Comparison of nutritive values of grasses and legume species using forage quality index
Fazel Amiri and Abdul Rashid b. Mohamed Shariff
pp. 577 - 586
Abstract
Understanding forage quality and the factors that affect its constituents will help improve livestock production by
making decisions that optimize forage nutritive value and intake. This investigation was conducted in Zagros semi-arid
rangeland center, Iran to determine forage quality of several grass and legume species. Samples were collected at early
bloom, from 5 m long and 0.10 m wide strips at a cutting height of 0.05 m. The samples were weighed for dry matter yield and
nutritive value measurements. Samples were dried and analyzed in the laboratory by standard methods to determine the
following parameters: nitrogen, crude protein (CP), ash, ether extract, neutral detergent fiber (NDF), acid detergent fiber
(ADF), digestible energy, dry matter digestibility (DMD), metabolizable energy, dry matter intake (DMI) and Relative Forage
Quality Index (RFQi
). Standard ANOVA procedures were used to analyze the data. Quality of forage species was classified
based on RFQi
similarity in Mosaic version 3.01 and PC-ORD environment software. There was a positive correlation
between CP, DMD, DMI and RFQi
for all species, and also a negative correlation between ADF, CP and RFQi
. The results of
statistical analysis show that, forage quality of species (Leguminoseae and Gramineae) were significantly different (P<0.05).
Species from gramineae showed significantly (P<0.05) higher ADF and NDF than leguminoseae species. At the stage of
sampling, gramineae had lower RFQi
than the legume family. The results of study showed the decision to practice grazing or
make hay or how to select the best hay available should be based on forage quality. A forage analysis is therefore important
to evaluate the nutritive value of the forage to be grazed or hay to be purchased or marketed. Knowing what affects forage
quality will also help in making appropriate selection of forages and supplements that will match livestock requirements and
result in economically optimum livestock performance.
