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GENETIC ANALYSIS OF DROUGHT TOLERANT EARLY MATURING WHITE MAIZE
(Zea mays L.) INBREDS WITH STRIGA RESISTANCE GENES FROM Zea diploperennis
BY
AKAOGU, IJEOMA CHINYERE
(10496580)
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL
FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF DOCTOR OF
PHILOSOPHY DEGREE IN PLANT BREEDING
WEST AFRICA CENTRE FOR CROP IMPROVEMENT
COLLEGE OF BASIC AND APPLIED SCIENCE
UNIVERSITY OF GHANA LEGON
DECEMBER, 2017
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DECLARATION
I hereby declare that except for references to work of other researchers, which have been duly
cited, this work is my original research and that neither part nor whole has been presented
elsewhere for the award of a degree.
.............................................
IJEOMA CHINYERE AKAOGU
(Student)
.............................................
PROF. SAMUEL KWAME OFFEI
(Supervisor)
.............................................
PROF. VERNON GRACEN
(Supervisor)
.............................................
PROF. PANGIRAYI TONGOONA
(Supervisor)
.............................................
DR. BAFFOUR BADU-APRAKU
(Supervisor)
.............................................
DR. DANIEL DZIDZIENYO
(Supervisor)
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ABSTRACT
Recurrent drought and parasitism by Striga hermonthica Del. Benth constitute the two most
important stresses limiting maize (Zea mays L.) production and productivity in sub-Saharan
Africa (SSA). Yield losses can reach up to 85% when the two stresses occur simultaneously in
the field. The use of resistant varieties is more sustainable, economical, and efficient for African
farmers. Several early (90-95 days to maturity) Striga resistant maize hybrids have been
commercialized in the sub-region. However, the levels of resistance are not as high as desired as
they still support Striga emergence thus increasing the Striga seed bank in the soil each season.
The International Institute of Tropical Agriculture (IITA) has developed new early maturing
maize inbreds containing novel Striga resistance genes from Zea diploperennis. Knowledge and
understanding of the mode of gene action conferring Striga resistance and drought tolerance in
these new early maturing maize inbreds would be invaluable in developing hybrids adapted to
both stresses in the sub-region. The objectives of this study were to: (i) determine the genetic
diversity and reaction of these early maturing maize inbred lines under Striga infestation and
drought environments, (ii) determine the mode of inheritance of Striga resistance in an early
maturing inbred line containing resistance genes from Zea diploperennis, (iii) determine the
combining abilities for grain yield and other agronomic traits and heterotic groups of 30 drought
tolerant, early maturing inbreds with the Striga resistance genes, (iv) identify high yielding and
stable hybrids under Striga-infested, drought and optimal growing conditions (v) examine the
inter-trait relationship of early maturing maize hybrids under the Striga infestation and drought.
Genetic diversity among 36 early maturing inbred lines was assessed using 8145 SNP markers.
The cluster analysis and population structure analysis separated the inbred lines into four distinct
groups based on their genetic distance indicating high level of genetic variability among the
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lines. Using the base indices for selection, 22% of the inbred lines combined resistance to Striga
and tolerance to drought. Generation mean analysis was used to study the inheritance of
resistance to Striga in the early maturing maize inbred line, TZdEI 352, containing genes from
Zea diploperennis to facilitate its effective use in resistance breeding programmes in SSA. Only
models that incorporated epistasis in addition to additive and dominance gene effects were
adequate in explaining variation in the six generations studied. Epistasis played an important role
in Striga resistance genes from Zea diploperennis in tropical maize. One hundred and fifty
hybrids derived from crosses involving the 30 inbreds utilizing North Carolina Design II plus six
hybrid checks were evaluated under artificial Striga infestation at Mokwa and Abuja, drought at
Ikenne, Bagauda, Minjibir and optimal environments at Ikenne, Mokwa and Abuja, in 2013 and
2015. Significant GCA and SCA effects for grain yield and most measured traits were detected
under the three research conditions. The higher values of GCA over SCA obtained for grain
yield, flowering traits, plant and ear heights, husk cover, Striga damage and number of emerged
Striga plants at 8 and 10 weeks after planting under Striga infested and optimal environments,
indicated that they were controlled by additive gene action. The non-additive gene action was
more important than the additive gene action for days to silking, anthesis-silking interval, ear
height, stalk and root lodging, ears per plants, ear and plant aspects while additive gene action
was more important for grain yield, plant height, husk cover, and stay green characteristics under
drought environments. There were no maternal effects in the expression of the traits either under
Striga infestation, drought or optimal growing environments. Inbreds TZdEI 268, TZdEI 352 and
TZdEI 173 had superior positive GCA-male and GCA-female effects for grain yield and
negative GCA-male and GCA-female effects for Striga damage and number of emerged Striga
plants under Striga infestation indicating that they contributed to higher grain yield in their
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hybrids and could be used to improve tropical germplasm for Striga resistance. The lines TZdEI
492 and TZdEI 378 with outstanding positive GCA effects for grain yield under drought
environments could be used to improve tropical germplasm for drought tolerance. The inbred
lines were classified into four heterotic groups across the research environments using GCA
effects of multiple traits. The inbred lines classified into each heterotic group may be recombined
to form populations that could be improved through recurrent selection. Grain yield ranged from
1134 kg ha-1 for TZEI 26 x TZEI 5 to 5362 kg ha-1 for TZdEI 173 x TZdEI 280 under Striga
infestation, 579 kg ha-1 for TZdEI 314 x TZdEI 378 to 3601 kg ha-1 for TZdEI 479 x TZdEI 260
under drought and 2376 kg ha-1 for TZdEI 82 x TZdEI 71 to 7769 kg ha-1 for TZdEI 260 x
TZdEI 396 under optimal conditions. The additive main effects and multiplicative interaction
analysis identified TZdEI 173 x TZdEI 280, TZdEI 173 x TZdEI 492, TZdEI 441 x TZdEI 260,
TZdEI 82 x TZdEI 260, TZdEI 71 x TZdEI 396, TZdEI 396 x TZdEI 131, TZdEI 396 x TZdEI
264, TZdEI 98 x TZdEI 352, TZdEI 157 x TZdEI 352, TZEI 18 x TZdEI 357, TZdEI 268 x
TZdEI 378, TZdEI 157 x TZdEI 280, TZdEI 492 x TZdEI 441 and TZEI 60 x TZEI 5 as the
highest yielding and stable hybrids with combined Striga resistance and drought tolerance genes.
Also, they had reduced Striga emergence and host plant damage. These hybrids should be tested
in multi-location and on-farm trials to confirm the consistency in performance and promoted for
release and commercialization in the Striga endemic areas with short duration of rainfall in West
and Central Africa to contribute to increased maize productivity, poverty alleviation and reduced
Striga seed bank in the soil. Striga resistant and drought tolerant hybrids with outstanding
performance across stress environments could be obtained through accumulation of favorable
alleles for stress tolerance in parental lines. Ear aspect was identified as the most reliable
secondary trait for indirect selection for grain yield under both Striga-infested and drought.
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DEDICATION
This work is dedicated to God Almighty for the strength and vision in carrying out this project
and to my parents, Mr. and Mrs. G.O. Akaogu and daughter; Kamsiyochukwu Pearl for their
love and support throughout the research.
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ACKNOWLEDGEMENTS
I thank the Almighty God for his blessings, guidance, protection, mercy and grace on me since
the beginning of my career till this level. I am most grateful to the Alliance for a Green
Revolution in Africa (AGRA), management and staff of West Africa Centre for Crop
Improvement (WACCI), Norman Borlaug Leadership Enhancement in Agriculture Program
(LEAP) for providing the scholarship to study in University of Ghana and complete the research
work.
I am most appreciative to my supervisors at WACCI, Prof. Samuel Kwame Offei, Prof. Vernon
Gracen, Prof. Pangirayi Tongoona and Dr. Daniel Dzidzienyo for their contributions to the
completion of this study. I owe my indebt gratitude to my in-country supervisor Dr. Baffour
Badu-Apraku for his invaluable contributions, mentoring and leadership in the course of my
thesis research. My sincere appreciation goes to the WACCI Director, Prof. Eric Yirenkyi
Danquah for his tremendous support and encouragement throughout my study. I am very grateful
to Dr. Edward S. Buckler and all staff and graduate students of the Buckler’s Laboratory,
Institute for Genomic Diversity, Cornell University Ithaca, New York for the use of their
facilities during the genotyping of my quantitative trait loci mapping population even though the
work is inconclusive at the moment.
I am very grateful to the management of National Biotechnology Development Agency
(NABDA) Abuja, Nigeria, especially to the past Director-Generals: Prof. Bamidele Ogba
Solomon and Prof. (Mrs.) Lucy Jumeyi Ogbadu for granting me the opportunity to pursue this
degree. I am also grateful to all the Staff of Stress Tolerant Maize for Africa (STMA) Project,
Maize Improvement Program of the International Institute of Tropical Agriculture (IITA),
Ibadan, Ikenne, Mokwa, Abuja and Bagauda stations for their technical support especially
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Abidemi Talabi for helping out with some of the analyses. My appreciation also goes to Dr.
Melaku Gedil and Nnanna Unachukwu for helping out with the molecular analyses and also to
Dr. Hernan Ceballos for his assistance with the generation mean analysis.
I am very indebted to my parents, Mr. and Mrs. G.O. Akaogu and my siblings for their
encouragement, prayers, love, support and loyalty at all stages of my study and career
development. I am most grateful to my husband, Mr. Ejike Stephen Adoro and my adorable,
amicable daughter Kamsiyochukwu Pearl who endured loneliness to enable me complete my
study. A special thanks to my mum, Mrs. O.C. Akaogu who left behind everything and helped to
take care of my daughter in order to allow me reasonable time for completion of the thesis write-
up. Their sacrifices and understanding are deeply appreciated. I thank God for them and cannot
think of a better family.
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Table of Contents
DECLARATION ............................................................................................................................. i
ABSTRACT .................................................................................................................................... ii
DEDICATION ................................................................................................................................ v
ACKNOWLEDGEMENTS ........................................................................................................... vi
LIST OF TABLES ......................................................................................................................... xi
LIST OF FIGURES ..................................................................................................................... xiii
LIST OF ABBREVIATIONS ...................................................................................................... xiv
CHAPTER ONE ............................................................................................................................. 1
1.0 GENERAL INTRODUCTION ........................................................................................ 1
CHAPTER TWO ............................................................................................................................ 6
2.0 LITERATURE REVIEW ................................................................................................. 6
2.1 Maize production ............................................................................................................... 6
2.2 Constraints to maize production and productivity ............................................................. 8
2.2.1. Parasitic weed Striga (witchweed) ............................................................................. 8
2.2.1.1 Effects of Striga on maize .................................................................................... 9
2.2.1.2 Striga hermonthica control methods .................................................................. 11
2.2.1.3 Novel Striga resistance from Zea diploperennis ................................................ 14
2.2.1.4 Genetics and mechanism of Striga tolerance/resistance in maize ...................... 15
2.2.2. Effects of drought on maize ..................................................................................... 17
2.2.2.1 Genetics and mechanism of drought tolerance in maize .................................... 18
2.3 Contribution of secondary traits to yield improvement under Striga infestation and
drought ............................................................................................................................... 20
2.4 Application of DNA markers in maize research with emphasis on resistance/tolerance to
Striga and drought ................................................................................................................. 22
Chapter Three ................................................................................................................................ 27
3.0 GENETIC DIVERSITY OF EARLY MATURING MAIZE INBREDS CONTAINING
Striga RESISTANCE GENES FROM Zea diploperennis ........................................................ 27
3.1 Introduction ..................................................................................................................... 27
3.2 Materials and methods ..................................................................................................... 28
3.2.1 Genetic materials ...................................................................................................... 28
3.2.2 Experimental locations and field layout ................................................................... 30
3.2.3 Data collection .......................................................................................................... 31
3.2.4 Data analyses ............................................................................................................ 33
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3.2.5 Molecular analysis .................................................................................................... 35
3.2.5.1 DNA extraction and genotyping using diversity arrays technology (DArT)
platform .......................................................................................................................... 35
3.2.5.2 Genetic diversity analysis ................................................................................... 36
3.2.5.3 Population structure analysis of inbred lines ...................................................... 36
3.3 Results ..................................................................................................................... 37
3.3.1 Performance and reaction of early maturing inbreds under Striga infested and
drought environments ..................................................................................................... 37
3.3.2 Genetic diversity assessment of the inbred lines ................................................... 42
3.3.3 Inter-trait relationship of the inbred lines under drought and Striga infestation ... 46
3.4 Discussion .................................................................................................................... 49
3.5 Conclusions .................................................................................................................. 52
Chapter Four ................................................................................................................................. 53
4.0 INHERITANCE OF RESISTANCE TO Striga hermonthica IN AN EARLY
MATURING MAIZE (Zea mays L.) INBRED LINES CONTAINING RESISTANCE GENES
FROM Zea diploperennis ........................................................................................................ 53
4.1 Introduction ..................................................................................................................... 53
4.2 Materials and methods ..................................................................................................... 54
4.3 Results ............................................................................................................................. 57
4.4 Discussion ................................................................................................................ 62
4.5 Conclusion ....................................................................................................................... 64
Chapter Five .................................................................................................................................. 65
5.0 COMBINING ABILITY AND HETEROTIC GROUPING OF Striga RESISTANT
AND DROUGHT TOLERANT EARLY MATURING MAIZE INBRED LINES AND
THEIR PERFORMANCE IN HYBRID COMBINATIONS ................................................... 65
5.1 Introduction ..................................................................................................................... 65
5.2 Materials and methods ..................................................................................................... 66
5.2.1 Genetic materials ...................................................................................................... 66
5.2.2 Field evaluation ......................................................................................................... 68
5.2.3 Data collection .......................................................................................................... 68
5.2.4 Data analyses ............................................................................................................ 69
5.3 Results ............................................................................................................................. 76
5.3.1 Performance of early maturing maize inbred lines under contrasting environments 76
5.3.3 General combining ability effects ............................................................................. 84
5.3.4 Performance and stability analysis of early maturing hybrids under Striga-infested,
drought and optimal environments .................................................................................... 90
5.3.5 Relationship between performance of parental inbred lines and their hybrids ....... 103
5.3.6 Interrelationship between traits under the different research conditions ................ 103
5.3.6.1. Relative importance of secondary traits to grain yield under Striga infestation and
drought ............................................................................................................................. 108
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5.3.7 Heterotic grouping of the early maturing inbred lines ............................................ 111
5.4 Discussion ...................................................................................................................... 113
5.5 Conclusions ................................................................................................................ 122
CHAPTER SIX ........................................................................................................................... 125
6.0 CONCLUSIONS AND RECOMMENDATIONS ...................................................... 125
6.1. General Conclusions ..................................................................................................... 125
6.2 Recommendations ......................................................................................................... 127
BIBLIOGRAPHY ....................................................................................................................... 129
APPENDICES ............................................................................................................................ 149
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LIST OF TABLES
Table 3.1: Pedigree of the 36 white early maturing maize Inbred lines used in the diversity study
........................................................................................................................................ 29
Table 3.2: Mean squares from analysis of variance for grain yield and other traits of 36 early
maturing white maize inbred lines screened under artificial Striga infestation at Mokwa
and Abuja, drought stress at Bagauda and Ikenne and across research conditions during
2014 and 2015 growing seasons. .................................................................................... 38
Table 3.3: Grain yield and other traits of the 36 early maturing white Zea diploperennis inbreds
screened under Striga infestation at Mokwa in 2014, Abuja and Mokwa in 2015. ....... 40
Table 3.4: Grain yield and other traits of the 36 early maturing white Zea diploperennis inbred
lines evaluated under drought stress at Ikenne in 2014, Bagauda and Ikenne in 2015. . 41
Table 3.5: Correlation coefficients between genetic distance (GD) and traits used as indices for
Striga resistance in early maturing maize inbred lines. .................................................. 45
Table 3.6: Correlation coefficients between genetic distance (GD) and traits used as indices for
drought tolerance in early maturing maize inbred lines. ................................................ 45
Table 4.1: Means of Striga damage and number of emerged Striga plants of the six generations
(P1, P2, F1, F2, BC1P1, and BC1P2) evaluated at Mokwa and Abuja in 2015 .................. 58
Table 4.2: Estimates of genetic components of means for Striga damage and number of emerged
Striga plants at Mokwa and Abuja in 2015 obtained by the weighted least square
(Mather and Jinks, 1982) ................................................................................................ 59
Table 4.3: Significant gene effects by weighted least square (Mather and Jinks, 1982) for Striga
traits and adequacy of associated models ....................................................................... 61
Table 5.1: Pedigree of the selected 30 white early maturing maize inbred lines used in North
Carolina Design II study ................................................................................................. 67
Table 5.2: Environments, locations, research conditions and years of evaluation of early
maturing maize hybrids under Striga-infested, drought and optimal environments in
Nigeria. ........................................................................................................................... 74
Table 5.3: Mean squares for grain yield and other agronomic traits of 156 early maturing maize
hybrids screened under Striga-infested environments at Abuja and Mokwa in 2013 and
2015. ............................................................................................................................... 77
Table 5.4: Mean squares for grain yield and other agronomic traits of 156 early maturing maize
hybrids screened under drought environments at Ikenne and Bagauda in 2013 and at
Ikenne, Bagauda and Minjibir in 2015 ........................................................................... 79
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Table 5.5: Mean squares for grain yield and other agronomic traits of 156 early maturing maize
hybrids evaluated under optimal environments at Ikenne, and Mokwa in 2013 and 2015
and at Abuja in 2015 ....................................................................................................... 80
Table 5.6: Mean squares for grain yield and other agronomic traits of 156 early maturing maize
hybrids evaluated across Striga-infested, drought and optimal environments in 2013 and
2015 ................................................................................................................................ 81
Table 5.7: Proportion (%) of the sums of squares for crosses attributable to general (GCA), and
specific combining ability (SCA) for grain yield and other agronomic traits of early
white maize inbred lines. ................................................................................................ 83
Table 5.8: General combining ability effects of early maturing inbred lines evaluated under
Striga-infested environments .......................................................................................... 85
Table 5.9: General combining ability effects of 30 early maturing maize inbred lines evaluated
under drought environments ........................................................................................... 87
Table 5.10: General combining ability effects of early maturing maize inbred lines evaluated
across optimal environments .......................................................................................... 89
Table 5.11: Grain yield and other agronomic traits of selected hybrids evaluated under artificial
Striga infestation (STR) at Mokwa and Abuja and under optimal growing conditions
(OPT) at Ikenne, Abuja and Mokwa in 2013 and 2015. ................................................. 91
Table 5.12: Grain yield and other traits of some hybrids evaluated under drought stress (DT) at
Ikenne and Bagauda and under optimal growing conditions (OPT) at Ikenne, Abuja and
Mokwa in 2013 and 2015. .............................................................................................. 93
Table 5.13: Grain yield and other traits of selected hybrids screened across Striga-infested,
drought and optimal conditions at 13 environments in 2013 and 2015. ......................... 94
Table 5.14: Average mid and high parent heterosis for grain yield and other agronomic traits
under Striga-infested and drought environments ......................................................... 104
Table 5.15: Estimates of genotypic (above diagonal) and phenotypic (below diagonal) correlation
coefficients for grain yield and other agronomic traits evaluated under artificial Striga
infestation in Mokwa and Abuja in 2013 and 2015. ..................................................... 105
Table 5.16: Estimates of genotypic (above diagonal) and phenotypic (below diagonal) correlation
coefficient for grain yield and other agronomic traits of maize inbred lines evaluated
under drought stress in Bagauda, Ikenne and Minjibir in 2013 and 2015. ................... 107
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LIST OF FIGURES
Figure 2.1: Changes in maize production in different continents of the world from 1963 to 2013
(FAOSTAT, 2012) ............................................................................................................ 7
Figure 3.1: Dendrogram of the 34 inbred lines based on Nei’s genetic distance estimated from
8145 SNP markers. ......................................................................................................... 43
Figure 3.2: Population structure of 34 early maturing maize inbred lines based on 8145 SNP
markers for k=3. .............................................................................................................. 44
Figure 3-3: Path analysis diagram displaying the relationship between traits of the inbreds
screened under drought conditions at Ikenne and Bagauda, 2014 – 2015 ...................... 47
Figure 3-4: Path analysis diagram displaying the relationship of traits of the inbreds screened
under artificial Striga infestation at Abuja and Mokwa 2014 – 2015 ............................ 48
Figure 5.1: Mean performance and stability of selected early maturing maize hybrids in terms of
grain yield as measured by principal components across three Striga-infested
environments in Nigeria between 2013 and 2015. ......................................................... 96
Figure 5.2: Mean performance and stability of selected early maturing maize hybrids in terms of
grain yield as measured by principal components across five drought environments in
Nigeria between 2013 and 2015. .................................................................................... 98
Figure 5.3: Mean performance and stability of selected early maturing maize hybrids in terms of
grain yield as measured by principal components across five optimal environments in
Nigeria between 2013 and 2015. .................................................................................. 100
Figure 5.4: Mean performance and stability of selected early maturing maize hybrids in terms of
grain yield as measured by principal components across eight stressed and five non-
stressed environments in Nigeria between 2013 and 2015. .......................................... 102
Figure 5.5: Path analysis diagram displaying relationship of measured traits of early maturing
inbred lines screened under drought at Ikenne, Minjibir and Bagauda, 2013 and 2015.
...................................................................................................................................... 109
Figure 5.6: Path analysis diagram displaying relationship of measured traits of early maturing
inbred lines screened under artificial Striga infestation at Abuja and Mokwa, 2013 and
2015. ............................................................................................................................. 110
Figure 5.7: Dendrogram of 30 early maturing maize inbreds constructed from GCA effects of
multiple traits (HGCAMT) using cluster analysis based on Ward’s minimum variance
across research environments. ...................................................................................... 112
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LIST OF ABBREVIATIONS
ABA: Abscisic Acid
AFLP: Amplified Fragment Length Polymorphism
ALS: Acetolactate Synthase
AMMI: Additive Main Effects and Multiplicative Interaction
ANOVA: Analysis of Variance
CIMMYT: International Maize and Wheat Improvement Center
CTAB: Cetyl Trimethyl Ammonium Bromide
DNA: Deoxyribonucleic acid
FAO: Food and Agriculture Organization
GCA: General Combining Ability
GD: Genetic Distance
GGE: Genotypes plus Genotypes by environments interactions
GEI: Genotype by environment interaction
GMA: Generation Mean Analysis
HGCAMT: Heterotic grouping based on GCA of Multiple Traits
HSGCA: Heterotic groups’ based on Specific and General Combining Ability
IITA: International Institute of Tropical Agriculture
IPCA: Interaction Principal Component Axes
NC II: North Carolina Design II
NCRPIS: North Central Regional Plant Introduction Station
PIC : Polymorphic Information Content
QTL : Quantitative Trait Loci
RAPD : Randomly Amplified Polymorphic DNA
RFLP : Restriction Fragment Length Polymorphism
SAS : Statistical Analytical Software
SCA: Specific Combining Ability
SNP: Single Nucleotide Polymorphisms
SNP-GD: Single Nucleotide Polymorphism based Genetic Distance
SSA: Sub-Saharan Africa
SSR: Simple Sequence Repeats
USA: United States of America
WAP: Weeks after Planting
WCA: West and Central Africa
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CHAPTER ONE
1.0 GENERAL INTRODUCTION
Maize (Zea mays L.) is a food security crop for millions of populace in sub-Saharan Africa
(SSA). It is commonly grown at subsistence levels by African farmers in the rural areas of the
sub-region (Shiferaw et al., 2011). It is ranked the third principal cereal after Oryza sativa L. and
Triticum aestivum L. in terms of its harvest area, nutritional value and consumption. All parts of
the crop can be used for food and non-food products (IITA, 2009). In developed nations such as
the United States of America (USA), the highest producer in the world with grain yield of about
7 tons per hectare, 37% of the maize produced is used largely for biofuel and 33% for livestock
feed. In contrast, Nigeria is the largest producing country in Africa with an average grain yield of
less than 2 ton/ha (FA0, 2012) and 80% of maize production is for human consumption.
The significance of maize in the savannas of SSA has been rapidly growing in the last twenty
years replacing traditional cereals such as millet (Pennisetum glaucum (L.) R.Br.) and sorghum
(Sorghum bicolor (L.) Moench) in regions of the savannas with good access to markets and
agricultural inputs such as fertilizers (Badu-Apraku and Fakorede, 2003). In addition to its high
yield potential and responsiveness to fertilizer, it has the tendency for alleviating the food
insecurity and malnutrition problems caused by the high levels of urbanization in West and
Central Africa (WCA) countries (Byerlee and Eicher, 1997). The increase of maize production in
the sub-region is largely due to the fact that the savannas, which are the maize belts of WCA, are
characterized by high incidence of in-coming radiation, low night temperatures and low humidity
as well as low pest and disease pressure. Despite the implementation of the Agricultural
Structural Adjustment Programmes and the huge potential of maize as an industrial and food
crop in the sub-region, its production and productivity have not met the increasing demands of
human population due to lack of improved seeds, unavailability/high cost of inorganic fertilizers
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and herbicides, and poor crop management practices as well as increasing levels of biotic and
abiotic stresses (Heerink, 2005). The abiotic stresses include recurrent drought and poor soil
fertility while the biotic stresses include insects such as stem borers, army worms and parasitic
weeds such as Striga spp and foliar diseases. Of these stress factors, infestation by Striga
hermonthica (Del.) Benth and drought are the two important stresses limiting maize (Zea mays
L.) production in WCA. Under field conditions, the two stresses can occur simultaneously
resulting in 85% yield reduction (Adetimirin et al., 2000). Therefore, there is a need to breed for
stress-resilient maize for the sub-region.
Three species namely Striga hermonthica, Striga asiatica and Striga aspera attack maize but
only the first two are of major economic importance in the savannas of WCA (Berner et al.,
1996). About 100% yield losses due to Striga hermonthica could be obtained depending on the
soil fertility, the type of genotype grown, severity of infestation and the prevailing environmental
conditions (Lagoke et al., 1991; Berner et al., 1996). Farmers have been forced to abandon their
fields heavily infested by Striga (Ejeta, 2007a). Several measures have been proposed for
reducing the effects of Striga including the use of resistant varieties, catch crops, crop rotation,
intercropping and high amount of nitrogen fertilizer. However, genetic resistance is the most
efficient and ecosystem friendly and sustainable approach for African farmers (Rich and Ejeta,
2008; Akaogu et al., 2012).
During the last two decades changes in the climatic conditions, movement of maize to marginal
areas by high value crops, low soil organic matter, water retention and soil infertility has
increased the occurrence of drought (Banziger et al., 2000). Effects of drought include delayed
silk emergence which gives rise to extended anthesis-silking interval, reduction in the quantity of
pollen shed, tassel blasting and ear barreness resulting in total crop failure (Banziger et al.,
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2000). According to Edmeades et al. (1995) average annual maize yield reduction of 15% occurs
in the savanna of WCA as a result of drought stress. About 40 to 90% reduction in grain yield
was recorded when moisture stress occurred from a few days before anthesis to the start of grain
filling (Menkir and Akintunde, 2001; Badu-Apraku et al., 2011a). Several chemical and cultural
methods have been used for Striga control but have proved ineffective and unsustainable for the
famers in the sub-region. The use of host plant resistance which is more sustainable (Rich and
Ejeta, 2008) has not been completely effective due to the limited knowledge on the genetic
factors controlling the life span of the parasite, the polygenic nature of Striga resistance and
complex genotype x environment interactions (Scholes and Press, 2008). Most resistance to
Striga spp. appears to be polygenic and quantitatively inherited. The International Institute of
Tropical Agriculture (IITA) maize programme has used resistance genes from diverse
germplasm sources including temperate and tropical materials identified following numerous
years of wide testing in the savannas of WCA. However, the Striga resistance genes have not
been as effective as desirable in the control of Striga because they allow the flowering and seed
production of the Striga plants thereby increasing the Striga seed bank in the soil. Consequently,
there has been an exploration for novel genes for resistance to Striga hermonthica in the wild
relative of maize, Zea diploperennis (Lane et al., 1997; Amegbor et al., 2017). Several early
maturing inbreds have been developed containing the novel Striga resistance genes from Z.
diploperennis in IITA. However, the combining ability and heterotic groups of the inbreds have
not yet been determined.
Hybrid development, promotion and adoption are promising strategies for significantly
increasing maize production and productivity and for revolutionalizing agriculture in WCA
(Akaogu et al., 2012). A number of seed companies have emerged in the sub-region during the
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last decade giving way to commercial hybrid production. However, there are limited early
maturing hybrids with combined tolerance/resistance to Striga and drought commercialized for
the areas of the savanna agro-ecologies with a short growing period. For successful hybrid
production and commercialization, information on the combining abilities and the heterotic
groupings of the inbreds are of primary importance to plant breeders. Identification of inbred
lines which show heterosis under drought and Striga infestation is an important step towards
addressing the challenges posed by these constraints. Information on heterotic patterns of the
lines can be obtained from crosses using different mating designs including the diallel, line x
tester and North Carolina (NC) II designs. However, each of these mating designs has its
limitations. When more than 20 inbred lines are involved for crossing, it is impracticable to use
diallel analysis to elucidate information on the heterotic patterns (Melchinger, 1999). However,
line x tester and NC II designs are suitable. When there are no testers of known heterotic
classification available, it is difficult to use the line x tester design analysis. However NC II
design can be used to sample a large number of inbred lines by dividing them into smaller groups
or sets. Studies have shown that classification of inbred lines using the specific combining ability
(SCA) of grain yield alone has not been a reliable method because the heterotic groups of inbred
lines is affected by the evaluation environments (Menkir et al., 2003). For example, Fan et al.
(2009) reported that the SCA effects of grain yield were often greatly influenced by the
environments, which often at times lead to assigning the same inbred line into different heterotic
groups under different studies. Therefore several other heterotic grouping methods such as
molecular markers (Reif et al., 2003; Flint-Garcia et al., 2009) and based on general combining
ability of multiple traits (HGCAMT) have been proposed (Badu-Apraku et al., 2013a).
Most tropical maize lines share some common parentage and Striga resistance gene sources,
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their exclusive use in hybrid development due to their novel Striga resistance gene could
heighten the risk of narrow genetic base in maize production field. Therefore, systemic
exploitation of these maize lines that will guarantee future gain from selection and minimize
narrow genetic base, requires thorough assessment of genetic relatedness in these maize lines
before their utilization in breeding programs. Liu et al. (2003) suggested that, utilization of
desirable genes from diverse sources of maize inbreds requires a total understanding of the
genetic and pedigree relationships, and also the genetic variation among them. Knowledge of the
genetic relatedness among inbred lines using molecular markers would help select inbreds that
have adequate genetic diversity, and optimized the discovery of single nucleotide polymorphisms
(Liu et al., 2003). There is lack of consistency between the hybrid performance and the genetic
distance (GD) derived from the molecular markers (Romay et al., 2013).
The objectives of the study were to:
(i) assess the genetic diversity and reaction of early maturing maize inbred lines
under Striga infestation and drought environments
(ii) determine the mode of inheritance of Striga hermonthica resistance in early
maturing maize containing Striga resistance genes from Zea diploperennis,
(iii) determine the combining abilities for yield and other agronomic traits and
heterotic groups of early maturing white Striga resistant and drought tolerant
maize inbreds,
(iv) identify high yielding and stable hybrids under Striga-infested, drought and
optimal growing conditions and
(v) assess the inter-trait relationship of early maturing maize hybrids under Striga-
infestation and drought.
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CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Maize production
Maize (Zea mays L.), known as corn in the USA, is a cross pollinated crop in the family
Poaceae. It originated from Mexico and Central America and has several wild species
including teosinte (Z. perennis, Z. nicaraguensis, Z. luxurians and Z. diploperennis) in the
genus Zea (Doebely, 1990). Maize is ranked the third most consumed grain cereal after
Triticum aestivum L. and Oryza sativa L. in the world (FAO, 2011). During the past twenty
years in WCA, maize has become a major food and cash crop and has witnessed rapid
increases in production (Fakorede et al., 2003). In Nigeria, maize is the second most consumed
cereal after rice. Its high energy content makes it essential in both human and animal diets;
hence it is rapidly replacing traditionally grown cereals such as Pennisetum glaucum (L.) R.Br
and Sorghum bicolor (L.) Moench in areas of the savannas with good fertilizer inputs and
markets (Badu-Apraku and Fakorede, 2003).
The total annual world production of maize is estimated to be about 883 million tons with the
USA as the leading producer (Fig. 2.1). In West Africa, Nigeria produces 9.2 million tons out
of the overall production of 16 million tons. However, the average grain yield of maize in
Nigeria is less than 2 tons per hectare (FAO, 2012) in comparison to USA which produces
about 7 tons per hectare.
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400
USA
350 China
300 Brazil
Africa
250 West Africa
200
150
100
50
0
1963 1973 1983 1993 2003 2013
Figure 2.1: Changes in maize production in different continents of the world from 1963 to 2013
(FAOSTAT, 2012)
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2.2 Constraints to maize production and productivity
The savannas of WCA, which is the maize belt of the region beacause of favourable climatic
conditions such as high incoming radiation, low prevalence of pests and diseases, and low
night temperatures is greatly constrained by several biotic and abiotic stresses, including
infestation by Striga hermonthica, recurrent drought, low soil fertility, heat stress, foliar
diseases, army worms and stem borers (Badu-Apraku et al., 2017). Among these stresses,
infestation by Striga hermonthica and recurrent drought are the most prominent living and non-
living factors limiting maize production and productivity in the savannas of WCA. Under field
conditions, Striga parasitism and drought occur simultaneously with severe consequences
(Badu-Apraku et al., 2010). Adetimirin et al. (2000) reported that drought has geometrically
increased the yield losses caused by Striga; while drought and Striga reduced average yield of
four maize hybrids by 27% and 53.7%, respectively, the combined effect of the two stresses
reduced maize yield in the hybrids by 85.5%. Therefore, for high and sustainable production
and productivity of maize in WCA, researchers need to address simultaneously the problems
posed by the two important stresses viz: Striga and drought.
2.2.1. Parasitic weed Striga (witchweed)
The genus Striga, known as witchweed, is an obligate parasite in the family Scrophulariaceae.
It depends completely on its host for its nutrients and water without contributing anything
beneficial in return. The genus Striga has about 28 species which parasitize both monocot and
dicot plants (Estep et al., 2011). The crops infested by Striga species in Africa are Sorghum
bicolor, Zea mays, Pennisetum glaucum, Eleusine coracana, Oryza glaberrima, Oryza sativa,
Vigna unguiculata and Saccharum officinarum (Rodenburg et al., 2006; Scholes and Press,
2008). The most common of these species which infest cereals are S. hermonthica (Del.)
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Benth., S. forbesii Benth, S. aspera (Willd.) Benth, S. asiatica (L.) Kuntze while S. gesneriodes
(Willd.) Vatke infests cowpea and tobacco (Estep et al., 2011). Across environments, there can
be different biotypes of S. hermonthica, an outcrossing species (Kim, 1994) which produces
more than 200,000 seeds per plant (Parker and Riches, 1993). The Striga seed banks are built
up by continuous growing of the host plants (Kunisch et al., 1991) and the seed can remain
dormant for more than 10 years in the soil (Gbèhounou, et al., 2003; Hearne, 2009) and cause
total crop failure resulting in yield losses depending on the prevailing climatic conditions and
the host plant (Parker and Riches, 1993). The growth of Striga plants on the host are
conditioned by chemical stimulants known as Strigolactones which are produced by the maize
plants under stress conditions (Bouwmeester et al., 2003). The Striga plants germinate from the
soil in response to the chemical exudates (Strigolactones) produced in the roots of the maize
plants (Matusova et al., 2005). Following germination, the Striga plants cannot survive except
by attachment to the host using haustoria to derive its water and nutrients for growth and
development. The Striga plants flower and set seeds about six weeks after emergence and
remain attached to the host, thereby increasing its seed bank. Prevailing climatic factors
determine the success of the parasitism between Striga and its host which are controlled by
several genetic and physiological processes (Ejeta, 2007b). The yield reduction in the host
plant is caused by phytotoxic effects which favour the source-sink partitioning of the
assimilates into the roots rather than the shoot, thus reducing grain yield (Ransom et al., 1996).
2.2.1.1 Effects of Striga on maize
S. hermonthica (Del.) Benth, S. aspera (Willd.) Benth, and S. asiatica (L.) Kuntze, are the
common species in WCA. S. hermonthica is the most prevalent and devastating of the three
affecting food grain with maize as the chief host (Berner et al., 1995; Yoshida, et al., 2010). In
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Africa, more than 24% of the total land cultivated with maize are heavily infested by Striga (de
Groote et al., 2008). In West Africa, about 64% of the cereal production areas are Striga
infested (Gressel et al., 2004) while 70% of Nigeria’s arable land is lost to Striga hermonthica
parasitism (Hartman and Tanimonure, 1991). Other cereals infested by S. hermonthica are
pearl millet (Pennisetum glaucum), finger millet (Eleusine coracana), sorghum (Sorghum
bicolor), and upland rice (Oryza sativa). Average yield reduction in cereals caused by Striga
infestation are estimated to be about 20 - 80% (Atera et al., 2011). Striga species totally
depend upon their hosts for food and water during growth below the soil surface. When they
appear above the soil surface, they produce chlorophyll and become semi-parasitic,
manufacturing some of their own food but continuing to depend largely upon the host (Stewart
et al., 1991). Striga causes reduction in host growth with effects being more severe under
limited soil moisture and nutrients. Other damage to host crops, especially maize, includes
chlorotic blotches, leaf scorching (firing) particularly around the margins of the leaves, leaf
drying resembling the situation under moisture stress (wilting), stunted growth, poor pollen
production and barreness. The severity and nature of damage depend on time of host
infestation. Early infestation causes reduction in plant height and yield, while late infection
reduces yield components such as cob length, cob diameter, ear and seed weight (Kim, 1991).
Striga reduces host plant growth via two processes. The first is competition for carbon by
direct transfer of the host plant carbon to the parasite. The second is impairment of
photosynthesis through a less efficient use of light in the fixation of carbon dioxide (Stewart et
al., 1991). Apart from the translocation of photosynthate carbon, there is movement of other
non-photosynthate compounds especially organic nitrogen. Synthesis of organic nitrogen
compounds occurs in the host roots and depends on a supply of sucrose from the host shoot and
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inorganic nitrogen from the soil. In sorghum and maize, decline in photosynthesis occurs
before the emergence of Striga and accounts for 80% of yield loss (Stewart et al., 1991).
