Genetic analyses of grain-filling rate and duration in maize1

Field Crops Research 61 (1999) 211±222

Genetic analyses of grain-®lling rate and duration in maize1 Guilin Wang, Manjit S. Kang*, Orlando Moreno Department of Agronomy, Louisiana Agricultural Experiment Station, Louisiana State University Agricultural Center, Baton Rouge, LA 70803-2110, USA Received 22 June 1998; accepted 4 November 1998

Abstract Grain-®lling rate and duration in¯uence grain yield in maize (Zea mays L.), but very little information on their inheritance exists. To devise effective breeding strategies, the genetic nature of these traits must be understood. The objectives of this study were to (1) examine general combining ability (GCA) and speci®c combining ability (SCA) for grain-®lling rate, grain®lling duration, and related agronomic traits via North Carolina Design II, (2) determine the inter-relationships among these traits and their in¯uence on yield via correlation and path coef®cient analyses, and (3) identify an indirect selection criterion for yield. Design II crosses among four inbred lines used as males and a set of four inbred lines as female parents were grown in 1996. Combining ability analyses indicated that both GCA (Vg) and SCA mean squares (Vs) were signi®cant for grain®lling rate (on a kernel or an ear basis) and effective ®lling duration. General combining ability was more important than SCA for both kernel-®lling rate and effective ®lling duration, whereas SCA effect was more important for ear-®lling rate. The ratio 2Vg/(2Vg ‡ Vs) was 0.85, 0.88, and 0.45 for kernel-®lling rate, effective ®lling duration, and ear-®lling rate, respectively. Kernel-®lling rate had a positive phenotypic correlation with kernel weight and was negatively correlated with midsilk date and effective ®lling duration. Kernel number per ear was more important than kernel-®lling rate in in¯uencing grain yield. These relationships were con®rmed by results from a 1997 experiment using nine commercial hybrids. Chlorophyll readings taken with SPAD chlorophyll meter at a late developmental stage gave a positive genetic correlation with single-plant yield (r ˆ 0.73). A path coef®cient analysis revealed that chlorophyll concentration had a small direct effect on grain yield, whereas it had a large indirect effect on grain yield via kernel number per ear and grain-®lling duration. Kernel weight and midsilk date could serve as indirect selection criteria for effective grain-®lling duration and kernel-®lling rate. Chlorophyll concentration at a late developmental stage could also be an indirect selection criterion for ®nal grain yield. # 1999 Elsevier Science B.V. All rights reserved. Keywords: Chlorophyll concentration; Combining ability; Grain-®lling duration; Grain-®lling rate; Maize; Path analysis

1. Introduction Grain yield in maize is primarily determined by kernel weight and kernel number (Poneleit and Egli, *Corresponding author. e-mail: [email protected] 1 Approved for publication by the director of the Louisiana Agricultural Experiment Station as manuscript no. 98-09-0288.

1979). Kernel weight is in¯uenced by grain-®lling rate and duration. During much of the grain-®lling period, grain dry weight accumulates at essentially a linear rate over time, beginning about 7±14 days post-midsilk (Sayre, 1948; Hanway, 1962; Duncan and Hat®eld, 1964). The linear grain-®lling period (the period between approximately 5±95% of the ®nal kernel weight) was termed `effective ®lling-period duration'

0378-4290/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved. PII: S0378-4290(98)00163-4

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G. Wang et al. / Field Crops Research 61 (1999) 211±222

(EFPD) (Daynard et al., 1971; Johnson and Tanner, 1972). Many studies have shown grain-®lling period to be positively correlated with yield (Daynard et al., 1971; Cross, 1975; Boyle et al., 1979; Wych et al., 1982). In the past several decades, increase in grain yield in maize was achieved mainly by lengthening grain®lling period and increasing population density, which, in turn, increased grain-®lling rate per unit land area. Grain-®lling duration was longer in the newer hybrids, even though harvest maturity remained unchanged (Cavalieri and Smith, 1985). The increase in grain-®lling duration was the result of delayed physiological (black layer) maturity rather than a change in ¯owering date. Physiological maturity occurred at a lower kernel moisture content in modern hybrids allowing hybrids with a longer grain-®lling period to remain in the same maturity group. Maturity group that Cavalieri and Smith referred to meant the time at which kernels dried down to a selected grain moisture percentage. In adverse environments, especially drought and cold conditions during the crop ripening period, early maturing, short-duration hybrids usually out-yielded their long-duration counterparts (Eastin, 1972). Genetic variability has been observed among maize lines and hybrids for grain-®lling rate and duration (Daynard et al., 1971; Carter and Poneleit, 1973; Poneleit and Egli, 1979). Similar genetic studies have been carried out on grain-®lling rate and duration in spring wheat and winter wheat (Bruckner and Frohberg, 1987; Darroch and Baker, 1990; Mou and Kronstad, 1994). Cross (1975) conducted a diallel analysis of seven early maize inbreds for grain-®lling duration and rate and found that the grain-®lling traits were genetically controlled. General combining ability (GCA) for EFPD and grain-®lling rate was more important than speci®c combining ability (SCA) effects, which indicated that cyclic selection should be effective in changing these traits within the material studied. Similar results were also reported in winter wheat (Triticum aestivum) (Mou and Kronstad, 1994) and spring wheat (Bruckner and Frohberg, 1987; Darroch and Baker, 1990). However, in those studies, grain-®lling rate was expressed on a kernel basis. Kang and Zuber (unpublished data) found a positive association between rate of grain ®lling and duration of grain ®lling (r ˆ 0.46) on a whole ear basis. Kang

