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Published online 27 March 2006
Published in Crop Sci 46:1124-1129 (2006)
© 2006 Crop Science Society of America
677 S. Segoe Rd., Madison, WI 53711 USA
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FORAGE & GRAZINGLANDS

Blending Hard White Wheat to Improve Grain Yield and End-Use Performances

Kyung-Min Leea, James P. Shroyerb, Timothy J. Herrmanc,* and Jane Lingenfelserd

a Dep. of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843
b Dep. of Agronomy, Kansas State University, Manhattan, KS66506
c Office of the Texas State Chemist, College Station, Texas A&M University, College Station, TX 77841
d Dep. of Grain Science and Industry, Kansas State University, Manhattan KS 66506

* Corresponding author (tjh{at}otsc.tamu.edu)


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Two hard white wheats (Triticum aestivum L.), ‘Betty’ with high protein and good processing quality and ‘Trego’ with good yield potential, were blended at percentages of 0, 10, 30, 50, 70, 90, and 100% Betty and planted at three locations in western Kansas during 2001 to 2003. Grain yield and wheat end-use quality were evaluated to assess the impact of blending two cultivars in an attempt to optimize yield and processing quality. A significant two-way interaction between blend and location was observed for grain yield, test weight, grain protein, single kernel properties, flour extraction, mixograph tolerance, and bread loaf volume per gram of protein. The different yield and quality response of the Betty–Trego blends among locations resulted, in part, from the presence of stripe rust (caused by Puccinia striiformis Westend.) during 2 of the 3 yr (2001 and 2003). Test weight, single kernel weight and size, grain yield, and flour extraction decreased in response to increasing Betty in the blend, whereas grain protein, kernel hardness index, mixograph water absorption, mixograph tolerance, and bread loaf volume increased with the blending level of Betty. Some variables did not exhibit a statistical linear response to the percentage of Betty, indicating that blending Betty and Trego was a good strategy to stabilize agronomic performance and end-use quality.

Abbreviations: ABS, mixograph flour water absorption • FLE, flour extraction • GYL, grain yield • HARD, SKCS kernel hardness index • HRW, hard red winter • PRO, grain protein • SIZE, SKCS kernel size • SKCS, single kernel characterization system • TW, test weight • VOL, bread loaf volume • VOLP, bread loaf volume per protein • WT, SKCS kernel weight


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
HARD WHITE WHEAT with hard endosperm and white bran is one of eight classes defined by the USDA's Federal Grain Inspection Service (Herrman and Reed, 2000). Demand for hard white wheat class is increasing for domestic and international markets, especially in Southeast Asia and Africa (Schumacher et al., 1999). Therefore, hard white wheat, to some extent, is expected to replace hard red winter (HRW) wheat cultivars that are widely adapted in the USA (Bequette and Herrman, 1994; Pike and MacRitchie, 2004).

Hard white wheat cultivars have some advantages over HRW wheat including a 1 to 2% higher extraction rate when milled to a common color specification, less bitter taste, and higher fiber (Chang and Chambers, 1992; McGuire et al., 1994; Schumacher et al., 1999; Ambalamaatil et al., 2002; Pike and MacRitchie, 2004). These advantages are important market characteristics that can lead to economic incentives for hard white wheat production. However, hard white wheats are more susceptible to preharvest sprouting, which decreases milling and baking quality (Nielsen et al., 1982; Pyler, 1988; Ambalamaatil et al., 2002).

It has been recognized that the grain lot mixtures of hard white and hard red wheats and the grain lot mixtures of hard white and soft white wheats could affect processing quality and end-use performance (Bequette and Herrman, 1994; Habernicht et al., 2002). Bean et al. (1990) showed a synergistic improvement in bread quality by blending low protein ‘Klasic’ and low protein ‘Anza’ flours in equal portion. Seed mixtures of two or more HRW wheat cultivars stabilized grain yield, compensated for injured varieties, and enhanced resistance to diseases or pests (Bowden et al., 2001). Cox et al. (2004) reported that blending reduced disease severity in susceptible cultivars as the proportion of the susceptible cultivar decreased in the blend.

