Determination of forage quality by near-infrared reflectance spectroscopy in soybean

Asekova, Sovetgul; Han, Sang-Ik; Choi, Hong-Jib; Park, Sang-jo; Shin, D. C.; Kwon, Chan Ho; Shannon, J. Grover; Lee, Jeong Dong

doi:10.3906/tar-1407-33

Cited by 31 publications

(38 citation statements)

References 35 publications

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“…For example, NIRS has been successfully used to predict the nutritive value of forages and hays through direct scanning of the forage samples or the extrusa obtained from esophageally fistulated animals [9]- [17]. Several other reports have also demonstrated successful application of NIRS in the analysis of anti-quality factors in forage [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (<i>Elymus glabriflorus</i>) Using Near-Infrared Reflectance Spectroscopy

Rushing¹,

Saha²,

Lemus³

et al. 2016

AJAC

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Southeastern wildrye (Elymus glabriflorus, Vasey ex L.H. Dewey) is a cool-season, perennial grass native to southeastern United States. Recently, there is a growing interest in its development as a grazing and haying forage crop due to its wide area of adaptation across this region. Consequently, there is a great need for the evaluation of its forage quality by rapid, but accurate analytical methods like Near-Infrared Reflectance Spectroscopy (NIRS). In this study, acceptable NIRS calibration models were developed for: dry matter, DM (n = 113, R 2 = 0.904, RSCD = 2.54, RSCIQ = 4.65); crude protein, CP (n = 113, R 2 = 0.974, RSCD = 5.16, RSCIQ = 5.92); acid detergent fiber, ADF (n = 116, R 2 = 0.896, RSCD = 2.35, RSCIQ = 1.28); neutral detergent fiber, NDF (n = 118, R 2 = 0.934, RSCD = 2.53, RSCIQ = 3.38); digestible dry matter, DDM (n = 116, R 2 = 0.895, RSCD = 2.36, RSCIQ = 1.35); dry matter intake, DMI (n = 115, R 2 = 0.924, RSCD = 2.40, RSCIQ = 2.53); and relative feed value, RFV (n = 114, R 2 = 0.932, RSCD = 2.94, RSCIQ = 2.81). Prediction of independent validation sets yielded good agreement between the NIRS predicted values and the laboratory reference values for each of: DM (n = 53, R 2 = 0.831, RPD = 2.45, RPIQ = 4.24); CP (n = 57, R 2 = 0.967, RPD = 5.37, RPIQ = 7.16); ADF (n = 49, R 2 = 0.895, RPD = 2.97, RPIQ = 1.51); NDF (n = 53, R 2 = 0.928, RPD = 3.75, RPIQ = 4.22); digestible dry matter, DDM (n = 55, R 2 = 0.860, RSCD = 265, RSCIQ = 1.15); dry matter intake, DMI (n = 156, R 2 = 0.845, RSCD = 2.48, RSCIQ = 2.11); and relative feed value, RFV (n = 55, R 2 = 0.916, RSCD = 3.45, RSCIQ = 3.04) contents, indicating that all seven calibration models had good quantitative information. Therefore, precise, accurate, and rapid analysis of these important forage quality attributes of southeastern wildrye can be routinely done using the developed NIRS calibration models.

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Section: Introductionmentioning

confidence: 99%

Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (<i>Elymus glabriflorus</i>) Using Near-Infrared Reflectance Spectroscopy

Rushing¹,

Saha²,

Lemus³

et al. 2016

AJAC

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“…In the present study the RPDc for all the calibrations were > 3.4 supporting the validity of the NIRS calibration models for routine analysis of the forage attributes measured. These RPDc values for the measured attributes were higher than the values reported previously (Alomar et al, 2009;Asekova et al, 2016;Campo et al, 2013;Cozzolino et al, 2006;Norman et al, 2015;Park et al, 1998) but lower than the RPDc for DMD and total N reported by Norman et al (2015) in temperate forages.…”

Section: Reference Values and Equation Developmentcontrasting

confidence: 53%

“…In the present study this criteria was met for all measurements except for the total N concentration prediction in soybean residue. Satisfactory RPDv values have been reported for total N and NDF and ADF prediction in soybean (Asekova et al, 2016). The poor prediction of the total N concentration in soybean in the present study may have been associated with the small population size (n=25) and that this material tends to be spectrally different to the other forages and crop residues.…”

Section: Equation Performancementioning

confidence: 47%

Options for improving the yield and nutritive value of maize and grain legume residues for ruminants in East African farming systems

