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COMBINING ABILITY BETWEEN DROUGHT AND HEAT STRESS TOLERANT
DONORS AND ADAPTED CIMMYT ZIMBABWE MAIZE INBRED LINES
By
PRECIOUS CHIMENE
A thesis submitted in fulfillment of the requirements for the degree of MASTER OF SCIENCE
(Msc.) IN CROP SCIENCE (Plant Breeding).
Department of Crop Science
Faculty of Agriculture
University of Zimbabwe
P.O.BOX MP167
Mount Pleasant
Harare
November 2014
ii
COMBINING ABILITY BETWEEN DROUGHT AND HEAT STRESS TOLERANT
DONORS AND ADAPTED CIMMYT ZIMBABWE MAIZE INBRED LINES
By
PRECIOUS CHIMENE
Submitted to the Department of Crop Science, Faculty of Agriculture of the University of
Zimbabwe in fulfillment of the requirements for the degree of
MASTER OF SCIENCE (Msc.) IN CROP SCIENCE (Plant Breeding)
Approved by:
Supervisors, Dr. S Dari ………………………………..
Dr. C. Magorokosho ………………………………..
Chairman, Dr. U. Mazarura ……………………………
iii
ABSTRACT
Drought and heat stress are major abiotic stresses limiting maize production in Zimbabwe and
Africa at large. It is of great importance to evaluate the breeding value of combined drought and
heat stress donor parents for development of new and locally adapted maize hybrids. A North
Carolina Design II (NCDII) mating scheme was used for crossing 10 combined drought and heat
stress tolerant donor lines and six adapted CIMMYT Zimbabwe lines. The cross combinations
that were successfully pollinated resulted in a total of 30 single cross hybrids and five stress
tolerant donor parents were dropped from the evaluations as they did not have all cross
combinations with the testers. These single cross hybrids were evaluated under optimum, sandy
and managed drought conditions using a 0.1 alpha lattice design with two replications in the
2013-14 summer and winter season. The objectives of this study were (i) to estimate combining
ability effects among the drought and heat stress tolerant donors and CIMMYT Zimbabwe
adapted maize inbred lines, (ii) to classify the stress tolerant donor lines into heterotic group A
and B using CML312 and CML444 as testers and (iii) to evaluate GXE interaction of the single
crosses developed. For grain yield and other secondary traits evaluated across environments,
significant GCA and SCA effects indicated the importance of both additive and non-additive
gene effects in the expression of these traits. Additive gene action contributed more to genotypic
variation amongst testcrosses for the traits measured as evidenced by the higher mean squares for
lines and testers than their interaction. For grain yield, additive gene action due to females had
much contribution to the genotypic variation therefore highlighting the importance of maternal
effects in the expression of this trait. The basis used for tester identification was good GCA
effects for grain yield. Lines CL1215159, CL133480, CML395and CML444 showed good GCA.
For heterotic grouping using CML312 and CML444 as testers, lines CL1215159, VL062656 and
CL1215158 were classified in heterotic group A and CL1215157 and CL133480 were classified
in group B. In heterotic group A, the single cross CL1215159 x CML312 was identified and in
heterotic group B, CL133480 x CML444 was identified as potential single cross testers. This
study was therefore able to identify genotypes to be incorporated in stress breeding programmes.
iv
DECLARATION
The thesis study was carried out at the International Maize and Wheat Improvement Centre
(CIMMYT- Southern Africa Regional Office) in collaboration with the University of Zimbabwe
under the supervision of Dr. C. Magorokosho and Dr. S. Dari.
I declare that the research presented in this thesis represents original work and has never been
submitted in any form for degree or diploma to any university. Where use has been made of the
work of others it is duly acknowledged in the text.
……………………………… ..…………………………....
Precious Chimene Date
v
ACKNOWLEDGEMENTS
My most sincere gratitude goes to CIMMYT for sponsoring this research project to its completion. I
gratefully acknowledge Dr. C. Magorokosho, Dr. P. Setimela and Dr. T. Ndhela for their tireless effort
and guidance that was of paramount importance to the success of this research. I am also greatly
indebted to Dr. S. Dari for her tireless effort in shaping up this thesis. Her guidance and supervision
during the course of the research is gratefully appreciated. I would like to thank the technical team at
CIMMYT for their technical assistance during the course of the research. Their effort is gratefully
appreciated. I would like to acknowledge the University of Zimbabwe’s Department of Crop Science
staff members for their suggestions and contributions that helped in the research and writing of this
thesis. I also acknowledge with heartfelt gratitude the support and assistance of my colleagues
including Tariro, Casper and Nyika. Sincere thanks also extend to my husband for his support, advice
and patient understanding throughout this project.
vi
DEDICATION
To my husband and son.
vii
LIST OF ABBREVIATIONS
ABA Abscisic acid
AD Days to mid anthesis
ANOVA Analysis of variance
ASI Anthesis silking interval
ART Agricultural Research Trust
CIMMYT International Maize and Wheat Improvement Center
CL CIMMYT Lines
CML CIMMYT maize line
CZH CIMMYT Zimbabwe Hybrid
EH Ear height
EPP Ears per plant
EPO Ear position
ET Exhollium turcicum
ER Ear rot
CRS Chiredzi Research Station
C.V Coefficient of variation
viii
FAO Food and Agriculture Organization
G Genotype
G x E Genotype by environment interaction
GLS Grey leaf spot
GYG Grain yield per hectare
ha Hectare
IPCC Intergovernmental Panel for Climate Change
KRS Kadoma Research Station
LSD Least significant difference
EH Ear height
GCA General combining ability
masl Meter (s) above sea level
mm millimeter (s)
MCR Masters of Crop Science
MS Mean squares
NCDII North Carolina Design II
PH Plant height
ix
PS Photo-system
SCA Specific combining ability
tha-1
Tonnes per hactare
VA Additive genetic variation
VD Dominance genetic variation
x
Table of Contents
DECLARATION ........................................................................................................................... iv
ACKNOWLEDGEMENTS ............................................................................................................ v
DEDICATION ............................................................................................................................... vi
LIST OF ABBREVIATIONS ....................................................................................................... vii
Table of Contents ............................................................................................................................ x
List of tables ................................................................................................................................. xiii
List of appendices ......................................................................................................................... xv
CHAPTER ONE ............................................................................................................................. 1
1.0 Introduction ........................................................................................................................... 1
1.1 Importance of maize .......................................................................................................... 1
1.2 Production of maize ........................................................................................................... 1
1.3 Production constraints ....................................................................................................... 2
1.4 Effort to curb production constraints ................................................................................. 4
1.4 Specific objectives ............................................................................................................. 5
1.5 Hypotheses......................................................................................................................... 5
CHAPTER TWO ............................................................................................................................ 6
2.0 LITERATURE REVIEW ...................................................................................................... 6
2.1 Maize production in the world........................................................................................... 6
2.2 Major abiotic constraints on maize production ................................................................. 7
2.2.1 Effects of drought on maize production ......................................................................... 8
2.2.2 Effects of heat stress on maize production ................................................................... 10
2.2.3 Combined effect of drought and heat stress on maize .................................................. 13
2.2.4 Challenges in breeding for drought and heat stress tolerance ...................................... 15
2.2.5 Progress in breeding for drought and heat stress tolerance in maize ............................ 15
2.2.6 Secondary traits used in selection for drought and heat stress tolerance...................... 17
2.2.6 Managed drought .......................................................................................................... 18
2.3 Combining ability of maize inbred lines ......................................................................... 18
2.4 Heterosis and heterotic groups ........................................................................................ 20
2.6 Mating designs in maize breeding ................................................................................... 21
2.7 Genotype by environment interaction ............................................................................. 23
xi
CHAPTER THREE ...................................................................................................................... 24
3.0 MATERIALS AND METHODS ........................................................................................ 24
3.1 Germplasm....................................................................................................................... 24
3.2 Testing environments ...................................................................................................... 24
3.2.1 Simulation for drought and heat stress ......................................................................... 25
3.3 Trial management ............................................................................................................ 26
3.4 Experimental Design and Data Collection ...................................................................... 27
3.5 Data analysis .................................................................................................................... 30
CHAPTER FOUR ......................................................................................................................... 32
4.0 RESULTS............................................................................................................................ 32
4.1 ANOVA and combining ability analysis ......................................................................... 32
4.1.1 Grain yield and other secondary traits measured under optimum conditions .............. 32
4.1.1.1 Testcross performance for grain yield measured under optimum conditions ........... 33
4.2.1 Grain yield and other secondary traits under sandy soils ............................................. 34
4.2.1.1 Testcross performance for grain yield evaluated under sandy soil conditions .......... 35
4.3.1: Secondary traits under managed drought conditions ................................................... 36
4.4.1 Grain yield performance across environments ............................................................. 37
4.4.1.1 Testcross performance for grain yield across environments ..................................... 38
4.5. General Combining Abilities .......................................................................................... 39
4.5.1 Line General Combining Ability effects under optimum sites ..................................... 39
4.5.2 Tester GCA effects for traits evaluated under optimum sites ...................................... 40
4.5.3 Line general combining ability effects at Chibhero Agricultural College ................... 41
4.5.4 Tester general combining ability effects at Chibhero Agricultural College ................. 43
4.5.5 Line general combining ability effects at Chiredzi Research Station .......................... 44
4.5.6 Tester general combining ability effects at Chiredzi Research Station ........................ 45
4.5.7 Line GCA effects for grain yield and other agronomic traits across environments ..... 46
4.5.8 Tester GCA effects for grain yield and other agronomic traits across environments .. 48
4.6 Specific Combining Abilities .......................................................................................... 49
4.6.1 Specific combining ability effects for grain yield under optimum conditions ............. 49
4.6.1.1 Specific Combining Ability Effects for anthesis days under optimum conditions ... 50
4.6.1.2 SCA effects for anthesis-silking interval evaluated under optimum conditions ....... 51
xii
4.6.2 SCA effects for grain yield evaluated under sandy soil conditions .............................. 52
4.6.2.1 SCA effects for anthesis days recorded under sandy soils ........................................ 52
4.6.3 SCA effects for grain yield measured under managed drought conditions .................. 54
4.6.3.2 SCA effects for anthesis-silking interval measured under managed drought
conditions............................................................................................................................... 56
4.6.4 SCA effects for grain yield measured across environments ......................................... 57
4.6.4.1 SCA effects for anthesis days evaluated across sites ................................................ 58
4.6.1.2. Specific Combining Ability Effects for anthesis-silking interval across environments
............................................................................................................................................... 59
4.7 SCA Effects: Heterotic Groups As Determined by Testers CML312 and CML444 ...... 60
CHAPTER 5 ................................................................................................................................. 61
5.0 Discussion ........................................................................................................................... 61
5.1 Grain yield and its components ....................................................................................... 61
5.1.1 Grain yield and its components under optimum conditions ......................................... 61
5.1.2 Grain yield and its components under sandy soil conditions ....................................... 62
5.1.3 Grain yield and its components under managed stress conditions ............................... 63
5.1.4 Grain yield and its components across environments .................................................. 64
5.2 GCA effects for grain yield and its components ............................................................. 65
5.3 SCA effects for grain yield .............................................................................................. 67
