Prediction of composition and ruminal degradability characteristics of barley straw by near infrared reflectance spectroscopy

Measurements of Chemical Compositions in Corn Stover and Wheat Straw by Near-Infrared Reflectance Spectroscopy

Simple Summary

Rapid and non-destructive methods play an important role in assessing forage quality. This study is aimed at establishing a calibration model that predicts the moisture, CP, NDF, ADF, and hemicellulose of corn stover and wheat straw by NIRS. In addition, we also intended to compared the predictive accuracy of combined calibration models to the individual models of chemical compositions for corn stover and wheat straw by NIRS. We show that accurately combining calibrated models would be useful for a broad range of end users. Furthermore, the accuracy of the calibration models was improved by increasing the sample numbers (the range of variability) of different straw species.

Abstract

Rapid, non-destructive methods for determining the biochemical composition of straw are crucial in ruminant diets. In this work, ground samples of corn stover (n = 156) and wheat straw (n = 135) were scanned using near-infrared spectroscopy (instrument NIRS DS2500). Samples were divided into two sets, with one set used for calibration (corn stover, n = 126; wheat straw, n = 108) and the remaining set used for validation (corn stover, n = 30; wheat straw, n = 27). Calibration models were developed utilizing modified partial least squares (MPLS) regression with internal cross validation. Concentrations of moisture, crude protein (CP), and neutral detergent fiber (NDF) were successfully predicted in corn stover, and CP and moisture were in wheat straw, but other nutritional components were not predicted accurately when using single-crop samples. All samples were then combined to form new calibration (n = 233) and validation (n = 58) sets comprised of both corn stover and wheat straw. For these combined samples, the CP, NDF, and ADF were predicted successfully; the coefficients of determination for calibration (RSQ_C) were 0.9625, 0.8349, and 0.8745, with ratios of prediction to deviation (RPD) of 6.872, 2.210, and 2.751, respectively. The acid detergent lignin (ADL) and moisture were classified as moderately useful, with RSQ_C values of 0.7939 (RPD = 2.259) and 0.8342 (RPD = 1.868), respectively. Although the prediction of hemicellulose was only useful for screening purposes (RSQ_C = 0.4388, RPD = 1.085), it was concluded that NIRS is a suitable technique to rapidly evaluate the nutritional value of forage crops.

Prediction of rumen degradability parameters of a wide range of forages and non-forages by NIRS

Kinetics of nutrient degradation in the rumen is an important component of feed evaluation systems for ruminants. The in situ technique is commonly used to obtain such dynamic parameters, but it requires cannulated animals and incubations last several days limiting its application in practice. On the other hand, feed industry relies strongly on NIRS to predict chemical composition of feeds and it has been used to predict nutrient degradability parameters. However, most of these studies were feedstuff specific, predicting degradability parameters of a particular feedstuff or category of feedstuffs, mainly forages or compound feeds and not grains and byproducts. Our objective was to evaluate the potential of NIRS to predict degradability parameters and effective degradation utilizing a wide range of feedstuffs commonly used in ruminant nutrition. A database of 809 feedstuffs was created. Feedstuffs were grouped as forages (FF; n=256), non-forages (NF; n=539) and of animal origin (n=14). In situ degradability data for dry matter (DM; n=665), CP (n=682) and NDF (n=100) were collected. Degradability was described in terms of washable fraction (a), slowly degradable fraction (b) and its rate of degradation (c). All samples were scanned from 1100 to 2500 nm using an NIRSystems 5000 scanning in reflectance mode. Calibrations were developed for all samples (ALL), FF and NF. Equations were validated with an external validation set of 20% of total samples. NIRS equations to predict the effective degradability and fractions a and b of DM, CP and NDF could be evaluated from being adequate for screening (r(2)>0.77; ratio of performance to deviation (RPD)=2.0 to 2.9) to suitable for quantitative purposes (r(2)>0.84; RPD=3.1 to 4.7), and some predictions were improved by group separation reducing the standard error of prediction. Similarly, the rate of degradation of CP (CP(c)) and DM (DM(c)) was predicted for screening purposes (RPD⩾2 and 2.5 for CP(c) and DM(c), respectively). However, the rate of degradation of NDF was not predicted accurately (NDF(c) : r(2)<0.75; RDP<2).

