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African Crop Science Journal
African Crop Science Society
ISSN: 1021-9730 EISSN: 2072-6589
Vol. 8, Num. 3, 2000, pp. 223-232
African Crop Science Journal, Vol. 8. No. 3, pp. 223-232

African Crop Science Journal, Vol. 8. No. 3, pp. 223-232

GENETIC ANALYSES OF ADAPTATION OF BARLEY TO LOW AND HIGH INPUT ENVIRONMENTS

R. MADAMBA
Laboratory of Plant Breeding, Wageningen Agricultural University, The Netherlands
Present mailing address: Department of Research and Specialists Services, Crop Breeding Institute, Box CY 550, Causeway, Harare, Zimbabwe

(Received 17 May, 1999; accepted 2 May, 2000)

Code Number: CS00024

INTRODUCTION

Genetic adaptation implies the shaping of a population or a species gene pool in response to environmental challenges (Perez-de-la-Vega and Tigerstedt, 1996). A crop’s ability to exploit its environment depends on many adaptive features that are controlled by multiple genes, interacting among themselves and with the environment in complex ways (Hawtin et al., 1997). The genotype by environment interaction (GxE) observed by plant breeders indicates differential responses of the varieties being tested to different environmental conditions and is a major complication in plant breeding (Ceccarelli and Hammer, 1996). GxE interactions reduce the correlation between the phenotype and the genotype.

Ceccarelli et al. (1996) suggested that if the genotype by environment (GxE) interaction is of the crossover type, genotypes developed in favourable environments do not perform well under harsh environments. This suggests that the genes for yield expressed in low and high input conditions are different. As a result, breeding procedures conducted under high input and uniform conditions might favour selection of cultivars adapted to intensive management and might eliminate individuals adapted to low input conditions (Ceccarelli, 1997).This could be the reason why research on crop improvement has not had as much impact in the smallholder farms compared to commercial farms. The idea of breeding for specific environments rather than broad adaptation is supported by Falconer (1990) and Kearsey and Pooni (1996).

Braun et al. (1997) however, suggested that it is possible to breed for wide adaptation provided that the genetic base is broad enough. The use of a shuttle programme (shuttling populations between localities with widely different environments) enables simulta-neous selection for both high and low yielding environments.

It was against this background that we initiated an experiment to obtain more insight into the genetic background of adaptation, both to specific and broad conditions at the molecular level. Knowledge of quantitative trait loci by enviro-nment interaction (QTL x E) is essential to be able to use the correct markers for marker assisted selection. The objective of the present study was to observe whether the same QTLs are involved in low, intermediate and high input environments (QTL by environment interaction) and whether the information on QTL by environment interaction can give some insights on breeding for specific and broad adaptation

MATERIALS AND METHODS

Ninety-four barley recombinant inbred lines (RILs), being progenies of reciprocal crosses of Apex and Prisma and the two parents, were used in this study. Apex contains the recessive mlo-mutant allele and is powdery mildew resistant and Prisma is susceptible to powdery mildew. Prisma contains the denso mutant allele that gives it a short stature.

Trials were conducted in a field with sandy soil at the Wageningen Agricultural University farm. It was grown in three different input environments namely; 1) 90 kg ha-1 total nitrogen + pesticides, representing the high input environment (NP); 2) 0 kg ha-1 applied nitrogen + pesticides, representing the intermediate input environment (P); and 3) 0 kg ha-1 applied nitrogen and no pesticides, representing the low input environment (O). The results of soil analysis indicated 26 mg (P2O5) l-1 of phosphorus, 13 mg (K2O)100 g-1 of potassium, 166 mg (MgO) kg-1 of magnesium and 0.31 mg (B) kg-1 of boron and these were deemed adequate for barley growth. The results for nitrogen were 35 kg N ha-1. The rates of nitrogen applied in the high input environment were based on the results from the soil analysis.

