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Published online 6 May 2005
Published in Crop Sci 45:981-987 (2005)
© 2005 Crop Science Society of America
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CROP BREEDING, GENETICS & CYTOLOGY

Quantitative Genetic Analysis of the Physiological Processes underlying Maize Grain Yield

E. A. Lee*, A. Ahmadzadeh and M. Tollenaar

Dep. of Plant Agriculture, Crop Science Bldg., Univ. of Guelph, Guelph, ON, Canada, N1G 2W1

* Corresponding author (lizlee{at}uoguelph.ca)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Few studies have examined the inheritance and interrelationships of both grain yield and the underlying physiological processes in maize (Zea mays L.). The objective of this study was to establish genetic relationships between the physiological components of grain yield and to examine the inheritance of grain yield and its component processes (i.e., additive and the nonadditive genetic effects). Twelve F1 hybrids, obtained by mating three male and four female inbred lines using a North Carolina Design II, were evaluated in trials conducted in Ontario from 2000 to 2002. Dry matter accumulation (DMA) at four stages of development, harvest index, leaf area index (LAI), stay green, and grain yield were measured. Variation among the 12 hybrids was significant for all traits evaluated, and the range in mean grain yield was 28% of the mean. Using the genetic effects partitioned by a Design II analysis, we dissected the physiological mechanisms that influenced favorable or unfavorable contributions to grain yield. Using the highest- and lowest-yielding hybrids in the study (i.e., maximum genetic variation), we attempted to dissect the physiological reasons for the difference in grain yield. This analysis, however, was unsuccessful in dissecting grain yield in terms of physiological mechanisms using a quantitative genetic model. Reasons for this failure may be, in part, (i) the relatively low contribution of statistically significant genetic effects to the differences between the hybrids; and (ii) partitioning of the difference between hybrids in four general combining ability (GCA) estimates and two specific combining ability (SCA) estimates results in small estimates relative to the grand mean.

Abbreviations: DMA, dry matter accumulation • GCA, general combining ability • LAI, leaf area index • SCA, specific combining ability


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
GENERAL AND SPECIFIC COMBINING ABILITY effects are important indicators of the potential value of inbred lines in hybrid combinations. General combining ability is the average performance of a line in hybrid combinations, while SCA is used to designate deviations of certain crosses from expectations on the basis of the average performance of the lines involved (Sprague and Tatum, 1942). Genetically, GCA is primarily associated with genes, which are additive in their effects, whereas SCA is attributed to the nonadditive genetic portion of the total genetic effects (Rojas and Sprague, 1952). Additive effects are the predictable portion of the genetic effects and are therefore useful to plant breeders. North Carolina Design II (Comstock and Robinson, 1948, 1952) is frequently used by plant breeders to assess the nature of the genetic effects influencing quantitative traits. One of the features of the design is that the variation due to hybrids is partitioned into components relating to GCAfemale and GCAmale (main effects: male, female) and to SCA (interactions: male x female). The relative importance of additive to nonadditive genetic effects governing the expression of a trait can be examined by expressing the portion of the total entry sums of squares that is partitioned into GCAmale plus GCAfemale (Pixley and Frey, 1991).

Numerous genetic studies on maize grain yield have examined the relative proportions of total genetic variance that are attributable to additive and nonadditive genetic effects. The average of nearly 100 estimates indicate that, assuming no epistasis and no linkage, additive genetic effects on average account for 61.2% and dominance accounted for 38.8% of total genetic effects (cf., Hallauer and Miranda, Fo., 1988). Attempts also have been made to estimate relative importance of additive and dominance effects for other traits such as plant height, ear height, number of ears, ear length, ear diameter, kernel-row number, kernel weight, kernel depth, and cob diameter in maize. The dominance-to-additive ratio on average is considerably lower for these traits than for grain yield, suggesting that the greatest proportion of the genetic effects can be attributed to additive effects (cf., Hallauer and Miranda, Fo., 1988). Although these traits may be associated with grain yield, in general, they do not represent processes that underlie grain-yield formation, and they contribute little or nothing to a mechanistic understanding of grain-yield formation.

