Genetic Factors Underlying Dry-Milling Efficiency and

Published July 28, 2016

Research

Genetic Factors Underlying Dry-Milling Efficiency and Flaking-Grit Yield Examined in US Maize Germplasm Joshua A. Macke, Martin O. Bohn, Kent D. Rausch, and Rita H. Mumm*

ABSTRACT A small but important proportion of US maize (Zea mays L.) grain is channeled to create breakfast cereals and other food products; yet, little focus has been devoted to genetic improvement of corn hybrids to meet needs of dry millers and other end users in the cereal pipeline. This study was designed to evaluate a broad range of US maize germplasm for key dry-milling traits: dry-milling efficiency (DME), the proportion of flaking grits produced from dry-milled maize grain, and flaking-grit yield (FGY), the amount of flaking grits produced per unit land. Genetic parameters for DME and FGY were characterized based on grain produced over 3 yr, and the associations between dry-milling, agronomic, ear, and kernel traits were assessed. Means for DME among experimental hybrids ranged from 24.0 to 36.0%. The DME was negatively correlated with grain yield and positively correlated with test weight; associations with other ear and kernel traits were weak despite being statistically significant in some cases. The DME is moderately heritable (h2 = 0.53) and controlled largely by additive gene action. A moderate amount (31%) of the variation in FGY was explained by multiple linear regression of grain yield and simple physical kernel properties: test weight, kernel thickness, and 100-kernel volume. Genetic variation for DME in US maize germplasm was demonstrated, which could be exploited to develop new maize hybrids with improved DME and, ultimately, FGY. However, more work is needed to develop indirect selection approaches to predict DME and FGY at or before harvest.

J.A. Macke, Dep. of Crop Sciences and the Illinois Plant Breeding Center, Univ. of Illinois at Urbana–Champaign, 1102 S. Goodwin Ave., Urbana, IL 61801 and Dow AgroSciences, Gibson City, IL; M.O. Bohn and R.H. Mumm, Dep. of Crop Sciences and the Illinois Plant Breeding Center, Univ. of Illinois at Urbana–Champaign, 1102 S. Goodwin Ave., Urbana, IL 61801; K.D. Rausch, Dep. of Agricultural and Biological Engineering, Univ. of Illinois at Urbana–Champaign, 1304 W. Pennsylvania Ave., Urbana, IL 61801. Received 14 Jan. 2016. Accepted 23 May 2016. *Corresponding author ([email protected]). Assigned to Associate Editor Adam Heuberger. Abbreviations: DME, dry-milling efficiency; FGY, flaking-grit yield; GCA, general combining ability; NSS, Nonstiff Stalk; PVP, Plant Variety Protection; SCA, specific combining ability; SSS, Stiff Stalk Synthetic.

I

n 2015, only a small proportion (1.4%) of the 361 t (14,216 million bushels) of maize grain produced in the United States was used in cereal and other food products (USDA–ERS, 2016). With a per capita maize consumption of ~73 kg yr−1, maize is an important human dietary component in the United States. From a dry-milling perspective, a maize kernel is comprised of three major components: endosperm, bran, and germ (Wolf et al., 1952) (Fig. 1). The endosperm, which makes up >80% of a maize kernel, is made up of hard translucent and soft floury endosperm. The bran or pericarp is a thin outer layer covering the maize kernel, and the germ contains the embryo. Dry milling is a process used to mechanically separate germ and bran from the endosperm by exploiting differences in their physical properties and chemical composition (Wolf et al., 1952). Separating major kernel components is critical for shelf life of endosperm products and affects the nutritional quality as well. The commercial dry-milling process is comprised of four key steps: tempering, degerminating, aspiration, and density separation. Published in Crop Sci. 56:1–11 (2016). doi: 10.2135/cropsci2016.01.0024 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA This is an open access article distributed under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

crop science, vol. 56, september– october 2016

www.crops.org 1

Fig. 1. Major components of a maize kernel: endosperm, germ, bran, and the tip cap (photo by Joshua A. Macke).

Tempering is the controlled addition of moisture to the maize kernel to create a moisture gradient in the maize kernel so that the bran and germ are at higher moisture than the endosperm. As a consequence, the bran and germ are more resilient to fragmentation without softening the endosperm (Mehra and Eckhoff, 1997). Degerminating is the mechanical process of separating bran and germ from endosperm. The bran is recovered by aspiration, while the germ is recovered by density differences using gravity tables. The remaining endosperm fraction is screened to sort particles by size. The largest endosperm particles, that is, flaking grits, are recovered as particles that pass through a 6.73-mm screen (3 mesh) but are retained in a 4.00mm screen (5 mesh). Other endosperm fractions include smaller grits, maize meal, and flour. Flaking grits are the most valued product of the drymilling process because of their role as an ingredient in breakfast cereals, snack foods, and malt beverages. Drymilling efficiency is measured as the proportion by weight of flaking grits produced from a 100-g grain sample and is typically expressed as a percentage; this characteristic is important to dry millers, but it is challenging as a breeding target. As a processing trait, DME becomes measurable considerably downstream of grain harvest. Furthermore, because of the previous lack of small-scale processing methods, it was not feasible to evaluate a large number of genotypes, which is necessary to understanding the genetic basis for the trait and required to facilitate selection. In the past, DME was estimated indirectly by assessing grain quality traits found to be related to DME such as test weight (Blandino et al., 2010; Kirleis and Stroshine, 1990; Lee et al., 2007a; Pan et al., 1996; Paulsen and Hill, 1985; Pomeranz and Czuchajowska, 1987), kernel hardness (Blandino et al., 2010; Kirleis and Stroshine, 1990; Lee et al., 2007a; Litchfield and Shove, 1990; Narváez-González et al., 2006), kernel density (Kirleis and Stroshine, 1990; Wu and Bergquist, 1991), breakage susceptibility (Paulsen and Hill, 1985), and protein content (Dorsey-Redding et al., 1991; Jackson et al., 1988; Lee et al., 2005, 2007a; Philippeau et al., 2000; Yuan and Flores, 1996). Ideally, in maize grown for dry-milling end use, selection for DME would result in the maximizing the amount of flaking grits produced per unit of land (FGY). The development of a small-scale dry-milling process (Rausch et al., 2009), which uses a 1-kg samples rather 2