Similar observations of reduction in crop growth prior to parasite emergence have been made
in cowpea (Emechebe et al., 1991). In WCA, farmers have abandoned their heavily Striga
infested fields (Ejeta, 2007a) which is a major biotic constraint limiting cereal production of
resource poor farmers. This Striga menace is aggravated by inherent soil infertility, recurrent
drought, lack of crop rotation, short fallow periods, and continuous cultivation of marginal
lands with little fertilizer input (Menkir et al., 2001). Striga parasitism is less severe in the
presence of good growing conditions such as high soil nitrogen and sufficient rainfall which is
not obtainable in Africa (Rich and Ejeta, 2008).
2.2.1.2 Striga hermonthica control methods
The effect of Striga parasitism on maize is determined by several factors such as ability to
withstand the parasite, inoculum density of Striga seed in the soil, the level of soil fertility and
amount of soil moisture (Kim and Adetimirin, 1997a; Kim and Adetimirin, 1997b). Several
control measures are available such as hand picking, crop rotations, use of trap crops, high
nitrogen fertilizer application to increase soil fertility (Oswald, 2005; Van Ast et al., 2005; Joel
et al., 2007) and the use of tolerant/resistant varieties (Badu-Apraku et al., 2004a; Menkir et
al., 2012, Badu-Apraku et al., 2013a). Also, intercropping with forage legume Desmodium
uncinatum (Khan et al., 2007) and use of herbicide coating on maize seeds (imidazolinone) are
used as control measures (Kanampiu et al., 2003). Despite all the numerous control strategies
aforementioned, none can completely eradicate the Striga infestation on maize fields (Oswald,
2005). Control strategies such as crop rotation, intercropping, and prolonged fallow help to
build the soil structure and fertility which combats the Striga menace (Kureh et al., 2000).
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However, the rapid increase in human population has given rise to rigorous land use with
reduced fallow periods (Webb et al., 1993), and in some regions, endless cereal cropping with
slight or zero application of both organic and inorganic fertilizer (Van Ast et al., 2005). This
has resulted in the decline of soil fertility and therefore reduced the success of the control
methods (Berner et al., 1996). The use of catch crop such as cotton which produces
germination stimulants that can cause spontaneous germination of Striga seeds in fields
previously infested, thereby decreasing its seed bank in the soil. Effective hand picking of the
mature Striga plants before they flower and produce seed can decrease the quantity of Striga
seeds in the soil, without appreciably increasing the crop yield (Verkleij and Kuiper, 2000)
because serious damage to the maize crop would have been done before the Striga plant
emerges from the soil (Parker and Riches, 1993). Furthermore, hand weeding is painstaking,
time consuming and impossible to use for Striga control on a large scale. In Kenya, the use of
herbicide coating imazapyr (imidazolinone) and pyrithiobac on the maize seeds increased grain
yield by 17% when grown in Striga-infested soils (Abayo et al., 1998). Dicamba (3, 6-
dichloro-methoxybenzoic acid), when applied at the time of Striga attachment, suppresses
emergence of the Striga plants and provides some yield protection (Odhiambo and Ransom,
1993). However, dicamba does not give the consistent control essential to make it cost
operative (Abayo et al., 1998). Although, the use of herbicides that can block acetolactate
synthase (ALS) pathway is an effective control method of parasitic plants, there is the risk of
mutation of traits conferring resistance to ALS-inhibiting herbicide. The use of ethylene gas in
Striga-infested soil to cause spontaneous germination of the Striga seeds in the absence of the
host before planting of crops is another effective Striga control method but very costly and the
African resource poor farmers can not afford it (Parker and Riches, 1993).
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Host-plant resistance, exemplified by a reduced level of Striga attachment to the host, or
tolerance, which is the potential of the host plant to produce a reasonably high yield despite
infestation, is the most economical, efficient and most environmentally-sustainable method in
Africa for decreasing the severe damage caused by Striga (DeVries, 2000; Badu-Apraku et al.,
2004a). Several maize populations, hybrids, open-pollinated varieties, and inbred lines with
Striga resistance or tolerance have been commericalized in WCA (Badu-Apraku et al., 2004a;
Badu-Apraku et al., 2005; 2007a; Menkir et al., 2012). Novel Striga resistance genes were
found in the wild maize relative, Z. diploperennis, and the resistance genes have been
introgressed into cultivated maize of tropical adaptation (Menkir, 2006; Amusan et al., 2008).
The maize plants have expressed resistance to Striga hermonthica through different
mechanisms including low or no stimulation of chemical exudates; production of strigolactones
which facilitates Striga seed germination (Kiruki et al., 2006); prevention of the attachment of
the parasites to its roots or death of the attached parasites resulting in fewer Striga plants and
production of yield higher than those of the susceptible genotypes (Badu-Apraku and
Akinwale, 2011). Striga tolerant genotypes support as many Striga plants as the intolerant
genotypes but produce more yield and show reduced damage syndrome than the susceptible
genotypes (Kim, 1994; Badu-Apraku et al., 2010). The use of Striga-resistant cultivars reduces
parasite seed reproduction and this depletes the Striga seed bank (Haussmann et al., 2004;
Badu-Apraku and Lum, 2007).
It has been documented that Striga control is most effective when a number of individual
technologies are combined in an integrated Striga control programme. This provides
sustainable approach over widespread of physical environments (Ellis-Jones et al., 2004). In
the USA, Striga damage has been brought under control with the aid of herbicides, ethylene
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injection, fumigation and application of very high amounts of ammonium nitrate fertilizer
(Doggett, 1984). A 46% decrease in Striga seed bank in the soil and 88% improvement in crop
productivity were observed with the integrated Striga control approach (Franke et al., 2006).
Adequate N fertilizer and herbicide applications are effective at reducing crop damage by
Striga as well as reducing Striga emergence (Kim, 1991; Mumera and Below, 1993). Striga
resistant maize grown after planting of soybean increased the benefit in two cropping seasons
by 100% when compared with continuous maize (Ellis-Jones et al., 2004). With all the control
methods available, the Striga problem still persists. The use of maize varieties that are Striga
and drought resistant/tolerant in an integrated Striga control approach will be the most
economically viable and practical approach for the farmers in WCA.
2.2.1.3 Novel Striga resistance from Zea diploperennis
Zea diploperennis is a wild relative of maize mostly found in the Sierra de Manantlán
Biosphere Reserve of Mexico and has a large economic potential for the improvement of
cultivated maize (Sánchez-Velásquez, 2002). Parker and Riches (1993) reported that resistance
genes may not exist in domesticated maize to combat the biotic stresses limiting maize
production. Consequently, there has been a search for novel genes for Striga resistance in the
wild relative of maize, Zea diploperennis (Lane et al., 1997; Amegbor et al., 2017). Novel
sources of Striga resistance were found in the wild maize, Zea diploperennis (teosinte) and
Trypsacum dactyloides as well as in land races and have been introgressed into maize (Kling et
al., 2000; Hearne, 2009). This has resulted in the development and registration of a Striga
resistant inbred line, TZSTR1108 (Menkir et al., 2006).
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2.2.1.4 Genetics and mechanism of Striga tolerance/resistance in maize
Improvement and utilization of Striga resistant varieties in WCA has been gradual because of
unavailable durable sources of resistance, and insufficient knowledge of the mechanism and
genetics of Striga resistance and/or tolerance. Information on genetic basis of resistance to
Striga is essential for breeders in their selection processes among segregating populations.
Striga tolerance refers to the capability of the host plant to survive and produce appreciable
yield in the presence of the attached Striga plants while Striga resistance is the ability of the
host plant to suppress the germination of the parasites, leading to reduced Striga emergence
(Kim, 1994). The complexity of the host parasite and environment interactions is the main
limiting factor in the development and deployment of Striga resistant varieties (Ejeta, 2007b).
The limited progress made so far in the improvement and commericalization of Striga-resistant
cultivars is mostly attributable to the scarce sources of resistance, the complexity of inheritance
of resistance, and scant knowledge about specific mechanisms associated with expression of
resistance in maize to the parasite (Amusan et al., 2008). Selection for host plant damage
symptoms and reduced Striga emergence has been effective in developing inbred lines, hybrids
and open-pollinated varieties with tolerance/resistance to S. hermonthica (Menkir et al., 2012,
Badu-Apraku et al., 2013a). Striga damage is used as the index of tolerance while the number
of emerged Striga plants is used as the index of resistance. According to DeVries (2000),
Striga tolerance is measured by the host damage on a scale of 1–9, where 1 = most tolerant and
9 = highly sensitive.
Several authors have reported that resistance to Striga hermonthica is polygenic and the
secondary traits for indirect selection for high grain yield under Striga infestation and breeding
for Striga tolerance and resistance are Striga damage and number of emerged Striga plants
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(Kim, 1994; Ejeta et al., 1997; Badu-Apraku et al., 1999). Contradictory results have been
reported on the gene action regulating grain yield, number of emerged Striga plants, and Striga
damage. Under Striga infestation, additive gene action played a more vital role than non-
additive gene action in controlling Striga damage and yield (Berner et al., 1995; Badu-Apraku
et al., 2007a). However, in other studies, dominance gene action was more important than the
additive in regulating the host plant damage while additive gene action played a vital role in the
control of the number of emerged Striga plants (Gethi and Smith, 2004; Badu-Apraku et al.,
2007b; Yallou et al., 2009). Knowledge and understanding of gene actions including additive,
dominance gene effects (a and d) and the three types of digenic interactions, that is, additive x
additive (aa), additive x dominance (ad) and dominance x dominance (dd) are very vital in
planning effective and efficient gene deployment schemes in a resistance development
programme.
Inbred lines of maize (TZi 3 and TZi 12) with resistance to Striga were first identified at IITA
in 1983 (Kim, 1991). These maize inbreds express tolerance to infection. This is characterized
by a reduced damage in the field despite a high Striga infection at the early stages of maize
growth (Buiel and Parlevliet, 1996). Various breeding methods have been employed to increase
Striga resistance in maize. At IITA, recurrent selection and inbred-hybrid methods have been
used to transfer favourable genes for Striga resistance in maize. However, farmers in the Striga
endemic zones of WCA are currently asking for varieties that possess resistance to multiple
stresses and are reluctant to accept maize varieties that do not meet this requirement (Badu-
Apraku et al., 2010). Therefore, there is a crucial need for introgressing drought and low N
tolerance genes into maize cultivars for increased productivity under Striga infestation since
the three stresses occur simultaneously in the field with adverse effect on yield
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2.2.2. Effects of drought on maize
Drought is rainfall deficit, leading to a protracted departure from normal water availability. The
effect of drought is very high in the savannas of WCA because of the unreliable and erratic
distribution of rainfall (Eckebil, 1991). Drought is caused by less than normal precipitation
over a period of time, usually a season or more to the extent that the amount of water available
in the soil for crop growth and development is not within reach. Drought is the second main
abiotic factor limiting the production and productivity of maize in WCA after poor soil fertility
(Badu-Apraku et al., 2010). On the average, It occurs two to three times each decade in sub-
Saharan Africa (DNRP-GAPCC, 2000) and is usually accompanied by climatic conditions such
as high temperatures, high wind, and low relative humidity, excess water loss through
evaporation and transpiration in many regions. The demand for food production in marginal
and semi-arid regions which are drought-prone will increase as a result of population growth as
well as global warming and ozone depletion (Curry et al., 1995) which have changed climatic
conditions leading to irregular, unreliable quantities and distribution of rainfall resulting in
several billions of USA dollars lost annually to drought (Badu-Apraku et al., 2011a). Drought
may occur at any time during maize growth and development, however some stages are more
sensitive than others. Its occurrence at flowering and grain-filling periods may cause losses in
yield of about 40 - 90% (NeSmith and Ritche, 1992; Menkir and Akintude, 2001). About 45 –
60% grain yield loss was obtained when drought occurred during silking (Campos et al., 2006).
Losses in maize yield caused by drought stress alone has been estimated at 12-15% in WCA
(Edmeades et al., 1995; Waddington et al., 1995). The most symptoms of adverse effects of
drought when it occurs during flowering in maize include: flowering asynchrony, delayed
silking resulting in extended anthesis-silking interval, tassel blasting, decrease in pollen
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potency and viability, reduced pistil receptivity and embryo abortion which can result in ear
barrenness and total yield loss (Banziger et al., 2000). When drought occurs after plant stand
establishment, there is undesirable expression of morphological characters such as reduced leaf
area and plant height as well as acceleration of leaf senescence from the leaves below the ear.
The stomata closure by plant in an attempt to reduce water loss through evapotranspiration
simultaneously reduce photosynthetic capacity. Therefore, assimilate partitioning to growing
sink is affected. In addition when drought occurs during linear grain growth, remobilization of
stem reserves can occur leading to lodging (Winkel et al., 2001).
2.2.2.1 Genetics and mechanism of drought tolerance in maize
Drought stress affects almost all plant functions, including developmental events, the ability of
leaves to assimilate carbon dioxide and nutrient uptake of roots (Schulze, 1991; Sari - Gorla et
al. 1999). Crops use physio-morphological characters such as stay-green of leaves, leaf area
and leaf chlorophyll concentration to overcome drought stress. For example, in Sorghum
bicolor L. Moench the stay-green of leaves is a post flowering adaptive trait that makes the
plant resistant to premature senescence and root lodging during grain production period (Crasta
et al., 1999). In maize, the stay-green trait contributes to improved overall plant health
resulting in tolerance to drought and resistance to stalk lodging (Duvick, 1992; Guei and
Wassom, 1992).
Physiological or biochemical mechanisms of drought tolerance related to the levels of proline
and abscisic acid (ABA) in plant leaves are well documented (Landip et al., 1995; Desai and
Singh, 2001). Landip et al. (1995) found that maize hybrids with higher leaf ABA
concentration had delayed pollen shed, reduced plant height and grain yield compared to those
with lower leaf ABA concentration. The ABA hormone acts as a catalyst to plant survival
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during moisture stress but does not contribute to grain yield production. During moisture
stresss, cell expansion such as leaf area, plant height, anthesis and silk emergence is delayed
(Banziger et al., 2000). Generally, plants produce an osmotically active substance which allows
them to take up more water from the soil in order to maintain the turgor and allow cellular
activities for a longer period of time during drought (Bolanos and Edmeades, 1991).
Studies on the mode of gene action controlling inheritance of grain yield of tropical maize
under moisture stress are limited and the few that are available are contradictory. According to
Guei and Wassom (1992) additive genetic effects played more important role than non-additive
genetic effects in the control of flowering traits while dominance effects were more important
than additive effects for ears per plant (EPP) and grain yield in two maize populations. In
contrast, Badu-Apraku et al. (2004b) reported additive genetic variance was more important
than non-additive effects for grain yield and other agronomic traits examined in a maize
population, Pool 16 DT, improved for grain yield under drought stress following eight cycles
of recurrent selection. Also, they indicated that non-additive gene action was large and should
be taken into considered during subsequent selection. Meseka et al. (2007) also found
significant effects of both additive and dominance variance in 24 late maturing tropical maize
inbreds used in line x tester design and screened under induced drought stress and well-watered
conditions. Under well-watered conditions, additive variance explained more than half of the
variation observed for all traits, excluding EPP.
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2.3 Contribution of secondary traits to yield improvement under Striga infestation and
drought
Under stress conditions, the heritability of grain yield is very low (Badu-Apraku et al., 2004b)
and selection for yield alone is ineffective. Therefore, plant breeders have focused attention on
reliable secondary traits alongside with yield to improve selection efficiency. A base index that
incorporates grain yield along with other secondary traits that are highly heritable, easy to
measure and highly correlated with yield could be used to improve selection efficiency
(Banziger and Lafitte, 1997; Banziger et al., 2000). For example, a selection index which
incorporated superior grain yield under Striga infestation with EPP, host plant damage and
Striga emergence count at 8 and 10 weeks after planting (WAP), number of emerged Striga
plants at 8 and 10 WAP are reliable secondary traits that have been used to improve yield and
Striga resistance and tolerance in maize (Badu-Apraku et al., 2009). Under drought, grain yield
with low value for plant and ear aspects, stay green characteristic, short anthesis-silking
interval (ASI), increased EPP and superior grain yield under optimal growing conditions have
been used to improve selection efficiency for higher levels of tolerance to drought and low soil
nitrogen in maize (Bolanos and Edmeades, 1996; Banziger and Lafitte, 1997; Badu-Apraku et
al., 2004b; Oyekunle and Badu-Apraku, 2013; Akaogu et al., 2017). Research in Pioneer Hi-
Bred and the International Maize and Wheat Improvement Centre (CIMMYT) indicates that
under drought the most important secondary traits used for selecting higher grain yield are ears
per plants, shortened ASI, and stay green (Banziger et al., 2000). Badu-Apraku et al. (2017)
selected top performing genotypes under multiple stresses (Striga infestation, recurrent drought
and low soil nitrogen) using multiple trait index that incorporated grain yield, anthesis-silking
interval, ears per plant, plant and ear aspects, stay-green characteristic, Striga damage and
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number of emerged Striga plants at 8 and 10 weeks after planting under multiple stress and
superior grain yield under optimal growing conditions.
Furthermore, plant breeders have used path analysis to examine the relationships among traits
during recurrent selection to ensure that selection in the desired direction has not resulted in
undesirable changes in the traits of interest. For example, Badu-Apraku et al. (2012a)
investigated the relative changes in genetic correlations during four cycles of recurrent
selection for increased yield and resistance to Striga hermonthica in maize population. The
authors observed that the increased grain yield was correlated with increased EPP, heights of
the plant and ears, shortened anthesis-silking intervals, ear aspect and Striga damage syndrome.
Based on these results, the base index was re-examined, since the index used for carrying out
selection under Striga infestation was designed to increase grain yield, ears per plant, improved
ear aspect and reduce number of emerged Striga plants while the other traits particularly plant
and ear heights were expected to remain constant. Information on the mode of expression of
the secondary traits in parental lines, their hybrid combinations and relationships with grain
yield could improve the efficiency in the improvement of hybrid performance under Striga
infestation and drought conditions.
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2.4 Application of DNA markers in maize research with emphasis on resistance/tolerance
to Striga and drought
Maize has become a model biological system for testing the potential application of
deoxyribonucleic acid (DNA) markers in crop improvement (Menkir, 1999). Recent progress
in the use of DNA markers on maize genome include characterization and quantification of
genomic variation among inbred lines within and between heterotic groups, broadening and
diversification of genetic base of adapted germplasm through introgression of unique alleles
from various donors, and identification and incorporation of marker-linked Quantitative Trait
Loci (QTL) into breeding populations (Menkir, 1999).
In assessing the genetic diversity, the use of DNA markers has been used to speed up the
process and offered a better alternative to the expensive and time consuming conventional
breeding procedure. Descriptions based on morphology do not involve the use of complex
laboratory procedures and are indeed essential for determining the agronomic potential of the
genetic materials. Genotypic differentiation of crops such as maize based on the physical
appearance could be defective and inconsistent due to the complexity of the genotype ×
environment interactions (Smith and Smith, 1988; Mohammadi et al., 2002). However, the
DNA markers used in assessing genetic diversity may be accompanied by some constraints
such as relatively low levels of polymorphism, insufficient genomic coverage and
developmental regulation as well as pleiotropic effects (Smith et al., 1997). In examining the
relationship between restriction fragment length polymorphism (RFLP) based distance of
parents and the yield potential of their hybrids, the results showed that the combination of lines
from different heterotic groups had a greater mean genetic distance estimate than the
combination of lines from the same heterotic group (Melchinger, et al., 1990). The study
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however concluded that RFLP data were not useful for predicting performance of hybrids
generated by crossing lines from genetically divergent heterotic groups.
In recent years, polymerase chain reaction (PCR) based markers such as amplified fragment
length polymorphism (AFLP) and simple sequence repeats (SSR) have become powerful tools
for studying genetic diversity (Prassana et al., 2002). Microsatellites, otherwise known as SSR,
are tandem repeats of sequence units generally less than 5 bp in length (Bruford and Wayne,
1993). They are co-dominant and their reproducibility makes them ideal for genome mapping
and population genetics studies (Dayanandan et al., 1998). Being genetically co-dominant, they
can distinguish between homozygous and heterozygous genotypes. Microsatellites have been
used in discriminating USA and European maize germplasm (Smith et al., 1997). Several
efforts have been devoted to characterizing tropical maize germplasm using SSR markers (Reif
et al., 2003; Menkir et al., 2005; Aguiar et al., 2008; Akaogu et al., 2012; Badu-Apraku et al.,
2013 a, b; Oyekunle et al., 2015). Reif et al. (2003) utilized 83 SSR markers to investigate
heterotic patterns among some early and intermediate subtropical germplasm and they
identified two heterotic groups comprising of a flint and dent mixture. They observed that the
pedigree information of the populations were in agreement with genetic distances obtained by
SSR markers. A similar finding was reported by Ifie (2013) who studied genetic diversity
between nine CIMMYT and 87 IITA early maturing inbreds using 31 SSR and 261 single
nucleotide polymorphisms (SNP) markers. They concluded that SSR markers were invaluable
complementation to conventional plant breeding for heterotic classification and could be used
to introgress elite germplasm. Different studies in maize have implored the use of SNP markers
including genetic diversity assessment, linkage map construction, linkage mapping, marker
assisted selection, genome wide association studies (GWAS), and genomic selection (Lu et al.,
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2009; Yang et al., 2011). Several authors have studied the genetic diversity of tropical maize
lines. For example, Menkir et al. (2005) assessed 41 late and intermediate tropical inbred lines
derived from four different genetic background using 21 AFLP and 31 SSR markers. Akaogu
et al. (2012) studied the genetic variation of 22 extra-early tropical Striga resistant lines using
23 pairs of SSR markers. Furthermore, Badu-Apraku et al. (2013b) studied the genetic
diversity of 17 extra-early inbreds, with combined resistance or tolerance to drought and Striga.
Correlation between genetic distance estimates of parental lines and the means of their F1
hybrid were insignificant for grain yield and most other traits. Also, these maize inbreds have
been phenotyped for reactions to Striga infestation and drought and it should be possible to
compare the pattern of observed Striga resistance and drought tolerance reactions in these
maize lines to the pattern of variability at different SNP loci to determine the relationship
between the phenotypic and genotypic expression of these inbred lines. SNP markers are
biallelic in nature and have lower information content when compared to SSRs. SNPs occur at
higher density in the genome with lower genotyping error rates and are amenable to high-
throughput technology (Rafalski, 2002; Kennedy et al., 2003). In addition, SNP genotyping can
provide improved marker data quality and quantity when compared with SSRs (Jones et al.,
2007; Hamblin et al., 2007).
Several hybrid groups generated by diallel or line × tester crosses have been studied under
stress and non-stress conditions during the last ten years. Badu-Apraku et al. (2016) studied
heterotic grouping of selected IITA and CIMMYT early maturing yellow maize inbreds using
three different methods namely: heterotic grouping based on specific and general combining
ability (HSGCA), genetic distance based on single nucleotide polymorphism markers (SNP-
GD) and general combining ability effects of multiple traits (HGCAMT) methods. They
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concluded that HGCAMT is an effective method for heterotic classification of the inbred lines
studied. Furthermore, the authors concluded that DNA markers could be used in heterotic
classification of inbreds that are yet to be screened in the field. Also, the efficacy of any of the
three heterotic classification approaches depends on the nature of genetic materials studied
(Annor and Badu-Apraku, 2016; BaduApraku et al., 2015, 2016).
During the last decade, several grouping methods such as the HSGCA, HGCAMT and DNA
markers; SNP and SSR have been useful in classifying the tropical inbred lines developed in
SSA into distinct heterotic classes (Badu-Apraku et al., 2015, 2016). In addition, DNA markers
have been widely used to investigate genetic variation and predict F1 hybrid performance or
heterosis in maize based on molecular markers’ genetic distance (Melchinger et al., 1990;
Smith et al., 1991; Melchinger, 1999). Several authors have studied the potential of DNA
markers in estimating the yield performance of maize hybrid (Melchinger et al., 1990;
Charcosset et al., 1998; Bernardo, 1992) but the available results cannot be considered
conclusive. The consistency of DNA markers in computing the distances between genes
depends on the number of markers, genomic coverage, type of gene action and the independent
information provided by each individual marker (Hahn et al., 1995). Dudley et al. (1991) found
that the correlation between genetic distance and hybrid yield was not significant. In several
studies, low but significant positive correlations were observed between RFLP marker and
yield performance of hybrid (Lee et al., 1989; Messmer et al., 1993). Mohammadi et al. (2008)
reported low and insignificant correlation between SSR markers’ genetic distance estimates
and SCA, on one hand, and significant correlation between genetic distance estimate and total
yield on the other. Munhoz et al. (2009) attributed the low correlation obtained between
randomly amplified polymorphic DNA (RAPD) based genetic distance and heterosis to the
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random distribution of the markers, inadequate coverage of the maize genome. The AFLP
markers were better than RFLP markers in predicting the value of parental variation in maize
(Ajmone-Marsan et al., 1998). In contrast, multidimensional scanning of 2,815 maize inbred
lines revealed genetic association between the maize inbred lines preserved at the North
Central Regional Plant Introduction Station (NCRPIS) germplasm bank determined using
principal coordinate analysis of the genetic distances matrix. The results showed that stiff stalk
and non-stiff stalk inbreds which showed the highest levels of heterosis were grouped very
closely together. Essentially the lines from the two groups overlapped while tropical lines that
did not combine as well with the US lines were well separated from the US lines (Romay et al.,
2013). The study also identified two MO17 related lines that shared 97% of DNA markers with
B73 with which they showed high heterosis. Most studies that examine a large number of
inbreds have concluded that markers do not identify heterosis potential.
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Chapter Three
3.0 GENETIC DIVERSITY OF EARLY MATURING MAIZE INBREDS
CONTAINING Striga RESISTANCE GENES FROM Zea diploperennis
3.1 Introduction
The successful introgression of novel genes for Striga resistance from the wild relative of
maize, Zea diploperennis, to diverse maize inbred lines (Kling et al., 2000; Menkir, 2006;
Menkir et al., 2006; Amegbor et al., 2017) makes them fundamental resources for genetic and
physiological studies, and their potential in breeding successful hybrids is encouraging. Since
these maize lines share some common parentage and Striga resistance gene sources, their
exclusive use in hybrid development, due to their novel Striga resistance gene, could widen the
genetic base in maize production fields. Therefore, systemic exploitation of these maize lines
that will allow gains from selection and minimize the narrow genetic base requires thorough
genetic diversity assessment in the maize lines. Knowledge and understanding of the genetic
diversity and genetic relatedness within a germplasm collection could be an invaluable aid in
deciding on breeding strategies (Semagn et al., 2012). DNA markers are invaluable in
determining the level of genetic diversity present within genetic materials (Senior et al., 1998).
Genetic diversity in maize has been assessed using several types of DNA markers. Application
of Restriction Fragment Length Polymorphism (RFLP), Randomly Amplified Polymorphism
DNA (RAPD), Simple Sequence Repeat (SSR) and Single Nucleotide Polymorphism (SNP)
markers have provided effective genotyping and are not affected by the different processes of
plant physiology or the environment. The genetic distance (GD) estimates may be useful for
placing inbred lines into distinct heterotic groups if used along with pedigree information so
that crosses between closely related lines are avoided (Lu et al., 2009). The use of SNP as
“marker of choice” is due to emergence of next generation sequencing technologies (Prasanna,
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2012).
The objectives of this study were to:
(i) determine the extent of genetic diversity within and between Striga resistant and
drought tolerant maize lines from different source populations,
(ii) classify the lines into distinct groups based on their genetic distances,
(iii) assess the yield performance of early maturing maize inbreds under drought and
Striga-infested environments
(iv) examine the correlation between Striga resistance, drought tolerance reactions and
SNP genotypic distance matrices and inter-trait relationship among the inbred lines
under drought and Striga infestation using sequential path analysis.
3.2 Materials and methods
3.2.1 Genetic materials
An extra-early drought tolerant and Striga resistant maize population, TZEE-W Pop STR C4
was crossed to four IITA intermediate maturing white inbreds, TZSTRI 104, TZSTRI 105,
TZSTRI 107, and TZSTRI 108 containing genes for Striga resistance from Zea diploperennis
in an effort to transfer Striga resistance genes into the population. The F1s were backcrossed
for two generations to the extra-early population to recover extra-earliness. The backcrosses
were selfed for six generations under Striga and drought conditions to develop several early
maturing white inbreds with resistance to Striga hermonthica, tolerance to low soil nitrogen
and drought. A total of thirty-six early maturing inbreds derived from diverse germplasm
sources were selected for the present study (Table 3.1).
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Table 3.1: Pedigree of the 36 white early maturing maize Inbred lines used in the diversity
study
Inbreds Pedigree Reaction to stress
Striga hermonthica drought
TZdEI 71 TZEE-W POP STR 104 S6 98/208-2/2-2/2-1/2-1/2 Susceptible Tolerant
TZdEI 124 TZEE-W POP STR 104 S6 83/208-1/1-2/2-3/4-1/3 Resistant Susceptible
TZdEI 202 TZE-W POP STR 108 S6 195/198-1/2-1/2-2/2-2/4 Susceptible Tolerant
TZdEI 315 TZEE-W POP STR 107 S6 53/254-2/2-3/3-1/3-2/3 Susceptible Susceptible
TZdEI 399 TZE-W POP STR 107 S6 118/254-2/2-1/1-2/2-1/3 Susceptible Tolerant
TZdEI 260 TZEE-W POP STR 108 S6 93/198-1/1-3/3-1/2-1/5 Tolerant Susceptible
TZdEI 479 TZEE-W POP STR 105 S5 126/253-1/2-1/2-2/3-1/4 Tolerant Susceptible
TZdEI 82 TZE-W POP STR 104 S6 98/208-2/2-2/2-1/2-2/3 Susceptible Susceptible
TZdEI 485 TZEE-W POP STR 105 S5 197/253-2/2-1/2-1/2-1/3 Susceptible Tolerant
TZdEI 352 TZE-W POP STR 107 S6 24/254-1/2-1/1-1/1-2/2 Resistant Tolerant
TZdEI 441 TZE-W POP STR 107 S6 232/254-1/1-1/4-2/3-1/2 Tolerant Susceptible
TZdEI 84 TZEE-W POP STR 104 S6 98/208-2/2-1/2-1/3-3/5 Susceptible Susceptible
TZdEI 280 TZE-W POP STR 108 S6 65/198-1/1-2/2-1/2-4/5 Susceptible Susceptible
TZdEI 357 TZE-W POP STR 107 S6 37/254-2/2-2/2-1/3-2/2 Resistant Susceptible
TZdEI 492 TZE-W POP STR 105 S6 2/253-1/1-2/2-1/2-1/2 Susceptible Tolerant
TZdEI 98 TZE-W POP STR 104 S6 83/208-1/1-2/2-2/4-5/5 Tolerant Tolerant
TZdEI 157 TZE-W POP STR 104 S6 22/160-1/3 Tolerant Tolerant
TZdEI 173 TZE-W POP STR 104 S6 41/160-1/2 Susceptible Tolerant
TZdEI 283 TZE-W POP STR 108 S6 34/198-1/2-1/3-1/2-2/3 Resistant Tolerant
TZdEI 105 TZE-W POP STR 104 S6 83/208-1/1-2/2-1/4-1/4 Tolerant Tolerant
TZdEI 120 TZEE-W POP STR 104 S6 18/208-2/2-3/4-1/2-2/2 Susceptible Susceptible
TZdEI 131 TZE-W POP STR 104 S6 83/208-1/1-2/2-3/4-3/4 Tolerant Susceptible
TZdEI 264 TZEE-W POP STR 108 S6 54/198-1/1-1/4-3/3-4/9 Susceptible Tolerant
TZdEI 378 TZE-W POP STR 107 S6 53/254-2/2-3/3-3/3-1/2 Susceptible Susceptible
TZdEI 268 TZEE-W POP STR 108 S6 1/198-1/1-2/2-2/2-1/4 Tolerant Susceptible
TZdEI 314 TZEE-W POP STR 107 S6 53/254-2/2-3/3-2/3-1/3 Susceptible Susceptible
TZdEI 396 TZE-W POP STR 107 S6 85/254-1/1-2/3-3/3-1/1 Susceptible Tolerant
TZEI 7 WEC STR S7 Inbred 12 Tolerant Tolerant
TZEI 18 TZE-W Pop STR Co S6 Inbred 136-3-3 Susceptible Tolerant
TZEI 31 TZE-W Pop x LD S6 Inbred 4 Susceptible Tolerant
TZdEI 425 TZE-W POP STR 107 S5 223/254-1/2-2/2-2/3-1/2 Susceptible Susceptible
TZdEI 551 TZE-W POP STR 105 S5 112/253-1/1-3/4-2/4-1/3 Susceptible Susceptible
Check 1-TZEI 2 TZE-W Pop X 1368 STR S7 Inbred 2 Tolerant Susceptible
Check 2- TZEI 3B TZE-W Pop X 1368 STR S7 Inbred 4 Tolerant Susceptible
Check 3- TZEI 26 WEC STR S8 Inbred 4 Susceptible Susceptible
Check 4- TZEI 65 TZE-W Pop STR Co S6 Inbred 141-1-2 Resistant Tolerant
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Twenty-nine out of the thirty-six inbreds shared common Striga resistance genes from the
wild relative of maize, Zea diploperennis, and are designated as TZdEI lines while the
remaining seven lines, without the genes, are designated as TZEI.
3.2.2 Experimental locations and field layout
The experiment was conducted under Striga-infested conditions in Abuja (9o15’N and 7o
20’E, 300 m altitude, 1,700 mm annual rainfall) in 2015 and Mokwa (9o18’N and 5o4’E,
457 m altitude, 1,100 mm annual rainfall) in 2014 and 2015, and terminal drought in
Bagauda (12°00′ N, 8°22′ E, 580 m altitude, 800 mm annual rainfall) in 2015 and induced
drought at Ikenne (6o 87’N, and 3o 7’E, 60 m altitude, 1500 mm annual rainfall) in 2014 and
2015. The trial was laid out as 6 x 6 lattice square design and replicated two times. A 4 m
single row plots spaced 0.75 m apart with 0.4 m between plants in each row were used. One
week before planting at Mokwa and Abuja, ethylene gas was plunged into the soil at a depth
of 12 cm and repeated at intervals of 1 m to induce sponateous germination of Striga seeds
present in the soil in order to ensure uniform infestation. The artificial Striga infestation
procedure as recommended by the IITA Maize Improvement Programme was used (Kim,
1991; Kim and Winslow, 1991). Seeds of the Striga plants were collected from Sorghum
bicolor (L.) Moench fields at end of the growing season and stored for at least six months.
The Striga seeds were mixed thoroughly with sand in the proportion of 1:99 by weight.
The Striga seed–sand mixture was applied using calibrated scoops, which supplied about
5000 germinable seeds in each planting hole. Three maize seeds were sown into each
infested hole.
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For the dry season evaluations at Ikenne, drought was induced by stopping water supply
from 28 days after planting till harvesting so that the maize plants depend on the available
soil moisture for physiological processes. The volume of the water level in the soil was
assessed on weekly basis using a device called Diviner 2000. The soil at Ikenne is eutric
nitrosol and is characterized by high water-holding capacity and the plots are flat and fairly
uniform. For all evaluations, number of plants were thinned to two plants per stand ten days
after planting giving a total of 66,667 plants ha-1.
For the drought experiment at Ikenne and Bagauda, 60 kg ha-1 each of N, P, and K was
applied as 15-15-15 NPK at planting. Additional 30 kg ha-1 N as urea was applied at 4
weeks after planting (WAP). In the Striga experiment, 20 kg ha-1 each of N, P, and K was
applied as 15-15-15 NPK at two weeks after planting. At 5 weeks after planting, 10 kg ha-1
N as urea was added. The delayed and reduced fertilizer rate was to stimulate the production
of Strigolactone and enhance Striga emergence because high levels of nitrogen fertilizer
suppresses growth of Striga plants (Kim, 1991). At all locations except in the Striga
experiments, weeds were controlled through the combination of PrimextraR and
GrammoxoneR (Paraquat) applied at 2 days after planting (DAP). In the Striga experiments,
weeds excluding Striga were controlled by hand picking.
3.2.3 Data collection
In all experiments, data for measured traits were recorded on individual plot basis. Data on
when half of the plants in a plot had started to shed pollen and produced silks were recorded
as number of days to 50% anthesis (DA) and 50% silking (DS). The interval between
anthesis-silking (ASI) was then calculated as the difference between DA and DS. The
distance from the plant base to the length of the first tassel branch was measured on ten
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plants per plot as the height of the plant while the ear height was measured as the distance
from the plant base to the node carrying the upper ear. Ears per plant (EPP) were calculated
as the total number of harvested ears per plot divided by the number of plants harvested.
Plant aspect which is the overall artchitecture of the plants in a plot was estimated on a scale
of 1 to 9 where 1 = excellent, uniform with no foliar diseases and 9 = very poor with several
diseased plants. Husk cover was scored on a scale of 1 to 9, where 1 = very tightly arranged
husks and extended beyond the ear tip and 9 = ear tips loose with kernels exposed. Ear
aspect which is the general appearance of the ears/cobs was recorded on a scale of 1 to 9,
where 1 = clean ears with no insect and disease symptom, large, uniform and tightly filled
ears and 9 = ears with about 90% - 100% diseased and insect damaged.
Leaf senescence data were recorded for the moisture stressed plots at 10 weeks after
planting on a scale of 1 to 9, where 1 = all leaves are green and 9 = almost all leaves are
dead. The data taken on the Striga-infested plots were similar to those of the drought stress
except that no data were taken on the leaf senescence (stay green characteristic).
Adiditional data, including number of emerged Striga plants and Striga damage at 8 and 10
weeks after planting (WAP) were recorded on the Striga–infested plots. Striga damage was
rated on individual plots on a scale of 1 to 9, where 1 = highly resistant plant with no Striga
damage, and 9 = highly susceptible plants with no ears (DeVries, 2000; Badu-Apraku and
Akinwale, 2011). Number of emerged Striga plants were subjected to logarithm
transformation using log (y +1) before analysis of variance to avoid multiplicative effects.
At physiological maturity, stalk lodging (number of plants broken below the highest ear
node) and root lodging (number of plants that fell from the root) were counted and
converted into percentages before square root transformation. Also, field weight of the ears
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were taken. Grain moisture was determined using moisture metre on a sample of ten ears
randomly picked per plot. Grain yield was computed based on 80% shelling percentage and
adjusted to 15% moisture. The formula used for calculating grain yield is shown below:
Grain yield (kg/ha) = field weight (kg/plot) x (100 – moisture) x 10000 x 80
85 0.4 x 0.75 100
For the drought experiment at Ikenne and Bagauda, all the harvested ears from each plot
were shelled to determine percent moisture which was used to obtain grain weight. The
grain yield was adjusted to 15% moisture and computed from the grain weight as follows:
Grain yield (kg/ha) = grain weight (kg/plot) x (100 – moisture) x 10000
85 0.4 x 0.75
3.2.4 Data analyses
Analysis of variance (ANOVA) was done separately on plot means for grain yield and other
measured traits under Striga-infested and drought conditions with PROC general linear
model in SAS using a RANDOM statement with the TEST option (SAS Institute, 2011).
