Visible and near-infrared (Vis-NIR, 350–2500 nm) diffuse reflection spectroscopy (DRS) models built from “as-collected” samples of solid cattle manure accurately predict concentrations of moisture and crude ash. Because different organic molecules emit different spectral signatures, variations in livestock diet composition may affect the predictive accuracy of these models. This study investigates how differences in livestock diet composition affect Vis-NIR DRS prediction of moisture and crude ash. Spectral signatures of solid manure samples (<i>n</i> = 216) from eighteen groups of cattle on six different diets were used to calibrate and validate partial least squares (PLS) regression models. Seven groups of PLS models were created and validated. In the first group, two-thirds of all samples were randomly selected as the calibration set and the remaining one-third were used for the validation set. In the remaining six groups, samples were grouped by livestock diet (ration). Each ration in turn was held out of calibrations and then used as a validation set. When predicting crude ash, the fully random calibration model produced a root mean square deviation (RMSD) of 2.5% on a dry basis (db), ratio of standard error of prediction to the root mean squared deviation (RPD) of 3.1, bias of 0.14% (db), and correlation coefficient <i>r</i><sup>2</sup> of 0.90., When predicting moisture, an RMSD of 1.5% on a wet basis (wb), RPD of 4.3, bias of –0.09% (wb), and <i>r</i><sup>2</sup> of 0.95 was achieved. Model accuracy and precision were not impaired by exclusion of any single ration from model calibration.

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