Soybeans have been a favored livestock forage for centuries. However, only a few studies have been conducted to estimate the forage quality of soybean by near-infrared reflectance spectroscopy (NIRS). In this study, 353 forage soybean samples were used to develop near-infrared reflectance (NIR) equations to estimate four forage quality parameters: crude protein (CP), crude fat (CF), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Samples included 181 recombinant inbred lines derived from PI 483463 (G. soja) × Hutcheson (G. max), 104 cultivated soybeans (G. max), and 68 wild soybeans (G. soja). Two NIR equations developed for CP and CF (2,5,5,1; multiple scatter correction [MSC]) and for NDF and ADF (1,4,4,1; MSC) were the best prediction equations for estimating these parameters. The coefficients of determination in the external validation set (r 2 ) were 0.934 for CF, 0.909 for CP, 0.767 for NDF, and 0.748 for ADF. The relative predictive determinant ratios for MSC (2,5,5,1) calibration indicate that the CP (3.25) and CF (3.85) equations were acceptable for quantitative prediction of soybean forage quality, whereas the NDF (2.07) and ADF (1.97) equations were useful for screening purposes. The NIR calibration equations developed in this study will be useful in predicting soybean forage quality for these four quality parameters.

Soybean [Glycine max (L.) Merr] with increased oleic acid is desirable to improve oxidative stability and functionality of soybean seed oil. Recently, soybean genotypes with high oleic acid (≥70 %) were developed by breeding programs. Efficient and effective identification of high oleic acid soybean genotypes using non‐destructive near infrared reflectance (NIR) on whole seeds would greatly enhance progress in breeding programs. The objective of this study was to develop a calibration equation for NIR determination of high oleic acid from single soybean seeds. A total of 600 intact, single F2 seeds were scanned by NIR. Spectral data were collected between 400 and 2,500 nm at 2 nm intervals. The relationship between NIR spectral patterns of each soybean seed and its oleic acid content was examined. The best predicted equations for oleic acid were selected on the basis of minimizing the standard error of cross‐validation and increasing the coefficient of determination. Validation demonstrated that the equations for determining total oleic acid and over 50 % oleic acid content had high predictive ability (r2 = 0.91 and r2 = 0.99, respectively). To validate the newly developed equation, F2 seeds from a different genetic background were tested. Again, high oleic acid from single soybean seeds was accurately predicted from various genetic backgrounds. Therefore, applying the calibration equations to NIR will be useful to rapidly and efficiently select high oleic acid soybean genotypes in breeding programs.