06 Badu - Maydica

Maydica 52 (2007): 205-217

GENETIC VARIANCES AND CORRELATIONS IN AN EARLY TROPICAL WHITE MAIZE POPULATION AFTER THREE CYCLES OF RECURRENT SELECTION FOR STRIGA RESISTANCE B. Badu-Apraku International Institute of Tropical Agriculture (IITA), C/o Lambourn (UK), Ltd, Carolyn House, 26 Dingwall Road, Croydon CR9 3EE, UK Received August 29, 2006

ABSTRACT - The magnitude and type of genetic variability are of prime importance to breeders in determining whether or not to improve a breeding population and the method to use. The early maturing population, TZE-W Pop DT STR, has gone through three cycles of S1 recurrent selection for improvement for grain yield and Striga resistance. Three hundred full-sib families within half-sib groups from cycle 3 of TZE-W Pop DT STR were evaluated under artificial Striga infestation in Ferkéssedougou, Côte d’Ivoire, 2002, and in Mokwa, Nigeria, 2003. Estimates of additive genetic variances were positive and moderate-to-large for all traits except lodging percentage. The additive genetic variance was much larger than the dominance variance for all traits except Striga emergence count. The dominance variance for Striga emergence was about twice as large as additive genetic variance at 8 WAP and about four times as large at 10 WAP. Narrow sense heritability (h2) estimate was 25% for grain yield and 0-90% for 13 other traits. The wide ranges, moderate-to-large additive genetic variance and expected gain/cycle of selection, and moderately low-to-high narrow sense heritability estimates observed in TZEE-W Pop DT STR C3 indicate that sufficient residual genetic variability still exists in the population to allow further improvement for grain yield, Striga resistance, and most other traits in the population. A wide range of genetic variation was observed among the full-sib families from TZE-W Pop DT STR C3 in Striga emergence count and Striga damage rating and several progenies were identified that combined reduced Striga emergence and Striga damage implying that it should be possible to extract from the population experimental varieties that combine both low Striga emergence and Striga damage. Grain yield had a large positive additive genetic correlation with EPP, a large negative genetic correlation with Striga damage ratings, and moderately large negative genetic correlations with flowering traits and Striga emergence count at 10 WAP. KEY WORDS: WCA, West and Central Africa; STR, Striga Resistance; SCA, Specific Combining Ability; GCA, General Combining Ability.

* For correspondence (e.mail: [email protected]).

INTRODUCTION Maize (Zea mays L.) is a major food crop in West and Central Africa (WCA) and has a great potential to contribute to food security in the semi - arid zone of the sub-region. One of the major biotic constraints to increased production of cereals [maize, sorghum (Sorghum bicolor L. Moench), and pearl millet (Pennisetum glaucum L.], cowpea [Vigna unguiculata (L.) Walp)] and groundnut (Arachis hypogea L.) in the semi-arid zone of WCA is the parasitic weed, Striga. The five most important species are S. hermonthica (Del.) Benth., S. asiatica (L.) Kuntze, S. aspera (Wild.) Benth., S. forbesii Benth., and S. gesneriodes (Wild.) Vatke. The species mainly parasitizing maize are S. hermonthica, S. asiatica and S. aspera. Of these, S. hermonthica is the most damaging (RAMAIAH, 1991) and widespread (LAGOKE, 1998). Even though the parasite has always been a serious pest on sorghum, pearl millet, groundnut and cowpea, maize is probably more seriously affected than the other cereals, largely because the crop is relatively new to WCA and has not coevolved with the parasite. Striga seeds germinate in response to stimulants in the root exudates of maize plants. Germinated Striga seedlings produce haustoria to establish contact with the host root and withdraw water, minerals, and organic compounds. Severe Striga infestation can cause yield losses ranging from 10 to 100%, depending on the crop variety and environmental conditions (LAGOKE, 1998; KROSCHEL, 1999), and has often compelled farmers to abandon maize cultivation entirely. Several strategies are available for the control of Striga. These include hand pulling, crop rotation, trap and catch crops, fertilizer use, fallow, seed treatments, and host plant resistance/tolerance (ODHIAMBO and RANSOM, 1994, 2000; SHAXSON and RICHES, 1998). However, the use of varieties toler-

206

B. BADU-APRAKU

ant/resistant to Striga species is considered the most economically feasible and sustainable approach available to resource-poor farmers for controlling the losses caused by this parasitic weed (DEVRIES, 2000; BADU-APRAKU et al., 2004). A Striga breeding program was initiated by the International Institute of Tropical Agriculture (IITA) in Côte d’Ivoire in 1994 to combat the threat posed by S. hermonthica in the savanna ecology of WCA. The emphasis of the program has been to develop maize source populations, inbred lines, and synthetic varieties that combine earliness or extra-earliness with tolerance/resistance to S. hermonthica and drought, using Striga resistance sources from the IITA Maize Program (BADU-APRAKU et al., 1999, 2001). In Striga research, tolerance denotes the ability of the host plant to withstand the effects of the parasitic plants that are already attached. Resistance refers to the ability of the host plant to prevent the parasites from attaching themselves to its roots (KIM, 1994). The strategy has been to introgress resistant genes from Striga resistant inbred lines (9030 STR, 1368 STR, and 9450 STR) from IITA (KIM et al., 1987) into the early and extra-early populations, pools, and varieties in plots artificially infested with S. hermonthica. The backcross method, inbreeding, hybridization and the S1 recurrent selection methods have all been successfully used in the program. The achievements include the development of two early maturing populations (90-95 days to maturity, one white, the other yellow), and two extra-early populations (80-85 days to maturity; one white, the other yellow). All of the populations combine drought tolerance with Striga resistance. In addition, several early and extra-early varieties, synthetics, and inbred lines, which combine Striga resistance with drought tolerance, have been developed in the program (BADU-APRAKU et al., 1999). Genetic variability for quantitative characters in maize populations is of prime importance to breeders for good progress from selection (HALLAUER and MIRANDA, 1988; BADU-APRAKU et al., 2004). In addition, the magnitude and type of genetic variability determine the breeding method to use for the genetic improvement of a population and the limits of selection for improvement. It is desirable in any breeding program to have a large genetic variation in the populations at the disposal of the breeder. Resistance to S. hermonthica is quantitatively inherited (KIM, 1994; EJETA et al., 1997; LANE et al., 1997). KIM (1994), in a diallel study involving 10 inbred lines, reported that general combining ability (GCA)