CHAPTER 6 ................................................................................................................................. 69
6.1 Conclusion ........................................................................................................................... 69
6.2 Recommendations ............................................................................................................... 69
REFERENCES ............................................................................................................................. 71
APPENDICES .............................................................................................................................. 81
xiii
List of tables
Table 3.1: Names and pedigree information of germplasm used to produce the single cross
hybrids........................................................................................................................................... 25
Table 3.2: Total rainfall received and amount of irrigation applied at each site .......................... 27
Table 3.3: The agronomic data that was recorded for hybrid trials .............................................. 29
Table 3.4: Skeleton ANOVA for the NCDII ................................................................................ 30
Table 4.1: ANOVA for grain yield and secondary traits measured under optimum sites ............ 33
Table 4.2: Mean grain yield (t/ha) measured under optimum conditions .................................... 34
Table 4.3: ANOVA for grain yield and other agronomic traits measured at Chibhero College .. 35
Table 4.4: Mean grain yield measured under sandy soil conditions ............................................. 36
Table 4.5: ANOVA for Agronomic traits under Managed Drought Conditions .......................... 37
Table 4.6: ANOVA for grain yield and other secondary traits measured across environments ... 38
Table 4.7: Mean grain yield evaluated across environments ........................................................ 39
Table 4.8: Line GCA effects for grain yield and other agronomic traits evaluated under optimum
conditions ...................................................................................................................................... 40
Table 4.9: Tester general combining ability effects for grain yield and other agronomic traits
under optimum conditions ............................................................................................................ 41
Table 4.10: Line general combining ability effects for grain yield and other agronomic traits
evaluated under sandy soils .......................................................................................................... 42
Table 4.11: Tester general combining ability effects for anthesis days and other agronomic traits
under sandy soils ........................................................................................................................... 44
Table 4.12: Line GCA effects for grain yield and other agronomic traits under managed drought
conditions ...................................................................................................................................... 45
Table 4.13: Tester general combining ability effects for grain yield and other agronomic traits
under managed drought conditions ............................................................................................... 46
Table 4.14: Line general combining ability effects for grain yield and other agronomic traits
across environments ...................................................................................................................... 48
Table 4.15: Tester general combining ability effects for grain yield and other agronomic traits
across environments ...................................................................................................................... 49
Table 4.16: Specific combining ability effects for grain yield under optimum conditions 50
Table 4.17: Specific Combining Ability Effects for anthesis days under optimum conditions ... 51
Table 4.18: SCA effects for anthesis-silking interval recorded under optimum conditions ......... 51
xiv
Table 4.19: SCA effects for grain yield evaluated under sandy soils ........................................... 52
Table 4.20: SCA effects for anthesis days recorded under sandy soils ........................................ 53
Table 4.21: SCA effects for anthesis-silking interval measured under sandy soil conditions ...... 54
Table 4.22: SCA effects for grain yield measured under managed drought conditions ............... 55
Table 4.23: SCA effects for anthesis days measured under managed drought conditions ........... 56
Table 4.24: SCA effects for anthesis-silking interval measured under managed drought
conditions. ..................................................................................................................................... 57
Table 4.25: SCA effects for grain yield evaluated across sites ..................................................... 58
Table 4.26: Specific Combining Ability Effects for anthesis days across sites ............................ 59
Table 4.27: Specific combining ability effects for anthesis-silking interval across sites ............. 59
xv
List of appendices
Appendix 1: SCA Effects For PH Under Optimum Conditions. .................................................. 81
Appendix 2: SCA Effects For PH Under Sandy Soil Conditions. ................................................ 81
Appendix 3: SCA Effects For PH Under Managed Drought Conditions ..................................... 81
Appendix 4: SCA Effects For Plant Height Across Environments .............................................. 82
Appendix 5: SCA Effects For EPP Under Optimum Conditions ................................................. 82
Appendix 6: SCA Effects For EPP Under Sandy Soil Conditions ............................................... 82
Appendix 7: SCA Effects For EPP Under Managed Drought Conditions ................................... 83
Appendix 8: SCA Effects For EPP Across Environments............................................................ 83
Appendix 9: SCA Effects For TEX Under Sandy Soil Conditions .............................................. 83
Appendix 10: SCA Effects For TEX Under Managed Drought Conditions ................................ 84
Appendix 11: SCA Effects For TEX Across Environments ......................................................... 84
Appendix 12: SCA Effects For ER Under Optimum Conditions ................................................. 84
Appendix 13: SCA Effects For ER Under Sandy Soil Conditions ............................................... 85
Appendix 14: SCA Effects For ER Under Managed Drought Conditions ................................... 85
Appendix 15: SCA Effects For ER Across Environments ........................................................... 85
Appendix 16: SCA Effects For ASI Under Optimum Conditions ................................................ 86
Appendix 17: SCA Effects For ASI Under Sandy Soil Conditions.............................................. 86
Appendix 18: SCA Effects For ASI Under Managed Drought Conditions .................................. 86
Appendix 19: SCA Effects For ASI Across Environments .......................................................... 87
Appendix 20: SCA Effects For AD Under Optimum Conditions ................................................ 87
Appendix 21:SCA Effects For AD Under Sandy Soil Conditions ............................................... 87
Appendix 22: SCA Effects For AD Under Managed Drought Conditions .................................. 88
Appendix 23: SCA Effects For AD Across Environments ........................................................... 88
Appendix 24: SCA Effects For GLS Under Optimum Conditions............................................... 88
Appendix 25: SCA Effects For ET Under Optimum Conditions ................................................. 89
Appendix 26: SCA Effects For SEN Under Managed Drought Conditions ................................. 89
1
CHAPTER ONE
1.0 Introduction
1.1 Importance of maize
Maize is an important crop in southern Africa which accounts for 40 to 50% of calories and
protein consumed in countries which depend mostly on it (Cairns et al., 2013). It is a major
source of income in developing countries to farmers most of which are resource poor. In eastern
and southern Africa it accounts for 30-50% of household expenditure for the poor. In eastern and
southern Africa 85% of maize produced is used as food and 95% in Africa as a whole in relation
to other parts of the world where maize is widely used as animal feed.
1.2 Production of maize
Maize ranks first in Africa and Latin America whilst third in Asia after rice and wheat (Doswell
et al., 1996). The area under maize production is more in developing countries than in developed
countries. Over 64% of the world maize production area is located in developing countries
(Doswell et al., 1996). Findings by FAOSTAT (2010) had shown that in this region 30% of the
land under cereal production is under maize cultivation and average maize yield has been one
sixth of that of the United States of America. Maize production needs to increase to meet the
increasing world population. By 2050, major cereal production which include maize has to
increase by 50% to meet the world`s growing population of both rural and urban people (Cairns
et al., 2013; Hall and Richards, 2012).
The hectarage under maize production worldwide amounts to about 144 million hectares (FAO,
2011) and approximately 96 million hectares is in the developing countries. Of the total world
maize area, 68% is in the developing countries and only 46% of world maize production is
2
accounted for by these countries (Pingali and Pandey, 2000). The developing countries’ low
average yields explain the huge gap between global share of area and of production. There is
yield difference between the developed countries and developing countries and the yields are
more than eight tons per hectare and less than three tons per hectare respectively (Pingali and
Pandey, 2000). Wide difference in climatic conditions together with agricultural technology
accounts for the yield difference of five tons per hectare between the two worlds.
There had been expansion of area under maize production in Zimbabwe for the last decades and
this was due to the introduction of improved varieties. The land reform programme has also
contributed to continued increase of area under maize production. Maize increased production
was also enhanced by a subsidized government credit system to commercial farmers together
with an agricultural input assistance programme facilitated by the government, United Nations
agencies and also other humanitarian organizations.
The country’s maize production greatly improved, but still there has been food insecurity with
about 1.68 million people requiring aid in the first quarter of 2011 (FAO, 2011). There is a
challenge in meeting this required amount due to abiotic stress which is becoming more
pronounced in the maize growing areas. Rainfall distribution and rise in temperatures has posed
a threat to meet the demand in the country, and world at large and in that fact the country has
every reason to develop maize varieties that are resilient to changing climate.
1.3 Production constraints
There are several factors which lead to limitations in boosting maize production to meet the
growing demand. Among the many limiting factors to maize production is abiotic stress mainly
drought and heat stress. These abiotic stresses have become a threat to maize production,
3
especially in Africa. The abiotic stress of drought and heat on crop production, in future is likely
to cause more impacts as their frequency and intensity is becoming more (Sanderson et al.,
2011). Drought and heat stress together in combination is likely to negatively affect maize
production (Cairns et al., 2012a). Regional yield loses as a result of drought was reported to be
around 70% under extreme conditions as compared to losses under optimal conditions
(Edemeades et al., 1999).
Climate change has impacted greatly on maize production throughout Africa due to recurrent
droughts (La Rovere et al., 2010), and that climatic projections had also indicated elevated
temperatures within drought-prone areas (Cairns et al., 2013). As climate change alters
precipitation patterns and causes temperatures to rise, estimates of up to 10 million tons of maize
yield may be lost yearly that could later affect up to 140 million people in the developing world
(Jones, 2003). Reports have suggested increasing growing temperatures and frequency of
drought in maize growing regions of sub Saharan Africa (IPCC, 2007).
In Zimbabwe there was a decline in maize production of about 70% between the years 1981 and
1982 (Rukuni et al., 2006). In the growing season of 1991-92 Zimbabwe experienced severe
yield losses in maize production due to drought and this also happened in the growing season of
2001-02 which left millions of people malnourished. Maize production has been low in
Zimbabwe especially in subsistence farmers as maize varieties used were poorly adapted to the
areas. Of late, research has been focusing on improving varieties targeted for high potential areas
which had resulted in these varieties being used in low potential areas resulting in poor yields.
Changes in rainfall pattern and high temperatures experience during the growing season had
resulted in a major challenge to maize production in Zimbabwe. Short growing season and
elevated temperatures have now characterized the summer season of the country. Drought and
4
heat stress have caused severe yield losses mostly in smallholder farming sector which accounts
for 91% of the country’s semi-arid area. Therefore, there is need to develop varieties that are
resilient to the changing climate.
1.4 Effort to curb production constraints
There is urgency in the need to develop varieties that are resilient to climate change as climate
change is posing a big threat to maize production in sub-Saharan Africa. Varieties with drought
and heat tolerance are needed as they are high yielding and also have stability (Banziger and
Araus, 2007). Technological development of cultivars that can escape or tolerate drought or heat
stress to alleviate the effects of the climate change is very useful. Relatively less effort has been
put to breeding for heat stress tolerance in maize as compared to effort devoted to breeding for
drought stress tolerance (Cairns et al., 2013). Identifying maize germplasm that has superior
drought and heat stress tolerance is of importance as it leads to a successful breeding programme.
Breeding for varieties with increased tolerance to drought and heat stress will help in curbing the
deleterious effects of climate change (Hellin et al., 2012).
The breeding value of promising parental lines to be used in a breeding programme is very
important therefore it is essential to evaluate parental lines’ breeding value. North Carolina
Design II NCDII is one of the most used mating designs used for evaluating gene action in a
population which is achieved through estimation of GCA and SCA variance. GCA is the average
performance of a line in its different cross combinations and represents additive gene action.