The design used was a randomised incomplete block design (a-design) in a split plot arrangement, with the fertiliser-pesticide levels as main plots and was replicated three times. The gross and net plots were 9.0 m2 . The spacing between sub-plots was 30 cm, but 3 m for the main plots.

Seventy-five kg N ha-1, in the form of Kas (Kalli ammon salpeter) at a rate of 275 kg ha-1, were applied on the 15 May, on plots that were to receive nitrogen to bring the total nitrogen (residual nitrogen was 35 kg ha-1) in the fertiliser-pesticide treatment to 90 kg N ha-1. The application rate of 75 kg N ha-1 instead of 55 kgha-1 was based on the efficiency of the machine. The Kas fertiliser contains 27% N of which 13.5% is in nitrate form for a long term effect and 13.5% in form of ammonium for immediate use. The trial was sprayed against powdery mildew with amistan (azoxystropin) and opus team (epoxiconazool/fen propimorf) at rates of one litre and one and half litres per hectare, respectively. The trial was sprayed against aphids with pirimor (pirimicarb) at a rate of 0.4 kg ha-1. The experiment received 3 l ha-1 of verigal D (bifenox/mecoprop-P), a herbicide against broad leafed weeds. The trial was planted on 31 March and 1 April 1998, at seeding rate of 90 kg ha-1 and was harvested on August 11 and 12, 1998.

The records taken included; grain yield in grams per plot, number of kernels per ear, weight of thousand kernels, number of ears per square metre, days to 50% flowering (the number of days from planting to the time when 50% of the main tilllers in a plot have exposed awns), days to 95% maturity (the number of days from planting, required by 95% of the plants to reach physiological maturity), lodging score and plant height. Lodging score was obtained as a fraction of the area lodged x lodging severity, where lodging severity is scored from 0 to 2; 0 represents no lodging, 1 plants bent but not touching the ground, and 2 plants are completely laying prostate on the ground.

A general analyses to test for outliers, equality of residual variance, genotype by environment interaction and generation of means was carried out on the phenotypic data using the statistical package SAS copyright 1989-1995 of the SAS institute. ANOVA was conducted for each environment and for combined environments. ANOVA and least squares means (means adjusted for the effect of incomplete blocks) for both the individual and combined analyses (for data with equal variances) were generated using the PROC GLM procedure. The PROC GLM procedure with the weighted command was used for the data with unequal variances. Bartlett’s and Levene’s tests (Gomez and Gomez, 1984; Snedecor and Cochran, 1989), were used to test for homogeneity of variance.

A genetic linkage map, which was constructed from the barley recombinant inbred lines (progenies of Apex and Prisma) at the Laboratory of Plant Breeding (Dourleijn, pers. comm.) was used for QTL analyses using the computer program MapQTL version 3.0, copyright 1997 of CPRO-DLO, Wageningen. The linkage map was constructed from amplified fragment length polymorphism (AFLP) markers. The AFLP used was constructed using 190 AFLP markers from a population of 94 recombinant inbred lines.

RESULTS

The residuals of the data for all the traits in the three different environments showed no deviation from a normal distribution. Variances for number of days to 50% flowering (DTF), plant height (PH) and grain yield over the three different environments were not significantly different. The variances for the data on number of days to 95% physiological maturity (DMAT), weight of a thousand kernels (TKW), number of kernels per ear (KE) and number of ears per square metre (EARM2) differed significantly (P<0.05). Both the logarithm and square root transformations failed to make the variances equal.

The individual analyses of variances (data not shown) showed highly significant (P<0.05) line effects for all the traits measured in all the three environments. ANOVA also indicated significant blocking effects for all the traits in each of the individual environments. The combined analyses of variance of all the treatments showed significant genotype by environment interactions effects for all the traits measured except DTF.

