基于土壤化学性质与神经网络的羊草碳氮磷含量预测

生态化学计量学是研究植物．土壤相互作用与从元素计量的角度分析生物地球化学元素区域循环规律的新思路，是当前生态化学计量学的研究热点和前沿。该文以羊草碳、氮、磷的含量为研究对象，选用能够模拟输入与输出层非线性关系的径向基函数（radialbasisfimction，RBF）神经网络，在土壤相关化学性质与羊草碳、氮、磷含量之间建立模型，构建最优的羊草碳、氮、磷含量的预测模型。研究结果显示，采用土壤营养元素及相关化学性质作为输入层，羊草碳、氮、磷含量作为输出层，利用Matlab软件建立RBF神经网络模型，模拟预测羊草碳氮磷平均质量分数分别为411．46，18．25和1．11mg／g，皆低于全球陆生植物...

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Published in农业工程学报 Vol. 30; no. 3; pp. 104 - 111
Main Author 李月芬 王冬艳 Viengsouk Lasoukanh 杨小琳 李文博 赵一赢 孙超
Format Journal Article
LanguageChinese
Published 吉林大学地球科学学院,长春,130061 2014
Subjects
吉林西部
土壤
模型
生态化学计量学
碳氮磷
神经网络
羊草
models
生态化学计量学
carbon,nitrogen and phosphorus
模型
neural networks
神经网络
碳氮磷
土壤
ecological stoichiometry
羊草
Western Jilin Province
Leymus chinensis
soils
吉林西部
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ISSN1002-6819
DOI10.3969/j.issn.1002-6819.2014.03.014

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Summary:生态化学计量学是研究植物．土壤相互作用与从元素计量的角度分析生物地球化学元素区域循环规律的新思路，是当前生态化学计量学的研究热点和前沿。该文以羊草碳、氮、磷的含量为研究对象，选用能够模拟输入与输出层非线性关系的径向基函数（radialbasisfimction，RBF）神经网络，在土壤相关化学性质与羊草碳、氮、磷含量之间建立模型，构建最优的羊草碳、氮、磷含量的预测模型。研究结果显示，采用土壤营养元素及相关化学性质作为输入层，羊草碳、氮、磷含量作为输出层，利用Matlab软件建立RBF神经网络模型，模拟预测羊草碳氮磷平均质量分数分别为411．46，18．25和1．11mg／g，皆低于全球陆生植物叶片碳氮磷的平均含量；羊草C／N值、C／P值和N／P平均值分别为24．70、429．24和17．92，皆高于全球陆生植物叶片C／N值、C／P值、N／P值；羊草N／P为17．92，其生长主要受P元素的限制。预测结果与实际情况比较符合，这说明RBF人工神经网络模型用于模拟预测羊草碳、氮、磷含量与土壤化学性质之间的关系是可行的，可以较准确地估测羊草碳氮磷含量，平均相对误差分别为1．39％，4．69％和7．65％。
Bibliography:11-2047/S
Ecological stoichiometry is an emerging discipline started in China in recent years. It is the science of studying the balance of energy and elements （i.e. carbon, nitrogen and phosphorus） in ecological processes and ecological interaction, providing an integrative approach to investigate the stoichiometric relationships and rules in the biogeochemical cycling and ecological processes. It has been one of the hotly-discussed issues in ecological research. The contents of carbon, nitrogen, and phosphorus is a core issue in ecological stoichiornetry studies. It is necessary to choose a method that can simulate and accurately predict the contents of plant carbon, nitrogen, and phosphorus in order to avoid destructive sampling. There is a complex nonlinear relationship between plant carbon, nitrogen, phosphorus, and soil physical and chemical properties. It is difficult to accurately predict plant carbon, nitrogen, and phosphorus by using traditional methods and models such as linear regression and a BP n
ISSN:1002-6819
DOI:10.3969/j.issn.1002-6819.2014.03.014