SCA is a measurement of a deviation of hybrid performance from the parental performance and
it represents non-additive gene action. Breeders thus use GCA and SCA as powerful tools in
selecting best parents to be used in hybrid formation. Combining ability studies help breeders in
detecting parental lines with good GCA, and those with good SCA in hybrid combination.
5
Therefore, a study on the combining ability of drought and heat stress tolerant donors and
adapted CIMMYT Zimbabwe maize inbred lines is necessary in identifying the best combiners
to exploit heterosis or accumulating productive genes.
The main objective of this study is to evaluate combining ability between combined drought and
heat tolerant donors and adapted CIMMYT Zimbabwe maize inbred lines for heat and drought
tolerance.
1.4 Specific objectives
1. To identify possible testers amongst the combined drought and heat stress tolerant donors and
CIMMYT Zimbabwe adapted maize inbred lines.
2. To classify the combined drought and heat stress donor lines into heterotic groups using
CML312 and CML444 as tester lines.
3. To evaluate GXE interaction of the single cross hybrids developed.
1.5 Hypotheses
1. The combined drought and heat tolerant donors and adapted CIMMYT Zimbabwe maize
inbred lines can produce possible single cross testers.
2. The combined drought and heat stress tolerant donor lines fall in different heterotic groups
3. Performance of the single crosses produced is not affected by the environment
6
CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Maize production in the world
Throughout the world, maize is grown in almost every country and is a staple food in many parts
of Africa. In the developing countries, the demand for maize will be expected to be more than
500 million tons by the year 2020 (Pingali and Heisey, 2001) and this is so as a result of
increasing maize demand for livestock feed. Its coverage in the region accounts for almost 27
metric hectares (Cairns et al., 2013). Over 85% of the population of people in the rural areas in
Africa grows maize due to its suitability in diverse farming systems and its ability for increased
yields when management practices are improved as compared to other cereal crops (Badu-
Apraku, 2013). Maize production improves the livelihoods of many households in the farming
communities of Africa. In West Africa, production of early and very early maturing maize
varieties has helped in alleviating food insecurity both seasonal and transitory (Badu-Apraku,
2013). Maize had been a major source of food to vulnerable people which include women and
children. In eastern and southern Africa, the crop of most importance for over 300 million people
is maize (Banziger et al., 2007), and accounts for 40 to 50% of calories and protein in the
countries which are most maize dependent (FAOSTAT, 2010). Population growth has raised the
need to boost maize production as yields are becoming less adequate to meet the needs of the
growing population. In the third world countries which include countries in sub-Saharan Africa,
the unabated population growth together with increasing poverty have increased pressure on food
maize demand (Pingali, 2001). Apart from the pressure from population growth, abiotic stress as
a result of climate change had also been a major constraint in boosting maize production in
Africa especially in the sub-Saharan Africa. Abiotic stress incidence on maize may increase as a
7
result of global climate change and maize displacement by high value crops to marginal areas
(Banziger and Cooper, 2001). The abiotic stress of drought has been seen to threaten maize
production in the eastern and southern parts of Africa (Banziger and Diallo, 2004). A
devastating case of drought was experienced in southern Africa in 1991-92 where maize
production was reduced by about 60% (Heisey and Edmeades, 1999).
2.2 Major abiotic constraints on maize production
Climate change throughout the world has resulted in unreliable rainfall leading to frequent
droughts and also elevated temperatures have also been noted. The Intergovernmental Panel for
Climate Change IPCC, (2007) had shown that from the projections of climate in eastern and
southern Africa, there is decreased rainfall and increasing temperatures in maize growing areas.
This has put maize production under threat as the crop is subjected to drought and heat stress at
its critical growth stages thereby resulting in reduced yields to lower levels. Drought has been
identified before as a major threat to maize production (Heisey and Edmeades, 1999) but heat
stress of late has not been of importance to maize production. The impact of heat stress alone or
together with drought stress can increasingly constrain production of maize (Cairns et al., 2013).
Temperature increase of 20C greatly lower yields as compared to a 20% rainfall decrease (Lobell
and Burke, 2010). Findings by Cairns et al. (2013) showed that projection of elevated
temperature by 2% lowered maize yield by 13% whilst a 20% increase of intra-seasonal rainfall
variability lowered maize yields by 4.2% only. The IPCC (2007) predicted elevated seasonal
temperatures up to extreme conditions which are coupled with intense drought. Rizhsky et al.
(2004) noted that drought stress is usually water and heat stress combined together due to a lower
transpirational cooling in conditions of limited water.
8
2.2.1 Effects of drought on maize production
Water is needed in maize growth as it acts as a medium for metabolic reactions and also for
transpirational cooling. The degree of soil drying, reduced transpiration relative to potential
evapotranspiration and also plant water status are used to quantify the severity of drought.
Drought affects yield of maize at most growth stages with flowering being the most susceptible
stage. At the stage of crop establishment, seedling tend to die due to water stress and there is
reduced plant population and this is a damaging effect as maize has no tillers thus no
compensation will occur. During the vegetative stage drought effect is not severe but
development of leaf area is lowered and leaf senescence is accelerated (Banziger et al., 2000).
Photosynthesis is reduced as maize plants have water shortage. This leads to reduced leaf
expansion hence light interception is also affected. The plants also tend to close the stoma when
there is water deficit in an endeavor to minimize water loss and this result in reduction of
photosynthesis and respiration due to photo-oxidation and enzyme damage (Edmeades et al.,
1993). Leaf senescence can also be a problem when plants experience water stress and there will
be reduced assimilates for grain filling hence yield potential.
Flowering is the growth stage that is more vulnerable to drought stress and extreme severity is at
the period between -2 to 22 days after silking with peak found at 7 days. Under drought
conditions where the plants are water stressed, there is no synchronization of flowering between
the male and female flowers and this is because the tassels will be having a stronger sink than the
growing ear so tassels grow faster than the ear. Silks are more sensitive to drought stress than
other parts of the plant because of their higher water content levels. As the plant is water
stressed, there is reduced silk elongation which results in poor pollination and fertilization. There
is delay of silk emergence whilst pollen is being shed leading to length of anthesis-silking
9
interval being longer, which correlate highly to setting of kennels (Edemeades et al., 2000a). In
conditions like this pollen can reach the silks when already desiccated or when silks have
senesced (Saini and Westgate, 2000). When silk emergence is delayed, it leads to pollen tube
growth failure or the zygote that has been newly formed is aborted. Abortion is a result of
inadequate supply of photo-assimilates to the developing ear. Maize yield is lowered if drought
stress occurs even after kernel fertilization and this is due to kernel abortion when drought hits at
this developmental stage. If maize plants are stressed in the period just before tassel emergence
up to the start of grain filling, complete barrenness can occur (Banziger et al., 2000).
The number of grains per plant during drought stress has been reported to rely heavily on the
current photosynthates at the two week bracketing flowering stage (Schussler and Westgate,
1995; Banziger et al., 2000). The photo-assimilate reserves of pre-flowering period are not
readily available to the developing ear as the sink strength will be impaired probably as a result
of the ovaries’ disrupted carbohydrate metabolism (Saini and Lalonde, 1998). When kernels
reach the linear phase of biomass accumulation, a strong sink is developed which attract stems
and husk reserve photosynthates. Therefore, the kernels will grow nearly 30% the weight of
kernels from plants that are not stressed even if drought severity is increased (Edmeades et al.,
1999). Harvest index is determined during 10-15 days before and after flowering. During periods
of drought stress at flowering stage, the tassel tend to have a stronger sink than the ear
(Edmeades et al., 2000) and this results in small ears per plant. When plants are drought stressed
during grain filling period, less assimilates are channeled to the developing grains. There is a
tendency of kernel and ear abortion and barrenness can occur. Remobilization of stalk
carbohydrate reserves to the grain can occur as the rate of photosynthesis is lowered, leading to
lodging (Banziger et al., 2000). If maize plants are water stressed at early stages of seed growth,
10
there is an increase in abscisic acid (ABA) concentration in the endosperm leading to a reduction
in number of endosperm cells and initiation of starch grains. During the period of drought stress,
cytokinin levels declines in the plant tissues and this has a negative effect on the plant as
cytokinins are important in establishment of kernel sink potential (Edmeades et al., 1998).
Some of the key physiological traits are affected by drought stress at cellular level and this
includes ABA accumulation in the plant causing the leaves to wilt, closure of stomata and leaf
senescence is accelerated. Cell division and expansion is lowered which in turn results in
reduced leaf area expansion, retarded growth of silks, reduced stem elongation and also
decreased root growth. This leads to severe negative effects of drought stress on maize plants.
Reduced plant height and size of the tassel was shown to be linked with a shorter anthesis-
silking interval than in tall and large tasseled plants (Fischer et al., 1987). Leaf growth together
with anthesis-silking interval is very crucial in source-sink relationship in maize due to their
interaction with light interception and harvest index respectively. In trying to counteract the
effect of water stress, the plant can resort to osmotic adjustment where assimilates will not be
channeled to the grain hence lowering the yield. Osmotic adjustment is more common in
sorghum, wheat and rice (Banziger et al., 2002). Plants that are under drought stress tend to have
proline accumulation. This proline serves as an osmolyte or protein structure protector as there is
no tugor. Under drought stress there is reduced enzyme activity like the acid invertase which is
responsible for conversion of sucrose to starch thus affecting starch accumulation in the plant.
2.2.2 Effects of heat stress on maize production
Temperature is a very important requirement in maize growth and development. It is needed for
the metabolic reactions to take place in the plants and an optimum temperature is needed.
Temperature is needed more in plants for growth and development rather than for
11
photosynthesis, and maize can grow best at temperature ranges of 24 to 30oC (Pingali, 2001) and
temperatures above this will affect the crop`s growth and development. Any rise of temperature
by a degree each day above 30oC was seen to lower final yield of maize in optimum and drought
conditions by 1% and 1.7% respectively (Lobell et al., 2011). Heat stress lowers yield in maize
by shortening the developmental stages, reduces light perception and affect processes which
involve carbon assimilation (Stone, 2001). Heat stress is defined by Cairns et al. (2012) as
“temperatures above a threshold level that results in irreversible damage to crop growth and
development.”. Heat stress causes an increase in respiration whilst photosynthesis decreases.
Temperatures above 35oC reduce photosynthesis by lowering the activity of ribulose 1.5-
biphosphate carboxylase (Griffin et al., 2004).
Excessive heat exposure of plants to temperatures above 5oC their optimal growing conditions
will trigger a set of cellular and metabolic responses needed for them to survive under these
temperatures. These include a reduction in normal protein synthesis, acceleration of transcription
and translation of heat shock proteins (Bray et al., 2000). Photo hormones such as ABA and anti-
oxidants are produced and there is also alteration of cellular structure organization. During the
vegetative stage, heat stress results in many physical and metabolic changes which include
changes in hormone homeostasis (Barnabas et al., 2007). Heat stress in maize can cause an
imbalance of photosynthesis and respiration which in turn will result in oxidative damage. Gong
et al. (1997) have noted that there is decrease in anti- oxidant enzyme activities during periods of
elevated temperatures in maize. Heat stress in plants cause changes in membrane function as a
result of membrane fluidity alteration. Heat stress in maize plants also affects the photochemical
efficiency of photosystem II which in photosynthesis, is the most temperature-sensitive
12
component. This stress is also associated with newly synthesized protein misfolding and old
protein denaturation (Barnabas et al., 2007).