Correlation coefficients for the traits measured in each of the three different environments. Grain yield (KGHA) was positively correlated with DTF, TKW, and EARM2 and negatively correlated with lodging score (LOG) in the NP environment (Table 1). Grain yield (KGHA) was positively correlated with DTF, DMAT and EARM2 in the P environment and positively correlated with TKW and EARM2 in the O environment. High negative correlations were observed between KE and EARM2 and TKW and EARM2 in all the three environments. The correlations between KE and TKW were positive. High positive correlations were also found between PH and KE and DMAT and DTF in all the three environments and between LOG and PH in the NP environment.

TABLE1. Correlation coefficients/Prob|R| under HO: RHO = 0/N = 100 in different environments
    KGHA1 KE EARM2 TKW DTF PH DMAT LOG
KE NP 0.13107              
    0.19372              
  P -0.05791              
    0.5671              
  O -0.03464              
    0.7323              
EARM2 NP 0.27046 -0.78023            
    0.006.5 0.0001            
  P 0.46036 -0.80932            
    0.0001 0.0001            
  O 0.48609 -0.77451            
    0.0001 0.0001            
TKW NP 0.22092 0.41385 -0.68666          
    0.0272 0.0001 0.0001          
  P 0.08524 0.34988 -0.58413          
    0.3991 0.0004 0.0001          
  O 0.22625 0.35604 -0.53183          
    0.0236 0.0003 0.0001          
DTF NP 0.205588 0.00782 0.11103 -0.0243        
    0.0399 0.9384 0.2714 0.8104        
  P 0.23144 -0.18307 0.30786 -0.21942        
    0.0205 0.0683 0.0018 0.0283        
  O -0.07962 -0.14107 0.21894 -0.36606        
    0.431 0.1615 0.0286 0.0002        
PH NP -0.1764 0.45419 -0.47942 0.21842 -0.57203      
    0.0791 0.0001 0.0001 0.029 0.0001      
  P -0.11484 0.53513 -0.50489 0.33201 -0.67103      
    0.2552 0.0001 0.0001 0.0007 0.0001      
  O 0.16239 0.46519 -0.415 0.53334 -0.66602      
    0.1065 0.0001 0.0001 0.0001 0.0001      
DMAT NP 0.06825 0.00729 -0.07444 0.17008 0.49967 -0.14198    
    0.4999 0.9426 0.4617 0.0907 0.0001 0.1588    
  P 0.40106 -0.43384 0.56198 -0.2463 0.6887 -0.64747    
    0.0001 0.0001 0.000 0.0135 0.0001 0.0001    
  O 0.05444 -0.31985 0.38042 -0.31208 0.63832 -0.54392    
  0.5906 0.0012 0.0001 0.0016 0.0001 0.0001      
LOG NP -0.3058 0.33718 -0.38166 0.06499 -0.46196 0.75323 -0.17395  
    0.002 0.0006 0.0001 0.5206 0.0001 0.0001 0.0835  
1KGHA = grain yield, KE = number of kernels per ear, 2EARM = Number of ears per square metre
TKW = weight of a thousand kernels, DTF = days to 50% flowering, PH = plant height
DMAT = days to physiological maturity, LOG = lodging score. Numbers in italics are the probability values

QTL analyses. Table 2 shows the results of QTL analysis for the traits measured in the three different environments. Five QTLs affecting days to 50% flowering (DTF) were detected on chromosomes 1, 2, 3 (2 QTLS) and 5, in the NP environment. Six were detected on chromosomes 1, 2, 3 (2 QTLs), 4 and 5, in the P environment and also six in the O environment on chromosomes 1, 2, 3, 4 (2 QTLs) and 5. Chromosome 3 at a position of about 126cM appeared to have a major effect on days to 50% flowering in all the three environments. The phenotypic variation explained by the QTL in this region was 56.9% for NP and 50.5% and 58.6% for the P and O environments, respectively. The additive effect of this QTL in the NP environment was 1.7 days, 1.5 days for the P environment and 1.8 days for the O environment.