and specific combining ability (SCA) were significant for Striga damage ratings and Striga emergence counts. However, GCA was higher for host plant damage and SCA was more important for Striga emergence. AKANVOU et al. (1997) reported similar findings and concluded that nonadditive gene action was more important than additive gene action for Striga emergence. On the other hand, GETHI and SMITH (2004) showed that the mean squares for SCA, while highly significant for some traits, were generally lower than those for GCA, thus suggesting that nonadditive gene action was of minor importance in the inheritance of Striga resistance. Reports also indicate that heritabilities of host damage and yield loss due to S. hermonthica are moderate (0.3 to 0.5) but heritabilities for Striga emergence are low (< 0.1) (BERNER et al., 1995; KLING et al., 2000). Available evidence indicates that genetic correlation between S. hermonthica emergence and level of plant damage is low, suggesting that different genes control S. hermonthica emergence and level of host plant damage in maize (KIM, 1994; BERNER et al., 1995; AKANVOU et al., 1997). TZE-W Pop DT STR is an early maturing white dent/flint, drought tolerant, and Striga resistant population. It was developed from diallel crosses involving outstanding drought tolerant and/or Striga resistant maize germplasm identified through several years of extensive testing in WCA (BADU-APRAKU et al., 1999, 2001). The population has gone through three cycles of S1 recurrent selection for the improvement of grain yield, Striga resistance, and other agronomic characters under artificial Striga infestation. The question now is whether there is adequate genetic variability in the population for continued good progress from the S1 family selection and whether the breeding scheme adopted is appropriate. The purpose of the present study was to determine the following for TZE-W Pop DT STR C3: (i) magnitude of the residual genetic variability after three cycles of recurrent selection for resistance to S. hermonthica; (ii) magnitude of additive genetic variance relative to other components of phenotypic variance (narrow-sense heritability); (iii) phenotypic and genetic correlation coefficients among the traits used in selecting for Striga resistance, and (iv) expected correlated responses among selected traits associated with Striga resistance. On the basis of the results obtained from the study, the traits are suggested for use in selecting for Striga resistance and the most appropriate breeding scheme to be

GENETIC VARIANCES FOR STRIGA RESISTANCE

employed to ensure maximum gains from further selection in the population for improved grain yield and Striga resistance.

MATERIALS AND METHODS Test population and S1 recurrent selection procedure Three hundred full-sib families derived from the source population, TZE-W Pop DT STR C3 were used for the study. The breeding population was formed from diallel crosses involving Pool 16 DT, Pool 16 Sequia × Pool 16 DT, inbred 5012, DR-White Pool BC1F1 and TZE Comp 4. Sources of drought tolerance and relatively high grain yield were Pool 16 DT, Pool 16 Sequia C2, DR-W Pool BC1F1 and the inbred line 5012; a source of Striga resistance was 1368 STR, an inbred line coded TZi 3 (KIM et al., 1987). Resistance in TZi 3 is inherited quantitatively by a multigenic system (KIM, 1994). The inbred line supports a reduced number of emerged Striga plants. Following four cycles of compositing and screening under artificial Striga infestation for Striga resistance and induced moisture stress for drought tolerance, the population was designated TZE-W Pop DT STR C0. Recurrent selection was initiated for improvement of Striga resistance in 1996 with progeny yield trials conducted in Ferkésedougou (hereafter referred to as Ferké) under artificial Striga infestation, Sinématialli (hereafter called Siné), a high-yield environment, both in Côte d’Ivoire, and in Kamboinse (drought stress environment), Burkina Faso, in 1997. Since 1998, the population has gone through three cycles of S1 recurrent selection with improvement for Striga resistance and other agronomic traits under artificial Striga infestation and in Striga-free conditions in Ferké, and also in Abuja and Mokwa (Nigeria). The number of progenies screened in each cycle ranged from 196 to 256, with a selection intensity of 25-30%. The top 25-30% progenies of each cycle of improvement were inter-mated to reconstitute the population for further cycles of improvement. Furthermore, the best 8-10 families identified based on the progeny trials of each cycle, were recombined to form experimental varieties. Selection is based on an index that includes grain yield, plant damage syndrome score, number of emerged Striga plants, ears/ plant, and plant and ear heights under infested and non-infested conditions. The selection index is similar to that used by BERNER et al. (1995), and is computed by standardizing observed values, multiplying them by assigned weights, and summing across variables. Genotypes with larger values were considered to have better performance. Experimental design Using the Design I mating system proposed by COMSTOCK and ROBINSON (1948), 300 full-sib families were extracted from non-inbred (S0) plants of TZEW-Pop DT STR C3 by crossing each of 75 random males to four, unrelated, random female plants to constitute a single male group (half-sib family). A total of 75 male groups were produced, resulting in 300 full-sib families. The 300 full-sib families were evaluated under artificial S. hermonthica infestation during the rainy seasons (July to November) of 2002 and 2003 at Ferké (Latitude 9°35′N, Longitude 5°14′W, elevation 325 m) and Mokwa (Latitude 9°18′N, Longitude 5°04′E, elevation 457 m). A randomized incomplete block design with two replications was used in each trial after subdividing the progenies into 15 sets, each containing 5 male groups; that is, 20 progenies. The

