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| 1 | 中国大豆花叶病抗源和抗性鉴别寄主的鉴定与评价显示文摘收集大豆抗大豆花叶病(SMV)抗源和鉴别寄主40份,通过接种Cho和Goodm an划分的抗大豆花叶病毒株系SMV G1-G7,了解这些材料对该株系的抗性反应。同时比较了其中部分材料对中国学者划分的SMV Sc1-Sc17株系的抗性反应。结果感病材料无论对SMV G1-G7株系,还是SMV Sc1-Sc17株系均表现感病;但是,齐黄1、科丰1、早18和8101等对SMV G1-G7株系均表现抗病的材料,对SMV Sc1-Sc17株系却表现出部分抗病;而另外一些材料,如:诱变30、徐豆1、文丰5、铁6915、齐黄10和Harosoy等对SMV G1-G7株系的抗性反应却与北美的鉴别寄主相同。结果表明:无论是对SMV G1-G7株系,还是对SMV Sc1-Sc17株系,抗病材料的抗性遗传基础是相似的;中国一些大豆花叶病毒株系的致病力强于国外的株系。因此,结合国外的SMV株系鉴定系统,创建一套统一的SMV株系鉴定系统是可行的。 | 廖林 Rajcan Istvan 陈鹏印 Buss Gleen Tolin Sue 杨振宇 董志敏 王曙明 | 2010 | 大豆科学2010，29，6： | 5 |
| 2 | 加拿大大豆育种和生产研究概况及展望显示文摘通过对加拿大大豆科研和生产概况的阐述,明确了加拿大大豆遗传育种方向、研究内容和方法;分析了加拿大大豆生产、工业发展和市场需求;同时指出了加拿大未来农业和大豆产业的发展趋势和策略。 | 廖林 Istvan Rajcan | 2008 | 大豆科学2008，27，2： | 3 |
| 3 | Biplot analysis of test sites and trait relations of soybean in Ontario显示文摘 | Yan W Rajcan I | 2002 | Crop Sci2002，42，： | 1 |
| 4 | Features and pathophysiology of acute coronary syndrome显示文摘 | Bergovec M Vrazi CH Rajcan Spoljari CI | | 0，，： | 1 |
| 5 | Biplot analysis of test sites and trait relations of soybean in Ontario显示文摘 | Yah W Rajcan I | 2002 | Crop Sci2002，42，： | 1 |
| 6 | Soybean cyst nematode:Challenges and opportunities 显示文摘 | Winter S M J Rajcan I Shelp B J | 2006 | Canadian Journal of Plant Science2006，86，1： | 1 |
| 7 | Note on relationship between leaf soluble carbohydrate and chlorophyll concertration in maize during leaf senescence显示文摘 | Irena Rajcan Lianne M D Matthijs T R | 1999 | Field Crops Research1999，63，： | 1 |
| 8 | Biplot analysis of test sites and trait rela- tions of soybean in Ontario显示文摘 | Yan W Rajcan I | 2002 | Crop Sci2002，42，： | 1 |
| 9 | Biplot analysis of test sites and trait relations of soybean in Ontario显示文摘 | Yan W K Rajcan I Li J | 2002 | Crop Science2002，42，1： | 1 |
| 10 | Genomic changes detected by array CGH in human embryos with developmental defects 显示文摘 | Rajcan S E Qiao Y Tyson C | 2010 | Mol Hum Reprod2010，16，2： | 1 |
| 11 | Biplot analysis of test sites and trait relations of soybean in Ontario 显示文摘 | Yan 'W Rajcan I | 2002 | Crop Sci2002，42，： | 1 |
| 12 | Biplot analysis of test sites and trait relations of soybean in Ontario显示文摘 | Yan W Rajcan I | 2002 | Crop Sci2002，42，： | 1 |
| 13 | Features and pathophysiology of acute coronary syndrome显示文摘 | Bergovec M Vrazi c' H Rajcan Spoljari c' I | | 0，，： | 1 |
| 14 | Understanding maize-weedcompetition: resource competition, light quality and the whole plant 显示文摘 | RAJCAN I SWANTON C J | 2001 | Field Crop Research2001，71，2： | 1 |
| 15 | Features and Pathophysiology of acute coronary syndrome显示文摘 | Bergovec M Vrazi C H Rajcan Spoljari C I | 2009 | Acta Med Croatica2009，63，1： | 1 |
| 16 | Biplot analysis of test sites and trait relations of soybean in ontario显示文摘 | Yan W Rajcan I | 2002 | Crop Sci2002，42，： | 1 |
| 17 | Source: sink ratio and leaf senescence of maize:I dry matter accumulation and partitioning during grain filling显示文摘 | Rajcan I Tollenaar M | 1999 | Field Crops Research1999，60，： | 1 |
| 18 | Prediction of cultivar performance based on single- versus multiple-year tests in soybean 显示文摘 | Yan W Rajcan I | 2003 | Crop Science2003，43，2： | 1 |
| 19 | Classification of Soybean Pubescence from Multispectral Aerial Imagery显示文摘The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration.Currently,soybean pubescence is classified visually,which is a labor-intensive and time-consuming activity.Additionally,the three classes of phenotypes(tawny,light tawny,and gray)may be difficult to visually distinguish,especially the light tawny class where misclassification with tawny frequently occurs.The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow,develop a set of indices for distinguishing pubescence classes,and test a machine learning(ML)classification approach.A principal component analysis(PCA)on hyperspectral soybean plot data identified clusters related to pubescence classes,while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability.Aerial images from 2018,2019,and 2020 were analyzed in this study.A 60-plot test(2019)of genotypes with known pubescence was used as reference data,while whole-field images from 2018,2019,and 2020 were used to examine the broad applicability of the classification methodology.Two indices,a red/blue ratio and blue normalized difference vegetation index(blue NDVI),were effective at differentiating tawny and gray pubescence types in high-resolution imagery.A ML approach using a support vector machine(SVM)radial basis function(RBF)classifier was able to differentiate the gray and tawny types(83.1%accuracy and kappa=0:740 on a pixel basis)on images where reference training data was present.The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel,indicating limitations of using aerial imagery for pubescence classification in some environmental conditions.High-throughput classification of gray and tawny pubescence types is possible using aerial imagery,but light tawny soybeans remain difficult to classify and may require training data from each field season. | Robert W.Bruce Istvan Rajcan John Sulik | 2021 | Plant Phenomics2021，3，1： | 1 |
| 20 | Biplot evaluation of test sites and trait relations of soybean in Ontario 显示文摘 | Yan W Rajcan I | 2002 | Crop Science2002，42，1： | 1 |
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