heart girth, paunch girth, face length, face width, horn length, horn diameter, distance between horns, ear length, ear width, neck length, neck diameter, tail length with switch, tail length without switch and distance between hip bones were $123.44{\pm}0.37$, $124.49{\pm}0.28$, $94.68{\pm}0.30$, $38.2{\pm}0.14$, $162.56{\pm}0.56$, $178.95{\pm}0.70$, $44.09{\pm}0.10$, $15.91{\pm}0.05$, $42.47{\pm}0.53$, $26.07{\pm}0.19$, $13.34{\pm}0.08$, $31.24{\pm}0.12$, $16.10{\pm}0.05$, $50.63{\pm}0.18$, $73.21{\pm}0.32$, $111.62{\pm}0.53$, $89.34{\pm}0.34$ and $17.28{\pm}0.10\;cm$, respectively. The correlation coefficients between different traits ranged from -0.806 (horn diameter and distance between horns) to 0.815 (heart girth and paunch girth). Most of the correlations were positive and significant. Factor analysis with promax rotation with power 3 revealed three factors which explained about 66.02% of the total variation. Factor 1 described the cow body and explained 38.89% of total variation. The second factor described the front view/face of the cow and explained 19.68% of total variation. The third factor described the back of the cow and explained 7.44% of total variation. It was necessary to include some more variables for factor 3 to obtain a reliable estimate of the back view of the cow. The lower communities shown for distance between horns, horn diameter, ear width and neck diameter indicated that these traits did not contribute effectively to explaining body conformation and can be dropped from recording, whereas all other traits are important and needed to explain body conformation in Kankrej cows. The result suggests that principal component analysis (PCA) could be used in breeding programs with a drastic reduction in the number of biometric traits to be recorded to explain body conformation.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.6
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• pp.868-872
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• 2005

Individual management of the animal is the first step towards reaching the goal of precision livestock farming that aids animal welfare. Accurate recognition of each individual animal is important for precise management. Electronic identification of cattle, usually referred to as RFID (Radio Frequency Identification), has many advantages for farm management. In practice, however, RFID implementations can cause several problems. Reading speed and distance must be optimized for specific applications. Image processing is more effective than RFID for the development of precision farming system in livestock. Therefore, the aim of this paper is to attempt the identification of cattle by using image processing. The majority of the research on the identification of cattle by using image processing has been for the black-and-white patterns of the Holstein. But, native Japanese and Korean cattle do not have a consistent pattern on the body, so that identification by pattern is impossible. This research aims to identify to Japanese black cattle, which does not have a black-white pattern on the body, by using image processing and a neural network algorithm. 12 Japanese black cattle were tested. Values of input parameter were calculated by using the face image values of 12 cows. The face was identified by the associate neural memory algorithm, and the algorithm was verified by the transformed face image, for example, of brightness, distortion, noise and angle. As a result, there was difference due to a transformation ratio of the brightness, distortion, noise, and angle. The algorithm could identify 100% in the range from -30 to +30 degrees of brightness, -20 to +40 degrees of distortion, 0 to 60% of noise and -20 to +30 degree of angle transformed images.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.7
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• pp.992-1006
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• 2018

Beef production extends over almost half of Australia, with about 47,000 cattle producers that contribute about 20% ($A12.7 billion gross value of production) of the total value of farm production in Australia. Australia is one of the world's most efficient producers of cattle and was the world's third largest beef exporter in 2016. The Australian beef industry had 25 million head of cattle in 2016-17, with a national beef breeding herd of 11.5 million head. Australian beef production includes pasture-based cow-calf systems, a backgrounding or grow-out period on pasture, and feedlot or pasture finishing. Feedlot finishing has assumed more importance in recent years to assure the eating quality of beef entering the relatively small Australian domestic market, and to enhance the supply of higher value beef for export markets. Maintenance of Australia's preferred status as a quality assured supplier of high value beef produced under environmentally sustainable systems from 'disease-free' cattle is of highest importance. Stringent livestock and meat quality regulations and quality assurance systems, and productivity growth and efficiency across the supply chain to ensure price competiveness, are crucial for continued export market growth in the face of increasing competition. Major industry issues, that also represent research, development and adoption priorities and opportunities for the Australian beef industry have been captured within exhaustive strategic planning processes by the red meat and beef industries. At the broadest level, these issues include consumer and industry support, market growth and diversification, supply chain efficiency, productivity and profitability, environmental sustainability, and animal health and welfare. This review provides an overview of the Australian beef industry including current market trends and future prospects, and major issues and opportunities for the continued growth, development and profitability of the industry.

• Journal of Embryo Transfer
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• v.20 no.3
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• pp.303-309
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• 2005

This study was conducted to examine the effects of OA on metaphase of meiosis II and the mitochondrial activity of cytoplasm in bovine cumulus oocytes complexes(COCs) during in vitro maturation. Hanwoo COCs were collected from the slaughterhouse cow ovaries and matured in TCM199 supplemented with $0.1\%$ PVA, 0.2 uM, 2 uM, 20 uM OA for the maturation rate of OA concentration. For the maturation effects between OA and cycloheximide(CX), COCs were matured in TCM199 with 25 ug/mL CX, 25 ug/mL CX (6 hrs culture) plus 2 uM OA or 2 uM OA only at a atmosphere $5\%\;CO_2,\;95\%$ air $39^{\circ}C$ for 6, 12, 24 hrs. To evaluate the nuclear types of matured COCs, cumulus cells were removedby $0.5\%$ hyaluronidase sol. and oocytes were fixed in 1:3 acetic acid ethyl alcohol for 30 sec. and then stained with $0.1\%$ basic Fuchsin sol. For the detection of fluoriscent intensity (FI) of matures oocytes, cumulus cells were removed same as performed above and were stained with 20 nM mite tracker for 20 min. at $39^{\circ}C$. Mitochondrial activity of FI in matured oocytes was imaged by laser conforcal microscopy (Fluoview, Olympus, Japan) and were measured scanned face on 5 um from median to endpoint of oocytes. Statical analysis of nuclear types observed the three replicates was carried out with ANOVA and Fisher's protected least significant difference test using the STATVIEW program. FI of matures oocytes was compared the multiples of the least intensity among the measured oocytes. Maturing in TCM199 supplemented with $0.1\%$ PVA, 0.2 uM, 2 uM, 20 uM OA, metaphase B were showed 72.0, 50.0, 70.0, $68.8\%$, respectively and there were different significant(p<0.05). In the case of treatment with OA and CX, metaphase were $73.8\%,\;8.2\%,\;45.5\%,\;73.7\%$ in $0.1\%$ PVA-TCM199, 25 ug/mL CX, 25 ug/mL CX plus OA or 2uM OA only, respeclively. FI was revealed the increasing tendency during the process of maturation. Whereas FI in CX was decreased about 3 times compared to the other treatments of 6 hrs maturation. We conclude that OA regulates bovine COCs maturation and induces the mitochondrial activity during the process of maturation.