四川盆地冬繁区小麦条锈病气象等级预测模型

四川盆地冬繁区是常年受小麦条锈病危害最重的地区之一.本研究利用盆地冬繁区代表站点1999—2016年的气象资料和条锈病资料,根据发病面积比将小麦锈病发生的气象条件划分为5个等级,采用多种分析方法确定了影响小麦条锈病发生的具有明确生物学意义的气象因子,并建立了小麦条锈病气象等级预测模型.结果表明: 四川盆地小麦条锈病的发生与平均(最高、最低)气温、降水量及距平百分率、相对湿度及距平百分率、平均风速、日照时数等多种气象因子显著相关,其中,平均气温和相对湿度距平百分率起主导作用.历史回代检验中,区(县)级样本准确率64%,市级样本准确率89%.对2017年盆地冬繁区小麦锈病发生气象等级进行预报,预测结果完全正确的样本占总样本量的62.8%;误差1个等级的样本占27.9%,误差2个或2个以上等级的样本仅占9.3%,预测效果较好,能达到从气象角度对小麦锈病发生进行预报的目的.     

抗条锈病小麦品种9365在抗病育种中的利用与评价

9365是陕西省小麦研究中心创制的抗条锈病小麦品种.经多年观察、利用发现,9365对条锈病表现高抗,其穗大、成穗率高、落黄好、高产、综合农艺性状好,是陕西省小麦抗条锈病育种可资利用的抗条锈小麦品种.其缺点是植株偏高、成熟偏晚、抗性受隐性基因控制.     

大元麦上的粘虫灰色灾变预测初探

本文应用灰色系统理论和方法探讨大元麦上的粘虫长期预测。对吴县1974～1986年大元麦粘虫发生量,经灾变映射,建立GM(1,1)灾变长期预测模型,用该模型回测历史拟合率达100％,对1987～1989年预测,均于实况相符。     

褐稻虱灰色灾变长期预测初探

本文应用灰色系统理论与方法探讨害虫的长期预测。对衙县1974～1986年晚稻主害代褐稻虱发生量构成的离散数列,建立了灰色系统GM(1,1)灾变长期预测模型,用该模型回测的历史拟合率达100％。对1987年晚稻褐稻虱发生量的跨年度长期预测,其结果与实测相吻合。     

一粒小麦抗白粉病和条锈病基因的分析

一粒小麦是普通小麦抗性改良的宝贵资源.本研究对24份一粒小麦分别进行了白粉病和条锈病混合菌种苗期接种鉴定,进一步分别用一套白粉病菌菌株(15个)对2份乌拉尔图小麦和条锈病菌小种(21个)对1份栽培一粒小麦进行接种鉴定,其中乌拉尔图小麦UR206能抵抗所有供试白粉菌菌株,UR204除对白粉菌菌株E11感病外,对其余菌株表现抗性;栽培一粒小麦MO205对不同条锈菌小种表现出不同的抗性反应,研究表明乌拉尔图小麦UR206、UR204和栽培一粒小麦MO205分别含有与已知抗白粉病和抗条锈病基因不同的新基因.对乌拉尔图小麦UR204、UR206和栽培一粒小麦MO205分别进行抗白粉和条锈病基因的遗传分析,结果表明乌拉尔图小麦UR204和UR206分别含有一对显性抗白粉病基因,栽培一粒小麦MO205含有两对独立遗传的显性抗条锈病基因.     

用GM(n,h)模型预测森林资源发展趋势

应用灰色系统理论,对森林资源的主要指标进行了预测,建立了灰色动态预测模型GM（1,1）．其预测结果误差小,精度高,为林业宏观决策,森林经营管理提供了科学依据和方法．     

基于GIS的四川盆区小麦条锈病风险评价与区划

利用四川盆区1961—2017年气象数据、1999—2016年小麦条锈病监测资料以及农业生产、地理信息等数据,基于自然灾害风险评估原理和四川盆区小麦条锈病发生机理,运用集优法、加权综合评价法、层次分析法等,构建了四川盆区小麦条锈病发生风险评价指标体系和评价模型。在GIS技术支持下,对条锈病孕灾环境敏感性、致灾因子危险性、承载体易损性和防灾减灾能力4要素分别进行了评价,对四川盆区小麦条锈病发生风险进行了评估和分区。结果表明:四川盆区整体气象环境对小麦条锈病越冬和春季流行适宜;危险性指数较高的区域主要分布在盆地的西部地区;盆西平原、盆中浅丘的易损性指数较高;盆西、盆南和盆中与盆东北相交的浅丘地区防灾减灾能力较强。研究将条锈病发生风险划分为轻、低、中、高4个不同等级,高风险区分布在盆地西部;中、低风险区分布面积相当广,前者呈零散状分布,后者主要集中在盆地西部和中部;轻风险区分布面积最广。     

