[Objective] This study was to reveal the correlation between leaf area and drought resistance in different varieties of alfalfa.[Method] Using various alfalfa varieties as experimental materials,the drought resistance...[Objective] This study was to reveal the correlation between leaf area and drought resistance in different varieties of alfalfa.[Method] Using various alfalfa varieties as experimental materials,the drought resistance of leaves of drought-stressed alfalfa plants was assessed by measuring the content of free proline for analyzing its correlation with leaf area.[Result] Under drought condition,the drought resistance of alfalfa is directly related to leaf area in a positive correlation.[Conclusion] Leaf area could be used as an institutional assistant index to reflect the resistance of different alfalfa varieties.展开更多
According to the preparation method commonly used for soy proteinαbased adhesives,alfalfa leaf protein was used as the raw material to prepare alfalfa leaf protein-based wood adhesive.Differential scanning calorimetr...According to the preparation method commonly used for soy proteinαbased adhesives,alfalfa leaf protein was used as the raw material to prepare alfalfa leaf protein-based wood adhesive.Differential scanning calorimetry analyzer(DSC),X-ray diffraction(XRD)and Fourier transform infrared spectroscopy(FTαIR)were used to characterize properties of the alfalfa leaf protein-based adhesive in this paper.The results revealed the following:(1)Chemical compositions and chemical structures of the alfalfa leaf protein were basically identical with those of the soy protein,both belonging to spherical proteins with the basis and potential for protein adhesives preparation,and spatial cross-linked network structures would be easily formed.(2)Alfalfa leaf protein and soy protein adhesives had the similar curing behaviors,curing temperature of alfalfa leaf protein-based adhesive was relaαtively lower,and the heating rate had minor influence on curing temperature of alfalfa leaf protein-based adhesive.At different heating rates,change tendencies of curing reaction degrees of both the two adhesives were not totally the same.(3)Activation energy and reaction frequency factor of the alfalfa leaf protein-based adhesive were higher than those of soy protein-based adhesive,indicating that the curing reaction of the alfalfa leaf protein adhesive was more difficult than soy protein-based adhesive,thus the dry shear strength and water resistance of alfalfa protein-based adhesive were lower than those of soy protein-based adhesive.Dynamics models of curing reactions of alfalfa leaf protein-based adhesive and soy protein-based adhesive are dα=dt/1.06×10^(13)e^(-97370/RT)(1-α)^(0.938) and dα/dt=1.09×10^(11)e^(-84260/RT) 1-α)^(0.928) respectively.The results of this study will expand the selection of raw materials for protein-based wood adhesives.展开更多
连续时序的叶面积指数(Leaf Area Index,LAI)可反映苜蓿长势的变化情况,预测苜蓿未来时段的LAI对指导田间管理决策具有重要作用。针对LAI数据采集困难,导致苜蓿时序LAI存在训练数据不足的问题,该文以生长天数为自变量,采用修正的Logisti...连续时序的叶面积指数(Leaf Area Index,LAI)可反映苜蓿长势的变化情况,预测苜蓿未来时段的LAI对指导田间管理决策具有重要作用。针对LAI数据采集困难,导致苜蓿时序LAI存在训练数据不足的问题,该文以生长天数为自变量,采用修正的Logistic模型对实测苜蓿LAI变化的动态过程进行建模,根据LAI模拟曲线进行数据插补,从而构建宁夏引黄灌区试验区3年的逐日苜蓿LAI数据集。在插补数据集的基础上,为解决苜蓿刈割后数据突变问题,提出了一种ME-BiLSTM模型。该模型集成移动累计和检验方法(MOSUM)以及基于双向长短期记忆网络(BiLSTM)的编码器-解码器神经网络。MOSUM方法可以实现LAI数据集中突变点检测,并剔除包含突变点训练批次,同时应用改进的BiLSTM模型进行预测。结果表明:ME-BiLSTM模型能较好地进行苜蓿LAI未来曲线变化的预测,其决定系数(R^(2))、均方根误差(RMSE)值分别为0.9985和0.0722。对于苜蓿生长的各个茬次,预测模型对于第1茬、第4茬的预测精度最高,第2茬和第3茬的预测精度稍有降低。展开更多
基金Supported by National Nonprofit Institute Research Grant(BRF090202)~~
文摘[Objective] This study was to reveal the correlation between leaf area and drought resistance in different varieties of alfalfa.[Method] Using various alfalfa varieties as experimental materials,the drought resistance of leaves of drought-stressed alfalfa plants was assessed by measuring the content of free proline for analyzing its correlation with leaf area.[Result] Under drought condition,the drought resistance of alfalfa is directly related to leaf area in a positive correlation.[Conclusion] Leaf area could be used as an institutional assistant index to reflect the resistance of different alfalfa varieties.