Similarly, ANOVA was conducted for all traits across research conditions. In the ANOVA,
the location–year combinations was the environment, replicates, and blocks were considered
as random factors while inbred lines (genotypes) were considered as fixed effects and the
adjusted means and standard errors were estimated. The base indices were computed and
used in ranking the inbred lines based on their reactions under both research conditions.
Under Striga infestation, the base index values were calculated as shown below:
BI = [(2 x YLD) + EPP – (SRD8 + SRD10) – 0.5 (NESP8 + NESP10)],
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Where YLD is the grain yield under Striga infestation, EPP is number of ears per plant,
SRD8 and SRD10 are host plant damage at 8 and 10 WAP, and NESP8 and NESP10 are
number of emerged Striga plants at 8 and 10 WAP.
Under induced drought stress, the base index values were computed using the equation
BI = [(2 x GY) + EPP – ASI – PASP – EASP - LD],
where GY is grain yield under drought, EASP is ear aspect, EPP is number of ears per plant,
PASP is plant aspect, and LD is stay green characteristic (leaf death scores) and ASI is
anthesis-silking interval.
Broad-sense heritability (H) of grain yield and other traits were estimated for each
environment as
H =σ 2 /(σ 2g g +σ
2
g×e / e +σ
2
e / re)
where σ 2g is variance for genotype, σ2gxe is variance for genotype x environment and σ 2e is
error variance, e is number of environments, and r is number of replications per
environment.
Sequential path co-efficient analyses were performed to explain the relationships among
traits under each and across research conditions using the method described by Mohammadi
et al. (2003). The stepwise regression was used to place the predictor traits into first, second
and third order based on their individual contributions to the total differences in grain yield
with minimized multicolinearity (Badu-Apraku et al., 2012b; 2014). At first, all other traits
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were regressed on grain yield and those with significant contributions to grain yield at P <
0.05 were identified as first order traits. Subsequently, traits that were not identified as first-
order traits were regressed on each of the first order traits to identify those with significant
contributions to grain yield through the first-order traits and they were categorized as
second-order traits. The procedure was repeated to identify traits in subsequent orders. The
path coefficients were the standardized b-values from the result of the regression analysis
(Mohammadi et al., 2003; Badu-Apraku et al., 2012b, 2014; Talabi et al., 2016). The
stepwise multiple regression analysis tested the significance of the path coefficients using t-
test at 0.05 level of probability and retained only traits with significant values and indicated
the percentage of the variation they contributed in the dependent variable.
3.2.5 Molecular analysis
3.2.5.1 DNA extraction and genotyping using diversity arrays technology (DArT)
platform
The 36 inbred lines, screened for their reactions to Striga hermonthica and drought (Table
3.1), were planted in the IITA green house in Ibadan for genetic diversity assessment. At
two weeks after planting, young leaves were collected from 15 - 20 seedlings of each
inbreds and stored at -80 °C. The leaf samples were lyophilized in console dry system from
Labconco (Labconco Inc., Missouri, USA) that dries at -50°C, 1-22 pascals pressure for 48
hours. DNA was extracted from the lyophilized leaves of the 36 inbreds using modified
CTAB (Cetyl Trimethyl Ammonium Bromide) protocol (Doyle and Doyle, 1987). The
quantity and quality of the DNA was tested on 1% agarose gel using λ-DNA standard and
were sent to the CIMMYT, Mexico, for genotyping using Diversity Arrays Technology
(DArT) sequence platform (www.diversityarrays.com/dart-application-dartseq). Two inbred
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lines had low quality genomic DNA and were removed from the molecular study. A total of
51,009 single nucleotide polymorphism (SNP) markers were obtained. SNP markers that
had 10% missing data points were filtered using TASSEL Software version 5.34 (Bradbury
et al., 2007) leaving a total of 8145 polymorphic markers for analysis.
3.2.5.2 Genetic diversity analysis
The PowerMarker software version 3.25 was used to compute the major allele frequency,
number of genes, heterozygosity, polymorphic information content (PIC) and allele
diversity value for all markers (Liu and Muse, 2005). The PIC was determined by the allele
sizes of the SNP markers at each locus using Nei’s (1972) genetic diversity formula as
reported by Smith et al. (1997):
PIC = 1 - Σ рі
Where pi is the frequency of the ith allele.
Cluster analysis was performed based on the genetic distance (GD) matrix using neighbor
joining trees for the SNP marker data and were viewed employing Dendroscope version
3.2.2 (Huson et al., 2007).
Using PROC CORR in SAS version 9.3, correlation analysis was perfomed using the data
from the GD matrix of the inbred lines and the means of the corresponding F1 hybrids for
traits recorded under Striga-infested and drought environments (SAS Institute, 2011).
3.2.5.3 Population structure analysis of inbred lines
For the SNP analysis, a model-based clustering method using the software package
STRUCTURE version 2.3.4 (Pritchard et al., 2000; Falush et al., 2003) was used to
establish the population structure among 34 early maturing maize inbred lines. The model
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assumed the number of clusters to be K. K ranged from 2 to 9 and each structure simulation
was repeated 3 times using the admixture model with a burn-in period of 100,000 iterations
and a Markov Chain Monte Carlo (MCMC) set at 100,000. The optimal sub-population
model was investigated by plotting the log probability L(K) and Δk of the data over runs, as
implemented in STRUCTURE HARVESTER (Earl and vonHoldt, 2012) and the most
likely value of K was determined. The K with the highest probability was used to assign
individual genotypes into groups. Individuals with membership probability greater than or
equal to 0.70 were placed in the same group while lines with membership probability less
than 0.70 were placed in a mixed group (Lu et al., 2009; Yang et al., 2011).
3.3 Results
3.3.1 Performance and reaction of early maturing inbreds under Striga infested and
drought environments
The two main effects, inbreds (G), environment (E) and their interactions (GEI), were
significant for all traits except environment for ear aspect, GEI for grain yield, days to
anthesis, Striga emergence count at 8 WAP and ears per plant (Table 3.2). In contrast, the
two main effects and their interactions showed significant mean squares for all traits except
GEI for grain yield and plant height under induced drought stress. Across the research
conditions, mean squares for the inbreds, environment and GEI were significant for grain
yield and other measured traits.
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Table 3.2: Mean squares from analysis of variance for grain yield and other traits of 36 early maturing white maize inbred lines screened under
artificial Striga infestation at Mokwa and Abuja, drought stress at Bagauda and Ikenne and across research conditions during 2014 and 2015
growing seasons.
Source DF Grain yield Days to Days to Antheis- Plant Ear Ears Striga damage Number of
(kg ha-1) anthesis silking silking height aspect per rating (scale 1-9) emerged Striga
interval (cm) (scale plant plants
1-9)
Striga infestation 8 WAP 10 WAP 8 10
WAP WAP
Environment (E) 2 6641695** 753.39** 750.56** 5.08** 9341.52** 0.31ns 0.14** 3.49** 5.91** 9.03** 12.10**
Genotype (G) 35 998643** 17.92** 20.85** 1.76* 246.79** 1.24** 0.07** 1.22** 2.18** 1.43** 1.80**
BLOCK (E*REP) 30 290682ns 4.53ns 5.25ns 1.30ns 132.77* 0.37ns 0.03ns 0.40ns 0.35ns 0.45ns 0.42ns
Replication/(E) 3 360056ns 8.50* 15.48** 7.17** 414.09** 1.99** 0.11** 0.22ns 0.70ns 1.17ns 0.46ns
G* E 70 310475ns 3.60ns 5.62* 1.81* 197.58** 0.77** 0.04ns 0.66** 0.84** 0.63ns 0.62*
Error 69 221604 2.92 3.43 1.13 82.49 0.32 0.03 0.33 0.32 0.50 0.39
Heritability 0.70 0.81 0.74 0 0.25 0.39 0.48 0.47 0.63 0.33 0.51
Plant
SGC aspect
Drought (1-9)
Environment (E) 2 15171868.14** 908.73** 728.58** 8.90** 20974.78** 23.13** 5.60** 19.13** 55.01**
Genotype (G) 35 306114.39** 12.93** 9.12** 3.00** 412.16** 1.79** 0.04* 2.58** 2.54**
BLOCK (E*REP) 30 83507.3ns 4.76** 4.39** 1.00ns 203.90ns 1.22* 0.03ns 0.81ns 0.74ns
Replication/(E) 3 103602.33ns 9.71** 7.90** 1.14ns 1134.84** 3.21** 0.07* 0.89ns 2.97**
G * E 70 138456.25ns 6.93** 7.36** 1.56** 247.54ns 1.16** 0.04* 1.29** 1.36**
Error 69 92911.2 2.47 2.20 0.82 195.64 0.67 0.02 0.59 0.65
Heritability 0.59 0.49 0.26 0.42 0.46 0.46 0.22 0.51 0.57
Across
Environment (E) 5 1878104 6.35** 690.7 6** 645.9 1** 9.60* * 20398 .19** 1 2.92** 2 .78**
Genotype (G) 35 939085** 22.52** 21.87** 2.63** 461.02** 2.05** 0.05**
BLOCK (E*REP) 60 188850ns 4.64** 4.82** 1.15ns 168.33ns 0.79** 0.03ns
Replication/(E) 6 231829ns 9.10** 11.69** 4.15** 774.47** 2.60** 0.09**
G * E 175 241428* 5.82** 6.86** 1.78** 223.33** 0.94** 0.04*
Error 96 175642 2.72 2.88 0.99 134.00 0.47 0.03
Heritability 0.78 0.76 0.71 0.23 0.02 0.58 0.39
SGC, stay green ch aracteristic; WAP, weeks after planting, *, **, Significant at 0.05 and 0.01probability l evels, resp ectively, an d ns, not significant
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The heritability values ranged from 25% for plant height to 81% for days to anthesis on plot-
mean basis under Striga infestation. Similarly, the heritability estimates under drought ranged
from 22% for ears per plant to 59% for grain yield. Across the research conditions, the
heritability estimates ranged from 2% for plant height to 78% for grain yield (Table 3.2).
Under Striga infestation, mean yield of the inbred lines varied from 909 kg ha-1 for TZdEI 84 to
2879 kg ha-1 for TZdEI 283 with a mean of 1547 kg ha -1. Striga damage at 8 WAP ranged from
3.0 to 5.17 with a mean of 4.09; while at 10 WAP it ranged from 3.50 to 6.33 with a mean of
4.83. The number of emerged Striga plants ranged from 0.14 to 2.75 at 8 WAP and from 1.0 to
3.1 at 10 WAP (Table 3.3). Only 18 inbreds, out of the 36 evaluated, had positive base indices
under Striga infestation. The base index is an indication of response of the inbred lines to Striga.
A positive base index meant resistance/tolerance to Striga while a negative base index meant
susceptibility to Striga. In contrast, grain yield varied from 176 kg ha-1 for TZdEI 173 to 1637 kg
ha-1 for TZdEI 283 with a grand mean of 754 kg ha-1 under drought. Stay green characteristic
ranged from 3.17 to 5.80 with a mean of 4.27. Out of the 36 inbred lines evaluated, only 15
showed positive base indices under drought indicating that they were tolerant to the stress with
10 of them producing more than the mean grain yield (Table 3.4). Eight inbred lines had positive
base indices across the two stress conditions: TZdEI 283, TZdEI 352, TZdEI 98, TZEI 7, TZEI
65, TZdEI 157, TZdEI 378, and TZdEI 396.
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Table 3.3: Grain yield and other traits of the 36 early maturing white Zea diploperennis inbreds screened under Striga infestation at Mokwa in 2014, Abuja and Mokwa in 2015.
Inbreds Grain yield Days to Days Anthesis Plant Striga damage Number of Stalk Ear Ears Base
(kg ha-1) anthesis to -silking height rating (WAP) emerged Striga lodging aspect per Index
silking interval (cm) (WAP) plant
8 10 8 10
TZdEI 283 2879 59 60 1.50 128.33 3.33 3.67 1.60 2.56 1.00 3.50 0.98 9.78
TZdEI 352 1628 61 64 2.50 124.17 3.00 3.50 1.33 1.94 2.07 4.50 1.15 7.49
TZdEI 124 1971 58 58 0.67 133.50 3.33 4.00 1.27 1.60 3.64 4.67 0.94 5.93
Check 4 - TZEI 65 1789 55 56 1.00 110.33 3.50 3.67 0.68 1.48 2.49 4.83 0.93 5.86
TZdEI 98 2414 58 59 0.50 123.83 3.83 4.17 1.30 1.96 2.42 4.00 0.86 5.51
TZdEI 357 1962 62 63 1.67 122.17 3.67 3.83 1.43 2.31 1.68 4.50 0.95 4.81
Check 1- TZEI 2 2291 59 61 1.33 125.17 4.17 4.50 1.12 1.93 1.71 4.17 0.93 4.61
TZdEI 131 1921 58 59 1.50 129.83 3.50 4.67 1.53 2.38 2.93 4.50 0.93 3.38
TZdEI 441 1319 62 64 2.00 119.00 3.50 4.17 1.19 1.80 2.16 4.83 0.98 2.73
TZdEI 260 1406 58 59 0.83 116.33 4.17 4.83 0.46 1.12 2.12 4.83 0.95 1.61
TZdEI 479 1607 57 58 1.33 120.33 4.00 4.67 1.24 2.04 2.40 4.67 0.95 1.60
TZdEI 173 1273 58 59 1.67 120.17 4.00 4.33 1.17 1.92 1.96 4.67 1.03 1.50
TZEI 7 1875 59 61 1.17 128.00 3.67 4.50 0.96 2.38 2.01 4.83 0.70 1.41
TZdEI 396 1791 60 61 0.83 128.50 4.17 5.00 1.81 2.61 2.17 4.67 0.99 0.92
TZdEI 82 1362 61 61 0.60 129.60 4.40 5.00 0.14 0.55 2.56 5.00 0.83 0.31
TZdEI 378 1593 59 60 1.17 118.50 4.33 4.83 1.75 2.41 3.02 4.50 0.99 0.19
TZdEI 157 1446 60 60 0.67 119.17 4.00 4.67 1.54 2.23 3.34 4.33 0.92 0.18
TZdEI 280 1815 58 60 1.83 99.00 4.17 5.00 1.79 2.12 1.94 4.83 0.85 0.16
Check 2 - TZEI 3B 1567 56 57 1.17 117.17 4.50 5.17 1.09 1.68 2.89 4.67 0.92 -0.19
TZdEI 399 1541 60 62 2.00 113.67 3.67 4.67 2.17 2.74 2.53 4.67 0.86 -0.26
TZdEI 105 1425 57 59 1.83 123.33 4.17 4.50 1.42 1.90 3.04 4.83 0.85 -0.26
TZdEI 268 1329 59 59 0.33 127.83 4.00 4.83 1.49 2.15 1.45 5.17 0.94 -0.27
TZdEI 485 1028 61 62 1.17 120.00 3.83 5.00 0.97 1.01 1.98 5.00 0.84 -1.07
TZdEI 492 1706 60 61 1.17 117.50 4.33 5.17 2.10 2.69 2.65 4.50 0.84 -1.76
TZdEI 71 2009 58 60 1.67 122.17 4.67 5.83 1.24 1.81 4.11 5.67 0.64 -2.43
TZEI 18 1778 58 61 2.33 113.33 4.33 4.83 2.42 3.10 3.74 4.17 0.74 -2.48
TZdEI 315 1181 58 59 1.00 111.67 4.50 5.33 1.28 2.01 3.04 4.83 0.90 -2.84
TZdEI 120 1332 57 58 0.83 128.33 4.67 5.17 1.04 1.60 2.82 4.50 0.75 -3.07
TZdEI 264 1255 59 61 1.67 108.33 4.00 5.67 2.04 2.75 1.00 5.17 0.91 -3.21
TZdEI 202 1085 56 57 1.67 123.00 4.50 5.00 1.01 1.65 3.34 4.67 0.75 -3.62
TZdEI 425 1011 63 65 1.50 107.00 4.25 5.50 1.60 2.40 2.09 5.50 0.92 -3.81
TZdEI 314 1239 57 58 1.00 119.67 4.33 5.33 2.24 2.92 2.40 4.83 0.84 -4.41
TZdEI 84 909 59 61 1.83 133.83 5.00 5.50 0.41 1.20 4.00 5.67 0.75 -5.33
TZEI 31 1009 59 61 1.33 111.33 4.50 5.67 1.87 2.77 3.35 5.17 0.81 -6.15
TZdEI 551 945 59 62 2.50 123.33 4.33 5.50 1.66 2.26 1.43 5.83 0.69 -6.32
Check 3- TZEI 26 948 56 57 1.17 120.83 5.17 6.33 2.75 2.99 3.92 5.33 0.73 -10.52
Mean 1548 59 60 1.36 120.59 4.09 4.83 1.42 2.09 2.54 4.77 0.88
Standard error 217 0.79 0.85 0.49 4.19 0.26 0.26 0.33 0.29 0.64 0.26 0.08
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Table 3.4: Grain yield and other traits of the 36 early maturing white Zea diploperennis inbred lines evaluated under drought stress at Ikenne in
2014, Bagauda and Ikenne in 2015.
Inbreds Grain yield Days to Days to Anthesis- Plant Stalk Plant Ear aspect Ears Stay green Base
(kg ha-1) anthesis silking silking height lodging aspect (scale 1-9) per characteristic Index
interval (cm) (scale 1-9) plant (scale 1-9)
TZdEI 283 1637 60 61 1.33 111.15 2.55 3.50 3.83 0.77 3.17 12.22
TZEI 7 1385 58 60 1.80 97.82 1.61 4.17 4.40 0.82 3.17 9.10
TZdEI 399 1195 60 61 1.00 107.76 2.80 3.80 4.40 0.77 3.60 8.56
Check 4 - TZEI 65 1319 61 62 1.00 90.23 1.83 4.67 4.75 0.86 3.60 8.37
TZdEI 98 1186 61 61 0.50 109.40 1.78 4.17 4.83 0.80 3.50 8.34
TZdEI 71 1051 58 59 1.00 121.62 2.42 4.40 4.60 0.80 3.60 6.98
TZdEI 425 1565 61 63 2.00 95.00 3.42 4.50 5.50 0.72 3.50 5.97
TZdEI 352 714 62 64 1.67 118.58 2.10 3.50 4.67 0.74 3.17 5.47
TZEI 18 1251 59 61 2.00 90.93 2.27 4.33 4.83 0.62 4.00 4.00
TZEI 31 1036 60 61 1.00 106.98 2.86 5.00 5.40 0.68 4.60 2.20
TZdEI 157 685 62 64 1.75 98.00 3.49 5.75 5.25 0.80 4.00 0.80
TZdEI 378 812 60 62 1.67 88.82 3.26 4.83 5.33 0.66 4.50 0.51
TZdEI 396 469 61 62 1.67 101.05 2.48 4.00 5.50 0.61 3.50 0.46
TZdEI 314 436 60 60 0.33 91.92 2.33 5.50 4.83 0.67 4.67 0.21
TZdEI 315 445 62 63 1.00 90.00 2.55 4.83 5.50 0.79 4.83 0.13
TZdEI 479 707 62 63 1.17 104.52 1.76 5.50 5.60 0.61 4.00 -0.53
TZdEI 264 639 61 63 2.20 91.63 3.26 5.50 5.83 0.76 3.67 -0.62
TZdEI 84 599 60 62 1.60 107.20 4.05 5.00 5.25 0.69 4.80 -0.69
TZdEI 492 699 61 63 1.40 110.28 3.65 4.60 5.60 0.61 4.80 -0.79
TZdEI 485 704 61 61 1.50 106.40 2.80 5.60 4.75 0.66 5.40 -1.11
TZdEI 280 415 61 63 2.00 88.55 2.56 4.33 5.50 0.54 3.67 -1.64
TZdEI 441 194 61 63 2.20 92.30 3.90 3.80 6.00 0.64 3.40 -1.76
TZdEI 105 446 59 61 1.20 99.63 3.09 5.67 5.33 0.61 4.17 -1.84
Check 1- TZEI 2 1162 59 62 3.20 83.48 2.99 5.40 5.20 0.65 5.80 -2.13
TZdEI 202 775 56 60 3.40 112.08 2.81 5.17 5.00 0.63 4.50 -2.14
TZdEI 82 520 61 62 1.17 97.60 2.03 5.83 5.00 0.58 4.83 -2.30
TZdEI 131 303 61 63 1.83 100.78 3.19 5.17 5.83 0.64 4.00 -3.05
TZdEI 357 511 64 65 1.33 104.43 1.99 5.33 6.33 0.50 3.83 -3.52
TZdEI 120 841 57 60 2.17 105.22 3.53 5.33 5.83 0.50 5.33 -4.23
TZdEI 260 521 57 60 2.83 81.63 2.99 6.17 5.80 0.74 5.00 -4.82
Check 2 - TZEI 3B 222 60 63 2.33 87.50 2.77 5.50 5.50 0.60 5.00 -5.72
TZdEI 173 176 62 64 2.00 98.90 2.58 6.20 5.75 0.64 5.00 -6.48
TZdEI 268 379 60 63 2.60 97.52 3.71 6.00 6.40 0.59 4.40 -6.75
TZdEI 124 496 59 61 2.33 108.50 3.16 5.83 6.33 0.50 4.67 -6.77
TZdEI 551 213 59 62 3.17 107.67 4.46 5.50 6.33 0.55 4.33 -7.83
Check 3- TZEI 26 437 57 60 3.33 100.22 3.00 5.67 6.00 0.53 5.67 -8.60
Means 755 60 62 1.79 100.01 2.81 5.00 5.37 0.66 4.27
Standard error 180.14 0.76 0.72 0.44 6.67 0.64 0.38 0.38 0.07 0.36
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3.3.2 Genetic diversity assessment of the inbred lines
The neighbor-joining tree plotted on the basis of Nei’s genetic distance matrix using the SNP
data divided the inbred lines into four groups. TZdEI 485, TZdEI 479, TZdEI 399, TZdEI
396, TZdEI 357, TZdEI 441, TZdEI 84, TZdEI 315, TZdEI 378, TZdEI 314, TZdEI 202 and
TZdEI 120 constituted the first group. TZdEI 551, TZdEI 492, TZEEI 66, TZEI 31, TZEI 2,
TZEI 3B, TZEI 18, TZEI 7 and TZEI 65 formed the second group. TZdEI352, TZdEI 283,
TZdEI 173, TZdEI 105, TZdEI 425, TZdEI 264, TZdEI 280 and TZdEI 260 made up the
third group while TZdEI 98, TZdEI 157, TZdEI 131, TZdEI 124, TZdEI 82 and TZdEI 71
formed the fourth group (Fig. 3.1). Similarly, the population structure analysis classified the
inbred lines into four distinct groups as presented with the different colours red (group 1),
green (group 2), blue (group 3) and yellow (group 4). The number of lines assigned to each
group differed with group 1 having the largest number of 22 inbreds, with 5 in group 2, 4 in
group 3 and 3 in group 4 (Fig. 3.2).
There were no significant correlation values between GD estimates of the inbred lines and
hybrid means for grain yields (r = 0·06), Striga damage at 8 WAP (r = −0·03) and 10 WAP
(r = −0·01), number of emerged Striga plants at 8WAP (r = 0·01) and 10 WAP (r = 0·04),
and EPP (r = 0.01) under Striga infestation (Table 3.5). In contrast, highly significant
correlations were observed between GD estimates of the inbred lines and hybrid means for
grain yield (r = 0·39), plant aspect (r = −0·25), ear aspect (r = −0·31) and ears per plant (r =
0·19), stay green characteristic (r = -0.30) but was not significant for ASI (r = 0·02) under
drought environments (Table 3.6).
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I
II
III
IV
Figure 3.1: Dendrogram of the 34 inbred lines based on Nei’s genetic distance estimated from
8145 SNP markers.
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Figure 3.2: Population structure of 34 early maturing maize inbred lines based on 8145 SNP
markers for k=3.
Each individual is represented by a single vertical line that is partitioned k=3 segments in the
x-axis, with lengths proportional to the estimated probability membership (y-axis)
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Table 3.5: Correlation coefficients between genetic distance (GD) and traits used as indices for Striga
resistance in early maturing maize inbred lines.
Striga damage Number of emerged
Grain Ears per rating
Striga plants
Yield plant
8 WAP 10 WAP 8 WAP 10 WAP
Genetic Distance 0.06ns 0.01ns -0.03ns -0.01ns 0.01ns 0.04ns
Grain yield 0.39** -0.74** -0.79** 0.09ns 0.07ns
Ears per plant -0.34** -0.33** -0.15ns -0.17*
Striga damage at 8 WAP 0.89** 0.02ns 0.02ns
Striga damage at 10 WAP -0.03ns 0.02ns
Number of emerged Striga
plants at 8 WAP 0.94**
*, **, Significant at 0.05 and 0.01probability levels, respectively, and ns, not significant
Table 3.6: Correlation coefficients between genetic distance (GD) and traits used as indices for
drought tolerance in early maturing maize inbred lines.
Grain Anthesis silking Ear Plant Ears per Stay green
Yield interval aspect aspect plant character
Genetic Distance 0.39** 0.02ns -0.31** -0.25** 0.19* -0.30**
Grain yield -0.35** -0.80** -0.57** 0.49** -0.46**
Anthesis-silking
0.36** 0.11ns -0.28** 0.26**
interval
Ear aspect 0.52** -0.45** 0.56**
Plant aspect -0.35** 0.52**
Ears per plant -0.36**
*, **, Significant at 0.05 and 0.01probability levels, respectively, and ns, not significant
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3.3.3 Inter-trait relationship of the inbred lines under drought and Striga infestation
The stepwise multiple regression result identified ear aspect (EASP) as the most important
trait with significant direct contribution to grain yield explaining 46% of the variation in grain
yield under drought conditions (Fig. 3.3). Four traits (EPP, SL, HUSK, DYSK) were
identified in the second order having indirect effects on grain yield through EASP. Three of
the four traits had positive indirect effects while only EPP had the highest and negative
indirect effect (-0.649) through EASP. There were five traits in the third order: STGR, RL,
ASI, PASP and DA. Only two traits, EHT and PHT, were classified as the fourth-order traits
with significant indirect effects on grain yield while EHT had indirect effects through three of
the third-order traits: STGR (-0.358), ASI (-0.330) and PASP (-0.402).
In the Striga environments, ear aspect (EASP) was also identified by step-wise multiple
regression analysis as the only first-order trait of direct contribution to grain yield accounting
for 51% of the total variation (Fig. 3.4). Four traits (RAT 2, CO_1, ASI, RAT 1) were
identified in the second order having indirect contributions to yield. Striga damage at 10
WAP (0.657) and ASI (0.278) had positive indirect contributions to grain yield whereas
number of emerged Striga plants at 8 WAP (-0.302) and Striga damage at 8 WAP (-0.494)
had negative indirect effects on the variation obtained in grain yield. Seven traits (HUSK,
CO_2, RL, EHT, DYSK, DA and SL) were identified in the third-order but only husk cover
made contribution through two of the second-order traits ( RAT 2 and RAT 1). Five of the
seven third-order traits had positive values ranging from 0.156 to 0.919. Only EHT (-0.130)
and DA (-3.434) had negative indirect effects through the second-order traits. Ears per plant
(EPP) and plant height (PLHT) consistuted the fouth-order traits.
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Figure 3-3: Path analysis diagram displaying the relationship between traits of the inbreds screened under drought conditions at Ikenne and Bagauda, 2014 – 2015
Values written in bold are the error effects; direct path coefficients are the values in parenthesis while other values are correlation coeeficients. R1 is error effects; DA, days to
5 0% anthesis; DYS, days to 50% silking,; ASI, anthesis-silking interval; EASP, ear aspect; HUS, husk cover; EPP, ears per plant; PASP, plant aspect; PHT, plant height; EHT,
ear height, STGR, stay green characteristics; RL, root lodging; SL, stalk lodging and YIELD, grain yield.
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Figure 3-4: Path analysis diagram displaying the relationship of traits of the inbreds screened under artificial Striga infestation at Abuja and Mokwa 2014 – 2015
Values written in bold are the error effects; the direct path coefficients are values in parenthesis and other values are correlation coeeficients. R1 is error effects; YIELD, grain
yield; DA, days to 50% anthesis; DYSK, days to 50% silking,; ASI, anthesis-silking interval; PHT, plant height; EASP, ear aspect; EPP, ears per plant; HUSK, husk cover; PASP,
plant aspect; EHT, ear height, RAT 1 and RAT 2, Striga damage score at 8 and 10 WAP; CO_1 and CO_2, number of emerged Striga plants at 8 and 10 WAP; RL, root lodging;
SL, stalk lodging..
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3.4 Discussion
The genotypic mean squares were highly significant for most traits under Striga infestation,
drought stress and across research conditions indicating that there was large genetic variation
among the inbreds which should enable selection for drought tolerance, Striga resistance and
increased grain yield under the two conditions. The significant environment mean squares
observed for most measured traits under Striga infestation and drought stress indicated that
the test locations were unique in discriminating among the inbreds and that testing the inbred
lines in a wide array of locations over years is required to identify the most stable lines for
hybrid production (Badu-Apraku et al., 2007a; 2011a). The significant environment x
genotype interaction mean squares for grain yield, Striga damage at 8 and 10 WAP and Striga
emergence count at 10 WAP indicated that the inbred lines varied in their response to
infestation at the different locations and that there could be different biotypes of Striga
hermonthica at the experimental locations. This finding corroborates with the results of Badu-
Apraku et al. (2008) and Badu-Apraku and Lum (2010).
The 36 inbred lines were characterized as Striga resistant/tolerant using the base index which
combined grain yield, ears per plant, Striga damage, and number of emerged Striga plants.
Fifty percent (50%) of the inbred lines had positive base indices and therefore, had some level
of resistance/tolerance to Striga. Also, the inbred lines were categorized as drought tolerant
using the selection index which combined grain yield, ears per plant, anthesis-silking interval,
plant and ear aspects, and stay green characteristics. Forty-two percent (42%) of the inbreds
had positive values of the base index indicating some level of tolerance to drought.
The lack of significant phenotypic correlation between grain yield and number of emerged
Striga plants indicated that the number of emerged Striga plants was not a consistent trait for
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selecting for Striga resistance. Similar findings were recorded by Kim and Adetimirin (1995)
and Badu-Apraku et al. (2007a). Similarly, the strong phenotypic correlations between
number of emerged Striga plants at 8 and 10 WAP and Striga damage at 8 and 10 WAP
suggested that either of the parameters would be adequate for selecting for Striga resistance
corroborating the findings of Badu-Apraku et al. (2007a). This result raised the question as to
whether collection of data for both Striga damage and number of emerged Striga plants at 8
WAP and 10 WAP was needed considering the labour and time involved in measuring these
traits. The data for the two traits could be recorded at 8 WAP or 10 WAP without much
sacrifice in precision. The significant phenotypic correlation between grain yield and stay
green characteristic under drought stress indicated that the stay green characteristic was one
of the consistent traits for selecting grain yield under drought stress. This is in disparity with
the results of Badu-Apraku et al. (2012b) who reported that the most consistent traits for
selecting for yield under drought stress in extra-early maturing maize were ear aspect,
anthesis-silking interval, plant aspect and number of ears per plant. The discrepancy in the
findings of the two studies could be due to the differences in the maturity groups of the
inbreds used.
Furthermore, the causal relationships among traits under Striga infestation and drought were
examined using sequential path co-efficient analyses. EASP was selected as the most
important trait contributing to the variation in grain yield obtained under both Striga
infestation and drought environments. In addition, EPP, stalk lodging, husk cover and days to
silking were identified as the second order traits contributing to the change in grain yield
under drought condition while Striga damage at 8 and 10 WAP, number of emerged Striga
plants at 8 WAP and ASI were identified as the second order traits under Striga infestation.
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Under drought, stay green characteristic, root lodging, ASI, PASP and days to anthesis were
identified as third-order traits while ear and plant heights were identified as the fourth-order
traits. However, ear height had indirect effect through three of the five third-order traits
(STGR, RL, ASI, PASP and DS) indicating its potential value for selecting drought tolerant
lines. Similar studies have been conducted in WCA. Talabi et al. (2016) identified EASP,
EPP, PASP, STGR and ASI as the most consistent secondary traits in selecting drought
tolerant genotypes in a study involving 250 early maturing full-sib progenies. Similarily,
Badu-Apraku et al. (2012b) identified EPP, PASP, EASP, DS, ASI, PHT and EHT as the
most reliable traits in selecting extra-early maize drought-tolerant inbred lines. Also, Banziger
and Lafitte (1997) identified EPP, ASI and STGR as the most reliable indirect traits for
selecting drought and low-N tolerant genotypes.
Obviously, reliable secondary traits in selecting outstanding drought tolerant and Striga
resistant/tolerant genotypes may vary depending on the nature of the genetic material used,
prevailing climatic condition and location of the experiment, but some traits appear to be
consistent under different research conditions. In this study, EASP was the most consistent
secondary trait in selecting drought tolerant and Striga resistant genotypes. Badu-Apraku et
al. (2012b) and Talabi et al. (2016) reported similar findings for extra-early maturing maize
inbreds and early maturing full-sib progenies under drought environments.
The low to moderately high genetic distances among the inbreds indicated that the lines are
distinct. The neighbour-joining tree and the model based clustering approach of the
population structure both classified the inbred lines into four groups indicating high
correspondence between the two methods in grouping the lines into divergent genetic types
for hybrid production.
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3.5 Conclusions
Significant genotypic variation existed among the inbreds for grain yield and other agronomic
traits under Striga infested and drought environments indicating that progress could be made
in selecting for Striga resistance and drought tolerance.
The level of drought tolerance and Striga resistance in the 36 early maturing maize inbred
lines were determined using the base index. About 42% and 50% of the inbreds were
identified as drought tolerant and Striga resistant/tolerant respectively.
The inbred lines were categorised into four divergent groups using the cluster analysis and
population structure analysis.
Ear aspect was identified as a reliable secondary trait for selection for high grain yield under
both research conditions.
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Chapter Four
4.0 INHERITANCE OF RESISTANCE TO Striga hermonthica IN AN EARLY
MATURING MAIZE (Zea mays L.) INBRED LINES CONTAINING RESISTANCE
GENES FROM Zea diploperennis
4.1 Introduction
In maize research, Striga resistance is the ability of the host plant to suppress the germination
and attachment of the Striga plants resulting in reduced number of emerged Striga plants
while Striga tolerance is the ability of the host plant to survive and produce reasonable yield
in the presence of the attached Striga plants (Kim, 1994). Several reports indicating that
several genes control resistance to Striga have been documented (Kim, 1994; Ejeta et al.,
1997). The maize plants have expressed resistance to Striga hermonthica through different
mechanisms including low stimulation of strigolactones which stimulate Striga germination
(Kiruki et al., 2006), suppression of growth of the parasites (Amusan et al., 2008), low
induction of haustorial (Gurney et al., 2003), inability to support Striga emergence (Lane et
al., 1997; Gurney et al., 2003; Amusan et al., 2008) and escape through root architecture
(Amusan et al., 2008). The use of recurrent selection in introgressing favourable alleles for
Striga resistance in maize has been documented (Menkir and Kling, 2007; Badu-Apraku et
al., 2006, 2008). Equal contribution of the favorable alleles for improvement are obtained
when the trait is governed by additive gene action (Badu-Apraku et al., 2009). Information on
the mode of gene action governing the inheritance of resistance would facilitate the
introgression of resistance genes and deployment of resistant varieties (Akanvou and Doku,
1998).
Generation mean analysis is very useful in determining gene effects for polygenic traits
(Mather and Jinks, 1982). It has the ability to compute the digenic genetic effects such as
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additive × additive [i], additive × dominance [j] and dominance × dominance [l] interactions
(Singh and Singh, 1992). The preponderance of additive gene action over dominance gene
action for grain yield and Striga traits has been documented in maize (Badu-Apraku et al.,
2015, 2016; Akaogu et al., 2012). Partitioning of the genetic effects into its components:
additive, dominance gene effects (d and h) and the three types of digenic interactions, that is,
additive x additive (i), additive x dominance (j) and dominance x dominance (l) effects will
provide very vital information for planning an effective gene deployment schemes in the
Striga resistance improvement programs of SSA.
The objective of this study was to examine the mode of inheritance of an early maturing
maize inbred line, TZdEI 352 containing Striga resistance genes from Zea diploperennis and
a Striga susceptible inbred line TZdEI 425.
4.2 Materials and methods
The Striga resistant maize inbred, TZdEI 352 was crossed to the Striga susceptible inbred,
TZdEI 425. The F1 progeny were selfed and backcrossed to the resistant and susceptible
parents to obtain F2, BC1P1 and BC1P2 generations. Field evaluations of the parents, F1, F2, and
backcrosses were carried out in 2015 at the IITA research stations at Mokwa and Abuja.
Both locations are in the southern Guinea savanna agro-ecology of Nigeria, where Striga is
endemic. The experimental design was randomized complete block design and the trial was
replicated four times under artificial infestation with Striga seeds. The plots were 4 m long
with 0.75 m apart and 0.4 m between plants in a row. The experimental units were three row
plots for the parental inbreds and F1 generations, six-row plots for the BC1P1 and BC1P2
generations, and a twelve-row plots for the F2 progeny generation. One week before planting,
ethylene gas was applied at both locations to cause suicidal germination of the seed of the
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parasite present in the soil. The ethylene gas was injected into the soil at a depth of 12 cm.
This was repeated at intervals of 1 m. At planting, 8.5 g sand/Striga mixture (5,000
germinable Striga seeds) was placed in each planting hole with three maize seeds which were
later thinned to two plants per hill at ten days after emergence giving a final population
density of 66,667 plants per hectare. About 20 kg ha-1 each of N, P, and K was applied as 15-
15-15 NPK two weeks after planting and additional 10 kg ha-1N in form of urea was applied
at five weeks after planting. Weeds other than Striga were removed by hand.
At each location, 30 plants from the homozygous generations (P1, P2 and F1), 60 plants from
backcross generations (BC1P1) and (BC1P2) and 120 plants from the F2 generations were
assessed for Striga damage and number of emerged Striga plants at 8 and 10 weeks after
planting (WAP) in each replicate. Striga damage was recorded on a scale of 1 to 9 (1 =
normal plant growth, no visible damage; 9 = severe damage or death) as proposed by Kim
(1991; 1994).