Grain yield is the product of DMA at maturity and the proportion of dry matter that is allocated to the grain, that is, harvest index. Therefore, any attempt to explain grain yield should consider these traits as the main physiological processes underlying grain yield. Subsequently, traits that underlie DMA and harvest index can be analyzed. Dry matter accumulation is the product of incident solar radiation at the earth's surface (I), the absorptance of solar irradiance by the maize canopy ({alpha}), and the efficiency of conversion of absorbed radiant energy into maize dry matter ({epsilon}) (Tollenaar and Dwyer, 1999). Dry matter accumulation at maturity is the sum of daily rates of DMA from planting to maturity (DMA = {Sigma} [I x {alpha} x {epsilon}]) and is, therefore, a function of the duration of the growing season, total I, LAI, canopy architecture, and leaf CO2 exchange rate (Tollenaar and Dwyer, 1999). Differences in grain yield and DMA at maturity between older and newer hybrids have been attributed, in part, to delayed leaf senescence, that is, stay green, of the newer hybrids (Tollenaar, 1991; Tollenaar and Wu, 1999). The component processes of grain yield, therefore, include DMA throughout the life cycle, harvest index, LAI, stay green, and leaf CO2 exchange rate.

The objective of this study was to establish genetic relationships between the physiological components of grain yield and to examine the inheritance of grain yield and its component processes (i.e., additive and the nonadditive genetic effects). The physiological basis of combining ability has received little attention, and limited information is available on the interrelationships within the additive and nonadditive portions for physiological traits influencing grain yield. Most physiological traits are quantitative in nature. They cannot be genetically analyzed in the same manner as traits controlled by a few genes. Nor are these traits easily measured characters in the field, as some require destructive sampling. Perhaps for these reasons, few studies have attempted to sort out the relative importance and the inheritance of these physiological processes for obtaining high and consistent grain yield. In this study we combine quantitative genetic modeling with crop physiology to further our knowledge regarding both the genetics and physiology underlying grain yield in maize. By using a factorial mating design, GCA and SCA estimates for all traits measured in this study were derived for each inbred line and hybrid combination. Using these estimates, we examine the relationships between grain yield and its component processes influenced by additive and nonadditive genetic effects.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Genetic Materials
Twelve single-cross hybrids were developed by crossing four female inbred lines (CG57, CG58, CG59, and CG69) to three male inbred lines (CG33, LH61, and LH145) in a factorial mating design or North Carolina Design II of Comstock and Robinson (1948)(1952). The CG lines were a sample from elite germplasm available for hybrid development at the University of Guelph and the LH lines were inbred lines developed by Holden's Foundation seed company (Table 1). The four female and three male lines used for this study are considered representative of the variability in elite maize short-season germplasm [<2800 OCHU (Ontario Crop Heat Units; Brown and Bootsma, 1993)].


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  		Table 1. Germplasm description of seven maize inbred lines used in this study.

 
Experimental Procedures
The 12 hybrids were grown at the Elora Research Station, Ontario, (43°38' N, 80°25' W, 380 masl) in 2000, 2001, and 2002. Between 1 May and 10 October, the average precipitation in the region is approximately 400 mm and average seasonal heat unit accumulation is around 2650 OCHU. The soil type is London loam (Aquic Hapludalf, USDA taxonomy). Fertility levels were adjusted for optimum grain yield by applying 100 kg N ha–1 as ammonium nitrate (34–0–0 N–P–K) when the crop was well established. Complete weed control was obtained with preplant and postemergence herbicides. Plots were hand weeded during the growing season as needed. The experiment was arranged in a randomized complete block design with four replications in each year. Each plot consisted of a 12-m long, six-row plot with 76-cm interrow spacing. Maize was sown with hand planters at two seeds per hill and thinned to one plant per hill at the seedling stage for a final plant density of 70000 plants ha–1. Two middle rows of each plot were used to measure the following characters.