TABLE 2. Results of Multiple QTL Mapping (MQM) for different agronomic traits
3Trait 1Inputs Chromosome QTL (cM) position 2Additive effects %Variation explained Lod score
DTF NP 1 30 0.9 17.5 8.85
2 120 0.5 4.8 3.78
3 126 1.7 56.9 22.59
3 65 -0.5 4.0 2.82
5 104 -0.6 6.6 4.51
P 1 30 0.9 17.1 7.33
2 120 0.5 4.8 3.16
3 126 1.5 50.5 19.40
3 65 -0.4 3.3 2.20
4 6 -0.4 3.1 2.05
5 104 -0.9 5.6 3.27
O 1 22 1.0 16.1 8.80
2 119 0.7 6.8 4.75
3 126 1.8 58.6 21.58
4 8 -0.5 5.5 3.11
4 79 -0.6 5.7 3.77
5 100 -0.4 3.2 2.34
DMAT NP 1 26 0.6 19.2 7.35
3 126 0.7 17.9 5.10
3 145 -0.4 8.5 3.27
4 67 -0.4 7.8 3.31
7 75 -0.5 11.1 4.60
P 1 30 0.9 12.7 3.86
3 126 1.8 51.5 10.96
7 75 -0.6 5.6 2.10
O 1 35 0.8 13.7 3.04
3 126 1.5 43.9 13.39
7 86 -0.6 7.1 3.12
PH NP 1 22 2.8 7.1 6.14
1 81 2.6 5.9 3.80
3 126 -9.2 73.3 29.88
4 129 2.7 6.3 3.62
5 105 2.4 5.0 2.97
P 1 23 1.3 1.3 2.16
1 126 1.9 2.8 3.56
2 68 1.3 1.3 2.17
2 120 -2.1 3.2 4.74
3 126 -9.6 75.1 40.87
4 129 2.0 3.1 2.59
5 104 2.1 3.4 5.25
O 1 22 1.8 2.3 3.23
1 85 1.9 2.8 3.95
1 126 2.1 3.3 4.31
2 68 1.6 1.9 3.20
3 126 -10.1 76.3 41.11
4 129 2.1 3.1 3.44
5 103 2.8 4.9 6.96
EARM2 NP 1 40 -52.0 33.5 3.46
2 125 -39.6 18.9 4.49
3 126 36.6 17.5 4.41
4 141 -41.3 21.3 4.85
6 93 -36.6 17.3 2.80
P 2 73 -24.4 19.0 4.92
3 126 26.9 23.6 7.21
4 129 -25.6 20.1 4.33
7 30 18.6 8.3 3.45
7 76 13.8 6.2 2.06
O 1 40 25.7 14.9 3.66
2 130 -15.1 7.4 3.28
3 126 24.3 20.9 6.99
4 134 -24.4 18.9 5.12
6 67 -15.6 8.5 2.10
7 26 16.7 7.0 3.28
7 76 17.7 11.2 3.31
KE NP 1 35 0.7 19.7 3.28
2 120 0.7 18.5 6.77
3 126 -0.8 12.3 4.23
4 141 0.4 8.1 2.31
7 30 -0.5 10.9 3.98
P 2 130 0.6 12.0 5.16
2 60 0.4 10.9 3.39
3 60 -0.5 9.3 2.99
3 126 -0.5 8.6 3.27
4 124 0.7 17.9 3.84
7 20 -0.5 7.5 3.40
O 2 130 0.7 20.0 7.47
2 65 0.4 7.5 3.17
3 60 -0.5 11.5 3.27
3 126 -0.4 8.7 3.30
7 30 -0.4 8.4 3.39
TKW NP 1 85 1.0 11.0 4.27
2 120 0.9 8.4 3.63
6 81 0.9 9.9 3.15
7 22 -1.0 7.7 3.43
7 76 -0.9 10.2 4.21
P 1 85 0.9 8.9 3.75
2 65 0.8 6.8 3.12
3 126 -1.0 13.2 5.13
4 79 -0.9 9.0 3.11
6 41 1.0 11.6 5.09
O 1 85 0.9 9.4 4.75
2 68 1.0 12.1 5.96
3 126 -1.3 21.3 9.47
4 75 -0.7 5.8 3.38
4 152 -0.8 5.7 3.29
6 44 1.0 11.3 6.00
7 76 -0.6 5.0 2.12
KG/HA NP 3 126 273.1 38.0 13.12
3 170 110.2 6.4 2.68
4 134 -213.7 22.2 7.43
7 0.0 -134.0 8.7 4.45
7 101 -122.6 7.8 3.80
P 1 35 202.1 45.9 9.43
3 126 124.3 18.0 6.19
4 136 -106.5 12.8 4.63
6 41 83.9 8.5 3.55
O 1 30 142.8 21.1 5.40
3 77 106.9 7.9 3.68
3 170 100.3 10.4 3.96
4 152 -139.4 18.3 7.79
7 0.0 -116.8 12.0 5.32
LOG NP 1 30 0.2 8.2 5.36
1 86 0.1 4.1 3.08
3 126 -0.5 61.5 25.18
6 40 0.1 5.7 4.14