207

sets were allocated randomly to the blocks; that is sets- in- reps or blocks (HALLAUER and MIRANDA, 1988), as were the progeny rows within each set. Single-row plots 5 m long with 26 plants per row were used. Row and hill spacing were 75 cm and 40 cm. Artificial Striga hermonthica infestation and field management The artificial field infestation method developed by IITA’s Maize Program (KIM, 1991; KIM and WINSLOW, 1991) was used to ensure uniform infestation and effective selection for resistance to Striga (reduced symptoms of host plant damage in the field). The Striga seeds used for artificial infestation were collected from fields of sorghum [Sorghum bicolor (L.) Moench] at the end of the previous growing season and stored until the next planting season. About one week before inoculation, the seed was mixed with finely sieved sand in the ratio of 1:99 by weight (seed: sand) and about 5000 germinable Striga seeds were placed in each planting hole made on ridges. The sand served as the carrier material and provided adequate volume for rapid and uniform infestation. Ethylene gas was used to stimulate suicidal germination of existing Striga seeds in the soil prior to artificial Striga infestation at Mokwa. The study conducted at Ferké was planted on a field where a soybean cultivar had been planted for two years in an effort to induce suicidal germination of the existing Striga seeds in the soil. Three maize seeds were placed in the same hole with the Striga seeds. The seedlings were later thinned to two, giving a plant population density of 66,666 plants ha-1. Fertilizer at the rate of 30 kg N ha-1, 26 kg ha-1 K, and 50 kg ha-1 P was applied as 15-15-15 NPK about 30 days after planting (DAP). Weeds other than Striga were controlled manually at 4 and 8 WAP. Data collection Grain yield/plot was recorded and converted to kg/ha at 15% moisture content. Other observations recorded in the trials included number of ears/plant (EPP), plant height (PHT), ear height (EHT), root lodging (percentage of plants leaning more than 30% from the vertical) and stalk lodging (percentage of plants broken at or below the top ear node). EPP was determined by dividing the total number of ears/plot by the number of plants harvested. Fifteen competitive plants were randomly chosen in each plot for the collection of flowering data (days to 50% anthesis and silking). ASI was determined as the difference between 50% silking and anthesis. The distances from the base of the plant to the node bearing the top ear and to the base of the tassel were measured after anthesis on competitive plants and the means were recorded as ear and plant height/plot. In addition, data were collected on the number of emerged Striga plants in the infested plots at 8 and 10 WAP, i.e., at 56 and 70 DAP. Similarly, the host plant damage syndrome rating was taken on all plants in the Striga-infested rows. The rating of the Striga damage syndrome was scored/plot on a scale of 1-9 where 1 = no damage, indicating normal plant growth and high resistance, and 9 = complete collapse or death of the maize plant; i.e., high susceptibility (KIM, 1991). Data were recorded for grain yield and grain moisture at harvest. A shelling percentage of 80% was assumed and grain yield (obtained from ear weight and converted to kg ha-1) was adjusted to 15% moisture. Statistical analysis Analysis of variance (ANOVA) for sets-in-replications design was performed on plot means of the individual characters com-

208

B. BADU-APRAKU

TABLE 1 - General form of the analysis of variance of Design 1 pooled over sets and repeated over environments for sets within replications, as used in this study (Adapted from HALLAUER and MIRANDA, 1988). –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Source of variation

D.F.

M.S.

Expected M.S.

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Formulae

Specific values

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Environments (e) Replications/e Sets/replications/e Males / sets Females / males / sets Environments × males / sets Environments × females / males / sets Pooled error Total

e-1 e(r-1) er(s-1) s(m-1) ms(f-1) s(m-1)(e-1) ms(f-1)(e-1) es(r-1)(mf-1) Sermf-1

1 2 56 60 225 60 225 570 1199

σ2 σ2 σ2 σ2 σ2

M5 M4 M3 M2 M1

+ + + +

rσ2 rσ2 rσ2 rσ2

ef/m ef/m ef/m

+ rfσ2 em + reσ2 + reσ2 f/m + rfσ2 em

f/m

+ ref σ2m

ef/m

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

r = number of replications per set = 2; f = number of females per male = 4; s = number of sets = 15; e = number of environments = 2; m = number of males in a set = 5; σ2e = environmental variance due to plots; σ2m = component of variance due to genetic differences among males; σ2f/m = component of variance due to females within males; σ2em = component of variance due to the interaction of males with environment; σ2ef/m = component of variance due to the interaction of females within males with environment.

bined for the sites using PROC GLM (SAS, 1987). The variance of Striga counts has been found to increase with the mean, therefore a log transformation {log (counts+1)} was used to reduce the heterogeneity of variance. Regression analyses to assess the genetic variation in host Striga damage and Striga emergence counts in the Design 1 progenies of TZE-W Pop DT STR C3 were performed using Striga damage rating as the independent variable and the corresponding Striga emergence counts as the dependent variable. Following the method of HALLAUER and MIRANDA (1988) variance components attributable to males (σ2m) and females within males (σ2f/m) were computed using the observed and expected mean squares presented in Table 1. The mean squares from the ANOVA were used to estimate the additive genetic variance (σ2a) and dominance variance (σ2d) as follows: σ2a = 4σ2M (inbreeding coefficient, F = 0 for non-inbred plants); σ2d = 4σ2 F/M – 4σ2M The standard errors of estimates of variance were calculated by taking the square root of the variance of the estimates. The estimated standard errors of the variances, based on Table 1 were computed as follows for the combined ANOVA: 16 x 2 V(σ2a) = ——— e2r2f2

 

M24 M23 M22 M25 ——––— + —––––— + ——––— + ——––— df5 + 2 df4 + 2 df3 + 2 df2 + 2

 

16 x 2 V(σ2d) = ——— e2r2f2

 

M25 M24 (f2+1) M23 M22 (f2+1) ——––— + —––––— + ——––— + —–—–— df5 + 2 df4 + 2 df3 + 2 df2 + 2

 

where e = number of environments, r = number of replications, f = number of females per male, M2, M3, M4 and M5 are observed mean squares in Table 1; df2, df3, df4 and df5 are degrees of freedom respectively associated with M2, M3, M4,

and M5. Narrow sense heritability (h2) among full-sib families was computed according to the method of HOLLAND et al. (2003) as follows:

h2 =

where σ2û σ2M σ2ME σ2F (M) σ2F (M) E

f (m-1) (1 + ––––––) σ2M mf - 1 –––––––––––––––––––––––––––––––––––––––––––––––––––– 2 f (m-1) σ ME σ2F (M) E s2û –––––––––– (σ2M + –––––––) + σ2F (M) + ––––––––– + –––– mf - 1 e e er

= = = = =

experimental error variance component male component male-by-environment interaction variance component female nested within male variance component female-within-male-by-environment interaction variance component.

