Evaluation of Land Productivity in Rubber Plantations Based on Data Mining

PENG Jinlian; TANG Zhaosheng; JIANG Jusheng; ZHAO Chunmei

doi:10.15886/j.cnki.rdswxb.2019.04.012

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The data of soil survey in rubber plantations in Hainan State Farms was analyzed to evaluate the land productivity in the rubber plantations by using four clustering algorithms（K-Means, Two-Step, Kohonen and TwoStep-AS）. According to the Silhouette coefficients, TwoStep-AS clustering models were established for cluster analysis of the indexes of the land productivity in the Hainan State Farm rubber plantations. After repeated iterations, the land in rubber plantations of Hainan State Farms was divided into three types, and the indexes affecting the land type of the rubber plantations were selected, including annual average rainfall, effective soil thickness, total nitrogen, north latitude, and soil-forming parent material.

Evaluation of Land Productivity in Rubber Plantations Based on Data Mining

1. School of Computer Science & Cyberspace Security, Hainan University, Haikou, Hainan 570228, China;

2. Hainan State Farms Academy of Sciences, Haikou, Hainan 570206, China;

3. Rubber Research Institute, CATAS, Danzhou, Hainan 571737, China

Received Date: 2018-10-19

Rev Recd Date: 2019-08-15

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Abstract: The data of soil survey in rubber plantations in Hainan State Farms was analyzed to evaluate the land productivity in the rubber plantations by using four clustering algorithms（K-Means, Two-Step, Kohonen and TwoStep-AS）. According to the Silhouette coefficients, TwoStep-AS clustering models were established for cluster analysis of the indexes of the land productivity in the Hainan State Farm rubber plantations. After repeated iterations, the land in rubber plantations of Hainan State Farms was divided into three types, and the indexes affecting the land type of the rubber plantations were selected, including annual average rainfall, effective soil thickness, total nitrogen, north latitude, and soil-forming parent material.

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Evaluation of Land Productivity in Rubber Plantations Based on Data Mining

doi: 10.15886/j.cnki.rdswxb.2019.04.012

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