et al. (1986) reported that grain-®lling rate had a direct positive effect on grain moisture reduction per thermal unit during the ®lling period, indicating that hybrids with relatively higher moisture reduction rate could also be fast ®llers. No genetic information is available, especially for maize, on grain-®lling rate on a plant or ear basis. Grain-®lling rate is dependent on both the partitioning ability of the crop species or cultivar and photosynthetic rate (Jurgens et al., 1978). Kernel-®lling rate was relatively more stable than grain-®lling duration; the latter was easily affected by changes in plant density and temperature whereas kernel growth rate was not affected (Poneleit and Egli, 1979; BaduApraku et al., 1983). Prioul and SÂchwebel-Dugue (1992) pointed out that excision of leaves did not affect kernel-®lling rate, whereas it greatly affected kernel number per ear. In soybean (Glycine max), Board et al. (1994) found that defoliation during mid to late seed ®lling signi®cantly reduced the effective ®lling period. Breeding programs might bene®t more by emphasizing grain-®lling rate on an ear or plant basis than grain-®lling duration to enhance grain yield. Daynard et al. (1971) and McGarrahan and Dale (1983) reported positive relationships between grain yield and grain-®lling duration in maize. Mou et al. (1994) found a negative correlation between grain®lling rate and grain-®lling duration, and a positive correlation between kernel weight and grain-®lling rate in wheat. Kernel weight was not associated with grain-®lling duration. Similar results were reported by Bruckner and Frohberg (1987), Darroch and Baker (1990) and Duguid and BruÃleÂ-Babel (1994) for different spring wheat lines. The larger the grain size, the higher the grain-®lling rate. Knott and Gebeyehou (1987) found in durum wheat that the lengths of vegetative and grain-®lling periods were negatively correlated. There was no optimum combination of grain-®lling duration and grain-®lling rate that maximized yield. Nitrogen concentration in leaves has been shown to in¯uence both the development of maize canopy (Muchow, 1988) and photosynthetic rate (Muchow and Davis, 1988; Greef, 1994), and it might have a possible relationship with grain-®lling traits. The staygreen characteristic in maize relates to nitrogen metabolism in leaves and has been associated with longer

G. Wang et al. / Field Crops Research 61 (1999) 211±222

photosynthetic period and increased grain yield. Recently, chlorophyll concentration in maize leaves has been shown to be a good indicator of the staygreen characteristic (D'Croz-Mason and Lindauer, 1997). Enhancement of resolution of photosynthetic measurements in maize leaves in the ®eld could help clarify the relationships between environmental stress, carbon assimilation, and grain yield (Tollenaar et al., 1994). Cumbersome gas exchange techniques have limited both the time and space resolution of photosynthetic measurements in ®eld studies. Chlorophyll concentration has been shown to be correlated with yield in some studies (Sprague and Curtis, 1933; Everett, 1960), but other researchers found no association between total chlorophyll and yield (Miller and Johnson, 1938; Fleming and Palmer, 1975). Leaf chlorophyll concentration has been suggested as an indicator of suf®ciency of nitrogen in maize (Piekielek and Fox, 1992). The traditional method of extracting and quantifying chlorophyll concentration is both laborious and destructive. Minolta Corporation developed a small, portable meter called chlorophyll meter (SPAD) that records instant, nondestructive, chlorophyll readings of plant leaves (Yadava, 1986). The reading in SPAD units indicates the relative chlorophyll concentration. Earl and Tollenaar (1997) reported a strong relationship (r ˆ 0.98) between maize leaf absorptance of photosynthetically active radiation and chlorophyll meter readings. Often, a complex trait is dif®cult to improve directly, but it may be more easily handled through indirect selection. Path coef®cient analysis has been used successfully to study relationships among yield and its component traits in sugarcane (Saccharum spp.) (Kang et al., 1983; Milligan et al., 1990; Kang et al., 1991) rice (Oryza sativa L.) (Gravois and Helms, 1992) barley (Hordeum vulgare L.) (Puri et al., 1982; Garcia del Moral et al., 1991) and maize (Djordjevic and Ivanovic, 1996). No information is available on the relative importance of direct and indirect effects of grain-®lling rate and duration, silking date, and chlorophyll concentration on grain yield in maize. The objectives of this investigation were to: (1) determine GCA and SCA effects for grain-®lling duration and grain-®lling rate on a kernel basis as well as on a plant basis to take into account kernel

213

number; (2) identify germplasm with relatively high grain-®lling rates; and (3) identify an indirect selection criterion for grain-®lling rate and yield, particularly to see via correlation analysis if chlorophyll meter readings could serve as a selection criterion for yield. 2. Materials and methods 2.1. 1996 field experiment In 1996, 16 crosses made according to Design II mating scheme (Comstock and Robinson, 1948) among eight inbred lines of dent maize were evaluated. Four lines were used as female parents (H111, L266, L668, and L729) and the other four as male parents (B73, L108, L329, and L654). The `L' lines (all full-season maturity) were developed at the Louisiana Agricultural Experiment Station and inbred lines H111 and B73 (early to medium maturity) were included as other public reference lines. The pedigrees of these inbred lines are given in Table 1 in Kang et al. (1995). Previously, these lines had been evaluated for maize weevils' (Sitophilus zeamais Motschulsky) preference/nonpreference for grain (Kang et al., 1995), rate of ear moisture loss (Zhang et al., 1996), and resistance to infection by Aspergillus ¯avus (Link ex Fries) (Zhang et al., 1997). The F1 crosses were grown in a randomized complete-block design with three replications at the Ben Hur Plant Science Farm (Commerce silt loam soil; ®ne-silty, mixed, nonacid, thermic Aeric Fluvaquent; USDA taxonomy), Baton Rouge, Louisiana (308N, 928W). Reciprocal crosses were not included. Each experimental unit consisted of a 6 m long, four-row plot with 102 cm inter-row spacing and 30 cm plant to plant spacing. The low plant density was used to provide each individual plant a minimal but equally competitive environment both within and between plots and to minimize effects of any differential adaptive ability of hybrids to high density. Planting was done with a mechanical planter on 20 May, 1996. Fertilizer applications were split: 67 kg P haÿ1 and 67 kg K haÿ1 were applied at sowing, followed by an application of 224 kg N haÿ1 as aqueous ammonia. The test was not irrigated and did not experience drought stress. Midsilk date (50% plants in a plot

