不同品系对小麦白粉病的抗性鉴定

通过田间自然发病和人工接种鉴定，对2000年华北北部203份材料中选育出来的抗病性、丰产性好的48份材料再次进行成株期和苗期抗白粉病鉴定，鉴定结果：成株期免疫材料有24份，高抗材料9份，中抗材料6份。苗期无免疫材料，高抗材料31份，中抗材料10份。     

Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices

In this study, we investigated the possibility of using ground-based remote sensing technology to estimate powdery mildew disease severity in winter wheat. Using artificially inoculated fields, potted plants, and disease nursery tests, we measured the powdery mildew canopy spectra of varieties of wheat at different levels of incidence and growth stages to investigate the disease severity. The results showed that the powdery mildew sensitive bands were between 580 and 710 nm. The best two-band vegetation index that correlated with wheat powdery mildew between 400 and 1000 nm wavelength were the normalized spectrum 570–590 and 536–566 nm bands for the ratio index, and 568–592 and 528–570 nm for the normalized difference index. The coefficients of determination (R ²) for both were almost the same. The optimum dual-green vegetation index was constructed based on a calculation of the ratio and normalized difference between the normalized spectrum within the two green bands. The coefficients of determination (R ²) of DGSR (584, 550) (dual-green simple ratio) and DGND (584, 550) (dual-green normalized difference) were both 0.845. The inverse models of disease severity performed well in the test process at the canopy scale, and indicated that, compared with the traditional vegetation indices of Lwidth, mND705, ND (SDr, SDb), SIPI, and GNDVI, the novel dual-green indices greatly improved the remote sensing detection of wheat powdery mildew disease. Following these results, combined disease severity and canopy spectra were shown to be of enormous value when applied to the accurate monitoring, prevention, and control of crop diseases.     

河南小麦品种对白粉病、叶枯病的抗性鉴定及评价

2006~2007年度对河南推广和后备品种120份进行成株期抗病性鉴定,结果表明,其中对白粉病免疫的品种29个,占鉴定总数的24.2%;高抗白粉病的品种有11个,占鉴定总数的9.2%;中抗品种有27个,占鉴定总数的22.5%;中感品种有18个,占鉴定总数的15%;高感品种有35个,占鉴定总数的29.2%。对叶枯病免疫的品种有南海1号和洛麦22,占鉴定总数的1.7%;高抗叶枯病的品种34个,占鉴定总数的28.3%;中抗品种有29个,占鉴定总数的24.2%;中感品种有23个,占鉴定总数的19.2%;高感品种有32个,占鉴定总数的26.7%。     

人工合成小麦对白粉病的抗性评价

本研究利用我国不同地区的20个小麦白粉菌菌株对28份合成小麦材料进行抗病性评价,并利用与基因连锁的分子标记检测这些合成小麦中Pm2抗白粉病基因。在供试的合成小麦材料中,C7、C14和C2分别抗14～16个菌株,另有6份材料能抗10个以上菌株,C16和C20不抗任何菌株。来自相同杂交组合的材料抗病性表现有很大不同。根据分子检测的结果,C9和C19的扩增片段与Pm2基因相同,但是抗谱分析表明这两个合成小麦对白粉菌的反应型与Pm2基因不同。     

基于多角度高光谱遥感的冬小麦叶片含水率估算模型

准确的作物水分监测对于旱情评估具有重要意义。在分析研究区冬小麦多角度光谱特征后,利用不同水分处理下冬小麦实测叶片含水率和实测多角度光谱数据,基于植被光谱指数法,建立不同观测角度下冬小麦光谱植被指数、水分敏感波段光谱指数与叶片含水率之间的数学模型。结果显示,相对方位角与相对天顶角越小时,观测到的光谱指数与叶片含水率的相关关系越优;敏感波段组合构建的光谱指数中,1450nm波段分别与其他波段组合的NDSI、RSI指数与叶片含水率相关性在各观测角度条件下均较好,1 450 nm波段是冬小麦叶片含水率研究的最佳敏感波段;选取常见的4种植被指数(NDVI、EVI、WI和NDII)中WI和NDVI在各观测角度下与叶片含水率的相关性优于其他两种指数,决定系数R2均在0.83以上,P0.01呈极显著相关;综上建立的多角度光谱叶片含水率估算模型,平均相对误差MRE均小于0.154、均方根误差RMSE均小于0.098,拟合效果较好,尤其是光谱指数NDSI1160,1450、NDSI980,1450和植被指数NDVI、WI;基于以上4种指数建立的最优观测角度(0°,30°)模型,其中植被指数WI的估算效果最好,相关系数在各角度均达到5%的相关显著水平,MRE0.03,可作为最优观测角度反演研究的最优植被指数。     

