期刊
SAUDI JOURNAL OF BIOLOGICAL SCIENCES
卷 24, 期 3, 页码 537-547出版社
ELSEVIER
DOI: 10.1016/j.sjbs.2017.01.024
关键词
Crop model; Crop simulation; Scaling up; Support vector machine; SBOCM
类别
资金
- National High Technology Research and Development Program of China (863 Program) [2007AA10Z220]
Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability. (C) 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.
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