Leaf rolling is one of the physiological characteristics used by maize plants in a bid to reduce
water loss through transpiration as not much of the leaf surface is being exposed. This in turn
will lower photosynthesis as some of the photosynthetic area is not exposed to sunlight and much
effect on potential grain yield will occur when leaf rolling occurs over a long time. Leaf
senescence also occurs when maize plants are subject to heat stress and this is usually as a result
of reduction in photosynthesis. There will be less assimilates made and the priority is given to
the developing ear hence senescence of the lower leaves. Nevertheless, the loss of three to four
lower leaves has less impact on yield potential of maize. The most impact of heat stress is at four
weeks around pollination stage which is the most critical stage for yield potential determination.
The floral structure of the maize plant which has female and male flowers separated makes it
more vulnerable to heat stress than any other cereal crop (Araus et al., 2012).
The most sensitive growth stage to heat stress in maize is the reproductive stage (Cairns et al.,
2013) with the female reproductive tissues being less vulnerable than the male reproductive
tissues. Heat stress on maize shortens the duration of pollen shedding by a tassel and also pollen
viability is also reduced. This is due to the location of tassels above the leaf canopy which
renders maximum exposure to high temperatures (Cairns et al., 2012). In low lying areas in the
tropics where temperatures of up to 45oC can be attained, pollen desiccation and drying of silk
can be experienced. At early reproductive stage, high temperatures delay silking thus results in
lowered flowering synchronization and also decrease fertilization (Cicchino et al., 2011).
Weather conditions which are hot and dry during pollination can cause tassel blasting and killing
of pollen before being shed (Schoper et al., 1987). Heat stress at two weeks prior to tassel
13
emergence lowers kernel numbers thus a smaller ear results. In some maize genotypes, tassel
temperatures of up to 38oC reduce the quantity of pollen and also viability (Schoper et al., 1987).
Premature death can occur if the plant is exposed to severe heat stress during grain filling stage
and there will be shortened grain filling stage and kernel weight is also lowered. Furthermore, a
plant tissue death result and there is no more mobilization of assimilates to the developing ear.
2.2.3 Combined effect of drought and heat stress on maize
It has been known that it is the simultaneous occurrence of many abiotic stresses that affects the
crops in the field rather than a single abiotic stress (Barnabas et al., 2007). Combined drought
and heat stress on maize is an example of simultaneous occurrence of different abiotic stress in
the field. Of great concern has been global climate change which is expected to elevate global
temperatures and alter rainfall distribution thus intensifying drought in arid and semi-arid areas
(Wigley and Raper, 2001). It was found that combined drought and heat stress on maize has a
significantly negative effect on growth and reproduction than the effect of a single stress alone
(Wang and Huang, 2004).
The combination of high temperature and severe drought has been noted to reduce the function
of Photosystem 11(PSII), lowers nitrogen anabolism, protein catabolism is strengthened and
affect lipid peroxidation (Xu and Zhou 2006). Heat stress alone in plants will allow the leaves to
open their stomata for cooling purposes but when combined with drought stress the leaves are
forced to close their stomata in an endeavor to minimize water loss. This will however, keep the
leaf temperature high (Rizhsky et al., 2002). In contrast to the effect of a single stress alone,
combined effect of drought and heat stress has been found to change the metabolism of plant in a
novel manner (Rizhsky et al., 2004). Simultaneous occurrence of drought and heat stress 3–4
weeks prior to flowering resulted in asynchrony in anthesis and silking of maize together with
14
growth and receptiveness of the style also being inhibited (Basetti and Westgate, 1993). It has
been noted that kernel numbers per ear did not increase when fresh pollen from plant that were
not stressed was used to pollinate late-appearing silks (Andrade and Suero, 1995).
The final growth stage in maize is the grain filling stage. At this stage fertilized ovaries will be
developing into caryopses. At maturation and ripening stages in maize, the major abiotic stresses
is drought and heat stress, and this is common in many maize growing areas. Combined drought
and heat stress during grain development cause substantial yield losses in cereals. The yield loss
is caused greatly by a reduction in starch accumulation as generally more than 65% of the grain
weight is accounted for by starch (Barnabas et al., 2007). The lower number of endosperm cells
at early stage of grain filling during periods of combined drought and heat stress accounts for the
reduction in grain weight (Nicolas et al., 1985). During the later stage of grain filling, stress
impairs starch synthesis as a result of short supply of assimilates to the grain (Blum, 1998).
Reduction of grain weight at this later stage can also be attributed to direct effects on the grain’s
synthetic processes (Yang et al., 2004b).
Drought and heat stress normally occur in the field and interact during the period of grain filling.
Nevertheless, there is limited information on the effect of combined drought and heat stress on
kernel development (Barnabas et al., 2007). Shah and Paulsen (2003) reported that combined
drought and heat stress reduced grain filling duration more than in either treatment alone. The
deleterious effect of drought stress on all physiological processes and developmental parameters
were noted to have more effect at high temperatures than at low temperatures (Altenbach et al.,
2003). Nonetheless, the simultaneous occurrence of drought and heat stress does not necessarily
lead to additive effect (Barnabas et al., 2007). This was supported by findings from Wardlaw,
(2002), where in kernel dry weight at maturity in wheat the effect of post-anthesis drought was
15
reduced by high temperatures. There is therefore a need to breed for maize varieties that are
tolerant to changing climate that is characterized by change in rainfall pattern and also elevated
temperatures.
2.2.4 Challenges in breeding for drought and heat stress tolerance
The biggest challenge in breeding for drought and heat stress tolerance is looking for ways to
guarantee good selection progress. Therefore, the conceptual framework implies the need of a
breeder to:
Have useful difference in characteristics that confer drought and heat stress tolerance.
Be able to assess precisely drought and heat stress tolerance under ideal conditions that
are alike to the target environment.
Be able to apply high selection intensity when selecting for drought and heat stress
tolerance.
2.2.5 Progress in breeding for drought and heat stress tolerance in maize
Amongst the cereal crops grown in the world, maize is one of the most vulnerable crops to
drought and heat stress. There has been progress made in breeding for drought tolerance in
maize. CIMMYT kick started a maize drought breeding programme in the 1970s using Tuxpeno
Sequia, an elite lowland tropical maize population (Bolanos et al., 1993). CIMMYT started
screening for managed stress experiments in 1975 using recurrent selection in the drought and
low nitrogen varieties. They started with a single population in 1975 and in 1985, the work
extended to seven populations. Varieties then started to be introduced and adapted in Africa and
Asia since 1996 and 2000 respectively (Prasanna, 2014). Under drought conditions the average
maize yield were 26kg ha-1
per cycle (Stevens, 2008). In cycles of full-sib recurrent selection of
16
over eight cycles for yield and synchronization in flowering, under drought stress, results showed
gains of 144kg ha-1
per year (Edemeades et al., 1999).
In southern Africa in late 1990s, CIMMYT again kick started a maize breeding programme
which was product orientated (Banziger et al., 2006). Varieties were subject to selection under
environments of optimum, low nitrogen and managed drought stress and the yield of CIMMYT
varieties were higher than commercial checks used (Cairns et al., 2013). A 40% yield advantage
was noted in CIMMYT hybrids as compared to commercial hybrids. In eastern and southern
African, the on-farm trial results showed yield advantage of 25 and 35% of new hybrids to
farmers’ varieties under high and low yielding environments respectively (Setimela et al., 2012).
Large gain from selections for drought tolerance was significantly noted as CZH016, the best
hybrid, outclassed the commercial check which was the most popular, released about 15 years
ago by 26% and 36% under low and high yielding environments respectively (Cairns et al.,
2013).
There have been 140 new drought tolerant varieties released in 13 countries in sub-Saharan
Africa since 2007 of which 81 are hybrids and 59 are improved open pollinated varieties
(Setimela et al., 2012). The new drought tolerant varieties which are estimated to be planted on
1.23 million hectares are said to be benefiting about 3 million households (Prasanna, 2014).
Regarding progress in breeding for drought tolerance a lot of work has been done, while
relatively not much has been done towards breeding for heat stress tolerance in maize (Cairns et
al., 2013). Studies done in temperate maize have shown negative effects of growing seasons
increased temperature (Cairns et al., 2013). In the US Corn Belt, a 10% yield loss resulted when
temperature increased from 22 o
C to 28 o
C during the period of grain filling (Thomson, 1966).
Similar studies have also been conducted where daily mean temperatures were increased by 6%
17
which resulted in yield loss of 42%. In southern Africa more than 20,000 historical maize yield
trials were recently analyzed and results have shown that there was a linear decrease in maize
production for every degree day accumulated above 30 oC (Lobell et al., 2011). The primary trait
used for selection under stress environments is grain yield. Selection for drought and heat stress
tolerance using grain yield only is insufficient as grain yield has low heritability and variance of
yield components. The use of secondary traits and grain yield in breeding for abiotic stress has
been reported to significantly make progress in selection (Mhike et al., 2011).
2.2.6 Secondary traits used in selection for drought and heat stress tolerance
Appropriate secondary traits to be used in selection must be related to grain yield genetically
under drought, must have high heritability, must be stable and easy to select for and not related
to yield loss under optimum growing environments (Edemeades et al., 2001). CIMMYT and
Pioneer Hi-bred have noted reduced prolificacy, stay green, anthesis-silking interval and leaf
rolling as crucial secondary traits to use under selection for drought (Banziger et al., 2000).
Measurement of secondary traits is justified through its contribution to yield improvement both
in optimum and stressed environments. The widely used secondary traits in breeding for abiotic
stress are prolificacy and anthesis silking interval. The physiological processes that are important
in increasing drought tolerance in maize is leaf photosynthesis sustenance throughout grain
filling period and kernel number increase as a result of more partitioning of assimilates to kernels
during the period of determining kernel number (Araus et al., 2008). Focusing on crucial traits
during phenotyping is found to make progress in abiotic stress breeding (Passioura, 2012).
Selection of genotypes is done under managed drought using performance on grain yield and
secondary traits.
18
2.2.6 Managed drought
Managed drought stress trials are carried out during the off-season in winter.. Genotypes are
screened for drought stress tolerance by exposing them to drought stress at either flowering or
grain filling stage. It is those good genotypes that respond well to the stress that are taken to be
having drought tolerance. There is a targeted yield reduction of about 15-30% of that under
optimum conditions when genotypes are subjected to an intermediate stress level. When
genotypes are exposed to severe stress levels which affect both flowering and grain filling stages,
a yield reduction of 30-60% of that realized under optimum conditions is expected (Banziger et
al., 2000). For a severe stress to occur, irrigation is designed in such a way that stress coincides
with flowering. Fourteen days after pollination, supplementary irrigation is applied to enhance
adequate grain filling of the formed grain. Intermediate stress is designed in a way that drought
stress coincides with grain filling only. Uniform application of irrigation is very crucial as it
gives uniform stress levels to the genotypes thus improved breeding progress (Banziger et al.,
2000).
