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a USDA-ARS Hard Red Spring and Durum Wheat Quality Lab., Harris Hall, North Dakota State Univ., Fargo, ND 58105
b Dep. of Plant Sciences, Loftsgard Hall, North Dakota State Univ., Fargo, ND 58105
* Corresponding author (Douglas_Doehlert{at}ndsu.nodak.edu)
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONREFERENCES |
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Test weight is the most commonly used method to evaluate oat quality (Forsberg and Reeves, 1992). Test weight is a measure of the density of oat grains as they are packed into a given volume. It is reported to be affected by kernel and groat size, groat density, hull thickness and length, and groat percentage as well as the presence of awns, diseases, and tertiary kernels (Murphy et al., 1940; Atkins, 1943; MacKey, 1959; Forsberg and Reeves, 1992). Several studies have reported genotype x environment interaction for oat test weight (Bartley and Weiss, 1951; Gullord and Aastveit, 1987).
Groat percentage is a measure of the proportion of the whole oat that is recovered as groat after dehulling. Groat percentage has long been recognized as an important indicator of oat quality (Love et al., 1925; Stoa et al., 1936; Atkins, 1943; Bartley and Weiss, 1951). Peek and Poehlman (1949) considered test weight to be a more valuable oat quality evaluation tool than groat percentage because hand-dehulling of oat was considered too tedious. Stoa et al. (1936) suggested that early maturing oat cultivars were superior in groat percentage, and rust susceptible lines were generally higher in percent hull. These conclusions were also supported by the findings of Bunch and Forsberg (1989). The studies of Bartley and Weiss (1951) indicated strong environmental effects on groat percentage and demonstrated positive correlations between groat percentage and yield, test weight and kernel weight. Youngs and Shands (1974) demonstrated that tertiary kernels had a higher groat percentage than primary and secondary kernels, although Palagyi (1983) found that genotypes with higher levels of tertiary kernels had lower groat percentage. He suggested that tertiary kernels compete with primary and secondary kernels for assimilate, preventing them from filling properly. Very little information is available concerning the control of oat groat weight, although one study (Gullord and Aastveit, 1987) indicated significant genotype x environment interactions for the trait.
Among the compositional components of oat, protein concentration often is ranked highly in importance because of its nutritional significance. Oat groats may contain from 124 to 244 g kg-1 protein, and this protein is of higher nutritional quality than most other grains (Peterson, 1992). Studies have shown genotypic and environmental effects on oat protein concentration (Jenkins, 1969; Forsberg et al., 1974; Saastamoinen et al., 1989). In particular, nitrogen supply strongly affects oat protein concentration (Ohm, 1976; Welch and Yong, 1980; Welch et al., 1991; Humphreys et al., 1994; Jackson et al., 1994).
Oat contains much higher oil concentrations than do other small grains (Youngs, 1986). Higher oil content is an advantage for animal feeding because of its higher caloric content. However, in food applications, higher oil concentrations are deleterious because of their potential for rancidity and production of off-flavors. Studies have indicated that both genotype and environment affect groat oil concentration (Brown et al., 1966; Saastamoinen et al., 1989; Welch, 1975; Humphreys et al., 1994). Cooler growth environments have been reported to stimulate oil accumulation in groats (Beringer 1971, Saastamoinen et al., 1989). Negative correlations between protein concentration and oil concentration among different oat genotypes have been reported (Brown et al., 1966; Forsberg et al., 1974). This relationship has been disputed (Youngs and Forsberg, 1979), and cultivars with both high protein and oil concentrations have been developed.
The ß-glucan component of oat has garnered increasing interest in recent years because of studies that indicated that ß-glucans associated with oat bran in diets can lower blood cholesterol in both animals and humans (Peterson, 1992). Variation in groat ß-glucan concentration among different oat genotypes and differing environmental conditions have been studied (Welch and Lloyd, 1989; Peterson, 1991; Welch et al., 1991; Brunner and Freed, 1994; Humphreys et al., 1994; Jackson et al., 1994; Peterson et al., 1995).
Although strong genotypic differences in ß-glucans can be demonstrated consistently, environmental effects have been more difficult to document (Peterson, 1991; Peterson et al., 1995). For example, of three recent studies where the effects of nitrogen fertilization on ß-glucan accumulation were examined, one study reported significant nitrogen x location and nitrogen x year interactions affecting ß-glucan concentration in groats, whereas, the other two studies found no significant main effect or interaction effects of nitrogen on ß-glucan concentration (Brunner and Freed, 1994; Humphreys et al., 1994; Jackson et al., 1994). Several studies have suggested that drought conditions may influence ß-glucan accumulation in oat (Peterson, 1991; Welch et al., 1991; Brunner and Freed, 1994; Peterson et al., 1995), but no study has conclusively demonstrated this.
