利用分子标记辅助选育白菜薹复等位基因型雄性不育系

为解决白菜薹杂种优势利用中的杂交制种手段问题,以大白菜复等位基因型雄性不育系为不育源,设计定向转育方案,采用连续回交的方法转育不育性和农艺性状,同时利用分子标记辅助选择目标基因型植株,向白菜薹自交系中转育不育基因,创制白菜薹雄性不育系。通过26对SSR引物的筛选鉴定,获得与显性雄性不育基因Ms紧密连锁、且同样可以标记同一位点恢复基因Msf和可育基因ms的分子标记GSSR1。经过3个世代的回交转育,创制出了具有100%不育度和100%不育株率的白菜薹复等位基因型雄性不育系BGMS-3。以BGMS-3为母本,与6个白菜薹自交系杂交,筛选出1个强优势组合C3。     

opaque-2玉米近等基因系的构建与赖氨酸含量快速检测

我国高赖氨酸玉米种质资源狭窄,opaque-2(o2)突变基因能大幅提高玉米赖氨酸含量,通过分子标记辅助选择构建o2玉米近等基因系并检测其赖氨酸含量具有重要意义。其中,要解决的两个关键问题是如何准确地将不同供体的o2突变基因导入受体系和如何快速地检测导入系的赖氨酸含量。本研究利用O2基因内紧密连锁的SSR共显性标记引物phi057检测玉米供体和受体自交系的多态性,利用其特异性和共显性构建o2近等基因系;参考已有研究,改进染料结合(DBL)法,测定18组构建成功的o2近等基因系的赖氨酸含量。结果表明,不仅在不同供体(CA339和山东2548)之间存在多态性,而且在不同受体系间也存在多态性,利用phi057能够成功地将不同供体的o2突变基因导入受体系,构建o2近等基因系;改进的DBL法分析表明,不同受体系赖氨酸含量变化较大,不同背景的受体系导入o2突变基因后赖氨酸含量增加的幅度差异也较大;普通玉米自交系间赖氨酸含量为0.223%~0.368%,构建成功的不同o2近等基因系间,赖氨酸含量为0.373%~0.527%,与受体亲本相比,赖氨酸增加幅度最低为13%,最高为74%。分析表明,phi057能准确筛选导入o2突变基因的受体系,结合改进的DBL法能快速地选择赖氨酸含量高的玉米。     

重力式计量包装机的设计与研究

介绍了定量包装机的结构、气缸选型以及料仓容积等参数的计算。该机配以PLC气动控制装置、嵌入式电脑核心的包装机控制仪,具有高精度、高速度及长期工作可靠性好等特点。它可以和自动取袋、封口机配合使用,根据不同物料的料性及工艺要求,进行自动称重包装,从而满足了国内粮食及其初深加工产品计量包装的性能指标要求,并广泛应用于粮食、化工等行业。     

玉米籽粒的特征选择算法——基于支持向量机与遗传算法

在基于数字图像的玉米品种自动识别的研究中,寻找对识别贡献大的新特征项,对玉米品种识别率的提高具有十分重大的意义.把遗传算法和支持向量机算法相结合,设计了具体的基于支持向量机和遗传算法的玉米籽粒特征选择算法,利用这种算法优选提取出的玉米籽粒特征,从玉米籽粒的胚部和冠部的颜色特征与形状特征中找出了对玉米品种识别贡献较大的新特征.     

Mass selection of wheat for grain filling without transient photosynthesis

Summary Post-anthesis chemical desiccation of wheat (Triticum aestivum L.) plants in the field eliminates transtent photosynthesis by killing all green tissues, thus revealing the plant's capacity for grain filling from stored stem reserves, as the case is for post-anthesis stress such as drought or leaf diseases. This study was conducted to investigate whether mass selection for large kernels under chemical desiccation would lead to the improve ment of grain filling in the absence of transient photosynthesis.Six crosses of common spring wheat were subjected to three cycles of mass selection from F₂ through F₁ when selection was performed for large kernels by sieving grains from plants that were erther chemically desiccated after anthesis, or not (controls). The resulting 36 bulks (six crosses by three selection cycles by two selection environments) were compared with their respective F₂ base populations, when tested with and without chemical desiccation.Selection for large kernels under potential conditions (without chemical desiccation) did not improve kernel weight under potnetial conditions, evidently because these materials were lacking in genetic variation for kernel weight under potential conditions. In four of the crosses, 3rd cycle selection for large kernels under potential conditions decreased kernel weight under chemical desiccation. On the other hand, selection for large kernels under chemical desiccation was effective in improving kernel weight and test weight under chemical desiccation, depending on the cross and the selection cycle, with no genetic shift in mean days to heading or mean plant height. Selection for large kernels under chemical desiccation was also effective in some cases in increasing kernel weight under potential conditions. The results are interpreted to show that selection under potential conditions and under chemical desiccation operate on two different sources for grain filling, namely transient photosynthesis and stem reserve utilization, respectively. In order to expose genetic variability for stem reserve utilization to selection pressure, transient photosynthesis must be eliminated, as done by chemical desiccation in this study.     

Estimation of genetic variance components and heritabilities for cut-flower yield in gerbera using least squares and maximum likelihood methods

Summary Genetic variance components and heritability were estimated for cut-flower yield of gerbera in the Davis population using ordinary least squares and maximum likelihood methods. The overall estimate of narrow-sense heritability is 0.33 based on least squares (LS) and 0.31 based on maximum likelihood (ML). The results of the study indicate that (1) ML and LS provide very similar results if sample size is large enough, suggesting both are useful for plant breeding programs; (2) about one third of the variation in gerbera cut-flower yield is additive, implying selection in cut-flower will be successful; and (3) although additive variation gradually decreased, heritability remained near 0.27 suggesting there is still potential variation in the population for further selection.     

