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

Tao Guo,Fangbin Wang,Fei Li,Guisheng Lan,Luming Dai,Faming Pan,Baipeng Yan,Fadi Li

doi:10.3390/ani11113328

Abstract

Simple SummaryRapid 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.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 (RSQC) 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 RSQC 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 (RSQC = 0.4388, RPD = 1.085), it was concluded that NIRS is a suitable technique to rapidly evaluate the nutritional value of forage crops.

Highlights

IntroductionCereal crops (namely corn and wheat) are major crops in China, with large amounts of these forage straw materials serving as important roughage sources for ruminant production
Cereal crops are major crops in China, with large amounts of these forage straw materials serving as important roughage sources for ruminant production
There are other uses of cereal straw, such as replacing fossil fuels in the energy sector and chemical industry, and bedding [2]. He et al demonstrated that basalt fiber addition is an effective way to enhance biohydrogen production from corn straw [3]

Summary

Introduction

Cereal crops (namely corn and wheat) are major crops in China, with large amounts of these forage straw materials serving as important roughage sources for ruminant production. There are other uses of cereal straw, such as replacing fossil fuels in the energy sector and chemical industry, and bedding [2]. He et al demonstrated that basalt fiber addition is an effective way to enhance biohydrogen production from corn straw [3]. The presence of beneficiary phytochemicals in straw, such as NDF, are important to stimulate rumen fermentation in ruminants.

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