2.3 Combining ability of maize inbred lines
The valuation of an inbred line relies on its combinations in relation to other lines. This is the
relative ability of an inbred line to pass on its desirable characteristics to its crosses which is
termed combining ability. Combining ability is a measure of the value of a genotype based on
performance of their offspring in some definite mating design (Allard, 1960). Populations,
varieties or inbred lines can be used as the genotypes. Combining ability studies is very
important in making an effective breeding programme. Information concerning combining
ability of parents and their resultant crosses is very useful in the development of desirable
hybrids in a breeding programme. Combining ability analysis helps in identification of best
19
combiners that are to be used in exploitation of heterosis or capturing of productive genes.
Combining ability encompasses General Combining Ability (GCA) and Specific Combining
Ability (SCA) and these were introduced by Sprague and Tatum (1942).
GCA is defined as the line’s average performance in hybrid combination expressed as a
deviation from overall mean of all crosses made from other parental lines (Falconer, 1981).
There can be either negative or positive deviations. Therefore, depending on trait under study
positive deviation can either be desirable or undesirable and it is also true with negative
deviation. For traits like yield, negative deviation is not favorable but with diseases it is
favorable. SCA is the crosses’ deviation based on average performance of the involved lines.
SCA is used to determine the value of best genotype combinations mostly in intra-group
matings. SCA estimates are very useful in final stages of identifying specific inbred lines to be
used in hybrid formation. The SCA measure which is high shows non-additive gene action.
Furthermore, SCA estimates are also used to classify inbred lines into heterotic groups. When
lines from different heterotic groups show high positive SCA estimates, they are said to
complement each other (Hallauer and Miranda, 1981).
GCA distinguishes additive gene effect whereas SCA distinguishes the non-additive gene effect
(Choukan, 2008). Statistically, GCA is the main effect whilst SCA is the effect of an interaction
statistically, meaning a deviation from additivity (Olfati et al., 2012). The additive gene effects
and non-additive gene effects are both of equal importance in expressing yield and its
influencing traits (Hefny, 2010). The predictable portion of genetic effects is very useful in plant
breeding. In a breeding programme, the use of GCA tests is important in preliminary screening
of lines from a large number of lines. These tests are also useful in determining type of gene
20
action which governs traits of interest. Additive gene action is attributed to a high GCA estimate
and genotypes with poor GCA are discarded.
2.4 Heterosis and heterotic groups
Heterosis is when F1 progeny exhibit superiority over both parents. Shull (1911) defined
heterosis as, “the superiority of heterozygous genotypes with respect to one or more characters in
comparison with the corresponding homozygotes.” Khan et al. (2008) defined heterosis as the
difference between a hybrid mean and its two parents. Maize hybrids usually give yield of two to
three times more as compared to their parents. Two lines which are extremely low yielding when
crossed, can give rise to a hybrid with high heterosis. However, it does not necessarily mean that
a superior hybrid is linked to high heterosis (Duvick, 1999).The superiority of a hybrid is not
only as a result of heterosis but also as a result of some heritable factors where heterosis is not an
influencing factor. Heterosis is also changed by genotype and environment interaction (Chapman
et al., 2000). Environmental conditions, species and trait under investigation other than parent
involved also determine level of heterosis in a hybrid (Chapman et al., 2000). Heterosis has been
shown to be higher in stress environments than under unstressed environments, due to the higher
sensitivity of inbred lines to stress than their hybrids (Ullustrup, 1970).
There are two types of heterosis and these are mid- parent heterosis and high-parent heterosis.
Lamkey and Edwards (1999) referred the difference between the hybrid and mean of the two
parents as mid-parent heterosis and the difference between the mean of F1 hybrid and that of the
highest performing parent producing the hybrid as high- parent heterosis. The characteristics that
suffer from inbreeding will show an improvement when the concerned inbred lines are crossed.
Heterotic effects in hybrids is said to be influenced by genetic distance between the parents
involved and their adaption level. Genetic distance can be enhanced between parental
21
populations thus enhancing the possibility of maximizing heterosis. Studies have shown that
genetic divergence between parents is needed for expression of heterosis (Miranda Filho, 1999).
For attaining heterosis, the lines involved for crossing need to be from different base populations
as the lines need to be unrelated. In exploitation of heterosis in breeding, the issue of heterotic
groups is very crucial.
Information concerning heterotic group of inbred lines help breeders to decide on best inbred line
combinations when developing varieties. A heterotic group consists of inbred lines with the same
performance when subjected to crossing with other inbred lines from a different heterotic group
and when crossed to each other, slight or no heterosis will be shown as the lines exhibit a close
relationship. Heterotic group classification in maize breeding helps breeders in determining
genetic distances between the inbred lines and mostly their potential vigor in cross combinations.
In hybrid breeding programmes, different heterotic groups are used for specific regions (Pratt et
al., 2003). Some heterotic groups are widely adapted and thus can be used across regions.
Halleur (1992) indicated that for maize breeding programmes in southern and eastern Africa,
elite inbred lines are classified into at least nine main heterotic groups. Establishment of heterotic
groups is not fully achieved and therefore systematic utilization of knowledge on heterotic
groups is practiced in the tropics by breeders (Pratt et al., 2003). Emphasis is put on the need to
exploit the diverse genotypes in the tropics for enhancement of heterotic groups for maize
hybrids. Putting inbred lines into heterotic groups will help in avoiding developing and
evaluating crosses that are to be discarded.
2.6 Mating designs in maize breeding
Knowledge of number of genes governing expression of a trait together with their respective
gene action is very useful in the successfully improvement of a trait in maize. Achievement is
22
reached when a specific mating or genetic design is used. Several mating designs have been
reviewed and have been used in estimation of genetic variance in maize populations (Hallauer
and Miranda, 1988). Diallel crosses, North Carolina (NC) design consisting of NCI, NCII and
NCIII are the major mating designs used in maize breeding. There is however a need to validate
some assumptions for adoption of any mating designs which are:
Each individual is diploid and act in a diploid manner during meiosis.
Absence of epistatic gene action or there is not any non-allelic interactions.
Absence of multiple alleles influencing the characters under study.
There are not non-genetic maternal influences or there are not any reciprocal differences.
Genes are not correlated or there is no linkage and there is independent assortment during
meiosis.
North Carolina design I (NCI) was introduced by (Hallauer, 1992) as a mating design that
allows a breeder to test a large number of genotypes from a population. It is a nested mating
design that uses different male parents, each crossed to different females. This design is very
crucial when there is an unequal number of a male and female parent as females are nested
within males. It gives an easy way of estimating additive genetic variance (VA) and dominance
variance (VD) through ensuring the between families statistic to be subdivided. This design is a
unique one in that factors are nested in one another not being crossed in a factorial design. In
maize breeding programmes, apart from diallel mating design, NCI is frequently used.
In North Carolina design II (NCII) males are crossed to females and all progeny families are
raised. The same group of females is crossed to each of the males. This design is very important
in cases where the number of females is not limiting. Each female genotype can be sampled the
23
number of times equivalent to the number of males. It estimates variance components plus GCA
and SCA. The major advantage of NCII is the ability to handle larger number of parents in an
experiment. GCA is used to estimate male and female mean squares whilst SCA variance of
diallel analysis is equal to interaction between males and females (Hallauer and Miranda, 1988).
From the mean squares, dominance variance is directly estimated (Falconer and Mackay, 1996).
2.7 Genotype by environment interaction
Vargas et al. (2001), defined Genotype by Environment (G x E) as the differential response of
cultivars to environmental changes. Exhibition of the same phenotypic characteristics of a certain
genotype varies under diverse environments and also diverse genotypes respond differently to a
specific environment. Environment plays a crucial role in modifying gene expression thus
genotypic expression heavily depends on the environment (Kang, 1998). Knowledge of G x E
interactions and yield stability are very crucial in breeding new varieties that have improved
adaptation to the target environments. Three common types of G x E interaction exists and these
are genotype x location interaction, genotype x year interaction and lastly genotype x location x
year interaction effects (Crossa, 1990). These interactions are explained by differences in
weather between and within seasons together with soil properties and many other environmental
factors. In G x E interaction there is what is called crossover interaction where the rank order of
performance of a genotype changes with regard to environment. Sometimes the rank order of
performance of a genotype does not change but the absolute difference of genotype performance
in different environments is that which changes and it is a non-crossover interaction. It is
crossover interaction that poses problems in plant breeding as it lowers selection progress as a
result of changing composition of genotypes selected in different environments (Cooper and
Delacy, 1994).
24
CHAPTER THREE
3.0 MATERIALS AND METHODS
3.1 Germplasm
Six adapted CIMMYT Zimbabwe lines were crossed to ten lines with combined drought and heat
tolerance (Table 3.1) using North Carolina DII mating design. The adapted CIMMYT Zimbabwe
lines were used as males and combined drought and heat tolerant donor lines were used as
females. From the potential 60 single cross hybrids that could be developed, only 30 single cross
hybrids were successfully developed. Therefore, CL133479, VL062571, CL106556, VL062626
and VL062650 were dropped from the evaluations as there was inadequate cross combinations
from them. These hybrids were then evaluated in five sites in 2013/14 summer season and in one
managed drought site in 2014 winter season.
3.2 Testing environments
The testing sites included four optimum sites, one sandy soil site and one managed drought site.
These were Harare Station (17.13oS, 31
oE, and 1406masl), Agricultural Research Trust (ART)
Farm (17.26oS, 31.5°E and 1480 masl), Devonia, Chibhero Agricultural College, Kadoma
Research Station (18.32°S, 30.90°E, 1 155 masl), Chiredzi Research Station (CRS) (21.02oS,
31.58oE, 433 masl). Harare Station, ART Farm, Devonia and Kadoma Research Station were the
optimum sites whilst Chiredzi Research Station was the managed drought site and Chibhero
Agricultural College was a sandy soil site. The trials were conducted during the 2013/14 summer
season at the optimum and sandy soil sites and in 2014 winter season at managed drought site.
25
Table 3.1: Names and pedigree information of germplasm used to produce the single cross
hybrids
Name Pedigree
CL1215159 (ATZTRLBA905-3-3P-1P-4P-2P-1-1-1-BxG9BC0RL23-1P-2P-3-
2P-3-2P-1P-BBB)-B-16TL-3-1-4-BB
CL1215157 CLQ-RCYQ40=(CML165xCLQ-6203)-B-9-1-1-B*9
CL133479 CLQ-RCYQ28=(CLQ6502*CLQ6601)-B-34-2-2-B*8-B
VL062571 DTPWC9-F24-4-3-1-B*5
CL106756 DTPYC9-F46-1-2-1-1-B*4
VL062626 DTPYC9-F46-1-2-1-2-B
CL133480 [CML-384 X CML-176](F3)100-2-7-B
VL062656 LaPostaSeqC7-F18-3-2-1-1-B*4
VL062650 LaPostaSeqC7-F64-2-6-2-2-B*4
CL1215158 POB502c3F210-3-2-1-B*8
CML539 CML539
CML442 CML442
CML312 CML312
CML395 CML395
CML444 CML444
CML546 CML546
3.2.1 Simulation for drought and heat stress
There was managed drought at Chiredzi Research Station and irrigation was used at critical times
only. Upto 280mm of irrigation was applied in the first eight weeks of plant growth. This
resulted in drought coinciding with flowering and grain filing stages. The stress applied resulted
in delay in silking making a longer anthesis silking interval and also kernel abortion occurred in
26
non-tolerant varieties. The stress level applied was targeted to achieve 15-20% (1-2tha-l) of
yields realized under optimum conditions. This stress level ensures that there was delay in
silking and also ear abortion occurs in those genotypes that are not tolerant to stress (Banziger et
al., 2000). An anthesis silking interval of between 4-8 days and ear number of 0.3-0.7 per plant is
achieved as a result of that stress (Banziger et al., 2000). The trials were planted in the last week
of June 2014 and these planting dates resulted in flowering stage being exposed to heat stress. In
this experiment flowering occurred in October 2014 and temperatures were above 30oC thus
there was heat stress at this critical period.