Dry Matter Accumulation
Dry matter accumulation was determined at the six-leaf stage (i.e., leaf tips), the 14-leaf stage, silking, and maturity by destructive whole-plant sampling during the 2001 and 2002 growing seasons; DMA was determined only at silking and maturity in 2000. Dry matter at the six-leaf stage was determined for each plot by taking a random sample of 10 plants in two middle rows from those pulled out at thinning at the six-leaf stage. Plant samples were dried at 80°C until constant weight. Three sample areas (each 3.04 m2) were used to determine DMA of each plot at the other three stages of development. Sample area was separated on each end by a 2-m long internal border and two border rows on each side. For the determination of DMA at the 14-leaf stage and at silking, all plants in the sample area were cut at ground level and divided into sample and subsample portions. The subsample consisted of five randomly selected plants in the harvest area. After recording the fresh weight of both portions, the subsample was separated into leaf and stem, and the sample portion was discarded. Moisture content of each subsample was determined by measuring its weight before and after drying at 80°C until weight did not change for two consecutive weighing dates. Total dry weight of the sample area was estimated by multiplying total sample fresh weight and percentage dry matter of the subsample. Total above-ground DMA at physiological maturity was estimated by cutting 10 subsample plants at ground level and separating the subsample in ear (kernels, cob, husk, and shank) and nonear portions. Only the ears of the remaining plants in the sample area were harvested. Dry weight of each subsample fraction and of the remaining ears in the sample area was determined after drying at 80°C until weight did not change for two consecutive weighing dates. Dry matter accumulation at maturity was computed from dry weights of the ear and nonear fractions of the subsample and the remaining ears in the sample area.

Grain Yield, Harvest Index, and Kernel Number
All ears from the sample area at maturity were shelled, and grain weight of the sample was determined. Grain yield was expressed at 0% moisture. Harvest index was estimated as the proportion of grain weight to total above-ground dry weight in the 10 subsample plants in each plot at maturity. The number of kernels per unit area was calculated from the grain sample at maturity using a seed counter.

Leaf Area Index and Stay Green
The LAI of each was recorded at silking using a LICOR leaf-area meter model 3100 (LI-COR, Lincoln, NE). For partly senesced leaves, the senesced portion was cut away from the leaf before measurement so that only green leaf area was determined. Leaf area index was estimated for each entry by dividing the leaf area of five randomly selected plants by the corresponding ground surface (i.e., five plants/seven plants m–2). In the first two years of the study, stay green was determined by visually assessing the degree of green tissue at 6 wk postsilking. To measure this trait more accurately, in the third year of the study, five plants were randomly selected and tagged within each plot, and leaf area was estimated by measuring leaf length and maximum leaf width of all leaves on each selected plant at silking. Leaf area was estimated as leaf length x leaf width x 0.75 (Montgomery, 1911) and summing for all leaves measured on each plant. Every leaf was again examined at 6 wk postsilking, and stay green of each entry was determined as the proportional decline of green leaf area at 6 wk postsilking relative to green leaf area at silking by visually rating the proportion of leaves remaining green (cf., Valentinuz and Tollenaar, 2004).

Silking and Maturity Dates
Number of days from planting to silking and maturity were determined for each entry. Silking date was the first day silks were visible on the topmost ear of at least 50% of plants in a plot. Date of maturity was defined as the first day when grain of at least 50% of plants in a plot attained black layer.

Statistical Analysis
A combined ANOVA was performed for each trait using PROC GLM of SAS ver. 8.2 (1996, SAS Institute, Cary, NC) using the following linear model:

where i = 1, 2, 3, 4; j = 1, 2, 3; q = 1, 2, 3; k = 1, 2, 3, 4; and cijkq denotes the value of the progeny of a mating of the ith female line, the jth male line, in the kth block, and in the qth year. The term µ is the grand mean, gi is the GCA effect common to all progeny of the ith female line, gj is the GCA effect common to all progeny of the jth male line, sij is the SCA effect specific to the progeny of mating the ith female line and the jth male line, yq is the average effect of the qth year, rk(yq) is the effect of the kth block nested within the qth year, (gy)iq and (gy)jq are the interactions between the GCA effects and year, (sy)ijq is the interaction between the SCA effect and year, and eijkq is the random experimental error.