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G. Wang et al. / Field Crops Research 61 (1999) 211±222

Table 1 Hybrid means of several traits for 16 maize single crosses (4  4 Design II mating scheme) Hybrid

Sowing to silking (days)

Black-layer maturity (days)

Kernel-filling rate (mg kernelÿ1 dayÿ1)

Ear-filling rate (g earÿ1 dayÿ1)

EFPDa (day)

Kernel number (earÿ1)

Kernel weight (mg)

Plant yield (g earÿ1)

L266  B73 H111  L329 L266  L654 H111  L108 L729  L108 L668  L654 L266  L108 H111  L654 L729  B73 L729  L654 L668  L329 L266  L329 L729  L329 H111  B73 L668  B73 L668  L108

56 62 58 58 62 59 58 58 56 61 61 59 64 56 55 61

92 100 106 99 107 102 102 98 93 105 99 99 109 91 92 98

9.36 9.63 11.11 9.72 9.10 9.86 8.53 11.16 9.64 9.86 8.52 8.63 8.32 11.21 9.32 8.76

7.35 6.58 6.89 6.61 6.97 6.78 6.30 6.64 7.60 5.84 6.07 5.31 6.20 5.16 5.80 5.10

21 24 23 25 24 23 24 23 22 25 21 23 25 20 22 22

743 763 664 672 716 696 637 589 699 601 637 596 658 542 583 627

198 230 248 238 219 221 199 247 210 240 180 199 208 219 198 189

168 168 167 166 162 161 146 145 141 134 126 123 121 118 116 113

0.24

0.41

LSD (0.05)

0.92

2.79

0.97

22

8.1

7.2

Grown in 1996 at Baton Rouge, Louisiana. EFPD: Effective filling period duration.

a

with silks emerged) for each plot was recorded on the basis of daily observations. Kernels were sampled from ears in central rows of a plot to estimate grain-®lling rate and duration and grain yield. Beginning at 15 days after midsilk, ears from eight adjacent plants were sampled at three weekly intervals before physiological maturity (black layer). Whole ears were harvested and dried in a forced-air oven at 608C for seven days. Kernels were separated from ears and weighed. 2.1.1. Statistical analyses Plot means of kernel weight were subjected to statistical analyses. Since the samples were taken during the period of linear grain-®lling phase beginning at about 15 days after midsilk and before black layer formation, a linear regression of kernel weight on time was used to derive a regression equation for each hybrid in each replication. Slopes (regression coef®cients) were estimated for grain-®lling rate over time. Day was used as a unit of time, instead of thermal unit, because uniform temperatures prevailed during the grain-®lling period of the experiment. Effective ®lling period duration was estimated

following Daynard et al. (1971) as: ®nal grain yield/ average grain-®lling rate during the linear grain-®lling phase. The EFPD was employed as a measure of grain®lling duration. Data were analyzed via SAS (SAS, 1989). The linear model for statistical analyses was: Yijk ˆ  ‡ i ‡ j ‡ k ‡ jk ‡ ijk where Yijk is the observed trait value,  the overall mean, i the block effect, j the male parent effect, k the female parent effect, jk the SCA effect for jkth cross, and, "ijk the residual effect. Importance of additive and nonadditive gene effects was estimated through GCA and SCA mean squares. GCA is a measure of the progeny (hybrid) performance of a line in crosses with other inbred lines. When the number of test lines is limited, average combining ability (ACA) is usually used to estimate GCA. In this study, ACA (average performance of a line minus the overall mean performance of all lines) was employed to estimate GCA due to the limited number of parents. For example, the average performance of inbred line H111 for grain yield was 149 g earÿ1, the overall mean for all 16 crosses was

G. Wang et al. / Field Crops Research 61 (1999) 211±222

142 g earÿ1, so the ACA of inbred line H111 for yield ˆ 149 ÿ 142 ˆ 7 g earÿ1. 2.2. 1997 field experiment Nine commercial hybrids (three hybrids from each of early, medium, and late maturity) obtained from four seed companies were grown in a ®eld different from that used for the 1996 experiment but of the same soil type at the Ben Hur Plant Science Farm. The experimental design was a randomized completeblock with three replications. The early hybrids were HS9502 (Agripro Seeds), 3394 (Pioneer Hi-Bred Int.), and TR1066 (Terra Int.). The medium maturity hybrids were 3223 (Pioneer Hi-Bred Int.), DK687 (Dekalb Genetics), and E1186 (Terra Int.). The three late-maturity hybrids were: HS9977 (Agripro Seeds), 3167 (Pioneer Hi-Bred Int.), and Bt-Hi (Pioneer HiBred Int.). The ®eld experiment was similar to that in 1996, except that there were ®ve rows per plot. Recording of midsilk date, sampling, determination of grain-®lling rate and duration, and single-plant yield were done as in the 1996 experiment. Chlorophyll meter (SPAD-502, Minolta, Tokyo, Japan) readings were recorded at 30 d after midsilk with the assumption that, at that time, maximal differentiation among hybrids relative to nitrogen utilization in the leaves had occurred. Chlorophyll readings were taken from an area 2/3 from the base toward the tip of the ear leaf from 10 adjacent maize plants from central rows of a plot. Plot means were computed and subjected to statistical analysis. 2.2.1. Path coefficient analysis Data were analyzed using the ANOVA procedure and its MANOVA option. Genetic correlation coef®cients among traits were determined from the variance and covariance components (Kang, 1994). Phenotypic correlation coef®cients among all traits also were computed. Direct and indirect path coef®cients were calculated via SAS, as described by Kang (1994). The traits analyzed were (i) grain-®lling rate, (ii) grain®lling duration, (iii) kernel number, (iv) midsilk date representing the end of vegetative period, (v) chlorophyll concentration, and (vi) single-plant grain yield. The generalized simultaneous equations for calculating direct effects of the ®ve component traits (1±5) on trait number 6, i.e., yield, are as follows:

215

P16 ‡ r12 P26 ‡ r13 P36 ‡ r14 P46 ‡ r15 P56 ˆ r16 r12 P16 ‡ P26 ‡ r23 P36 ‡ r24 P46 ‡ r25 P56 ˆ r26 r13 P16 ‡ r23 P26 ‡ P36 ‡ r34 P46 ‡ r35 P56 ˆ r36 r14 P16 ‡ r24 P26 ‡ r34 P36 ‡ P46 ‡ r45 P56 ˆ r46 r15 P16 ‡ r25 P26 ‡ r35 P36 ‡ r45 P46 ‡ P56 ˆ r56 In the above equations, the Pijs represent direct effect of respective ith trait on trait number 6, and the rijs represent correlation coef®cients between ith and jth traits (i ˆ 1±5, and j ˆ 2±6). 3. Results and discussion 3.1. 1996 experiment Hybrid means for grain-®lling rate and duration and other agronomic traits are presented in Table 1. Differences were observed for all traits, but there was no single best combination of all of the traits to represent plant yield. There always was a compromise among traits. 3.1.1. Combining ability The ANOVA revealed that the GCA mean squares for both male and female parents and SCA mean square for all traits were signi®cant at 0.01 level, except the SCA mean square for kernel-®lling rate, which was signi®cant (p ˆ 0.05) (Table 2). The SCA sum of squares was much smaller in magnitude than the GCA sum of squares, except for ear-®lling rate, kernel number, and single-plant yield, which indicated that there was a preponderance of GCA effects for midsilk date, kernel-®lling rate, physiological maturity (black layer) date, effective grain-®lling duration, and kernel weight. On the contrary, both GCA and SCA were important in determining ear-®lling rate, kernel number, and single-plant yield. Actually, in the case of ear-®lling rate, the ratio 2Vg/(2Vg ‡ Vs) was as low as 0.45, showing that the SCA effect was twice as important as the GCA effect. The different genetic effects between kernel-®ll rate and ear-®ll rate probably resulted from the genetic effect of kernel number per ear for which both additive and non additive gene effects were equally important. This also indicated

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Table 2 Analyses of variance for grain-filling traits and other selected traits measured from 16 maize single crosses (4  4 Design II mating scheme) Source of variation

Mean squares df

Sowing to silking (days2)

Black-layer maturity (days2)

Kernel-filling rate (mg kernelÿ1 dayÿ1)2

Ear-filling rate (g earÿ1 dayÿ1)2

EFPDa (days2)

Kernel number

Kernel weight (mg)2

Plant yield (g earÿ1)2

Reps. GCA-Mb GCA-Fd SCA Error R2 CV (%)

2 3 3 9 30

0.40 60.31c 35.92c 4.25c 0.31 0.97 0.95

1.65 314.96c 103.18c 21.96c 2.81 0.94 1.69

0.16 7.51c 4.32c 0.51e 0.02 0.98 1.9

0.02 0.61c 0.82c 2.47c 0.06 0.93 3.91

0.09 14.70c 8.63c 2.88c 0.34 0.90 2.68

162.2 1829.3c 3460.7c 15496.0c 247.8 0.92 5.68

14 3221c 2800c 225c 24 0.96 2.27

89.9 852.3c 1017.4c 1378.7c 96.7 0.97 4.64

Grown in 1996 at Baton Rouge, Louisiana. EFPD: Effective filling-period duration. b GCA-M ˆ general combining ability of male parents. c Significant at 1% probability levels. d GCA-F ˆ general combining ability of female parents. e Significant at 5% probability level. a

possible compensation of grain-®lling rate with kernels per ear. However, the male and female GCA mean squares were not very similar in magnitude. The possible reason for this could be a bias introduced by reciprocal effects or the limited number of parents used in the study. The computation of average combining ability (ACA) can be a good measure of general combining ability. Line L654 as male parent had the highest ACA

for kernel-®lling rate, kernel weight, second highest ACA for ear-®lling rate, and the third highest ACA for effective ®lling duration, which resulted in the highest ACA for single-plant yield among male parents (Table 3). Line H111 had the highest ACA for kernel-®lling rate, kernel weight, and the second highest ACA for effective grain ®lling duration, which resulted in the highest ACA for single-plant yield among female parents. The parents with a higher than

Table 3 Average combining ability for kernel weight, kernel-fill rate, ear-fill rate, EFPDa, and single-plant yield from 16 maize single crosses (4  4 Design II mating scheme) Parents

Kernel filling rate (mg kernelÿ1 dayÿ1)

Ear filling rate (g earÿ1 dayÿ1)

EFPD (days)

Kernel weight (mg)

Males L654 B73 L108 L329 LSD (0.05)

0.956 0.343 ÿ0.513 ÿ0.764 0.122

0.144 0.173 ÿ0.050 ÿ0.266 0.205

0.243 ÿ1.630 0.803 0.583 0.489

24 ÿ9 ÿ4 ÿ12 4

9.52 ÿ6.39 ÿ4.64 ÿ7.76 3.61

Females H111 L266 L729 L668 LSD (0.05)