1NP = Extra nitrogen + pesticide, P = No extra nitrogen + pesticide, and O = No extra nitrogen + no pesticide 2Additive effects = (Prisma - Apex)/2; 3 DTF = days to 50% flower, DMAT = days to physiological maturity PH = plant height, EARM2 = number of ears per square metre, KG/HA = grain yield in kg/ha
TKW = weight of a thousand kernels, KE = number of kernels per ear, and LOG = lodging score

In terms of days to 95% physiological maturity (DMAT), five QTLs were detected in the NP environment on chromosomes 1, 3 (2 QTLs), 4 and 7. Three QTLs were detected in chromosomes 1, 3 and 7, in the P and O environments. On chromosomes 1 and 3 (position 126 cM) in all the three environments the Prisma alleles had positive effects on days to 95% physiological maturity and had negative effects on it on chromosomes 3 (position 145 cM), 4 and 7. In the P and O environments, the QTL on chromosome 3 (position 126 cM) had large effects on days to 95% physiological maturity, with phenotypic variation explained by 51.5% and 43.9%, respectively. The respective additive effects were 1.8 and 1.5 days.

Five QTLs significantly affecting plant height (PH) were detected on chromosomes 1 (2 QTLs), 3, 4 and 5, in the NP environmemt, seven in the P environment on chromosomes 1 (2 QTLs), 2 (2 QTLs), 3, 4 and 5. Seven QTLs were detected in the O environment on chromosomes 1 (3 QTLs) 2, 3, 4 and 5. The Prisma alleles had positive effects on plant height on chromosomes 1 (position 68 cM), 4 and 5 in all the environments. The Prisma allele had negative effects on plant height on chromosomes 2 (position 120 cM) and 3. The QTL on chromosome 3 had a large and dramatic effect on plant height with % variation explained of 73.3% (NP), 75.1% (P) and 76.3% (O) environments. The respective additive effects were -9.2, -9.6 and -10.1 cm.

Five QTLs affecting number of ears per square metre were detected in the NP environment on chromosomes 1, 2, 3, 4 and 6 and five in the P environment on chromosomes 2, 3, 4 and 7 (2 QTLs) and seven on chromosomes 1, 2, 3, 4, 6 and 7 (2 QTLs) in the O environment. The Prisma alleles had positive effects on number of ears per square metre on chromosome 3 and 7.

Five QTLs affecting number of kernels per ear were detected on chromosomes 1, 2, 3, 4 and 7 in the NP environment, six QTLs in the P environment on chromosomes 2 (2 QTLs), 3 (2 QTLs) 4 and 7 and five in the O environment on chromosomes 2 (2 QTLs), 3 (2 QTLs) and 7. The Prisma alleles had positive effects on number of kernels per ear on chromosomes 1, 2 and 4 but had negative effects on it on chromosomes 3 and 7.