Fine mapping of powdery mildew resistance gene PmTm4 in wheat using comparative genomics

Powdery mildew,caused by Blumeria graminis f.sp.tritici,is one of the most severe wheat diseases.Mining powdery mildew resistance genes in wheat cultivars and their appliance in breeding program is a promising way to control this disease.Genetic analysis revealed that a single dominant resistance gene named PmTm4 originated from Chinese wheat line Tangmai 4 confers resistance to prevailing isolates of B.graminis f.sp.tritici isolate E09.Detailed comparative genomics analyses helped to develop closely linked markers to PmTm4 and a fine genetic map was constructed using large F_2 population,in which PmTm4 was located into a 0.66-c M genetic interval.The orthologous subgenome region of PmTm4in Aegilops tauschii was identified,and two resistance gene analogs(RGA)were characterized from the corresponding sequence scaffolds of Ae.tauschii draft assembly.The closely linked markers and identified Ae.tauschii orthologs in the mapping interval provide an entry point for chromosome landing and map-based cloning of PmTm4.     

三唑类杀菌剂Folicur与Caramba防治小麦白粉病的研究

通过室内苗期和成株期田间小区试验,测定了不同浓度的Folicur和Caramba对小麦白粉病的防治效果。结果表明,苗期1.15mL/L的Folicur,3.75mL/L的Caramba对小麦叶片具有很好的保护作用,对已发病的小麦叶片能够抑制叶片内病菌的生长。成株期发病前施药,Folicur和Caramba对小麦白粉病的防治效果分别为86.57%和82.09%,均高于常规用量粉锈宁的防治效果(68.16%);发病后用药,防治效果可达68%以上,仍高于常规用量粉锈宁的防治效果(65.74%)。     

洛阳市小麦白粉病测报技术研究

以历史资料为据．建立了气象因素对小麦白粉病影响的数学模型．经过回验，可以作为超长期预测模型应用；用多元回归法筛选因子，对其进行标准化处理．建立模糊关系进行聚类分析，把洛阳市小麦白粉病的生态地理划分为3类；当经济允许损失水平为3．1％时．防治指标为3月中下旬的病情指数9。     

小麦抗白粉病基因的分子标记

小麦白粉病是威胁我国小麦生产的重要常见病害之一。培育抗病品种是防治小麦白粉病的一项既安全又经济有效的措施。分子标记技术的迅速发展使得该措施正在成为小麦技病基因研究工作的重要手段之一。到目前为止，小麦中正式定名的抗白粉病基因位点巳达30个(Pml-Pm30)，其中16个位点的18个基因巳成功地标记和作图，为这些技病基因的鉴别和遗传学研究以及分子标记辅助育种奠定了良好的基础。本文对BFLP、BAPD、AFLP、SSR等技术在小麦抗白粉病基因的分子标记研究方面的现状进行了综述。     

基于概率模型的冬小麦白粉病监测研究

【目的】利用多源数据对白粉病易发区进行病害发生监测研究能够提供大面积、快速、客观的病害发生信息,为农业植保部门开展科学防控提供有效的指导。研究以结合遥感与气象数据监测冬小麦白粉病并获取其精细空间信息为目的。【方法】利用中国高分辨率对地观测系统高分一号卫星(GF-1/WFV)遥感影像提取了研究区小麦种植区及归一化植被指数(normalized difference vegetation index,NDVI)、增强型植被指数(enhanced vegetation index,EVI),通过气候灾害组站点红外雨量数据(climate hazards group infra Red precipitation with station data,CHIRPS)及美国中分辨率成像光谱仪(moderate-resolution imaging spectroradiometer,MODIS)获取了研究区气象相关数据。采用基于概率模型的方法筛选并定量化表达了特征因子与小麦患病情况间的关系,生成了研究区小麦患病概率分布数据、研究区小麦白粉病监测结果、监测结果错分概率分布数据。【结果】基于概率模型的白粉病监测方法总体精度为81.25%,与目前较为流行的分类与回归树(classification and regression tree,CART)和随机森林(random forests,RFs)2种分类监测方法相比具有较高的监测精度;小麦患病概率分布数据能够详细地展现研究区小麦患病概率的空间分布及高患病概率麦区向低患病概率麦区的过渡情况;监测结果的错分概率分布数据与实际错分情况具有较好的一致性。【结论】基于概率模型的白粉病监测方法能够应用于区域尺度冬小麦白粉病发生发展状况监测研究。     

