Corn silage quality index: An index combining milk yield, silage nutritional and fermentation parameters

Development of indexes based on milk yield of lactating dairy cows fed corn silage-based diets combined with silage quality parameters can provide clear guidance to assess the overall quality of corn silage produced. This study was done to determine the most important minimum number of silage quality parameters for corn silage quality evaluation among commonly used parameters for nutritional and fermentation quality and to develop an integrated corn silage quality index (CSQI) using standard scoring functions and weighting assignment. Principal component analysis (PCA) and multiple regression analysis (MRA) were used to select the most important silage quality parameters and to assign parameter weights, whereas standard scoring functions were used to normalize silage quality parameters. A variety of corn silage samples (n = 390) representing spatial and seasonal heterogeneity, were collected from 195 intensive dairy farms in China and analyzed for 16 frequently used chemical and fermentation parameters. Concurrent with silage sampling, average daily milk yield respective to each silage was collected from 50 mid-lactating dairy cows fed corn silage-based diets (i.e., 39–48 % DM corn silage in TMR) and used as the dependent variable in MRA. The silage quality parameters used in developing the index were; digestible NDF after 30-h in vitro incubation (g/kg NDF), and concentrations (DM basis) of starch, crude protein, ether extract, ammonia, and lactic acid. The CSQI was developed by summing normalized and weighted quality parameters. The CSQI was subsequently converted to corn silage quality scores (CSQS, 0–100). Based on the CSQS, silages were grouped into five quality grades; poor, fair, average, good and excellent having grade mean index score of 51, 62, 69, 78 and 89. The new indexes were evaluated against observed daily milk yield measurements and Milk2006 milk yield estimates. The CSQS were found to be positively correlated with observed daily milk yield measurements and Milk2006 index. The linear relationship between MRA based CSQS and observed daily milk yield measurement was higher than that of the relationship between PCA based CSQS and Milk2006 index (R2 = 0.72 vs. 0.60). Further, accuracy and precision of predicting milk yield by MRA based CSQI were higher than those of the PCA based CSQI against both observed daily milk yield measurements and Milk2006 index based on corn silage quality. Thus, among the two multivariate approaches, the MRA based CSQI method was a more accurate method for predicting performance between corn silage of different qualities.