0.882 ÿ0.139 ÿ0.313 ÿ0.429 0.122

ÿ0.048 0.163 0.263 ÿ0.366 0.205

ÿ0.027 ÿ0.077 1.083 ÿ0.977 0.489

19 ÿ4 3 ÿ18 4

6.93 8.94 ÿ2.91 ÿ12.96 3.61

Grown in 1996 at Baton Rouge, Louisiana. EFPD: Effective filling-period duration.

a

Plant yield (g earÿ1)

G. Wang et al. / Field Crops Research 61 (1999) 211±222

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Table 4 Phenotypic correlation coefficients among grain-filling traits and other agronomic traits measured from 16 maize single crosses (4  4 Design II mating scheme) Trait

Black-layer maturity

Kernel-fill rate

Ear-fill rate

EFPDa

Kernel number

Kernel weight

Plant yield (g earÿ1)

Sowing to silking (days) Black-layer maturity (days) Kernel-fill rate (mg kernelÿ1 dayÿ1) Ear-fill rate (g earÿ1 dayÿ1) EFPD (day) Kernel number Kernel weight (mg)

0.77b

ÿ0.45b ÿ0.21

ÿ0.18 0.04 0.14

0.46b 0.68b ÿ0.32c 0.11

0.20 0.21 ÿ0.23 0.73b 0.33c

ÿ0.05 0.27c 0.75b 0.26 0.38b 0.02

ÿ0.08 0.21 0.28c 0.74b 0.35b 0.73b 0.52b

Grown in 1996 at Baton Rouge, Louisiana a EFPD, Effective filling-period duration. b Significant at 1% probability levels. c Significant at 5% probability levels.

zero ACA can be used to form a population for recurrent selection because traits, such as kernel-®lling rate, effective grain ®lling duration, kernel weight, and midsilk date, were controlled by additive gene effects.

increase in kernel-®lling rate in two of three populations subjected to recurrent selection for seed size in soybean.

3.1.2. Phenotypic correlations The associations between grain-®lling characters and other agronomic traits were studied using phenotypic correlation coef®cients (Table 4). An interesting ®nding was that days from sowing to midsilk date were positively correlated with black-layer maturity date and effective grain-®lling period duration and negatively correlated with kernel-®lling rate. Researchers of other crops have indicated, however, that longer grain-®lling duration was often associated with early heading date (Wych et al., 1982; Sayed and Gadallah, 1983; Metzger et al., 1984; van Sanford, 1985). Optimum midsilk date may serve as an indirect selection criterion for high kernel-®lling rate without sacri®cing yield. An example of this would be hybrid L266  B73, as it took 56 days from sowing to midsilk date, had a relatively high kernel-®lling rate of 9.36 mg dÿ1, a black-layer maturity of 92 days, and the highest single-plant yield (Table 1). While kernel®lling rate and EFPD had signi®cant positive correlations with kernel weight, only ear-®lling rate showed signi®cant positive correlations with kernel number and single-plant yield. The correlation between kernel weight and kernel-®lling rate means that kernel size might serve as a potential selection criterion for kernel-®lling rate. Tinius et al. (1992) found a linear

The ANOVA indicated signi®cant differences among hybrids for all traits (Table 5), verifying that genetic variation existed for grain-®lling rate and duration. Chlorophyll meter readings at 30 days post-midsilk also exhibited genetic variation among hybrids. Therefore, selection for and improvement of these traits is possible.

3.2. 1997 experiment

3.2.1. Phenotypic and genetic correlations The sign of the phenotypic correlation coef®cients usually matched that of the respective genetic correlation coef®cient (Table 6). The similar magnitudes of the phenotypic and genetic correlation coef®cients meant that environmental variance and covariance had been minimized (Kang et al., 1983). Grain-®lling rate and duration and midsilk date showed similar relationships as those observed in 1996. Although there were positive relationships between grain-®lling rate and plant yield and between grain-®lling duration and plant yield, they were not signi®cant. Chlorophyll meter readings were positively correlated with grain®lling duration, kernel number, and plant yield. Smith and Nelson (1986) reported a positive relationship between the seed-®lling period and yield in soybean and suggested that yield might be enhanced by selecting for longer seed-®lling period. Salado-

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Table 5 Range, mean, standard error, and coefficient of variation for six traits measured from nine commercial maize hybrids Trait ÿ1

Filling rate (mg kernel Plant yield (g earÿ1) Filling-duration (days) Silk date (daysb) Kernel number Chlorophyll conc.

ÿ1

day )

Range

Mean  SE

CV (%)

F-test

0.83±1.17 145±183 23±34 53±58 488±627 48±62

1.04  0.11 163  12.2 28  2.8 55  1.6 560  35 56  3.3

4.4 2.2 6.7 1.2 9.5 2.2

** ** ** ** ** **

Grown in 1997 at Baton Rouge, Louisiana. Significant at 0.01 probability level. b Days after sowing. **

Table 6 Phenotypic (upper value) and genetic (lower value) correlation coefficients among six traits measured from nine commercial maize hybrids Trait

EFPDa

Midsilk date

Kernel no.

Chlorophyll conc.

