摘要
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(radial basis function,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1.11 mg/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%。
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 neural network. As a new artificial neural network model, a RBF （radial basis function） neural network has some advantages of fast learning, getting in the local minimum, and approximating any arbitrary accuracy of the global nonlinear relationship. Therefore, a RBF neural network can show an ability to handle a complex nonlinear relationship. Currently, a RBF neural network is one of the most accepted prediction methods. Taking the prediction of 38 samples as a research sample, this paper established a prediction model based on a RBF Neural network from seven impact indexes including pH, the total soluble salt, total carton, total nitrogen, total phosphorus, available nitrogen, and available phosphorus. Taking the prediction of five samples as a test sample, the results indicated that the relative errors of carbon, nitrogen, and phosphorus contents were only 1.39%, 4.69%, and 7.65%, respectively, and the correlation coefficients were 0.5, 0.93, and 0.94 respectively. Therefore, a RBF neural network had higher prediction accuracy. The statistical results showed that the average contents of carbon, nitrogen, and phosphorus in Leymus chinensis （103 samples） were 411.46, 18.25, and 1.11 mg/g, respectively. They are all lower than the global average contents of carbon, nitrogen, and phosphorus in a terrestrial plant. The values of C/N, C/P, and N/P were 24.70, 429.24, and 17.92, respectively in Leymus chinensis. They were all higher than those in a global terrestrial plant. The N/P was 17.92 in Leymus chinensis. The growth of Leyrnus chinensis in the research area was limited by phosphorus.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2014年第3期104-111,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
吉林省自然科学基金项目(201215017)
关键词
神经网络
模型
土壤
生态化学计量学
羊草
碳氮磷
吉林西部
neural networks
models
soils
ecological stoichiometry
Leymus chinensis
carbon, nitrogen andphosphorus
Western Jilin Province