3.3 Trial management
The trial evaluation was done using an alpha (0, 1) lattice design. Planting was done using two
seeds per planting station and compound D fertilizer was applied as basal fertilizer. The trials
were replicated twice and each entry was planted in one row plots with a length of 4m and
measuring 0.75m inter-row and 0.25m intra-row spacing. At three weeks after crop emergence
thinning was done leaving one plant per planting station to achieve a plant population of 53 000
plants per hectare and this was done across all sites.
Basal fertilizer application was done by broadcasting and then disced into the soil before
planting. The application rate was the same at all sites with a rate of 400kg per hectare being
applied. Topdressing fertilizer application rate was 350kg per hectare except for Chibhero site
which had sandy soils and a rate of 400kg per hectare was applied. Topdressing was split applied
at four weeks and eight weeks after crop emergence at a rate of 200kg per hectare in the first
split and 150kg per hectare applied in the second split. Pest control was also carried out using
chemicals and problem pests were maize stalk borer and termites. Stalk borer was controlled by
27
dipterex granules application into funnels of each plant and application rate was 4kg per hectare.
Confidor was used for the control of termites.
Trials were grown at Chiredzi Research Station during the rain-free period that is in the winter
season. Irrigation water was applied at the start of the growing season to enhance good crop
germination and establishment. Thereafter, irrigation was stopped to enable the crop to
experience drought stress at critical flowering and graining filling stages. It is these two stages
that are heavily affected by stress. This stress resulted in average yields of less than three tons
per hectare. The crop was also exposed to heat stress by planting at the time that will ensure that
flowering and grain filling stages will coincide with the stress. These trials being planted in late
June2014 resulted in those critical stages being subjected to heat stress.
Table 3.2: Total rainfall received and amount of irrigation applied at each site
SITE AMOUNT OF RAINFALL (mm)
HARARE
1013
ART FARM
902
DEVONIA
874
CHIBHERO
932
KADOMA
800
CHIREDZI 389
3.4 Experimental Design and Data Collection
The experiment consisted of hybrid trials. The hybrid trials consisted of 30 hybrids and five
hybrid checks which included two of these hybrid checks coming from CIMMYT and three
coming from SEEDCO. Planting of the hybrid trials was done using 0.1 Alpha lattice design
28
with two replications. Hybrid trials were planted in one row each and row length was 4m with
inter-row spacing of 0.75m and in-row spacing of 0.25m.
Agronomic data that was collected included grain yield (GYD), anthesis date (AD) which was
measured as number of days when 50% of plants start shedding pollen from planting date and
silking date measured as number of days when 50% of the plants have emerged silk from
planting date. Important secondary traits were also recorded and these were plant height (PH),
ear height (EH), root lodging (RL). Anthesis silking interval was also calculated from anthesis
and silking dates. Disease scores of grey leaf spot and turcicum were done using a scale of 1-5
with score of 1 being disease free and 5 being severely diseased.
29
Table3.2: The agronomic data that was recorded for hybrid trials
Trait
Abbreviation Description
Grain yield GY Shelled grain weight per plot adjusted to 12.5% grain
moisture and converted to tons per hectare
Anthesis date AD Measured as number of days after planting when 50% of the
plants shed pollen.
Plant Height PH Measured as the height between the bases of a plant to the
insertion of the first tassel branch of the same plant.
Ear Height EH Measured as the height between the base of a plant to the
insertion of the top ear of the same plant
Ear aspect scores EA Rated on a scale from 1 (= good) to 5 (= poor). This
parameter is recorded at harvest.
Root lodging RL Measured as percentage of plants that show root lodging,
that is those stems that are inclining by more than 45o.
Husk cover HC Measured as percentage of plants with ears that are not
completely covered by the husks.
Ear rot ER Percentage of ears those are rotten.
Grain moisture MOI Percent water content of grain as measured at harvest.
30
Grey leaf spot GLS Score for the severity of gray leaf spot (Cercosporazea-
maydis) symptoms rated from 1(= clean, no infection) to 5
(= severly diseased).
Exohiliumtecicum ET Score for the severity of maize streak virus symptoms rated
from 1 (= clean, no infection) to 5(= severely diseased).
3.5 Data analysis
Analysis of variance (ANOVA) for hybrid trial was done at individual site and across sites using
the PROC MIXED procedure of SAS (SAS Institute, 2002). The fixed effects were genotypes
and random effects were replications and incomplete blocks. Combined analysis of variance
across all sites was done to be able to identify genotypes with good performance across diverse
environments rendering them to have general adaption. Five parental lines were dropped as a
result of poor synchronization leaving the report being based on a 5 x 6 line by tester analysis.
Table 3.3: Skeleton ANOVA for the NCDII
Source Df Expected Mean Squares
Males m-1 σ2
e+rσ2fm+rfσ
2m
Females f-1 σ2
e+rσ2fm+rmσ
2f
Males*females (m-1)(f-1) σ2
e+rσ2fm
Error (r-1)(mf-1) σ2
e
Total rmf-1
31
GCA and SCA effects were computed using the line x tester analysis of SAS Programme. Line x
tester model which is presented below was used.
Yijk = μ + gi + gj + sij + rk + eijk
Where:
Yijk = mean value of a character measured on cross i x j in kth replication
gi = GCA effect of ith parent
gj = GCA effect of the parent j
sij = SCA effect of cross i x j
rk = replication effect
eijk = environmental effect peculiar to (ijk)th individual
μ = population mean effect
Estimation of GCA effects:
Lines: gi = xi…/tr – y…/lrt
Testers: gt = x.j./lr – x…/ltr
Estimation of SCA effects:
sij = xij./r – xi…/tr – x.j./lr – x…/ltr
Where:
l = number of lines
t = number of testers
r = number of replications
32
CHAPTER FOUR
4.0 RESULTS
4.1 ANOVA and combining ability analysis
4.1.1 Grain yield and other secondary traits measured under optimum conditions
Table 4.1 below shows analysis of variance for grain yield under the optimum conditions and
shows significant differences for testcross and line x tester interaction. There was no significant
difference observed for lines and testers. Table 4.1 below also shows ANOVA of secondary
traits that were used in helping in selection of high yielding genotypes. The secondary traits
measured under optimum conditions shows that there were highly significant differences for
anthesis date on testcross, lines and testers. Significant differences were observed on line x tester
interaction. For plant height, highly significant differences were shown on lines with significant
differences observed on testcross and tester mean squares. There was however non-signification
differences observed for plant height on line x tester interaction.
The trait ears per plant had significant differences on testcross, tester and line x tester interaction
and non-significant difference was shown on line mean squares. Significant differences were
also observed on all sources of variation that is testcross, line, tester and line x tester interaction.
The testcross hybrids and lines showed highly significant differences for ear texture. Significant
differences were observed on tester mean squares and on line x tester interaction for ear texture
Table 4.1 below shows that there were significant differences for grey leaf spot on testcross
hybrids, on line and tester parents whist non-significant differences were recorded on line x
tester interaction. For the disease Exorhilium turcicum, highly significant differences were
shown on testcross hybrids, on lines and tester mean squares with significant differences
observed on line x tester interaction.
33
Table 4.1: ANOVA for grain yield and secondary traits measured under optimum sites
SOURCE DF GYD AD PH EPP ER TEX GLS ET
TESTCROSS 29 4.02* 14.67*** 565.37** 0.15** 10.63** 22.57*** 0.39* 0.68***
LINE 4 2.96 19.55*** 2002.08*** 0.17ns 21.17** 67.01*** 0.53* 2.78***
TESTER 5 4.55 42.63*** 802.42* 0.22* 15.43** 32.65** 0.59* 0.81***
LINE*TESTER 20 4.1* 6.70** 218.77ns 0.12* 7.32* 11.16* 0.31ns 0.23*
ERROR 119 2.95 2.63 265 0.07 4.21 5.66 0.2 0.13
Df: Degrees of Freedom ns: not significant ***: p< 0.001 **: p <0.01 *: p< 0.05
4.1.1.1 Testcross performance for grain yield measured under optimum conditions
The ANOVA for grain yield under optimum conditions indicated that there were significant
differences on testcross and Line x Tester interaction. Best cross combinations were observed
from L5T5 (7.23t/ha) and L2T2 (7.22t/ha) and the least yielding cross combinations was from
L1T3 (3.92t/ha) and L5T3 (3.93t/ha) (Table 4.2). The site mean grain yield was 5.98t/ha.
Average grain yield among testcrosses due to lines ranged from 5.48t/ha to 6.48t/ha whist that
due to testers ranged from 4.96t/ha to6.45 t/ha. From the testcrosses, lines 1, 2 and 3 and testers
1, 4, 5 and 6 conferred higher grain yield than the trial mean with lines 4 and 5 and testers 2 and
3 conferred lower yields below the trial mean.
34
Table 4.2: Mean grain yield (t/ha) measured under optimum conditions
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546 MEAN(t/ha-1
)
CL1215159 7.28 6.91 4.66 6.48 7.77 7.37 6.75
CL1215157 7.42 7.28 7.81 6.91 7.65 6.49 7.26
CL133480 6.88 7.45 7.24 6.78 5.90 6.35 6.76
VL062656 6.18 4.49 5.79 6.85 7.27 7.20 6.29
CL1215158 6.75 5.97 5.06 8.28 8.44 5.00 6.58
MEAN 6.90 6.42 6.11 7.06 7.41 6.48 6.73
4.2.1 Grain yield and other secondary traits under sandy soils
Table 4.3 below shows analysis of variance for grain yield under sandy soils at Chibhero
College. These results show that there were significant differences for Testcross and Line x
Tester interaction. There was no significant difference for Line and Tester. The secondary traits
measured at Chibhero College showed significant differences for anthesis date on lines and
testers. Mean squares for anthesis-silking interval for the testcross hybrids and for line x tester
interaction were also significant whilst significant differences for plant height were observed for
testers. Non-significant differences were observed on anthesis date for testcross hybrids and for
line x tester interaction, and for anthesis-silking interval for line and tester and plant height for
testcross, line and line x tester interaction. The table shows highly significant differences on ear
texture for testcross, line and tester whilst non-significant differences were shown on line x tester
interaction.