Fig. 2. Participants in the maize–cereal pipeline and their respective goals and interests.

than 40-kg samples required for pilot-scale studies and produces results representative of DME obtained in industry, is ideal for comparing numerous lines and hybrids and has facilitated exploration of the genetics underlying DME. Seebauer et al. (2010) indicated that genetic factors play an important role in the relative contribution of bran, germ, and endosperm to kernel tissue composition. Differences in kernel tissue composition account for a significant proportion of the phenotypic variation observed among hybrids for dry-milling yield (Rausch et al., 2009). The maize–cereal pipeline is an excellent example of an agricultural production system that provides primary ingredients for dietary staples (Fig. 2). It is a complex pipeline involving groups with different interests and expertise including seed companies, farmers, dry millers, food companies, and consumers. Since multiple segments of the agricultural food chain are involved, benefits need to be maximized for each. This study focused on the front end of the cereal pipeline, centering on characteristics that are beneficial for both farmers and dry millers and that can be improved through plant breeding. Thus, our overall goal was to explore the relationship of DME to agronomic performance with the aim of finding ways to enhance both simultaneously. We evaluated a diverse set of inbred lines that represent the genetic diversity in US commercial corn germplasm and all possible hybrid combinations of those inbreds for agronomically important traits and dry-milling properties. Our overall goal was to assess the feasibility of improving US maize for dry-milling characteristics while simultaneously improving agronomic performance. Specifically,

www.crops.org


Table 1. Category, description, basis, and unit of measurement for the phenotypic traits collected. Category Agronomic Dry Milling

Ear

Trait

Basis

Description

Unit

Grain yield

Whole plot

Weight of grain per plot at 15.5% moisture

Mg ha−1

Test weight

Whole plot

Weight of 1 bushel of grain adjusted to 15.5% moisture

kg hL−1

Dry-milling efficiency (DME)

Whole plot

%

Flaking-grit yield (FGY)

Derived

Proportion by weight (db) of flaking grits obtained from 1 kg dry-milled grain Amount of flaking grits produced per unit of land; computed as grain yield  DME

Cob length

Five ears per plot

Length of whole ear including cob

mm

Width of cob at median length

mm

Ear fill length

Length of ear with kernels

mm

Ear width

Width of ear at median length

mm

Ear rows

Number of kernel rows per ear

Count

Kernels per row

Number of kernels per row

Count

100-kernel volume

100 kernels measured in graduated cylinder

mm3

Cob width

Kernel

Mg ha−1

100-kernel weight

Weight of 100 kernels

g

Kernel width

Length of 10 kernels arranged side by side

mm

Kernel length

Length of 10 kernels arranged tip to crown

mm

Kernel thickness

Length of 10 kernels stacked on top of each other

mm

Kernel size Sphericity

Length  width  thickness kernel size1/3/kernel length; range from 0 to 1, 0 = flat 1 = round

mm3 Value

10 kernels 2 per plot

Derived

our objectives were to (i) characterize genetics underlying key dry-milling traits in the US maize germplasm base, namely amount of genetic variation, heritability, type of gene action involved, general and specific combining ability, and environmental influence on performance; (ii) highlight potential sources of favorable alleles to improve DME and FGY; (iii) determine the association between dry-milling performance and agronomic, ear, and kernel traits; and (iv) estimate quantitative genetic parameters for insight into potential breeding approaches.

MATERIALS AND METHODS Plant Materials Twelve genetically diverse lines that represent important heterotic subgroups in the current US maize commercial germplasm base were used in this study ( Johnson, 2008; Mikel and Dudley, 2006), among these, were two public inbreds and 10 proprietary inbreds for which Plant Variety Protection (PVP) has expired (Mikel, 2006) (Supplemental Table S1). These lines served as ancestors in the development of current elite inbreds and represent a vast amount of the genetic diversity in the current US maize commercial germplasm pool. Five of the inbreds (B73, LH1, PHG39, PHJ40, and 4676A) trace their ancestry to Iowa Stiff Stalk Synthetic (SSS), which represents the female side of the heterotic pattern used in the United States, while the other seven (LH123HT, LH82, Mo17, PH207, PHG47, PHG84, and PHZ51) are of Nonstiff Stalk (NSS) background, composed largely of Iodent, Minnesota13, Oh07, Midland Yellow Dent, Oh43, Lancaster, and broad-based germplasm generally representing the male side. In hybrid combination, all inbreds are suited to the climate of central Illinois (among other US Corn Belt locations), according to the range of growing degree units to silking (1390 to 1690) recorded in inbred PVP applications (Germplasm Resources Information Network, 2012) and Iowa State University website (USDA–ARS, 2008). Seed of crop science, vol. 56, september– october 2016

the inbred lines was obtained from the USDA North–Central Regional Plant Introduction Station in Ames, IA. The inbreds were crossed using a half-diallel design (without reciprocals) to generate seed for 66 F1 test hybrids at the University of Illinois Crop Sciences Research and Education Center.

