[Objective] The paper was to study the distribution of main nutrients in seedlings of umbrella bamboo (Fargesia murielae) in Shennongjia National Nature Reserve. [Method] The study was conducted in Liangfengya of Shen...[Objective] The paper was to study the distribution of main nutrients in seedlings of umbrella bamboo (Fargesia murielae) in Shennongjia National Nature Reserve. [Method] The study was conducted in Liangfengya of Shennongjia National Nature Reserve. In the field investigation, six clumps of umbrella bamboo grown independently were randomly selected and sampled. The total nitrogen, total phosphorus and total potassium of umbrella bamboo were detected by regular plant analysis method. The age classes of bamboo seedlings were ascertained by age grade backtracking method. [Result] In different organs, N, P, K contents in branches and leaves were significantly higher that than in stems. Along age grades, N and P contents performed "M" shape in branches and leaves, while K content approximately performed as normal distribution. [Conclusion] The nutrients distribution pattern of these seedlings is likely formed by its nutrition mechanism which allocates nutrients according to different needs or by external interference of environmental features. However, the specific causes still need further investigation.展开更多
Background: The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the n...Background: The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the natural regeneration of Dacrydium pectinatum communities in China, designing advanced and accurate estimation methods is necessary.Methods: This study uses machine learning techniques created a series of comprehensive and novel models from which to evaluate soil nutrient content. Soil nutrient evaluation methods were built by using six support vector machines and four artificial neural networks.Results: The generalized regression neural network model was the best artificial neural network evaluation model with the smallest root mean square error(5.1), mean error(-0.85), and mean square prediction error(29). The accuracy rate of the combined k-nearest neighbors(k-NN) local support vector machines model(i.e. k-nearest neighbors-support vector machine(KNNSVM)) for soil nutrient evaluation was high, comparing to the other five partial support vector machines models investigated. The area under curve value of generalized regression neural network(0.6572) was the highest, and the cross-validation result showed that the generalized regression neural network reached 92.5%.Conclusions: Both the KNNSVM and generalized regression neural network models can be effectively used to evaluate soil nutrient content and quality grades in conjunction with appropriate model variables. Developing a new feasible evaluation method to assess soil nutrient quality for Dacrydium pectinatum, results from this study can be used as a reference for the adaptive management of rare and endangered tree species. This study, however, found some uncertainties in data acquisition and model simulations, which will be investigated in upcoming studies.展开更多
文摘[Objective] The paper was to study the distribution of main nutrients in seedlings of umbrella bamboo (Fargesia murielae) in Shennongjia National Nature Reserve. [Method] The study was conducted in Liangfengya of Shennongjia National Nature Reserve. In the field investigation, six clumps of umbrella bamboo grown independently were randomly selected and sampled. The total nitrogen, total phosphorus and total potassium of umbrella bamboo were detected by regular plant analysis method. The age classes of bamboo seedlings were ascertained by age grade backtracking method. [Result] In different organs, N, P, K contents in branches and leaves were significantly higher that than in stems. Along age grades, N and P contents performed "M" shape in branches and leaves, while K content approximately performed as normal distribution. [Conclusion] The nutrients distribution pattern of these seedlings is likely formed by its nutrition mechanism which allocates nutrients according to different needs or by external interference of environmental features. However, the specific causes still need further investigation.
基金financially supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFBB2017ZB004)。
文摘Background: The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the natural regeneration of Dacrydium pectinatum communities in China, designing advanced and accurate estimation methods is necessary.Methods: This study uses machine learning techniques created a series of comprehensive and novel models from which to evaluate soil nutrient content. Soil nutrient evaluation methods were built by using six support vector machines and four artificial neural networks.Results: The generalized regression neural network model was the best artificial neural network evaluation model with the smallest root mean square error(5.1), mean error(-0.85), and mean square prediction error(29). The accuracy rate of the combined k-nearest neighbors(k-NN) local support vector machines model(i.e. k-nearest neighbors-support vector machine(KNNSVM)) for soil nutrient evaluation was high, comparing to the other five partial support vector machines models investigated. The area under curve value of generalized regression neural network(0.6572) was the highest, and the cross-validation result showed that the generalized regression neural network reached 92.5%.Conclusions: Both the KNNSVM and generalized regression neural network models can be effectively used to evaluate soil nutrient content and quality grades in conjunction with appropriate model variables. Developing a new feasible evaluation method to assess soil nutrient quality for Dacrydium pectinatum, results from this study can be used as a reference for the adaptive management of rare and endangered tree species. This study, however, found some uncertainties in data acquisition and model simulations, which will be investigated in upcoming studies.
