NEAR-INFRARED REFLECTANCE ESTIMATES OF GRAIN PROTEIN AND MALT EXTRACT IN HILL AND ROW PLOT EVALUATIONS OF SPRING MALTING BARLEY

1990 ◽  
Vol 70 (1) ◽  
pp. 71-78 ◽  
Author(s):  
S. TRAGOONRUNG ◽  
P. M. HAYES ◽  
S. L. BROICH

Expensive, time-consuming analyses can limit selection responses for grain protein and malt extract in a malting barley improvement program. Alternative breeding strategies, such as doubled haploid recurrent selection, rapidly produce more genotypes than can be evaluated in conventional plots. Prior to implementing a doubled haploid recurrent selection program for malting quality we sought to test the utility of hill plot evaluation and near-infrared reflectance (NIR) prediction for grain protein and malt extract. Five- and six-wavelength calibration equations were generated for prediction of grain protein and malt extract, respectively. The multiple correlation coefficient of the protein equation (0.96) was higher than that of the malt extract equation (0.88). Calibration equations for both traits based on separate locations and spike classes (two-row vs. six-row) were less robust than the multiple environment, combined equations. The grain protein and malt extract equations had acceptable predictive power for both row and hill plot samples. However, in view of differential trait expression in hill and row plots, NIR prediction based on hill plot evaluation is appropriate for grain protein. NIR prediction of malt extract is best deferred until genotypes are evaluated in row plots.Key words: Malting quality, NIR, hill plots, barley

1990 ◽  
Vol 70 (1) ◽  
pp. 61-69 ◽  
Author(s):  
S. TRAGOONRUNG ◽  
P. M. HAYES ◽  
B. L. JONES

Provided they reliably predict row plot performance, hill plots should be useful for doubled haploid recurrent selection in malting barley (Hordeum vulgare L.). The primary objective of this research was to compare hill and row plot expression of agronomic and malting quality traits in an array of elite spring habit barley germplasm grown under irrigated conditions. A supporting objective was to identify an appropriate seeding rate for hill plot evaluation. Eight-replicate hill plots at four seeding rates (10, 20, 30, and 40 seeds per hill) were compared with adjacent four-replicate row plots in each of three environments. Genotype and genotype × environment interactions were significant for most agronomic traits in both plot types. Significant, linear genotype responses to hill plot seeding rates were observed for most agronomic traits. Seeding rate had no consistent effect on the expression of malting quality. The percentage of lines in common in the two plot types at 25 and 50% selection intensities was the most useful comparison statistic and indicated hill plot selection should be effective for most agronomic and malting quality traits. Although yield heritability estimates were consistently high in both hill and row plots, there was little relationship between trait expression in the two plot types. Differential tillering in response to hill plot competition is likely responsible. A seeding rate of 10 seeds per hill should be appropriate in preliminary screening for traits amenable to hill plot selection in irrigated spring habit malting barley.Key words: Hordeum vulgare L., malting quality, breeding methods, barley


NIR news ◽  
2020 ◽  
Vol 31 (7-8) ◽  
pp. 14-19
Author(s):  
Omar Vergara-Díaz ◽  
Shawn Kefauver ◽  
José Luis Araus ◽  
Iker Aranjuelo

