Development and validation of near-infrared spectroscopy for the prediction of forage quality parameters in Lolium multiflorum

Zhongfu Yang,Yan Zhang,Gang Nie,Ling Pan,Linkai Huang,Xiao Ma,Xinquan Zhang

doi:10.7717/peerj.3867

Zhongfu Yang, Yan Zhang + Show 5 more

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https://doi.org/10.7717/peerj.3867

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Journal: PeerJ	Publication Date: Oct 3, 2017
Citations: 33	License type: CC BY 4.0

Affiliation: Sichuan Agricultural University

Abstract

Italian ryegrass (Lolium multiflorum) is an important cool-season, annual forage crop for the grassland rotation system in Southern China. The primary aim of breeding programs is always to seek to improve forage quality in the animal productivity system; however, it is time- and labor-consuming when analyzed excessive large number of samples. The main objectives of this study were to construct near-infrared reflectance spectroscopy (NIRS) models to predict the forage chemistry quality of Italian ryegrass including the concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and water soluble carbohydrate (WSC). The results showed that a broader range of CP, NDF, ADF and WSC contents (%DM) were obtained (4.45–30.60, 21.29–60.47, 11.66–36.17 and 3.95–51.52, respectively) from the samples selected for developing NIRS models. In addition, the critical wavelengths identified in this study to construct optimal NIRS models were located in 4,247–6,102 and 4,247–5,450 cm-1 for CP and NDF content, and both wavelengths 5,446–6,102 and 4,247–4,602 cm-1 could for ADF and WSC. Finally, the optimal models were developed based on the laboratory data and the spectral information by partial least squares (PLS) regression, with relatively high coefficients of determination (R2CV, CP = 0.99, NDF = 0.94, ADF = 0.92, WSC = 0.88), ratio of prediction to devitation (RPD, CP = 8.58, NDF = 4.25, ADF = 3.64, WSC = 3.10). The further statistics of prediction errors relative to laboratory (PRL) and the range error ratio (RER) give excellent assessments of the models with the PRL ratios lower than 2 and the RER values greater than 10. The NIRS models were validated using a completely independent set of samples and have coefficients of determination (R2V, CP = 0.99, NDF = 0.91, ADF = 0.95, WSC = 0.91) and ratio of prediction to deviation (RPD, CP = 9.37, NDF = 3.44, ADF = 4.40, WSC = 3.39). The result suggested that routine screening for forage quality parameters with large numbers of samples is available with the NIRS model in Italian ryegrass breeding programs, as well as facilitating graziers to monitor the forage development stage for improving grazing efficiency.

Highlights

Italian ryegrass (Lolium multiflorum) is one of the most famous annual forage grasses, and is widely used for the cereal-forage rotation system in south China owing to its high productivity and palatability, excellent resprouting and easy plantability (Carámbula & Carámbula, 1977; MAIA, 1995)
The results showed that the best model with lowest root mean square error of cross-validation (RMSECV) and the highest R2CV values (RMSECV = 0.68, R2CV = 0.99) was the crude protein (CP) calibration model, while the highest RMSECV and the lowest R2CV value was obtained from the water soluble carbohydrate (WSC) calibration model, indicating that the near-infrared reflectance spectroscopy (NIRS) model for predicting CP content was well suitable in Italian ryegrass (Table 3)
The most critical quality parameters comprising CP, neutral detergent fiber (NDF), acid detergent fiber (ADF) and WSC contents were detected in 34 Italian ryegrass accessions at different development stage and the results showed that the range of most parameters was broader than the range in previous studies (Asekova, 2016; Fox et al, 2012; Williams & Norris, 1987a), which represented a wider application of our NIRS model constructed based on these data

Summary

Introduction

Italian ryegrass (Lolium multiflorum) is one of the most famous annual forage grasses, and is widely used for the cereal-forage rotation system in south China owing to its high productivity and palatability, excellent resprouting and easy plantability (Carámbula & Carámbula, 1977; MAIA, 1995). The content of crude protein (CP), neutral detergent fiber (NDF) and acid detergent fiber (ADF), and water soluble carbohydrate (WSC) are the three most important forage quality parameters which determine the forage intake and digestibility for livestock (Mott & Moore, 1970). The high CP content increased the milk and milk protein yield (Mäntysaari et al, 2004) and the NDF, ADF are well correlated with digestibility for livestock animals (Agbagla-Dohnani et al, 2001; Agnihotri et al, 2003; Dutta, Sharma & Hasan, 1999; Suksombat, 2004; Yépez et al, 2004), as well as the WSC may improve the balance and synchrony of the nitrogen and carbon supply to the rumen (Miller et al, 2001). The appropriate content of the WSC could prevent clostridial from fermenting, which is a critical parameter for silage production (Haigh, 1990; Pettersson & Lindgren, 1990). Traditional methods for determining the contents of CP, NDF, ADF, and WSC are based on standard wet chemistry analytical techniques; it is unsuitable for a large number of samples due to its high costly, time-consuming, laborious, and produces pollution in Italian ryegrass (Kong et al, 2005; Wittkop, Snowdon & Friedt, 2012)

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