35
Table 4.3: ANOVA for grain yield and other agronomic traits measured at Chibhero
College
SOURCE DF GYD AD ASI PH TEX
TESTCROSS 29 2.72** ns 0.83* ns 0.74**
LINE 4 2.22ns 35** 0.25ns 510ns 2.15***
TESTER 5 1.82ns 28.36* 0.89ns 639* 1.6***
LINE*TESTER 20 3.04** 5.31ns 0.93* 220.25ns 0.25ns
ERROR 119 1.11 8.53 0.41 215.83 0.18
Df: Degrees of Freedom ns: not significant ***: p< 0.001 **: p <0.01 *: p< 0.05
4.2.1.1 Testcross performance for grain yield evaluated under sandy soil conditions
Grain yield at Chibhero College as shown by the analysis of variance indicates that there were
significant differences for grain yield for testcross and line x tester interaction. Table 4.4 below
showed that best SCA effects were from cross combinations of L3T2 (5.51t/ha) and L2T6
(5.49t/ha). The testcrosses that have least yields were L3T5 (1.39t/ha) and L4T3 (1.89t/ha).
Calculated means of lines and testers shows that the highest mean yield for lines was recorded
for Line 2 (4.31t/ha) and best mean for testers was recorded for Tester 4 (4.25t/ha) with the site
mean being 3.81t/ha. Average grain yield among testcrosses due to lines had a range from
3.21t/ha to 4.31t/ha whilst that due to testers ranged from 3.14t/ha to 4.25t/ha. Among the
testcrosses, Lines 1, 2 and 3 performed above the mean grain yield of the trial with Lines 4 and 5
performing below the mean grain yield of the trial. Testers 1, 2, 4 and 6 conferred higher grain
yields than the trial mean yield with testers 3 and 5 conferring lower yields than trial mean yield.
36
Table 4.4: Mean grain yield measured under sandy soil conditions
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546 MEAN(t/ha)
CL1215159 4.48 4.57 3.01 3.95 4.06 4.31 4.06
CL1215157 3.55 3.49 5.21 2.99 5.18 5.50 4.32
CL133480 4.04 5.52 3.53 5.97 1.40 2.78 3.87
VL062656 2.68 3.35 1.89 3.60 3.68 4.09 3.22
CL1215158 5.46 2.17 2.08 4.76 3.03 3.94 3.57
MEAN 4.04 3.82 3.14 4.25 3.47 4.12 3.81
4.3.1: Secondary traits under managed drought conditions
There were highly significant differences among testcross hybrids, lines, testers and significant
differences for line x tester interaction for anthesis date. Significant differences were also noted
for anthesis-silking interval on testcross and line x tester interaction with significant differences
observed on lines and no significant differences observed on testers. The Table 4.5 below shows
analysis of variance of secondary traits of ear rot and ear texture. For ear rot, significant
differences were shown on testcross, line and line x tester interaction with insignificant
differences shown on Tester. Significant differences were also noted on Testcross and Tester for
ear texture and insignificant differences noted on Line and Line x Tester interaction.
37
Table 4.5: ANOVA for Agronomic traits under Managed Drought Conditions
SOURCE DF AD ASI ER TEX
TESTCROSS 29 20.82*** 6.75** 52.41* 0.69*
LINE 4 26.54*** 8.14** 94.13* 0.37ns
TESTER 5 69.94*** 3.07ns 35.53ns 1.19*
LINE*TESTER 20 7.39** 7.4** 48.29* 0.63ns
ERROR 119 2.02 1.65 24.43 0.37
Df: Degrees of Freedom ns: not significant ***: p <0.001 **: p< 0.01 *: p <0.05
4.4.1 Grain yield performance across environments
Table 4.6 below shows the ANOVA for grain yield of testcross, line, testers and their respective
interactions. Significant differences were observed among sites, testcross, line and line x tester
interaction. However, there was no significant difference for tester, site x testcross, site x line,
site x tester and site x line x tester.
The line x tester ANOVA done across site for anthesis date, plant height, ears per plant, ear rot
and ear texture are presented in Table 4.6 below. Highly significant differences for anthesis date
were observed across environments. For the same trait testcross, line, tester, site x testcross, site
x tester, site x line and line x tester were highly significant. Non-significant differences were
observed for anthesis date for site x line x tester interaction. Highly significant differences for
plant height were observed on site, testcross, line and tester with non-significant differences
observed on all the interactions.
The table below shows that there were highly significant differences for ears per plant on site,
testcross, line, tester, site x testcross and site x tester with moderate significant difference on site
x line and significant difference recorded on line x tester. Non-significant difference was
observed on site x line x tester interaction. For ear rot significant differences were observed on
site, testcross, line, tester and their interactions with exception of site x tester and line x tester
interactions which were not significantly different. For ear texture, all sources of variation were
significantly different with site x line x tester having non-significant differences.
38
Table 4.6: ANOVA for grain yield and other secondary traits measured across
environments
SOURCE DF GYD AD PH EPP ER TEX
SITE 4 226.14*** 11472.46*** 36173.64*** 1.51*** 143.73*** 9.14***
TESTCROSS 29 7.39** 21.01*** 895.91*** 0.11* 22.8** 1.75***
LINE 4 10.17* 44.43*** 2566.93*** 0.13ns 71.59*** 2.53***
TESTER 5 6.18ns 54.61*** 1588.77*** 0.18* 30.54** 5.9***
SITE*TESTCROSS 116 3.78ns 7.97*** 264.54ns 0.06ns 17.28** 0.28**
SITE*LINE 16 1.62ns 9.06** 259.49ns 0.04ns 21.42** 0.45**
SITE*TESTER 20 4.31ns 22.05*** 331.58ns 0.07ns 12.98ns 0.41**
LINE*TESTER 20 7.13** 7.92** 388.49ns 0.08ns 11.1ns 0.56***
SITE*LINE*TESTER 80 3.79ns 4.23ns 248.78ns 0.07ns 17.52** 0.21ns
ERROR 150 3.06 3.6 278.43 0.06 9.38 0.17
Df: Degrees of Freedom ns: not significant ***: p <0.001 ** : <p 0.01 * :< p 0.05
4.4.1.1 Testcross performance for grain yield across environments
The analysis of variance for grain yield across sites indicated that there were significant
differences on testcross, line and line x tester interaction (Table 4.7). Best cross combinations
were observed from L5T4 (6.74t/ha) and L2T3 (6.64t/ha) and L1T5 (5.89t/ha) and the least
yielding cross combinations were from L4T3 (3.47t/ha) and L5T3 (3.51t/ha) (Table 4.7). Line
and tester means were calculated and the highest mean yield for lines was L2 (5.34t/ha) whilst
that for testers was T4 with 5.23t/ha. The across environment mean grain yield was 4.98t/ha.
Average grain yield among testcrosses due to lines ranged from 4.35t/ha to 5.34t/ha whilst that
due to testers ranged from 4.68t/ha to 5.23t/ha. From the testcrosses, lines 1, 2 and 3 and testers
1, 4 and 5 conferred higher grain yields than the trial mean with lines 4 and 5 and testers 2, 3 and
6 conferring lower yields below the trial mean. From the assigned ranks of lines and testers
across environments, the overall rankings show that L1 and T4 have been stable inbred lines.
39
Table 4.7: Mean grain yield evaluated across environments
TESTER
LINE 1 R 2 R 3 R 4 R 5 R 6 R AY AR OR
1 5.56 1 5.41 2 3.96 3 4.73 3 5.89 2 5.53 1 5.18 2.00 1
2 5.22 2 4.96 3 6.64 1 4.7 4 5.41 3 5.09 3 5.34 2.67 3
3 5.06 3 5.61 1 5.8 2 5.28 2 3.78 5 5.22 2 5.13 2.50 2
4 4.34 5 3.67 5 3.47 5 4.69 5 4.83 4 5.07 4 4.35 4.67 5
5 5.01 4 4.36 4 3.51 4 6.74 1 5.97 1 3.95 5 4.92 3.17 4
OR 3
5
6
1
2
4
AY 5.04 4.80 4.68 5.23 5.18 4.97 4.98
R=rank AY=average yield AR=average rank OR=overall rank
4.5. General Combining Abilities
4.5.1 Line General Combining Ability effects under optimum sites
The line with the best GCA effects for grain yield under optimum conditions was CL1215157
(0.49) whilst the line with the poorest GCA effect was VL062656 (-0.50). The GCA effects for
the secondary traits: anthesis date, anthesis silking interval, plant height, ears per plant, ear rot,
ear texture, Exorhilium turcicum and grey leaf spot are presented in Table 4.8 below.
CL1215157 had the least negative GCA effects for anthesis days (-0.78) and the highest positive
GCA effects for anthesis date was recorded for CL1215158 (0.99). For anthesis-silking interval,
CL1215159 (0.35) had the highest positive GCA effects with CL12155158 (-0.32) having the
highest negative GCA effects. LCL133480 (5) had the highest positive GCA effects for plant
height whilst CL1215158 (-8.96) had the highest negative GCA effects. For the trait ears per
plant, VL062656 (0.07) had the highest GCA effects and C133480 (-0.09) had the highest
negative GCA effects. Line with highest positive GCA for ear rot was CL1215158 (0.73) whilst
that with the highest negative GCA was CL1215159 (-1.20). For the foliar diseases recorded,
40
CL133480 (0.13) had the highest positive GCA effects for grey leaf spot whilst CL1215157 and
CL1215158 had equal negative GCA effects of -0.16 and for Exorhilium turcicum. The highest
positive GCA effects were shown on CL1215157 (0.31) and the highest negative GCA effects
were shown on CL1215159 (-0.32).
Table 4.8: Line GCA effects for grain yield and other agronomic traits evaluated under
optimum conditions
LINE GYD AD ASI PH EPP ER GLS ET
CL1215159 0.15 -0.17 0.35 3.96 0.01 -1.20 0.09 -0.32
CL1215157 0.49 -0.78 -0.19 -4.79 0.02 -0.08 -0.16 0.31
CL133480 0.05 0.14 0.27 5.00 -0.09 0.37 0.13 0.19
VL062656 -0.50 -0.23 -0.11 4.79 0.07 0.16 0.11 0.08
CL1215158 -0.19 0.99 -0.32 -8.96 -0.01 0.73 -0.16 -0.27
GYD=grain yield; AD=anthesis days; ASI=anthesis silking interval; PH=plant height;
EPP=ears per plant; ER=ear rot; GLS=Grey Leaf Spot; ET=Exorhilium turcicum
4.5.2 Tester GCA effects for traits evaluated under optimum sites
The tester with the best GCA effects for grain yield was CML395 (0.47) whilst the tester with
the poorest GCA effect was CML539 (-0.20). The GCA effects for the secondary traits: anthesis
days, anthesis silking interval, plant height, ears per plant, ear rot, ear texture, Exorhilium
turcicum and Grey Leaf Spot are presented in Table 4.9 below. The tester with the highest
negative GCA effects for anthesis days was CML539 (-0.86) whilst that for anthesis-silking
interval was CML546 (-0.27). The highest positive GCA effects for anthesis days was recorded
for CML395 (1.53) and the highest anthesis-silking interval was recorded for CML442 (0.53).