Plant yield (g earÿ1)

Filling rate (mg kernelÿ1 dayÿ1)

ÿ0.86b ÿ0.92

ÿ0.62b ÿ0.77 0.52b 0.82

ÿ0.28 ÿ0.44 0.13 0.61 0.45c 0.53

ÿ0.24 ÿ0.37 0.40c 0.57 0.38 0.48 0.47c 0.79

0.06 0.07 0.20 0.24 0.19 0.22 0.67b 0.82 0.65b 0.73

EFPD (days) Midsilk date (daysd) Kernel number (earÿ1 ) Chlorophyll conc. (SPAD reading)

Grown in 1997 at Baton Rouge, Louisiana. EFPD: Effective filling-period duration. b Significant at 0.01% probability levels. c Significant at 0.05% probability levels. d Days after sowing. a

Navarro et al. (1986) reported, however, that while higher-yielding soybean cultivars usually had a longer seed-®lling period, selection for this trait would not necessarily be correlated with yield. 3.2.2. Path coefficient analysis The interrelationships among single-plant grain yield and other traits illustrated in Fig. 1. The direct effects indicated that the single-plant yield in the nine maize hybrids was in¯uenced mainly by grain-®lling rate, grain-®lling duration, and kernel number per ear (Table 7). Grain-®lling rate had the largest direct effect on yield. It was counter-balanced, however, by the negative indirect effects of grain-®lling duration and kernel number. There was a large positive indirect effect of grain®lling duration on yield via kernel number, indicating

that grain-®lling duration modi®ed kernel number. But there also was a large negative effect via grain-®lling rate. Midsilk date had a small direct effect on grain yield. It mainly affected yield by elongating grain®lling duration and increasing kernel number. Kernel number affected yield mainly through its direct effect, since the negative effect via grain-®lling rate and positive effect via grain-®lling duration almost canceled each other out. Chlorophyll concentration had a negligible direct effect on yield. It had a large positive indirect effect on yield by elongating grain-®lling duration and increasing kernel number. However, it had a negative indirect effect via grain-®lling rate. The negative association between chlorophyll and grain®lling rate indicated that higher grain-®lling rate probably resulted from higher transfer rate of nitrogen from leaf to storage, which, in turn, lowered chlor-

G. Wang et al. / Field Crops Research 61 (1999) 211±222

219

Fig. 1. 273Path diagram showing causal relationships of five predictor variables with a response variable (single plant yield) in maize. Onedirectional arrows (!) represent direct paths (P) and two-directional arrows ($) represent correlations (r).

ophyll concentration in the leaf. Thus, chlorophyll readings could be a suitable criterion to select for high grain yield. For grain yield genetic analyses, 1 ÿ P2x was nearly equal to one and the residual effects were nearly zero. In a phenotypic path coef®cient analysis, a large residual effect usually indicates that there are traits other than those included in pathways that contribute to the dependent variable. However, the interpretation of the residual in a genetic path analysis is different in that it represents the failure of the estimated genetic correlations among the variables to account for the total genetic variation in a trait (Sidwell et al., 1976). So the estimation of genetic correlations among traits with respect to kernel number was, although acceptable, not perfect. 4. Summary and conclusions The study revealed genotypic differences in kernel®lling rate, ear-®lling rate, effective ®lling duration, midsilk date, and chlorophyll concentration. The large GCA mean squares for traits other than ear-®lling rate

indicated that a recurrent selection procedure should improve these traits. Ear-®lling rate had a larger effect than kernel-®lling rate on ®nal grain yield however, and it was controlled chie¯y by nonadditive gene effects, which also re¯ected importance of kernels per ear. Selection effect of kernel-®lling rate may be masked because of heterosis for ear-®lling rate. Furthermore, although the phenotypic correlation between kernel-®lling rate and grain yield was signi®cant, it was smaller in magnitude than the correlation between ear-®lling rate and grain yield. Therefore, although selection for kernel-®lling rate would be effective, its effect on ®nal grain yield would be much less than that of ear-®lling rate that correlated with kernel number. Maize maturity cannot be delayed inde®nitely because of the limited length of growing seasons. To increase maize yield further, ear-®lling rate improvement should be emphasized. Although there was a positive correlation between effective grain-®lling duration and single-plant yield (1996: signi®cant; 1997: not signi®cant), some exceptional hybrids with higher grain-®lling rate and shorter ®lling duration that resulted in high grain yield were identi®ed (L266  B73 and H111  L654). Midsilk

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Table 7 Genotypic path coefficient analyses showing effects of kernelfilling rate, effective filling duration, midsilk date, kernel number, and chlorophyll concentration on plant yield (g earÿ1) Pathway Filling rate ! Plant yield Direct effect, P16 Indirect effect via: filling duration, r12 P26 midsilk date, r13P36 kernel no., r14 P46 chlorophyll, r15 P56 Correlation, r16 Filling duration ! Plant yield Direct effect, P26 Indirect effect via: filling rate, r21 P16 midsilk date, r23 P36 kernel no., r24 P46 chlorophyll, r25 P56 Correlation, r26 Midsilk date ! Plant yield Direct effect, P36 Indirect effect via: filling rate, r31 P16 filling duration, r32 P26 kernel no., r34 P46 chlorophyll, r35 P56 Correlation, r36 Kernel no. ! Plant yield Direct effect, P46 Indirect effect via: filling rate, r41 P16 filling duration, r42 P26 midsilk date, r43 P36 chlorophyll, r45 P56 Correlation, r46 Chlorophyll ! Plant yield Direct effect, P56 Indirect effect via: filling rate, r51 P16 filling duration, r52 P26 midsilk date, r53 P36 kernel no., r54 P46 Correlation, r56 1 ÿ P2x

1.24 ÿ0.76 ÿ0.03 ÿ0.36 ÿ0.02 0.07 0.83 ÿ1.13 0.03 0.49 0.03 0.24 0.04 ÿ0.94 0.68 0.43 0.02 0.23 0.82 ÿ0.55 0.51 0.02 0.04 0.84 0.05 ÿ0.45 0.48 0.02 0.64 0.74 1

Data for nine commercial maize hybrids grown in 1997 at Baton Rouge, Louisiana.