Experimental Design Field trials were conducted at the University of Illinois Crop Sciences Research and Education Center at Urbana, IL, in the summer seasons of 2009, 2010, and 2011. The 66 test hybrids, six current commercial hybrid checks, and the 12 parental inbreds were grown in a resolvable incomplete block design with three replications. Because of differences in plant height and vigor between inbreds and hybrids, genotypes were nested within generation to prevent interplot effects with two incomplete blocks of inbreds and 12 incomplete blocks of hybrids, each block containing six genotypes. Genotypes were assigned randomly to blocks within a generation using CycDesignN (Whittaker et al., 1999; software available through VSNi at http://www.vsni.co.uk/ software/cycdesign), which helps to balance physical distances between any pair of genotypes across replications. Plots were comprised of four rows with observations recorded from only the center two rows to minimize potential effects from height and maturity differences from neighboring plots and to maximize pollination within the entry and limit effects of xenia in the grain. The plot length was 5.3 m with 0.76-m row spacing. Field trials were machine planted with 35 kernels per row, which was later thinned back to 30 plants corresponding to a final plant density of ~74,000 plants ha−1.

Phenotypic Evaluation Entries were evaluated for traits related to agronomic performance, dry-milling performance, ear characteristics, and kernel characteristics (Table 1). Grain yield was recorded on a per-plot basis, as was test weight; both were adjusted to 15.5% moisture using the per-plot measurement for grain moisture at harvest.

www.crops.org 3

and DME calculated as the proportion by weight on a dry basis of flaking grits obtained from dry milling a 1-kg grain sample, and it was expressed as a percentage (Table 1). The DME values for each subsample were averaged to obtain a plot mean. Flaking-grit yield (Mg ha−1) was calculated as FGY = grain yield (Mg ha−1)  DME (%).

Statistical Analyses An analysis of variance (ANOVA) was performed to estimate generation least square means. Single-year analyses were conducted to test for homogeneity of error variances among environments before a combined analysis across environments was performed. Shapiro–Wilk’s test of normality was used to test whether residuals follow a normal distribution. The linear mixed model for the combined analysis across environments was as follows: yijklm =  + Ei + R (i)j +  k + E ik + R (i)jk + B (ijk)l + G (ijkl)m +  ijklm

Fig. 3. Small-scale dry-milling procedure adapted from Rausch et al. (2009).

Cob length, cob width, fill length, ear width, number of kernel rows, number of kernels per row, 100-kernel weight, and 100kernel volume are the ear traits that were collected. Kernel traits that were collected include kernel length, kernel width, and kernel thickness; from these, kernel size and sphericity were computed. Ear and kernel characteristics are considered to be grain-yield component traits as well as potential dry-milling performance factors. Prior to harvest, five ears were hand collected at random from the center of each plot for ear and kernel trait evaluation. All plots were hand harvested to facilitate collection of the large sample required for grain analysis and the dry-milling process. Ears were dried and shelled using a corn ear sheller (Model SCS-2, Agriculex Inc.); grain moisture and test weight were recorded. The five ears collected preharvest were added back into a bulk grain sample to calculate total grain yield for each plot. Grain yield estimates were adjusted to 15.5% moisture content for plot comparison. Two 1-kg grain subsamples from each plot were dry-milled using an adaption of the laboratory dry-milling procedure from Rausch et al. (2009). Kernels were separated after degermination into streams that passed over (+5) or through (−5) a 4.0-mm screen (5 mesh). The +5 fraction was roller-milled and a subsequent sieving over a 1.68-mm screen (10 mesh) separated the large-grit fraction from fractions containing small grits, fines, pericarp, and germ (Fig. 3). All materials that passed through the 4.0- and 1.68-mm (5 and 10 mesh) screens were combined without further separating small grits, fines, pericarp, or germ. Recovered flaking-grit fractions per subsample were weighed 4

where yijklm was the phenotypic observation for the mth genotype in the ith environment, the jth replication, the kth generation, and the lth block;  was the overall mean; Ei was the fixed effect of the ith environment; R (i)j was the random effect of the jth replication nested within the ith environment;  k was the fixed effect of the kth generation; E ik was the interaction of the kth generation and the ith environment; R (i)jk was the interaction between the jth replication nested within the ith environment and the kth generation; B (ijk)l was the random effect of the lth block nested within the ith environment, jth replication, and the kth generation; G (ijkl)m was the random effect of the mth genotype nested within the ith environment, the jth replication, the kth generation, and the lth block; and  ijklm was the random error of the ijklmth observation. General combining ability and SCA, as well as heritability estimates, were calculated using the 66 F1 test hybrids. Variance components were estimated using the linear mixed model: yijkl =  + Ek + R (k)l + Gi + Gj + Sij + EGik + EGjk + ESijk +  ijkl where yijkl was the phenotypic observation for the cross between inbreds i and j in the lth replication nested within the kth environment;  was the overall mean; Ek denoted the random effect of the kth environment; R (k)l was the random effect of the lth replication nested within the kth environment; Gi or Gj were the general combining ability (GCA) effects of the ith and jth inbreds, respectively; Sij was the specific combining ability (SCA) effect of the cross between the ith and jth inbred; EGik and EGjk were the interaction effects between GCA of the ith or jth inbred with the kth environment; ESijk was the interaction effect between SCA of the cross between ith and jth inbred with the kth environment; and  ijkl is the random error of the ijklth observation. In this model, GCA and SCA effects as well as their interactions with environments were regarded as random effects. Heritabilities, broad-sense (H 2) and narrowsense (h2), respectively, were estimated as outlined in Zare et al. (2011) in keeping with Teklewold and Becker (2005):

www.crops.org


Table 2. Summary statistics for 17 agronomic, dry-milling, ear, and kernel traits for 66 test hybrids and 12 parental inbreds evaluated in Urbana, IL, in 2009, 2010, and 2011. Test hybrids Phenotype

SD

Min.

