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首页> 外文期刊>Journal of the Science of Food and Agriculture >A review of smart agriculture and production practices in Japanese large-scale rice farming
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A review of smart agriculture and production practices in Japanese large-scale rice farming

机译:A review of smart agriculture and production practices in Japanese large-scale rice farming

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摘要

Japanese farms currently occur on a small but expanding average physical scale and annual sales, in a decreasing and aging labor force, increasingly abandoned farmland and massive access of corporations powerful in capital and management. The government has proposed to accelerate agricultural growth through adopting high-level smart technologies in large-scale farms, with a series of favorite policies and measures. This paper reviews the connotations, characteristics and technologies of smart agriculture in the views of Japanese scholars, the practice of smart agriculture in Japan, followed by a case study on the smart rice production model 'NoshoNavi1000'. The results show that Japanese scholars have conducted in-depth and massive studies in smart agriculture, with regional characteristics in the orientations on labor-saving, precise management, disaster reduction and inheriting traditional farming skills. The government's ambitious objective is supported by its all-round policy package, including project demonstration, supporting services, environmental improvement, education and training, and overseas outreaching. Closely combined with the planting structure of Japanese agriculture, rice production is an important sector for the R&D and application of smart farming in the form of either individual technologies or models. The policies and technologies have achieved benefits in saving labor and production costs, and improving the profit margin. Through years of on-farm application, 'NoshoNavi1000' has been developed to involve the major technological components of smart agriculture. It has contributed to improving rice yield, production efficiency and profitability of large-scale farms, through real-time data collection, comprehensive and professional data mining, and specific and practical feedbacks. (c) 2022 Society of Chemical Industry.

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