基金This work was supported by Science-technology Support Foundation of Guizhou Province of China(No.[2019]2325,[2019]2308 and[2020]1Y125)National Natural Science Foundation of China(No.31870546)Forestry Department Foundation of Guizhou Province of China(No.[2017]14,[2018]13).
文摘According to the preparation method commonly used for soy proteinαbased adhesives,alfalfa leaf protein was used as the raw material to prepare alfalfa leaf protein-based wood adhesive.Differential scanning calorimetry analyzer(DSC),X-ray diffraction(XRD)and Fourier transform infrared spectroscopy(FTαIR)were used to characterize properties of the alfalfa leaf protein-based adhesive in this paper.The results revealed the following:(1)Chemical compositions and chemical structures of the alfalfa leaf protein were basically identical with those of the soy protein,both belonging to spherical proteins with the basis and potential for protein adhesives preparation,and spatial cross-linked network structures would be easily formed.(2)Alfalfa leaf protein and soy protein adhesives had the similar curing behaviors,curing temperature of alfalfa leaf protein-based adhesive was relaαtively lower,and the heating rate had minor influence on curing temperature of alfalfa leaf protein-based adhesive.At different heating rates,change tendencies of curing reaction degrees of both the two adhesives were not totally the same.(3)Activation energy and reaction frequency factor of the alfalfa leaf protein-based adhesive were higher than those of soy protein-based adhesive,indicating that the curing reaction of the alfalfa leaf protein adhesive was more difficult than soy protein-based adhesive,thus the dry shear strength and water resistance of alfalfa protein-based adhesive were lower than those of soy protein-based adhesive.Dynamics models of curing reactions of alfalfa leaf protein-based adhesive and soy protein-based adhesive are dα=dt/1.06×10^(13)e^(-97370/RT)(1-α)^(0.938) and dα/dt=1.09×10^(11)e^(-84260/RT) 1-α)^(0.928) respectively.The results of this study will expand the selection of raw materials for protein-based wood adhesives.
文摘连续时序的叶面积指数(Leaf Area Index,LAI)可反映苜蓿长势的变化情况,预测苜蓿未来时段的LAI对指导田间管理决策具有重要作用。针对LAI数据采集困难,导致苜蓿时序LAI存在训练数据不足的问题,该文以生长天数为自变量,采用修正的Logistic模型对实测苜蓿LAI变化的动态过程进行建模,根据LAI模拟曲线进行数据插补,从而构建宁夏引黄灌区试验区3年的逐日苜蓿LAI数据集。在插补数据集的基础上,为解决苜蓿刈割后数据突变问题,提出了一种ME-BiLSTM模型。该模型集成移动累计和检验方法(MOSUM)以及基于双向长短期记忆网络(BiLSTM)的编码器-解码器神经网络。MOSUM方法可以实现LAI数据集中突变点检测,并剔除包含突变点训练批次,同时应用改进的BiLSTM模型进行预测。结果表明:ME-BiLSTM模型能较好地进行苜蓿LAI未来曲线变化的预测,其决定系数(R^(2))、均方根误差(RMSE)值分别为0.9985和0.0722。对于苜蓿生长的各个茬次,预测模型对于第1茬、第4茬的预测精度最高,第2茬和第3茬的预测精度稍有降低。