Starch is the major storage component in oat. However, because most of the value of oat lies in the nonstarch components, its concentration is usually not considered in quality analyses. The remaining components of oat composition include ash and fiber components other than ß-glucans. Fiber components, which include pentosans (arabinoxylans), cellulose, lignin, and galactomannans (Aspinall and Carpenter, 1984; Henry, 1987) are important to quality because of their contribution toward total dietary fiber. Ash represents the mineral components of oat and is primarily composed of phosphorus, calcium, potassium, copper, manganese, iron, sodium, and magnesium (Peterson et al., 1975). Although many of these are considered essential minerals to be included in the diet, they are generally not considered in selection of oat for quality.
In this study, 12 oat genotypes adapted for production in North Dakota and divergent in protein, oil, ß-glucan, and groat size were grown at four different locations over three years. Detailed environmental data were gathered at these sites. Our goals were to determine the relative effects of specific meteorological factors on oat grain yield and quality, and to determine sources of quality trait variation observed in the oat breeding program.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONREFERENCES |
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A seeding rate of 2.47 x 106 kernels ha-1 was used for all experiments. Herbicide treatments consisted of pre-emergence application of 3.93 kg ha-1 propaclor (2-chloro-N-isopropylacetanilide) and post-emergence application at the 3-leaf stag with a tank mix of 0.14 kg ha-1 thifensulfuron {methyl3 [[[[(4-methoxy-6-methyl-1,3,5-triazin-2yl)amino]carbonyl]amino]sulfonyl]-2-thiophenecarboxylate}, 0.07 kg ha-1 tribe-nuron {methyl 2-[[[[N-(methoxy-6-methyl-1,3,5-triazin-2yl)methylamino] carbonyl] amino] sulfonyl] benzoate}, and 0.14 kg ha-1 clopyralid (3,6-dichloro-2-pyridinecarboxylic acid, monoethanolamine salt). Experimental units consisted of four rows spaced 0.3 m apart and 2.4 m long. The two center rows were harvested with a two-row binder and threshed with a plot thresher. Seed was cleaned with an air screen cleaner to remove chaff. Test weight was determined by weighing a fixed volume of grain. Planting and harvest dates are shown in Table 1.
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Sample Preparation
Sound grain was stored in paper bags and envelopes. Whole oat samples were steam-treated in a vegetable steamer for 20 min to inactivate enzymes. Samples were dehulled with a Codema Laboratory Oat Huller (Codema Inc., Eden Prairie, MN)1. The groat proportion was obtained by weighing the sample before and after dehulling. Dehulled groats were cleaned by hand to ensure that all hulls and broken groats were removed. Oil concentration and groat weight were determined on whole groats. Groats for starch, protein, ß-glucan, and ash analyses were milled in a Retsch model ZM-1 centrifugal mill with a 0.5-mm collar screen (Brinkmann Instruments, Westbury, NY). Flour was stored in small sealable plastic bags and placed in a desiccator at room temperature until analyzed.
Chemical Analyses
Moisture of flour samples was determined by heating a 2-g flour sample for 2 h in a convection oven at 130°C. Samples were allowed to cool in a desiccator and reweighed. Moisture was proportional to the weight loss during the heat treatment. All chemical analyses are expressed on a dry weight basis.
Oil analysis was performed on whole groats with an Oxford 4000 NMR (Abingdon, England). Groats were dried in a convection oven at 130°C for 2 h to eliminate the interference of water to the oil signal. Samples were allowed to cool in a desiccator before analysis. Calibration of the instrument had been established by comparison of signals with groats with known oil concentration, established by Soxhlet extraction with petroleum ether. Starch was analyzed according to the American Association of Cereal Chemists (1995) method 76-11.
Protein was analyzed by combustion analysis with a Leco FP-428 Nitrogen Analyzer (Leco Corporation, St. Joseph, MI). Total nitrogen was converted to protein by multiplying by 6.25.
Ash of a 2-g sample was determined in an ashing oven by initially incubating samples in crucibles for 1-h at 350°C, then increasing the oven temperature to 450°C, and 590°C after 1-h intervals, then maintaining 590°C for 18 h. After ashing, crucibles were removed from the ashing oven and allowed to cool in a desiccator before measuring ash weight. Total (1
3), (14)-ß-D-glucan (ß-glucan) was determined by the method of McCleary and Glennie-Holmes (1985).
Groat weight was derived from the number of kernels in a 10-g sample.
Experimental Design and Statistical Analyses
Experimental plots were arranged in a randomized complete block design with three replicates within each environment. Analysis of variance was performed with the SAS General Linear Model procedure (SAS Institute, Cary, NC), where all environments were considered random and genotypes were considered fixed. Pearson correlation matrixes were calculated across all environments for each genotype with the Statistix computer package (Analytical Software, Tallahassee, FL) and were pooled and their homogeneity determined by procedures described by Steel et al. (1997)(p. 295–297). Significance of individual correlation coefficients were evaluated using 108 degrees of freedom, according to Steel et al (1997)(p. 295).
