龙菁1号紫花苜蓿新品种的选育
Breeding of new variety of Medicago sativa L.cv.Longjing No.1
王建丽 1牟林林 1庄煦 1张冬梅 1尤佳 1邸桂俐 2王晓龙 3韩微波1
作者信息
- 1. 黑龙江省农业科学院草业研究所,黑龙江哈尔滨 150086
- 2. 黑龙江省农业科学院经济作物研究所,黑龙江哈尔滨 150086
- 3. 黑龙江省农业科学院畜牧兽医分院,黑龙江齐齐哈尔 161005
- 折叠
摘要
试验为解决寒区优质紫花苜蓿种质资源匮乏的难题,以抗寒、高产为育种目标,2002年起对9个国内外紫花苜蓿品种经过混合单株选择法及四代田间表型混合选择,选育龙菁1号紫花苜蓿品种.结果显示:在品种比较试验中,龙菁1号紫花苜蓿的干草产量、越冬率和粗蛋白含量最高,分别为10 920.2 kg/hm2、95.2％、19.22％;在区域试验中,龙菁1号紫花苜蓿在吉林白城地区的干草产量(11 866.18 kg/hm2)最高;在生产试验中,与龙牧803、WL323HQ相比,龙菁1号紫花苜蓿在杜蒙地区的干草产量(10 088 kg/hm2)最高.研究表明,龙菁1号紫花苜蓿品种的耐寒性、干草产量、粗蛋白含量均优于龙牧803和WL323HQ,是适合东北寒区人工草地建植、退化草原改良、粮改饲和草田轮作的优质牧草.
Abstract
The experiment aimed to address the scarcity of high-quality alfalfa germplasm resources in cold regions,with cold resistance and high yield as the breeding objectives.Since 2002,nine domestic and foreign alfalfa varieties have been selected through a mixed single-plant selection method and four generations of field phenotypic mixed selection,resulting in the development of the Longjing No.1 alfalfa variety.The results showed that in variety comparison trials,Longjing No.1 alfalfa had the highest dry hay yield,overwintering rate,and crude protein content,at 10 920.2 kg/hm2,95.2％,and 19.22％,respectively.In regional trials,Longjing No.1 alfalfa had the highest dry hay yield in the Baicheng area of Jilin(11 866.18 kg/hm2).In production trials,compared with Longmu 803 and WL323HQ,Longjing No.1 alfalfa had the highest dry hay yield in the Dumeng area(10 088 kg/hm2).The study indicates that the Longjing No.1 alfalfa variety excels in cold tolerance,dry hay yield,and crude protein content compared to Longmu 803 and WL323HQ,making it a high-quality forage suitable for the establishment of artificial grasslands,improvement of degraded grasslands,crop-to-fodder conversion,and crop-rotation systems in the cold northeastern regions.
关键词
龙菁1号紫花苜蓿/新品种/选育/抗寒/干草产量Key words
Medicago sativa L.cv.Longjing No.1/new variety/breeding/cold resistance/dry hay yield引用本文复制引用
基金项目
科技创新2030-重大项目(2022ZD04012)
黑龙江省农业科学院"农业科技创新跨越工程"专项(CX23GG06)
出版年
2024
国家科技期刊平台
NSTL
万方数据