Abstract
The study investigated the suitability of stage of maturity and botanical fractions of whole crop rice (WCR) to predict yield and nutritive value of ensiled WCR for dairy cows. Eight varieties of WCR (i.e., Akichikara, Fukuhibiki, Habataki, Hamasari, Hokuriku 168, Kusanami, Tamakei 96, Yumetoiro) were harvested at four stages of maturity (i.e., 10, 22, 34, 45 days after flowering [DAF]) and ensiled. Dry matter (DM) yield at each harvest was determined. Silage samples were fractionated into four botanical fractions being: leaf blade, leaf sheath, stem and head. Silage samples were also analyzed for chemical composition, fermentation characteristics, in situ DM and N disappearance. Metabolizable energy (ME) and metabolizable protein (MP) content of samples were estimated according to Terada et al. (1988) and AFRC (1993), respectively. Relationships between maturity or proportions of botanical fractions and contents of WCR silage in terms of DM, ME and MP, and their yields, were estimated by correlation and regression analysis. Stage of maturity was positively related (P<0.001) to ME content (R2=0.46; y=4.53+0.08X) and MP content (R2=0.56; y=22.26+0.76X), and DM yield (R2=0.63; y=9.21+0.12X), ME yield (R2=0.68, y=36931+1708X) and MP yield (R2=0.72, y=161.0+14.15X) of WCR. Proportion of leaf was negatively related to yields and nutritive value of ensiled WCR, whilst proportion of head was positively related (P<0.05 to <0.001). Proportion of head was best related to the ME content (R2=0.72; y=3.26+0.009X), MP content (R2=0.72; y=12.31+0.079X), and DM yield (R2=0.41; y=9.02+0.009X), ME yield (R2=0.76, y=19494+165.5X), and MP yield (R2=0.75, y=34.37+1.32X) of WCR. Results suggest that to optimize yield and nutritive value, WCR should be ensiled within 40 DAF and the proportion of head should be equal to or more than 500g per kgDM of WCR silage. Stage of maturity and proportion of head of WCR predict yields of DM, ME and MP of WCR, and their contents, in WCR silage with acceptable accuracy. However, these relationships need to be validated using large data sets and in vivo studies.
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