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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (2): 110-120.doi: 10.12133/j.smartag.SA202205003

• Topic--Smart Animal Husbandry Key Technologies and Equipment • Previous Articles     Next Articles

Development of China Feed Nutrition Big Data Analysis Platform

XIONG Benhai1(), ZHAO Yiguang1, LUO Qingyao1, ZHENG Shanshan1, GAO Huajie2   

  1. 1.State Key Laboratory of Animal Nutrition, Institute of Animal Sciences/China Feed Database Information Network Center, Chinese Academy of Agricultural Sciences, Beijing 100193, China
    2.Beijing DaBeiNong Technology Group Co. , Ltd. , Beijing 100080, China
  • Received:2022-05-09 Online:2022-06-30
  • corresponding author: XIONG Benhai, E-mail:xiongbenhai@caas.cn
  • Supported by:
    Data Platform Project of Ministry of Science and Technology (NASDC2022XM02); "14th Five-Year Plan" National Major Science and Technology Research Project (2021YFD2000800); Key Project of Chongqing Technology Innovation and Application Development (cstc2019jscx-gksbX0091)


The shortage of feed grain is continually worsening in China, which leads to the transformation of feed grain security into national food security. Therefore, comprehensively integrating the basic data resources of feed nutrition and improving the nutritional value of all available feed resources will be one of the key technical strategies to ensure national food security in China. In this study, based on the description specification and attribute data standard of 16 categories of Chinese feed raw materials, more than 500,000 pieces of data on the types, spatial distribution, chemical composition and nutritional value characteristics of existing feed resources, which were accumulated through previous projects from the sixth Five-Year Plan to the thirteenth Five-Year Plan period, were digitally collected, recorded, categorized and comprehensively analyzed. By using MySQL relational database technology and PHP program, a new generation of feed nutrition big data online platform (http://www.chinafeeddata.org.cn/) was developed and web data sharing service was provided as well. First of all, the online platform provided visual analysis of all warehousing data, which could realize the visual comparison of a single or multiple feed nutrients in various graphic forms such as scatter diagram, histogram, curve line and column chart. By using two-dimensional code technology, all feed nutrition attribute data and feed entity sample traceability data could be shared and downloaded remotely in real-time on mobile phones. Secondly, the online platform also incorporated various regression models for prediction of feed effective nutrient values using readily available feed chemical composition in the datasets, providing dynamic analysis for feed raw material nutrient variation. Finally, based on Geographic Information System technology, the online platform integrated the data of feed chemical composition and major mineral element concentrations with their geographical location information, which was able to provide the distribution query and comparative analysis of the geographic information map of the feed raw material nutrition data at both provincial and national level. Meanwhile, the online platform can also provide a download service of the various datasets, which brought convenience to the comprehensive application of existing feed nutrition data. This research also showed that expanding feed resource data and providing prediction and analysis models of feed effective nutrients could maximize the utilization of the existing feed nutrition data. After embedding online calculation modules of various feed formulation software, this platform would be able to provide a one-stop service and optimize the utilization of the feed nutrition data.

Key words: feed grain, feed nutrition data, big data, data mining, GIS, feed security

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