Betty and Trego are hard white wheat cultivars developed by the Kansas Agricultural Experimental Station (Sears et al., 1999; Martin et al., 2000). Betty showed good milling and baking properties and average yield compared to HRW wheat (Sears et al., 1999), while Trego had good test weights, flour extraction rate, and superior yield potential (Martin et al., 2000). Combining these traits through mixing seed of these cultivars to predetermined ratios offers the possibility of overcoming production and processing constraints. Therefore, the objective of this study was to define the appropriate blend for optimum field and processing performance for hard white wheat in Kansas and to compare blends with single varietal performance.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Field Trial
Betty and Trego were blended at different weight ratios, Betty:Trego—0:100, 10:90, 30:70, 50:50, 70:30, 90:10, and 100:0. Five blended and two unblended samples were grown in a randomized complete block design with four replications at three locations, Colby, Hays, and Tribune, in western Kansas during the 2001 through 2003 seasons. The crop rotation at Hays and Colby was a wheat–fallow rotation, while at Tribune it was a wheat–row crop–fallow rotation. At all locations the planted and harvested plot sizes were 1.5 by 12.2 m and 0.9 by 10.6 m, respectively. Soil at the Colby site was a Keith silt loam (fine-silty, mixed, superactive, mesic Aridic Argiustoll) at the Northwest Research and Extension Center, Colby, KS (39°24' N, 101°4' W). Soil at the Hays site was a Harney silt loam, 0 to 1% slopes (fine, smectitic, mesic Typic Argiustoll) (38°51' N, 99°20' W). Soil at the Tribune site was a Richfield silt loam (fine, smectitic, mesic Aridic Argiustoll) at the Southwest Research and Extension Center-Tribune Branch (38°30' N, 101°41' W). Seedbed preparation was typical of western Kansas wheat production. Minimal tillage occurred to reduce soil moisture loss. Tillage was performed by a sweep plow through the fallow period to control weed growth, and a field cultivator was used just before fall planting. Seeding rate was 67 kg ha–1. Nitrogen was applied at 56 kg ha–1 before planting at all locations. Planting dates were during the optimal planting time, generally the last week of September and early October. Harvest dates varied by location and year, but generally harvest was completed by the first week of July.

Agronomic Quality Evaluation
Collected wheat samples were cleaned using the MCI Kicker dockage tester (Mid-Continent Industries, Inc., Newton, KS) before grain and processing quality tests. Grain sample moisture and protein concentration were measured by using near-infrared transmittance (NIT) 1229 Whole Grain Analyzer (Foss North America, Eden Prairie, MN). It had been calibrated by the manufacturer and validated annually using newly harvested wheat samples analyzed for protein concentration by a Leco model FP-428 N analyzer (St. Joseph, MI). Grain protein concentration was expressed on a 120 g kg–1 moisture basis. Grain yields were reported on a per hectare basis and further standardized as a ratio by subtracting Trego from the average yield of blends for each location and year. This procedure was employed for quality characteristic analyses as well. Test weight was measured as described in the USDA Federal Grain Inspection Service's protocol (USDA, 1990). The Perten SKCS 4100 single kernel characterization system (Perten Instruments North America, Reno, NV) was used to measure single kernel weight, size, and grain hardness index.

Milling and Baking
Hard white wheat samples were adjusted to 150 g kg–1 moisture concentration with distilled water and held for 16 h for tempering before milling. Tempered wheat samples were milled on a Brabender Quadromat Sr. Mill (C.W. Brabender, Hackensack, NJ). Temperature (25°C) and humidity (70%) were kept constant during milling. Flour was prepared by combining break and reduction flour streams for subsequent dough and bread tests. Flour extraction (g kg–1) was determined by calculating the amount (g) of flour produced per kg of wheat. Bran, short, break flour, and reduction flour were also calculated as percentages of total products recovered during milling. Moisture of flour samples was determined by Approved Method 44–15A (AACC, 2000). The mixing characteristics of flour samples such as water absorption (%), peak time (min), and mixing tolerance were evaluated using the 10 g Mixograph (National Manufacturing Co., Lincoln, NE) according to Approved Method 54–40A (AACC, 2000). Mixing tolerance score, ranging from 0 (very weak) to 7 (very strong), was estimated by visually comparing the band width and the descending slope of the mixogram to those of standard reference mixograph charts classified according to protein concentrations (Pomeranz, 1987).

