Abstract
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.
Highlights
For many years dairy and beef cattle breeding have focused on improving the production and profitability of animals through genetics, nutrition, and management, often at the expense of other relevant traits
ACorrelation coefficient (r) transformed to coefficient of determination (R2). bBayes B methodology employed; multibreed (Mul); Norwegian Red (Nor); Brown Swiss (Bro); Crossbreed (Cro); Simmental (Sim); Holstein (Hol); number of folds (n-F) in the cross-validation, leave-one-out cross-validation (LOOCV), train and test cross-validation defined by splitting the data set randomly (R-Tr/Te) or not (Tr/Te), external or independent validation. *The validation strategy defined as “CV” was assigned for the reviewed paper that did not completely describe the validation method adopted or the authors defined that cross-validation was employed
ABulk milk samples. bBackward interval partial least squares (BiPLS), number of folds (n-F) in the cross-validation, leave-one-out cross-validation (LOOCV), train and test cross-validation defined by splitting the data set randomly (R-Tr/Te), external or independent validation. cCorrelation coefficient (r) transformed to coefficient of determination (R2). *The validation strategy defined as “CV” was assigned for the reviewed paper that did not completely describe the validation method adopted or the authors defined that cross-validation was employed
Summary
For many years dairy and beef cattle breeding have focused on improving the production and profitability of animals through genetics, nutrition, and management, often at the expense of other relevant traits. Several technologies (e.g., sensors, infrared spectrometry, and image analysis, among others) have been used to generate novel complex traits in dairy and beef cattle, with infrared spectrometry being one of the most relevant tools used in livestock to date (De Marchi et al, 2014; Dixit et al, 2017; Bell and Tzimiropoulos, 2018). NIR and MIR are the main regions used in livestock applications (Griffiths and de Hasenth, 2007). This technology is fast, non-invasive, nondestructive, and has great potential for on-line measurement (De Marchi et al, 2014; Dixit et al, 2017)
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