黄土高原13种栽培牧草营养成分NIRS模型分析

发布时间：2018-11-09 11:02

【摘要】：对2012-2013年黄土高原种植的13个牧草品种、780份干草样品的营养成分建立了近红外光谱(near infrared reflectance spectroscopy,NIRS)的检测模型。豆科牧草的粗脂肪(EE)、酸性洗涤纤维(ADF)和粗灰分(Ash)建模结果最好,其定标决定系数(RSQ)0.94,交叉验证相关系数(1-VR)0.7最高,定标标准分析误差(SEC)在0.071~0.713,交叉校验定标标准分析误差(SECV)在0.160~2.751。禾本科牧草的EE和可溶性糖(WSC)建模结果最好,RSQ分别达0.916和0.859,1-VR分别为0.609和0.810,SEC和SECV分别是0.250、1.488和0.505、3.172。菊科和车前科牧草的模型,除ADF外,其它指标预测的稳定性和准确性较为理想,RSQ在0.85以上,1-VR在0.70以上,SEC和SECV分别在0.361~3.557和0.495~4.602。NIRS对豆科粗蛋白(CP)和WSC的数值预测较差,RSQ仅0.55,对禾本科CP、ADF、中性洗涤纤维(NDF)、Ash及菊科和车前科的ADF的预测稍差,RSQ0.7。
[Abstract]:A near infrared spectrum (near infrared reflectance spectroscopy,NIRS) model was established for the determination of nutrient components of 780 hay samples planted in the Loess Plateau in 2012-2013. The modeling results of crude fat (EE), acid detergent fiber (ADF) and crude ash fraction (Ash) were the best, the calibration determination coefficient (RSQ) was 0.94, and the cross-validation correlation coefficient (1-VR) was the highest. The analysis error of calibration standard (SEC) is 0.071 / 0. 713, and the error of cross calibration standard analysis is 0. 160 / 2.751. The results of EE and soluble sugar (WSC) modeling of grasses were the best, with RSQ of 0.916 and 0.810 ~ (-1) VR of 0.609 and 0.810 ~ (-1) SEC and SECV of 0.250 ~ 1.488 and 0.505 ~ 3.172, respectively. The model of Compositae and Chlorophyllaceae forage, except ADF, the stability and accuracy of other indexes were satisfactory. The RSQ and 1-VR were above 0.85 and 0.70, respectively. The values of SEC and SECV for (CP) and WSC in the family Leguminosae were poor at 0.361 ~ 3.557 and 0.495~4.602.NIRS, respectively, but only 0.55 for RSQ and (NDF), for CP,ADF, neutral detergent fiber of Gramineae. The prediction of Ash and ADF of Compositae and Plantago is slightly worse than that of RSQ0.7..
【作者单位】：草地农业生态系统国家重点实验室兰州大学草地农业科技学院;宁夏农林科学院动物科学研究所;贵州省草业研究所;
【基金】：长江学者和创新团队发展计划(IRT13019) 甘肃省2016年草牧业试验试点和草业技术创新联盟科技支撑(GCLM2016001)
【分类号】：S816

【相似文献】