Application of fecal near-infrared reflectance spectroscopy profiling for the prediction of diet nutritional characteristics and voluntary intake in beef cattle

Use of near-infrared reflectance spectroscopy on feces to estimate digestibility and dry matter intake of dietary nutritional characteristics under grazing conditions in Colombian creole steers

Digestibility and intake are parameters difficult and expensive to estimate under grazing conditions; therefore, the aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations applied to feces (F-NIRS) and evaluate their accuracy to predict dry matter digestibility (DMD) and dry matter intake (DMI) of Colombian creole cattle. Five digestibility trials using creole steers were conducted; indigestible neutral detergent fiber (iNDF) was used as internal marker and Cr₂O₃ and TiO₂ as external markers. A total of 249 forage and 396 fecal samples from individual animals were collected, dried, and grinded for conventional chemical analysis. For spectral analysis, fecal samples were pooled across collection periods (77 samples). Chemometric analysis was performed using WinISI V4.10 software applying the modified partial least squares method. Cross-validation was performed to avoid overfitting the models. The goodness-of-fit statistics considered were the coefficient of determination in cross-validation and prediction sets (R²cv and r², respectively) and the ratio performance deviation (RPD). Fecal NIRS calibrations developed for forage and supplement DMD showed a satisfactory fit (R²cv =0.87 and RPD=2.77 and R²cv=0.92 and RPD=3.50, respectively). The accuracy of fecal output equations using chromium (Cr) and titanium (Ti) was similar in terms of R²cv (0.92) and RPD (3.63 vs. 3.57). Total DMI equations using Ti performed better compared to Cr (R²cv = 0.82 vs. 0.78; RPD=2.41 vs. 2.17, respectively). The F-NIRS models were validated using a completely independent set of fecal samples showing a moderate fit (r²>0.8 and RPD>2.0). This study showed that F-NIRS is a feasible tool to predict DMD and DMI of creole steers under grazing conditions. However, previous to socialization, this requires an improvement in accuracy of the calibrated equations related to grazing animals in different production contexts.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.