The generation means were utilized in the analyses of variance for all tests. The data were
first analyzed using the F-test to detect differences in mean performance of the generations
studied. Generation mean analysis (GMA) was conducted on Striga damage and number of
emerged Striga plants at each location. Homogeneity of variances was tested using Bartlett’s
test (Bartlett, 1937) to determine if the data from the two locations could be pooled for
combined analysis and the test was significant, hence data from individual environments were
analysed separately. Data were subjected to generation mean analyses using the sequential
model fitting procedure to determine the simplest and yet adequate model to describe the
data. Since the additive-dominance model was found to be inadequate to explain the observed
variations, estimates of additive, dominance and digenic gene interaction were computed as
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described by Mather and Jinks (1982). The mean effect m, pooled additive effect [d], pooled
dominance effect [h], pooled additive x additive interaction effect [i], pooled additive x
dominance interaction effect [j] and pooled dominance x dominance interaction effect [l] are
related to the generation means according to the following equations:
m = (P1)/2 + (P2)/2 + 4F2 - BC1P1 - BC1P2
[d] = (P1)/2 - (P2)/2
[h] = -(3P1)/2 - (3P2)/2 - F1 - F2 + 6BC1P1 + 6BC1P2
[i] = -4F2 + 2BC1P1 + 2BC1P2
[j] = -P1 + P2 + 2BC1P1 - 2BC1P2
[l] = P1 + P2 + 2F1 + 4F2 - 4BC1P1 - 4BC1P2
The genetic parameters were estimated by unweighted regression analyses. The adequacy of
each model was based on statistical significance of the genetic parameters and included the
coefficient of determination, R2, the residual mean squares, F-test and the chi-square test of
agreement between the observed and predicted mean of each generation estimated from the
fitted mode. Significance of the estimates was tested by the standard error of each of the
parameters.
Significant parameters were re-estimated by the weighted least-square method using matrix
procedure (Mather and Jinks, 1982). Weights were computed as reciprocals of the variances
of generation means. Expected generation means were estimated only from significant gene
effects by the following equations:
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P1 = m + [d] + [i]
P2 = m – [d] + [i]
F1 = m + [h] + [l]
F2 = m + ½[h] + ¼[l]
BC1P1 = m + ½[d] + ½[h] + ¼[i] + ¼[j] + ¼[l]
BC1P2 = m - ½[d] + ½[h] + ¼[i] - ¼[j] + ¼[l]
4.3 Results
The means of the six generations for Striga damage and number of emerged Striga plants
evaluated in Mokwa and Abuja are presented in Table 4.1. Although parental lines were fixed
inbreds (S8), there were genetic variation in levels of Striga damage and number of emerged
Striga plants among the lines studied. The generation mean analysis of Striga damage and
number of emerged Striga plants predicted from additive and dominance gene effects alone
deviated significantly from the observed means. Therefore, the simple additive-dominance
model was inadequate to explain the differences in Striga damage and number of emerged
Striga plants of the generations. Hence, the digenic interactions was included in the simple
addidtive-dominance model (Table 4.2). The genetic effects were higher in Mokwa than
Abuja for the traits studied. Among the main effects, additive (d) was higher than the
dominance (h) component for Striga damage at 8 and 10 WAP in Mokwa while dominance
(h) was greater than the additive (d) component for Striga damage at 10 WAP in Abuja, and
number of emerged Striga plants at both location.
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Table 4.1: Means of Striga damage and number of emerged Striga plants of the six generations (P1, P2, F1, F2, BC1P1, and BC1P2)
evaluated at Mokwa and Abuja in 2015
Abuja Mokwa
Number of emerged Striga Number of emerged Striga
Generation Striga damage plants Striga damage plants
8 WAP 10 WAP 8 WAP 10 WAP 8 WAP 10 WAP 8 WAP 10 WAP
P 1 3.00±0.0 3.33±0.33 3.00±0.57 5.33±0.88 2.50±0.50 3.75±0.75 14.25±2.36 15.75±2.06
P2 5.00±0.0 6.33±0.33 21.67±7.22 28.33±7.54 4.25±0.25 6.25±0.25 35.25±14.96 36.00±16.04
F1 2.00±0.0 2.67±0.33 10.33±7.13 12.33±8.25 3.50±0.5 4.25±0.63 25.00±8.38 26.75±8.66
F2 2.70±0.05 3.17±0.06 0.31±0.11 0.35±0.11 3.59±0.05 5.12±0.06 1.48±0.27 1.85±0.28
BC1P1 2.30±0.04 2.48±0.05 0.31±0.13 0.36±0.13 2.44±0.07 3.84±0.06 1.96±0.41 2.36±0.42
BC1P2 3.00±0.08 3.44±0.10 0.56±0.19 0.62±0.19 3.86±0.06 5.19±0.22 1.86±0.46 2.14±0.44
Mean 2.70 3.08 0.51 0.58 3.37 4.81 1.98 2.34
SE ± 0.03 0.04 0.10 0.11 0.04 0.04 0.23 0.23
Mid-parent 4.00 4.83 12.34 16.83 3.38 5.00 24.75 25.88
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Table 4.2: Estimates of genetic components of means for Striga damage and number of emerged Striga plants at Mokwa and Abuja in
2015 obtained by the weighted least square (Mather and Jinks, 1982)
Parameter Abuja Mokwa
Striga damage Number of emerged Striga Striga damage Number of emerged Striga
10 WAP 8 WAP 10 WAP 8 WAP 10 WAP 8 WAP 10 WAP
m 2.91±0.19 0.42±0.27 0.46±0.31 3.45±0.11 4.70±0.15 1.82±0.73 2.24±0.86
[d] 1.24±0.44 -0.24±0.70 -0.18±0.86 1.25±0.30 1.48±0.35 -1.07±2.12 -1.57±2.40
[h] -2.59±0.91 -5.81±2.30 -10.39±3.35 0.46±1.45 -0.24±1.89 -20.39±12.09 -23.21±11.09
[i] 0.14±0.94 0.62±1.65 0.68±1.67 -1.51±0.52 -2.18±0.43 2.94±5.56 2.89±5.80
[j] -0.72±1.46 -9.35±12.94 -8.82±14.41 0.90±2.34 0.62±3.64 18.38±34.93 22.94±36.15
[l] 2.68±1.53 25.95±26.21 34.63±28.40 -0.28±4.66 -0.95±6.63 72.73±20.54 72.21±19.30
*WAP, weeks after planting; m, mean effect; d, pooled additive effect; h, pooled dominance effect; i, pooled additive x additive interaction effect; j, pooled
additive x dominance interaction effect; l, pooled dominance x dominance interaction effect.
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Among the interactions, additive x additive interactions (i) were larger than additive x
dominance (j) and dominance x dominance (l) for Striga damage while dominance x dominance
interactions (l) were larger than (i) and (j) for number of emerged Striga plants.
Estimates of additive gene effects showed variation in magnitude and sign for number of
emerged Striga plants at both locations while the dominance effects were similar. In both
locations, the (d) and (dd) were in opposite directions for Striga damage and number of
emerged Striga plants except for Striga damage at 10 WAP in Mokwa indicating that the
nature of epistasis is duplicate. The models that incorporated digenic gene interaction were
adequate to explain the variation in Striga damage and Striga emergence at Abuja and Mokwa
(Table 4.3).
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Table 4.3: Significant gene effects by weighted least square (Mather and Jinks, 1982) for Striga
traits and adequacy of associated models
Parameter Gene
Location Trait Number type df X2 value
Abuja Striga damage at 10 WAP 5 m,d,h,i,l 1 5.76
Number of emerged Striga
plants at 8 WAP 5 m,d,h,j,l 1 0.59
Number of emerged Striga
plants at 10 WAP 5 m,d,h,j,l 1 0.75
Mokwa Striga damage at 8 WAP 5 m,d,h,i,l 1 4.42
Striga damage at 10 WAP 5 m,d,h,i,l 1 0.09
Number of emerged Striga
plants at 8 WAP 5 m,d,h,j,l 1 1.08
Number of emerged Striga
plants at10 WAP 5 m,d,h,j,l 1 0.93
WAP, weeks after planting; m, mean effect; d, pooled additive effect; h, pooled dominance effect; I, pooled additive
x additive interaction effect; j, pooled additive x dominance interaction effect; l, pooled dominance x dominance
interaction effect
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4.4 Discussion
The resistant parent had lower means for number of emerged Striga plants and Striga damage
than the susceptible parent indicating that Striga resistance was expressed by low value for
Striga damage and fewer number of emerged Striga plants.. Similar results have been
reported in Zea mays (Kim et al., 1999; Mbogo et al., 2015) and Sorghum bicolor (Oliver et
al, 1991; Arnaud et al., 1999). The BC1P1 means for number of emerged Striga plants at 8
and 10 WAP and Striga damage at 10 WAP skewed towards the resistant parent P1. This
indicated that Striga resistance/tolerance is quantitatively inherited and controlled by several
genes. Also, the distribution of the population when backcrossed to the susceptible parent
(BC1P2) was skewed towards TZdEI 425, the susceptible parent P2. In general, backcrossing
to the susceptibile parent increased allele frequency for susceptibility while alleles frequency
for resistance increased when backcrossing to the resistant parent therefore there was a shift
towards the resistance direction.
The significant dominant genetic effects (d) for number of emerged Striga plants and Striga
damage at Abuja and the high mean values of dominance over additive mean values implied
that dominance effects controls the inheritance of Striga resistance and tolerance. This finding
corroborated that of Akanvou et al. (1997), who found out that dominance genetic effects
were more important than additive genetic effects in regulating inheritance of number of
emerged Striga plants. In contrast, Gethi and Smith (2004), reported that additive gene effects
played more important role than dominance gene effects in regulating all the measured
resistance traits.
At Mokwa, additive genetic effects were significant for Striga damage while dominance
genetic effects were significant for number of emerged Striga plants at 10 WAP only. The
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effects of the additive gene action were higher than dominance effects for Striga damage
while dominance effects were higher than additive effects for number of emerged Striga
plants. This implied that additive gene actions controlled Striga damage while dominance
gene actions controlled number of emerged Striga plants. Similar results have been reported
by Akanvou et al. (1997). The differences in expression of the Striga damage at Abuja and
Mokwa suggested the presence of genotype and environment interactions and that probably
different biotypes of Striga hermonthica existed at Mokwa and Abuja test locations. Similar
results were reported by Badu-Apraku et al. (2015). The models that included the digenic
gene interactions in addition to additive and dominance effects were adequate in explaining
the variation for Striga damage and number of emerged Striga plants among the generations
studied, there was no need for fitting a higher order model incorporating trigenic interactions.
This suggested the involvement of epistasis in inheritance of Striga resistance from Zea
diploperennis background in tropical maize germplasm. This is in agreement with the results
of Adetimirin et al. (2001) who concluded that epistasis was involved in maize host resistance
to Striga.
According to Mather and Jinks, (1982), genetic interaction is said to be duplicating when the
(d) and (dd) estimates have the opposite signs and are complementary when they are similar.
Gene interactions for Striga damage and number of emerged Striga plants were of duplicate
type since the (d) and (dd) estimates had opposite signs. The presence of duplicate type of
gene interaction confirmed the iinvolvement of epistatic effects.
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4.5 Conclusion
The model that incorporated digenic interactions was adequate in explaining the variation
observed in inheritance to Striga resistance among the generations studied. Striga resistance
genes from Zea diploperennis showed duplicate epistatic interactions which is similar to
those found in tropical maize germplasm. Improvement of traits using recurrent selection will
not be the most appropriate since dominance effects were more important than additive
effects. It is of pratical application in hybrid breeding.
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Chapter Five
5.0 COMBINING ABILITY AND HETEROTIC GROUPING OF Striga RESISTANT
AND DROUGHT TOLERANT EARLY MATURING MAIZE INBRED LINES
AND THEIR PERFORMANCE IN HYBRID COMBINATIONS
5.1 Introduction
The availability of early maturing varieties (90–95 days to maturity) and extra-early maturing
varieties (80–85 days to maturity) has allowed maize to expand into the dry savannas of
WCA, replacing millet (Pennisetum glaucum (L.) R. Br.,) and sorghum (Sorghum bicolor
(L.) Moench), the traditional cereals thus reducing food scarcity in the middle of the year,
when food reserves are almost empty after the long dry season. However, Striga hermonthica
(Del.) Benth parasitism and recurrent drought are the two major constraints to maize
production in the sub-region. Consequently, breeding for early and extra-early maize cultivars
with improved tolerance to drought stress and tolerance to Striga that fit into areas with a
short duration of the growing season is essential to improve productivity and ensure stable
production of the crop.
Development of hybrid varieties, their promotion and adoption, are promising strategies for
an appreciable increase in maize production and to revolutionize agriculture in WCA. A
number of seed companies have sprung up in the sub-region in the last decade, setting the
stage for commercial hybrid seed production. However, there are no commercial early
maturing hybrids with resistance/tolerance to Striga and drought available to these companies
to produce for the farmers in the savanna where there are short growth periods and Striga
hotspots. Fortunately, the IITA maize programme has developed a large number of early and
extra-early inbred lines with improved tolerance to Striga and drought at the flowering and
grain filling stages (Badu-Apraku and Oyekunle, 2012). Information on the general and
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specific combining abilities of inbred lines is very vital in identifying productive hybrids for
commercial hybrid production without making all possible crosses among the parental
inbreds. The major challenge facing IITA’s maize breeders presently is to test these inbreds in
hybrid combinations to identify and promote commercialization of productive hybrids with
tolerance to Striga and drought in WCA.
The objectives of the present study were to:
(i) determine the combining abilities and heterotic grouping of the early maturing
maize inbreds under Striga infested and drought environments.
(ii) identify high yielding and stable hybrids across Striga-infested, drought,
optimal growing environments and across the three research environments for
commercialization in the sub-region.
(iii) determine the inter-trait relationship of early maturing maize hybrids under the
Striga-infested and drought conditions.
5.2 Materials and methods
5.2.1 Genetic materials
The genetic materials used for this study were 30 inbred lines (Table 5.1) selected from the 36
lines screened for Striga resistance and drought tolerance in chapter three of this thesis. The
lines were selected based on their performance under Striga infestation and drought. The 30
inbreds were divided into six sets, with each set comprising five inbred lines. The five inbred
lines in each set were used as females and crossed with five inbred lines in another set used as
males as proposed in the NC II mating design (Comstock and Robison, 1948). Each inbred
line was used as a female parent in one set and as a male parent in another set. A total of 150
crosses (6 sets x 25 hybrids) were made.
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Table 5.1: Pedigree of the selected 30 white early maturing maize inbred lines used in North
Carolina Design II study
Inbreds Pedigree Reaction to stress
Striga hermonthica Drought
TZdEI 71 TZEE-W POP STR 104 S6 98/208-2/2-2/2-1/2-1/2 Susceptible Tolerant
TZdEI 124 TZEE-W POP STR 104 S6 83/208-1/1-2/2-3/4-1/3 Resistant Susceptible
TZdEI 202 TZE-W POP STR 108 S6 195/198-1/2-1/2-2/2-2/4 Susceptible Tolerant
TZdEI 315 TZEE-W POP STR 107 S6 53/254-2/2-3/3-1/3-2/3 Susceptible Susceptible
TZdEI 399 TZE-W POP STR 107 S6 118/254-2/2-1/1-2/2-1/3 Susceptible Tolerant
TZdEI 260 TZEE-W POP STR 108 S6 93/198-1/1-3/3-1/2-1/5 Tolerant Susceptible
TZdEI 479 TZEE-W POP STR 105 S5 126/253-1/2-1/2-2/3-1/4 Tolerant Susceptible
TZdEI 82 TZE-W POP STR 104 S6 98/208-2/2-2/2-1/2-2/3 Susceptible Susceptible
TZdEI 485 TZEE-W POP STR 105 S5 197/253-2/2-1/2-1/2-1/3 Susceptible Tolerant
TZdEI 352 TZE-W POP STR 107 S6 24/254-1/2-1/1-1/1-2/2 Resistant Tolerant
TZdEI 441 TZE-W POP STR 107 S6 232/254-1/1-1/4-2/3-1/2 Tolerant Susceptible
TZdEI 84 TZEE-W POP STR 104 S6 98/208-2/2-1/2-1/3-3/5 Susceptible Susceptible
TZdEI 280 TZE-W POP STR 108 S6 65/198-1/1-2/2-1/2-4/5 Susceptible Susceptible
TZdEI 357 TZE-W POP STR 107 S6 37/254-2/2-2/2-1/3-2/2 Resistant Susceptible
TZdEI 492 TZE-W POP STR 105 S6 2/253-1/1-2/2-1/2-1/2 Susceptible Tolerant
TZdEI 98 TZE-W POP STR 104 S6 83/208-1/1-2/2-2/4-5/5 Tolerant Tolerant
TZdEI 157 TZE-W POP STR 104 S6 22/160-1/3 Tolerant Tolerant
TZdEI 173 TZE-W POP STR 104 S6 41/160-1/2 Susceptible Tolerant
TZdEI 283 TZE-W POP STR 108 S6 34/198-1/2-1/3-1/2-2/3 Resistant Tolerant
TZdEI 105 TZE-W POP STR 104 S6 83/208-1/1-2/2-1/4-1/4 Tolerant Tolerant
TZdEI 120 TZEE-W POP STR 104 S6 18/208-2/2-3/4-1/2-2/2 Susceptible Susceptible
TZdEI 131 TZE-W POP STR 104 S6 83/208-1/1-2/2-3/4-3/4 Tolerant Susceptible
TZdEI 264 TZEE-W POP STR 108 S6 54/198-1/1-1/4-3/3-4/9 Susceptible Tolerant
TZdEI 378 TZE-W POP STR 107 S6 53/254-2/2-3/3-3/3-1/2 Susceptible Susceptible
TZdEI 268 TZEE-W POP STR 108 S6 1/198-1/1-2/2-2/2-1/4 Tolerant Susceptible
TZdEI 314 TZEE-W POP STR 107 S6 53/254-2/2-3/3-2/3-1/3 Susceptible Susceptible
TZdEI 396 TZE-W POP STR 107 S6 85/254-1/1-2/3-3/3-1/1 Susceptible Tolerant
TZEI 7 WEC STR S7 Inbred 12 Tolerant Tolerant
TZEI 18 TZE-W Pop STR Co S6 Inbred 136-3-3 Susceptible Tolerant
TZEI 31 TZE-W Pop x LD S6 Inbred 4 Susceptible Tolerant
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5.2.2 Field evaluation
One hundred and fifty single cross maize hybrids plus six hybrid checks were assessed under
end of season drought at Bagauda during the 2013 and 2015 growing seasons and Minjibir in
2015, managed drought stress at Ikenne during the 2013/2014 and 2015/2016 dry seasons.
The hybrids were also screened under artificial Striga infestation at Mokwa and Abuja and
optimal conditions (rain-fed, no drought and no artificial Striga infestation) at Mokwa, and
Ikenne during the 2013 and 2015, and at Abuja in 2015 growing seasons in Nigeria. The trials
were laid out as 12 x 13 randomized incomplete block design with two replications. Single
rows of 4 m long spaced 0.75 m apart and 0.4 m between plants in a row were used. At two
weeks after planting, number of plants were reduced to two plants/hill resulting in a
population density of 66,667 plants ha-1 at all sites. The hybrids were screened under artificial
Striga infestation at Mokwa and Abuja using the IITA Striga infestation method (Kim, 1991)
that ensures uniform infestation of each hill with about 5000 Striga seeds. The managed
drought stress at Ikenne was achieved by stopping irrigation water from 28 days after planting
till harvesting, so that the maize plants depend on available water in the soil for physiological
processes. Standard agronomic practices as described in Chapter three of this thesis were
applied to the hybrid trials at all sites.
5.2.3 Data collection
Data recorded for the optimum experiment are days to 50% silking (DS) and 50% anthesis
(DA), anthesis-silking interval (ASI), plant and ear heights, number of ears/plot (EPP), husk
cover, root and stalk lodging, ear aspect and plant aspect. Grain yield was calculated in kg/ha
and estimated based on 80% shelling percentage and adjusted to 15% moisture. Observations
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made on the Striga-infested and drought environments are as described in Chapter three of
this thesis.
5.2.4 Data analyses
Data on the number of emerged Striga plants was transformed using [log (counts+1)] to
reduce the heterogeneity of variances for Striga counts while data for stalk and root lodging
in percentages were transformed using arcsine. Analysis of variance (ANOVA) was
performed separately on plot means for grain yield and other measured traits under Striga-
infested, drought and rain-fed conditions in SAS with PROC general linear model using a
RANDOM statement with the TEST option (SAS Institute, 2011). Similarly, ANOVA was
done for all traits across research conditions. In the ANOVA, the location–year combinations
(environment), blocks and replicates were considered as random factors while hybrids
(genotypes) were considered as fixed effects and the adjusted means and standard errors
estimated. The ANOVA of NC II crosses for each location-year combination was performed
on the entries without the checks using PROC general linear model in SAS using a
RANDOM statement with the TEST option (SAS Institute, 2011).
The general linear model for NC II mating design is:
Where,
th th
Xijklm = the observed value of the progeny of the i female, jth male in the k replication
within set l and in the mth environment
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µ = population mean;
= th Si average effect of the l set;
= GCA effect common to all hybrids of the ith female nested within lth set
= th GCA effect common to all hybrids of the j male nested within lth set;
= th th SCA effect of hybrid from the i female and j male nested within lth set;
= average effect of the mth environment;
= effect of the kth replication nested within the lth set and mth environment;
=Interaction between the set effect and the environment;
and = Interaction between environment and GCA nested within sets;
= Interaction between environment and SCA nested within sets
= the experimental error
The genotype (hybrid) component of variance was partitioned into variance due to female
parent, male parent and their interactions (female x male). The effects of the female sets and
male sets is the GCA effect while the female x male sets interaction represents SCA effects
(Hallauer and Miranda, 1988). The F-test for GCA-female, GCA-male and SCA mean
squares was calculated using the mean squares values for their interaction with the
environment. The mean squares for environment x SCA were tested using the pooled error
mean squares.
The proportionate contribution for each trait was calculated as percentage of the sum of
squares for the genotype attributed to GCA and SCA as follows:
Contribution of GCAmale = [ssm / (ssm + ssf + ssmf) x 100]
Contribution of GCAfemale = [ssf / (ssm + ssf + ssmf) x 100]
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Contribution of SCA (%) = [ssmf / (ssm + ssf + ssmf) x 100]
Where:
ssm = sum of squares due to males within sets,
ssf = sum of squares due to females within sets,
ssmf = sum of squares due to male x female within sets interaction
The relative GCAmales, GCAfemales and SCA effects for grain yield were computed from
the adjusted means using the line x tester approach (Singh and Chaudhary, 1985)
GCAmales = Xj -Y
GCA females = Xi – Y
SCA=Xij –Xi –Xj +Y
Where:
Xj = the mean of hybrids with a given male averaged over replicates, environments and
females,
Xi = the mean of hybrids with a given female averaged over replicates, environments and
males,
Xij = the mean of a given hybrid averaged over replicates, environments and females,
Y = the experimental mean.
Standard errors for GCA effects were computed as described by Cox and Frey (1984):
1⁄2 1⁄2
SEGCA=(MSfe[f-1)/mfer] or (MSme[m-1)/mfer]
1⁄2
SE SCA = ([MSfme(m-1)(f-1) / mfer])
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Where, MSfe, MSme and MSfme are the respective female x environment, male x environment
and female x male x environment interaction mean squares and were multiplied by the
appropriate proportion of total number of observations (female x male x replicate x
environment). The test for the significance of the general and specific combining ability
effects was done using t–tests, with t = GCA / SEGCA or SCA / SE SCA, respectively.
The mid-parent (MPH) and better parent (BPH) heterosis for grain yield, days to 50%
anthesis and silking, ear aspect, plant aspect, ears per plant, Striga damage at 8 and 10 WAP,
number of emerged Striga plants at 8 and 10 WAP and the stay-green characteristic for a
cross was computed as follows:
Heterosis values for mid-parent (MPH) and better-parent (BPH) was calculated as:
!"# = ('( − !")!" + 100
."# = (/0123)23 + 100
where, F1 is the mean of the hybrid and MP = (P1 + P2)/2 in which P1 and P2 are the
respective means of the inbred parents, BP = the mean of the better parent.
Broad-sense heritability (H) of grain yield and other traits were estimated for each
environment as
2 2 2 2
H =σ g /(σ g +σ g×e / e +σ e / re)
where σ 2g is variance for genotype, σ2gxe is variance for genotype x environment and σ 2e is
error variance, e is number of environments, and r is number of replications per environment.
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Repeatability of the traits (Falconer and Mackay, 1996) under drought, Striga infested,
optimal and across environments were computed on genotype-mean basis using the following
formula: environments
where r is the number of replicates per environment; e is the number of environments; σ 2g is
variance due to genotypes; σ2ge is variance due to genotype × environment interactions; σ2
represents the estimate of experimental error variance. The restricted maximum likelihood
(REML) method in SAS MIXED procedure was used to estimate variances. One out of the 14
environments with heritability of grain yield less than 0.30 were removed from all analyses
(Table 5.2).
The base indices as described in chapter three for Striga resistance and drought tolerance
were used in identifying Striga resistant and drought tolerant single-cross hybrids for
commercial production.
The grain yield of the selected 35 early hybrids were subjected to the additive main effects
and multiplicative interaction (AMMI) analysis to examine the relationships among hybrids
(G), environments (E) and G × E interaction and to select stable and high yielding hybrids for
commericalization in WCA. The AMMI model was described by Zobel et al. (1988), Gauch
and Zobel (1988), and Crossa (1990). This analysis uses principal component analysis to
break down (G × E) effects into a number of interaction principal component axes (IPCAs).
The genotype main effect plus G × E interaction (GGE) biplot software Windows application
that fully automates biplot analysis (Yan, 2001) was used for the AMMI analysis. The AMMI
model equation of Sadeghi et al. (2011) was used.
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Table 5.2: Environments, locations, research conditions and years of evaluation of early maturing maize hybrids under Striga-infested, drought
and optimal environments in Nigeria.
Grain yield
Environment Location Research condition Year (kg ha-1) Repeatability
1 Mokwa Striga-infested 2013 3765 0.72
2 Mokwa Striga-infested 2015 3091 0.27
3 Abuja Striga-infested 2013 2807 0.68
4 Abuja Striga-infested 2015 3767 0.61
5 Ikenne Managed Drought 2013/2014 2367 0.77
6 Ikenne Managed Drought 2015/2016 698 0.51
7 Minjibir Managed Drought 2015 547 0.34
8 Bagauda Terminal Drought 2013 3966 0.63
9 Bagauda Terminal Drought 2015 4159 0.58
10 Mokwa Rainfed 2013 5539 0.69
11 Mokwa Rainfed 2015 7203 0.68
12 Ikenne Rainfed 2013 4210 0.81
13 Ikenne Rainfed 2015 6575 0.68
14 Abuja Rainfed 2015 6041 0.36
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The AMMI biplot was used to obtain information on the hybrids that were the most suitable
under each stress conditions as well as optimal environments and to investigate the stability of
hybrids in the contrasting environments. Sequential path co-efficient analyses were performed to
explain the causal relationships among traits under each research conditions as described by
Mohammadi et al. (2003). The regression analysis was used to place the predictor traits into first,
second, third and fourth order based on their individual contributions to the total variation in
grain yield with minimized multicolinearity (Badu-Apraku et al., 2012a; 2014, Talabi et al.,
2016). At first, all other traits were regressed on grain yield and those with significant
contributions to grain yield at P < 0.05 were identified as first order traits. Subsequently, traits
that were not identified as first-order traits were regressed on each of the first order traits to
identify those with significant contributions to grain yield through the first-order traits and they
were categorized as second-order traits. The procedure was repeated to identify traits in
subsequent orders. The path coefficients were the standardized b-values from the output of the
stepwise regression analysis (Mohammadi et al., 2003; Badu-Apraku et al., 2012a; 2014, Talabi
et al., 2016). The stepwise multiple regression analysis tested the significance of the path
coefficients using t-test at 0.05 level of probability and retained only traits with significant path
coefficients and indicated the percentage of the variation they accounted for in the dependent
variable.
The HGCAMT method proposed by Badu-Apraku et al. (2013b) was adopted for the grouping
of the 30 early maturing maize.. The GCA effects of 10 traits that had significant mean squares
across research conditions were standardized. The traits included grain yield, days to 50% silking
and anthesis, ASI, plant and ear height, plant and ear aspects, root lodging and ears per plant.
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Using SAS software version 9.3, the standardised GCA effects were used for the Ward‟s
minimum variance cluster analysis to construct for the groupings of the inbred lines (SAS
Institute, 2011).
5.3 Results
5.3.1 Performance of early maturing maize inbred lines under contrasting environments
The combined ANOVA of the 156 early maturing single cross hybrids evaluated across two
locations (Mokwa and Abuja) under Striga infestation in 2013 and 2015, showed highly
significant (P < 0.01) hybrid (G), environment (E) and hybrid x environment interaction (GEI)
mean squares for all measured traits (Table 5.3). Significant (P < 0.01) sets and environment x
sets interaction effects were obtained for most measured traits except sets mean squares for
Striga damage at 8 WAP and environments x sets interactions mean squares for ASI and ears per
plant (EPP). Similarly, the variation among male (GCA-male), female (GCA-female) in sets and
the male x female interactions (SCA) were highly significant (P < 0.01) for all measured traits
except SCA for ASI and EPP.
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Table 5.3: Mean squares for grain yield and other agronomic traits of 156 early maturing maize hybrids screened under Striga-
infested environments at Abuja and Mokwa in 2013 and 2015.
Source of variation df Grain yield Days to Anthesis Striga damage score Number of emerged Ear Ears
( kg ha-1) 50% - silking (scale 1-9) Striga plants aspect per
silking Interval plant
(days)
8 WAP 10 WAP 8 WAP 10 WAP
Environment (E) 2 87669271.9** 2769.24** 45.18** 10.56** 2.82* 62.30** 22.92** 4.33** 1.46**
SET 5 3590873.0** 83.89** 9.80** 1.22ns 1.89* 17.77** 8.95** 2.23** 0.08**
E*SET 10 5133848.0** 17.07** 1.03ns 2.26** 1.26* 1.61** 1.74** 1.39** 0.03ns
REP(E*SET) 15 518716.4ns 4.82ns 2.44ns 0.46ns 0.71ns 0.67ns 0.42ns 0.57ns 0.02ns
BLOCK(E*REP) 72 1955079.9** 9.12** 2.74** 2.32** 2.01** 0.72** 0.57** 1.46** 0.01ns
HYBRID (G) 155 2895635.2** 18.79** 3.25** 3.08** 3.19** 2.56** 1.65** 1.68** 0.04**
GCAM/SET 24 4989075.8** 23.03** 5.52** 5.12** 5.02** 3.88** 2.42** 2.13** 0.04**
GCAF/SET 24 4731992.5** 33.47** 4.40** 5.69** 6.66** 3.36** 2.52** 3.80** 0.06**
SCA/SET 96 1320972.3** 7.78** 1.60ns 1.35** 1.06** 1.10** 0.75** 0.82** 0.02ns
G * E 310 1951558.3** 7.01** 2.18** 1.10** 1.07** 0.76** 0.64** 0.81** 0.03**
E*GCAM/SET 48 2729848.9** 7.10** 2.56** 0.97ns 1.02** 0.90** 0.85** 0.86** 0.04**
E*GCAF/SET 48 2374399.6** 7.85** 2.66** 1.26** 1.66** 1.10** 0.99** 1.01** 0.03**
E*SCA/SET 192 1404975.6** 5.63** 1.87ns 0.93* 0.87* 0.58** 0.45** 0.67** 0.02ns
Error 359 622139.0 3.67 1.60 0.72 0.67 0.43 0.33 0.43 0.02
*, **, Significant at 0.05 and 0.01probability levels, respectively, and ns, not significant
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Furthermore, interactions of the GCA-male and GCA-female with the environment were highly
significant for yield and other traits except E x GCA-male for Striga damage at 8 WAP. Also,
the SCA x environment interactions were significant (P < 0.05) for most measured traits except
ASI, and EPP. The variation due to GCA-male and GCA-female were higher than those of SCA
for all measured traits under Striga infestation.
Under drought conditions, highly significant effects were observed among hybrids,
environments, hybrid x environment interactions, GCA-male, GCA-female, SCA, and the
interactions of GCA-male, GCA-female and SCA with the environment, for all measured traits
except SCA and the interactions of GCA-female and SCA with the environment for ASI (Table
5.4). The GCA-male and GCA-female mean squares were substantially larger than SCA effects
for all traits under drought.
Under optimal environments, significant differences were observed among hybrids, environment,
G x E interaction, GCA-male, GCA-female and GCA-male x E interaction effects for all traits
(Table 5.5). However, the SCA effect, GCA-female x E interaction and SCA x E interaction
mean squares were significant for all traits except SCA and GCA-female x E interaction mean
square for ASI and SCA x E interaction effects for ASI, EPP and root lodging. The GCA-male
and GCA-female effects were substantially larger than SCA effects for all traits.
Across the three research conditions (Striga infestation, drought and optimal growing
conditions), ANOVA showed that variation due to hybrids, environments, GCA-male, GCA-
female, SCA and their interactions were highly significant (P < 0.01) for grain yield and other
agronomic traits common across the research environments (Table 5.6). The GCA-male and
GCA-female effects were substantially larger than SCA effects for all traits.
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Table 5.4: Mean squares for grain yield and other agronomic traits of 156 early maturing maize hybrids screened under drought
environments at Ikenne and Bagauda in 2013 and at Ikenne, Bagauda and Minjibir in 2015
Source of df Grain Yield (kg Days to Days to Anthesis- Plant height Ear Root Plant Ears Stay green
variation ha-1) 50% 50% silking (cm) aspect lodging aspect per characteristic
anthesis silking interval (scale 1- (scale 1- plant (scale 1-9)
9) 9)
Environment (E) 4 950272343.00** 3355.96** 5575.23** 492.03** 240290.02** 337.23** 40.91** 663.86** 19.98** 436.97**
SET 5 11609167.00** 137.26** 154.48** 5.29ns 805.28** 7.44** 1.74** 3.75** 0.16** 1.44ns
E * SET 20 10213470.00** 51.38** 46.49** 4.48* 2075.93** 2.50** 2.05** 3.37** 0.13** 3.86**
REP(ENV*SET) 25 8028967.00** 6.37** 7.93* 1.99ns 250.58ns 0.70* 0.46ns 1.34ns 0.04 0.78ns
BLOCK(E*REP) 122 5307794.00** 9.80** 15.81** 3.66** 604.30** 1.22** 0.60** 3.10** 0.08** 2.96**
HYBRIDS (G) 155 5374750.00** 20.10** 23.13** 3.63** 633.55** 2.03** 0.65** 2.35** 0.11** 2.76**
GCAM/SET 24 6638086.00** 22.87** 23.67** 4.29* 860.34** 2.72** 0.75** 2.47** 0.15** 4.53**
GCAF/SET 24 7505775.00** 22.77** 27.16** 4.51* 1130.53** 2.49** 0.76** 3.29** 0.11** 4.97**
SCA/SET 96 4239579.00** 11.41** 14.65** 3.09ns 442.93** 1.42** 0.54** 1.85** 0.09** 1.81**
G * E 620 4143954.00** 10.08** 10.99** 3.13** 469.39** 1.14** 0.60** 1.52** 0.06** 1.43**
ENV*GCAM/SET 96 3853886.00** 13.76** 14.62** 3.40* 657.06** 2.19** 0.72** 1.81** 0.08** 1.97**
ENV*GCAF/SET 96 4173701.00** 14.63** 16.63** 2.96ns 819.11** 1.51** 0.87** 2.08** 0.08** 1.41**
ENV*SCA/SET 384 3937195.00** 5.54** 6.70** 2.94ns 241.46* 0.63** 0.46** 1.12* 0.05** 1.09**
Error 592 3515561.00 3.66 4.88 2.66 207.60 0.46 0.35 0.93 0.04 0.78
*, **, Significant at 0.05 and 0.01probability levels, respectively, and ns, not significant
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Table 5.5: Mean squares for grain yield and other agronomic traits of 156 early maturing maize hybrids evaluated under optimal
environments at Ikenne, and Mokwa in 2013 and 2015 and at Abuja in 2015
Source of df Grain Yield Days to Days to Anthesis- Plant Ear height Root Husk Plant Ear Ears
variation (kg ha-1) 50% 50% silking height (cm) (cm) lodging cover aspect aspect per
anthesis silking interval (scale 1- (scale 1- (scale 1- plant
9) 9) 9)
Environment (E) 4 263742713.00** 149.46** 157.20** 9.51** 60394.10** 35942.13** 331.83** 227.90** 177.54** 116.51** 0.34**
SET 5 18872443.00** 80.39** 98.40** 1.33** 2138.22** 1657.50** 9.94** 1.91** 12.91** 2.40** 0.02
E*SET 20 4791105.00** 10.78** 13.82** 0.74** 362.94** 150.64** 3.69** 1.08** 1.38** 2.10** 0.01
REP(E*SET) 25 693839.00ns 1.85ns 1.72ns 0.31ns 77.28ns 68.72ns 0.79ns 0.31ns 0.40ns 0.29ns 0.01ns
BLOCK(E*REP) 122 1382316.00** 3.42** 3.32** 0.37ns 234.45** 120.92** 1.63** 0.33** 0.85** 0.46** 0.01*
HYBRIDS (G) 155 6541812.00** 12.81** 14.56** 0.79** 486.40** 399.53** 2.23** 0.61** 2.36** 1.45** 0.02**
GCAM/SET 24 10693735.00** 19.11** 21.53** 1.42** 737.03** 657.69** 1.89* 0.59** 3.44** 1.83** 0.03**
GCAF/SET 24 11581716.00** 18.54** 22.55** 1.14** 878.39** 773.25** 2.98** 1.31** 3.16** 1.89** 0.03**
SCA/SET 96 3032926.00** 5.95** 6.10** 0.47ns 237.80** 169.90** 1.55* 0.36** 1.06** 0.98** 0.02**
G * E 620 1664380.00** 3.32** 3.54** 0.45** 184.66** 112.81** 1.58** 0.40** 0.77** 0.68** 0.01*
E * GCAM/SET 96 2141306.00** 4.18** 3.86** 0.68** 229.53** 163.52** 1.64** 0.51** 0.78** 0.92** 0.02**
E * GCAF/SET 96 2410645.00** 3.95** 4.31** 0.42ns 253.04** 154.92** 1.68** 0.58** 0.77** 0.89** 0.02**
E * SCA/SET 384 1108390.00** 2.50** 2.67** 0.39ns 143.29** 85.08** 1.36ns 0.30** 0.73** 0.45** 0.01ns
Error 592 859535.00 1.71 1.85 0.37 97.47 64.29 1.18 0.24 0.42 0.30 0.01
*, **, Significant at 0.05 and 0.01probability levels, respectively, and ns, not significant
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Table 5.6: Mean squares for grain yield and other agronomic traits of 156 early maturing maize hybrids evaluated across Striga-
infested, drought and optimal environments in 2013 and 2015
Source of df Grain yield Days to Days to Anthesis- Plant height Ear height Ear Root Husk Ears
variation (kg ha-1) 50% 50% Silking (cm) (cm) aspect lodging cover per
anthesis silking Interval (scale 1- (scale 1- (scale 1- plant
9) 9) 9)
Environment (E) 12 1015531669** 3374.00** 5529.64** 482.41** 161274.76** 78294.18** 174.69** 221.87** 570.89** 8.31**
SET 5 11060187** 232.03** 312.69** 9.19** 1048.72** 2467.27** 8.34** 8.92** 4.43** 0.08**
E * SET 60 4699875** 24.65** 24.81** 2.42** 1062.25** 580.56** 2.06** 2.90** 4.82** 0.07**
REP(E*SET) 65 651016** 3.50* 4.82* 1.45ns 157.86ns 83.210ns 0.51* 0.63ns 0.51ns 0.02ns
BLOCK(E*REP) 314 1363620** 6.53** 9.42** 2.17** 423.40** 195.86** 0.98** 1.12** 1.03** 0.04**
Hybrid (G) 155 6351613** 35.15** 42.34** 3.21** 882.02** 628.84** 2.98** 1.65** 2.15** 0.08**
GCAM/SET 24 8195967** 46.14** 50.81** 4.91** 1362.82** 933.96** 3.33** 1.81** 2.66** 0.10**
GCAF/SET 24 12572731** 51.68** 67.00** 5.10** 1862.97** 1379.15** 4.22** 1.65** 5.13** 0.11**
SCA/SET 96 3125655** 15.09** 16.51** 1.68ns 475.30** 258.92** 1.90** 1.13** 0.99** 0.06**
G * E 1860 1617195** 5.94** 7.21** 1.94** 317.06** 192.76** 0.93** 1.11** 1.13** 0.04**
E * GCAM/SET 288 2442897** 7.73** 8.69** 2.30** 444.23** 252.49** 1.46** 1.14** 1.66** 0.05**
E * GCAF/SET 288 2287304** 7.57** 9.54** 1.99** 495.45** 296.22** 1.30** 1.38** 1.90** 0.04**
E * SCA/SET 1152 9745151** 3.96** 5.10** 1.71* 193.15** 124.67** 0.58** 0.91** 0.58** 0.03**
Error 1544 617889 2.60 3.43 1.53 153.32 86.93 0.39 0.76 0.47 0.02
*, **, Significant at 0.05 and 0.01probability levels, respectively, and ns, not significant
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5.3.2 Relative contributions of combining ability effects
The relative contributions of additive and dominance effects was determined as the proportion of
GCA effects to the total genetic effects using the sum of squares. Under Striga-infested
environments, the overall contributions of GCA (GCA-male plus GCA-female) sum of squares
to the total variation among hybrids varied from 49% for stalk loding to 78% for ear height while
that of SCA varied from 21% for ear height to 51% for stalk lodging (Table 5.7, Appendix 5.7a).