粗山羊草抗条锈病新基因YrY206遗传分析和微卫星 标记

从小麦野生近缘属——粗山羊草中挖掘小麦条锈病抗病基因, 拓展小麦抗病性的遗传基础。利用抗小麦条锈病与感小麦条锈病的粗山羊草间杂交, 从粗山羊草[Aegilops tauschii (Coss.) Schmal] Y206中鉴定出1个显性抗小麦条锈病基因, 暂定名为YrY206。应用分离群体分组法(Bulked segregant analysis, BSA)筛选到Wmc11a、Xgwm71c、Xgwm161和Xgwm183标记, 与该基因之间的遗传距离分别为4.0、3.3、1.5和9.3 cM。根据连锁标记所在小麦微卫星图谱的位置, YrY206被定位在3DS染色体上。分析基因所在染色体的位置、抗病性特征, 认为YrY206是一个新的抗小麦条锈病基因。     

小麦抗条锈基因Yr69的连锁标记开发

抗条锈病基因Yr69对我国小麦条锈菌(Puccinia striiformis f.sp.tritici)小种具有广谱抗性,在小麦抗条锈病育种中具有重要价值.为提高分子标记辅助选择育种的效率,加快Yr69在小麦抗病育种中的应用,本研究利用条锈菌小种CYR34对包含340个小麦家系的'Taichung29/CH7086'...     

高粱蚜灰色灾变长期预测模型

本文应用灰色系统灾变预测理论建立了吕梁地区高粱蚜发生量预测模型．     

小麦条锈菌大片段基因组DNA的提取方法研究

由条锈菌Puccinia striiformis引致的小麦条锈病是小麦最重要的病害之一。由于其活体寄生的特点,对小麦条锈菌的遗传学和分子生物学研究十分有限,大片段核DNA的提取研究还未见报道。高分子量基因组DNA是开展大片段基因组文库构建、基因组分析以及基因组重建的重要基础,通过系统建立和优化小麦条锈菌大片段基因组DNA的分离方法,成功获得分子量大于1Mb高质量的病菌基因组DNA。     

Characterization of genetic loci conferring adult plant resistance to leaf rust and stripe rust in spring wheat.

Leaf (brown) and stripe (yellow) rusts, caused by Puccinia triticina and Puccinia striiformis, respectively, are fungal diseases of wheat (Triticum aestivum) that cause significant yield losses annually in many wheat-growing regions of the world. The objectives of our study were to characterize genetic loci associated with resistance to leaf and stripe rusts using molecular markers in a population derived from a cross between the rust-susceptible cultivar 'Avocet S' and the resistant cultivar 'Pavon76'. Using bulked segregant analysis and partial linkage mapping with AFLPs, SSRs and RFLPs, we identified 6 independent loci that contributed to slow rusting or adult plant resistance (APR) to the 2 rust diseases. Using marker information available from existing linkage maps, we have identified additional markers associated with resistance to these 2 diseases and established several linkage groups in the 'Avocet S' x 'Pavon76' population. The putative loci identified on chromosomes 1BL, 4BL, and 6AL influenced resistance to both stripe and leaf rust. The loci on chromosomes 3BS and 6BL had significant effects only on stripe rust, whereas another locus, characterized by AFLP markers, had minor effects on leaf rust only. Data derived from Interval mapping indicated that the loci identified explained 53% of the total phenotypic variation (R2) for stripe rust and 57% for leaf rust averaged across 3 sets of field data. A single chromosome recombinant line population segregating for chromosome 1B was used to map Lr46/Yr29 as a single Mendelian locus. Characterization of slow-rusting genes for leaf and stripe rust in improved wheat germplasm would enable wheat breeders to combine these additional loci with known slow-rusting loci to generate wheat cultivars with higher levels of slow-rusting resistance.     