10.73 71.4

0.85 1.4

8.51 68.3

12.50 74.1

4.20 70.9

18.00 2.1

% Mg ha−1

29.4 3.2

2.6 0.3

24.0 2.4

36.0 3.9

31.9 1.3

mm mm mm mm Count Count mL g

172.1 27.3 153.9 43.8 15.2 34.6 40.7 28.3

11.0 1.0 11.9 1.8 1.6 2.4 3.7 2.8

150.1 24.5 132.8 39.4 11.7 29.5 31.5 21.6

205.0 29.2 190.4 47.3 19.0 40.8 49.7 34.5

mm mm mm mm3

7.9 11.7 4.3 394.2 0.6

0.5 0.5 0.2 38.7 1 trait.

For example, public records of the parentage of ex-PVP inbreds suggest that Mo17 was not used in the development of the NSS heterotic group by DuPont Pioneer (M.A. Mikel, University of Illinois, personal communication, 2015). Grain yield means among the commercial hybrid checks ranged from 13.28 to 13.97 Mg ha−1 with an overall mean of 13.50 Mg ha−1 (Table 2). As expected, all of the commercial check hybrids out-yielded the test hybrids (Supplemental Table S2), demonstrating the progress of plant breeding made over the past decades. Dry-Milling Efficiency Means for DME ranged from 24.0 to 36.0% among test hybrids (Table 2) and from 24.5 to 31.4% among the commercial checks hybrids (Supplemental Table S2). Overall, DME means of test hybrids (29.4%) and commercial check hybrids (27.3%) were not significantly different at the  = 0.05 probability level. This finding suggests that DME has not been a prime target for improvement in US maize breeding to date. Among the test hybrids, PHJ40  LH123HT had the highest DME at 36.0%, and LH1  PHG47 had the lowest DME at 24.0% (Table 3; Supplemental Table S2). Thirteen of the test hybrids out-performed the best commercial check for DME. Inbreds PHJ40, B73, and 4676A, on the female side, and LH123HT, PHG84, and LH82, on the male side of the US heterotic pattern, are implicated as potential sources of favorable alleles to improve DME (Table 3) Rausch et al. (2009) evaluated 11 commercial hybrids with varying milling properties for DME; they reported DME values ranging from 29.6 to 47.0% with an average 6

of 39.2%. Results from our study also showed substantial variation for DME, but overall DME values were larger in the selected set of 11 hybrids used by Rausch et al. (2009). Blandino et al. (2010) reported DME means that ranged from 40.4 to 60.0% for 13 hybrids used for food processing in northern Italy. However, Blandino et al. (2010) performed dry milling only on typical, flat-shaped, whole kernels from the middle part of the ear to reduce variability in kernel size. In general, DME appears to be higher with grain of high test weight as shown by this study and other work (Blandino et al., 2010; Kirleis and Stroshine, 1990; Lee et al., 2007b; Pan et al., 1996; Paulsen and Hill, 1985; Pomeranz and Czuchajowska, 1987). Many of the hybrids tested by Rausch et al. (2009) and Blandino et al. (2010) had test weights greater than 75 kg hL−1, which is not typical of most yellow dent hybrids in the US Corn Belt. Parental inbred means for DME ranged from 24.3 to 39.2% with a mean of 31.9% (Table 2). The overall parental DME performance was significantly greater than the average DME of test hybrids. Comparing overall means between test hybrids and parental inbreds, we found that the parental inbreds showed decreased kernel width and kernel length but increased kernel thickness (Table 2). These findings are in agreement with Blandino et al. (2010), who reported phenotypic correlations of DME with kernel length (r = −0.60), kernel width (r = −0.13), and kernel thickness (r = 0.46). Furthermore, our results highlight the importance of kernel traits in maximizing value from the dry-milling process.

www.crops.org


Fig. 4. (A) Graphic depiction of mean performance for grain yield, dry-milling efficiency (DME), and flaking-grit yield (FGY) for each of 66 test hybrids evaluated in Urbana, IL, in 2009, 2010, and 2011. Green dots represent the five best hybrids for grain yield, orange dots represent the five best hybrids for DME, and showing the balance in contribution to performance for FGY. (B) Histograms showing the frequency distribution of test hybrids by trait.

Flaking-Grit Yield Estimates of FGY for test hybrids ranged from 2.4 to 3.9 Mg ha−1, with an overall mean of 3.2 Mg ha−1 (Table 2). For the commercial check hybrids, means for FGY ranged from 3.18 to 4.15 Mg ha−1 with an overall mean of 3.61 Mg ha−1 (Supplemental Table S2). Although PHJ40  LH123HT exhibited the highest DME, B73  PHG84 had the highest FGY among test hybrids at 3.89 Mg ha−1 (Supplemental Table S2). Although FGY is a function of DME and grain yield, results indicate that hybrids with high grain yield or DME are not necessarily those with high FGY. The top five ranking hybrids out of the 35 SSS  NSS for grain yield, DME, and FGY are given in Table 3. For example, B73  PHG84, which ranked 22nd for grain yield and fourth for DME, was top ranked among the 35 SSS  NSS crosses for FGY. Figure 4 shows how hybrids were able to achieve high FGYs in various ways, highlighting the balance of grain yield and DME represented by FGY. Test Weight and Kernel Characteristics Test hybrids showed expression of physical kernel traits typical for yellow dent maize (Table 2). Test weight, 100kernel volume, 100-kernel weight, kernel width, kernel length, kernel thickness, kernel size, and sphericity varied significantly (P  0.01) among test hybrids with mean ranges of 68.3 to 74.1 kg hL−1, 31.5 to 49.7 mL, 21.6 to 34.5 crop science, vol. 56, september– october 2016

g, 6.7 to 9.2 mm, 10.7 to 12.8 mm, 3.9 to 4.7 mm, 297.7 to 482.2 mm3, and 0.6 to 0.7, respectively. Test hybrids PHJ40  PHG84 and B73  PHG39 exhibited the highest test weight means, each at 74.1 kg hL−1 (Supplemental Table S2). Over 80% of test hybrids displayed test weights above the commercial standard of 70 kg hL−1 (56 lb bu−1). The overall test weight mean of test hybrids was not significantly different from the average commercial check hybrid test weight. Overall means for kernel width and kernel depth were similar for test hybrids and commercial check hybrids but kernel length was significantly (P  0.001) greater for the commercial check hybrids.

