Dry matter intake （DMI） prediction models of NRC （2001）, Fox et aL （2004） and Fuentes-Pila et aL （2003） were targeted in the present study, and the objective was to evaluate their prediction accuracy with feed...Dry matter intake （DMI） prediction models of NRC （2001）, Fox et aL （2004） and Fuentes-Pila et aL （2003） were targeted in the present study, and the objective was to evaluate their prediction accuracy with feeding trial data of 32 lactating Holstein cows fed two total mixed rations with different forage source. Thirty-two cows were randomly assigned to one of two total mixed ration groups： a ration containing a mixed forage （MF） of 3.7% Chinese wildrye, 28.4% alfalfa hay and 26.5% corn silage diet and another ration containing 33.8% corn stover （CS） as unique forage source. The actual DMI was greater in MF group than in CS group （P=0.064）. The NRC model to predict DMI resulted in the lowest root mean square prediction error for both MF and CS groups （1.09 kg d-1 vs. 1.28 kg d-1） and the highest accuracy and precision based on concordance correlation coefficient for both MF and CS diet （0.89 vs. 0.87）. Except the NRC model, the other two models presented mean and linear biases in both MF and CS diets when prediction residuals were plotted against predicted DMI values （P〈0.001）. The DMI variation in MF was caused by week of lactation （55.6%）, milk yield （13.9%）, milk fat percentage （7.1%） and dietary neutral detergent fiber （13.3%）, while the variation in CS was caused by week of lactation （50.9%）, live body weight （28.2%）, milk yield （8.4%）, milk fat percentage （5.2%） and dietary neutral detergent fibre （3.8%）. In a brief, the NRC model to predict DMI is comparatively acceptable for lactating dairy cows fed two total mixed rations with different forage source.展开更多
The number of bolls, individual boll weight, and lint percentage are three important yield components of lint yield of cotton. In the present study, nine parents, twenty F1, and twenty F2 crosses of intraspecific hybr...The number of bolls, individual boll weight, and lint percentage are three important yield components of lint yield of cotton. In the present study, nine parents, twenty F1, and twenty F2 crosses of intraspecific hybrids of sea island cotton （Gossypium barbadense L.） were grown at Tarim University, Alar, Xinjiang, China, in 2000 and 2001. Lint yield and its three component traits were measured and analyzed by an extended conditional mixed linear model approach. Lint percentage made the largest contribution to additive, additive x environment, and dominance x environment variations for lint yield. The contribution ratios of number of bolls, individual boll weight, and combined contribution of these two traits to additive x environment and dominance x environment variations for lint yield were not statistically significant. Lint yield of different parents could be affected differently by lint percentage. Lint yield of some parents was closely correlated with lint percentage, whereas for other parents, the behavior of individual boll weight and number of bolls played much more important roles on lint yield than that of lint percentage. It was shown by the conditional and conventional predicted additive x environment interaction effects of parents that the environment condition could influence different parents with varied effects.展开更多
基金financially supported by the National Natural Science Foundation of China(31572435)the National Key Research and Development Plan(2016YFD0700205,2016YFD0700201)
文摘Dry matter intake （DMI） prediction models of NRC （2001）, Fox et aL （2004） and Fuentes-Pila et aL （2003） were targeted in the present study, and the objective was to evaluate their prediction accuracy with feeding trial data of 32 lactating Holstein cows fed two total mixed rations with different forage source. Thirty-two cows were randomly assigned to one of two total mixed ration groups： a ration containing a mixed forage （MF） of 3.7% Chinese wildrye, 28.4% alfalfa hay and 26.5% corn silage diet and another ration containing 33.8% corn stover （CS） as unique forage source. The actual DMI was greater in MF group than in CS group （P=0.064）. The NRC model to predict DMI resulted in the lowest root mean square prediction error for both MF and CS groups （1.09 kg d-1 vs. 1.28 kg d-1） and the highest accuracy and precision based on concordance correlation coefficient for both MF and CS diet （0.89 vs. 0.87）. Except the NRC model, the other two models presented mean and linear biases in both MF and CS diets when prediction residuals were plotted against predicted DMI values （P〈0.001）. The DMI variation in MF was caused by week of lactation （55.6%）, milk yield （13.9%）, milk fat percentage （7.1%） and dietary neutral detergent fiber （13.3%）, while the variation in CS was caused by week of lactation （50.9%）, live body weight （28.2%）, milk yield （8.4%）, milk fat percentage （5.2%） and dietary neutral detergent fibre （3.8%）. In a brief, the NRC model to predict DMI is comparatively acceptable for lactating dairy cows fed two total mixed rations with different forage source.
基金the Natural Science Foundation of Zhejiang Province,China (Y306107)National Natural Science Foundation of China (30500365)+1 种基金Project of General Administration of Quality Supervision,Inspection and Quarantine of China (2006QK25)Scientific Research Fund of Education Department Zhejiang Province,China (20060522)
文摘The number of bolls, individual boll weight, and lint percentage are three important yield components of lint yield of cotton. In the present study, nine parents, twenty F1, and twenty F2 crosses of intraspecific hybrids of sea island cotton （Gossypium barbadense L.） were grown at Tarim University, Alar, Xinjiang, China, in 2000 and 2001. Lint yield and its three component traits were measured and analyzed by an extended conditional mixed linear model approach. Lint percentage made the largest contribution to additive, additive x environment, and dominance x environment variations for lint yield. The contribution ratios of number of bolls, individual boll weight, and combined contribution of these two traits to additive x environment and dominance x environment variations for lint yield were not statistically significant. Lint yield of different parents could be affected differently by lint percentage. Lint yield of some parents was closely correlated with lint percentage, whereas for other parents, the behavior of individual boll weight and number of bolls played much more important roles on lint yield than that of lint percentage. It was shown by the conditional and conventional predicted additive x environment interaction effects of parents that the environment condition could influence different parents with varied effects.