The standard errors for heritability estimates were estimated using the method of HALLAUER and MIRANDA (1988). Expected response to selection (R) was estimated using R=ihσA, where i is the standardized selection differential (in this case selection intensity of 10% was used), h is the square root of the narrow sense heritability, and σA is an estimate of the standard deviation of breeding values (square-root of the additive genetic variance). Genotypic and phenotypic correlation coefficients and their standard errors were computed according to the method described by MODE and ROBINSON (1959). A genetic correlation coefficient was declared significant at σ = 0.05 and 0.01 levels of probability if it exceeded its standard error by two and three times, respectively. Correlated responses to selection were computed using ih raXY σA where h is the square root of the heritability of the directly selected trait (X), raXY is the genetic correlation between the directly selected trait and the indirectly selected trait, Y (FALCONER, 1981). A random mating population in linkage equilibrium without epistasis was assumed in this study.


209

FIGURE 1 - Regression of Striga emergence counts at 10 WAP on the Striga damage rating at 10 WAP of 300 full-sib families of TZE-W Pop DT STR C3 evaluated under artificial Striga infestation at Ferké in 2002 and Mokwa in 2003.

RESULTS Components of variance and heritability Analyses of variance combined across the two Striga-infested environments showed that all components of variation, including replications, sets-in-reps, males/sets-in-reps, and the females in males/sets-inreps had significant mean squares for most traits studied (data not shown). However, the mean squares for most traits were not significant for the interaction of the males-within-set and females in males-within-set with the environments. The means and ranges across environments in this study were very high for all traits (Table 2). The regression analyses depicted vividly the wide range of genetic variation among the full-sib families from TZE-W Pop DT STR C3 in Striga emergence count and Striga damage rating (Fig 1). Several progenies that combined reduced Striga emergence and Striga damage could be identified. The lowest host damage score was 3, on a scale of 1-9

suggesting the need for further improvements in the level of host damage in the population. Similarly, the minimum Striga emergence count was 23 plants per plot, which is considered too high. The estimates of additive genetic variances were positive and in most cases moderately large for the traits except for percentage lodging (which was negative) and were therefore equated to zero (Table 3). Also, the additive genetic variance was much larger than the dominance variance for all traits except Striga emergence counts at 8 and 10 WAP. The dominance variance for Striga emergence was about twice as large as additive genetic variance at 8 WAP and about four times as large at 10 WAP (Table 3). Five of the thirteen traits studied had negative dominance variances (equated to zero). Days to silking and anthesis had negative dominance variances. Six of the dominance × environment interaction variances of the traits studied were also negative. On the other hand, apart from percentage lodging, all traits had positive additive × environment interaction

TABLE 2 – Means ± S.E and ranges for grain yield and agronomic traits of full- sib families developed from TZE-W Pop DT STR C3 evaluated under Striga infestation in Ferké in 2002 and Mokwa in 2003.

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Trait

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Grain yield, kg/ha Ears per plant Plant height, cm Ear height, cm Days to anthesis Days to silk Anthesis-silk interval, days Root lodging, % Stalk lodging, % Striga rating at 8 WAP Striga rating at 10 WAP Striga emergence count at 8 WAP Striga emergence count at 10 WAP Mean ± S.E

1900 ± 550.00 0.8 ± 0.12 139 ± 11.60 62 ± 6.75 55 ± 1.08 59 ± 2.04 4 ± 1.66 7 ± 2.79 4.5± 1.98 4.8 ± 0.80 5.3 ± 0.85 127.5 ± 43.05 123 ± 45.89

Range

0.71-3.62 0.4-2.2 98-169 47-77 52-61 53-65 0.5-7.5 0.9-13.3 0.4-19.1 3.0-6.9 3.9-7.9 24-258 23-232

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

SE = Standard error of the mean of grain yield.

variances. The environment, dominance × environment interaction, and dominance variances were much larger than the additive variances, resulting in large phenotypic variances and a negative additive variance for percentage stalk lodging. The environmental variance was positive and large for all traits. The dominance × environment and additive × environment interaction variances for grain yield were strikingly high, resulting in high phenotypic variance and, hence, low narrow sense heritability (25%). The narrow sense heritability estimates ranged from 0 to 90% for 13 other traits. It is striking to note the low dominance variance, moderately high additive genetic variances, and narrow sense heritability obtained for Striga damage rating at 10 WAP. Moderateto-large predicted gain/cycle of selection was obtained for grain yield, plant height, ear height, days to anthesis, ASI, Striga damage at 8 WAP and 10 WAP and Striga emergence count at 8 WAP and 10 WAP. The percentage gain/cycle ranged from 3.75 for ears/plant to 22.5 for ASI.

Correlations and correlated response among traits The phenotypic correlation coefficient (Table 4) was strikingly high between Striga emergence count at 10 WAP and at 8 WAP (rp=0.90**), ASI and days to mid-silking (rp=0.85**), EHT and PHT (rp =0.75**), Striga damage score at 10 WAP and at 8 WAP (rp=0.73**). Grain yield had significant correla2487607.470 3608575.906

3706.88

4210.99

893799.896

1432603.474

29821.346 3570.571 20.191 +0.000 8.266 56.246 6.5020 0.0002 0.008 0.324 0.664

77311442032.25

856.94 ± 727.13

671.08 ± 671.23

63564.75 ± 128519.12 20.85 ± 60.49 +0.00 ± 20.20 0.84 ± 2.62 0.13 ± 2.00 +0.00 ± 0.93 +0.00 ± 2.38 0.61 ± 1.20 0.003 ± 0.01 +0.00 ± 0.40 0.16 ± 0.24 +0.00 ± 0.26

212.25 ± 337.10

386.47 ± 331.41

125652.3 ± 83848.89 110.73 ± 39.07 39.20 ± 13.23 1.30 ± 1.40 0.00 ± 1.14 3.54 ± 0.79 6.78 ± 1.86 1.18 ± 0.77 0.005 ± 0.00 0.007 ± 0.21 0.25 ± 0.15 0.46 ± 0.17