Relationship between Traits Dry-milling efficiency was negatively correlated with grain yield (r P = −0.50) and positively correlated with test weight (r P = 0.52) (Table 4). Likewise, Lee et al. (2007b) found a moderate but highly significant correlation between DME and test weight (r P = 0.42) in a set of 114 commercial hybrids grown in the US Corn Belt. Very tight positive phenotypic relationships between DME and test weight were reported by Pan et al. (1996) in a small set of six high-oil maize hybrids (r P = 0.91) and by Blandino et al. (2010) when 13 commercial hybrids cultivated in northern Italy were evaluated (r P = 0.84). However, Dorsey-Redding et al (1991) suggested that test weight is not a precise indicator of any specific grain

www.crops.org 7

Table 4. Pearson correlation coefficients (rP) for the relationship between phenotypic performance for all traits with grain yield, test weight, dry-milling efficiency (DME), and flakinggrit yield (FGY) among 66 test hybrids. Trait

Grain yield

Test weight

DME

−0.42**

−0.50**

FGY

Agronomic and dry-milling traits Grain yield

1

0.55**

Test weight

−0.42**

1

0.52**

0.04

DME

−0.50**

0.52**

1

0.44**

FGY

0.55**

0.04

0.44**

1

Ear traits Cob length

0.10*

Cob width

−0.12**

−0.04

0.10*

0.08

0.15**

0.30**

0.20**

Ear fill length

0.24**

−0.06

Ear width

0.09*

−0.16**

0.14**

0.26**

Ear rows

0.18**

−0.14**

0.02

0.21**

Kernels per row

0.38**

−0.24**

−0.16**

0.25**

100-kernel volume

0.13**

−0.09*

0.10**

0.26**

100-kernel weight

0.11*

0.05

0.14**

0.26**

−0.08

0.16**

Kernel traits Kernel width

−0.14**

0.14**

0.13**

Kernel length

0.40**

−0.30**

−0.22**

Kernel thickness

0.06

−0.01

−0.10*

Kernel size

0.13**

−0.06

−0.07

Sphericity

−0.33**

0.26**

0.17**

−0.04 0.21** −0.03 0.06 −0.18**

* Significant at the 0.05 probability level. ** Significant at the 0.01 probability level.

quality trait. Also, DME was highly significantly correlated with a number of ear and kernel traits including cob width, cob size, ear width, kernel width, kernel length, and sphericity; however, all correlation coefficients indicated weak relationships (i.e., |r P| < 0.50) (Table 4). Flaking-grit yield was highly positively correlated with grain yield and DME. Correlations between FGY and ear traits tended to be greater and more highly significant than those with DME (Table 4). Flaking-grit yield was positively correlated with kernel length, whereas DME was negatively correlated. However, as was seen with DME, correlations of FGY with ear and kernel traits indicated weak relationships and limited promise of using ear or kernel traits for indirect selection. Additive genetic correlation coefficients (rA) were calculated to assess the genetic relationship among grain yield, test weight, DME, and FGY. Genetic correlations can be caused by pleiotropy and linkage, leading to phenotypic relationships that can be difficult or impossible to alter. The genetic correlation between grain yield and DME was estimated at rA = −0.43. This finding suggests that simultaneous improvement of both grain yield and DME may be difficult because selection for favorable genes for one trait may have a negative impact for the other trait. This result is taken with caution though, 8

as genetic correlations are comprised of three estimated variance components and standard errors associated with genetic correlations are very large (Bernardo, 2010). Trait correlations are important for using indirect selection to exploit a strong positive phenotypic relationship with another trait of interest, particularly one that may be difficult or expensive to measure (Chen and Lübberstedt, 2010). Furthermore, trait correlations are important in devising multitrait selection schemes, particularly when a negative relationship exists between traits targeted for improvement. Given that FGY is positively correlated with grain yield, all the ear traits measured, and kernel length, it may be possible to effectively predict performance using a combination of these traits for the purposes of selection. Furthermore, results suggest that progress in genetically improving FGY along with grain yield, and test weight if desired, is feasible. Interestingly, FGY shows no association with test weight, although it is positively correlated with DME, which is influenced by test weight. This result may suggest that non-US germplasm (e.g., flint types) may be a source of favorable alleles to improve FGY, which is function of both grain yield and DME.

