The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis. The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60-70%. Conversely, this reduction in accuracy was relatively contained (10-15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts. The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.

Paper mulberry (PM) and mulberry (MU) have been considered potential substitutes for traditional forages in response to the increasing demand for high-protein feed for livestock. To improve the utility of these two typical woody forages, our study investigated the effects of sucrose and lactic acid bacteria (LAB) additives on the fermentation quality, nutritive value, and protein fractions of their leaf silages. Collected leaves were separately subjected to ensiling treatments, either with or without sucrose (S), in combination with Lactobacillus plantarum (LP), or Lactobacillus casei (LC). The silage was sampled and analyzed for fermentation parameters, carbohydrates, and protein fractions after ensiling for 60 days. The pH value of paper mulberry silages with S was 19% lower than that without S, while LAB-treated mulberry silages showed decreased ammonia nitrogen (by 71%) and fraction A in crude protein (by 15%) compared with no LAB additives. In summary, adding S improved the fermentation quality, with no positive effect on protein fractions, in PM silage, whereas LAB additives improved the potential utilization of protein in MU silage.

Portable near infrared (NIR) spectrometers are now readily available on the market and with their smaller size, weight and cost have provided the opportunity to analyze forages both on farms and directly in the field. As new technologies and new portable NIR instruments become available on the market, calibrations for these instruments become a major constraint due to the costs and time necessary to collect reference data. This study evaluated techniques to transfer calibrations for alfalfa and grass forage samples that were developed for a scanning benchtop monochromator (FOSS 6500, 400–2498 nm, LAB) to a diode array instrument (AuroraNir, 950–1650 nm, DA), a digital light processing instrument (NIR-S-G1, 950–1650 nm, DLP) and a short wavelength instrument (SCiO, 740–1070 nm, SCIO). Alfalfa (N = 612) and grass (N = 516) samples from eight agronomic studies were analyzed by wet chemistry for crude protein, neutral detergent fiber (NDF), acid detergent fiber (ADF), in-vitro digestibility (IVTD) and NDF digestibility (NDFD) and divided into calibration, test-set, standardization and inoculation/prediction datasets. Different calibration transfer strategies were evaluated: Spectral Bias Correction (SBC), Shenk and Westerhaus algorithm (SW), Piecewise Direct Standardization (PDS), Dynamic Orthogonal Projection (DOP) or creating a new calibration using LAB predictions of the inoculation/prediction dataset as reference values. All computations for trimming, calibration, validation and standardization were developed using R. SBC with inoculation was an effective method to transfer calibrations for DA. Validation errors for DA transferred calibrations were about 15% lower than LAB for alfalfa data but 6% greater for grass data. For SCIO after DOP spectral adjustment, predicting errors were slightly greater than LAB for both data sets, while prediction errors with DLP were two to three times greater than LAB even after inoculation. PDS created spectral artifacts in the spectra of all three portables, which then resulted in large validation errors. Using LAB predictions as reference values was suitable only for DA, while DLP and DA had large prediction errors. This study showed that calibration sharing between a benchtop and portable instruments is challenging, but possible depending on the portable technologies and the transfer method. Spectral bias correction plus inoculation was the best method to transfer multivariate models for the forage components’ prediction from LAB to handhelds, particularly for DA. Application of DOP was beneficial for SCIO to successfully maintain performance of the original calibration, while for DLP the prediction models were not accurate. Additional studies are necessary to verify these transferring techniques can also be applied to fresh forages, allowing an easier and extended implementation of NIR analysis directly in fields.