The percentage contribution of the GCA-male sum of squares were larger than that of GCA-
female for grain yield, ASI and number of emerged Striga plants at 8 WAP, while the
contribution of GCA-female was greater than the GCA-male for days to 50% anthesis and
silking, plant and ear heights, stalk lodging, husk cover, ear aspect, ears per plant, Striga damage
at 8 and 10 WAP and number of emerged Striga plants at 10 WAP (Appendix 5.7b). GCA
accounted for about 65% of the sum of squares for grain yield, 67% and 73% for Striga damage
at 8 and 10 WAP, 62% each for number of emerged Striga plants at 8 and 10 WAP, respectively.
Under drought, the contribution of GCA to genotypic sum of squares ranged from 39% for stalk
lodging to 65% for husk cover while that of SCA varied from 35% for husk cover to 61% for
stalk lodging (Table 5.7, Appendix 5.7a). The percentage contribution of the GCA-male to the
sum of squares were larger than those of GCA-female for days to anthesis, ear aspect and ears
per plant while the contribution of the GCA-female was greater than the GCA-male for grain
yield, days to silking, ASI, plant and ear heights, stalk and root lodging, husk cover, plant aspect
and stay green characteristic (Appendix 5.7c). The contribution of SCA, GCA-male, and GCA-
female to genotypic sum of squares for grain yield were 23, 36 and 41%, respectively. The
contribution of GCA-male, GCA-female and SCA to genotypic sum of squares for stay-green
characteristic were 27, 30 and 43.
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Table 5.7: Proportion (%) of the sums of squares for crosses attributable to general (GCA), and specific combining ability (SCA) for
grain yield and other agronomic traits of early white maize inbred lines.
Traits Striga-infested Drought Optimum Condition
GCA SCA GCA SCA GCA SCA
male female male female male female
Grain yield 33.25 31.54 35.21 36.10 40.83 23.06 31.08 33.66 35.26
Days to anthesis 25.80 31.05 43.15 25.06 24.95 49.99 31.09 30.17 38.74
Days to silking 26.29 38.20 35.51 21.63 24.82 53.55 31.43 32.92 35.64
Anthesis-silking interval 33.81 26.92 39.27 20.29 21.33 58.37 32.16 25.77 42.07
Plant height 35.55 37.81 26.64 22.87 30.05 47.09 28.72 34.22 37.06
Ear height 33.98 44.42 21.60 11.72 30.73 57.55 31.16 36.64 32.20
Stalk lodging 22.94 25.68 51.38 12.49 26.78 60.74 16.14 30.86 53.00
Root lodging - - - 20.36 20.71 58.94 17.06 26.88 56.06
Husk cover 30.76 31.91 37.33 24.66 40.31 35.04 17.76 39.23 43.00
Ear aspect 23.12 41.31 35.58 25.03 22.86 52.10 23.90 24.79 51.32
Plant aspect - - - 18.77 24.99 56.24 31.71 29.20 39.09
Ears per plant 21.11 29.26 49.63 23.95 18.42 57.64 21.03 24.85 54.13
Striga damage rating at 8 WAP 31.60 35.11 33.29 - - - - - -
Striga damage rating at 10 WAP 31.50 41.79 26.71 - - - - - -
Number of emerged Striga plants at 8
WAP 33.35 28.88 37.77 - - - - - -
Number of emerged Striga plants at 10
WAP 30.42 31.74 37.84 - - - - - -
Stay green characteristics - - - 27.04 29.65 43.32 - - -
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GCA contributions to genotypic sum of squares varied from 44% for root lodging to 68% for ear
height under optimal environments while SCA varied from 32% for ear height to 56% for root
lodging (Table 5.7, Appendix 5.7a). In general, GCA was more important than SCA for grain
yield under the three research conditions. However, for most measured traits, GCA was more
important than SCA under Striga infestation, SCA was more important than GCA under drought
and GCA was as important as SCA under optimal conditions.
5.3.3 General combining ability effects
The significant GCA-male effects for grain yield under Striga infestation ranged from -18 for
TZdEI 315 to 996 for TZdEI 173 while significant GCA-female effects ranged from -20 for
TZdEI 399 to 893 for TZdEI 268 (Table 5.8). Inbred lines TZdEI 173 had significant positive
GCA-male and GCA-female effects for grain yield; TZdEI 260 had only significant positive
GCA-male effects while TZdEI 352 had only significant GCA-female for grain yield. In
contrast, inbreds TZEI 31 and TZdEI 84 both had significant negative GCA-male and GCA-
female effects for grain yield while TZdEI 202 and TZdEI 378 had only significant negative
GCA-male effect for grain yield (Table 5.8). Inbreds TZEI 31, TZdEI 84, TZEI 18 and TZdEI
202 both had significant positive GCA-male and GCA-female effects for Striga damage at 8 and
10 WAP while Inbreds TZdEI 173 and TZdEI 352 both had significant negative GCA-male and
GCA-female effects for Striga damage at 8 and 10 WAP. The inbreds TZdEI 124 and TZdEI 131
had significant negative GCA-male effects for Striga damage at both 8 and 10 WAP. The inbred
TZdEI 268 showed significant negative GCA-female effects for Striga damage at 8 WAP but
significant negative GCA-female and GCA-male effects for Striga damage at 10 WAP.
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Table 5.8: General combining ability effects of early maturing inbred lines evaluated under Striga-infested environments
Striga damage at Striga damage Number of emerged Number of emerged Inbred line Grain yield Days to silking 8WAP at10WAP Striga plants Striga plants 8WAP 10WAP
GCAM GCAF GCAM GCAF GCAM GCAF GCAM GCAF GCAM GCAF GCAM GCAF
TZdEI 71 -443.53 197.11 0.21 -1.16* 0.55** -0.03 0.49* -0.07 -0.37* -0.06 -0.32* -0.1
TZdEI 124 325.70 40.81 -0.99* -0.65 -0.41** -0.21 -0.55** -0.17 0.02 0.59** 0.00 0.37*
TZdEI 202 -711.23* -422.78 0.78 1.12* 0.42** 0.60** 0.45* 0.39 0.00 -0.20 -0.12 -0.26
TZdEI 399 43.71 -20.20 1.08* 1.14* -0.25 -0.63** -0.31 -0.51* 0.54** 0.21 0.45** 0.30
TZdEI 260 785.34* 205.06 -1.09* -0.53 -0314 0.27 -0.08 0.36 -0.19 -0.53** -0.00 -0.30
TZdEI 268 449.57 893.59** -0.55 -0.91* -0.28 -0.71** -0.54** -0.62** -0.67** -0.35* -0.56** -0.32*
TZdEI 314 46.98 10.35 -0.79 -0.38 0.16 -0.34 003 -0.42 0.20 0.11 0.09 -0.13
TZdEI 396 212.68 77.92 0.87* 0.35 -0.46** -0.31 -0.24 -0.22 -0.04 0.31 0.03 0.24
TZEI 7 -171.96 -481.62 -0.23 0.79 0.24 0.49* 0.16 0.58* 0.09 -0.12 0.07 -0.05
TZEI 31 -537.28* -500.25* 0.71 0.15 0.67** 0.86** 0.59** 0.68** 0.42* 0.27 0.37* 0.25
TZdEI 315 -18.09 -152.17 -1.59** -1.39** 0.13 0.25 0.21 -0.08 0.99** 0.65** 0.70** 0.49**
TZdEI 479 -369.27 -91.22 0.25 -1.02 0.13 0.12 0.27 0.52* -0.85** -0.54** -0.53* -0.32*
TZdEI 82 480.30 351.36 -0.62 -0.85 0.09 -0.01 -0.19 -0.25 0.04 -0.08 -0.10 -0.05
TZdEI 485 -246.67 -157.88 0.68 1.35* -0.01 0.09 0.11 0.09 -0.51** -0.63** -0.37* -0.57**
TZdEI 441 153.72 49.91 1.28* 1.91** -0.34* -0.45* -0.39* -0.28 0.41* 0.60* 0.31 0.45*
TZdEI 352 410.03 632.85* 0.49 1.28* -0.75** -0.64** -0.59** -0.89** -0.05 0.14 -0.15 -0.06
TZdEI 84 -759.90* -949.04** -0.91* -1.42** 0.45* 0.63* 0.47* 1.07** -0.44* -0.19 -0.32* -0.22
TZdEI 280 358.73 -152.47 -0.71 -0.85 -0.05 0.06 0.04 0.14 -0.01 0.14 -0.06 0.16
TZdEI 357 -145.54 137.60 0.92* 0.41 -0.05 -0.41* -0.13 -0.59* 0.25 0.16 0.22 0.39*
TZdEI 492 136.67 331.06 0.22 0.58 0.41* 0.36* 0.21 0.27 0.25 -0.25 0.30* -0.27
TZdEI 98 -91.65 181.42 -0.01 -0.85 -0.29 -0.15 0.05 -0.13 -0.24 -0.07 -0.21 -0.18
TZdEI 157 -408.50 -163.55 0.33 1.45** 0.24 -0.19 0.21 0.11 0.46 0.24 0.42* 0.28
TZdEI 173 996.70** 671.98* -1.941** -1.78** -0.83** -0.62** -0.99** -0.76** -0.06 -0.47* -0.12 -0.38
TZdEI 283 -221.39 -233.95 0.89* 0.15 0.54** 0.21 0.35 0.24 -0.05 0.10 -0.04 0.06
TZEI 18 -275.16 -455.90 0.73 1.02* 0.34* 0.75** 0.38* 0.54* -0.12 0.20 -0.05 0.22
TZdEI 105 194.83 -189.63 -0.88* -0.74 -034* -0.03 -0.32* 0.01 -0.03 -0.03 0.07 0.00
TZdEI 120 -57.93 134.39 -0.58 0.13* 0.16 0.27 0.08 -0.05 -0.30 -0.20 -0.35 -0.24
TZdEI 131 461.84 -315.13 -0.75 0.06 -0.64** 0.07 -0.55** -0.05 0.03 0.09 0.12 0.02
TZdEI 264 -50.63 -21.88 1.391** 1.49** 0.33* 0.01 0.41* 0.31 0.12 -0.38 -0.04 -0.24
TZdEI 378 -548.11* 348.50 -0.94* -0.4 0.49** -0.33 0.38* -0.32 0.18 0.52* 0.19 0.46*
SE ± 269.81 251..63 0.46 0.46 0.16 0.18 0.17 0.21 0.15 0.17 0.15 0.16
GCAm, GCA effects of the inbreds used as male parents, GCAf, GCA effects of the inbreds used as female parents, WAP, weeks after planting; *, **,
Significant at 0.05 and 0.01probability levels, respectively.
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However, TZdEI 441 had significant negative GCA-female and GCA-male effects for Striga
damage at 8 WAP but only the GCA-male effect was significant for Striga damage at 10 WAP.
In contrast, inbreds TZdEI 268, TZdEI 479 and TZdEI 485 had significant negative GCA-male
and GCA-female effects for number of emerged Striga plants at 8 and 10 WAP while TZdEI 71,
TZdEI 84 and TZdEI 120 had significant negative GCA-male effect for number of emerged
Striga plants at 8 and 10 WAP. On the other hand, TZdEI 173 had significant negative GCA-
female effect for number of emerged Striga plants at 8 and 10WAP. TZdEI 399, TZdEI 441 and
TZdEI 264 showed significant positive GCA-male and GCA-female effects for days to 50%
silking. Also, TZdEI 396, TZdEI 357 and TZdEI 283 had significant positive GCA-male effects
while TZdEI 124, TZdEI 260, TZdEI 378 and TZdEI 105 had only significant negative GCA-
male effect for days to silking. In contrast TZdEI 202, TZdEI 485, TZdEI 352, TZEI 18 and
TZdEI 157 had only significant positive GCA-female effect for days to silking while TZdEI 71,
TZdEI 268 and TZdEI 479 had only significant negative GCA-female effect (Table 5.8).
Under drought conditions, the significant GCA-male for grain yield varied from -737 for TZdEI
479 to 671 for TZdEI 492 while GCA-female varied from -574 for TZdEI 314 to 642 for TZEI 7
(Table 5.9). Inbred lines, TZdEI 492 and TZdEI 378 had positive and significant GCA effects
(significant GCA-male and GCA-female) for grain yield. However, only the GCA-male effect
for grain yield were significant and positive for TZdEI 260 and TZdEI 315. The inbred TZEI 7
had only significant positive GCA-female effect for grain yield.
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Table 5.9: General combining ability effects of 30 early maturing maize inbred lines evaluated under drought environments
Inbred lines Grain yield ASI Ear aspect Plant aspect Ears per plant SGC
GCAm GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm GCAf
T ZdEI 71 -249.70 161.23 -0.05 0.00 0.09 -0.05 0.04 -0.06 -0.01 0.00 -0.15 -0.44*
TZdEI 124 -189.44 -408.51* 0.66* 0.24 0.20 0.24 0.14 0.36* -0.01 -0.01 0.75** 0.54**
TZdEI 202 -43.88 -76.99 0.00 0.70 0.04 0.05 -0.12 -0.15 -0.02 0.01 -0.31 0.06
TZdEI 399 204.18 38.24 -0.08 -0.34 -0.09 -0.31 0.01 -0.12 0.01 -0.04 -0.09 -0.16
TZdEI 260 278.83* 286.02 -0.54* -0.60* -0.23 0.07 -0.08 -0.03 0.03 0.05 -0.21 0.02
TZdEI 268 12.29 -179.64 -0.58* 0.26 0.06 0.13 -0.13 -0.01 0.03 -0.02 0.08 0.07
TZdEI 314 60.65 -573.93** -0.32 0.14 -0.29 0.34* 0.28 0.32 0.08* 0.01 0.20 0.69**
TZdEI 396 242.79 228.56 -0.06 -0.42 -0.10 -0.34* -0.33 -0.36* -0.03 0.03 -0.40 -0.57**
TZEI 7 -205.97 642.36** 0.38 0.01 0.34 -0.14 0.03 -0.05 -0.04 0.02 -0.14 -0.41*
TZEI 31 -109.76 -117.35 0.58* 0.00 -0.01 -0.01 0.15 0.12 -0.04 -0.04 0.28 0.23
TZdEI 315 472.63** -113.36 -0.33 -0.34 -0.54* 0.08 -0.22 0.08 0.14 0.05 0.00 0.25
TZdEI 479 -736.72** 474.74** 0.03 0.28 0.43* -0.01 0.36 0.14 -0.01 0.11 -0.03 0.03
TZdEI 82 115.65 -241.79 -0.26 0.03 -0.15 -0.24 -0.28 -0.23 0.01 -0.04 -0.49* -0.47**
TZdEI 485 -97.46 -211.71 0.41 -0.26 0.41* 0.48 0.51** 0.65** -0.13 -0.09* 0.86** 0.59**
TZdEI 441 245.90 92.12 0.15 0.28 -0.15 -0.30 -0.38* -0.65** -0.01 -0.03 -0.34 -0.41*
TZdEI 352 -230.27 176.22 0.15 -0.01 0.00 -0.14 -0.31 -0.10 -0.03 0.05 -0.05 -0.02
TZdEI 84 -131.27 -251.60 0.05 0.09 0.13 0.11 0.67** 0.39* -0.03 -0.01 -0.03 0.08
TZdEI 280 -92.89 -386.16* 0.11 0.44* -0.07 0.26 -0.22 -0.01 0.03 0.02 0.03 -0.24
TZdEI 357 -216.43 143.65 -0.07 -0.47* 0.06 -0.12 -0.01 0.02 -0.01 -0.05 0.01 -0.08
TZdEI 492 670.87** 317.88* -0.23 -0.04 -0.14 -0.11 -0.15 -0.32 0.05 -0.01 0.03 0.26
TZdEI 98 -352.59* 19.44 -0.02 -0.35 0.26 -0.18 0.20 0.36* -0.04 -0.02 0.31 0.27
TZdEI 157 92.57 88.88 -0.22 -0.17 -0.29 -0.10 -0.04 0.09 0.03 0.07 -0.15 -0.17
TZdEI 173 69.39 -7.28 -0.01 0.13 0.05 0.13 -0.14 -0.23 -0.06 0.00 0.14 0.05
TZdEI 283 -20.27 -306.09 0.16 0.15 0.17 0.34* 0.07 -0.13 0.07 -0.02 -0.15 0.09
TZEI 18 210.90 205.06 0.10 0.25 -0.20 -0.21 -0.11 -0.11 0.00 -0.04 -0.15 -0.25
TZdEI 105 -291.25* -180.01 0.06 -0.29 0.07 0.12 0.14 0.05 -0.06 -0.08* 0.27 0.40*
TZdEI 120 132.18 27.77 0.34 -0.08 -0.31 -0.11 -0.01 -0.11 0.06 0.01 -0.29 -0.07
TZdEI 131 -358.47* -183.96 0.01 0.24 0.11 0.27 0.04 0.09 -0.05 -0.01 0.09 0.21
TZdEI 264 72.55 -69.55 -0.36 0.08 -0.07 0.19 -0.26 0.25 0.02 0.01 -0.33 -0.43*
TZdEI 378 444.99** 405.75* -0.06 0.06 0.18 -0.48** 0.11 -0.30 0.03 0.08* 0.27 -0.11
SE± 138.43 157.75 0.23 0.22 0.19 0.16 0.17 0.18 0.04 0.04 0.18 0.15
ASI, anthesis-silking interval; SGC, stay green characteristics, GCAm, GCA effects of the inbreds used as male parents, GCAf, GCA effects of the
inbreds used as female parents SE, standard error.
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Furthermore, TZdEI 260 had significant negative GCA-male and GCA-female effects for ASI.
Inbreds TZdEI 124 andTZEI 31 had significant positive GCA-male effects for ASI while TZdEI
268 had significant negative GCA-male effects. Also, TZdEI 280 had significant positive GCA-
female while TZdEI 357 had significant negative GCA-female effect for ASI. Positive values for
the GCA-male and GCA-female effects for stay-green characteristic were observed for TZdEI
124 and TZdEI 485, only GCA-female effect for TZdEI 314 and TZdEI 105 indicate early
senescence. In contrast, significant negative GCA-male and GCA-female values were observed
in TZdEI 82, only GCA-female effect for TZdEI 71, TZdEI 396, TZdEI 441 and TZdEI 264
indicate delayed senescence. The lines with the poor values for plant and ear aspects were those
with positive GCA-female and/or GCA-male effects (Table 5.9).
The GCA effects due to males within sets (GCA-male) for grain yield under optimal condition
varied from -778 for TZdEI 98 to 782 for TZdEI 396 while the GCA effects due to females
within sets (GCA-female) ranged from -1020 for TZdEI 124 to 955 for TZdEI 260 (Table 5.10).
Under optimal conditions, Inbreds TZdEI 260, TZdEI 396, TZdEI 479 and TZdEI 173 had
significant (P < 0.5) positive GCA for grain yield when used as either male or female parents
whereas TZdEI 485, TZdEI 280 and TZdEI 283 had significant positive GCA effects for grain
yield when used as male parent only. Similarly, TZdEI 71, TZdEI 492 and TZdEI 378 had
significant positive GCA effects for grain yield only when used as female parent. In contrast,
Inbreds TZEI 31 and TZdEI 120 had significant (P < 0.5) negative GCA-male and GCA-female
effects for grain yield whereas TZdEI 71, TZdEI 315, TZdEI 352, TZdEI 98 and TZdEI 157 had
significant negative GCA-male effect for grain yield while TZdEI 399, TZdEI 314, TZdEI 82
and TZdEI 84 had significant negative GCA-female effect for grain yield.
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Table 5.10: General combining ability effects of early maturing maize inbred lines evaluated across optimal environments
INBREDS Grain Yield Days to anthesis Days to silking ASI Plant aspect Ear aspect Ears per plant
GCAm GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm GCAf GCAm GCAf
TZdEI 71 -556.78** 526.36* 0.37 -0.30 0.21 -0.40 -0.12 -0.12 0.24 -0.04 0.36** 0.03 -0.06** 0.00
TZdEI 124 -37.16 -1020.42** 0.06 0.12 -0.12 0.10 -0.24* -0.03 0.11 0.05 0.21 0.21 -0.02 0.01
TZdEI 202 55.86 39.13 -0.51 0.04 -0.30 0.14 0.18 0.14 -0.14 -0.11 -0.19 -0.26* 0.03 0.04*
TZdEI 399 -222.98 -500.41* 0.87** 0.84** 1.05** 0.96** 0.20* 0.09 -0.13 -0.28* -0.04 -0.13 0.01 -0.07**
TZdEI 260 761.06** 955.34** -0.79** -0.69* -0.84** -0.79** -0.02 -0.08 -0.07 0.38* -0.35* 0.15 0.04* 0.02
TZdEI 268 -17.26 -36.54 0.76* 0.44 0.65* 0.43 -0.14 -0.03 0.21 -0.16 -0.02 -0.03 0.00 -0.01
TZdEI 314 -52.52 -452.58* -0.93** -0.52* -0.86** -0.63* 0.08 -0.05 0.44** 0.37* -0.08 0.04 -0.01 0.02
TZdEI 396 782.39** 690.16** 0.18 -0.04 -0.07 -0.18 -0.22* -0.14 -0.54** -0.03 -0.26* -0.26* 0.01 0.02
TZEI 7 -336.56 319.83 -0.55* -0.21 -0.32 -0.05 0.21* 0.15 0.10 -0.32* 0.34* 0.06 -0.02 -0.02
TZEI 31 -376.06* -520.87* 0.54* 0.34 0.60* 0.43 0.07 0.07 -0.21 0.14 0.01 0.20 0.02 0.00
TZdEI 315 -463.86* -84.24 -1.02** -1.13** -1.25** -1.18** -0.19 -0.04 -0.25 0.01 -0.09 0.10 0.00 -0.01
TZdEI 479 529.41* 461.19* 0.24 -0.75** -0.09 -0.90** -0.34** -0.19* 0.64** 0.60** 0.15 -0.08 0.03 0.05
TZdEI 82 -327.16 -611.04** -0.24 0.04 -0.26 0.06 -0.05 0.08 -0.26 -0.21 -0.11 0.15 -0.03 -0.03
TZdEI 485 609.08** 96.82 -0.22 0.38 -0.03 0.26 0.15 -0.16* 0.43** 0.40* 0.20 0.30* 0.00 -0.03
TZdEI 441 -347.46 137.27 1.24** 1.45** 1.63** 1.77** 0.44** 0.31** -0.56** -0.80** -0.15 -0.47** 0.00 0.02
TZdEI 352 -488.81* -78.45 0.71* 0.59* 0.88** 0.82** 0.18 0.25** 0.01 -0.01 0.27* 0.07 0.01 0.01
TZdEI 84 -287.01 -771.12** -0.31 -0.65* -0.23 -0.82** 0.06 -0.18* 0.17 0.16 0.33* 0.12 -0.01 -0.03
TZdEI 280 681.96** 77.55 -0.84** -0.57* -0.80** -0.40 0.02 0.20* 0.02 0.19 -0.10 -0.08 0.01 0.01
TZdEI 357 -181.23 211.21 0.40 0.53* 0.22 0.40 -0.20* -0.15 0.04 -0.10 -0.12 -0.06 0.00 0.02
TZdEI 492 275.08 560.82* 0.04 0.10 -0.07 -0.01 -0.06 -0.13 -0.23 -0.25 -0.38** -0.05 -0.01 -0.01
TZdEI 98 -777.52** 152.63 0.58* 0.28 0.47 -0.03 -0.10 -0.27** 0.27 0.17 0.31* 0.04 -0.04* -0.03
TZdEI 157 -477.91* -361.73 0.54* 0.96** 0.31 0.84** -0.16 -0.15 0.06 0.02 0.07 0.05 -0.01 0.00
TZdEI 173 469.32* 512.88* -0.67* -0.49 -0.86** -0.60* -0.17 -0.12 -0.34* -0.05 -0.20 -0.13 0.01 0.01
TZdEI 283 651.95** -387.14 -0.28 -0.59* -0.05 -0.37 0.18 0.20 0.05 -0.14 -0.15 0.14 0.02 0.03
TZEI 18 134.16 371.62 -0.16 -0.29 0.14 -0.10 0.25* 0.22* -0.03 -0.01 -0.03 -0.24* 0.03 -0.01
TZdEI 105 187.57 -212.33 0.25 -0.38 0.35 -0.50 0.14 -0.13 -0.38* 0.00 -0.11 0.04 0.01 -0.02
TZdEI 120 -437.84* -398.64* -0.49 0.01 -0.58* -0.08 -0.09 0.01 0.08 -0.20 0.01 0.05 0.03 0.00
TZdEI 131 78.01 -176.89 -0.16 -0.02 -0.22 -0.09 -0.07 -0.02 -0.04 0.07 0.14 0.17 -0.03 -0.03
TZdEI 264 37.56 -103.33 0.66* 1.03** 0.66* 1.15** -0.01 0.07 0.34* 0.38* -0.10 0.24 0.00 0.01
TZdEI 378 134.70 681.07** -0.26 -0.58* -0.22 -0.48 0.02 0.04 0.00 -0.30* 0.05 -0.46** -0.01 0.03
SE ± 185.10 196.39 0.26 0.25 0.25 0.26 0.10 0.08 0.14 0.14 0.12 0.12 0.02 0.02
GCAm =GCA effects of the inbreds used as male parents, GCAf = GCA effects of the inbreds used as a female parents, * and **significant at 5 and 1% probability
levels, ASI, anthesis-silking interval
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The inbred lines TZdEI 399, TZdEI 441, TZdEI 352 and TZdEI 264 displayed significant
positive GCA-male and GCA-female effects for both days to anthesis and silking while inbreds
TZdEI 260 and TZdEI 314 had significant negative GCA effects when used as either male or
female parents for both days to anthesis and silking. Significant negative GCA-female effects
for plant aspect were recorded for inbreds TZdEI 399, TZEI 7 and TZdEI 378, GCA-male for
TZdEI 396, TZdEI 173 and TZdEI 105 and both GCA-male and GCA-female for TZdEI 441
while significant positive GCA effects for both GCA-male and GCA-female were observed for
TZdEI 314, TZdEI 479, TZdEI 485 and TZdEI 264 (Table 5.10). Inbreds TZdEI 71, TZEI 7,
TZdEI 352, TZdEI 84 and TZdEI 98 showed significant positive GCA-male for ear aspect while
TZdEI 485 had significant positive GCA-female for ear aspect. In contrast, significant negative
GCA-male effects of ear aspect was observed for TZdEI 260 and TZdEI 492 as well as GCA-
female effects of ear aspect for TZdEI 202, TZdEI 441, TZEI 18 and TZdEI 378 and both GCA-
male and GCA-female for TZdEI 396. The EPP had significant and positive GCA-male for
inbred TZdEI 260, GCA-female for TZdEI 202 as well as significant negative GCA-male for
TZdEI 71 and TZdEI 98, and GCA-female for TZdEI 399 (Table 5.10).
5.3.4 Performance and stability analysis of early maturing hybrids under Striga-infested,
drought and optimal environments
Under Striga infestation, the mean grain yield of hybrids ranged from 1134 kg ha-1 for the Striga
susceptible check, TZEI 26 x TZEI 5 to 5362 kg ha-1 for TZdEI 173 x TZdEI 280 with a mean of
3146 kg ha-1 (Table 5.11, Appendix 5.11). The highest yielding Striga resistant hybrid, TZdEI
173 x TZdEI 280 out-yielded the commercial hybrid check TZEI 60 x TZEI 86 by 152%.
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Table 5.11: Grain yield and other agronomic traits of selected hybrids evaluated under artificial Striga infestation (STR) at Mokwa and Abuja and under optimal
growing conditions (OPT) at Ikenne, Abuja and Mokwa in 2013 and 2015.
Grain yield (kg ha-1) Days to 50% SDR‡ (WAP) NESP (WAP) Ear aspect Ear per plant
Hybrids silking (scale 1 -9) BI
STR OPT STR OPT 8 10 8 10 STR OPT STR OPT
T ZdEI 173 x TZdEI 352 4676 4347 58 53 1.67 2.33 2.64 2.79 3.67 4 0.87 0.96 11.05
TZdEI 173 x TZdEI 280 5362 6816 57 51 2.67 3.50 2.80 3.16 3.83 3.8 0.97 0.96 10.82
TZdEI 352 x TZdEI 315 4821 5969 60 52 2.50 3.17 3.26 3.36 4.17 4.05 1.05 0.94 10.31
TZdEI 71 x TZdEI 268 4432 7068 57 52 3.67 4.00 1.70 2.44 4.67 4.7 1.00 0.97 8.05
TZdEI 82 x TZdEI 260 4946 6856 55 50 3.33 4.67 2.24 3.04 3.83 4 0.96 0.99 7.61
TZdEI 260 x TZdEI 268 4193 6370 56 50 3.67 4.67 1.19 2.13 4.67 4.4 1.04 0.99 7.59
TZdEI 357 x TZdEI 82 4230 6246 58 51 3.00 3.50 2.40 3.09 4.67 4.1 0.97 0.99 7.58
TZdEI 314 x TZdEI 105 4385 5109 58 51 3.00 3.67 2.88 3.26 3.83 4.55 0.98 0.98 7.42
TZdEI 378 x TZdEI 173 4688 6272 58 52 2.50 3.50 3.38 3.74 4.33 4.05 0.90 0.97 7.34
TZdEI 268 x TZdEI 105 4215 4971 58 54 2.83 3.50 2.63 3.06 3.83 4.25 0.91 0.97 6.88
TZdEI 280 x TZdEI 485 3510 5880 62 52 3.33 4.17 1.20 1.93 5.33 4.6 1.02 0.96 6.77
TZdEI 268 x TZdEI 131 4681 5383 57 51 3.00 4.00 2.86 3.35 4.17 4.5 0.87 0.93 6.41
TZdEI 105 x TZdEI 173 3993 5448 57 51 2.83 3.33 2.94 3.19 4.50 4.45 0.93 0.98 6.39
TZdEI 352 x TZdEI 485 3814 6305 60 53 3.33 4.33 2.09 2.53 4.67 4.8 1.03 1.01 6.26
TZdEI 98 x TZdEI 352 4368 5868 61 54 2.67 3.67 2.81 3.17 4.17 4.3 0.83 0.97 6.15
TZdEI 441 x TZdEI 260 3821 7033 58 52 2.33 4.00 2.43 3.24 4.50 3.5 0.94 1.01 6.14
TZdEI 268 x TZdEI 120 4023 5581 58 51 3.17 4.33 2.69 2.98 4.50 4.35 1.02 1.08 6.12
TZdEI 485 x TZdEI 124 3415 5111 58 51 3.17 4.17 1.80 2.23 5.17 5.3 0.99 0.91 5.66
TZdEI 120 x TZdEI 173 4264 5388 59 52 3.83 4.00 2.90 3.10 4.50 4.2 0.98 0.95 5.57
TZdEI 492 x TZdEI 441 3963 6245 62 55 4.00 4.83 2.28 2.74 4.83 4.1 1.08 0.99 5.43
TZdEI 485 x TZdEI 260 3589 6442 59 51 3.83 5.00 1.33 2.23 4.83 4.9 1.07 0.93 5.39
TZdEI 124 x TZdEI 268 3440 3967 59 53 3.60 4.00 2.83 3.12 4.20 4.55 1.13 0.95 5.36
TZdEI 82 x TZdEI 399 3745 5910 57 51 2.67 3.83 2.78 3.31 4.33 4.15 0.92 0.98 5.13
TZdEI 479 x TZdEI 124 3482 6455 58 51 3.00 4.83 1.67 2.95 4.67 4.1 1.03 1.00 5.05
TZdEI 352 x TZdEI 82 4215 5881 61 52 3.17 4.00 3.06 3.31 4.83 4.05 0.88 1.01 4.90
Check 2 - TZEI 188 x TZEI 98 2681 5605 59 52 4.17 5.50 2.80 3.41 5.50 4.55 0.71 0.91 -4.53
Check 3 - TZEI 60 x TZEI 5 3143 6876 64 54 4.83 5.67 2.94 3.33 5.50 3.3 0.68 0.97 -4.73
TZdEI 105 x TZdEI 98 2151 4308 61 53 4.67 6.00 2.93 3.31 6.33 4.85 0.73 0.84 -7.09
TZEI 7 x TZdEI 378 2033 5921 62 51 5.17 6.00 3.31 3.73 5.83 4.45 0.81 0.93 -7.79
TZdEI 84 x TZdEI 485 1816 5731 59 52 5.67 6.33 2.25 2.71 5.83 4.8 0.75 0.98 -8.52
Check 1 - TZEI 60 x TZEI 86 2128 5333 62 53 5.00 6.17 3.01 3.58 5.67 4.65 0.67 0.98 -8.76
Check 5 - TZEI 2 x TZEI 87 1838 4167 61 52 5.17 6.17 3.24 3.64 6.00 5.175 0.74 0.91 -9.23
TZEI 31 x TZdEI 264 2108 5356 62 52 5.50 6.50 3.86 3.94 6.17 4.6 0.82 0.97 -9.24
Check 4 - TZEI 31 x TZEI 63 1961 4823 61 53 5.50 6.33 3.39 3.74 5.33 4.4 0.66 0.96 -10.73
Check 6 - TZEI 26 x TZEI 5 1134 4176 63 53 6.00 7.33 2.78 3.41 6.67 4.9 0.67 0.84 -14.14
Means 3146 5601 60 52 3.97 4.91 2.82 3.29 5.12 4.45 0.89 0.97
Heritability 0.30 0.76 0.64 0.76 0.65 0.67 0.67 0.64 0.54 0.54 0.38 0.44
SE± 343 312 0.86 0.46 0.37 0.36 0.30 0.25 0.29 0.19 0.06 0.04
SDR: Striga Damage Rating at weeks after planting (WAP); NESP: Number of Emerged Striga Plants, BI: base index
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Grain yield of the hybrids ranged from 579 kg ha-1 for TZdEI 314 x TZdEI 378 to 3601 kg ha-1 for
TZdEI 479 x TZdEI 260 with a mean of 2307 kg ha-1 under drought and 2376 kg ha-1 for TZdEI 82 x
TZdEI 71 to 7769 kg ha-1 for TZdEI 260 x TZdEI 396 with a mean of 5601 kg ha-1 under optimal
growing environments (Table 5.12, Appendix 5.12). Under drought stress, the highest yielding hybrid
TZdEI 479 x TZdEI 260, produced more grains than the commercial hybrid check TZEI 60 x TZEI 86
by 99% while the highest yielding hybrid, TZdEI 260 x TZdEI 396 under optimal growing condition
out-yielded the commercial check TZEI 60 x TZEI 86 by 45%. Days to silking varied from 55 for
TZdEI 260 x TZdEI 396 to 63 for TZdEI 485 x TZdEI 124 with a mean of 59. The average yield
reduction of the hybrids was 44% and 59% under Striga infestation and drought conditions. The
reduction in grain yield of the hybrids was accompanied by increased days to silking, ASI, bareness and
poor ear aspect across Striga infested and drought stress environments, and poor plant aspect under
drought environments (Tables 5.11 and 5.12, Appendix 5.11 and 5.12). The hybrid, TZdEI 173 x
TZdEI 352 showed outstanding performance across the two stress environments using the IITA base
index. Yield range across the three research conditions was 1748 kg ha-1 for TZdEI 82 x TZdEI 71 to
4976 kg ha-1 for TZdEI 260 x TZdEI 396 with a mean of 3768 kg ha-1 (Table 5.13).
Under Striga infestation, the heritability estimates varied from 0.30 for grain yield to 0. 67 for Striga
damage at 10 WAP and number of emerged Striga plants at 8 WAP while under drought, the value
ranged from 0.29 for plant height to 0.55 for grain yield. Under optimal growing condition, the
heritability estimates ranged from 0.44 for ears per plant to 0.76 for grain yield and days to silking
(Tables 5.11 and Tables 5.12). Across research conditions, heritability estimates ranged from 0.34 for
root lodging to 0.84 for days to 50% anthesis and silking (Table 5.13).