陆地棉高品质系数量性状的遗传变异与选择指数

 研究了20个高品质陆地棉品系12个数量性状的遗传变异、广义遗传力、遗传相关及产量的选择指数。结果表明,单株产量和株铃数具有较大的遗传变异,纤维品质性状的遗传变异较小,说明继续改良高品质系的产量性状潜力较大。纤维品质性状的广义遗传力介于79.07%~89.47%之间,产量及产量组分性状中以衣分和铃重最高,广义遗传力分别为89.1%和81.85%,单株皮棉重最低,为55.05%。单株皮棉重与株铃数、株铃数与果枝数成极显著遗传正相关。利用与单株皮棉重遗传相关较强的多个性状构建的选择指数,比单纯选择单株皮棉重的效果好,其中以单株皮棉重、株铃数、果枝数和果节数结合选择的效果最佳。     

基于地相位优化估计的RVoG三阶段森林冠层高度反演

极化干涉合成孔径雷达（PolInSAR）估测森林结构参数中,数据受基线长度、信噪比、环境地形以及雷达波长的影响,尤其在复杂森林环境条件下,会导致观测到的复相干存在误差,从而影响最终的反演结果。为解决此问题,首先探讨了体相干选择对RVoG三阶段森林冠层高度反演的影响,以地相位为参考逐像素选择距离地相位最远的相干性作为体相干。其次改进了地相位估计方法,采用戴明回归（DMR）和正交回归（OGR）2种相干直线拟合方法来改进地相位的估计,并在DMR拟合方法中设置了不同的误差比（0.3和0.6）来比较地相位估计方法对RVoG三阶段森林冠层高度反演的影响。研究结果表明：以地相位为参考逐像素选择体相干的反演结果相较于直接使用HV极化通道的复相干γ_HV为体相干的反演精度有明显提升,决定系数（R²）由0.349增加到0.383,均方误差由7.097 m²降低到5.755 m²。在体相干优化选择的基础上,采用了戴明回归和正交回归对地相位估计方法进行了改进。表明基于最小二乘回归（LSR）地相位估计的RVoG三阶段反演精度最...     

基于全子集-分位数回归的土壤含盐量反演研究

为提高植被覆盖条件下卫星遥感对土壤含盐量的估测精度,以河套灌区解放闸灌域为研究区,以高分一号卫星影像为数据源,同步采集不同深度土壤含盐量,通过全子集筛选法(Best subset selection)分析不同波段和光谱指数对于不同深度土壤含盐量的敏感性,并采用人工神经网络(Artificial neural network,ANN)、支持向量机(Support vector machine,SVM)和分位数回归(Quantile regression,QR) 3种方法,构建全子集筛选前后0～20 cm、20～40 cm、0～40 cm、40～60 cm、0～60 cm等不同深度下的土壤含盐量反演模型。结果表明,B4、BI、SI1、SI3是0～20 cm、0～40 cm处土壤含盐量的敏感变量组合,B4、BI、NDVI为20～40 cm、40～60 cm、0～60 cm处土壤含盐量的敏感变量组合;在各深度下,分位数回归模型的精度最高,模型的决定系数R2c1、R2v1均在0. 4以上,均方根误差RMSEc1、RMSEv1均小于0. 4%,SVM次之,ANN最差;在20～40 cm深度下QR反演模型效果优于其他深度,为本文土壤含盐量估算的最优模型,其建模和验证的决定系数R2c1、R2v1分别为0. 611和0. 671,建模和验证均方根误差RMSEc1、RMSEv1分别为0. 177%和0. 160%。本研究可为卫星遥感大范围监测植被覆盖条件下土壤盐渍化程度提供参考。     

基于随机森林的高寒湿地地区土地覆盖遥感分类方法

高寒湿地是青藏高原典型独特的生态系统,是全球气候变化的敏感地带和预警区。利用遥感技术快速、准确地分类提取高寒湿地的土地覆盖信息,对当地生态安全监测和保护具有重要意义。本文以若尔盖湿地国家级自然保护区为研究区,首先,以高分一号(GF-1)遥感影像为数据源,融合光谱特征、水体指数、地形特征、植被指数和纹理信息等26个变量进行随机森林(Random forest,RF)分类实验;然后,根据袋外数据(Out of bag,OOB)的特征变量重要性得分和精度评价结果,选出高寒湿地地区土地覆盖类型的最优分类方案和特征;最后,对特征变量进行降维,并基于相同的变量,采用极大似然法(Maximum likelihood classification,MLC)、支持向量机(Support vector machine,SVM)、人工神经网络(Artificial neural network,ANN)和RF等方法进行分类,比较不同方法的优适性。结果表明:结合GF-1影像光谱、水体、植被、纹理特征和地形信息,使用26个变量的RF模型的分类精度最高,总体精度(Overall accuracy,OA)为90.07%,Kappa系数为0.86;通过RF模型的变量重要性分析可以有效选出重要的特征信息,在降低特征变量维度的同时,还能保证较高的分类精度; 4种分类方法中,RF算法是高寒湿地地区较合适的分类方法,OA比MLC基准方法高17.63个百分点,比SVM和ANN等机器学习算法分别高6.98、6.56个百分点。