Molecular mapping of a non-host resistance gene YrpstY1 in barley (Hordeum vulgare L.) for resistance to wheat stripe rust

Cultivated barley (Hordeum vulgare L.) is considered as a non-host or inappropriate host species for wheat stripe rust caused by Puccinia striiformis f. sp. tritici. Most barley cultivars show a broad-spectrum resistance to wheat stripe rust. To determine the genes for resistance to wheat stripe rust in barley, a cross was made between a resistant barley line Y12 and a susceptible line Y16. The two parents, F(1) and 147 BC(1) plants were tested at seedling stage with Chinese prevalent race CYR32 of Puccinia striiformis f. sp. tritici by artificial inoculation in greenhouse. The results indicated that Y12 possessed one dominant resistance gene to wheat stripe rust, designated YrpstY1 provisionally. A total of 388 simple sequence repeat (SSR) markers were used to map the resistance gene in Y12 using bulked segregant analysis. A linkage map, including nine SSR loci on chromosome 7H and YrpstY1, was constructed using the BC(1) population, indicating that the resistance gene YrpstY1 is located on chromosome 7H. It is potential to transfer the resistance gene into common wheat for stripe rust resistance.     

小麦条锈菌国际鉴别寄主Vilmorin23的抗条锈病基因YrV23微卫星标记

Vilmorin23是小麦条锈菌国际鉴别寄主和国际上重要抗源材料。采用SSR技术,利用由Vilmorin23为基因供体转育而成的小麦抗条锈近等基因系Taichung29*6/YrV23,选用YrV23所在2B染色体上的55对SSR引物,对Taichung29*6/ YrV23及其轮回亲本Taichung29和抗性基因供体Vilmorin23的基因组DNA进行PCR扩增和聚丙烯酰胺凝胶电泳分析。结果显示,引物Xwmc356在近等基因系与轮回亲本间扩增出特异性DNA片段,经F₂代群体150个抗、感单株检测证实,该片段位点与抗条锈病基因YrV23有连锁关系,遗传距离为9.4 cM。Xwmc356可作为抗条锈基因YrV23的SSR标记。      

小麦与条锈菌互作中过氧化物酶的细胞化学研究

采用细胞化学方法对小麦与条锈菌互作过程中过氧化物酶的分布及其活性大小进行了研究,结果表明：过氧化物酶主要分布于细胞壁和细胞间隙中;在未行接种的小麦叶片中,抗病品种和感病品种的过氧化物酶活性均比较低;条锈菌侵染后,诱导抗、感病品种叶片中的过氧化物酶活性升高,且抗病品种升高的幅度明显大于感病品种;感病品种中过氧化物酶活性在侵染位点附近细胞壁上表现升高,而抗病品种中该酶的活性在侵染点细胞以及远离侵染点的叶肉细胞的细胞壁和细胞间隙中均显著升高。高活性的过氧化物酶是小麦抗条锈性的生化标记和重要机制之一。     

Application of thermal and visible imaging to estimate stripe rust disease severity in wheat using supervised image classification methods

Reliable and accurate estimation of plant disease severity at the field scale is a key factor for predicting yield losses, disease management and food security. A field experiment was designed and conducted during 2017–18 and 2018–19 with 24 wheat cultivars to estimate the stripe rust severity by supervised classification of thermal and visible images using parallelepiped, minimum distance, mahalanobis distance, maximum likelihood, support vector machine and neural network methods of image classification. Results demonstrated the potential of thermal and visible imaging techniques to estimate wheat stripe rust severity with good accuracy. For both visible and thermal images used in this study, support vector machine gave the best estimates of the rust severity, while the parallelepiped method was the worst-performing method. Support vector machine and neural network methods showed d-index, Nash-Sutcliffe efficiency and coefficient of determination values above 85%, with accuracies above 98% and kappa coefficient above 0.97 for both thermal and visible images. Comparison of thermal and visible image classification performance revealed that for all the methods except support vector machine, the estimated rust severity, overall accuracy and kappa coefficient of thermal images were better than visible images. The present study clearly showed that both thermal and visible image analysis can be applied as a rapid non-destructive technique to estimate the wheat rust severity under field conditions. The study also provided a comparative insight into thermal and visible image classification methods that have great potential for sustainable plant disease management in modern agriculture.