2325.71

2421.65

473248.5 309.88 95.22 7.01 6.27 6.12 12.99 4.64 0.03 1.61 0.83 0.93

7.79± 0.15

14.92± 0.14

24.51± 0.18 42.98± 0.13 47.49± 0.14 13.35± 0.20 0.00 ± 0.18 89.55±0.13 68.14± 0.14 24.64± 0.17 19.67±0.12 0.58± 0.13 26.13 ± 0.19 44.82± 0.18

6.76

12.63

291.84 11.47 7.18 -0.69 -0.00 2.96 3.57 0.90 0.05 0.01 0.43 0.76

5.50

9.91

5.38 6.05 22.50 3.75 10.67 8.96 14.34

15.36 8.25 11.58 9.86 ++

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– + Negative variances were equated to zero. ++ Gain from selection was not calculated because of zero σ2a. *,** significant at 0.05 and 0.01 levels of probability, respectively.

5615.843 0.000 13.135 0.377 +0.000 +0.000 3.385 0.000 0.000 0.209 0.000

269.33 91.09 15.58 7.86 2.34 8.29 5.51 0.03 1.02 1.28 1.45

Plant height, cm Ear height, cm Root lodging, % Stalk lodging, % Days to anthesis Days to silking Anthesis-silking interval, days No. of ears per plant Harvest moisture, g/kg Striga rating at 8 WAP Striga rating at10 WAP Striga emergence count at 8 WAP (plants/plot) Striga emergence count at 10 WAP (plants/plot)

39110182944.75

615281.00

Grain yield, kg/ha

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Trait σ2e σ2 de σ2ae σ2d ± S.E σ2a ± S.E σ2ph h2, % ± S.E R % Gain/cycle ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

TABLE 3 - Components of variance, heritability estimates, expected response/cycle (R), and % gain/cycle of grain yield and agronomic traits among Design I progenies from TZE-W Pop DT STR C3 evaluated under artificial Striga infestation in Ferkè in 2002 and Mokwa in 2003.

210 B. BADU-APRAKU

No. ears per plant

Plant height, cm

Ear height, cm

Days to anthesis

Days to silk

AnthesisRoot silking lodging interval, days

Striga rating, 8 WAP


Striga Striga emergence emergence count, count, 8 WAP 10 WAP

0.50**

-0.37**

-0.59**

-0.49**

-0.10*

-0.69**

-0.72**

-0.20**

Ear height, cm

Days to anthesis

Days to silk

Anthesis-silking interval, days

Root lodging



Striga emergence count, 8 WAP

-0.07

-0.08*

-0.39**

-0.35**

-0.14**

-0.29**

-0.29**

-0.10*

0.19**

0.25**

0.81± 0.163**

-0.10*

-0.08*

-0.58**

-0.53**

-0.12**

-0.36**

0.44**

-0.27**

0.75**

-0.14± 0.298

0.54± 0.236*

-0.04

-0.01

-0.48**

-0.48**

-0.07

-0.31**

-0.40**

-0.27**

0.97± 0.012**

0.18± 0.290

0.91± 0.057**

-0.05

-0.01

0.30**

0.20**

0.01

0.06

0.58**

0.25± 0.137

0.21± 0.142

-0.58± 0.140**

-0.48± 0.178*

0.01

-0.03

0.57**

0.47**

0.04

0.85**

0.93± 0.016**

0.08± 0.173

0.19± 0.170

-1.02± -0.010**

-0.65± 0.159**

-0.03

-0.03

0.50**

0.45**

0.04

0.78± 0.104**

0.51± 0.166**

-0.25± 0.299

0.08± 0.321

-1.43± -0.480**

-0.73± 0.235**

-0.13**

-0.09*

0.12**

0.06

-0.61± 0.451

0.58± 0.260*

0.01± 0.329

-1.12± -0.120**

-0.94± 0.055**

-0.16± 0.657

-0.07± 0.738

0.19**

0.19**

0.73**

0.97± 0.044**

0.27± 0.465

0.34± 0.242

0.31± 0.208

-0.79± 0.123**

-0.30± 0.302

-1.22± -0.230**

-0.85± 0.143**

0.15**

0.13**

0.89± 0.079**

0.40± 0.460

0.56± 0.256*

0.47± 0.158*

0.33± 0.152*

-0.51± 0.180*

-0.25± 0.231

-1.02± -0.014**

-0.83± 0.119**

0.90**

0.57± 0.293

0.07± 0.582

0.57± 0.565

0.65± 0.329

0.49± 0.236*

0.30± 0.237

-0.17± 0.361

-0.36± 0.328

-0.91± 0.092**

-0.24± 0.553

0.99± 0.092**

0.09± 0.617

0.10± 0.829

0.48± 0.923

1.34± -0.649*

0.99± 0.009**

0.60± 0.239*

-0.69± 0.279*

-1.01± -0.011**

-1.49± -0.934

-0.56± 0.577

*,** significantly different from zero at 0.05 and 0.01 levels of probability, respectively.

––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

-0.23**

0.59**

Plant height, cm

Striga emergence count, 10 WAP

0.39**

No. ears per plant

Grain yield, Mg/ha

––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Grain Yield, Mg/ha

––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

TABLE 4 – Phenotypic (below diagonal) and genetic (above diagonal) correlations among traits of full- sib families developed from TZE-W Pop DT TR C3 evaluated under Striga infestation in Ferké in 2002 and Mokwa in 2003.