Using this model, hybrid sums of squares were partitioned into sources of variation due to females, males, and the female x male interaction. Hybrid x year sums of squares were similarly partitioned into male x year, female x year, and male x female x year sources. The pooled error term [pooled error(a)] was used to test the components of hybrid x year interaction. Tests of significance for female, male, and male x female were made by testing these mean squares with their respective year interaction mean squares. When the mean squares for interaction (hybrids or their partitions x year) were not significant, mean squares for the interaction and the pooled error(a) were pooled to form a single denominator (pooled error(b)) for the F-test of hybrids and their partitions (Cochran and Cox, 1957). The expected variation due to female and male parents corresponds to GCA, and that due to the male x female interaction corresponds to SCA (Hallauer and Miranda, Fo., 1988). Since there are two sets of parents in a North Carolina Design II, there are two independent estimates of GCA. For each character and if the combining ability effects were significant, then the GCA estimates (gi or gj) for all parental lines and SCA estimates (sij) for all hybrid genotypes were calculated according to Beil and Atkins (1967) as follows:

where yij is the mean of the hybrid of mating the ith female and the jth male parent, yi. is the mean of all hybrids involving the ith female parent, y.j is the mean of all hybrids involving the jth male parent, and y.. is the mean of all hybrids. Standard errors for gi or gj estimates and sij estimates were calculated by using the methods described in Cox and Frey (1984), where SEGCA = {MSfy [(f – 1)/mfyr]}1/2 or {MSmy [(m – 1)/mfyr]}1/2 for females or males, respectively. MSfy and MSmy are the respective female x year and male x year mean squares and are multiplied by the appropriate proportion of total number of observations [males x females x reps(year) x years]. Standard errors for sij estimates were calculated as SESCA = {MSfmy [(m – 1)(f – 1)/mfyr]}1/2 where MSfmy is the female x male x year mean square. When the MSfy, MSmy, and MSfmy were not significant, they were replaced by the pooled error(b) to calculate standard errors for gi or gj and sij estimates, respectively. Two-tailed t tests were used to test the significance of the gi or gj and sij estimates from zero, where t = GCA/SEGCA and t = SCA/SESCA, respectively (Singh and Chaudhary, 1977).

To assess the relative importance of additive and nonadditive genetic effects for each trait, the ratio of (male sums of squares + female sums of squares)/hybrid sums of squares was computed (Pixley and Frey, 1991). The closer the ratio is to unity, the greater the influence of additive genetic effects on the trait. An approximate F test of the ratio of the GCA mean squares (i.e., MSmale + MSfemale) to SCA mean squares (i.e., MSmalexfemale) was used to test the significance of the amount of additive vs. nonadditive variation, assuming that SCA is randomly and normally distributed with constant variance (Satterthwaite, 1946; Lin and Binns, 1991).


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
CONCLUSIONS
 REFERENCES
 
Phenotypic and Genetic Variation
Variation among the 12 hybrids was significant for all traits evaluated (Table 2). The range in mean grain yield among the hybrids from the lowest value (5.19 Mg ha–1) to the highest value (6.90 Mg ha–1) was 28% of the mean grain yield (Table 3). The two components of grain yield, harvest index and DMA at maturity, displayed a similar range in values as a proportion of the hybrid mean. Mean harvest index ranged from 0.47 to 0.55 among hybrids, which is 18% of the hybrid mean (Table 3). The range from the lowest to the highest value for DMA at maturity was 3.10 Mg ha–1, which is 26% of the hybrid mean for DMA at maturity (Table 3). The difference in DMA at silking between the highest and lowest values was 1.24 Mg ha–1, which is 19% of the hybrid mean. In contrast, the difference between the highest and lowest values for DMA during the grain-filling period was 2.89 Mg ha–1, which is 52% of the hybrid mean (Table 3). A large proportion of DMA is accumulated during the grain-filling period ({approx}45% of the total) in these hybrids (Tollenaar et al., 2004). Mean values for component processes of DMA ranged from 2.49 to 3.36 for LAI, and from 15 to 71% for stay green (Table 3). The combined ANOVAs indicated that hybrid x year interactions were significant for all traits except DMA at the 14-leaf stage, DMA during the grain-filling period, and DMA at maturity, suggesting that genotype x environment effects on grain yield are not caused by genotype x environment effects on DMA during the later phases of the plant's life cycle. Instead, the genotype x environment effect on grain yield appears to be a result of the genotype x environment effect on harvest index.