General and Specific Combining Ability, Heritability, and Gene Action The diallel design has been used successfully by geneticists to gain a better understanding of the nature of gene action involved in quantitative traits (Gardner and Eberhart, 1966). In particular, the diallel design facilitates estimation of GCA and SCA effects and their variances. The variance among test hybrids was partitioned into GCA 2 2 ) and SCA ( sSCA ) variance components and their ( sGCA interactions with the environment (Table 5). Estimates 2 were significant (P  0.05) for all traits except of sGCA for DME and FGY. Twelve out of 17 traits showed sig2 estimates. Except for cob width nificant (P  0.05) sSCA 2 2 ) or SCA  E ( sSCA ) and kernel width, GCA  E ( sGCA interaction variances were significant (P  0.05). Variance estimates can be compared within a trait; however, the absolute values cannot be compared across traits because of the differences in the units of measure. The relative importance of GCA and SCA can be expressed as a ratio of GCA/SCA or interactions of GCA/ SCA with environment, and these ratios can be compared across traits to assess the relative importance of additive and nonadditive gene action. Variance of GCA for DME was more than double the SCA variance component, indicating a strong additive component. The magnitude of the variance for interactions of SCA with environment was greater than SCA, while GCA with environment was similar to GCA, suggesting that DME is influenced by a nonadditive component interaction with the environment. A similar trend was observed for FGY; predominantly additive gene action is indicated.

www.crops.org


Table 5. Variance component estimates, broad-sense (H2) and narrow-sense (h2) heritabilities, and average levels of dominance ( d ) for all traits based on 66 test hybrids evaluated in Urbana, IL, in 2009, 2010, and 2011. Variance components 2 GCA

Trait Agronomic and dry-milling traits Grain yield Test weight Dry-milling efficiency Flaking-grit yield Ear traits Cob length Cob width Ear fill length Ear width Ear rows Kernels per row 100-kernel volume 100-kernel weight Kernel traits Kernel width Kernel length Kernel thickness Kernel size Sphericity

2 SCA

s2GCA×E

s2SCA×E

s2error

0.21** 0.27*** 0.91* 0.01

0.02 0.18** 2.41** 0.04**

0.29** 0.18** 1.79*** 0.03**

9.00* 0.07 7.08 0.59* 0.14** 0.57 1.30* 0.72**

18.61** 0.06 17.44** 0.40* 0.10** 0.49* 1.50** 0.91**

3.38 0 8.29 0 0.10** 0.90* 2.33*** 1.04***

s

s

0.17* 0.46* 2.05† 0.03 52.42* 0.32* 65.03* 0.95* 1.39* 2.34* 6.09* 3.55* 0.14* 0.08* 0.02* 730.09* 40  10 −5*

H2

h2

d

1.13*** 0.97*** 4.36*** 0.18***

0.70 0.80 0.65 0.49

0.43 0.62 0.53 0.43

1.57 1.08 0.66 -/-

99.82*** 2.63*** 122.78*** 8.16*** 0.51*** 7.11*** 6.31*** 3.66***

0.82 0.69 0.83 0.68 0.95 0.79 0.84 0.85

0.76 0.62 0.79 0.52 0.91 0.70 0.76 0.77

0.59 0 0.47 1.11 0.45 0 0.65 0.64

0.94 0.85 0.82 0.90 0.92

0.89 0.65 0.75 0.84 0.82

0.53 1.12 0 0.52 0.71

0.02* 0.05*** 0.003 100.52*

0.004 0.02* 0.003* 74.43*

0.01 0.02* 0.001 68.67

0.13*** 0.18*** 0.05*** 899.05***

10  10 −5***

3  10 −5*

5  10 −5*

4  10 −5***

* Significant at the 0.05 probability level. ** Significant at the 0.01 probability level. *** Significant at the 0.001 probability level. † Significant at the 0.1 probability level.

For grain yield, GCA variance was smaller than the SCA variance, indicating the importance of nonadditive gene action, which is widely recognized. For test weight, the variance of GCA was larger than the SCA variance indicating additive gene action. This finding concurs with that of Lee et al. (2012). The interaction of both GCA and SCA with the environment was lower than GCA or SCA, respectively, suggesting that the environment does not lead to changes in hybrid rank. Narrow- and broad-sense heritabilities were estimated for each of the 17 traits (Table 5). Narrow-sense heritabilities for all traits ranged from 0.43 to 0.91 for the test hybrids (Table 5). In particular, moderate narrow-sense heritabilities (0.40  h2  0.70) were estimated for grain yield, test weight, DME, and FGY. High narrow-sense heritabilities (0.80) were associated with number of ear rows, kernel width, kernel size, and sphericity.

Predicting Performance The trait FGY is a measure of productivity and dry-milling value on a per-unit-of-land basis. However, its measurement requires estimation of DME, which is laborious and considerably downstream of harvest. We used the SAS procedure PROC REG to develop models for FGY based on variables collected at or near harvest. The model is a linear function of grain yield, test weight, kernel thickness, and 100-kernel volume. The model was constructed crop science, vol. 56, september– october 2016

using the first 2 yr of data and validated using the third year of data. The test for lack of fit was not significant at p  0.05, indicating that the model fits and is not biasing our prediction. However, the model explained only 31% of the phenotypic variance among the test hybrids for FGY. The usefulness of prediction models was demonstrated by improving silage quality of maize hybrids with the final goal to increase milk production in dairy cattle. A model has been constructed that estimates energy intake of dairy cattle from maize silage and subsequently the milk production per acre and used to facilitate selection among maize silage hybrids (Schwab et al., 2003). Several previous studies explored methods to select for drymilling qualities applying prediction models. Paulsen and Hill (1985) proposed a prediction equation for DME that used test weight and breakage susceptibility, accounting for 82% of the phenotypic variation (R 2 = 0.82). Kirleis and Stroshine (1990) found that kernel density was highly correlated (r = 0.88) with a milling evaluation factor, calculated by combining the amount of endosperm that remains on the 3.5 mesh wire, 5 mesh wire, and 7 mesh wire after dry milling. Adding test weight and kernel density resulted in a higher correlation with the milling evaluation factor (r = 0.95) (Kirleis and Stroshine, 1990). Lee et al. (2007a) reported a model to predict DME that included test weight, protein content, pycnometer density, time to grind in Stenvert Hardness Tester, and kernel size

