Real-Time Dry Matter Prediction in Whole-Plant Corn Forage and Silage Using Portable Near-Infrared Spectroscopy

Portable near-infrared reflectance spectroscopy (NIRS) offers the opportunity of a rapid measurement of forage dry matter concentration, allowing producers to make faster adjustments in real time. This study compared dry matter (DM) concentration predictions of three units of a portable near-infrared reflectance spectrometer (pNIRS) with conventional forced-air oven drying (48 h at 60 °C) using corn forage and silage samples. Moreover, a common on-farm method (Koster tester) was also compared. The reflectance curve used by pNIRS to predict DM was obtained by scanning WPCS samples and developed by SciO. A total of 113 whole-plant corn forage (WPCF) and 27 whole-plant corn silage (WPCS) samples from 66 corn hybrids were obtained from three separate experiments conducted between 2018 and 2019. These three experiments were completely independent of each other, with sample collections over different periods. In Experiment 1, all treatments were tested in WPCF, and the DM concentration of the forced-air oven differed from Koster testers (35.4 vs. 34.3% DM, on average, respectively) and all three pNIRS units (35.4 vs. 30.7% DM, on average, respectively), with no differences among pNIRS. All treatments were positively correlated with the forced-air oven treatment DM values. Experiment 2 evaluated the Koster tester and pNIRS in WPCF on farms, and the Koster tester differed from pNIRS (37.2 vs. 33.3% DM, on average, respectively) treatments. All pNIRS were positively correlated with Koster tester treatment. In Experiment 3, all treatments were tested in WPCS, and the DM concentration of the forced-air oven was greater than other treatments (35.3 vs. 32.8% DM, on average, respectively). Overall, Koster tester predictions for both Experiments 1 and 3 were better correlated with the forced-air oven than pNIRS. Additionally, pNIRS showed a lower mean bias, although low coefficients of determination and concordance correlation were observed in Experiment 3 compared to Experiments 1 and 2, which might be related to the prediction curve. Further calibrations of the predictive curve with forage samples would be needed to reasonably estimate the DM concentration of WPCF, whereas a greater number of samples could account for the variations in WPCS due to large heterogeneity in particle composition (e.g., leaves, stem, and kernel), size, and distribution.