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Botanical Composition Definition of Tall Fescue‐White Clover Mixtures by Near Infrared Reflectance Spectroscopy 1
Author(s) -
Petersen J. C.,
Barton F. E.,
Windham W. R.,
Hoveland C. S.
Publication year - 1987
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci1987.0011183x002700050050x
Subject(s) - trifolium repens , near infrared reflectance spectroscopy , composition (language) , calibration , pasture , grazing , biology , legume , festuca arundinacea , standard error , chemical composition , agronomy , zoology , poaceae , botany , horticulture , mathematics , near infrared spectroscopy , chemistry , statistics , linguistics , philosophy , neuroscience , organic chemistry
Near infrared reflectance spectroscopy (NIRS) was evaluated for determining botanical composition of preformed mixtures containing ‘Regal’ iadino clover ( Trifolium repens L.) and ‘Kentucky 31’ lowendophyte tall fescue ( Festuca arundinacea Schreb.) as well as the botanical composition of samples harvested from grazing paddocks. Both species were grown in separate plots, harvested at monthly intervals, and hand‐mixed. This provided combinations from 100% grass to 100% legume, resulting in a total of 60 samples that were used for NIRS calibration. The NIRS calibration equations for percentage tall fescue and white clover resulted in standard errors of calibration of 2.98 and 2.80%, respectively. Synthetic mixtures composed of material from grazing paddocks were used for validation of the equations. Species composition was predicted with excellent precision and accuracy showing biases of ‐1.64 and 1.80%, and standard errors of prediction (SEP) of 3.16 and 3.08% for the tall fescue and white clover equations, respectively. High R 2 was also obtained when predicting the percentage of both component species. The NIRS prediction of pasture samples, whose actual composition was determined by hand separation, yielded higher SEP and bias. This additional variation (compared to the prediction of synthetic mixtures) was assumed to be made up of error associated with the hand‐separation procedure and the variation of the prediction samples not present in the calibration set. These data indicate that with the proper selection of calibration samples, wavelengths, and data transformation, NIRS can be used to determine the botanical composition of grass‐legume mixed samples.