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  		Table 2. Mean squares from combined ANOVAs for dry matter accumulation at maturity (DMM), at silking (DMS), during the grain-filling period (DMGF), at the six-leaf stage (DM6), and at the 14-leaf stage (DM14); and grain yield, harvest index, leaf area index (LAI), and stay green in a North Carolina Design II experiment during 3 or 2 yr.

 

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  		Table 3. Hybrid means across 3 yr for grain yield, dry matter accumulation at maturity (DMM) and at silking (DMS), harvest index, leaf area index (LAI), and stay green; and across 2 yr for dry matter accumulation at the 14-leaf stage (DM14) and the six-leaf stage (DM6).

 
Both additive and nonadditive genetic effects influenced grain yield in this study (Table 2). The SS ratio for grain yield was 0.74 (P < 0.05), which is similar to the relative importance of additive genetic variance reported in other maize studies (Hallauer and Miranda, Fo., 1988). Grain yield is the product of DMA at maturity and harvest index. The SS ratio for harvest index was 0.91 (P < 0.05), indicating that additive effects were primarily influencing this trait in our study. Except for DMA at the six-leaf stage, no differences were observed between proportions of additive to nonadditive genetic effects on DMA during the life cycle. Component processes of DMA are LAI, stay green, and net leaf CO2 exchange rate. In contrast to DMA, the SS ratios were significant (P < 0.05) for maximum LAI (0.91) and the maintenance of green leaf area during the grain-filling period, that is, stay green (0.90), indicating that additive effects accounted for most of the variability for these traits among the hybrids. Results of other studies also have shown that LAI is affected to a greater extent by additive than by nonadditive genetic effects (Rutger et al., 1971; Mason and Zuber, 1976). The inheritance and genetic effects influencing potential net leaf CO2 exchange rate for this genetic material was examined in another study (Ahmadzadeh, 2003; Ahmadzadeh et al., 2004). Differences in leaf CO2 exchange rate among the hybrids were not significant until 6 wk after silking, and the SS ratio for leaf CO2 exchange rate at 6 wk after silking was 0.78 (P < 0.05) (Ahmadzadeh, 2003; Ahmadzadeh et al., 2004).

Relationship between Genetic Effects on Grain Yield and its Component Processes
The contribution to the performance of the hybrid that the inbred lines made within the additive (GCA) and nonadditive (SCA) portion of the genetic variance was estimated (Tables 4 and 5). For grain yield, only additive genetic effects associated with females were significant (Table 2). Within the group of female inbred lines, a significant positive gi estimate for grain yield was associated with inbred line CG59, and a significant negative gi estimate for grain yield was associated with inbred line CG58 (Table 4). Within the nonadditive portion of the genetic effects, two genotypes exhibited favorable contributions to grain yield and two genotypes exhibited unfavorable contributions to grain yield (Table 5).


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  		Table 4. Estimates of GCA effects for dry matter accumulation at maturity (DMM), at silking (DMS), at the 14-leaf stage (DM14), and at the six-leaf stage (DM6); and grain yield, harvest index, leaf area index (LAI), and stay green of female and male lines.

 

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  		Table 5. Estimates of SCA effects for dry matter accumulation at maturity (DMM), at silking (DMS), at the 14-leaf stage (DM14), and at the six-leaf stage (DM6); and grain yield, harvest index, and leaf area index (LAI).

 
Is it possible to dissect the underlying physiological mechanisms influencing these favorable or unfavorable contributions to grain yield? Grain yield is the product of DMA at maturity and harvest index and, consequently, additive genetic effects on grain yield should be associated with additive genetic effects on either or both DMA at maturity and harvest index. We will attempt to illustrate this approach by using the highest yielding hybrid (CG59 x LH61) and lowest yielding hybrid (CG58 x LH145).

