Description:
<jats:p>Legume content in legume–grass mixtures is a key parameter for the quantification of N<jats:sub>2</jats:sub> fixation, forage, and diet quality. This study was conducted (i) to develop a near infrared reflectance spectroscopy (NIRS) based method to estimate the legume content in multispecies legume–grass mixtures as in widespread use in Western Europe, (ii) to compare end‐points and artificial mixture calibration strategies and (iii) to evaluate the effect grinding may have on the NIRS predictions of legume content. Calibration samples were taken in 1999 and 2000 in legume–grass fields that comprised a broad variation of site conditions. The samples were hand‐sorted, dried, and ground. End‐points calibrations derived from sets of legume samples (=100% legume content) and sets of grass samples (=0% legume content) were compared with calibrations where 63 spectra of artificially mixed samples (increments of 5% legume content) were added to represent a continuum of possible values of legume content. The influence of the preparation protocol of defined dry mixtures was compared by preparing duplicate mixtures where one replicate was prepared from fresh material, dried, and ground as a mixture and the other mixed from dry, ground material. Log (1/<jats:italic>R</jats:italic>) (<jats:italic>R</jats:italic> = reflectance) spectra were taken of all samples. Partial least squares regression was applied to develop calibration algorithms in the spectral range of 7500 to 3950 cm<jats:sup>−1</jats:sup> (1333–2532 nm). First derivative combined with vector normalization proved to be the best data pretreatment. For each strategy, three models were developed: One model was based on all samples validated with a one‐leave‐out cross‐validation, and two models were based on half of the samples validated by the other half. Prediction errors were between 2.2 and 4.0%, and coefficients of determination of all validations were greater than 99% so that no remarkable differences between the models existed. At least 70% of the selected spectral regions were in common for all models. These regions do not describe legumes themselves but rather the information that discriminates them from grasses. It is emphasized that the calibrations introduced have the potential for a broad use that needs to be proved by further validations.</jats:p>