date had a negative correlation with grain-®lling rate and positive correlation with effective ®lling duration. Kernel weight and midsilk date could be selection

criteria for effective grain-®lling duration and grain®lling rate. Grain-®lling rate, grain-®lling duration, and ear kernel number had the largest direct effects on ®nal grain yield but only effective ®lling-period duration and kernel number had relatively large correlation coef®cients with single-plant yield. Chlorophyll concentration had a large indirect effect on yield via kernel number and grain-®lling duration. Kernel number was found to be the most in¯uential factor affecting grain yield. Chlorophyll meter readings at a late developmental stage may serve as a selection criterion for increasing kernel number and grain yield. A high correlation between chlorophyll meter readings ofearleaf and visuallyevaluated stay greenhasbeen reported (D'Croz-Mason and Lindauer, 1997). Direct selection for grain-®lling duration should be relatively easy because ofits strong correlation with stay green trait that can be determined by using the chlorophyll meter. Our results also indicated that later silking can serve as a selection index for longer grain-®lling duration. However, selection for grain-®lling rate is relatively more dif®cult. Selection for kernel size and/or silking date might help improve grain-®lling rate. A key to shortening days from sowing to harvest without sacri®cing yield and even increasing yield is rapid grain-®lling rate. Ear-®lling rate was most conducive to increasing ®nal grain yield because it re¯ected importance of kernel number per ear. Kernel-®lling rate and kernel number per ear in¯uenced ear-®lling rate. Kernel-®lling rate was mostly controlled by additive gene action; however, both additive and dominant gene actions were equally important for kernel number. Recurrent selection should be effective for improving both kernel- and ear-®lling rates. Grain®lling rate might be improved by simultaneous selection for increased chlorophyll concentration and early silking. Because of correlations between early silking date and large kernel size and between chlorophyll meter reading at a late developmental stage and kernel number and grain-®lling duration, kernel weight, kernel number and maturity could be simultaneously improved. References Badu-Apraku, B., Hunter, R.B., Tollenaar, M., 1983. Effect of temperature during grain filling on whole plant and grain yield in maize (Zea mays L.). Can. J. Plant Sci. 63, 357±363.

G. Wang et al. / Field Crops Research 61 (1999) 211±222 Board, J.E., Wier, A.T., Boethel, D.J., 1994. Soybean yield reductions caused by defoliation during mid to late seed filling. Agronom. J. 86, 1074±1079. Boyle, M.G., Enievel, D.F., Shannon, J.C., 1979. Assimilate partitioning to kernels of maize differing in maturity. Agronom. Abstr. Am. Soc. Agronom., Madison, Wisconsin, p. 9. Bruckner, P.L., Frohberg, R.C., 1987. Rate and duration of grain fill in spring wheat. Crop Sci. 27, 451±455. Carter, M.W., Poneleit, C.G., 1973. Black layer maturity and filling period among inbred lines of corn (Zea mays L.). Crop Sci. 13, 436±439. Cavalieri, A.J., Smith, O.S., 1985. Grain filling and field drying of a set of maize hybrids released from 1930 to 1982. Crop Sci. 25, 856±860. Comstock, R.E., Robinson, H.F., 1948. The components of genetic variance in populations of biparental progenies and their use in estimating the average degree of dominance. Biometrics 4, 254±266. Cross, H.Z., 1975. Diallel analysis of duration and rate of grain filling of seven inbred lines of corn. Crop Sci. 15, 532±535. Darroch, B.A., Baker, R.J., 1990. Grain filling in three spring wheat genotypes: Statistical analysis. Crop Sci. 30, 525±529. D'Croz-Mason, N.E., Lindauer, M.V., 1997. Visual and chlorophyll meter comaprisons for stay green maize genotypes. Agronom. Abstr. Am. Soc. Agronom., Madison, WI, p. 79. Daynard, T.B., Tanner, J.W., Duncan, W.G., 1971. Duration of grain filling period and its relation to grain yield in corn, Zea mays L. Crop Sci. 11, 45±47. Djordjevic, J.S., Ivanovic, M.R., 1996. Genetic analysis for stalk lodging resistance in narrow-base maize synthetic population ZPS14. Crop Sci. 36, 909±913. Duguid, S.D., BruÃleÂ-Babel, A.L., 1994. Rate and duration of grain filling in five spring wheat (Triticum aestivum L.) genotypes. Can. J. Plant Sci. 74, 681±685. Duncan, W.G., Hatfield, J.L., 1964. A method for measuring the daily growth of corn kernels. Crop Sci. 4, 550±551. Earl, H.J., Tollenaar, M., 1997. Maize leaf absorptance of photosynthetically active radiation and its estimation using a chlorophyll meter. Crop Sci. 37, 436±440. Eastin, R.A., 1972. Photosynthesis and translocation in relation to plant development. In: Rao, N.G.P., House, R.L. (Eds.), Sorghum in Seventies. Oxford IBH, New Delhi, pp. 214± 246. Everett, H.L., 1960. Isolation of a genetic series controlling chloroplast pigment production in Zea mays L. Genetics 35, 104. Fleming, A.A., Palmer, J.H., 1975. Variation in chlorophyll content of maize lines and hybrids. Crop Sci. 15, 617±620. Garcia del Moral, L.F., Ramos, J.M., Garcia del Moral, M.B., Jimenez-Tejada, M.P., 1991. Ontogenic approach to grain production in spring barley based on path-coefficient analysis. Crop Sci. 31, 1179±1185. Gravois, K.A., Helms, R.S., 1992. Path analysis of rice yield and yield components as affected by seeding rate. Agron. J. 84, 1±4. Greef, J.M., 1994. Productivity of maize in relation to morphological, physiological characteristics under varying amounts of nitrogen supply. Crop Sci. 172, 317±326.