Grain Yield
Mean grain yield of a hybrid across years and replications (cij) can be estimated as cij = µ + gi + gj + sij, where µ is the grand mean, gi and gj are the GCA effects of the ith female and jth male, respectively, and sij is the SCA effect specific to the hybrid of the ith female line and the jth male line. The components of this model can be estimated using the data in Table 3 and the statistically significant g and s estimates have been depicted in Tables 4 and 5. Grain yield of CG59 x LH61 (6.90 Mg ha–1) is the sum of the grand mean (6.07 Mg ha–1), the gi estimate for CG59 (0.33 Mg ha–1), the gi estimate for LH61 (0.32 Mg ha–1), and the sij estimate for CG59 x LH61 (0.19 Mg ha–1). Similarly, grain yield of CG58 x LH145 (5.19 Mg ha–1) is the sum of the grand mean (6.07 Mg ha–1), the gi estimate for CG58 (–0.19 Mg ha–1), the gi estimate for LH145 (–0.50 Mg ha–1), and the sij estimate for CG58 x LH145 (–0.18 Mg ha–1). Although the gj estimates for LH61 and LH145 were numerically large, those estimates were not statistically significant, in part because of the relatively high male x year sum of squares, that is, the error term (Table 2). The difference in grain yield between the two hybrids (1.71 Mg ha–1) can be attributed to statistically significant additive effects for CG59 (+0.33 Mg ha–1) and CG58 (–0.19 Mg ha–1), for a total difference of 0.51 Mg ha–1, and the remainder of the difference (1.20 Mg ha–1) is attributable to nonsignificant effects. Only a relatively small proportion of the genetic effects (i.e., 0.51/1.20 = 43%) were statistically significant.

Harvest Index
Harvest index of CG59 x LH61 was significantly greater than that of CG58 x LH145 (Table 3). About 24%, or 0.41 Mg ha–1 of the 1.71 Mg ha–1 difference in grain yield between the two hybrids was attributable to their difference in harvest index (0.04). Genetically, the difference in harvest index between the two hybrids was attributable, in part, to a significant positive contribution CG59 (gi = +0.01) and a significant negative contribution by CG58 (gi = –0.01). Neither the gj estimates for LH61 and LH145, nor the sij estimates for the two hybrids were statistically significant (Tables 4 and 5). Even though 24% of the difference in grain yield could be attributed to harvest index, only 50% of the genetic effects controlling harvest index were statistically significant.

Dry Matter Accumulation at Maturity
Dry matter accumulation at maturity was also significantly greater for CG59 x LH61 than for CG58 x LH145 (Table 3). About 76% of the difference in grain yield between the two hybrids was attributable to their difference in DMA at maturity (2.9 Mg ha–1). Genetically, the difference in DMA at maturity between the two hybrids was attributable, in part, to a significant favorable gi estimate for CG59 (+0.4 Mg ha–1) and a significant favorable sij estimate for CG59 x LH61 (+0.8 Mg ha–1). Neither the gi estimates for CG58, LH61, and LH145, nor the sij estimate for CG58 x LH145 were statistically significant (Tables 4 and 5). As with harvest index, even though 76% of the difference in grain yield was due to differences in DMA at maturity, only a relatively small percentage of the genetic effects were statistically significant (41%).

Dry Matter Accumulation During the Life Cycle
Differences in DMA between the two hybrids were not statistically significant until after silking (Table 3). Almost all of the difference in DMA at maturity between the two hybrids was attributable to differences in DMA during the grain-filling period. The difference in DMA during the grain-filling period between the two hybrids (2.6 Mg ha–1) was attributable, in part, to a significant favorable gi estimates for CG59 (+0.6 Mg ha–1) and LH61 (+0.6 Mg ha–1), and a significant favorable sij estimate for CG59 x LH61 (+0.8 Mg ha–1). Neither the gi estimates for CG58 and LH145, nor the sij estimate for CG58 x LH145 were statistically significant (Tables 4 and 5). Unlike the traits previously discussed, a rather large proportion (75%) of the genetic effects governing DMA during the grain-filling period was statistically significant, and the trait essentially explained all of the difference in DMA at maturity.