Measurement of rumen dry matter and neutral detergent fiber degradability of feeds by Fourier-transform infrared spectroscopy

This study explored the potential of partial least squares (PLS) and Fourier-transform infrared spectroscopy (FTIR) to predict rumen dry matter (DM) and neutral detergent fiber (NDF) degradation parameters of a wide range of feeds for ruminants, as an alternative to the in situ method. In total, 663 samples comprising 80 different feed types were analyzed. In situ DM and NDF degradabilities were determined as follows: effective degradability (ED), rumen soluble fraction (A), degradable but not soluble fraction (B), rate of degradation of the B fraction (C), and indigestible NDF (iNDF). Infrared spectra of dry samples were collected by attenuated total reflectance from 600 to 4000cm(-1). Feeds were randomly classified into 2 subsets of samples with representation of all feed types; one subset was used to develop regression models using partial least squares, and the second subset was used to conduct an external validation of the models. This study indicated that universal models containing all feed types and specific models containing concentrate feeds could provide only a relatively poor estimation of in situ DM degradation parameters because of compositional heterogeneity. More research, such as a particle size distribution analysis, is required to determine whether this lack of accuracy was due to limitations of the FTIR approach, or simply due to methodological error associated with the in situ method. This latter hypothesis may explain the low accuracy observed in the prediction of degradation rates if there was physical leakage of fine particles from the mesh bags used during in situ studies. In contrast, much better predictions were obtained when models were developed for forage feeds alone. Models for forages led to accurate predictions of DMA, DMB, NDFED, and NDF concentration (R(2)=0.91, 0.89, 0.85, and 0.79, standard error = 4.34, 5.97, 4.59, and 4.41% of DM, respectively), and could be used for screening of DMED, NDFC, and iNDF. These models relied on certain regions of the FTIR spectrum (900-1150 and 1500-1700cm(-1)), which are mainly compatible with absorption of plant cell wall components, such as cellulose, pectin, lignin, cutin, and suberin, but also with nonstructural carbohydrates and certain active compounds. In conclusion, FTIR spectroscopy could be considered a low-cost alternative to in situ measurements in feed evaluation.

Using NIRS to predict fiber and nutrient content of dryland cereal cultivars

Residue from cultivars of spring wheat (Triticum aestivum L.), winter wheat, and spring barley (Hordeum vulgare L.) was characterized for fiber and nutrient traits using reference methods and near-infrared spectroscopy (NIRS). Calibration models were developed for neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), carbon (C), sulfur (S), nitrogen (N), and C:N. When calibrations were tested against validation sets for each crop year, NIRS was an acceptable method for predicting NDF (standard error of prediction (SEP)<0.87; R2>0.90) and ADF (SEP< 0.81; R2>0.92) and moderately successful for ADL in 1 year of the study (SEP=0.44; R2=0.81) but less successful for C, S, N, and C:N (R2 all<0.57). These results indicate that NIRS can predict the NDF and ADF of cereal residue from dryland cropping systems and is a useful tool to estimate residue decomposition potential.

Prediction of aspects of neutral detergent fibre digestion of forages by chemical composition and near infrared reflectance spectroscopy

Sixty-two herbage samples, harvested in natural meadows located in the mountains of León (north-west Spain), and characterised by a diverse botanical composition and different stages of maturity of the plants, were used to evaluate the ability of chemical composition and near infrared (NIR) spectroscopy to predict in vitro digestibility and in sacco degradability of the neutral detergent fibre (NDF) fraction. In vitro digestibility was performed as described by the Goering and Van Soest procedure. Three dry Holstein-Friesian cows fitted with a rumen cannula were used to incubate the herbage samples. A Bran+Luebbe InfraAlyzer 500 spectrophotometer was used to obtain the NIR spectra corresponding to the 62 original herbage samples. Prediction equations for the estimation of in vitro digestibility and in sacco degradability parameters of the NDF fraction were generated using NIR spectra or chemical data as independent variables. The results showed that the in vitro digestibility and kinetic parameters of degradation of the NDF fraction could not be predicted accurately, probably as a consequence of the errors corresponding to the reference methods. In contrast, these errors did not greatly affect the extent of disappearance of the NDF fraction at later times, so the accuracy of prediction of these parameters was higher, especially when NIR spectra were used as independent variables. This is probably due to the close relationship that the parameters showed with the chemical data, since this kind of information, together with some physical characteristics of the samples, is included in the NIR spectra.