Allometric models for leaf area and leaf mass predictions across different growing seasons of elm tree(Ulmus japonica)

Convenient and effective methods to determine seasonal changes in individual leaf area(LA) and leaf mass(LM) of plants are useful in research on plant physiology and forest ecology.However,practical methods for estimating LA and LM of elm(Ulmus japonica) leaves in different periods have rarely been reported.We collected sample elm leaves in June,July and September.Then,we developed allometric models relating LA,LM and leaf parameters,such as leaf length(L) and width(W) or the product of L and W(LW).Our objective was to find optimal allometric models for conveniently and effectively estimating LA and LM of elm leaves in different periods.LA and LM were significantly correlated with leaf parameters(P<0.05),and allometric models with LW as an independent variable were best for estimating LA and LM in each period.A linear model was separately developed to predict LA of elm leaves in June,July and September,and it yielded high accuracies of 93,96 and 96%,respectively.Similarly,a specific allometric model for predicting LM was developed separately in three periods,and the optimal model form in both June and July was a power model,but the linear model was optimal for September.The accuracies of the allometric models in predicting LM were 88,83 and 84% for June,July and September,respectively.The error caused by ignoring seasonal variation of allometric models in predicting LA and LM in the three periods were 1–4 and 16–59%,respectively.

我国基础教育数字资源及服务:现状、问题与对策

本研究对我国基础教育阶段数字教育资源及服务进行系统研究。首先,通过问卷调查和深度访谈对全国31个省(自治区、直辖市)中小学数字教育资源建设与应用现状,从教育管理者、教师、学生三个关键涉众的视角,运用社会调查法进行深入调查,从数字教育资源使用基本情况、数字教育资源使用过程等维度揭示了我国基础教育数字资源现状。其次,结合内容分析法对32个政府类教学资源平台和39个企业类教学资源平台进行数据挖掘和深入分析,从各类平台的建设管理、服务、资源共建共享、服务效果等方面着手,深入了解中小学数字教育资源服务现状。以数字教育资源供给一般过程为框架,深度分析和挖掘我国基础教育数字资源供给与需求各阶段存在的问题。最后,提出了顶层规划、统筹协调、构建多元化数字资源供给机制、开发一体化数字资源供给平台等对策建议。