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黄土高原半干旱区引进燕麦种质的适应性评价 被引量:10

High-yield and high-resistant oat germplasm selection in Semi-arid areas of the Loess Plateau
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摘要 为探讨国外引进的燕麦种质在黄土高原半干旱区榆中县地区的适应性,筛选出适宜在该地区种植的优异种质。在黄土高原半干旱区榆中县对引自加拿大、澳大利亚、荷兰、新西兰和俄罗斯的6份裸燕麦种质和8份皮燕麦种质从生育期、生长性状和抗病性等方面进行综合评价。结果表明:14份参试种质除VAO-7和伽利略外都属于早熟型,且均能在当地正常成熟;主成分分析和抗病性评价表明,从加拿大引进的裸燕麦DA92-1和皮燕麦Rigdon的综合得分较高,种子产量分别高达2 243.15、3 652.78 kg/hm^2,在株高和叶长方面表现较好,抗病性较强,为引进燕麦种质中适合在黄土高原半干旱区榆中县地区种植的品种。 the aim of the current work is r to explore the adaptability of oat germplasm introduced to Yuzhong County of semi-arid area of the Loess Plateau and to find out high-yield and high-resistance excellent germplasm materials adapted to the area.This study evaluated and compared the adaptability of six naked oats and eight hulled oats introduced from five countries including Canada,Australia and Helan in Yuzhong County of semi-arid area of the Loess Plateau.The traits including growth period,growth traits and disease resistance were measured.The results showed that the tested materials were all showed early maturity except VAO-7 and Jialilue,and all of them had a good adaptability to the local conditions.The principal component analysis and disease tolerance analysis on the tested germplasm materials showed that DA92-1 and Rigdon introduced from Canada had excellent comprehensive performance with higher seed yield(2243.15 kg/hm^2、3652.78 kg/hm^2),plant height,longer leaf length and better disease resistance,and were the most suitable oat variety in Yuzhong County of semi-arid area of the Loess Plateau.
作者 聂秀美 赵桂琴 柴继宽 孙雷雷 王军 NIE Xiu-mei;ZHAO Gui-qin;Chai Ji-kuan;SUN Lei-lei;WANG Jun(College of Grassland Science,Gansu Agricultural University/Key Laboratory for Grassland Ecosystem of Ministry of Education/Pratacultural Engineering Laboratory of Gansu Province/Sino-U.S.Centers for Grazing Land Ecosystem Sustainability,Lanzhou 730070,China)
出处 《草原与草坪》 CAS CSCD 2019年第2期25-31,38,共8页 Grassland and Turf
基金 国家燕麦荞麦产业体系(CARS-7-C) 农业部牧草种质资源保护项目(2013014)资助
关键词 燕麦 适应性 半干旱区 主成分分析 oat adaptability semi-arid area Yuzhong County principal component analysis
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