www.crops.org 9

distribution; it explained 52% of the variation for DME. The models reported by Paulsen and Hill (1985), Kirleis and Stroshine (1990), and Lee et al. (2007a) are time consuming and labor intensive; furthermore, they require specialized equipment and the destruction of grain samples to obtain measurements to predict DME. Our model was based on physical kernel traits that could rapidly be collected soon after harvest without specialized equipment to predict flaking-grit productivity. Moreover, we sought to predict FGY rather than DME, a more useful measure for maize breeders. Unfortunately, size and weight characteristics of the kernel alone will not provide enough detail to predict the yield of flaking grits accurately. A better assessment of kernel composition and physical properties may be needed. There is a necessity to explore methods involving simple, fast, and easy-to-use techniques to identify hybrids best suited for dry milling. Our FGY prediction model provides the first insight to improving both grain yield and DME simultaneously. Based on the findings of Wu and Bergquist (1991), Kirleis and Stroshine (1990), and Paulsen and Hill (1985), we suggest that including breakage susceptibility, kernel density, or both in the regression model may improve the prediction of FGY.

CONCLUSIONS This survey of US germplasm indicated that genetic diversity is present for DME, which could be exploited to improve performance for this trait and increase FGY, along with grain yield per se, in new maize hybrids. Drymilling efficiency is largely controlled by additive gene action, suggesting that both parents of new hybrids could be improved to enhance DME performance. Dry-milling efficiency has a negative phenotypic correlation with grain yield, highlighting the need for a selection index to make simultaneous gains for both traits. In addition, the negative genetic correlation observed between grain yield and DME further suggests that linkage or pleiotrophy exists between these traits. Marker-assisted selection may be useful in breaking unfavorable linkages and selecting for chromosomal regions of the genome that are associated with positive performance for both traits or positive performance of one trait and neutrality for the other. Performance for FGY may be predicted, to some extent, based on simple physical kernel characteristics such as kernel thickness and 100-kernel volume. Together with grain yield and test weight, these kernel characteristics explained 31% of the variation for FGY. However, further work is needed to improve the predictability of FGY. A next step would be to evaluate other kernel characteristics, such as kernel density and breakage susceptibility, as possibilities to obtain better predictability of FGY. Most importantly, this study provides the first insight into the relationship between DME and grain yield among a broadly representative set of US germplasm. 10

Supplemental Information Available Supplemental Table S1 presents detailed pedigree and genetic background information of the inbred lines used in this study. Supplemental Tables S2 and S3 provide means of grain yield, test weight, dry-milling efficiency, and flaking-grit yield for all hybrids and inbreds. Supplemental Table S4 presents trait means, standard deviations, and the range observed for six commercial maize hybrids used as checks in this study. Acknowledgments This work was supported in part by a USDA Hatch grant, award ILLU-802-354, and gifts from the W.K. Kellogg Institute. JAM was supported in part in his graduate studies through the Carl T. Calkins Fellowship in Plant Breeding and the William B. and Nancy L. Ambrose Fellowship in Crop Sciences.

References Bernardo, R. 2010. Breeding for quantitative traits in plants. 2nd ed. Stemma Press, Woodbury, MN. Blandino, M., M.C. Mancini, A. Peila, L. Rolle, F. Vanara, and A. Reyneri. 2010. Determination of maize kernel hardness: Comparison of different laboratory tests to predict dry-milling performance. J. Sci. Food Agric. 90:1870–1878. Chen, Y., and T. Lübberstedt. 2010. Molecular basis of trait correlations. Trends Plant Sci. 15:454–461. doi:10.1016/j.tplants.2010.05.004 Dorsey-Redding, D., L.A. Hurburgh, L. Johnson, and S.R. Fox. 1991. Relationships among maize quality factors. Cereal Chem. 68:602–605. Gardner, C., and S. Eberhart. 1966. Analysis and interpretation of the variety cross diallel and related populations. Biometrics 22:439–452. doi:10.2307/2528181 Germplasm Resources Information Network. 2012. USDA–ARS National Plant Germplasm System. http://www.ars-grin.gov/ npgs/index.html (accessed 18 May 2016). Holland, J.B. 2006. Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS PROC MIXED. Crop Sci. 46:642– 654. doi:10.2135/cropsci2005.0191 Jackson, D., L. Rooney, O. Kunze, and R. Waniska. 1988. Alkaline processing properties of stress-cracked and broken corn (Zea mays L.). Cereal Chem. 65:133–137. Johnson, G.R. 2008. A commercial-grade mapping population. In: Proc. of the 44th Annual Illinois Corn Breeders School, Champaign, IL. 3–4 Mar. 2008. Univ. of Illinois, Urbana, IL. Kirleis, A., and R. Stroshine. 1990. Effects of hardness and drying air temperature on breakage susceptibility and dry-milling characteristics of yellow dent corn. Cereal Chem. 67:523–528. Kutner, M.H., C.J. Nachtsheim, and J. Neter. 2004. Applied linear regression models. McGraw-Hill/Irwin, New York. Lee, E.A., J.A. Young, J.A. Frégeau-Reid, and B.G. Good. 2012. Genetic architecture underlying kernel quality in food-grade maize. Crop Sci. 52:1561–1571. doi:10.2135/cropsci2011.10.0545 Lee, K., T.J. Herrman, S.R. Bean, D.S. Jackson, and J. Lingenfelser. 2007a. Classification of dry-milled maize grit yield groups using quadratic discriminant analysis and decision tree algorithm. Cereal Chem. 84:152–161. doi:10.1094/CCHEM-84-2-0152 Lee, K., T.J. Herrman, J. Lingenfelser, and D.S. Jackson. 2005. Classification and prediction of maize hardness-associated properties