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Table 5.12: Grain yield and other traits of some hybrids evaluated under drought stress (DT) at Ikenne and Bagauda and under optimal growing
conditions (OPT) at Ikenne, Abuja and Mokwa in 2013 and 2015.
Hybrids Grain yield (kg ha- Days to 50% Plant height (cm) Ear aspect (scale Plant aspect Ear per plant SGC Base
1) silking 1 - 9) (1-9) (1-9) Index
DT OPT DT OPT DT OPT DT OPT DT OPT DT OPT
TZdEI 479 x TZdEI 260 3601 6622 56 50 167.90 188.10 4.05 4.30 4.15 4.90 1.07 1.12 2.30 13.85
TZdEI 396 x TZdEI 264 3167 6018 58 53 169.40 180.70 4.35 4.30 3.65 5.65 0.96 0.99 2.40 9.20
TZdEI 378 x TZdEI 157 3086 5564 57 53 160.30 188.60 3.75 4.00 3.90 3.95 0.90 0.97 3.20 8.80
TZdEI 71 x TZdEI 396 3172 6601 56 51 165.00 188.90 3.90 4.15 3.80 3.35 0.73 1.02 2.10 7.79
TZdEI 157 x TZdEI 280 2880 5992 59 52 152.80 176.10 4.00 4.35 4.25 3.90 1.07 1.03 3.00 7.77
TZdEI 260 x TZdEI 396 3122 7769 55 50 159.10 197.10 4.65 4.60 3.75 4.40 0.87 0.98 2.80 7.57
TZdEI 399 x TZdEI 268 2443 4306 57 54 159.50 180.90 3.65 4.70 3.90 4.20 0.92 0.91 2.60 7.46
TZdEI 492 x TZdEI 315 3155 6010 57 51 158.90 178.40 3.85 4.20 4.20 3.25 0.82 0.90 3.40 6.91
TZdEI 396 x TZdEI 378 2909 6295 58 52 158.10 180.20 4.10 3.95 4.05 3.60 0.87 0.97 2.60 6.85
TZdEI 396 x TZdEI 131 2697 6680 55 50 164.10 192.50 4.35 4.15 4.15 3.60 0.82 0.98 2.50 6.82
TZdEI 396 x TZdEI 120 2742 5797 57 52 155.90 185.40 3.90 4.10 4.20 3.75 0.90 0.96 2.90 6.52
TZdEI 399 x TZdEI 314 2704 4891 56 51 151.60 181.80 3.95 4.40 4.35 4.40 0.94 0.92 3.20 6.42
TZdEI 314 x TZdEI 120 2796 4827 57 52 155.30 178.40 4.25 4.50 4.40 4.45 1.02 1.00 2.90 6.20
TZdEI 441 x TZdEI 71 2548 5153 58 53 163.60 191.00 3.85 4.35 3.75 3.70 0.82 0.93 2.20 5.96
TZEI 18 x TZdEI 84 2980 6045 58 53 154.00 176.60 4.15 4.15 4.10 3.55 0.82 0.93 2.50 5.89
TZdEI 71 x TZdEI 314 2606 6064 56 51 159.20 184.50 4.00 4.40 4.50 4.65 0.95 0.97 2.70 5.80
TZEI 7 x TZdEI 264 2681 5554 59 53 159.70 181.40 4.20 4.55 4.25 4.00 0.80 0.94 2.60 5.71
TZdEI 283 x TZdEI 492 2670 6352 58 52 152.10 179.10 4.15 3.85 4.45 3.40 0.98 1.07 3.20 5.66
TZdEI 260 x TZEI 31 2764 5847 57 52 157.80 185.00 4.25 4.80 4.40 4.20 0.99 1.01 3.30 5.50
TZdEI 173 x TZdEI 352 2545 4347 61 53 153.50 161.25 3.80 4.00 4.00 2.38 0.85 0.96 2.80 5.42
TZdEI 315 x TZdEI 399 2659 5759 56 52 150.70 179.70 4.05 4.15 4.40 3.85 0.89 1.03 3.10 5.39
TZdEI 98 x TZdEI 492 2791 5877 58 53 164.60 180.50 4.35 3.95 4.60 3.50 0.95 0.95 3.90 5.08
TZEI 18 x TZdEI 352 2789 5799 61 54 152.80 178.60 4.05 4.45 4.35 3.60 0.85 1.00 2.50 5.06
TZdEI 378 x TZEI 18 2810 5723 59 52 149.80 185.10 3.95 4.25 4.60 3.95 0.88 0.95 2.90 4.98
TZdEI 260 x TZEI 7 2735 5694 56 51 144.40 181.30 4.40 4.85 4.40 4.10 0.92 0.90 2.90 4.92
Check 1 - TZEI 60 x TZEI 86 1804 5333 58 53 148.00 183.70 5.00 4.65 4.70 4.20 0.81 0.98 3.50 -2.16
TZdEI 492 x TZdEI 485 2078 7723 60 52 142.90 196.40 5.40 4.10 5.00 4.10 0.55 0.99 4.90 -10.30
TZdEI 283 x TZdEI 352 1180 4043 61 53 145.60 182.80 5.75 5.05 4.95 4.15 0.63 0.99 4.00 -10.85
TZdEI 357 x TZdEI 479 1050 7058 62 53 126.20 198.50 5.65 4.30 5.75 4.50 0.63 1.05 3.60 -11.32
TZdEI 131 x TZdEI 98 1178 3290 61 54 147.70 174.20 5.70 5.30 5.40 4.95 0.65 0.82 4.10 -12.15
TZdEI 84 x TZdEI 479 839 5801 63 51 126.33 194.70 5.58 4.70 5.83 4.60 0.86 0.97 3.13 -12.46
TZdEI 280 x TZdEI 485 1229 5880 60 52 129.70 187.30 5.85 4.60 5.50 4.80 0.68 0.96 3.90 -12.87
TZdEI 485 x TZdEI 124 1582 5111 61 51 136.20 194.10 5.55 5.30 5.70 5.00 0.68 0.91 5.10 -13.01
TZdEI 82 x TZdEI 71 671 2376 63 53 146.13 161.80 5.44 6.00 5.56 5.25 0.46 0.80 3.78 -16.12
TZdEI 314 x TZdEI 378 579 4596 61 52 118.50 162.30 6.30 5.15 6.45 4.75 0.78 0.93 5.30 -17.36
Means 2307 5601 59 52 153.08 184.31 4.62 4.45 4.65 4.51 0.82 0.97 3.27
Heritability 0.55 0.76 0.54 0.76 0.29 0.66 0.46 0.54 0.38 0.62 0.44 0.44 0.5
SE± 638.02 311.50 0.75 0.46 4.97 3.38 0.23 0.19 0.34 0.22 0.07 0.04 0.3
SGC, stay green characteristic.
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Table 5.13: Grain yield and other traits of selected hybrids screened across Striga-infested, drought and optimal conditions at 13 environments in 2013 and 2015.
Hybrid Grain yield Days to Days to Anthesis- Plant height Ear Plant Root Husk Ear per
(kg ha-1) 50% 50% silking (cm) aspect aspect lodging cover plant
anthesis silking interval
TZdEI 260 x TZdEI 396 4976 53 54 1.00 172.08 4.63 4.08 1.67 3.56 0.93
TZdEI 173 x TZdEI 280 4782 53 55 1.23 165.35 4.06 3.78 1.54 3.06 0.93
TZdEI 71 x TZdEI 268 4777 55 55 0.54 168.77 4.75 4.40 1.97 3.27 0.98
TZdEI 173 x TZdEI 492 4765 55 56 1.46 158.92 3.98 3.80 1.31 3.27 0.96
TZdEI 479 x TZdEI 260 4715 53 54 0.85 171.73 4.37 4.53 1.23 3.85 1.07
TZdEI 441 x TZdEI 260 4657 55 56 1.54 170.58 4.00 3.53 1.20 3.00 0.91
TZdEI 82 x TZdEI 260 4625 52 53 1.15 164.04 4.19 3.80 1.55 3.17 0.95
TZdEI 71 x TZdEI 396 4540 53 55 1.42 174.23 4.25 3.58 1.35 3.13 0.88
TZdEI 396 x TZdEI 131 4499 53 54 0.73 172.88 4.46 3.88 2.11 3.23 0.91
TZdEI 492 x TZdEI 485 4480 55 57 1.88 164.27 4.88 4.55 1.26 3.77 0.82
TZdEI 378 x TZdEI 173 4452 55 56 1.42 165.00 4.31 4.05 1.25 3.50 0.89
TZdEI 98 x TZdEI 280 4402 54 55 1.23 174.88 4.54 4.45 1.52 3.52 0.90
Check 3 - TZEI 60 x TZEI 5 4379 57 59 1.92 174.69 4.21 3.75 1.22 3.52 0.84
TZdEI 396 x TZdEI 264 4379 55 56 1.35 170.69 4.40 4.65 1.71 3.23 0.98
TZdEI 352 x TZdEI 315 4360 55 57 1.69 169.23 4.12 3.88 1.75 3.19 0.98
TZdEI 357 x TZdEI 485 4357 56 57 1.92 167.23 4.92 4.45 1.15 3.77 0.83
TZEI 18 x TZdEI 357 4328 56 57 1.50 160.31 4.33 4.20 1.47 3.75 0.93
TZdEI 98 x TZdEI 352 4305 56 58 1.62 171.62 4.19 3.85 1.61 3.13 0.88
TZdEI 479 x TZdEI 124 4301 53 55 1.65 172.85 4.54 4.75 1.00 3.75 0.96
TZdEI 260 x TZdEI 314 4298 53 54 1.00 167.27 4.81 5.30 1.32 4.23 1.01
TZdEI 492 x TZdEI 315 4294 54 55 1.54 161.96 4.37 3.73 1.31 3.46 0.88
TZdEI 260 x TZdEI 268 4289 53 54 0.81 161.69 4.73 4.68 1.84 3.75 0.90
Check 2 - TZEI 188 x TZEI 98 3787 55 56 1.42 160.12 4.63 4.15 1.42 3.60 0.85
Check 1 - TZEI 60 x TZEI 86 3259 56 57 1.15 161.50 5.02 4.45 1.27 3.71 0.84
Check 4 - TZEI 31 x TZEI 63 3198 55 57 2.00 157.46 4.67 4.26 1.94 3.88 0.85
TZdEI 84 x TZdEI 441 3031 56 58 2.19 174.31 4.98 4.43 1.88 3.12 0.87
Check 5 - TZEI 2 x TZEI 87 2921 54 57 2.42 160.08 5.13 4.65 1.73 3.85 0.84
TZdEI 105 x TZdEI 98 2914 56 58 1.35 163.85 5.33 4.83 1.84 3.65 0.69
TZdEI 105 x TZdEI 157 2856 55 56 1.12 172.38 4.92 4.70 1.72 3.65 0.90
TZdEI 124 x TZEI 31 2782 54 57 2.19 165.58 5.10 4.68 2.33 3.90 0.88
TZdEI 283 x TZdEI 352 2642 56 58 1.92 160.19 5.38 4.55 1.57 3.75 0.84
Check 6 - TZEI 26 x TZEI 5 2588 56 58 2.08 153.46 5.50 4.75 1.95 3.96 0.76
TZdEI 314 x TZdEI 378 2474 57 58 1.27 135.62 5.75 5.60 2.22 4.37 0.84
TZdEI 131 x TZdEI 98 2230 57 59 1.92 157.27 5.58 5.18 1.76 3.94 0.75
TZdEI 82 x TZdEI 71 1748 57 59 1.83 152.88 5.73 5.39 1.64 3.86 0.72
Means 3768 55 56 2 164.86 4.67 4.34 1.66 3.55 0.89
Heritability 0.66 0.84 0.84 0.38 0.67 0.70 0.64 0.34 0.47 0.56
SE 165.02 0.34 0.39 0.26 2.63 4.86 0.21 0.21 0.14 0.03
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The yield performance and stability of the selected 35 (best 25 and worst 10 hybrids
using the base index) early maturing maize hybrids evaluated under drought, Striga
infestation, optimal environments and across the three research conditions are presented
in the AMMI biplots (Fig. 5.1, 5.2, 5.3 and 5.4). In the AMMI biplot, the grand mean for
grain yield represents the vertical dotted line, while the horizontal dotted line (y ordinate)
represents the interaction principal component axes 1 (IPCA1) value of zero. Hybrids
located near the horizontal line have little interactions with the environment and are
considered to be more stable than those farther apart. The farther a cultivar is to the right
side of the grand mean line, the higher the grain yield.
Under Striga infestation, E (environment), G (hybrids), and the IPCA1 accounted for
6.76, 51.47, and 30.9% of the total variation in the sum of squares for grain yield,
respectively, giving a total sum of 89.1%. This indicated that the biplot was effective in
explaining both the main effects as well as in decomposing the G × E interaction under
Striga-infested environments (Fig. 5.1). The hybrids 2 (TZdEI 173 x TZdEI 280), 5
(TZdEI 82 x TZdEI 260), 7 (TZdEI 357 x TZdEI 82), 8 (TZdEI 314 x TZdEI 105), 11
(TZdEI 280 x TZdEI 485), 13 (TZdEI 105 x TZdEI 173), 15 (TZdEI 98 x TZdEI 352), 16
(TZdEI 441 x TZdEI 260), 17 (TZdEI 268 x TZdEI 120) and 20 (TZdEI 492 x TZdEI
441) produced yields greater than the grand mean and had near zero IPCA1 score,
indicating that they were stable under Striga-infested environments. The hybrids 1
(TZdEI 173 x TZdEI 352), 3 (TZdEI 352 x TZdEI 315), 7 (TZdEI 357 x TZdEI 82), 9
(TZdEI 378 x TZdEI 173), 10 (TZdEI 268 x TZdEI 105), 12 (TZdEI 268 x TZdEI 131),
19 (TZdEI 120 x TZdEI 173), 20 (TZdEI 492 x TZdEI 441), 23 (TZdEI 82 x TZdEI 399)
and 25 (TZdEI 352 x TZdEI 82) produced yields greater than the grand mean but had
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Entry Hybrid
1 TZdEI 173 x TZdEI 352
2 TZdEI 173 x TZdEI 280
3 TZdEI 352 x TZdEI 315
4 TZdEI 71 x TZdEI 268
5 TZdEI 82 x TZdEI 260
6 TZdEI 260 x TZdEI 268
7 TZdEI 357 x TZdEI 82
8 TZdEI 314 x TZdEI 105
9 TZdEI 378 x TZdEI 173
10 TZdEI 268 x TZdEI 105
11 TZdEI 280 x TZdEI 485
12 TZdEI 268 x TZdEI 131
13 TZdEI 105 x TZdEI 173
14 TZdEI 352 x TZdEI 485
15 TZdEI 98 x TZdEI 352
16 TZdEI 441 x TZdEI 260
17 TZdEI 268 x TZdEI 120
18 TZdEI 485 x TZdEI 124
19 TZdEI 120 x TZdEI 173
20 TZdEI 492 x TZdEI 441
21 TZdEI 485 x TZdEI 260
22 TZdEI 124 x TZdEI 268
23 TZdEI 82 x TZdEI 399
24 TZdEI 479 x TZdEI 124
25 TZdEI 352 x TZdEI 82
26 TZEI 188 x TZEI 98
27 TZEI 60 x TZEI 5
28 TZdEI 105 x TZdEI 98
29 TZEI 7 x TZdEI 378
30 TZdEI 84 x TZdEI 485
Figure 5.1: Mean performance and stability of selected early maturing maize hybrids in terms of grain yield 31 TZEI 60 x TZEI 86
as measured by principal components across three Striga-infested environments in Nigeria 32 TZEI 2 x TZEI 87 33 TZEI 31 x TZdEI 264
between 2013 and 2015. 34 TZEI 31 x TZEI 63
35 TZEI 26 x TZEI 5
E1 =Abuja, 2013; E2 = Mokwa, 2013; E3 = Abuja, 2015.
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positive interactions with IPCA1 indicating that they were adapted to high yield
environments (E2) while hybrids 4 (TZdEI 71 x TZdEI 268), 6 (TZdEI 260 x TZdEI
268), 14 (TZdEI 352 x TZdEI 485), 21 (TZdEI 485 x TZdEI 260) and 24 (TZdEI 479 x
TZdEI 124) yielded higher than the grand mean but showed strong negative interaction
with IPCA1 indicating that they were adapted to low yield environments (E3).
Under drought, E (environment), G (hybrids), and the IPCA1 accounted for 69.55, 16.11,
and 6.7% of the total variation in the sum of squares for grain yield, respectively giving a
total sum of 92.4%. This indicated that the biplot was effective in explaining both the
main effects as well as in decomposing the G × E interaction under drought environments
(Fig. 5.2). The hybrids 3 (TZdEI 378 x TZdEI 157), 4 (TZdEI 71 x TZdEI 396), 12
(TZdEI 399 x TZdEI 314), 15 (TZEI 18 x TZdEI 84), 16 (TZdEI 71 x TZdEI 314), 17
(TZEI 7 x TZdEI 264), 19 (TZdEI 260 x TZEI 31), 21 (TZdEI 315 x TZdEI 399), 22
(TZdEI 98 x TZdEI 492), 24 (TZdEI 378 x TZEI 18), and 25 (TZdEI 260 x TZEI 7)
produced yields greater than the grand mean and had near zero IPCA1 score, indicating
that they were the most stable under drought environments. The hybrids 1 (TZdEI 479 x
TZdEI 260), 8 (TZdEI 492 x TZdEI 315), 9 (TZdEI 396 x TZdEI 378), 2 (TZdEI 396 x
TZdEI 264), 6 (TZdEI 260 x TZdEI 396), 10 (TZdEI 396 x TZdEI 131), 23 (TZEI 18 x
TZdEI 352), 11 (TZdEI 396 x TZdEI 120), 18 (TZdEI 283 x TZdEI 492) and 20 (TZdEI
173 x TZdEI 352) produced yields greater than the grand mean but had positive
interactions with IPCA1 indicating that they were adapted to high yield environments
while hybrid 5 (TZdEI 157 x TZdEI 280) yielded higher than the grand mean but showed
strong negative interaction with IPCA1 indicating that they were adapted to low yield
environments.
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Entry Hybrid
1 TZdEI 479 x TZdEI 260
2 TZdEI 396 x TZdEI 264
3 TZdEI 378 x TZdEI 157
4 TZdEI 71 x TZdEI 396
5 TZdEI 157 x TZdEI 280
6 TZdEI 260 x TZdEI 396
7 TZdEI 399 x TZdEI 268
8 TZdEI 492 x TZdEI 315
9 TZdEI 396 x TZdEI 378
10 TZdEI 396 x TZdEI 131
11 TZdEI 396 x TZdEI 120
12 TZdEI 399 x TZdEI 314
13 TZdEI 314 x TZdEI 120
14 TZdEI 441 x TZdEI 71
15 TZEI 18 x TZdEI 84
16 TZdEI 71 x TZdEI 314
17 TZEI 7 x TZdEI 264
18 TZdEI 283 x TZdEI 492
19 TZdEI 260 x TZEI 31
20 TZdEI 173 x TZdEI 352
21 TZdEI 315 x TZdEI 399
22 TZdEI 98 x TZdEI 492
23 TZEI 18 x TZdEI 352
24 TZdEI 378 x TZEI 18
25 TZdEI 260 x TZEI 7
26 TZdEI 352 x TZdEI 485
27 TZdEI 315 x TZdEI 124
28 TZdEI 492 x TZdEI 485
29 TZdEI 283 x TZdEI 352
30 TZdEI 357 x TZdEI 479
31 TZdEI 131 x TZdEI 98
32 TZdEI 280 x TZdEI 485
33 TZdEI 485 x TZdEI 124
34 TZdEI 82 x TZdEI 71
35 TZdEI 314 x TZdEI 378
Figure 5.2: Mean performance and stability of selected early maturing maize hybrids in terms of grain yield as measured by principal components
across five drought environments in Nigeria between 2013 and 2015.
E1 = Bagauda, 2013; E2 = Ikenne, 2013/2014; E3 = Minjibir, 2015; E4 = Bagauda, 2015; E5 = Ikenne, 2015/2016.
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Under optimal environments, the variation in grain yield attributable to E, G, and the
IPCA1 were 13.09, 60.38, and 16.5%, respectively. The hybrids 3 (TZdEI 357 x TZdEI
485), 4 (TZdEI 98 x TZdEI 280), 10 (TZEI 18 x TZdEI 357), 12 (TZdEI 485 x TZdEI
202) and 21 ( TZdEI 71 x TZdEI 396) produced yields greater than the grand mean and
had near zero IPCA1 score, indicating their superior stability under optimal environments
(Fig. 5.3). Hybrids 1 (TZdEI 260 x TZdEI 396), 2 (TZdEI 492 x TZdEI 485), 6 (TZdEI
71 x TZdEI 268), 7 (TZdEI 357 x TZdEI 479), 9 (TZdEI 492 x TZdEI 479), 11 (TZdEI
280 x TZdEI 479), 15 (TZdEI 260 x TZdEI 314), 19 (TZdEI 479 x TZdEI 71), 20 (TZdEI
479 x TZdEI 260), 22 (TZdEI 479 x TZdEI 124) and 25 (TZdEI 485 x TZdEI 260)
yielded higher than the grand mean but showed strong positive interaction with IPCA1
indicating that they were adapted to high yield environments while hybrids 5 (TZdEI 173
x TZdEI 492), 8 (TZdEI 441 x TZdEI 260), 10 (TZEI 18 x TZdEI 357), 13 (TZEI 60 x
TZEI 5), 14 (TZdEI 82 x TZdEI 260), 16 (TZdEI 173 x TZdEI 280), 17 (TZdEI 378 x
TZdEI 283), 18 (TZdEI 396 x TZdEI 131), 23 (TZEI 18 x TZdEI 280) and 24 (TZEI 7 x
TZdEI 105) yielded higher than the grand mean but showed strong negative interaction
with IPCA1 indicating that they were adapted to low yield environments (Ikenne and
Mokwa 2015).
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Entry Hybrid
1 TZdEI 260 x TZdEI 396
2 TZdEI 492 x TZdEI 485
3 TZdEI 357 x TZdEI 485 4 TZdEI 98 x TZdEI 280
5 TZdEI 173 x TZdEI 492
6 TZdEI 71 x TZdEI 268 .
7 TZdEI 357 x TZdEI 479
8 TZdEI 441 x TZdEI 260
9 TZdEI 492 x TZdEI 479
10 TZEI 18 x TZdEI 357
11 TZdEI 280 x TZdEI 479
12 TZdEI 485 x TZdEI 202
13 TZEI 60 x TZEI 5
14 TZdEI 82 x TZdEI 260
15 TZdEI 260 x TZdEI 314
16 TZdEI 173 x TZdEI 280
17 TZdEI 378 x TZdEI 283
18 TZdEI 396 x TZdEI 131
19 TZdEI 479 x TZdEI 71
20 TZdEI 479 x TZdEI 260
21 TZdEI 71 x TZdEI 396
22 TZdEI 479 x TZdEI 124
23 TZEI 18 x TZdEI 280
24 TZEI 7 x TZdEI 105
25 TZdEI 485 x TZdEI 260
26 TZdEI 399 x TZdEI 268
27 TZEI 31 x TZdEI 120
28 TZEI 26 x TZEI 5
29 TZEI 2 x TZEI 87
30 TZdEI 105 x TZdEI 157
31 TZdEI 283 x TZdEI 352
32 TZdEI 124 x TZdEI 268
33 TZdEI 124 x TZEI 31
34 TZdEI 131 x TZdEI 98
35 TZdEI 82 x TZdEI 71
Figure 5.3: Mean performance and stability of selected early maturing maize hybrids in terms of grain yield as measured by principal components
across five optimal environments in Nigeria between 2013 and 2015.
E1 = Ikenne, 2013; E2 = Mokwa, 2013; E3 = Ikenne, 2015; E4 = Mokwa, 2015; and E5 = Abuja, 2015.
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Across research environments, the variation in grain yield attributable to E, G, and the
IPCA1 were 68.5, 13.74, and 6%, respectively, giving a sum of 88.2%. The hybrids 2
(TZdEI 173 x TZdEI 280), 3 (TZdEI 173 x TZdEI 492), 5 (TZdEI 441 x TZdEI 260), 7
(TZdEI 82 x TZdEI 260), 8 (TZdEI 71 x TZdEI 396), 10 (TZdEI 396 x TZdEI 131), 12
(TZdEI 396 x TZdEI 264), 13 (TZdEI 98 x TZdEI 352), 15 (TZdEI 157 x TZdEI 352), 17
(TZEI 18 x TZdEI 357), 20 (TZdEI 268 x TZdEI 378), 21 (TZdEI 157 x TZdEI 280), 23
(TZdEI 492 x TZdEI 441) and 24 (TZEI 60 x TZEI 5) produced yields greater than the
average mean and had near zero IPCA1 score, indicating their superior stability across
research environments (Fig. 5.4). Hybrids 11 (TZdEI 378 x TZdEI 173), 16 (TZdEI 352 x
TZdEI 315) and 22 (TZdEI 82 x TZdEI 399) yielded higher than the grand mean but
showed strong positive interaction with IPCA1 indicating that they were adapted to high
yield environments while hybrids 1 (TZdEI 260 x TZdEI 396), 4 (TZdEI 71 x TZdEI
268), 6 (TZdEI 479 x TZdEI 260), 9 (TZdEI 98 x TZdEI 280), 14 (TZdEI 492 x TZdEI
485), 18 (TZdEI 357 x TZdEI 485), 19 (TZdEI 479 x TZdEI 124) and 25 (TZdEI 260 x
TZdEI 268) yielded higher than the grand mean but showed strong negative interaction
with IPCA1 indicating that they were adapted to low yield environments.
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Entry Hybrid
1 TZdEI 260 x TZdEI 396
2 TZdEI 173 x TZdEI 280
3 TZdEI 173 x TZdEI 492
4 TZdEI 71 x TZdEI 268
5 TZdEI 441 x TZdEI 260
6 TZdEI 479 x TZdEI 260
7 TZdEI 82 x TZdEI 260
8 TZdEI 71 x TZdEI 396
9 TZdEI 98 x TZdEI 280
10 TZdEI 396 x TZdEI 131
11 TZdEI 378 x TZdEI 173
12 TZdEI 396 x TZdEI 264
13 TZdEI 98 x TZdEI 352
14 TZdEI 492 x TZdEI 485
15 TZdEI 157 x TZdEI 352
16 TZdEI 352 x TZdEI 315
17 TZEI 18 x TZdEI 357
18 TZdEI 357 x TZdEI 485
19 TZdEI 479 x TZdEI 124
20 TZdEI 268 x TZdEI 378
21 TZdEI 157 x TZdEI 280
22 TZdEI 82 x TZdEI 399
23 TZdEI 492 x TZdEI 441
24 Check 3 - TZEI 60 x TZEI 5
Figure 5.4: Mean performance and stability of selected early maturing maize hybrids in terms of grain 25 TZdEI 260 x TZdEI 268
yield as measured by principal components across eight stressed and five non-stressed 26 TZEI 31 x TZdEI 131
environments in Nigeria between 2013 and 2015. 27 TZdEI 84 x TZdEI 441 28 TZdEI 105 x TZdEI 157
29 Check 5 - TZEI 2 x TZEI 87
E1 = Abuja Striga-infested, 2013; E2 = Mokwa Striga-infested, 2013; E3 = Abuja Striga-infested, 30 TZdEI 124 x TZEI 31
2015; E4 = Bagauda drought, 2013; E5 = Ikenne drought, 2013/2014; E6 = Minjibir drought, 2015; 31 TZdEI 283 x TZdEI 352
E7 = Bagauda drought, 2015; E8 = Ikenne drought, 2015/2016; E9 = Ikenne optimal, 2013; E10 = 32 Check 6 - TZEI 26 x TZEI 5
Mokwa optimal, 2013; E11 = Ikenne optimal, 2015; E12 = Mokwa optimal, 2015; E13 = Abuja 33 TZdEI 314 x TZdEI 378
optimal; 2015. 34 TZdEI 131 x TZdEI 98
35 TZdEI 82 x TZdEI 71
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5.3.5 Relationship between performance of parental inbred lines and their hybrids
The average mid and high parent heterosis values for grain yield (267% and 191%) were
higher under drought than under Striga-infested environments (96% and 73%). Negative
values for mid and high parent heterosis were recorded for the flowering traits (days to
50% silking and anthesis) under Striga and drought environments; stay green
characteristic and ear aspect only under drought. On the other hand, positive mid and
high parent heterosis values were recorded for the Striga traits and ear aspect under
Striga-infested environments (Table 5.14).
5.3.6 Interrelationship between traits under the different research conditions
Significant phenotypic correlations were obtained between grain yield and other traits
under Striga infestation (Table 5.15). The phenotypic correlation (rp) between number of
emerged Striga plants and grain yield was (-0.07*) at 8 WAP and (0.06*) at 10 WAP,
indicating that genotypes with fewer number of emerged Striga plants yielded higher than
those with more number of emerged Striga plants. Significant positive genotypic
correlations (rg ) were obtained between the number of emerged Striga plants at 8 WAP
and at 10 WAP (0.95**) and between host plant damage at 8 and 10 WAP (0.99**).
Also, large genotypic correlations existed among several other traits. Grain yield had
moderately high positive, genotypic correlation with EPP (0.47*), PHT (0.54*), and EHT
(0.53**). In contrast, grain yield had small negative genotypic correlation with the
flowering traits, and number of emerged Striga plants at 8 and 10 WAP.
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Table 5.14: Average mid and high parent heterosis for grain yield and other agronomic traits
under Striga-infested and drought environments
Mid-parent heterosis High parent heterosis
Traits (%) (%)
Striga Striga
infested Drought infested Drought
Grain yield 96.2 266.58 73.07 191.15
Days to anthesis -2.95 -7.08 -4.27 -8.37
Days to silking -2.28 -5.25 -3.76 -6.39
Ear aspect 9.35 -12.46 15.26 -5.50
Plant aspect - -5.19 - 5.67
Ears per plant 3.97 23.93 -2.59 14.18
Striga damage rating at 8 WAP 1.62 - 8.94 -
Striga damage rating at 10 WAP 5.22 - 13.45 -
Number of emerged Striga plants at 8
WAP 93.21 - 238.39 -
Number of emerged Striga plants at 10
WAP 48.9 - 94.23 -
Stay green characteristics - -21.56 . -13.37
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Table 5.15: Estimates of genotypic (above diagonal) and phenotypic (below diagonal) correlation coefficients for grain yield and other
agronomic traits evaluated under artificial Striga infestation in Mokwa and Abuja in 2013 and 2015.
TRAIT YIELD DA DS ASI PHT EHT SDR1 SDR2 NESP1 NESP2 SL HC EASP EPP
YIELD 0.05 -0.06 -0.20 0.54** 0.53** -0.95** -0.95** 0.25 0.13 0.41 -1.00** -1.00** 0.47*
DA -0.08* 0.96** 0.60* 0.05 0.39* 0.02 0.08 0.01 0.05 -0.50 0.13 0.04 -0.27
DS -0.11** 0.65** 0.79** 0.07 0.46** 0.14 0.20 0.01 0.08 -0.50 0.28* 0.16 -0.47**
ASI -0.07* 0.05 0.79 ** 0.14 0.55* 0.33 0.37 0.02 0.14 -0.28 0.52* 0.32 -0.77*
PHT 0.19** -0.11** -0.11** -0. 04 0.75** -0.55** -0.65** -0.13 -0.11 0.63 -0.53** -0.44** 0.16
EHT 0.20** -0.01 -0.03 -0.03 0.64 ** -0.45** -0.49** 0.19 0.17 1.00 -0.50** -0.40** -0.01
SDR1 -0.42** 0.14** 0.25** 0.21** -0.35** -0.3 3** 0.95** 0.07 0.09 0.21 1.00** 0.83** -0.58**
SDR2 -0.45** 0.10* 0.20** 0.17** -0.38** -0.35** 0.78 ** 0.00 0.04 0.24 1.00** 0.92** -0.63**
NESP1 -0.07* -0.05 -0.01 0.03 0.03 0.13** 0.07 0.1 0* 0.99** 0.54 0.06 0.10 -0.17
NESP2 -0.06* -0.05 -0.02 0.01 -0.01 0.12** 0.07 0.14** 0.89 ** 0.76 0.08 0.09 -0.27
SL -0.15** -0.11** -0.11** -0.03 0.12** 0.05 0.11** 0.12** 0.07* 0.0 4 1.00 0.01 -0.74
HC -0.22** 0.04 0.12** 0.12** -0.26** -0.14** 0.52** 0.65** 0.14** 0.19** -0. 01 1.00** -0.89**
EASP -0.48** 0.12** 0.19** 0.15** -0.25** -0.23** 0.61** 0.64** 0.13** 0.11** 0.17** 0.44 ** -0.78**
EPP 0.21** -0.13** -0.16** -0.11** 0.09** 0.07* -0.32** -0.33** -0.15** -0.12** -0.15** -0.18** -0.2 8**
DA, days to anthesis; DS, days to silking; ASI, anthesis-silking interval; PHT, plant height; EHT, ear height; SDR1, Striga damage at 8 weeks after planting; SDR2, Striga dama ge
at 10 weeks after planting; NESP1, number of emerged Striga plants at 8 weeks after planting, NESP2, number of emerged Striga plants at 10 weeks after planting; SL, stalk
lodging; HC, husk cover; EASP, ear aspect; EPP, ears per plant; *, **, Significant at 0.05 and 0.01probability levels, respectively,
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Significant large negative genetic correlation was also obtained with Striga damage at 8
and 10 WAP. Large-to-moderately large and significant genetic correlation coefficients
were also observed between plant height (PHT) and ear height (EHT); days to anthesis
and days to silking as well as ASI; days to silking and ASI; ASI and EHT, husk cover
(HC) and Striga damage at 8 and 10 WAP, and husk cover and ear aspect.
Under drought, significant phenotypic correlations were obtained between grain yield and
all measured traits except root and stalk lodging (Table 5.16). The phenotypic correlation
between grain yield and stay green characteristic (SGC) was rp= -0.12*, ASI rp= -0.08*,
EPP rp= 0.16**, PASP rp= -0.26**, and EASP rp= -0.32** indicating that the variation
in grain yield explained by these traits was very small. The values for genotypic
correlations were generally larger than the phenotypic correlations. Grain yield showed
significant negative genetic correlations with plant aspect (rg= -0.91**), ear aspect (rg= -
1.00**), SGC (rg= -0.80**) and significant positive genetic correlations with ears per
plant (rg= 0.99**). Other large-to-moderately large and significant genotypic correlation
coefficients (rg) observed were days to anthesis with days to silking; days to silking with
ASI, EASP, EPP and SGC; ASI with EASP and SGC; PHT with HC, PASP, EASP and
SGC; HC with PASP, EASP and SGC; PASP with EASP, EPP and SGC; EASP with
EPP and SGC; and EPP with SGC.
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Table 5.16: Estimates of genotypic (above diagonal) and phenotypic (below diagonal) correlation coefficient for grain yield and other
agronomic traits of maize inbred lines evaluated under drought stress in Bagauda, Ikenne and Minjibir in 2013 and 2015.
Yield DA DS ASI PLHT EHT RL SL HC PASP EASP EPP SGC
Yield -0.17 -0.33 -0.82 0.50 1.00 -1.00 -1.00 -0.74* -0.91** -1.00** 0.99** -0.80**
-0.17** 0.97** 0.29 0.07 0.54 0.44 0.19 0.07 0.24 0.47* -0.49** 0.33
DA
DS -0.20** 0.84** 0.51* -0.11 0.24* 0.46 0.22 0.22 0.30 0.58** -0.61** 0.45**
ASI -0.08* -0.17** 0.40** -0.79 -1.00 0.33 0.02 0.71 0.37 0.70* -0.71 0.74*
PHT 0.22** -0.44** -0.45** -0.0 8* -0.38 -0.48 -0.59 -0.57* -0.72** -0.96** 0.27 -0.66**
EHT 0.15** -0.39** -0.37** -0.01 0.68 ** -1.00 -0.77 0.98 -0.96 -1.00 -0.22 -0.75
RL -0.06* -0.05 0.02 0.10** 0.0 7* -1.00 0.70 0.64 0.20 0.29 -0.10
SL 0.0 3 -0.07* -0.15** -0.15** 0.07* 0.05 0.22 ** 1.00 1.00 0.12 -0.48 0.90
HC -0.21** 0.28** 0.29** 0.05 -0.48** -0.22** -0.13** -0. 01 0.60** 1.00** -0.44* 0.89**
PASP -0.26** 0.33** 0.34** 0.05 -0.47** -0.28** 0.00 0.01 0.49 ** 0.73** -0.54** 0.80**
EASP -0.32** 0.19** 0.27** 0.17** -0.37** -0.08* 0.04 -0.03 0.42** 0.42 ** -0.71* 0.59**
EPP 0.16** -0.25** -0.29** -0.10** 0.30** 0.21** 0.02 0.03 -0.23** -0.24** -0.2 8** -0.60**
SGC -0.12** 0.05 0.12** 0.12** -0.24** -0.01 0.00 0.08* 0.38** 0.36** 0.44** -0.1 7**
DA, days to anthesis; DS, days to silking; ASI, anthesis-silking interval; PLHT, plant height; EHT, ear height; RL, root lodging; SL, stalk lodging; HC, husk cover; PASP, plant
aspect; EASP, ear aspect; EPP, ears per plant SGC, stay green characteristic; *, **, Significant at 0.05 and 0.01probability levels, respectively,
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5.3.6.1. Relative importance of secondary traits to grain yield under Striga
infestation and drought
Under drought, the stepwise multiple regression analysis identified EASP, EHT, DYSK,
PASP and PLHT as the important traits with significant direct contribution to grain yield.
These traits explained about 72% of the variation in grain yield (Fig. 5.5). Among the
five primary traits, ear aspect had the highest direct effect (-0.786) on grain yield. Only
EHT and PLHT had positive direct effects on grain yield. Six traits (HC, EPP, STGR,
DA, SL, ASI) were identified in the second-order, only DA contributed indirectly to yield
through four of the five first-order traits. Also, days to 50% anthesis had the highest
positive indirect contribution through days to 50% silking. The indirect contribution of
the remaining five secondary traits are clearly illustrated in Fig. 5.5. Only root lodging
was identified in the third-order as having positive indirect contribution through stalk
lodging.
In the Striga environments, EASP and RAT 2 were identified by step-wise multiple
regression analysis as the primary traits, accounting for 80% of the total variation in grain
yield (Fig. 5.6). Six traits (RAT 1, HUSK, EPP, PLHT, CO_1, and CO_2) were identified
as the second order contributors to grain yield with RAT 1 and EPP being the only
secondary traits that contributed indirectly to grain yield through all the primary traits.
