• Journal of Embryo Transfer
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• v.28 no.3
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• pp.177-184
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• 2013

The objective of this study was to analyze the accuracy of estrus detection of heat detector and analysis of estrus behavior (mounting and mounted), and the evaluation of conditions required for improving reproductive efficiency in Holstein dairy cows fitted with a estrous detector. The heat detection system consists of estrous detector based on wireless sensor and an electric bulletin board displayed estrus behavior data. When cow mounting other cows, the accuracy of estrus behavior displayed an electric bulletin board were 87.5% (mounting other cows only), 100% (mounting other cows but not standing), 80.0% (mounting other cows with standing for 1~4 seconds), 90.0% (mounting other cows but not standing for 1~4 seconds), 80% (mounting other cows with standing for more than 5 seconds) and 90.0% (mounting other cows but not standing for more than 5 seconds). When cow mounted other cows, the accuracy of estrus behavior displayed an electric bulletin board were 100% (mounted other cows but not standing), 100% (mounted other cows with standing for 1~4 seconds), 100% (mounted other cows but not standing for 1~4 seconds) and 100% (mounted other cows with standing for more than 5 seconds). Circadian distribution of first observed in estrus were 59.1% (am 8~pm 6) and 40.9% (pm 6~am 8). Distribution for the number of estrus behavior were 40.9% (less than 3 times), 36.4% (4~6 times) and 22.7% (more than 4 times). The conception rates relative to interval from first estrus behavior to insemination for estrus periods were 23.1% (less than 11 hours) and 55.6% (12~20 hours).

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.175-186
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• 2019

Efforts have been made to introduce the climate smart agriculture (CSA) for adaptation to future climate conditions, which would require collection and management of site specific meteorological data. The objectives of this study were to identify requirements for construction of agricultural meteorology information service system (AMISS) using technologies that lead to the fourth industrial revolution, e.g., internet of things (IoT), artificial intelligence, and cloud computing. The IoT sensors that require low cost and low operating current would be useful to organize wireless sensor network (WSN) for collection and analysis of weather measurement data, which would help assessment of productivity for an agricultural ecosystem. It would be recommended to extend the spatial extent of the WSN to a rural community, which would benefit a greater number of farms. It is preferred to create the big data for agricultural meteorology in order to produce and evaluate the site specific data in rural areas. The digital climate map can be improved using artificial intelligence such as deep neural networks. Furthermore, cloud computing and fog computing would help reduce costs and enhance the user experience of the AMISS. In addition, it would be advantageous to combine environmental data and farm management data, e.g., price data for the produce of interest. It would also be needed to develop a mobile application whose user interface could meet the needs of stakeholders. These fourth industrial revolution technologies would facilitate the development of the AMISS and wide application of the CSA.