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

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.

Spatial and Temporal Monitoring of Pasture Ecological Quality: Sentinel-2-Based Estimation of Crude Protein and Neutral Detergent Fiber Contents

“…The SWIR region is also widely used for CP and NDF estimation in forage [6,7,13,29,76,78,[92][93][94][95][96][97][98]. Our results are in agreement with other studies that have also reported the importance of the SWIR region, and specifically for the wavelengths of 2202 nm (CP in rapeseed, R 2 = 0.98 [99] and 2204 nm (NDF in forage, R 2 = 0.97 [16].…”

Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

“…In comparison to ADF, the NDF concentration of tepary samples were less accurately predicted by all three techniques (Table 3). Similar differences between prediction accuracy of ADF and NDF were also noticed for guar in this study, and also reported earlier in NIRS studies involving Brassica napus [26], Lolium multiflorum [11], and Oryza sativa [27]. Though PLS performed better at predicting IVTD in tepary bean compared to other two, all three techniques resulted in relatively low prediction accuracy with R 2 cv and R 2 v ranging between 0.75-0.79 and 0.75-0.88, respectively.…”

“…The method of applying NIRS to predict forage quality involves analyzing a particular forage with both traditional lab analysis and NIRS, and then developing a predictive equation by pairing the information in a calibration dataset ( Figure 1). The NIRS has been widely used in forage quality predictions of crops including alfalfa (Medicago sativa) [9], maize (Zea mays) [10], ryegrass (Lolium multiflorum) [11], tall fescue (Festuca arundinacea) [12], and other species. However, the technique has been underutilized to provide predictions of forage quality for many warm-season legumes.…”

Use of near infrared spectroscopy for the evaluation of forage for ruminants

“…Forage can be dried or oven-dried and afterwards ground to 1 mm (Bezada et al, 2017;Simeone et al, 2015;Andueza et al, 2016;Molano et al, 2016;Durmic et al, 2017;Ullmann et al, 2017;Parrini et al, 2018) or 2 mm (Zhang et al, 2017). This is done for the sake of standardization, especially because the quality of the calibration can be increased with sample preparation and measurement standardization (Reddersen et al, 2013).…”