GENETIC VARIANCES FOR STRIGA RESISTANCE 211

212

B. BADU-APRAKU

TABLE 5 - Expected correlated response to selection for grain yield, plant height, Striga damage rating at 8 WAP, Striga emergence at 10 WAP and anthesis-silking interval of full- sib families developed from TZE-W Pop DT STR C3 evaluated under Striga infestation in Ferké in 2002 and Mokwa in 2003. –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Directly selected trait

Indirectly selected trait

Actual for indirectly selected trait

Predicted response for indirectly selected trait

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

Grain yield

Anthesis-silking interval, days

4.00

Plant height

Ear height, cm

Striga damage rating at 8 WAP

Striga damage at 10 WAP, score

Striga emergence count at 8 WAP

Striga emergence at 10 WAP, plants/plot

62 5.30 123.0

6.53 51 5.13 92.6

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

tions with all other traits studied. The phenotypic correlation between Striga emergence count and grain yield was rp=-0.20** at 8 WAP and rp=-0.23** at 10 WAP, indicating that only a small percentage of the variation in grain yield was explained by variation in the number of emerged Striga at 8 and 10 WAP. The genetic correlations were generally higher than the phenotypic correlations. Large and positive genetic correlations were observed between Striga emergence count at 8 WAP and at 10 WAP (ra=0.99**) and between Striga damage scores at 8 WAP and 10 WAP (ra=0.89**). Also, large genetic correlations existed among several other traits. For example, grain yield showed positive, large-to-moderately large genetic correlation with EPP (ra=0.81**), PHT (ra=0.54*), and EHT (ra=0.91**). It had a large-to-moderately large negative genetic correlation with the flowering traits, and Striga damage rating at 8 and 10 WAP. Other large and significant genetic correlation coefficients were PHT with EHT; days to anthesis with days to silking, ASI, Striga damage at 10 WAP, and Striga emergence at 10 WAP; days to silking with ASI, root lodging, Striga damage at 10 WAP, Striga emergence at 8 and 10 WAP; ASI with Striga damage at 10 WAP and Striga emergence at 10 WAP; and root lodging with Striga damage at 8 WAP. The genetic correlation between several traits (e.g., EPP and Striga damage rating at 8 and 10 WAP) was larger than 1.00, which is theoretically impossible. These high estimates may be attributed to sampling or experimental errors or a combination of both. The relative efficiency of direct selection for grain yield instead of ASI, plant height instead of ear height, Striga damage rating at 8 WAP instead of at 10 WAP and Striga emergence count at 8 WAP instead of at 10 WAP was examined to determine the possibility of using only one of the two parameters

in each pair of traits for the evaluation and selection of genotypes for Striga resistance. Results revealed the expected correlated responses for all the indirectly selected traits as positive but ASI was higher than the corresponding actual value while ear height and Striga emergence count at 10 WAP were lower than the actual values (Table 5). However, the predicted correlated response of Striga damage rating at 10 WAP (score of 5.13) with Striga damage rating at 8 WAP as the directly selected trait was strikingly close to the actual value (score of 5.30).

DISCUSSION The amount and type of genetic variability are of major importance in a breeding program. The amount of genetic variability determines the limits of selection for improvement, while the type of variability helps the breeder to determine the most appropriate breeding method to use for the genetic improvement of a population. The presence of moderate-to-large additive genetic variances, and the high means and wide ranges of the traits in this study suggest that there is substantial residual genetic variability in the source population to allow good progress from further selection for Striga resistance and grain yield. The additive genetic variance was much larger than the dominance variance for all traits except Striga emergence counts at 8 and 10 WAP. In Striga research, host-plant rating (Striga damage) is used as the index of tolerance while Striga emergence count (number of emerged Striga plants) plus yield performance is used as the index of resistance. Results of this study indicate that inheritance of Striga tolerance is controlled primarily by additive genetic variance while resistance is controlled by non-additive gene action. These results corroborate the findings of KIM (1994) and AKANVOU


et al. (1997). On the other hand, the larger dominance variance obtained for Striga emergence in this study compared to additive genetic variance (dominance variance is about twice as large as the additive genetic variance at 8 WAP and about four times larger at 10 WAP) indicates that nonadditive gene action is more important than additive gene action for Striga emergence. This finding is in agreement with KIM (1994) and AKANVOU et al. (1997) but contradicts those of GETHI and SMITH (2004), who showed that GCA mean squares for Striga emergence were about 7 times larger than SCA mean squares at 9 WAP and about 8 times larger at 12 WAP and concluded that additive gene action was more important than nonadditive gene action in the inheritance of this trait. The differences in the results of these studies could be attributed to that fact that different genetic materials were used in the studies. Whereas TZi lines and their derivatives were used in the present study as well as those by KIM (1994) and AKANVOU et al. (1997), the study conducted by GETHI and SMITH (2004) involved a wider range of germplasm with possibly introduced genes that had a different mode of action. The larger genetic variance observed for most traits studied, compared to the dominance variance in TZE-W Pop DT STR C3 is not surprising, considering the fact that this population has not been intensely selected but has gone through only three cycles of selection. According to HALLAUER and MIRANDA (1988), in a previously selected maize germplasm, the variance for SCA (related to dominance variance) is larger than that for GCA, an indicator of additive genetic variance, the converse being the case in unselected populations. Results of this study corroborate those of LINDSEY et al. (1962) who showed that the estimates for additive genetic variances obtained in two open-pollinated maize varieties exceeded the estimates of dominance variance for all characters studied, including grain yield. However, this finding is in disagreement with GUEI and WASSOM (1992) who reported, in a study of the inheritance of some drought adaptive traits in two maize populations, that there was greater dominance deviation for grain yield and number of ears/plant, even though additive genetic variance was more important than dominance variance in the expression of flowering traits. Another way to quantify the amount and type of usable genetic variability in a breeding population is to express the additive variance as a proportion of the phenotypic variance. This is termed heritability in the narrow sense. Heritability is a measure of