In the case of weight of a thousand kernels (TKW), five QTLs were mapped on chromosomes 1, 2, 6 and 7 (2 QTLs) in the NP environment, five in the P environment on chromosomes 1, 2, 3, 4 and seven QTLs on chromosomes 1, 2, 3, 4 (2 QTLs), 6 and 7 in the O environment. On chromosomes 1, 2 and 6 the Prisma alleles had positive effects on weight of a thousand kernels. They had negative effects on it on chromosomes 3, 4 and 7.

Five significant QTLs located on chromosomes 3 (2 QTLs), 4 and 7 (2 QTLs) affected grain yield in the NP environment. Four QTLs on chromosomes 1, 3, 4 and 6 were detected for grain yield in the P environment and five on chromosomes 1, 3(2 QTLs), 4 and 7 in the O environment. The Prisma alleles had positive effects on yield on chromosomes 1 and 3 and had negative effects on yield on chromosomes 4 and 7 (position 0.0 cM) in all environments.

There was no lodging observed in the P and O environments. Five QTLs affecting lodging were detected on chromosomes 1 (2 QTLs), 3 (2 QTLs) and 6. The Prisma allele had negative effects on lodging only on chromosomes 3 (position 126 cM), but positive effects on lodging on all other chromosome regions. The QTL on chromosome 3 (position 126 cM) had a large effect on lodging. It explained 61.5% of the phenotypic variation and the additive effect was -0.5.

DISCUSSION

The alpha-lattice design used in this study was efficient, as there were highly significant blocking effects. As a result, adjusted means, that is LSMEANS, were used for QTL analyses and estimation of trait correlation coefficients. The use of means in the estimation of correlation coefficients is more appropriate than the use of raw data, because this reduces the experimental error in correlation analyses and hence avoids confounding the residual squares with the experimental error (Gomez and Gomez, 1984). The significant line effects shown by the different traits indicated the existence of genetic variation within the population used in the study.

Significant correlation coefficients among traits indicated that genes for these traits are either linked, have a pleiotropic effect or are influenced similarly by the environment (Aasteveit and Aastveit, 1993 quoted by Veldboom, 1996). The lack of correlation among the traits may suggest that the loci affecting these traits are independent from each other.

It could be expected that at least some of the genes underlying QTLs would show QTL x environment interaction, because of the large GxE interaction observed in all agronomic traits except days to 50% flowering as suggested by Hayes et al. (1993). QTL x environment interaction would be expressed as 1) significant effects detected only in a subset of the total number of environment, in other words, change in the position or disappearing of a QTL due to change of environment; 2) significant changes in the magnitude of QTL effects across environments; and, 3) significant opposite favourable alleles of a QTL in distinct environments otherwise called cross-over interactions (Hayes et al., 1993). Some of the QTLs, observed in this study, with large effects on traits showed QTL x environment interaction. This might indicate that genes for the different traits were being expressed differently in different environments.

Of the eight major yield QTLs identified, only one QTL on chromosome 4 behaved homo-geneously across the three environments. Favourable alleles on chromosome 4 were contributed by Apex and probably due to its mlo-mutant allele. This locus was well balanced in terms of many favourable effects. The Apex allele’s positive effects on grain yield and number of ears per square metre is quite a favourable situation. Both number of ears per square metre and grain yield were positively correlated. Its positive effect on number of ears per square metre could have increased grain yield. The loci’s positive effects on weight of a thousand kernels in the O environment could have also contributed to high yields. Weight of a thousand kernels was also earlier on found being positively correlated with grain yield in this particular environment. Although the QTL had negative effects on number of kernels per ear in the NP and P environments, this did not cause significant yield reduction. This is not surprising as the correlation between number of kernels per ear and yield was not significant. The QTL’s negative effects on number of kernels per ear could have been due to its large and positive effects on number of ears per square metre, a trait that was earlier on found to be highly and negatively correlated with it. This balance of favourable effects showed that this locus was adapted to all the three environments used and hence showed broad adaptation.

