The optimal lignin quantification method to breed for an improved cell wall digestibility in perennial ryegrass

Frederik van Parijs, Chris Van Waes, Bart Vandecasteele, Geert Haessaert, Isabel Roldán-Ruiz, Hilde Muylle

    Onderzoeksoutput: Bijdrage aan tijdschriftA1: Web of Science-artikelpeer review

    Uittreksel

    Perennial ryegrass (Lolium perenne) is an important source of protein and energy for dairy cattle. To improve the protein/energy ratio of this forage, focus is now on improving its cell wall digestibility. The in vitro assessment of the digestible fraction of the neutral detergent fibre (NDFD) is a superior method for determining the cell wall digestibility, but requires the use of ruminal fluid, which has a highly variable composition and is often not readily available. As lignin is considered the main cell wall component that impedes NDFD, we investigated whether this “subtrait” could serve as alternative breeding selection criterion to improve NDFD. Therefore, we assessed the accuracy of two lignin quantification methods: van Soest (ADL) and Klason lignin (KL). We also considered KL estimates corrected for the solubilized lignin (total lignin or TL) and non-solubilized protein (TL’). Although the latter is considered the truest possible lignin content, it was not always the most correlated to NDFD, due to the limited accuracy of protein quantification on lignin residue. TL is most correlated to NDFD and we therefore recommend it for use in conventional
    breeding if NDFD determination is not a possibility. However, NDFD is still a superior selection criterion, as it combines the effect of several subtraits and not just lignin.
    For marker–trait association studies, a more accurate estimate of lignin content
    is more important than a high correlation with NDFD, but also here, TL performs
    best.
    Oorspronkelijke taalEngels
    TijdschriftGrass and Forage Science
    Volume73
    Exemplaarnummer1
    Pagina's (van-tot)101-111
    ISSN0142-5242
    DOI's
    PublicatiestatusGepubliceerd - 2018

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