213

the genetic relationship between parent and progeny and has been widely used to assess the degree to which a character may be transmitted from parent to offspring. It also indicates the relative importance of hereditary (additive, dominance, epistatic) and environmental variances in the expression of traits. The low-to-moderately high heritability esimates (0.3-0.9) obtained for grain yield, plant height, ear height, flowering traits, and host damage scores at 8 and 10 WAP indicate that there is adequate variation to allow further improvement of these traits in TZE-W Pop STR C3. In particular, Striga damage rating is clearly more heritable than Striga emergence count thus providing further evidence to support the earlier conclusion that Striga tolerance is controlled by additive, while emergence is controlled by nonadditive gene action. As noted by HALLAUER and MIRANDA (1988), epistatic effects have been assumed to be absent in estimation of genetic variances in many earlier studies, as was the case in the present study. But since epistasis is purely a statistical description and does not define a physiological function, its presence, whatever the magnitude, will bias downwards the estimates of additive genetic variance, hence narrow-sense heritability. Although the presence of epistatic effects could cause the low heritability estimates obtained in this study, the values compare favorably well with those reported in the literature even for late-maturing maize germplasm. For example, KLING et al. (2000), found for TZL Comp. 1, h2 estimates of 33% for Striga damage rating scores, 32% for grain yield under Striga infestation and 14% for Striga emergence counts. The estimate of the genetic correlation between Striga emergence counts and grain yield under infestation was -0.22. These results, along with those obtained in our study suggest that Striga effects constitute severe stress on the maize plants, which will normally result in low heritability estimates. These findings are in disagreement with those of GUEI and WASSOM (1992) who reported larger heritability and additive variance estimates for flowering traits, number of ears per plant and grain yield in the maize population, La Posta Sequia in stress (drought) than non-stress environments (wellwatered). HAUSSMANN et al. (1988) reported similar results for grain yield in Striga hermonthica in Sorghum. They showed that lower estimates of average midparent heterosis for grain yield usually result from crosses of adapted parent lines, while high estimates are frequently obtained from studies which involved exotic germplasm or conducted under envi-

214

B. BADU-APRAKU

ronmental stress. Furthermore, HAUSSMANN et al. (2000a) reported high estimates of broad-sense heritability for all measured traits in sorghum and attributed these partly to the use of high number of replications and test locations and improved plot layout. Therefore, to improve the heritability of grain yield and other measured traits in maize, it might be desirable to increase the number of replications and locations so as to increase the accuracy of estimated entry means, and thus the heritability. The environment mean squares were large and significant for most traits, indicating that the environment plays an important role in Striga parasitism and that Striga tolerant genotypes from the population should be tested in several environments to identify those that are superior. The lack of significant interaction of the males-within-set and females in males-within-set with the environments for most traits indicates that the performance of the Striga resistant genotypes was quite stable in the different environments. Experience has shown that varieties developed for S. hermonthica resistance in WCA provide useful levels of resistance in many lowland environments throughout sub-Saharan Africa, under infestation by both S. hermonthica and S. asiatica. Invariably, a highly resistant variety in one environment has been found to be highly resistant in another, once the variety is grown within its region of adaptation (KLING et al., 2000). Of greater relevance to the subject of this study are the interactions of σ2a and σ2d with the environment. Relative to Striga emergence count, σ2ae and σ2de for Striga rating were much smaller. This is an asset in the breeding program. In other words, the large additive relative to nonadditive variance in TZE-W Pop STR C3 for Striga tolerance is not influenced by the environment to an appreciable extent. On the other hand, emergence count is greatly influenced by the environment. This implies the need for effective field screening methods such as the artificial inoculation of planting holes at the time of planting (KIM, 1991; KIM and WINSLOW, 1991; HAUSSMANN et al., 2000b), combined with improved plot layout (such as the lattice experimental design), a high number of replications, and the use of appropriate resistance indices (HAUSSMANN et al., 2000b). Results of this study show that there is a preponderance of additive genetic variance for grain yield, host damage scores, EPP, and most other traits studied; dominance variance is of major importance for Striga emergence in C3 of TZE-W Pop DT STR. To ensure good progress from further selection in this

source population, a breeding scheme that capitalizes largely on additive genetic variance for host damage, such as recurrent selection (S1 family selection, S2 family selection, half-sib family selection with testcrosses, full-sib family selection), could be adopted to increase the frequency of Striga resistance genes and to accumulate additive genetic variance. In particular, the full-sib recurrent selection and the half-sib family selection with testcrosses would also afford the opportunity to take advantage of the large dominance variance (nonadditive gene action) for Striga emergence in the population to improve this trait. Inbreeding and hybridization would be very effective in developing Striga resistant populations and varieties. The moderate-to-large expected gain/cycle of selection observed for grain yield, plant height, ear height, days to anthesis, ASI, Striga damage and Striga emergence is a confirmation that good progress from selection for the traits could be made using S1 recurrent selection methods to increase the frequency of favorable alleles. It is interesting to note that several progenies of TZE-W Pop DT STR C3 combined reduced Striga emergence and Striga damage. This implies that it should be possible to extract from the populations, experimental varieties that combine both low Striga emergence and Striga damage. However, the low heritability estimates obtained for Striga emergence counts at 8 and 10 WAP suggest that improvement in grain yield and Striga emergence through recurrent selection in advanced generations would be slow. For fast progress from selection for Striga resistance, it might be desirable to introgress novel Striga resistance genes into the population. Genetic variances by definition are never negative (ROBINSON et al., 1955). However, in practice, negative variance components do occur for various reasons and are conventionally equated to zero. The negative dominance variance component estimates obtained for Striga rating at 10 WAP, harvest moisture, days to silking, days to anthesis, and EHT could be attributed to sampling error in the production of progenies for evaluation or lack of randomating assumed by statistical models to estimate the variance components (HALLAUER and MIRANDA, 1988; GOUESNARD and GALLAIS, 1992). In the present study, plants designated as male or pollen parents and those designated as females or seed parents were planted on the same date during the dry season of 2002 at Ferké. The high temperatures and humidity that normally characterize the dry season during the pollinating period might have reduced the duration of pollen


