Maize silage has become the major forage component in the ration of dairy cows over the last few decades. This review provides information on the mean content and variability in chemical composition, fatty acid (FA) profile and ensiling quality of maize silages, and discusses the major factors which cause these variations. In addition, the effect of the broad range in chemical composition of maize silages on the total tract digestibility of dietary nutrients, milk production and milk composition of dairy cows is quantified and discussed. Finally, the optimum inclusion level of maize silage in the ration of dairy cows for milk production and composition is reviewed. The data showed that the nutritive value of maize silages is highly variable and that most of this variation is caused by large differences in maturity at harvest. Maize silages ensiled at a very early stage (dry matter (DM) < 250 g kg(-1)) were particularly low in starch content and starch/neutral detergent fibre (NDF) ratio, and resulted in a lower DM intake (DMI), milk yield and milk protein content. The DMI, milk yield and milk protein content increased with advancing maturity, reaching an optimum level for maize silages ensiled at DM contents of 300-350 g kg(-1), and then declined slightly at further maturity beyond 350 g kg(-1). The increases in milk (R(2) = 0.599) and protein (R(2) = 0.605) yields with maturity of maize silages were positively related to the increase in starch/NDF ratio of the maize silages. On average, the inclusion of maize silage in grass silage-based diets improved the forage DMI by 2 kg d(-1), milk yield by 1.9 kg d(-1) and milk protein content by 1.2 g kg(-1). Further comparisons showed that, in terms of milk and milk constituent yields, the optimum grass/maize silage ratio depends on the quality of both the grass and maize silages. Replacement of grass silage with maize silage in the ration, as well as an increasing maturity of the maize silages, altered the milk FA profile of the dairy cows, notably, the concentration of the cis-unsaturated FAs, C18:3n-3 and n-3/n-6 ratio decreased in milk fat. Despite variation in nutritive value, maize silage is rich in metabolizable energy and supports higher DMI and milk yield. Harvesting maize silages at a DM content between 300 and 350 g kg(-1) and feeding in combination with grass silage results in a higher milk yield of dairy cows.

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Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore–Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.

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