Four traits were grouped into third order and only ear height and days to 50% silking
contributed indirectly to yield through four of the six secondary traits. Only DA was
identified in the fourth order having indirect effect through three of the third-order traits.
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Figure 5.5: Path analysis diagram displaying relationship of measured traits of early maturing inbred lines screened under drought at Ikenne, Minjibir and
Bagauda, 2013 and 2015.
Bold value is the residual effect; values in parenthesis are direct path coefficients while other values are correlation coefficients. R1 is residual effects; ASI,
anthesis-silking interval; DA, days to 50% anthesis; DYSK, days to 50% silking; EASP, ear aspect; EPP, ears per plant; HC, husk cover; PASP, plant aspect;
PLHT, plant height; EHT, ear height; RL, root lodging; SL, stalk lodging; STGR, stay green characteristic and YIELD, grain yield
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Figure 5.6: Path analysis diagram displaying relationship of measured traits of early maturing inbred lines screened under artificial Striga infestation at Abuja and
Mokwa, 2013 and 2015.
Bold value is the residual effect; values in parenthesis are direct path coefficients while other values are correlation coefficients. R1 is residual effects; ASI,
anthesis-silking interval; DA, days to 50% anthesis; DYSK, days to 50% silking; EASP, ear aspect; EPP, ears per plant; HUSK, husk cover; PASP, plant aspect;
PLHT, plant height; EHT, ear height; RAT 1 and RAT 2, Striga damage score at 8 and at 10 WAP; CO_1 and CO_2, number of emerged Striga plants at 8 and
at 10 WAP; RL, root lodging; SL, stalk lodging and YIELD, grain yield
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5.3.7 Heterotic grouping of the early maturing inbred lines
The result of the dendogram constructed based on the HGCAMT (Fig. 5.7), showed four
heterotic groups. Group 1 consisted of TZdEI 71, TZdEI 98, TZdEI 378 and TZdEI 479. group 2
comprised of TZdEI 124, TZdEI 131, TZdEI 84, TZdEI 314, TZdEI 280, TZdEI 485, TZdEI
202, TZdEI 352, TZEI 7, TZEI 31, TZdEI 283, TZEI 18 and TZdEI 264, group 3 is made up of
TZdEI 399, TZdEI 268, TZdEI 120, TZdEI 357, TZdEI 157, TZdEI 396, TZdEI 82, TZdEI 105
and TZdEI 441 ; while TZdEI 260, TZdEI 315, TZdEI 492 and TZdEI 173 constituted group 4
(Fig 5.7). Group 1 and 2 comprises of lines with varied reaction to Striga and drought. About
55% and 44% of white lines in group 3 were tolerant to Striga and drought, while group 4 had
25% and 50% Striga resistant and drought tolerant lines.
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Figure 5.7: Dendrogram of 30 early maturing maize inbreds constructed from GCA effects of
multiple traits (HGCAMT) using cluster analysis based on Ward’s minimum variance
across research environments.
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5.4 Discussion
The significant genotypic mean squares for most measured traits under Striga infestation, and all
traits under drought stress and optimal growing conditions indicated that there was large genetic
variation among the hybrids which should allow progress from selection for Striga resistance,
induced drought stress tolerance and improved grain yield under the three contrasting
environments. The significant mean squares of the environment for all traits under Striga
infestation, drought and optimal growing conditions indicate that the environments were unique
in discriminating among the hybrids and highly variable, implying the need for testing in more
than one environment over several years for each of the contrasting environments as reported by
Badu-Apraku et al. (2007a, 2011a).
The significant genotype x environment interaction mean squares for grain yield, Striga damage
and number of emerged Striga plants under Striga infestation is an indication that the hybrids
varied in their response to Striga infestation at the different sites suggesting that the biotypes of
S. hermonthica at the different sites could be different. Several authors in WCA have reported
similar result (Yallou et al., 2009; Menkir et al., 2010; Badu-Apraku and Lum, 2010; Badu-
Apraku et al., 2010). The highly significant interactions between genotype x environment for
grain yield and other traits under drought and optimal growing environments was an indication
that the environment influenced the expression of the hybrids in the different drought
environments for the traits. This provided a justification for evaluating the hybrids across the two
distinct environments in an effort to identify those with consistent performance across the
environments. These results are consistent with the findings of Badu-Apraku et al. (2011b) and
Badu-Apraku and Oyekunle (2012). The average yield reduction of the hybrids under Striga
infestation was 44% relative to performance under optimal environments. This is quite similar to
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the results of Badu-Apraku et al. (2004a) and Ifie et al. (2015) who reported yield reduction of
42% and 44%, respectively, under similar conditions. However, it is lower than the 53.7%
reported by Adetimirin et al. (2000), 68% by Kim et al. (2002), 65% by Badu-Apraku et al.
(2010) and 55% by Akaogu et al. (2012). It is, however, higher than the yield reduction of 23%
reported by Badu-Apraku et al. (2011b). The yield reduction of 47% suggested that the intensity
of infestation in this study was high enough to allow the identification of hybrids that possessed
genes for Striga resistance/tolerance.
Despite the high severity of infestation in this study, the novel Striga resistance genes from the
wild relatives of maize, Zea diploperennis, in the genotypes allowed them to suppress the
emergence of the Striga plants and produced high yields. The high yield of the hybrids under
Striga infestation was accompanied by reduced number of emerged Striga plants as well as
reduced Striga damage. Also, the average yield reduction of 59% under drought indicated that
the levels of drought imposed during flowering and grain-filling were severe enough to allow
selection for drought tolerance among the hybrids. The yield reduction observed under drought
was within the range reported by other researchers (Nesmith and Ritchie, 1992; Badu-Apraku et
al., 2011b; Akaogu et al., 2017). Under drought, the reduction in grain yield was accompanied
by a decrease in ASI, barrenness, stay green characteristic, and poor plant and ear aspects. These
results are consistent with those of earlier scientists (Bolanos et al., 1993; Edmeades et al., 1995)
who reported that ears per plant and ASI are important secondary traits when selecting for
drought tolerance and yield potential in tropical maize.
The relatively high heritability (> 60%) obtained for days to 50% anthesis and silking, ear height,
Striga damage rating, number of emerged Striga plants and ear aspect under Striga infestation;
grain yield, days to silking and anthesis, plant height, plant and ear aspect under optimal
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conditions indicated that these traits could be easily transferred and direct phenotypic selection
could also be done since additive gene action was more important in the inheritance of the traits
more than the non-additive genetic effects. On the other hand, the relatively low heritability
estimates obtained for grain yield, plant height, ear and plant aspect, ears per plant under
drought; grain yield, anthesis-silking interval and ears per plant under Striga-infested indicated
that direct phenotypic selection for these traits may not be effective suggesting the use of indirect
selection or secondary traits to determine the underlying genetic merits of the traits as suggested
by Mhike et al. (2011).
The significant GCA-male, GCA-female and SCA for grain yield and other agronomic traits
except for ASI under Striga infestation suggested that the performance of the inbreds differed
when used as either male or female parents in hybrid combinations. This also suggested that
additive and non-additive gene actions were important in the inheritance of grain yield and other
measured traits under Striga infestation. The high GCA over SCA mean squares for Striga
damage and number of emerged Striga plants under Striga infestation indicated that additive
gene action was more important in controlling both host plant damage and number of emerged
Striga plants. The results of this study are, in part, contradictory to the findings of Gethi and
Smith (2004), Badu-Apraku et al. (2007a) who showed that non-additive gene action was more
important than additive gene action in controlling the inheritance of host plant damage, while
additive gene action was more important in controlling the number of emerged Striga plants.
Furthermore, the findings of this study are contradictory to the results of Kim (1994), Akanvou
et al. (1997); Badu-Apraku et al. (2007b) who reported that additive gene action controlled
Striga damage while non-additive gene action controlled the number of emerged Striga plants.
The discrepancy in this results and those of the earlier authors may be attributed to the fact that
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the early maturing inbreds used in the present study were derived from two diverse germplasm
sources, which might possess some genes with different modes of action for Striga resistance.
Under drought and optimal growing condition, the significant GCA-male, GCA-female and SCA
for grain yield and other agronomic traits except SCA for ASI, was an indication that the inbred
lines responded differently when used as either male or female parents in hybrid combinations.
This implied that additive and non-additive gene actions were important in the inheritance of
grain yield and most measured traits except ASI. These results revealed consistent trends in the
mode of gene action controlling the inheritance of resistance to Striga and tolerance to drought in
the set of early maturing inbred lines studied. Additive gene action was more important than non-
additive gene action for grain yield and most measured traits observed under both stresses.
Similar results have been reported for resistance to Striga and tolerance to drought in early white
(Badu-Apraku et al., 2011b) and extra-early maturing maize inbreds (Badu-Apraku and
Oyekunle, 2012, Akaogu et al., 2012, 2017). This implied that appreciable breeding progress
could be made using breeding methods which capitalize on additive gene action such as the S1
family recurrent selection, backcrossing and hybridization for the development of drought
tolerant cultivars, and synthetics as well as for population improvement. This is in support of the
results of Vasal et al. (1992) and Zambezi et al. (1994).
The significant GCA-male x environment and GCA-female x environment interactions for grain
yield and most measured traits under Striga infestation and drought, indicated that there is
significant variation in the combining ability of the lines across the Striga and drought
environments. This suggested that the selection of Striga resistant/tolerant and drought tolerant
hybrids would be more reliable if based on performance across a range of Striga environments.
The significant SCA x environment interaction effects for grain yield, days to 50% anthesis,
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Striga damage, number of emerged Striga plants and ear aspect indicated that the response of the
hybrids with respect to these traits varied in the Striga infested environments. This result is
consistent with those of Badu-Apraku et al. (2013b). Similarly, the significant SCA x
environment interactions for grain yield and other agronomic traits except ASI under drought
indicated that the expression of these traits in specific hybrids would vary in the different
drought environments. This is also consistent with the findings of several authors (Badu-Apraku
and Oyekunle 2012).
In this study, the percentage contributions of GCA-male and GCA-female effects did not
significantly vary for grain yield and other traits under Striga-infested, drought and optimal
growing conditions which implied that maternal or cytoplasmic genes did not have any influence
on these traits. Derera et al. (2008) reported maternal effects for grain yield under drought, and
anthesis-silking interval, prolificacy and ear aspect under drought and non-drought environments
in maize hybrids. Similarly, Jumbo and Carena (2012) reported maternal effects for ear height in
elite early maturing maize population hybrids. Furthermore, Ifie (2013) reported maternal effects
for days to silking and paternal effects for ears per plant in early maturing maize hybrids under
Striga infested environments.
Inbred lines TZdEI 268, TZdEI 352 and TZdEI 173 had superior positive GCA (GCA-male
and/or GCA-female) effects for grain yield under Striga infestation indicating that the inbred
lines contributed to higher grain yield in their hybrids under Striga infestation. TZdEI 492 and
TZdEI 378 had superior positive GCA effects for grain yield under drought environments
suggesting that the inbred lines contributed to higher grain yield of their hybrids under drought
while TZdEI 260, TZdEI 396, TZdEI 479 and TZdEI 173 had superior positive GCA effects for
grain yield under optimal environments. These inbred lines are expected to contribute higher
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grain yield to their hybrids under optimal environments. The inbred lines identified with
significant positive GCA effects for grain yield would likely contribute favorable alleles in a
recurrent selection program and such lines could be used as parents to form a synthetic
population that could be improved for Striga resistance and drought tolerance if the heterotic
orientations are taken into consideration to avoid mixing up of heterotic groups. Subsequently,
new inbred lines with improved levels of Striga resistance and drought tolerance could be
extracted from the improved populations following cycles of recurrent selection.
Using the base indices, TZdEI 173 x TZdEI 352 showed good performance under both Striga
infestation and drought stress. This result is interesting and encouraging as Striga and drought
stress occur simultaneously in the savannas of WCA and farmers in the sub-region are requesting
hybrids with combined resistance/tolerance to the two stress factors. The superior yielding
hybrids identified in this study should be evaluated extensively in contrasting environments in
multi-location and on-farm trials to confirm the superior performance and release to farmers in
WCA for production.
The mid-parent and high parent heterosis values for grain yield were higher under drought than
under Striga-infested environments. The average mid- and high- parent heterosis for grain yield
was 96% and 73% under Striga infestation; 267% and 191% under drought. The positive values
obtained for grain yield indicated that the hybrids produced more grain yield than their inbred
parents. Negative mid- and high- parent heterosis values obtained for days to 50% anthesis and
silking indicate that the hybrids flowered earlier than their corresponding inbred parents under
both drought and Striga-infested environments. This is in support of the results of Meseka et al.
(2006), who reported negative heterotic values for days to silking and positive values for plant
height and grain yield under low and high nitrogen environments. Also, the positive mid- and
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high- parent heterosis for Striga damage and number of emerged Striga plants at 8 and at 10
WAP indicated that the hybrids suffered higher Striga damage and allowed the emergence of
more Striga plants than their corresponding parental lines.
Another objective of this study was to examine the relationships among the measured traits under
the contrasting environments. Phenotypic and genotypic correlation analysis provides
information on the type, strength and direction of the relationship between a pair of traits. In
contrast, sequential path analysis provides information on cause and effect relationships among
traits by identifying traits with significant contributions to grain yield and ranking them as first
and second order traits in decreasing order of their relative importance in explaining the observed
variation in grain yield. The traits identified in the first order are considered as traits of value,
while the second order traits are considered as traits of potential importance (Badu-Apraku et al.,
2014; Talabi et al., 2016). In this study, phenotypic and genotypic correlations as well as
sequential path analysis were employed to provide an insight into the inter-trait relationships
among the measured traits. The significant genotypic correlation suggested that there was genetic
relationship between most of the traits studied. The negative genotypic correlations between
grain yield and days to silking and anthesis as well as Striga damage at 8 and 10 WAP were
expected because Striga affects the growth and development and grain yield of infested plants.
The negative correlations between grain yield and flowering traits could be due to the intensity
of artificial Striga infestation on the hybrids. The significant negative phenotypic correlations
between grain yield and Striga damage at 8 and 10 WAP suggested a negative relationship
between the two traits. This result corroborated the findings of Amusan et al. (2008), Badu-
Apraku et al. (2012a) and Karaya et al. (2012). The positive phenotypic and genotypic
correlations between Striga damage at 8 and 10 WAP and number of emerged Striga plants at 8
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and at 10 WAP suggested that either of the parameters will suffice as a selection parameter for
the evaluation of genotypes for Striga resistance. Considering the resources and time involved in
taking Striga damage and number of emerged Striga plants, this result suggested that the data for
these traits may be taken at either 8 or 10 WAP without any serious loss of precision. This result
is consistent with the findings of Badu-Apraku (2007).
The high negative genotypic and moderate phenotypic correlations between grain yield and
Striga damage at 8 and 10 WAP suggested that selection for high grain yield and Striga
resistance under artificial Striga infestation could be realized in the breeding program. The
positive phenotypic and genotypic correlations obtained between Striga damage and number of
emerged Striga plants at 8 WAP, on one hand, Striga damage and number of emerged Striga
plants at 10 WAP on the other, suggested that Striga resistance was not controlled by the number
of Striga plants attached to the host and that several genes regulate S. hermonthica emergence
and severity of host plant damage in maize. This result corroborated the findings of other
researchers Kim (1994); Akanvou et al. (1997) and Badu-Apraku (2007). The generally low
phenotypic and genetic correlations between grain yield and the number of emerged Striga plants
implied that the two traits were genetically independent and could be improved separately. This
results suggested that Striga damage should be preferred to number of emerged Striga plants for
selection for high grain yield and Striga resistance/tolerance because of the high heritability
estimates and the negative phenotypic and genotypic correlations between grain yield and Striga
damage at 8 and 10 WAP.
However, for maximum gain from selection for increased grain yield and resistance to Striga, a
combination of host plant damage and number of emerged Striga plants should be used to
improve the two traits simultaneously. Similar findings in Sorghum bicolor was reported by
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Haussmann et al. (2000). Selection for host damage and number of emerged Striga plants could
be effectively carried out simultaneously using an appropriate selection index under Striga-
infested and non-infested environments. Overall, grain yield had significant phenotypic
correlations with all the traits used in computation of the base indices for selection of Striga
resistant varieties.
Under drought environments, the negative genotypic and phenotypic correlations between grain
yield and flowering traits as well as the stay green characteristic suggested that the level of
drought stress imposed was very severe and high enough to elicit differences between the
germplasm in terms of tolerance and susceptibility to drought. In this study, the strong genotypic
correlation between grain yield and ASI, PASP, EASP as well as SGC justified their addition in
the IITA base index for increased yield under drought conditions.
Ear aspect was identified as the most important trait contributing to the differences in grain yield
under Striga-infested and drought, thus, confirming its reliability as a secondary trait for indirect
selection under the both research conditions.
It is striking to note that five hybrids (TZdEI 173 x TZdEI 280, TZdEI 82 x TZdEI 260, TZdEI
98 x TZdEI 352, TZdEI 441 x TZdEI 260, and TZdEI 492 x TZdEI 441) were identified by
AMMI biplots as high yielding and stable across the research environments as well as under
Striga infestation. Also three hybrids (TZdEI 396 x TZdEI 264, TZdEI 71 x TZdEI 396 and
TZdEI 157 x TZdEI 280) were identified among the top 25 highest yielding under drought
environments as well as across research conditions. In addition, TZdEI 71 x TZdEI 268, TZdEI
260 x TZdEI 396 and TZdEI 173 x TZdEI 280 were selected based on their yield performance
across contrasting environments. These hybrids should be extensively tested in on-farm trials in
WCA to confirm the consistency in performance and vigorously promoted for adoption and
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commercialization to contribute to food security and improved livelihoods of resource poor
farmers in the sub-region.
The set of inbreds were classified into four heterotic groups using the GCA effects of multiple
traits. The inbreds in each heterotic group may be recombined to form Striga resistant and
drought tolerant populations which could be improved through recurrent selection.
5.5 Conclusions
A total of 156 single cross hybrids were screened under Striga infested, drought and optimal
environments in an effort to determine their combining abilities and the mode of gene action
conditioning Striga resistance and tolerance to drought, classify the inbred lines into heterotic
groups, identify outstanding hybrids with consistent performance across the three contrasting
environments and examine the trait relationships. Striga infested, drought and optimal
environments were highly variable and unique in discriminating among the hybrids. The genetic
differences among the hybrids was enough to allow the selection of Striga resistant/tolerant and
drought tolerant hybrids. The preponderance of GCA over SCA mean squares for most of the
traits under Striga-infested and optimal environments indicated that additive gene action was
more important than non-additive gene action for the measured traits although both GCA and
SCA effects accounted for the differences among the 156 hybrids evaluated in this study. In
contrast both additive and non-additive gene action were important for yield and other
agronomic traits under drought environments. The maternal or cytoplasmic genes did not have
any influence on the inheritance of grain yield and other agronomic traits under the contrasting
research conditions (Striga-infested, drought and optimal growing environment).
Three inbred lines (TZdEI 268, TZdEI 479 and TZdEI 485) with significant negative GCA
(GCA-male and GCA-female) effects for number of emerged Striga plants under Striga
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infestation would be useful in contributing favorable alleles for breeding for Striga resistance in
tropical germplasm. Inbreds TZdEI 173 and TZdEI 352 had significant positive GCA effects for
grain yield and produced one of the highest yielding hybrids under Striga infestation indicating
they would contribute favourable alleles for breeding for improved grain yield under Striga. The
inbreds, TZdEI 492 and TZdEI 378 with significant positive GCA (GCA-male and GCA-female)
effects for grain yield and TZdEI 479 with significant positive GCA-female and TZdEI 260 with
significant positive GCA-male for grain yield under drought would be useful in contributing
favorable alleles for breeding for tolerance to drought. These lines identified as outstanding
under both stresses, could contribute favourable alleles when used in breeding for high grain
yield under drought and Striga infestation.
The additive main effects and multiplicative interaction analysis identified TZdEI 173 x TZdEI
280, TZdEI 173 x TZdEI 492, TZdEI 441 x TZdEI 260, TZdEI 82 x TZdEI 260, TZdEI 71 x
TZdEI 396, TZdEI 396 x TZdEI 131, TZdEI 396 x TZdEI 264, TZdEI 98 x TZdEI 352, TZdEI
157 x TZdEI 352, TZEI 18 x TZdEI 357, TZdEI 268 x TZdEI 378, TZdEI 157 x TZdEI 280,
TZdEI 492 x TZdEI 441 and TZEI 60 x TZEI 5 as the highest yielding and stable hybrids across
the three contrasting research environments. These hybrids should be tested in multi-location and
on-farm trials to confirm the consistency in performance and promoted for release and
commercialization in the sub-region. These hybrids will be useful in Striga endemic and drought
prone areas of the sub-region. High yielding hybrids (TZdEI 173 x TZdEI 352, TZdEI 173 x
TZdEI 280, TZdEI 352 x TZdEI 315, TZdEI 71 x TZdEI 268, TZdEI 82 x TZdEI 260, TZdEI
260 x TZdEI 268, TZdEI 357 x TZdEI 82, TZdEI 314 x TZdEI 105, TZdEI 378 x TZdEI 173,
TZdEI 268 x TZdEI 105 and TZdEI 268 x TZdEI 131) with reduced Striga emergence and host
plant damage were identified.
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The sequential path analyses identified ear aspect as the most reliable secondary trait for indirect
selection for grain yield under both Striga-infested and drought conditions. The significant
phenotypic correlation between grain yield with Striga damage at 8 and 10 WAP, number of
emerged Striga plants at 8 and 10 WAP, and ears per plant justified their inclusion in the base
index as secondary traits for indirect selection for grain yield under Striga infested environments.
Also, the significant genotypic and phenotypic correlation between grain yield and anthesis-
silking interval, plant aspect, ear aspect and stay green characteristic justified their addition in the
base index for selection for grain yield under drought environments.
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CHAPTER SIX
6.0 CONCLUSIONS AND RECOMMENDATIONS
6.1. General Conclusions
Striga hermonthica parasitism, stem borers, low inherent soil feritilty and recurrent drought, are
the major limiting factors to maize production and productivity in the savanna agro-ecologies of
WCA which have the highest yield potential because of high incoming solar radiation, low night
temperature and low severity of pest and diseases. Severe yield reduction is obtained when the
stresses occur simultaneously in the field. The genetic relatedness of the 36 early maturing maize
inbred lines with the novel Striga resistance genes from the wild relatives Zea diploperennis
were assessed using SNP markers. The mode of inheritance to Striga resistance in an early
maturing tropical maize inbred line, TZdEI 352 containing genes for Striga resistance from Zea
diploperennis was determined using generation mean analysis. A total of 156 single cross
hybrids generated using the NC II design were evaluated under Striga-infested, drought and
optimal growing conditions in Nigeria for two years. The objectives were to determine the
genetic diversity, mode of inheritance and combining ability for grain yield and heterotic groups
of the early maturing maize inbreds under contrasting environments, identify high yielding and
stable hybrids with combined Striga resistance and drought tolerance genes.
The findings of this study are summarized as follows:
There was correspondence between the cluster analysis and population structure analysis in
grouping the early maturing inbred lines into four distinct classes using the information based on
their genetic distance.
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Based on the base indices as selection criteria, 42% and 50% of the inbred lines were identified
as drought tolerant and Striga resistant respectively while 22% of the lines combined
resistance/tolerance to both Striga and drought.
Inheritance study showed that epistasis played an important role in Striga resistance genes from
Zea diploperennis in tropical maize. Also, resistance to Striga hermonthica is site/location
specific.
Striga parasistism reduced grain yield by 47% relative to yield under optimal environments
suggesting that the level of infestation was high enough to elucidate hybrids that possessed novel
Striga resistance genes from the wild relative Zea diploperennis. The grain yield reduction of
hybrids under drought was 59% of the optimal environments indicating that the drought stress
imposed from 4 weeks after planting till physiological maturity was adequate to elicit the genetic
variation among the hybrids.
General combining ability (GCA) effects were greater than specific combining ability (SCA)
effects for most traits under Striga-infested and optimal growing conditions suggesting that
additive gene action was more important than non-additive gene action in the 30 early maturing
inbreds. In addition, additive gene action was as important as non-additive gene action under
drought conditions.
Broad sense heritability ranged from 30% for grain yield to 67% for days to anthesis, Striga
damage at 10 WAP and number of emerged Striga plants at 8 WAP under Striga infestation, and
29% for plant height to 55% for grain yield under drought. This result indicated that many of
these traits can be readily transmitted from the parents to their offspring and direct phenotypic
selection can be done since there was preponderance of additive gene effects for most of the
measured traits.
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The 30 inbred lines were classified into four heterotic groups based on the GCA effects of
multiple traits of the inbreds.
Inbred lines TZdEI 173, TZdEI 352, TZdEI 268, TZdEI 479 and TZdEI 485 showed significant
positive GCA effects for grain yield and significant negative GCA effects for number of emerged
Striga plants under Striga infestation while inbreds TZdEI 492, TZdEI 378, TZdEI 479 and
TZdEI 260 showed positive GCA effects for grain yield under drought. These inbred lines would
contribute favorable alleles for Striga resistance and drought tolerance which could be
introgressed into the breeding populations of national maize programs of West and Central
Africa for improvement.
Maternal or cytoplasmic genes did not have any influence on the inheritance of grain yield and
other agronomic studied traits under Striga-infested, drought and optimal growing environments.
6.2 Recommendations
The findings from this study will help plant breeders understand how to use the inbred lines in
other breeding activities such as selecting parental lines for hybrid seed production, classification
into heterotic groups and creating a core set of germplasm.
The schemes used in this study can be exploited by other breeder for efficient parental lines and
hybrid production under drought, Striga-infested and optimal growing conditions in the sub-
region.
The inbred lines identified with combined resistance/tolerance to Striga and drought would
contribute favourable alleles for introgression of genes for resistance/tolerance to both stresses in
hybrid production, population improvement and inbred lines recycling.
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The ten hybrids (TZdEI 173 x TZdEI 280, TZdEI 82 x TZdEI 260, TZdEI 98 x TZdEI 352,
TZdEI 441 x TZdEI 260, TZdEI 492 x TZdEI 441, TZdEI 396 x TZdEI 264), (TZdEI 71 x
TZdEI 396, TZdEI 157 x TZdEI 280, TZdEI 71 x TZdEI 268, and TZdEI 260 x TZdEI 396 )
identified as high yielding and stable under the individual research conditions and across
environments should be tested extensively in on-farm trials to confirm the consistency of
performance and promoted for release and commercialization in the sub-region. Also, they
would be useful as female parents for the development of three-way or double cross hybrids.
The inbred lines that showed positive GCA effects for grain yield under Striga-infested and
drought environments as well as negative GCA effects for Striga damage, number of emerged
Striga plants and stay green characteristics could be used in pedigree selection to develop
synthetic populations and high yielding hybrids with combined resistance/tolerance to Striga and
drought.
Inbred TZdEI 352 was identified as a tester and could be used in classifying other tropical early
maturing inbred lines into heterotic groups.
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APPENDICES
Appendix 5.11: Performance of 156 single cross hybrids under Striga infestation (STR) and
optimal (OPT) environment
Grain yield Striga damage Striga emergence Ear aspect Base
Hybrid (kg ha-1) Index
STR OPT 8 WAP 10 WAP 8 WAP 10 WAP STR OPT
TZdEI 399 x TZEI 7 3881 5297 2.83 3.67 2.66 3.49 4.50 4.45 4.4 0
TZdEI 378 x TZdEI 98 3966 4969 2.50 4.17 3.62 3.91 4.67 4.55 4.24
TZdEI 173 x TZdEI 492 4495 7184 3.83 4.50 3.37 3.77 3.83 3.55 3.92
TZdEI 314 x TZdEI 131 3306 5131 3.00 4.00 2.99 3.45 4.67 4.60 3.89
TZdEI 399 x TZdEI 396 3609 5298 2.50 4.17 3.06 3.65 4.33 4.25 3.85
TZdEI 98 x TZdEI 357 3845 4760 3.00 4.17 3.30 3.69 4.83 4.50 3.85
TZdEI 82 x TZdEI 124 3661 5205 3.50 4.00 2.41 2.86 5.00 4.60 3.78
TZdEI 173 x TZdEI 357 3781 5737 3.50 4.33 3.02 3.51 4.67 4.30 3.76
TZdEI 352 x TZdEI 441 3997 5331 3.83 4.00 2.69 3.17 4.50 4.55 3.71
TZdEI 396 x TZdEI 131 3867 6680 3.00 4.17 3.61 3.94 5.17 4.15 3.71
TZdEI 71 x TZdEI 314 3753 6064 3.17 4.17 2.92 3.22 4.83 4.40 3.71
TZdEI 357 x TZdEI 77 3483 7058 3.83 4.83 1.81 3.11 4.50 4.30 3.57
TZdEI 283 x TZdEI 280 3780 5856 3.17 4.17 3.13 3.50 4.67 4.65 3.39
TZdEI 157 x TZdEI 352 3963 5840 2.83 4.17 3.40 3.56 4.50 4.25 3.30
TZdEI 357 x TZdEI 441 3634 5471 3.00 3.50 2.83 3.49 4.67 4.30 3.27
TZdEI 268 x TZdEI 378 3616 5988 3.33 4.67 3.03 3.37 4.33 4.25 2.97
TZdEI 98 x TZdEI 492 3868 5877 3.50 4.17 3.30 3.57 4.83 3.95 2.96
TZdEI 264 x TZdEI 173 3814 5330 3.17 4.50 3.07 3.38 5.00 4.65 2.78
TZdEI 260 x TZdEI 396 3411 7769 4.17 5.00 1.45 2.70 4.67 4.60 2.72
TZdEI 492 x TZdEI 82 4101 6253 4.67 5.00 2.47 2.69 4.33 4.40 2.71
TZdEI 77 x TZdEI 260 3394 6622 4.17 5.33 1.71 2.42 5.00 4.30 2.59
TZdEI 399 x TZdEI 268 3041 4306 3.17 4.00 2.59 3.12 4.50 4.70 2.55
TZdEI 396 x TZdEI 264 3680 6018 3.67 4.50 3.49 3.71 4.67 4.30 2.52
TZdEI 124 x TZdEI 396 3684 5731 3.33 4.33 3.65 3.83 5.33 4.45 2.47
TZdEI 202 x TZdEI 268 3129 6187 4.17 4.50 2.28 2.82 5.00 4.15 2.33
TZdEI 71 x TZdEI 396 3404 6601 3.00 4.50 2.74 3.26 5.00 4.15 2.31
TZdEI 492 x TZdEI 211 3080 7723 3.83 5.17 1.57 2.50 5.33 4.10 2.12
TZdEI 120 x TZdEI 98 3560 4811 3.83 4.50 2.32 2.78 4.67 4.60 1.93
TZdEI 314 x TZdEI 120 3109 4827 3.83 4.67 2.81 3.06 5.00 4.50 1.41
TZdEI 124 x TZdEI 314 3314 4574 3.33 4.50 3.77 4.09 5.17 4.50 1.28
TZdEI 211 x TZdEI 71 2903 6043 4.17 5.17 1.83 2.46 5.17 4.60 1.20
TZdEI 268 x TZdEI 264 3737 5330 4.33 5.17 2.65 3.07 5.00 4.25 1.10
TZdEI 280 x TZdEI 82 3738 6046 4.33 4.83 2.49 3.23 5.17 4.40 1.06
TZdEI 260 x TZdEI 314 2860 6823 4.17 5.50 1.82 2.48 5.17 4.90 0.96
TZdEI 357 x TZdEI 211 2993 7311 3.50 4.83 2.32 3.38 5.17 4.60 0.87
TZdEI 131 x TZdEI 173 3532 5971 3.50 4.50 3.31 3.58 5.00 4.35 0.76
TZdEI 441 x TZdEI 399 3045 5359 3.50 4.50 3.29 3.77 5.17 4.40 0.67
TZdEI 283 x TZdEI 84 3054 5557 3.67 4.83 3.34 3.54 5.17 4.35 0.54
TZdEI 77 x TZdEI 202 2443 5410 4.33 5.33 1.45 1.96 5.17 4.60 0.48
TZdEI 157 x TZdEI 492 3451 5624 3.83 4.83 3.32 3.87 4.50 4.05 0.38
TZdEI 211 x TZdEI 399 2579 4897 3.83 4.67 2.10 2.61 5.00 5.20 0.34
TZdEI 315 x TZdEI 399 3291 5759 4.00 4.33 3.85 4.07 4.83 4.15 0.34
TZdEI 315 x TZdEI 124 2876 5380 4.17 4.50 2.55 2.92 5.00 5.40 0.17
TZdEI 315 x TZdEI 260 3191 5816 4.00 5.00 3.19 3.79 5.17 4.40 -0.18
TZdEI 396 x TZdEI 120 3055 5797 4.00 4.83 3.05 3.19 5.00 4.10 -0.20
TZdEI 314 x TZdEI 264 2947 5200 3.83 5.00 3.35 3.46 5.17 3.95 -0.23
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Appendix 5.11 continued. Performance of 156 single cross hybrids under Striga (STR) infestation and optimal
(OPT) environments.
Grain yield (kg Striga damage Striga emergence Ear aspect Base
Hybrid ha-1) Index
STR OPT 8 WAP 10 WAP 8 WAP 10 WAP STR OPT
TZdEI 399 x TZdEI 314 2991 4891 4.00 4.67 3.12 3.50 5.33 4.40 -0. 27
TZdEI 441 x TZdEI 124 3210 5604 3.33 4.17 3.55 3.76 5.33 4.20 -0.42
TZdEI 157 x TZdEI 84 2747 4849 3.50 5.00 2.85 3.46 5.67 4.80 -0.52
TZEI 18 x TZdEI 352 3326 5799 3.83 5.00 3.19 3.67 5.00 4.45 -0.55
TZdEI 396 x TZdEI 105 2884 5624 3.67 5.00 3.43 3.95 5.67 4.40 -0.66
TZdEI 98 x TZdEI 280 3107 7225 4.33 5.83 2.32 2.57 5.17 4.30 -0.74
TZdEI 280 x TZdEI 77 2812 6891 4.50 5.33 1.93 2.54 5.00 4.30 -0.77
TZdEI 84 x TZdEI 441 2568 4732 3.50 5.00 2.81 3.08 5.67 4.70 -0.79
TZdEI 202 x TZdEI 396 2944 6066 4.33 4.67 2.81 3.13 5.67 3.90 -0.81
TZdEI 441 x TZdEI 71 2762 5153 4.17 5.17 2.63 3.00 5.00 4.35 -0.87
TZdEI 357 x TZdEI 315 2795 5476 4.33 4.67 3.44 3.78 5.33 4.40 -0.97
TZdEI 260 x TZEI 31 3348 5847 4.17 5.17 3.15 3.64 5.00 4.80 -0.97
TZdEI 264 x TZdEI 98 2957 4723 3.33 4.83 2.96 3.42 5.17 4.80 -0.99
TZEI 18 x TZdEI 357 3036 6897 3.83 4.33 3.53 3.87 5.33 3.80 -1.06
TZdEI 131 x TZEI 18 2623 5337 4.17 5.00 2.57 3.21 5.33 4.50 -1.13
TZdEI 280 x TZdEI 441 3054 5940 3.67 5.00 3.39 3.99 5.00 3.85 -1.13
TZdEI 283 x TZdEI 492 3294 6352 4.50 5.33 3.16 3.76 4.83 3.85 -1.14
TZdEI 315 x TZdEI 202 2573 5198 4.00 5.00 3.35 3.81 5.83 4.50 -1.30
TZdEI 264 x TZdEI 283 2709 5490 4.67 5.67 1.78 2.30 5.50 5.40 -1.36
TZdEI 264 x TZEI 18 2952 5484 4.17 5.67 2.77 3.12 5.50 4.65 -1.37
TZdEI 124 x TZEI 31 2892 3880 4.33 5.00 3.30 3.54 5.17 5.00 -1.38
TZdEI 315 x TZdEI 71 2322 5445 4.33 5.17 2.15 2.61 5.83 5.00 -1.54
TZdEI 157 x TZdEI 280 3395 5992 4.00 5.00 3.36 3.88 4.50 4.35 -1.59
TZdEI 173 x TZdEI 84 2068 6026 4.33 5.17 0.99 2.16 5.17 5.00 -1.63
TZdEI 77 x TZdEI 399 2573 5517 4.00 5.50 2.53 3.22 4.83 4.80 -1.67
TZEI 7 x TZdEI 264 3078 5554 4.50 5.67 2.88 3.06 5.83 4.55 -1.69
TZdEI 105 x TZEI 18 3020 5717 4.17 5.00 3.16 3.55 5.33 4.40 -1.73
TZEI 31 x TZdEI 131 2975 4549 4.00 4.67 3.15 3.64 5.33 5.10 -1.82
TZdEI 283 x TZdEI 352 2740 4043 4.33 5.50 2.89 3.37 5.33 5.05 -1.87
TZdEI 492 x TZdEI 315 3319 6010 5.00 5.67 3.21 3.66 5.50 4.20 -1.87
TZdEI 98 x TZdEI 84 2742 4456 4.83 5.17 3.12 3.38 5.17 4.00 -2.00
TZdEI 283 x TZdEI 357 2984 5527 4.50 5.00 3.15 3.41 4.83 4.20 -2.12
TZdEI 82 x TZdEI 202 2277 5717 4.67 5.33 2.43 2.78 5.50 4.30 -2.33
TZdEI 399 x TZEI 31 2365 5183 4.00 4.83 3.52 4.17 5.33 4.30 -2.43
TZdEI 77 x TZdEI 71 2667 6638 4.33 6.00 1.81 2.55 5.33 4.50 -2.54
TZdEI 157 x TZdEI 357 2648 5093 4.00 5.17 3.45 3.90 5.67 4.15 -2.55
TZEI 31 x TZdEI 120 2971 4180 5.00 5.50 2.96 3.20 5.17 4.75 -2.58
TZdEI 82 x TZdEI 71 2143 2376 5.00 5.33 1.60 2.50 5.67 6.00 -2.59
TZdEI 378 x TZdEI 157 2785 5564 3.83 4.67 4.19 4.32 5.33 4.00 -2.71
TZdEI 124 x TZEI 7 2862 4600 4.00 5.17 3.33 3.70 5.50 5.20 -2.74
TZEI 7 x TZdEI 105 2644 6443 4.00 5.00 3.07 3.49 5.50 4.40 -2.76
TZEI 7 x TZdEI 131 3285 5702 4.00 5.17 3.16 3.67 5.50 4.40 -2.77
TZdEI 120 x TZdEI 283 2804 4990 4.67 5.33 3.25 3.43 5.17 4.06 -2.83
TZdEI 260 x TZEI 7 3201 5694 4.83 5.33 3.65 3.96 5.50 4.85 -2.93
150
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Appendix 5.11 continued: Performance of 156 single cross hybrids under Striga infestation
(STR) and optimal (OPT) environments.