221

Hanway, J.J., 1962. Corn growth and composition in relation to soil fertility I. Growth of different plant parts and relation between leaf weight and grain yield. Agronom. J. 54, 145±148. Johnson, D.R., Tanner, J.W., 1972. Calculation of the rate and duration of grain filling in corn (Zea mays L.). Crop Sci. 12, 485±489. Jurgens, S.K., Johnson, R.R., Boyer, J.S., 1978. Dry matter production and translocation in maize subjected to drought during grain filling. Agronom. J. 70, 678±682. Kang, M.S., 1994. Applied Quantitative Genetics. M. S. Kang Publishers, Baton Rouge, LA. Kang, M.S., Miller, J.D., Tai, P., 1983. Genetic and phenotypic path analyses and heritability in sugarcane. Crop Sci. 23, 643± 647. Kang, M.S., Tai, P., Miller, J., 1991. Genetic and path analyses in sugarcane: artificially created relationships. Crop Sci. 31, 1684±1686. Kang, M.S., Zhang, Y., Magari, R., 1995. Combining ability for maize weevil preference of maize grain. Crop Sci. 35, 1556± 1559. Kang, M.S., Zuber, M.S., Colbert, T.R., Horrocks, R.D., 1986. Effects of certain agronomic traits on and relationship between rates of grain-moisture reduction and grain fill during the filling period in maize. Field Crops Res. 14, 339±347. Knott, D.R., Gebeyehou, G., 1987. Relationship between the lengths of the vegetative and grain filling periods and agronomic characters in three durum wheat crosses. Crop Sci. 27, 857±860. McGarrahan, J.P., Dale, R.F., 1983. A trend toward a longer grainfilling period for corn. A case study in Indiana. Agronom. J. 76, 518±522. Metzger, D.D., Czaplewski, S.J., Rasmusson, D.C., 1984. Grainfilling duration and yield in spring barley. Crop Sci. 24, 1101± 1105. Miller, E.S., Johnson, I.J., 1938. The relation between leaf tissue pigment concentration and yield in corn. J. Am. Soc. Agronom. 30, 941±946. Milligan, S.B., Gravois, K.A., Bischoff, K.P., Martin, F.A., 1990. Crop effects on genetic relationships among sugarcane traits. Crop Sci. 30, 927±931. Mou, B., Kronstad, W.E., 1994. Duration and rate of grain filling in selected winter wheat populations I. Inheritance. Crop Sci. 34, 833±837. Mou, B., Kronstad, W.E., Saulescu, N.N., 1994. Grain filling parameters and protein content in selected winter wheat populations II. Associations. Crop Sci. 34, 838±841. Muchow, R.C., 1988. Effect of nitrogen supply on the comparative productivity of maize and sorghum in a semi arid tropical environment I. Leaf growth and leaf nitrogen. Field Crops Res. 18, 1±16. Muchow, R.C., Davis, R., 1988. Effect of nitrogen supply on the comparative productivity of maize and sorghum in a semi arid tropical environment II. Radiation interception and biomass accumulation. Field Crops Res. 18, 17±30. Piekielek, W.P., Fox, R.H., 1992. Use of a chlorophyll meter to predict sidedress nitrogen requirements for maize. Agronom. J. 84, 59±65.

222

G. Wang et al. / Field Crops Research 61 (1999) 211±222

Poneleit, C.G., Egli, D.B., 1979. Kernel growth rate and duration in maize as affected by plant density and genotype. Crop Sci. 19, 385±388. Prioul, J.L., SÂchwebel-DugueÂ, N., 1992. Source±sink manipulations and carbohydrate metabolism in maize. Crop Sci. 32, 751±756. Puri, Y.P., Qualset, C.O., Williams, W.A., 1982. Evaluation of yield components as selection criteria in barley breeding. Crop Sci. 22, 927±931. Salado-Navarro, L.R., Sinclair, T.R., Hinson, K., 1986. Yield and reproductive growth of simulated field grown soybean I. Seed filling duration. Crop Sci. 26, 966±970. SAS, 1989 (SAS Institute Inc.) SAS Language Guide. Release 6.03 Edition, SAS Institute Inc., Cary, NC. Sayed, H.I., Gadallah, A.M., 1983. Variation in dry matter and grain filling characteristics in field cultivars. Field Crops Res. 7, 61±71. Sayre, J.D., 1948. Mineral accumulation in corn. Plant Physiol. 23, 267±281. Sidwell, R.J., Smith, E.L., McNew, R.W., 1976. Inheritance and interrelationships of grain yield and selected yield-related traits in hard red winter wheat cross. Crop Sci. 16, 650±654. Smith, J.R., Nelson, R.L., 1986. Relationship between seed-filling period and yield among soybean breeding lines. Crop Sci. 26, 469±472.

Sprague, H.B., Curtis, N., 1933. Chlorophyll content as an index of the productive capacity of selfed lines of corn and their hybrids. J. Am. Soc. Agronom. 25, 709±724. Tinius, C.N., Burton, J.W., Carter, T.E., 1992. Recurrent selection for seed size in soybean II. Indirect efects on seed growth rate. Crop Sci. 32, 1480±1483. Tollenaar, M., McCullogh, D.E., Dwyer, L.M., 1994. Physiological basis of the genetic improvement of corn. In: Slafer, G.A. (Ed.), Genetic Improvement of Field Crops, Marcel Dekker, New York, pp. 183±236. van Sanford, D.A., 1985. Variation in kernel growth characters among soft red winter wheat. Crop Sci. 25, 626±630. Wych, R.D., McGraw, R.L., Stuthman, D.D., 1982. Genotype x year interaction for length and rate of grain filling in oats. Crop Sci. 22, 1025±1028. Yadava, Umedi L., 1986. A rapid and nondestructive method to determine chlorophyll in intact leaves. Hortsci. 21, 1449± 1450. Zhang, Y., Kang, M.S., Magari, R., 1996. A diallel analysis of ear moisture loss rate in maize. Crop Sci. 36, 1140±1144. Zhang, Y., Kang, M.S., Magari, R., 1997. Genetics of resistance to kernel infection by Aspergillus flavus in maize. Plant Breed. 116, 146±152.