Baking tests were performed with the following ingredients: 100 g flour (140 g kg–1 moisture basis), 0.2 g malted barley flour, 1 g dry yeast, 3 g shortening, 6.0 g sugar, and 2.2 g NaCl. Ascorbic acid was added to the baking formula at 100 g kg–1. Bake water absorption was determined visually based on dough condition after adding the amount of water estimated by 10 g mixograph test. Dough mixing time was optimized by performing the stretch test on dough. Dough was fermented for 160 min including first and second punch step. The fermented doughs were molded and proofed for 55 min. After proofing, dough was baked for 22 min at 215°C. The bread loaf volume (ml) was measured by rape seed (Brassica campestris L.) displacement method after immediately removing loaf from the oven.

Statistical Analyses
All data analyses were performed using SAS software (SAS Institute, 2004). Relative differences (Betty and Trego blend response minus Trego response) for grain yield and quality traits, flour extraction, dough rheology, and bread loaf volume within a location and among locations were evaluated using a mixed model analysis in which fixed effects were blend, location, and blend x location interaction, while year and year x location were considered as random effects. Orthogonal polynomial contrasts were tested to determine the nature of the response of selected agronomic traits and quality characteristics to the percentage of Betty. Pearson correlation coefficients were calculated among grain and processing characteristics using averages across locations and years. Regression and stepwise regression analyses to predict actual bread loaf volume were performed using SAS procedures.


   RESULTS AND DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
There was year x location interaction for all responses except mixograph absorption and peak mixing time (Table 1). Eight of the eleven response variables displayed a significant (P < 0.05) two-way interaction between blend and location (Table 1). Those characteristics that did not display a two-way interaction (mixograph absorption, mixograph peak time, and loaf volume) had a significant blend effect. None of the quality characteristics displayed a significant (P < 0.05) location effect.


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  		Table 1. Significance (P value) of random effects (year and year x location) and fixed effects (blend, location, and blend x location) for the relative difference (‘Betty’ and ‘Trego’ – Trego response) of selected agronomic traits and milling and baking quality characteristics.

 
Grain Properties
Test Weight
The blend effect differed among locations (Fig. 1 ) for relative test weight. Test weight increased as the proportion of Betty increased at Hays and decreased in response to an increasing proportion of Betty at Colby and Tribune. Tribune test weight declined approximately 2 kg hL–1, and a 1 kg hL–1 decline occurred for the 100% Betty treatment at Colby. The different test weight response to percentage of Betty in the blend among locations likely resulted from the presence of stripe rust, which was most pronounced at Hays. Trego tends to exhibit higher test weight in the absence of disease pressure than Betty (Roozeboom, 2002).


Figure 1
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  		Fig. 1. Relative difference (‘Betty’ and ‘Trego’ blend response – Trego response) for test weight (kg hL–1) among blends with different percentage of Betty at three Kansas locations (Colby [linear, P = 0.0005], Hays [linear, P < 0.0001], and Tribune [linear, P < 0.0001]).

 
Test weight is closely associated with flour extraction, flour quality, break release and stream distribution, ash composition, and milling capacity (Gwirtz et al., 1996; Ambalamaatil et al., 2002; Matus-Cadiz et al., 2003), which was partially confirmed in this study. Average test weight was correlated positively with average kernel weight and it was correlated negatively with average grain protein concentration, which was more pronounced at Colby (r = –0.82, P < 0.001) than the other two locations (Table 2).


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  		Table 2. Simple correlations of selected agronomic traits and milling and baking quality characteristics for the blend of ‘Betty’ and ‘Trego’ hard white wheat at each location and all three locations in western Kansas during 2001, 2002, and 2003.{dagger}

 
Kernel Weight, Size, and Hardness
Blending ratio and blend x location interaction significantly affected relative single kernel weight, size, and hardness index (Table 1). Relative kernel weight decreased as the percentage of Betty in the blend increased at Colby and Tribune (Fig. 2 ). At Hays, the response was less dramatic and the 90 and 100% Betty blend displayed the only significant decline in kernel weight compared to the 100% Trego treatment. Average kernel weight was correlated positively with kernel size (r = 0.83, P < 0.001) and flour extraction (r = 0.74, P < 0.001), but not with kernel hardness index (r = 0.12) (Table 2).


Figure 2
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  		Fig. 2. Relative difference (‘Betty’ and ‘Trego’ blend response – Trego response) for SKCS single kernel weight (mg) among blends with different percentage of Betty at three Kansas locations (Colby [linear, P < 0.0001], Hays [linear, P = 0.0086], and Tribune [linear, P < 0.0001]).