Dry Matter Accumulation and Component Processes
Is it possible to dissect DMA during the grain-filling period further? Dry matter accumulation is the product of light interception, duration of light interception, and the utilization of absorbed irradiance by the crop canopy (i.e., canopy photosynthesis) throughout the life cycle. Light interception is a function of LAI. In this study, we measured LAI at silking (approximately maximum LAI) and the proportion of leaf area that remained green through the grain-filling period, that is, stay green. We cannot attribute the differences in DMA during the grain-filling period to LAI; however, the differences could be a function of stay green. The maximum LAI of the two hybrids did not differ (Table 3). Stay green of CG59 x LH61 was significantly greater than that of CG58 x LH145, 72 and 38% green leaf area at 6 wk after silking, respectively (Table 3). The SS ratio for stay green was 0.91 (P < 0.05), indicating that primarily additive effects were influencing this trait in our study, but surprisingly, both of the male parents, LH61 and LH145, had significant favorable gi estimates for stay green, 16 and 8%, respectively (Table 4). Neither the gi estimates for CG58 and CG59 nor the sij estimates for the two hybrids for stay green were statistically significant (Tables 4 and 5).

These differences in DMA during the grain-filling period do not appear to be related to relative maturity (which could influence duration of light interception) nor to potential leaf CO2 exchange rate. The two hybrids did not differ either in duration from planting to silking, or from planting to maturity (P > 0.05). Mean duration from planting to silking was 72.8 d for CG59 x LH61 and 73.3 d for CG58 x LH145 and mean duration from planting to maturity was 128.5 d for CG59 x LH61 and 127.6 d for CG58 x LH145. In another study involving the same set of genetic materials, potential leaf CO2 exchange rate of the hybrids was not significantly different until 6 wk after silking (Ahmadzadeh et al., 2004). However, CG59 x LH61 and CG58 x LH145 did not differ significantly in potential leaf CO2 exchange rate at 6 wk after silking (Ahmadzadeh et al., 2004).

The differences in DMA during the grain-filling period between these two hybrids cannot be easily attributed to the underlying physiological mechanisms using this methodology. The LAI was not significantly different; therefore, it was not a result of differences in potential light interception. Relative maturity was not significantly different between these hybrids; therefore, it was not likely a result of differences in duration of light interception. Potential leaf CO2 exchange rates were not significantly different between these two hybrids; therefore, the difference in DMA during the grain-filling period cannot be attributed to potential photosynthesis. Only stay green was significantly different between the two hybrids. However, the only significant genetic effects were for the gj estimates of the two males and they were both favorable, adding even more confusion to our attempt to understand the mechanisms driving the differences in DMA during the grain-filling period between the two hybrids.


   CONCLUSIONS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
REFERENCES
 
In this study, we combined quantitative genetic modeling with crop physiology to further our knowledge regarding both the genetics and physiology underlying grain yield in maize. Using the genetic effects partitioned by a Design II analysis, we dissected the physiological mechanisms that influenced favorable or unfavorable contributions to grain yield and the underlying physiological mechanisms. This analysis, however, was unsuccessful in attributing physiological factors underlying grain yield in terms of quantitative genetics. Reasons for this failure may be, in part, (i) the relatively low contribution of statistically significant genetic effects to the differences between the hybrids; and (ii) partitioning of the difference between hybrids in four g estimates and two s estimates results in small estimates relative to the grand mean. For instance, the gi estimate for harvest index for the line CG59 was 0.01, which is about 2% of the grand mean for harvest index, but a coefficient of variability in a good field study is in the order of 4 to 8%. Results of this study show that quantitative genetic analyses of major physiological processes underlying grain yield in our genetic material was unsuccessful and these results suggest that quantitative genetic analyses of single genes or quantitative trait loci associated with grain yield will be even more challenging.


   ACKNOWLEDGMENTS
 
Technical support by A. Aguilera, M.J. Ash, and B. Good is gratefully acknowledged.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Part of a dissertation submitted by A. Ahmadzadeh in partial fulfillment of the requirements for a Ph.D. Financial support, in part, from the Ontario Ministry of Agriculture and Food, Natural Science and Engineering Research Council, and Ontario Corn Producers' Association. A. Ahmadzadeh was supported by a postgraduate scholarship from CIMMYT.

Received for publication October 14, 2003.


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


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A. J. Lorenz, J. G. Coors, N. de Leon, E. J. Wolfrum, B. R. Hames, A. D. Sluiter, and P. J. Weimer
Characterization, Genetic Variation, and Combining Ability of Maize Traits Relevant to the Production of Cellulosic Ethanol
Crop Sci., January 28, 2009; 49(1): 85 - 98.
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