Rapid determination of leaf water content for monitoring waterlogging in winter wheat based on hyperspectral parameters

Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events. Leaf water content(LWC) is an important waterlogging indicator, and hyperspectral remote sensing provides a non-destructive, real-time and reliable method to determine LWC. Thus, based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage. Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature. Combined with methods such as vegetation index construction, correlation analysis, regression analysis, BP neural network(BPNN), etc., we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC. LWC decreased faster under severe stress than under slight stress, but the effect of long-term slight stress was greater than that of short-term severe stress. The sensitive spectral bands of LWC were located in the visible(VIS, 400–780 nm) and short-wave infrared(SWIR, 1 400–2 500 nm) regions. The BPNN Model with the original spectrum at 648 nm, the first derivative spectrum at 500 nm, the red edge position(λr), the new vegetation index RVI(437, 466), NDVI(437, 466) and NDVI′(747, 1 956) as independent variables was the best model for inverting the LWC of waterlogging in winter wheat(modeling set: R~2=0.889, RMSE=0.138; validation set: R~2=0.891, RMSE=0.518). These results have important theoretical significance and practical application value for the precise control of waterlogging stress.     

小麦抗白粉病及分子标记研究进展

小麦白粉病是威胁小麦的主要病害。实践证明,选育和应用抗病品种是解决小麦白粉病最经济、安全、有效的措施。近年来,分子标记技术的快速发展为小麦抗白粉病基因的研究奠定了基础。对小麦白粉病菌的形态特征、毒性频率、抗性基因来源、染色体定位、生理抗性、抗性基因应用等方面研究进展进行了综述,旨为小麦抗病育种提供参考依据。由于病原菌生理小种极容易变异,导致克服寄主原有抗性,可通过基因聚合使不同抗性基因实现有效结合,拓宽其抗谱,提高其抗性持久性,对小麦白粉病抗性研究具有重要理论和实践意义。     

四川抗白粉病育种概况

本文简要的慨述了四川小麦抗白粉病育种情况,提出了小麦抗白粉病育种的方法:常规杂交育种、回交转育育种、DNA分子标记的辅助育种、诱变育种等,强调既要加强垂直抗性为主的抗白粉病育种,又要进行持久抗性品种的选育与利用。     

Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution

The use of remote sensing to monitor nitrogen(N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden. In this study, we model the total leaf N concentration(TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution(VND). The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model. The results show that:(1) the vertical distribution law of LNC was distinct, presenting a quadratic polynomial tendency from the top layer to the bottom layer.(2) The effective layer for remote sensing detection varied at different growth stages. The entire canopy, the three upper layers, the three upper layers, and the top layer are the effective layers at the jointing stage, flag leaf stage, flowering stages, and filling stage, respectively.(3) The TLNC model considering the VND has high predicting accuracy and stability. For models based on the greenness index(GI), mND705(modified normalized difference 705), and normalized difference vegetation index(NDVI), the values for the determining coefficient(R2), and normalized root mean square error(nRMSE) are 0.61 and 8.84%, 0.59 and 8.89%, and 0.53 and 9.37%, respectively. Therefore, the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field.     

抗白粉病小偃麦衍生系的遗传学研究

对八倍体小偃麦与普通小麦杂交得到的抗小麦白粉病衍生系XM-5312和XM-6281进行白粉病抗性鉴定及抗性遗传分析.结果表明:衍生系XM-5312、XM-628、亲本八倍体小偃麦XIN818均对白粉病表现免疫.亲本忻麦6160对白粉病表现高感;抗白粉病衍生系XM-5312、XM-6281分别与高感品种中国春正反交,杂种...     

小麦品种Bogatka抗白粉病基因的分子标记定位

为明确小麦品种Bogatka抗白粉病性状的遗传基础,利用感病亲本薛早和Bogatka以及其杂交所得"薛早/Bogatka"F1与薛早回交得到的BC1群体,进行遗传分析和分子标记定位。结果表明,Bogatka中含有1个显性抗白粉病基因,暂命名为MlBogatka。进一步利用BSA法对BC1分离群体进行分子标记检测,得到与MlBogatka基因连锁的分子标记STSBCD135、Xgwm501和Xwmc332,并构建遗传连锁图。根据这些分子标记的染色体定位信息,该基因位于小麦2B染色体长臂。综合对该基因的标记定位和Pm6基因特异分子标记检测结果,推测该基因可能是Pm6或与Pm6位点紧密连锁的抗白粉病基因。本研究结果为Bogatka在小麦抗白粉病育种中的利用提供了依据。     