 
Stripe rust occurred during the 2001 and 2003 growing seasons and was most prevalent at the Hays site although no disease data were taken from these plots. Cox et al. (2004) reports that mixtures of resistant and susceptible cultivars slow disease development on the susceptible cultivar. Trego is moderately to highly susceptible to stripe rust, while Betty has excellent resistance (Stack and Sloderbeck, 2004). The difference in trend between Hays and the other two locations, Colby and Tribune, are largely attributable to stripe rust pressure. While this response was unanticipated, it illustrates the benefit of blending wheat to adapt to a wide array of environmental influences.

Grain Protein
At Colby and Hays, the grain protein increase in response to an increasing percentage of Betty was linear based on the orthogonal polynomial contrast test (Fig. 3 ). There was no effect of blend on grain protein at Tribune. Tribune experiences the lowest amount of annual precipitation, resulting in high protein levels (e.g., >140 g kg–1) independent of the cultivar. Variation in protein concentration due to environmental conditions has been reported in previous studies of hard white wheat cultivars (Huang and Varriano-Marston, 1980; McGuire et al., 1994; Lang et al., 1998).


Figure 3
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  		Fig. 3. Relative difference (‘Betty’ and ‘Trego’ blend response – Trego response) for grain protein concentration (g kg–1) among blends with different percentage of Betty at three Kansas locations (Colby [linear, P < 0.0001], Hays [linear, P < 0.0001], and Tribune [not significant]).

 
Grain protein concentration of hard white wheat is a crucial factor in determining noodle, bread, and tortilla quality (McGuire et al., 1994; Qarooni et al., 1994; Lang et al., 1998; Wang and Flores, 1999; Ambalamaatil et al., 2002). Grain protein displayed a significant positive correlation with mixograph absorption and negative correlations with test weight, kernel weight, grain yield, and flour extraction. However, the strength of the correlations was moderate to weak. Betty expresses a higher protein concentration due in part to its genetic composition, resulting from the presence of ‘Plainsman V’ in its parentage (Sears et al., 1999).

Grain Yield
At Colby, grain yield declined as the percentage of Betty increased. Trego possesses a higher yield potential than Betty (Roozeboom, 2002), thus, this outcome would be expected in the absence of stripe rust. There was a yield difference of nearly 550 kg ha–1 from the 100% Betty to the 100% Trego treatment (Fig. 4 ). This dramatic trend was not observed at either Hays or Tribune, where the 100% Betty was 150 kg ha–1 lower or higher, respectively, than 100% Trego. The study's premise indicated that blending cultivars may either stabilize yield or optimize economic return by combining cultivars with distinct yield or quality traits. In this study, conducted for 3 yr at three locations, we observed both situations.


Figure 4
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  		Fig. 4. Relative difference (‘Betty’ and ‘Trego’ blend response – Trego response) for grain yield (kg ha–1) among blends with different percentage of Betty at three Kansas locations (Colby [linear, P < 0.0001], Hays [not significant], and Tribune [not significant]).

 
Grain yield is the principal economic determinant of revenue for wheat production in much of Kansas. This is not the case in parts of Texas and Oklahoma where wheat grazing is a common practice and grain yield represents a second harvest and economic return. Blending wheat cultivars to stabilize yield is a common practice in Kansas, which, according to Kansas agricultural statistics, comprised 7 to 15% of the total planted hectares from 2000 to 2005 (Kansas Agricultural Statistics Service, 2005).

Milling and Baking Qualities
Flour Extraction
The relative flour extraction response increased with an increasing percentage of Betty in the blend at Hays and decreased with increasing Betty in the blend at Colby and Tribune (Fig. 5 ). These trends were consistent with test weight and kernel weight responses, which are supported by the correlation coefficients of 0.44 and 0.74, respectively. During 2 of the 3 yr of the study, stripe rust was present in Kansas. The disease was most pronounced at Hays, resulting in lower test weights, kernel weight, and grain yield for Trego and blends containing a higher percentage of Trego. Flour extraction represents an important economic variable to millers. Baker et al. (1999) quantified the economic significance of flour extraction and mixograph absorption in a single variable defined as dough factor. Further characterization of the economic benefit of blending hard white wheats should utilize this tool in addition to exploring the benefit of blending on protein and grain yield.


Figure 5
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  		Fig. 5. Relative difference (‘Betty’ and ‘Trego’ blend response – Trego response) for flour extraction (g kg–1) among blends with different percentage of Betty at three Kansas locations (Colby [linear, P = 0.0030], Hays [linear, P < 0.0001], and Tribune [linear, P = 0.0022]).

