| 基于主成分与灰色关联分析的饲草小黑麦品种筛选与配套技术研究 |
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| 引用本文： | 张阳,张伟,赵威军,邵荣峰,王官,薛丁丁,李金梅.基于主成分与灰色关联分析的饲草小黑麦品种筛选与配套技术研究[J].作物杂志,2020,36(3):117-91. |
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| 作者姓名： | 张阳 张伟 赵威军 邵荣峰 王官 薛丁丁 李金梅 |
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| 作者单位： | 山西省农业科学院高粱研究所,030600,山西晋中 |
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| 基金项目： | 山西省农业科学院农业创新课题“雁门关牧区饲用小黑麦和甜高粱一年两作配套栽培技术集成研究”(YCX2017D2111) |
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| 摘 要： | 为了筛选在山西省雁门关地区最适宜种植的小黑麦品种以及配套的高产优质栽培技术,利用主成分分析法和灰色关联度分析法综合评价不同小黑麦品种在不同密度和肥料条件下的产量和饲草品质。结果表明,小黑麦饲草品质指标中糖类和蛋白类指标变异较大,但是总的可消化养分以及能量品质变化较小。相关性分析表明,相对饲料价值（RFV）与粗蛋白含量、瘤胃降解蛋白含量、醇溶糖含量、总可消化养分、产奶净能、维持净能、增重净能和相对饲料质量（RFQ）均呈极显著正相关。RFV主要通过酸性和中性洗涤纤维计算而来,在单一指标中最能反映饲草品质。综合考虑灰色关联度和主成分分析方法,晋饲草1号（播种量150kg/hm2,复合肥750kg/hm2、尿素150kg/hm2）最适宜雁门关地区推广种植。
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| 关 键 词： | 主成分分析 灰色关联度分析 小黑麦 优质高产 综合评价 |
| 收稿时间： | 2019-07-09 |
Variety Screening and Study of Cultivation Technology for Forage Triticale Varieties Based on Principal Component and Grey Relation Analysis |
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| Zhang Yang,Zhang Wei,Zhao Weijun,Shao Rongfeng,Wang Guan,Xue Dingding,Li Jinmei.Variety Screening and Study of Cultivation Technology for Forage Triticale Varieties Based on Principal Component and Grey Relation Analysis[J].Crops,2020,36(3):117-91. |
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| Authors: | Zhang Yang Zhang Wei Zhao Weijun Shao Rongfeng Wang Guan Xue Dingding Li Jinmei |
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| Institution: | Sorghum Research Institute, Shanxi Academy of Agricultural Sciences, Jinzhong 030600, Shanxi, China |
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| Abstract: | This study was designed to select the best triticale variety with optimum cultivation technology for good-quality and high-yield in Yanmenguan region of China. Principal component analysis and grey relation analysis were used to evaluate the yield and forage quality of different triticale varieties under different densities and fertilization conditions. The results showed that the variations of sugar and protein in the quality indexes of triticale were large, whereas, the variations of total digestible nutrients and energy quality were little. The correlation analysis showed that relative feed value (RFV) significantly correlated with crude protein content, rumen degradable protein content, alcohol soluble sugar content, total digestible nutrients, net energy of milk production, net energy of maintenance, net energy of weight gain, and relative feed quality (RFQ). Therefore, RFV could best reflect the quality of forage grass in a single indicator. Taking grey relational degree and principal component analysis method into consideration, Jinsicao1 (150kg/ha, compound fertilizer 750kg/ha, and urea 150kg/ha) was most suitable for planting in Yanmenguan area. |
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| Keywords: | Principal component analysis Grey relation analysis Triticale Good-quality and high-yield Comprehensive evaluation |
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