Regionalization of wheat powdery mildew oversummering in China based on digital elevation

Blumeria graminis f. sp. tritici, the pathogen that causes wheat powdery mildew, is one of the most important diseases affecting wheat production in China, and the oversummering is the key stage of wheat powdery mildew epidemic. The more oversummering regionalization of wheat powdery mildew has played an important role in disease prediction, prevention and control. In this study, we analyzed the correlation between oversummering data of wheat powdery mildew and the meteorological factors over the past years, and determined that temperature was the key meteorological factor influencing oversummering of wheat powdery mildew. The average temperature at which wheat powdery mildew growth was terminated(26.2°C) was used as the threshold temperature to regionalize the oversummering range of wheat powdery mildew. This regionalization was done using the GIS ordinary kriging method combined with the Digital Elevation model(DEM) of China. The results showed that annual probability of oversummering region based on Model 26.2 were consistent with the actual survey of the more summer wheat powdery mildew. Wheat powdery mildew oversummering regions in China mainly cover mountainous or high-altitude areas, and these regions form a narrow north-south oversummering zone. Oversummering regions of wheat powdery mildew is mainly concentrated in the high-altitude wheat growing areas, including northern and southern Yunnan, northwestern Guizhou, northern and southern Sichuan, northern and southern Chongqing, eastern and southern Gansu, southeastern Ningxia, northern and southern Shaanxi, central Shanxi, western Hubei, western Henan, northern and western Hebei, western Liaoning, eastern Tibet, eastern Qinghai, western Xinjiang and other regions of China.     

白粉病胁迫下小麦叶片光谱响应与智能分类识别

目的 开展基于高光谱技术的白粉病胁迫下田间小麦光谱的响应研究,实现小麦白粉病感染等级的快速确定。方法 采用光纤光谱仪配合积分球和叶片夹采集大田活体小麦叶片可见-近红外光谱;通过光谱数据拟合得到的SF-SPAD (Spectrum fitting SPAD)值来反映叶绿素含量,对叶片感染白粉病进行初步判定;使用PROSPECT模型进行光谱敏感度分析确定敏感波段;结合主成分分析(Principal component analysis, PCA)降维和支持向量机(Support vector machine,SVM)建模,实现对光谱数据的二分类;根据二分类模型判断的病点百分比对小麦病虫害感染程度进行分级。结果 SF-SPAD值随自下而上的叶序的增大而逐渐上升;SF-SPAD值≤0.90的全是病点,≥1.05的全是好点。光谱敏感度分析确定了敏感波段为可见光波段440~500和540~780 nm,降低了数据维度。确定了感染等级(R)与病点百分比(%)的关系为R₁：0~30%、R₂：30%~50%、R₃：50%~70%、R₄：70%~100%。本研究所建模型适用的检测株数最少为20株。结论 结合SF-SPAD值和光谱PCA-SVM二分类建立的监测模型可以准确、快速地判定小麦白粉病感染与否及感染等级,同时可以降低采样数量、减少地面检测工作量、提高检测效率,是一项实用性强、简单、易推广的智能化监测技术。     

基于GAHP的小麦白粉病流行动态预测方法研究

针对目前小麦白粉病流行动态研究中存在的影响因子单一、预测结果有误报等问题,提出一种基于GAHP的小麦白粉病流行动态预测方法。该方法引入影响白粉病发生的多源因素,采用AHP算法定性分析影响因子和预测方案并定量计算出权重大小,同时聚类分析群组中各专家权重值,最后加权求和得出白粉病的流行动态。通过对2012年淮北地区小麦白粉病流行动态进行实例验证,对比发布的预测结果,结果准确,证明该方法的可行性和有效性较好。     

已知抗白粉病基因小麦种质的HMW-GS组成分析

通过聚丙烯酰胺电泳技术(SDS-PAGE)分析了25个具有已知抗白粉病基因小麦种质的高分子量麦谷蛋白亚基(HMW-GS)组成。研究表明，在供试材料中亚基组成类型极其丰富。在Glu-A1、Glu-B1和Glu-D1几个位点上分别检测到2、7和4种不同的亚基组成类型。其中，在Glu-A1位点上大多数品种都具有null亚基，占该位点亚基的84％；在Glu-B1位点上的变异类型最丰富，7 8亚基出现频率最高，为36％；在Glu-D1位点上，劣质亚基2 12出现频率最高，为76％，被世界公认的优质亚基5 10出现频率较低，仅为12％。利用这些已知抗白粉病基因小麦种质进行种质创新，可向育种提供优点突出而无突出缺点的亲本。