Dejene¹

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The role and importance of livestock in smallholder crop-livestock systems and the importance of crop residues (CR) as ruminant feedstuffs in such systems are well established. The effects of genotype (G), environment (E) and crop management (M) factors on yields of grain and CR and the nutritive value of the CR of maize (Zea mays) and common bean (Phaseolus vulgaris) were investigated in an East African context. Genotype effects were examined in early and medium maturing (EM and MM, respectively) maize hybrids and popular varieties of common bean. Environment was examined at three sites for maize in the 2013 and 2014 cropping seasons and at four sites for common bean in 2013. Management effects examined were plant density (D) (5 and 7 plants/m 2 ), height of cutting at harvest in maize, and harvest at early and final seed maturity in common bean. Quality attributes (nitrogen (N), neutral detergent fibre (NDF) and acid detergent fibre (ADF) contents and dry matter digestibility (DMD)) were analysed using near infrared spectroscopy (NIRS) and calibrations expanded and validated with reference samples of CR representing the experimental sample set.There was substantial variation (P<0.05 to P<0.0001) among maize genotypes in yields of grain and stover and quality of stover as a feedstuff. Dual purpose genotypes were identified but G x E interactions for some yield and quality attributes showed that the variation among genotypes can depend on environment. In general the higher D increased yields of grain by 9-15% and stover by 21-31%, but had only minor effects on stover quality attributes. Year (Y) x density (D), G x E and G x D interactions were observed for some of the yield and stover attributes.High cutting height of maize (two internodes below the lowest ear) allowed harvest of most of the stover (on average 63-72% in EM and MM genotypes, respectively) as feedstuff while leaving stubble in the field available for other uses. This is established practice by farmers in some regions. The amount and quality as a feedstuff of the upper and lower stover components varied (P<0.01) among genotypes. N and DMD were 0.05-0.09% and 1.7-3.6%, respectively higher (P<0.0001) in the upper fraction of the stover than in whole stover. Thus evaluation and estimation of the amount and quality of maize stover available as a feedstuff should consider harvest at high cutting height to increase quality. G x E interactions were observed in yield and in some stover attributes in the upper and lower fractions, but these were not consistent.ii Common bean varieties varied (P<0.05 to P<0.0001) in yields of seed and haulm and in the nutritive value of haulm fractions (leaf, stem and pod wall) when harvested at seed maturity. There was extensive leaf loss at seed maturity which decreased haulm quality.Seed and haulm yields were correlated (r=0.87). Seed yield was generally not related to haulm quality as N% or DMD%. The variation in yield and haulm feedstuff quality was also examined in common bean varieties harvested at early se...

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“…The NIRS technique has not been exploited for forage quality predictions in soybean. A single report investigated modified PLS and multiple scatter correction methods for NIR predictions of CP, NDF, and ADF concentrations, using 353 soybean samples collected at one (R6) growth stage [31]. In comparison, calibrations developed in the present study, used data on IVTD and CP with just 70 soybean samples collected across a range of different growth stages.…”

Section: Soybeanmentioning

confidence: 99%

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

Baath

Gowda

et al. 2020

Sensors

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Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes—guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)—using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R2c = 0.94–0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R2cv/R2v = 0.92–0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R2cv/R2v = 0.42–0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.

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Determination of forage quality by near-infrared reflectance spectroscopy in soybean

Cited by 31 publications

References 35 publications

Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (<i>Elymus glabriflorus</i>) Using Near-Infrared Reflectance Spectroscopy

Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (<i>Elymus glabriflorus</i>) Using Near-Infrared Reflectance Spectroscopy

Options for improving the yield and nutritive value of maize and grain legume residues for ruminants in East African farming systems

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

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Determination of forage quality by near-infrared reflectance spectroscopy in soybean

Cited by 31 publications

References 35 publications

Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (&lt;i&gt;Elymus glabriflorus&lt;/i&gt;) Using Near-Infrared Reflectance Spectroscopy

Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (&lt;i&gt;Elymus glabriflorus&lt;/i&gt;) Using Near-Infrared Reflectance Spectroscopy

Options for improving the yield and nutritive value of maize and grain legume residues for ruminants in East African farming systems

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

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Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (<i>Elymus glabriflorus</i>) Using Near-Infrared Reflectance Spectroscopy

Analysis of Some Important Forage Quality Attributes of Southeastern Wildrye (<i>Elymus glabriflorus</i>) Using Near-Infrared Reflectance Spectroscopy