www.crops.org


using multivariate statistical analyses. J. Cereal Sci. 41:85–93. doi:10.1016/j.jcs.2004.09.006 Lee, K., T.J. Herrman, L. Rooney, D.S. Jackson, J. Lingenfelser, K.D. Rausch, J. McKinney, C. Iiams, L. Byrum, J. Hurburgh, R. Charles, L.A. Johnson, and S.R. Fox. 2007b. Corroborative study on maize quality, dry-milling and wet-milling properties of selected maize hybrids. J. Agric. Food Chem. 55:10751–10763. doi:10.1021/jf071863f Litchfield, J.B., and G.C. Shove. 1990. Dry milling of U.S. hardendosperm corn in japan: Product yield and corn properties. Appl. Eng. Agric. 6:629–634. doi:10.13031/2013.26440 Mehra, S.K., and S. Eckhoff. 1997. Single-stage short-duration tempering of corn for dry-milling. Cereal Chem. 74:484–488. doi:10.1094/CCHEM.1997.74.4.484 Mikel, M.A. 2006. Availability and analysis of proprietary dent corn inbred lines with expired U.S. plant variety protection. Crop Sci. 46:2555–2560. doi:10.2135/cropsci2006.05.0358 Mikel, M.A., and J.W. Dudley. 2006. Evolution of North American dent corn from public to proprietary germplasm. Crop Sci. 46:1193–1205. doi:10.2135/cropsci2005.10-0371 Mode, C.J., and H.F. Robinson. 1959. Pleiotropism and genetic variance and covariance. Biometrics 15:518–537. doi:10.2307/2527650 Narváez-González, E.D., J. de Dios Figueroa-Cárdenas, S. Taba, E.C. Tostado, R.Á.M. Peniche, and F.R. Sánchez. 2006. Relationships between the microstructure, physical features, and chemical composition of different maize accessions from Latin America. Cereal Chem. 83:595–604. doi:10.1094/CC-83-0595 Pan, Z., S. Eckhoff, M. Paulsen, and J. Litchfield. 1996. Physical properties and dry-milling characteristics of six selected high-oil maize hybrids. Cereal Chem. 73:517–520. Paulsen, M., and L. Hill. 1985. Corn quality factors affecting dry milling performance. J. Agric. Eng. Res. 31:255–263. doi:10.1016/0021-8634(85)90092-7 Philippeau, C., J. Landry, and B. Michalet-Doreau. 2000. Influence of the protein distribution of maize endosperm on ruminal starch degradability. J. Sci. Food Agric. 80:404–408. doi:10.1002/10970010(200002)80:33.0.CO;2-Z Pomeranz, Y., and Z. Czuchajowska. 1987. Laboratory tests to predict the commercial yield of flaking or large grits in dry corn milling. J. Food Sci. 52:830–830. doi:10.1111/j.1365-2621.1987. tb06740.x Rausch, K.D., L.E. Pruiett, P. Wang, L. Xu, R.L. Belyea, and M. Tumbleson. 2009. Laboratory measurement of yield and


composition of dry-milled corn fractions using a shortened, single-stage tempering procedure. Cereal Chem. 86:434–438. doi:10.1094/CCHEM-86-4-0434 SAS Institute. 2008. SAS software, version 9.2. SAS Inst. Inc., Cary, NC. Schwab, E.C., R.D. Shaver, J.G. Lauer, and J.G. Coors. 2003. Estimating silage energy value and milk yield to rank corn hybrids. Anim. Feed Sci. Technol. 109:1–18. doi:10.1016/S03778401(03)00210-4 Seebauer, J.R., G.W. Singletary, P.M. Krumpelman, M.L. Ruffo, and F.E. Below. 2010. Relationship of source and sink in determining kernel composition of maize. J. Exp. Bot. 61:511–519. doi:10.1093/jxb/erp324 Sughroue, J.R., and A.R. Hallauer. 1997. Analysis of the diallel mating design for maize inbred lines. Crop Sci. 37:400–405. doi:10.2135/cropsci1997.0011183X003700020016x Teklewold, A., and H.C. Becker. 2005. Heterosis and combining ability in a diallel cross of Ethiopian mustard inbred lines. Crop Sci. 45:2629–2635. doi:10.2135/cropsci2005.0085 USDA–ARS. 2008. Entry 001. Stiff stalk synthetic (SSS). Iowa State Univ., Ames, IA. http://www.public.iastate.edu/~usdagem/Field_Day/Handouts/GEM_FD_08_Sings.ppt (accessed 1 June 2012). USDA–ERS. 2016. Agricultural Baseline Projections. UDDA, Economic Research Service. Washington, DC. http://www.ers.usda. gov/topics/farm-economy/agricultural-baseline-projections/ baseline-related-historical-data.aspx (accessed 13 May 2016). Whittaker, D., E.R. Williams, and J.A. John. 1999. CycDesign: A package for the computer generation of experimental designs, Version 1.2. CSIRO Forestry and Forest Products and The Univ. of Waikato, Canberra and Hamilton. Canberra, Australia. Wolf, M., C. Buzan, M.M. MacMasters, and C. Rist. 1952. Structure of the mature corn kernel. 1. Gross anatomy and structural relationships. Cereal Chem. 29:321–333. Wu, Y.V., and R.R. Bergquist. 1991. Relation of corn grain density to yields of dry-milling products. Cereal Chem. 68:542–544. Yuan, J., and R.A. Flores. 1996. Laboratory dry-milling performance of white corn: Effect of physical and chemical corn characteristics. Cereal Chem. 73:574–578. Zare, M., R. Choukan, E.M. Heravan, M.R. Bihamta, and K. Ordookhani. 2011. Gene action of some agronomic traits in corn (Zea mays L.) using diallel cross analysis. Afr. J. Agric. Res. 6:693–703.

www.crops.org 11