Hybrid Grain yield (kg Striga damage Striga emergence Ear aspect Base
ha-1) Index
STR OPT 8 WAP 10 WAP 8 WAP 10 WAP STR OPT
TZEI 7 x TZdEI 105 2644 6443 4.00 5.00 3.07 3.49 5.50 4.40 -2.76
TZEI 7 x TZdEI 131 3285 5702 4.00 5.17 3.16 3.67 5.50 4.40 -2.77
TZdEI 120 x TZdEI 283 2804 4990 4.67 5.33 3.25 3.43 5.17 4.06 -2.83
TZdEI 260 x TZEI 7 3201 5694 4.83 5.33 3.65 3.96 5.50 4.85 -2.93
TZdEI 120 x TZdEI 157 2724 5042 4.50 5.17 3.40 3.64 5.33 4.65 -3.14
TZdEI 396 x TZdEI 378 2708 6295 4.33 5.17 3.59 3.85 5.00 3.95 -3.16
TZdEI 84 x TZdEI 77 1903 5801 4.33 6.17 1.21 2.38 6.00 4.70 -3.17
TZdEI 202 x TZEI 7 2204 5498 4.33 5.17 1.78 2.30 5.50 4.80 -3.20
TZdEI 105 x TZdEI 283 2925 5473 4.33 5.50 3.25 3.73 5.17 4.20 -3.23
TZdEI 105 x TZdEI 157 2270 4102 3.83 5.00 3.46 3.79 5.50 4.90 -3.34
TZdEI 378 x TZdEI 283 2901 6742 4.83 5.33 3.50 3.86 5.83 3.50 -3.50
TZdEI 280 x TZdEI 315 2571 5527 4.17 5.67 3.67 4.01 5.33 4.50 -3.55
TZdEI 71 x TZEI 7 2980 5096 4.83 5.33 2.98 3.35 6.00 5.10 -3.57
TZdEI 84 x TZdEI 315 2849 5164 4.33 6.17 3.37 3.59 6.00 4.40 -3.58
TZdEI 84 x TZdEI 82 2565 4540 5.00 6.00 1.36 2.06 6.33 4.40 -3.71
TZdEI 131 x TZdEI 157 2502 4940 4.33 5.33 3.76 3.73 5.50 4.75 -3.86
TZdEI 441 x TZdEI 202 2426 5558 3.67 5.17 2.98 3.22 5.50 4.15 -3.88
TZEI 18 x TZdEI 280 3171 6448 4.67 5.33 3.53 3.87 5.50 3.95 -3.91
TZdEI 120 x TZEI 18 2627 4407 4.50 5.50 2.99 3.41 5.33 4.70 -4.19
TZdEI 378 x TZEI 18 2709 5723 4.67 5.50 3.81 4.01 5.50 4.25 -4.37
TZdEI 264 x TZdEI 157 2984 4833 4.67 5.67 3.37 4.17 5.50 4.45 -4.41
TZdEI 131 x TZdEI 98 2214 3290 4.17 5.50 2.87 3.09 5.83 5.30 -4.47
TZEI 188 x TZEI 98 2681 5605 4.17 5.50 2.80 3.41 5.50 4.55 -4.53
TZEI 18 x TZdEI 84 2611 6045 5.00 5.83 2.67 3.17 5.67 4.15 -4.67
TZEI 60 x TZEI 5 3143 6876 4.83 5.67 2.94 3.33 5.50 3.30 -4.73
TZdEI 71 x TZEI 31 2403 5519 4.83 5.50 3.27 3.65 6.00 4.30 -5.16
TZEI 7 x TZdEI 120 2357 5027 5.00 5.83 2.62 3.26 5.50 4.65 -5.73
TZEI 31 x TZdEI 105 2651 5540 5.00 6.00 3.47 4.04 5.83 4.25 -5.83
TZdEI 314 x TZdEI 378 2109 4596 4.83 5.33 3.05 3.57 5.83 5.15 -5.92
TZEI 31 x TZdEI 378 2598 5138 5.00 5.50 3.54 3.86 5.33 4.35 -6.09
TZdEI 211 x TZdEI 202 1740 6885 4.67 5.83 1.65 2.35 5.83 4.20 -6.25
151
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Appendix 5.12: Performance of 156 single cross hybrid under drought (DT) and optimal (OPT)
environments
Hybrid Grain yield Days to Ear aspect Ears per SGC BI
(kg ha-1) silking plant
DT OPT DT OPT DT OPT DT OPT
TZdEI 479 x TZdEI 260 3601 6622 56 50 4.05 4.30 1.07 1.12 2.30 13.85
TZdEI 396 x TZdEI 264 3167 6018 58 53 4.35 4.30 0.96 0.99 2.40 9.20
TZdEI 378 x TZdEI 157 3086 5564 57 53 3.75 4.00 0.90 0.97 3.20 8.80
TZdEI 71 x TZdEI 396 3172 6601 56 51 3.90 4.15 0.73 1.02 2.10 7.79
TZdEI 157 x TZdEI 280 2880 5992 59 52 4.00 4.35 1.07 1.03 3.00 7.77
TZdEI 260 x TZdEI 396 3122 7769 55 50 4.65 4.60 0.87 0.98 2.80 7.57
TZdEI 399 x TZdEI 268 2443 4306 57 54 3.65 4.70 0.92 0.91 2.60 7.46
TZdEI 492 x TZdEI 315 3155 6010 57 51 3.85 4.20 0.82 0.90 3.40 6.91
TZdEI 396 x TZdEI 378 2909 6295 58 52 4.10 3.95 0.87 0.97 2.60 6.85
TZdEI 396 x TZdEI 131 2697 6680 55 50 4.35 4.15 0.82 0.98 2.50 6.82
TZdEI 396 x TZdEI 120 7038 5797 57 52 3.90 4.10 0.90 0.96 2.90 6.52
TZdEI 399 x TZdEI 314 2704 4891 56 51 3.95 4.40 0.94 0.92 3.20 6.42
TZdEI 314 x TZdEI 120 2796 4827 57 52 4.25 4.50 1.02 1.00 2.90 6.20
TZdEI 441 x TZdEI 71 2548 5153 58 53 3.85 4.35 0.82 0.93 2.20 5.96
TZEI 18 x TZdEI 84 2980 6045 58 53 4.15 4.15 0.82 0.93 2.50 5.89
TZdEI 71 x TZdEI 314 2606 6064 56 51 4.00 4.40 0.95 0.97 2.70 5.80
TZEI 7 x TZdEI 264 2681 5554 59 53 4.20 4.55 0.80 0.94 2.60 5.71
TZdEI 283 x TZdEI 492 2670 6352 58 52 4.15 3.85 0.98 1.07 3.20 5.66
TZdEI 260 x TZEI 31 2764 5847 57 52 4.25 4.80 0.99 1.01 3.30 5.50
TZdEI 173 x TZdEI 352 2545 4347 61 53 3.80 4.00 0.85 0.96 2.80 5.42
TZdEI 315 x TZdEI 399 2659 5759 56 52 4.05 4.15 0.89 1.03 3.10 5.39
TZdEI 98 x TZdEI 492 2791 5877 58 53 4.35 3.95 0.95 0.95 3.90 5.08
TZEI 18 x TZdEI 352 2789 5799 61 54 4.05 4.45 0.85 1.00 2.50 5.06
TZdEI 378 x TZEI 18 2810 5723 59 52 3.95 4.25 0.88 0.95 2.90 4.98
TZdEI 260 x TZEI 7 2735 5694 56 51 4.40 4.85 0.92 0.90 2.90 4.92
TZdEI 441 x TZdEI 260 2702 7033 59 52 4.20 3.50 0.79 1.01 2.70 4.77
TZEI 7 x TZdEI 378 5924 5921 58 51 4.25 4.45 0.97 0.93 2.80 4.61
TZdEI 82 x TZdEI 399 2733 5910 58 51 4.20 4.15 0.82 0.98 2.40 4.48
TZdEI 71 x TZdEI 268 2692 7068 57 52 4.85 4.70 0.99 0.97 3.40 4.33
TZdEI 157 x TZdEI 492 2740 5624 60 54 4.00 4.05 0.82 0.98 3.00 4.11
TZdEI 357 x TZdEI 441 2804 5471 60 54 4.20 4.30 0.68 0.96 2.70 3.99
TZdEI 131 x TZdEI 283 2479 5853 56 52 4.60 4.40 0.98 0.97 3.10 3.97
TZdEI 82 x TZdEI 260 2202 6856 56 50 4.60 4.00 0.89 0.99 2.30 3.97
TZEI 188 x TZEI 98 2642 5605 58 52 4.20 4.55 0.87 0.91 2.80 3.91
TZdEI 98 x TZdEI 352 2706 5868 60 54 4.10 4.30 0.83 0.97 3.10 3.89
TZdEI 157 x TZdEI 357 2430 5093 60 55 4.65 4.15 0.96 1.00 2.50 3.67
TZdEI 264 x TZEI 18 2666 5484 60 54 4.30 4.65 0.83 1.00 2.80 3.57
TZdEI 268 x TZdEI 378 2962 5988 58 53 4.45 4.25 0.83 0.96 3.30 3.44
TZEI 31 x TZdEI 105 2527 5540 57 53 4.30 4.25 0.82 0.97 3.20 3.29
TZdEI 352 x TZdEI 315 2487 5969 60 52 4.15 4.05 0.97 0.94 3.10 3.26
TZdEI 260 x TZdEI 314 2635 6823 56 50 4.50 4.90 1.01 1.02 3.80 3.13
TZdEI 173 x TZdEI 280 2511 6816 56 51 4.45 3.80 0.87 0.96 3.20 3.12
TZdEI 378 x TZdEI 283 2611 6742 58 51 4.15 3.50 0.93 0.99 2.90 3.09
152
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Appendix 5.12 continued. Performance of 156 single cross hybrids under drought (DT) and
optimal environments.
Hybrid Grain yield Days to Ear aspect Ears per plant SGC BI
(kg ha-1) silking
DT OPT DT OPT DT OPT DT OPT
TZEI 7 x TZdEI 120 2888 5027 57 50 4.20 4.65 0.88 0.99 2.40 3.05
TZdEI 202 x TZdEI 2707 6187 59 52 4.35 4.15 0.87 1.03 3.30 3.03
268
TZdEI 173 x TZdEI 5609 7184 58 52 4.50 3.55 0.93 1.02 3.30 2.91
492
TZEI 18 x TZdEI 357 2534 6897 59 53 4.25 3.80 0.88 1.04 3.60 2.77
TZdEI 82 x TZdEI 202 2184 5717 56 51 4.30 4.30 0.90 0.99 2.70 2.74
TZEI 60 x TZEI 5 2612 6876 60 54 4.35 3.30 0.81 0.97 3.40 2.62
TZdEI 357 x TZdEI 2453 5476 59 53 4.20 4.40 0.86 1.03 3.40 2.54
315
TZdEI 399 x TZdEI 2450 5298 58 53 4.55 4.25 0.75 0.88 2.70 2.49
396
TZdEI 352 x TZdEI 82 2440 5881 60 52 4.25 4.05 0.84 1.01 2.60 2.41
TZdEI 82 x TZdEI 124 2177 5205 56 51 4.40 4.60 0.81 0.97 2.70 2.40
TZdEI 98 x TZdEI 280 2356 7225 55 52 4.40 4.30 0.87 0.93 3.60 2.30
TZdEI 378 x TZdEI 98 2308 4969 58 53 4.25 4.55 0.94 1.08 3.50 2.13
TZdEI 105 x TZdEI 173 2425 5448 59 51 4.38 4.45 0.78 0.98 3.75 2.12
TZdEI 283 x TZdEI 84 2671 5557 57 53 4.50 4.35 0.83 1.03 3.30 2.00
TZdEI 120 x TZdEI 173 2450 5388 59 52 4.45 4.20 0.80 0.95 3.00 1.99
TZdEI 268 x TZdEI 264 2311 5330 60 53 4.75 4.25 0.97 0.97 2.90 1.97
TZdEI 315 x TZdEI 71 2386 5445 55 49 4.90 5.00 0.87 0.90 3.10 1.95
TZdEI 120 x TZEI 18 2525 4407 59 54 4.35 4.70 0.79 1.08 3.20 1.93
TZdEI 479 x TZdEI 71 2368 6638 60 51 5.10 4.50 1.05 1.01 3.10 1.83
TZdEI 124 x TZdEI 396 2271 5731 57 51 4.40 4.45 0.91 1.03 3.30 1.73
TZEI 31 x TZdEI 264 2583 5356 59 52 4.55 4.60 0.77 0.97 3.10 1.70
TZdEI 131 x TZdEI 157 2272 4940 58 52 4.35 4.75 0.86 0.94 3.40 1.64
TZdEI 202 x TZdEI 396 2289 6066 59 52 4.55 3.90 0.87 0.99 2.60 1.63
TZdEI 485 x TZdEI 202 2212 6885 58 51 5.25 4.20 0.75 1.00 2.40 1.49
TZdEI 492 x TZdEI 82 2109 6253 59 53 4.80 4.40 0.82 0.97 2.60 1.44
TZdEI 84 x TZdEI 315 2471 5164 58 50 4.45 4.40 0.87 0.98 3.60 1.35
TZEI 18 x TZdEI 492 2467 5733 60 54 4.30 4.10 0.75 0.91 2.70 1.34
TZdEI 264 x TZdEI 98 2076 4723 60 54 4.70 4.80 0.89 0.95 2.70 0.93
TZdEI 441 x TZdEI 399 2359 5359 61 54 4.90 4.40 0.77 0.99 2.80 0.92
TZEI 31 x TZdEI 378 2597 5138 58 52 4.45 4.35 0.84 0.99 3.70 0.90
TZdEI 71 x TZEI 31 2443 5519 58 51 4.65 4.30 0.79 0.96 2.70 0.89
TZdEI 283 x TZdEI 357 2103 5527 59 52 4.70 4.20 0.86 0.94 3.00 0.87
TZEI 7 x TZdEI 131 2283 5702 58 52 4.55 4.40 0.78 0.93 3.10 0.86
TZdEI 260 x TZdEI 268 2266 6370 57 50 5.10 4.40 0.72 0.99 2.80 0.65
TZdEI 357 x TZdEI 82 2318 6246 59 51 4.65 4.10 0.77 0.99 2.90 0.52
TZdEI 399 x TZEI 31 2415 5183 58 52 4.10 4.30 0.76 0.89 3.50 0.52
TZdEI 268 x TZdEI 120 2432 5581 60 51 4.35 4.35 0.76 1.08 2.80 0.39
TZdEI 202 x TZdEI 314 2250 5417 57 51 4.25 4.05 0.89 0.94 3.40 0.17
TZdEI 124 x TZdEI 314 2200 4574 57 51 4.40 4.50 0.89 0.93 3.40 0.16
TZdEI 378 x TZdEI 173 2486 6272 59 52 4.55 4.05 0.80 0.97 3.80 0.08
TZdEI 120 x TZdEI 157 2262 5042 59 52 4.35 4.65 0.80 0.93 3.30 0.07
TZdEI 264 x TZdEI 157 2161 4833 61 54 4.55 4.45 0.83 0.95 2.50 0.02
TZdEI 124 x TZdEI 268 2045 3967 58 53 4.90 4.55 0.92 0.95 3.80 0.01
TZdEI 399 x TZEI 7 2271 5297 60 53 4.75 4.45 0.74 0.93 2.70 -0.06
TZdEI 315 x TZdEI 202 2048 5198 58 50 4.95 4.50 0.91 1.00 3.20 -0.08
153
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Appendix 5.12 continued. Performance of 156 single cross hybrids under drought (DT) and
optimal (OPT) environments.
Hybrid Grain yield Days to Ear aspect Ears per plant SG BI
(kg ha-1) silking C
DT OPT DT OPT DT OPT DT OPT
TZdEI 314 x TZdEI 264 2368 5200 59 52 4.45 3.95 0.76 1.00 3.70 -0.14
TZdEI 98 x TZdEI 357 2459 4760 60 54 4.15 4.50 0.71 0.93 3.60 -0.17
TZEI 31 x TZdEI 120 2551 4180 59 52 4.40 4.75 0.88 0.97 3.90 -0.23
TZEI 31 x TZEI 63 2317 4823 60 53 4.55 4.40 0.86 0.96 3.67 -0.26
TZdEI 202 x TZEI 31 2310 5263 59 52 4.35 4.20 0.81 1.08 3.30 -0.30
TZdEI 492 x TZdEI 441 2481 6245 61 55 4.80 4.10 0.82 0.99 3.60 -0.33
TZEI 18 x TZdEI 280 2560 6448 60 52 4.20 3.95 0.70 0.97 3.40 -0.39
TZdEI 120 x TZdEI 98 2208 4811 58 51 4.45 4.60 0.86 0.95 4.00 -0.45
TZdEI 283 x TZdEI 280 2151 5856 59 53 4.60 4.65 0.80 1.04 2.90 -0.68
TZdEI 105 x TZEI 18 2071 5717 58 52 4.60 4.40 0.81 0.94 3.60 -0.73
TZdEI 120 x TZdEI 283 1967 4990 59 53 4.90 4.06 0.83 1.05 3.00 -0.87
TZdEI 396 x TZdEI 105 2375 5624 58 52 4.25 4.40 0.74 0.99 3.10 -0.99
TZdEI 280 x TZdEI 82 1919 6046 57 52 4.70 4.40 0.81 0.95 2.40 -1.06
TZdEI 71 x TZEI 7 1985 5096 57 51 4.90 5.10 0.81 0.95 2.40 -1.19
TZdEI 479 x TZdEI 124 2639 6455 57 51 4.90 4.10 0.86 1.00 3.90 -1.26
TZdEI 173 x TZdEI 357 2314 5737 60 53 4.55 4.30 0.70 1.03 3.30 -1.26
TZdEI 202 x TZEI 7 2151 5498 57 51 5.30 4.80 0.86 0.96 3.20 -1.61
TZEI 7 x TZdEI 105 2183 6443 60 52 4.75 4.40 0.82 0.95 3.40 -1.82
TZdEI 441 x TZdEI 124 2068 5604 59 52 4.75 4.20 0.85 0.97 3.40 -2.06
TZdEI 105 x TZdEI 157 1955 4102 59 52 4.60 4.90 0.83 0.96 3.70 -2.07
TZdEI 157 x TZdEI 84 2148 4849 60 53 4.55 4.80 0.81 0.94 3.30 -2.15
TZEI 60 x TZEI 86 1804 5333 58 53 5.00 4.65 0.81 0.98 3.50 -2.16
TZdEI 173 x TZdEI 84 2376 6026 58 52 5.35 5.00 0.83 0.98 3.60 -2.56
TZEI 2 x TZEI 87 2327 4167 58 52 4.55 5.18 0.82 0.91 3.70 -2.63
TZdEI 315 x TZdEI 260 1755 5816 57 50 5.10 4.40 0.85 1.00 3.20 -2.70
TZdEI 479 x TZdEI 399 2389 5517 60 51 5.15 4.80 0.92 0.95 3.80 -2.75
TZdEI 441 x TZdEI 202 1960 5558 60 53 4.95 4.15 0.69 1.01 3.10 -2.79
TZdEI 105 x TZdEI 283 2181 5473 57 53 4.85 4.20 0.73 0.94 3.70 -2.82
TZdEI 485 x TZdEI 71 1955 6043 58 51 5.30 4.60 0.84 0.97 3.30 -3.00
TZdEI 280 x TZdEI 315 1925 5527 59 50 5.05 4.50 1.03 1.04 3.70 -3.04
TZdEI 268 x TZdEI 105 2162 4971 59 54 4.85 4.25 0.66 0.97 3.70 -3.18
TZdEI 314 x TZdEI 131 2091 5131 58 51 4.50 4.60 0.79 1.01 3.60 -3.20
TZdEI 131 x TZEI 18 2256 5337 60 53 4.85 4.50 0.72 0.95 3.60 -3.40
TZdEI 131 x TZdEI 173 2169 5971 58 51 4.90 4.35 0.78 0.99 3.70 -3.42
TZdEI 280 x TZdEI 441 2090 5940 60 53 5.10 3.85 0.74 0.94 3.10 -3.46
TZdEI 264 x TZdEI 173 2089 5330 60 52 5.00 4.65 0.61 0.96 3.30 -3.80
TZdEI 98 x TZdEI 84 2091 4456 59 50 4.10 4.00 0.75 1.07 3.10 -3.80
TZdEI 314 x TZdEI 105 2044 5109 57 51 4.85 4.55 0.83 0.98 4.30 -3.84
154
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Appendix 5.12 continued. Performance of 156 single cross hybrids under drought (DT) and
optimal environments.
Hybrid Grain yield Days to Ear aspect Ears per SGC BI
(kg ha-1) silking plant
DT OPT DT OPT DT OPT DT OPT
TZdEI 352 x TZdEI 479 1627 6174 60 53 5.30 4.90 0.85 1.03 3.40 -3.99
TZEI 26 x TZEI 5 1872 4176 59 53 5.40 4.90 0.72 0.84 3.00 -4.02
TZdEI 268 x TZdEI 131 1982 5383 60 51 4.90 4.50 0.85 0.93 4.00 -4.03
TZdEI 479 x TZdEI 202 2554 5410 57 50 4.90 4.60 0.72 0.99 3.30 -4.08
TZdEI 124 x TZEI 7 1920 4600 57 51 4.90 5.20 0.76 0.98 3.60 -4.57
TZdEI 264 x TZdEI 283 1934 5490 60 54 5.40 5.40 0.93 1.05 3.40 -4.63
TZdEI 485 x TZdEI 399 2057 4897 60 53 5.40 5.20 0.72 0.96 3.70 -5.07
TZdEI 84 x TZdEI 441 1656 4732 61 54 4.85 4.70 0.79 0.94 3.10 -5.26
TZdEI 492 x TZdEI 479 1894 6908 61 53 5.00 4.90 0.77 0.98 4.00 -5.33
TZdEI 84 x TZdEI 485 1985 5731 60 52 4.80 4.80 0.61 0.98 3.50 -5.60
TZEI 31 x TZdEI 131 1902 4549 59 53 4.90 5.10 0.66 0.89 3.60 -5.70
TZdEI 357 x TZdEI 485 2221 7311 59 53 5.10 4.60 0.64 0.98 4.20 -5.81
TZdEI 84 x TZdEI 82 1919 4540 59 53 5.25 4.40 0.62 0.86 4.25 -6.38
TZdEI 280 x TZdEI 479 1035 6891 59 52 5.00 4.30 0.68 1.02 2.90 -7.06
TZdEI 485 x TZdEI 260 2312 6442 59 51 5.05 4.90 0.63 0.93 4.70 -7.33
TZdEI 105 x TZdEI 98 1741 4308 60 53 5.20 4.85 0.51 0.84 4.10 -8.49
TZdEI 124 x TZEI 31 1612 3880 60 53 5.15 5.00 0.74 0.99 4.10 -9.58
TZdEI 352 x TZdEI 485 2128 6305 60 53 5.30 4.80 0.66 1.01 5.00 -9.63
TZdEI 315 x TZdEI 124 1763 5380 57 51 5.55 5.40 0.80 0.94 4.90 -9.73
TZdEI 492 x TZdEI 485 2078 7723 60 52 5.40 4.10 0.55 0.99 4.90 -10.30
TZdEI 283 x TZdEI 352 1180 4043 61 53 5.75 5.05 0.63 0.99 4.00 -10.85
TZdEI 357 x TZdEI 479 1050 7058 62 53 5.65 4.30 0.63 1.05 3.60 -11.32
TZdEI 131 x TZdEI 98 1178 3290 61 54 5.70 5.30 0.65 0.82 4.10 -12.15
TZdEI 84 x TZdEI 479 839 5801 63 51 5.58 4.70 0.86 0.97 3.13 -12.46
TZdEI 280 x TZdEI 485 1229 5880 60 52 5.85 4.60 0.68 0.96 3.90 -12.87
TZdEI 485 x TZdEI 124 1582 5111 61 51 5.55 5.30 0.68 0.91 5.10 -13.01
TZdEI 82 x TZdEI 71 671 2376 63 53 5.44 6.00 0.46 0.80 3.78 -16.12
TZdEI 314 x TZdEI 378 579 4596 61 52 6.30 5.15 0.78 0.93 5.30 -17.36
155
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Appendix 5.13: Performance of the 156 single cross hybrids across Striga infested, drought and
optimal conditions at 13 environments in 2013 and 2015
Hybrid Grain Days Anthesis Plant Ear Plant Ears
yield (kg to -silking height aspect aspect per
ha-1) silking interval (cm) plant
TZdEI 268 x TZdEI 378 4278 56 1.50 161.58 4.35 4.28 0.91
TZdEI 378 x TZdEI 283 4272 56 1.96 164.92 4.29 4.00 0.94
TZdEI 492 x TZdEI 441 4255 58 2.20 170.27 4.54 3.50 0.94
TZdEI 357 x TZdEI 82 4229 56 1.27 170.35 4.44 4.20 0.90
TZdEI 283 x TZdEI 492 4229 56 1.46 163.00 4.19 3.93 1.00
TZdEI 492 x TZdEI 479 4225 58 1.62 166.19 4.96 4.45 0.90
TZdEI 98 x TZdEI 492 4224 56 0.85 169.23 4.31 4.05 0.94
TZEI 18 x TZdEI 280 4206 57 1.96 152.69 4.40 4.33 0.82
TZdEI 157 x TZdEI 280 4201 57 1.46 160.38 4.25 4.08 0.99
TZdEI 82 x TZdEI 399 4196 55 1.69 170.50 4.21 3.88 0.91
TZdEI 485 x TZdEI 260 4195 56 1.15 162.00 4.94 5.55 0.85
TZdEI 396 x TZdEI 378 4166 56 1.35 163.81 4.25 3.83 0.91
TZdEI 71 x TZdEI 314 4160 54 1.00 169.23 4.35 4.58 0.94
TZdEI 202 x TZdEI 268 4143 56 1.35 168.00 4.42 4.10 0.96
TZdEI 157 x TZdEI 352 4141 58 1.62 169.46 4.33 3.93 0.90
TZdEI 492 x TZdEI 82 4131 57 1.23 165.85 4.54 3.83 0.90
TZdEI 352 x TZdEI 82 4124 57 2.04 172.58 4.31 3.85 0.92
TZdEI 352 x TZdEI 485 4124 58 2.27 161.85 4.96 5.00 0.88
TZdEI 479 x TZdEI 71 4080 56 1.08 162.15 4.92 4.73 0.98
TZdEI 260 x TZEI 31 4079 55 1.35 165.42 4.63 4.30 0.97
TZEI 18 x TZdEI 352 4076 59 1.88 160.65 4.42 3.98 0.91
TZEI 18 x TZdEI 84 4075 56 1.96 162.42 4.50 3.83 0.86
TZdEI 105 x TZdEI 173 4073 55 0.67 176.58 4.44 3.97 0.90
TZdEI 157 x TZdEI 492 4012 58 1.38 168.58 4.13 4.00 0.90
TZdEI 268 x TZdEI 120 4012 56 1.58 168.65 4.38 4.18 0.94
TZdEI 315 x TZdEI 399 4000 55 1.38 161.04 4.27 4.13 0.96
TZdEI 357 x TZdEI 441 3985 58 1.85 177.88 4.35 3.63 0.81
TZdEI 396 x TZdEI 120 3985 56 1.12 165.23 4.23 3.98 0.92
TZdEI 120 x TZdEI 173 3984 56 1.23 164.31 4.37 4.08 0.90
TZdEI 173 x TZdEI 357 3980 57 1.77 165.35 4.48 4.20 0.89
TZdEI 260 x TZEI 7 3974 55 1.65 157.92 4.83 4.25 0.90
TZdEI 378 x TZdEI 157 3970 56 1.08 168.58 4.21 3.93 0.92
TZdEI 131 x TZdEI 173 3944 55 1.69 165.23 4.71 4.20 0.87
TZdEI 283 x TZdEI 280 3942 57 1.92 166.62 4.63 4.00 0.91
TZdEI 202 x TZdEI 396 3939 56 1.58 177.96 4.56 3.78 0.92
TZdEI 268 x TZdEI 131 3937 56 1.42 168.27 4.58 4.38 0.88
TZdEI 124 x TZdEI 396 3937 55 1.31 171.04 4.63 4.10 0.96
TZEI 7 x TZdEI 105 3928 56 1.73 170.58 4.79 3.90 0.87
TZdEI 357 x TZdEI 479 3922 58 1.00 158.04 4.87 5.13 0.88
TZdEI 280 x TZdEI 82 3915 56 2.23 163.58 4.69 3.98 0.87
156
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Appendix 5.13 continued. Performance of the hybrids across Striga-infested, drought and optimal
conditions in 13 environments in 2013 and 2015.
Hybrid Grain Days Anthesis Plant Ear Plant Ears per
yield (kg to -silking height aspect aspec plant
ha-1) silking interval (cm) t
TZdEI 378 x TZEI 18 3905 57 1.81 161.31 4.42 4.28 0.91
TZdEI 485 x TZdEI 202 3900 56 1.23 165.31 4.98 4.53 0.84
TZEI 7 x TZdEI 264 3876 58 1.73 164.42 4.71 4.13 0.87
TZdEI 283 x TZdEI 84 3872 56 2.00 174.54 4.60 4.05 0.94
TZEI 7 x TZdEI 131 3870 56 1.68 168.08 4.71 4.23 0.82
TZdEI 131 x TZdEI 283 3865 55 1.46 170.00 4.65 4.33 0.95
TZdEI 352 x TZdEI 441 3844 60 1.85 164.38 4.60 3.93 0.86
TZdEI 399 x TZEI 7 3822 57 1.46 167.50 4.58 4.15 0.83
TZdEI 399 x TZdEI 396 3822 57 1.27 163.73 4.38 3.93 0.83
TZdEI 268 x TZdEI 264 3800 57 1.27 156.58 4.62 4.53 0.95
TZdEI 264 x TZEI 18 3791 58 2.12 153.92 4.71 4.38 0.91
TZEI 188 x TZEI 98 3787 56 1.42 160.12 4.63 4.15 0.85
TZdEI 280 x TZdEI 441 3775 57 1.92 161.46 4.60 3.80 0.85
TZdEI 314 x TZdEI 105 3767 55 1.54 165.62 4.50 4.40 0.93
TZEI 18 x TZdEI 492 3754 58 1.96 149.19 4.46 4.33 0.82
TZdEI 485 x TZdEI 71 3746 56 1.15 173.42 5.00 4.65 0.92
TZdEI 396 x TZdEI 105 3742 56 1.46 171.00 4.63 4.85 0.89
TZdEI 202 x TZdEI 314 3721 55 1.85 162.19 4.42 4.38 0.91
TZEI 31 x TZdEI 105 3717 56 1.42 172.85 4.63 4.15 0.88
TZdEI 264 x TZdEI 173 3717 57 1.23 159.50 4.87 4.45 0.80
TZdEI 268 x TZdEI 105 3714 57 1.19 165.92 4.38 4.35 0.84
TZdEI 378 x TZdEI 98 3714 56 1.15 167.62 4.46 4.38 0.99
TZdEI 173 x TZdEI 84 3709 56 1.58 170.92 5.17 4.63 0.90
TZdEI 441 x TZdEI 124 3700 57 1.85 176.69 4.67 4.15 0.88
TZdEI 357 x TZdEI 315 3698 57 1.58 172.58 4.54 3.98 0.95
TZdEI 280 x TZdEI 479 3697 56 1.50 165.73 4.73 4.70 0.86
TZEI 7 x TZdEI 378 3697 56 1.65 157.23 4.69 4.03 0.92
TZdEI 173 x TZdEI 352 3694 57 1.29 161.25 3.83 3.28 0.89
TZdEI 105 x TZEI 18 3691 56 1.46 164.19 4.69 4.28 0.87
TZdEI 82 x TZdEI 124 3686 54 0.96 170.15 4.62 4.00 0.89
TZdEI 441 x TZdEI 399 3675 58 1.77 172.54 4.77 3.80 0.89
TZdEI 315 x TZdEI 260 3648 54 1.15 155.35 4.85 4.45 0.93
TZdEI 314 x TZdEI 120 3648 55 1.19 162.58 4.52 4.43 1.00
TZdEI 352 x TZdEI 479 3638 58 1.77 168.15 5.00 4.68 0.92
TZdEI 479 x TZdEI 399 3635 56 1.38 155.85 4.94 5.33 0.93
TZdEI 479 x TZdEI 202 3627 54 2.27 168.46 4.85 5.15 0.89
TZdEI 283 x TZdEI 357 3620 57 1.35 171.31 4.54 3.95 0.89
TZdEI 105 x TZdEI 283 3618 56 1.96 167.73 4.67 4.28 0.83
TZdEI 71 x TZEI 31 3611 55 1.54 162.92 4.83 4.28 0.87
TZdEI 98 x TZdEI 357 3610 57 1.42 169.58 4.44 4.30 0.85
157
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Appendix 5.13 continued. Performance of 156 single cross hybrids across Striga infested, drought and
optimal conditions in 13 environments in 2013 and 2015.
Hybrid Grain Days Anthesis Plant Ear Plant Ears
yield (kg to -silking height aspect aspect per
ha-1) silking interval (cm) plant
TZdEI 399 x TZdEI 314 3605 55 1.12 162.50 4.44 4.38 0.93
TZdEI 84 x TZdEI 315 3597 54 1.31 164.19 4.79 4.28 0.92
TZEI 7 x TZdEI 120 3597 55 1.73 164.19 4.67 4.85 0.90
TZdEI 314 x TZdEI 264 3590 57 1.38 151.15 4.42 4.45 0.90
TZEI 31 x TZdEI 378 3572 56 1.69 155.27 4.62 4.45 0.88
TZdEI 441 x TZdEI 71 3571 57 1.58 174.58 4.31 3.73 0.89
TZdEI 82 x TZdEI 202 3570 55 1.81 168.73 4.58 4.05 0.95
TZdEI 315 x TZdEI 71 3548 53 0.81 163.00 5.15 4.80 0.89
TZdEI 280 x TZdEI 485 3544 58 2.35 154.88 5.25 5.15 0.87
TZEI 31 x TZdEI 264 3541 57 1.65 155.50 4.94 4.45 0.86
TZdEI 314 x TZdEI 131 3541 55 1.65 166.38 4.58 4.75 0.92
TZdEI 131 x TZEI 18 3530 57 1.85 159.04 4.83 4.43 0.86
TZdEI 120 x TZdEI 98 3524 56 1.27 171.46 4.56 4.28 0.89
TZdEI 157 x TZdEI 357 3503 59 1.46 165.38 4.69 4.45 0.96
TZdEI 264 x TZdEI 283 3480 59 2.12 154.73 5.42 5.35 0.97
TZdEI 399 x TZEI 31 3468 56 1.73 161.62 4.46 4.05 0.86
TZdEI 441 x TZdEI 202 3465 58 2.27 178.04 4.77 3.78 0.82
TZdEI 280 x TZdEI 315 3460 55 1.88 150.81 4.90 4.40 1.01
TZdEI 202 x TZEI 7 3450 55 2.15 171.81 5.15 4.20 0.88
TZdEI 202 x TZEI 31 3442 57 1.77 166.00 4.67 4.08 0.93
TZdEI 120 x TZdEI 157 3439 57 1.65 162.42 4.69 4.30 0.87
TZdEI 71 x TZEI 7 3412 55 1.69 156.12 5.23 4.40 0.86
TZdEI 315 x TZdEI 124 3411 54 1.58 168.42 5.37 5.18 0.88
TZdEI 84 x TZdEI 485 3387 56 1.69 170.54 5.04 4.90 0.78
TZdEI 264 x TZdEI 157 3385 59 1.50 153.15 4.73 4.70 0.88
TZdEI 84 x TZdEI 479 3385 56 1.55 161.45 5.30 5.06 0.92
TZdEI 124 x TZdEI 314 3371 55 1.23 164.81 4.62 4.53 0.92
TZdEI 315 x TZdEI 202 3364 55 1.35 161.58 4.98 4.33 0.97
TZdEI 485 x TZdEI 124 3362 56 1.42 161.96 5.37 5.35 0.84
TZdEI 131 x TZdEI 157 3347 56 1.04 168.88 4.77 4.35 0.90
TZdEI 157 x TZdEI 84 3328 58 1.50 171.54 4.90 4.80 0.88
TZdEI 264 x TZdEI 98 3322 58 1.35 159.62 4.85 4.73 0.89
TZdEI 399 x TZdEI 268 3301 56 1.19 168.23 4.25 4.05 0.91
TZdEI 84 x TZdEI 82 3280 57 1.13 169.33 5.17 4.25 0.76
TZEI 31 x TZdEI 120 3276 56 1.69 159.12 4.71 4.58 0.92
TZdEI 120 x TZEI 18 3273 57 1.88 154.85 4.71 4.35 0.90
TZdEI 485 x TZdEI 399 3270 58 1.46 158.88 5.23 5.08 0.86
TZEI 60 x TZEI 86 3259 57 1.15 161.50 5.02 4.45 0.84
TZEI 31 x TZEI 63 3198 57 2.00 157.46 4.67 4.26 0.85
TZdEI 120 x TZdEI 283 3191 58 1.38 157.13 4.69 4.17 0.92
TZEI 31 x TZdEI 131 3168 57 1.54 167.62 5.08 4.83 0.78
TZdEI 124 x TZEI 7 3167 54 1.85 158.62 5.15 4.63 0.86
TZdEI 124 x TZdEI 268 3094 56 0.72 164.12 4.62 4.50 0.97
TZdEI 98 x TZdEI 84 3046 56 1.13 164.08 4.33 5.33 0.91
158
90 GCA
SCA
80
70
60
50
40
30
20
10
0
STR DT OPT STR DT OPT STR DT OPT STR DT OPT STR STR STR STR DT
Grain yield Anthesis-silking interval Ear aspect Ear per plant SDR1 SDR2 NESP1 NESP2 Stay
green
Appendix 5.7a. Percentage contribution of GCA and SCA under Striga, drought and optimal growing environments
159
41.31 41.79
37.81 GCA male
35.55 35.11 GCA female
33.25 33.81 33.35
31.54 31.6 31.5 31.74
30.42
29.26 28.88
26.92
23.12
21.11
Appendix 5.7b. Additive variance due to male and female for yield and other agronomic traits
under Striga infestation
160
40.83
GCA male
36.1
GCA female
30.05 29.65
27.04
25.0624.95 25.03 24.99
23.95
22.87 22.86
21.33
20.29
18.77 18.42
Appendix 5.7c. Additive variance due to male and female for yield and other agronomic traits
under drought
161