All the other chromosomes had QTLs that were heterogeneously expressed. Chromosome 1 had a site with large and significant effects on yield only in the P and O environments. The QTL had positive effects on days to 95% physiological maturity and days to 50% flower in these environments. The increased growth duration could have favoured the high yields observed in the two environments. This QTL had also positive effects on number of ears per square metre in the O environment, a trait that was found to be highly correlated with yield. However, the QTL did not have significant effects on yield in the NP environments. For the NP environment there was a counter balance of favourable and unfavourable effects. The large plant height could have contributed to lodging score, resulting in poor light interception and hence low yields. The advantage of having a longer grain filling duration was negated by lodging score. The positive effects of the QTL on number of kernels per ear, to have favourable effects on yield could have been counter balanced by its negative effects on number of ears per square metre. The locus is not adapted to the NP environment but to the P and O environments.

The QTL on chromosome 3 (the suspected region of the denso mutant allele) had significant effects on yield in the NP and P environments. In these environments, this locus showed a balance in some important yield determining traits. Its positive effects on yield in the NP and P environments could have been due to its positive effects on days to 50% flower, days to 95% physiological maturity and number of ears per square metre and its negative effects on plant height, coupled with low lodging score especially in the NP environment. The loci’s negative effects on number of kernels per ear in the NP and number of kernels per ear and weight of a thousand kernels in the P environments could have been balanced by its positive effects on number of ears per square metre which probably compensated for the low number of kernels per ear and weight of a thousand kernels. This region of the denso mutant allele did not have significant effects on yield in the O environment. It seemed that, it was no longer an advantage to have dwarf statured plants in this particular environment. This is expected as such environments do not favour lodging to have negative effects on yield. This locus therefore, showed adaptation to the NP and P environments.

The QTLxE interaction was more complex in chromosomes 3 (position 77 cM), 6 and 7 (position 101 cM) as these were characterised by cross-over of favourable alleles in the different environments in grain yield, although it was not significant (Lod Score >3). The QTL on chromosome 3 (position 77 cM) had significant positive effects on yield in the O environments but had negative effects on it, although not significant, in the NP environments. The QTL on chromosome 6 had significant and positive effects on yield in the P environment and negative effects on it, even though not significant, in the O environment. The QTL on chromosome 7 had significant negative effects on yield in the NP environment and had positive effects on it, although not significant, in the O environment. The positive effects on yield of the QTL on chromosome 6 in the P environment could have been due to its positive effects on weight of a thousand kernels. Its positive effects on lodging score could have caused low yields in the NP environment. This locus appeared to be more adapted only to the P environment. The two yield QTLs on chromosomes 7 and 3 (position 170 cM) did not share their region with other yield related traits. However, because of their differential expressions across the three environments, the QTLs on chromosome 7 (position 0.0) and 3 (position 170) are more suitable for the NP and O environments.

CONCLUSIONS

The regions of the genome controlling yield and related traits were differentially expressed across the three environments. This implied that genes for yield were being expressed differently in different environments. Breeding programmes designed for marginal areas should therefore be conducted under those environments.

ACKNOWLEDGEMENTS

This paper is part of an Msc. thesis submitted to the Laboratory of Plant Breeding, Wageningen Agricultural university. I wish to thank Dr. Johan Dourleijn and Prof. Piet Stam for supervising this project. Mr Masselink the farm manager at the university provided valuable technical input in this project.

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  12. Veldboom, L.R. and Lee, M. 1996. Genetic mapping of quantitative trait loci in maize in stress and nonstress environments: II. Plant height and flowering. Crop Science 36:1320-1327.
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