Su Ying-Xue, Xu Huan, Yan Li-Jiao
College of Life Sciences, Zhejiang University, 310058 Hangzhou, Zhejiang Province, PR China.
Saudi J Biol Sci. 2017 Mar;24(3):537-547. doi: 10.1016/j.sjbs.2017.01.024. Epub 2017 Jan 30.
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.
现有的作物模型产生的模拟结果不尽人意且操作复杂。然而，本研究展示了统计作物模型在大规模模拟方面的独特优势。以水稻作为研究作物，通过整合发育阶段和产量预测模型，开发了一种基于支持向量机的开放式作物模型（SBOCM）。使用了中国地面气象观测站获取的基础地理信息以及中国科学院发布的1:1000000土壤数据库。基于建模数据的尺度兼容性原则，设计了一个开放阅读框，用于动态每日输入气象数据以及输出水稻发育和产量记录。以此生成水稻发育阶段和产量预测模型，并将其整合到SBOCM系统中。对参数、方法、误差来源等因素进行了分析。尽管SBOCM不是作物生理模拟模型，但在一定尺度范围内可用于多年生模拟和单季水稻预测。它便于数据采集，具有区域适用性，参数简单，并且在多尺度因子整合方面有效。它有潜力在未来与广泛的社会经济因素相结合，以提高预测准确性和实用性。