shedding and silk receptivity for the plants. This might have led to assortative mating in the production of the full-sib families within half-sib groups. In this case, it was expected that matings involving early flowering males might have been largely restricted to early silking females, intermediate flowering males primarily to intermediate silking females, and late flowering males to late silking females. According to LINDSEY et al. (1962), such a situation would lead to an upward bias in the estimate of additive genetic variance and a downward bias in the estimate of dominance variance and might have resulted in negative dominance variances. On the other hand, the negative additive estimates, σ2a, obtained for stalk lodging may have resulted from sampling error, experimental error, or a combination of both. Genotypic correlations between traits are important to breeders because they indicate the direction and magnitude of correlated responses to selection, the relative efficiency of indirect selection and they allow the computation of appropriate multiple trait selection indices (FALCONER and MACKAY, 1996). The statistically significant large-to-moderately large genetic correlations suggest that there may be considerable genetic association between most of the characters studied. The negative genetic correlations between grain yield and the flowering traits as well as Striga damage rating at 8 and 10 WAP were not surprising since Striga affects the physiology and yield of infested plants. Normally, the correlations between flowering traits and grain yield are positive. The negative correlations between the two traits could be due to the severe stress on the progenies from high, artificial Striga infestation. It is not uncommon for the flowering traits to have negative correlations with grain yield under severe stress (BADU-APRAKU et al., 2004). The large positive phenotypic and genetic correlations between Striga emergence at 8 WAP and at 10 WAP and between host damage scores at 10 WAP and at 8 WAP suggests that either of the parameters of each trait will suffice as a selection parameter for the evaluation of genotypes for Striga resistance. The strikingly close predicted correlated response of Striga damage rating at 10 WAP using Striga damage rating at 8 WAP as the directly selected trait and the moderately close predicted correlated response between Striga emergence count at 8 WAP and at 10 WAP, and between plant height and ear height confirms that either of the pair of each trait will suffice as a selection parameter. Considering the labour and time involved in recording Striga damage ratings and Stri-

215

ga emergence, this result suggests that the data for these traits may be taken at 8 WAP without any serious loss of precision. The lower correlated response between grain yield and ASI despite the strong genetic correlation may be attributed to low additive variances of both traits. Further studies are needed to confirm these results so that data for Striga emergence count and Striga damage rating could be taken at either 8 or 10 WAP to save labour. The high negative phenotypic and genetic correlations between grain yield and Striga damage scores at 10 WAP and at 8 WAP are a confirmation of the reliability of the traits for selection for the improvement of grain yield and Striga resistance. This result also suggests that simultaneous improvement of grain yield and Striga damage under artificial infestation can easily be achieved in the population. The small and positive phenotypic and genetic correlations obtained between Striga damage rating and Striga emergence count at 8 WAP, on the one hand, and Striga damage rating and Striga emergence count at 10 WAP on the other, suggest that resistance to Striga is not controlled by the number of Striga plants attached to the host and that different genes control S. hermonthica emergence and levels of host plant damage in maize. This is in agreement with the findings of KIM (1994) and AKANVOU et al. (1997). The generally low phenotypic and genetic correlations between grain yield and Striga emergence counts implies that the two traits are genetically independent (no linkage or pleiotropy) and that each trait could be improved separately. However, Striga emergence count alone is not reliable for the evaluation of resistance, particularly when maize materials are highly susceptible, because such materials suffer more damage even when few Striga plants are attached to the roots of the host plant (KIM et al., 1998). The results of this study suggests that host damage scores should be preferred to Striga emergence counts for selection for improved Striga resistance and grain yield because of the higher heritability estimates and the high negative phenotypic and genetic correlations between grain yield and Striga damage scores at 8 WAP and at 10 WAP. However, for maximum gain from selection for Striga resistance and increased grain yield, it would be desirable to use a combination of host damage rating and Striga emergence counts to improve both traits simultaneously. HAUSSMANN et al. (2000b) also recommended a combination of host damage rating and Striga emergence counts for evaluation of resistance in

216

B. BADU-APRAKU

sorghum. COMPTON and LONNQUIST (1982) reported that selection indices are useful because they permit uniform application of a multiple trait selection criteria and are an efficient way of improving simultaneously several quantitative traits. Therefore, selection for host damage rating and Striga emergence counts could be effectively carried out simultaneously using an appropriate selection index under Striga-infested and non-infested conditions. The large phenotypic and genetic correlations observed between grain yield and EPP, Striga damage rating, plant and ear heights, days to silking and anthesis as well as ASI in this study justify their inclusion in the selection index for yield improvement in Strigaprone environments as earlier reported by ADETIMIRIN et al. (2000) and BADU-APRAKU et al. (2004).

damage rating, and the several progenies identified that combined reduced Striga emergence and Striga damage indicate that it should be possible to extract from the population experimental varieties that combine both low Striga emergence and Striga damage. The strikingly close predicted correlated response of Striga damage rating at 10 and at 8 WAP and the moderately close predicted correlated response between Striga emergence count at 8 WAP and at 10 WAP, and between plant height and ear height confirm that either of the pair of each trait will suffice as a selection parameter. ACKNOWLEDGEMENTS - Financial support of USAID for this study is gratefully acknowledged. The author is also grateful to the staff of the IITA Maize Program in Ibadan for technical assistance and to A. Lum Fontem, B. Bossey, and K. Masseka for the statistical analyses. The manuscript has been submitted with the approval of IITA as manuscript number IITA/06/JA/18.

SUMMARY The wide ranges, the presence of moderate-tolarge additive genetic variance and moderately lowto-high narrow sense heritability estimates indicate that sufficient residual genetic variability still exists to allow further improvement of grain yield, Striga resistance, and most other traits in the TZE-W Pop DT STR C3 maize population. This was confirmed by the moderate-to-large predicted gain/cycle of selection for grain yield, plant height, ear height, days to anthesis, ASI, Striga damage rating at 8 WAP and 10 WAP and Striga emergence count at 8 WAP and 10 WAP. However, the low heritability estimates obtained for Striga emergence counts at 8 and 10 WAP suggest that for fast progress from selection for Striga resistance, it might be desirable to introgress novel Striga resistance genes into the population. Dominance variance was larger than additive genetic variance for Striga emergence and should be taken into consideration in future selection programs. A breeding scheme that capitalizes largely on additive and also dominance variance, such as the fullsib or the half-sib recurrent selection with testcrosses, should be adopted for further improvement of the population. The best approach for breeding for improved grain yield and Striga resistance is to select simultaneously for a combination of high grain yield, low Striga damage syndrome rating, and reduced Striga emergence under Striga-infested conditions and high grain yield under non-infested conditions. The wide range of genetic variation observed among the full-sib families from TZE-W Pop DT STR C3 in Striga emergence count and Striga

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