The expansion of world population requires the development of new strategies and tools for agriculture. Extensive breeding and agronomic efforts over the past 50 years have been responsible for tripling cereal yields, while advances in grain quality have been less evident. Continuing advances in the techniques available to breeders offer the potential to increase the rate of genetic improvement aiming to develop resilient crop and better (more resource use efficient) varieties. Plant breeders want to be able to phenotype large numbers of lines rapidly and accurately identify the best progeny. For this purpose, different methodological approaches have been proposed to evaluate these traits in the field: (1) proximal (remote) sensing and imaging, (2) laboratory analyses of samples, and (3) lab-based near-infrared reflectance spectroscopy analysis in the harvestable part of the crop. However, near-infrared reflectance spectroscopy-based field evaluation of yield and grain quality is currently a real option. Thus the development of new technological approaches, such as the use of hyperspectral imaging sensors or near-infrared reflectance spectroscopy under field conditions may be critical as a phenotypic approach for efficient breeding as well as in field management of crops. This article reports the description of the CropYQualT-CEC project funded by the H2020-MSCA-RISE program. This project pursues the main objective of generating a common solid knowledge basis within the context of precision agriculture and digital farming. Further, within the project context, the article also provides a case study in which prediction models for total grain protein content, based on the reflectance spectrum of wheat canopies, are presented. Measurements were performed at around anthesis, using a full range near-infrared reflectance spectroscopy field spectrometer. Several models explaining >60% of grain protein variance in field trials illustrate the predictive capacity and robustness of this methodology for inferring grain quality traits well in advance of harvest.


1991 ◽  
Vol 42 (8) ◽  
pp. 1399 ◽  
Author(s):  
KF Smith ◽  
SE Willis ◽  
PC Flinn

Near infrared reflectance spectroscopy (NIR) was used to develop calibration equations to measure the magnesium concentration in perennial ryegrass herbage (Lolium perenne). A subset of 72 samples was selected on the basis of spectral variation from 400 samples grown in 1988-1989. Three alternative equations were chosen using stepwise multiple linear regression, with standard errors ranging from 0.4 to 0.3 g/kg DM with corresponding squared multiple correlation coefficients ( R2) of 0.68 to 0.82. The equations had 2, 4 and 4 wavelength terms respectively. When these equations were tested on an independent population of perennial ryegrass samples, a significant bias existed when using the 4 term equations but there was no bias when the 2 term equation was used. We conclude that NIR can be used to screen large numbers of perennial ryegrass plants for magnesium concentration. However, for the calibration equations to be used for the analysis of other populations equation performance must be monitored by comparing reference and NIR analyses on a small number of samples.


2005 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
I. T. Kadim ◽  
W. Al-Marzooqi ◽  
O. Mahgoub ◽  
K. Annamalai

Near-infrared reflectance spectroscopic (NIRS) calibrations were developed for the prediction of the content of dry matter (DM); nitrogen (N), ether extract (EE), neutral detergent fibre (NDF), acid detergent fibre (ADF), gross energy (GE), calcium (Ca) and phosphate (P) in broiler excreta samples. The chemical composition of broiler excreta was determined by the conventional chemical analysis methods in the laboratory and compared with NIRS. Excreta samples (n = 72) were oven dried (60 oC) and analyzed for DM, N, EE, NDF, ADF, GE, Ca and P. The determined values (mean ± SD) were as follows: DM: 31.46 ± 7.65 (range:19.14 - 44.51), N: 5.85 ± 2.88 (range: 4.85 -7.00), EE: 1.37 ± 0.25 (range: 0.88-1.99), ADF: 16.71 ± 1.99 (range: 12.11-19.97), NDF: 26.26 ± 1.63 (range: 22.03-30.21), GE: 15.27 ± 0.33 (range: 14.52-16.11), Ca: 2.57 ± 0.22 (range: 2.16-3.01), P: 1.79 ± 0.15 (range: 1.41-2.11). The samples were then scanned in a NIRS model 5000 analyzer and the spectra obtained for each sample. Calibration equations and prediction values were developed for broiler excreta samples. The software used modified partial least square regression statistic, as it is most suitable for natural products. For broiler excreta samples, the coefficient of determination (R2) and the standard error of prediction (SEP) was DM = 0.97, 1.27, N = 0.95, 0.72, EE = 0.92, 0.07, ADF = 0.87, 0.78, NDF = 0.88, 0.72, GE = 0.89; 0.24, Ca = 0.96, 0.06, P = 0.93, 0.09, respectively. The results indicate that it is possible to calibrate NIRS to predict major constituents in broiler excreta samples.


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