 
Mixograph Mixing Property
The increasing percentage of Betty in the blend resulted in a highly significant (P < 0.01) increase in mixograph water absorption. Increased water absorption represents value to bakers because they add more water to the flour, thus increasing product yield and shelf-life. The absence of a blend x location interaction indicates that mixograph absorption appears to be driven by genetic composition of the cultivars.

Dough mixing properties (water absorption, peak time, peak height, tolerance, and flour protein) determined by mixograph are interrelated to flour protein composition, mainly gluten, and bread-making potential (Neufeld and Walker, 1990; Bean et al., 1990; Dong et al., 1992; Campbell et al., 2001). Flour water absorption determined by mixograph is an important character that is highly associated with the quality of bread, tortillas, and noodles (Qarooni et al., 1994; Lang et al., 1998; McGuire et al., 1994). Prior research reported that water absorption has been correlated with NIR hardness and mill flour streams (Morris, 1992), however, neither of these relationships was observed in this study.

Mixograph tolerance score is a subjective measure of dough strength and stability to over-mixing. Blending ratio and blending x location interaction significantly affected the relative difference in mixograph tolerance score (Table 1). The linear increase in dough strength is expected with increasing percentage of Betty in the blend. Mixograph tolerance score showed significant correlation with test weight (r = 0.52, P < 0.001), protein concentration (r = –0.27, P < 0.001), single kernel weight (r = 0.74, P < 0.001), size (r = –0.44, P < 0.001), hardness index (r = –0.61, P < 0.001), and loaf volume (r = 0.44, P < 0.001). The linear increase in mixograph tolerance score and its relationship with other traits may reflect changes in protein quantity and quality due to different ratios of Betty and Trego in the blend.

Baking
Loaf volume increased significantly (P < 0.001) as Betty increased in the blend. Blend treatments containing 50% Betty or more displayed loaf volumes that were significantly (P < 0.05) greater than blends with higher ratios of Trego. Bread loaf volume was moderately correlated to protein concentration (r = 0.41, P < 0.001) and mixograph water absorption (r = 0.49, P < 0.001). Bread loaf volume was predicted with an equation created by quality characteristics estimated in this study. Grain protein concentration, as expected, was an important predictor of bread loaf volume, accounting for about 60% of the total observed variation in loaf volume (R2 = 0.70, P < 0.001).

Formula

Bread-making performance of hard white wheat is largely influenced by dough properties (Ambalamaatil et al., 2002). Typically, hard white wheat cultivars and their blends have shown good bread-making performance (Bean et al., 1990; Chang and Chambers, 1992; Morris, 1992; McGuire et al., 1994; Lang et al., 1998; Campbell et al., 2001; Ambalamaatil et al., 2002; Habernicht et al., 2002). Pike and MacRitchie (2004) reported the similar protein composition data in terms of polymeric protein, gliadin, and unextractable polymeric protein concentrations between Betty and Trego.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
The magnitude and direction of responses observed among agronomic traits and quality characteristics for the Trego–Betty blend changed in a linear or nonlinear pattern depending on location. Blending advantage and, in some cases, synergistic effects were observed to depend on stripe rust and other environmental conditions. The blending study helps differentiate those response variables that are more dependent on genotype versus those which respond to a genotype by environment interaction. Study results indicate that blending hard white wheat cultivars represents a sound strategy to stabilize yield and end-use quality. Results from this study can be evaluated using economic optimization techniques.


   ACKNOWLEDGMENTS
 
We thank the Kansas Wheat Commission, FINNUP Foundation, Kansas Agric. Exp. Stn., and the Anderson Endowment administered through the Ohio Agricultural Research and Development Center of the Ohio State University for their assistance in this research. Study cooperators who managed the field plots were Dr. Alan Schlegel, Dr. Joe Martin, and Patrick Evans at Tribune, Hays, and Colby, respectively. We thank the editor and reviewers for their useful comments.

Received for publication July 6, 2005.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 





This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF) Free
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Right arrow Citing Articles via Web of Science (1)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Lee, K.-M.
Right arrow Articles by Lingenfelser, J.
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PubMed
Right arrow Articles by Lee, K.-M.
Right arrow Articles by Lingenfelser, J.
Agricola
Right arrow Articles by Lee, K.-M.
Right arrow Articles by Lingenfelser, J.
Related Collections
Right arrow Wheat
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