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONREFERENCES |
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Correlation Analyses
Phenotypic correlations of oat grain yields and quality characteristics with environmental conditions were calculated to determine environmental conditions associated with oat characteristics (Table 5). Grain yield was correlated positively with both high and low temperatures in April and May, indicating that warm spring weather was favorable for higher yields. These conditions provided for earlier planting and accelerated seedling development. Both July and August low temperatures were correlated negatively with yield. This suggested that the high night temperatures during the final stages of grain development may reduce grain yields through excessive respiration. Several previous studies have found negative relationships between high night temperatures and grain yield in maize and other crops (Peters et al., 1971; Christy and Williamson, 1985). They suggested that excessive respiration at night depleted photosynthate that would have otherwise contributed to grain yield. A negative correlation between seasonal precipitation and grain yield (Table 5) suggested that most of the environments had adequate moisture to sustain growth. Excessive rain in July probably contributed to more severe crown rust infections, which were associated with negative effects on yields. Seasonal solar radiation was highly and correlated positively with yield (Table 5). The importance of solar radiation to yield suggests that gross photosynthesis may have been a major limiting factor to plant growth. Many previous investigators have attempted to link photosynthesis and yield, and most have failed (Gifford et al., 1984), usually because so many factors influence yield. However, shading experiments have resulted in decreased yields in maize, Zea mays L., (Early et al., 1967; Reed et al., 1988) and in soybeans, Glycine max (L.) Merr., (Christy and Porter, 1982). In soybeans, 50% shade resulted in a 25 to 35% yield decrease (Christy and Porter, 1982).
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Groat weight differed from yield, test weight and groat percentage, in that it was less strongly correlated with warmer spring temperatures, but was more strongly negatively correlated with warmer summer temperatures. This suggested that groat weight was more strongly influenced by temperatures occurring during the grain filling period.
Groat starch was correlated positively with warmer temperatures in most months (Table 5). It was also correlated positively with precipitation for the season, and for April and August. Physiological reasons for mechanisms by which these environmental factors would affect groat starch are not clear at this time.
Groat protein was significantly correlated with relatively few environmental factors (Table 5). Of particular interest was a positive correlation with June precipitation and a negative correlation with July precipitation. June precipitation may have stimulated vegetative growth that allowed oat plants to accumulate nitrogen prior to grain filling, and July precipitation may have washed any remaining soil nitrogen out of the root zone, preventing its accumulation in grain. Alternatively, July precipitation may have stimulated starch accumulation, which would have diluted the protein concentration.
Groat lipid concentration was correlated negatively with warm spring temperatures but correlated positively with warmer summer temperatures. This might appear to be inconsistent with earlier studies that suggested that cooler temperatures during grain fill increased oil accumulation (Beringer, 1971; Saastamoinen et al., 1989). It should be noted that very little overall variation in groat oil concentration could be attributed to the environment, and that genotypic effects accounted for most of the variation (Table 4).
Groat ß-glucan concentration was correlated with many of the same environmental factors as groat starch (Table 5). This suggests that these two complex polymers of glucose responded in about the same way to environmental conditions. Several earlier studies had suggested that drought conditions might stimulate ß-glucan concentrations in oat groats (Peterson, 1991; Welch et al., 1991; Brunner and Freed, 1994; Peterson et al., 1995). In the current study, precipitation in July and August was correlated positively with ß-glucan concentration, suggesting the opposite to these previous studies.
Groat ash concentration was correlated positively with warmer temperatures in most months and with precipitation in July and August. Physiological reasons for these correlations are not clear.
Attempts to correlate quality characteristics with each other across environments were partially unsuccessful because of excessive heterogeneity of correlation coefficients among genotypes (Table 6). However, it was apparent that groat percentage was correlated positively with yield and test weight. This suggests that conditions leading to higher yields generally also lead to improved quality. This analysis also indicated that across environments, starch concentration was correlated positively with ash and ß-glucan concentration, protein concentration was correlated positively with groat weight, and lipid concentration was correlated negatively with yield, test weight and groat percentage and weight.
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NOTES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONREFERENCES |
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Received for publication June 1, 2000.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSNOTESRESULTS AND DISCUSSIONREFERENCES |
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3), (14)-ß-D-glucan concentrations in endosperm and whole grain of wheat, barley, oats and rye. J. Cereal Sci. 6:253–258.3), (14)-ß-glucan in barley and malt. J. Inst. Brew. 91:285–295.3), (14)-ß-D-glucan content of oat cultivars and wild Avena species and its relationship to other characteristics. J. Cereal Sci. 13:173–178.3) (14)-ß-D-glucan content of oat genotypes. J. Cereal Sci. 9:35–40.This article has been cited by other articles:
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  A. Banas, H. Debski, W. Banas, W. K. Heneen, A. Dahlqvist, M. Bafor, P.-O. Gummeson, S. Marttila, A. Ekman, A. S. Carlsson, et al. Lipids in grain tissues of oat (Avena sativa): differences in content, time of deposition, and fatty acid composition J. Exp. Bot., July 11, 2007; (2007) erm125v3. [Abstract] [Full Text] [PDF]
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 D. M. Peterson, D. M. Wesenberg, D. E. Burrup, and C. A. Erickson Relationships among Agronomic Traits and Grain Composition in Oat Genotypes Grown in Different Environments Crop Sci., May 27, 2005; 45(4): 1249 - 1255. [Abstract] [Full Text] [PDF]
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