For plant height, the highest positive GCA was recorded for CML395 (5.67) whilst the highest
negative effect recorded for CML539 (-5.46) and with ears per plant, CML312 (0.10) had the
41
highest positive GCA effect with the highest negative effect shown on CML395 (-0.12). Tester
with highest positive GCA effects for ear rot was CML312 (1.01) whilst that with highest
negative GCA effects was CML444 (0.81). For the foliar diseases recorded, CML442 (0.20) had
the highest positive GCA effects for grey leaf spot whilst CML312 had highest negative GCA
effects of -0.22 and for Exorhilium turcicum, the highest positive GCA effects were shown on
CML442 (0.49) and the highest negative GCA effects were shown on CML546 (-0.28).
Table 4.9: Tester general combining ability effects for grain yield and other agronomic
traits under optimum conditions
TESTER GYD AD ASI PH EPP ER GLS ET
CML539 0.13 -0.86 -0.14 -5.46 -0.02 -0.37 -0.05 -0.26
CML442 -0.21 -0.67 0.53 -1.33 0.00 0.33 0.20 0.49
CML312 -1.02 -0.43 0.16 -1.08 0.10 1.01 -0.23 -0.12
CML395 0.47 1.53 -0.04 5.67 -0.12 0.02 0.15 -0.17
CML444 0.46 1.10 -0.24 5.17 -0.01 -0.81 -0.15 0.33
CML546 0.17 -0.78 -0.27 -2.96 0.06 -0.19 0.10 -0.28
GYD=grain yield; AD=anthesis days; ASI=anthesis silking interval; PH=plant height;
EPP=ears per plant; ER=ear rot; TEX=ear texture GLS=Grey Leaf Spot; ET=Exorhilium
turcicum
4.5.3 Line general combining ability effects at Chibhero Agricultural College
Three lines that are CL121559, CL1215157 and CL133480 had positive GCA effects for grain
yield under sandy soils conditions with lines VL062656 and CL1215158 having negative GCA
effects. The poorest line was which VL062656 had a GCA effect of -0.59. GCA effects for the
other agronomic traits under sandy soil conditions are also presented in the Table 4.10 below.
Three lines had negative GCA effects for anthesis days that are CL121559, CL1215157 and
42
VL062656 which indicates earliness in those lines. CL1215157 had a GCA effect for anthesis
days of -1.25 followed by VL062656 with a GCA effect of -0.58. CL1215158 had the highest
positive GCA effect for anthesis days of 2.17 followed by CL133480 with GCA effects of 1.42.
Anthesis-silking interval is one of the important traits to consider in line selection. Lines
CL1215159, CL1215157 and VL062656 recorded negative GCA effects for anthesis silking
interval of -0.19, -0.03 and -0.03 respectively with lines CL133480 and CL1215158 showing
positive GCA effects of 0.21 and 0.06 respectively. For plant height, CL1215159, CL133480 and
VL062656 had positive GCA effects with CL1215159 having the highest positive effects and
CL1215157 and CL1215158 had negative GCA effects as shown in the table. Ears per plant is
another trait which is important in selection and it shows that CL1215157 (0.01), VL062656
(0.027) and CL1215158 (0.05) had positive GCA effects whist the rest had negative effects with
CL133480 (-0.08 having the highest negative GCA effects. From the table below, CL133480 and
VL062656 had the highest positive GCA effects of 0.34 with CL1215159 (-0.58) having the
highest negative GCA effects for ear texture.
Table 4.10: Line general combining ability effects for grain yield and other agronomic
traits evaluated under sandy soils
LINE GYD AD ASI PH EPP ER TEX
CL1215159 0.25 -1.25 -0.19 7.67 -0.01 -1.41 -0.58
CL1215157 0.51 -1.75 -0.03 -1.50 0.01 2.23 0.22
CL133480 0.06 1.42 0.21 3.50 -0.08 -0.78 0.34
VL062656 -0.59 -0.58 -0.03 0.17 0.02 0.01 0.34
CL1215158 -0.24 2.17 0.06 -9.83 0.05 -0.05 -0.33
GYD=grain yield; AD=anthesis days; ASI=anthesis silking interval; PH=plant height;
EPP=ears per plant; ER=ear rot; TEX=ear texture
43
4.5.4 Tester general combining ability effects at Chibhero Agricultural College
The tester with the best positive GCA effect for grain yield was CML395 (0.45) followed by
CML546 (0.32) as shown in the table 4.11 below. Four testers that is Tester CML539, CML442,
CML395 and CML546 had positive GCA effects for grain yield under sandy soils conditions
with CML312 and CML444 having negative GCA effects. The poorest tester was CML312
which had a GCA effect of -0.66. GCA effects for the other agronomic traits under sandy soil
conditions are also shown in the table below. Testers which had least GCA effects for anthesis
date is CML312 (-1.6) and T5 had the highest positive GCA effect for anthesis days of 2.8.
Anthesis silking interval is one of the important traits to consider in tester selection. Testers
CML442, CML312 and CML444 recorded negative GCA effects for anthesis silking interval of -
0.01, -0.21 and -0.50 respectively with Testers CML539, CML395 and CML546 showing
positive GCA effects of 0.29 and 0.29 and 0.09 respectively. For plant height CML444 (11.0)
had the highest positive effects and CML442 (-10.5) had highest negative GCA effects as shown
in the table. Ears per plant are important in selection and it shows that Testers CML44 and
CML546 had positive GCA effects whist the rest of the testers had negative effects. The tester
with the highest GCA effect is T6 (0.06) and that with the least GCA effect being CML442 (-
0.1). For the trait ear rot, four testers recorded negative GCA effects with a good tester for this
trait being CML444 (-1.04) and two testers had positive GCA effects with CML312 (1.51) being
a poor one for this trait. Testers showing the highest positive and negative GCA effects for ear
texture were CML442 (0.75) and CML444 (-0.3) respectively.
44
Table 4.11: Tester general combining ability effects for anthesis days and other agronomic
traits under sandy soils
TESTER GYD AD ASI PH EPP ER TEX
CML539 0.23 -1.20 0.29 -4.00 -0.02 -0.55 0.15
CML442 0.01 -0.30 -0.01 -10.50 -0.03 1.14 0.75
CML312 -0.66 -1.60 -0.21 7.50 0.00 1.51 -0.15
CML395 0.45 1.20 0.29 0.00 -0.10 -0.87 -0.30
CML444 -0.34 2.80 -0.50 11.00 0.07 -0.19 -0.20
CML546 0.32 -0.90 0.09 -4.00 0.08 -1.04 -0.25
GYD=grain yield; AD=anthesis days; ASI=anthesis silking interval; PH=plant height;
EPP=ears per plant; ER=ear rot; TEX=ear texture
4.5.5 Line general combining ability effects at Chiredzi Research Station
Lines with positive GCA effects for grain yield under managed drought conditions were lines
CL1215159, CL133480 and CL1215158 whilst CL1215157 and VL062656 had negative GCA
effects. The line with the best GCA effects was CL1215159 (0.51) whilst the line with the
poorest GCA effect was VL062656 (-1.09). The GCA effects for the secondary traits: anthesis
days, anthesis silking interval, plant height, ears per plant, ear rot, ear texture and senescence are
presented in Table 4.12 below. CL1215157 had the least negative GCA effects for anthesis days
(-1.80), anthesis silking interval (-1.42) and plant height (-11.9). The highest positive GCA
effects for anthesis date was recorded for VL062656 (1.56), for anthesis-silking interval, GCA
effects was CL133480 (0.77) and for plant height it was 10.93 for CL133480. Line with highest
positive GCA for ear rot was CL1215158 (4.24) whilst that with the highest negative GCA was
CL1215159 (-3.30). The GCA effects for ear texture shows that the line with highest positive
effects was CL133480 (0.22) whist the line with the highest negative effects was CL1215159 (-
45
0.2). The trait senescence shows the highest positive GCA effects on CL1215157 (0.46) and the
highest negative GCA effects were on CL133480 (-0.50).
Table 4.12: Line GCA effects for grain yield and other agronomic traits under managed
drought conditions
LINE GYD AD ASI PH EPP ER TEX SEN
CL1215159 0.51 -1.36 0.04 5.10 0.12 -3.30 -0.20 -0.23
CL1215157 -0.15 -1.80 -1.42 -11.90 -0.02 -1.52 0.09 0.46
CL133480 0.44 0.29 0.77 10.93 0.00 0.39 0.22 -0.50
VL062656 -1.09 1.56 -0.04 -6.23 -0.10 0.20 0.05 0.19
CL1215158 0.29 1.29 0.52 2.10 -0.01 4.24 -0.16 0.08
GYD=grain yield; AD=anthesis days; ASI=anthesis silking interval; PH=plant height;
EPP=ears per plant; ER=ear rot; TEX=ear texture; SEN=leaf senescence
4.5.6 Tester general combining ability effects at Chiredzi Research Station
Testers with positive GCA effects for grain yield under managed drought conditions were testers
CML 539, CML312 and CML546 whilst testers CML442, CML395 and CML444 had negative
GCA effects. The tester with the best GCA effects was CML539 (1) whilst the tester with the
poorest GCA effect was CML539 (-0.35). The GCA effects for the secondary traits of anthesis
days, anthesis silking interval, plant height, ears per plant, ear rot, ear texture and senescence are
presented in Table 4.13 below. The tester with the least negative GCA effects for anthesis days
was CML539 (-3.23) whist that for anthesis-silking interval was CML444 (-0.54). The highest
positive GCA effects for anthesis days was recorded for CML444 (4.76) and for anthesis-silking
interval CML312 and CML395 (0.63). For plant height, the highest positive GCA was recorded
for CML395 (8.57) whilst the highest negative effect was recorded for CML539 (-18.43) and
with ears per plant, CML442 (0.18) had the highest positive GCA effect with the highest
46
negative effect shown on CML444 (-0.08). Tester with highest positive GCA effects for ear rot
was CML442 (1.94) whilst that with highest negative GCA effects was CML546 (-3.54). Testers
which recorded positive GCA effects for ear texture are CML442 (0.44) and CML444 (0.34)
whilst the rest of the testers had negative GCA effects with CML312 (-0.51) having the highest
negative. Highest positive GCA effects were shown on CML546 (0.51) with CML312 (-0.94)
having the highest negative effect.
Table 4.13: Tester general combining ability effects for grain yield and other agronomic
traits under managed drought conditions
TESTER GYD AD ASI PH EPP ER TEX SEN
CML539 1.00 -3.23 0.26 -18.43 0.01 1.02 -0.11 0.00
CML442 -0.11 0.37 -0.34 5.57 0.18 1.94 0.44 -0.26
CML312 0.68 -2.33 0.63 0.37 -0.08 0.69 -0.51 -0.94
CML395 -0.11 1.77 0.63 8.57 -0.01 0.05 -0.06 0.25
CML444 -0.24 4.76 -0.64 5.77 -0.08 -0.15 0.34 0.44
CML546 0.13 0.07 -0.54 -1.83 -0.03 -3.54 -0.11 0.51
GYD=grain yield; AD=anthesis days; ASI=anthesis silking interval; EPP=ears per plant;
PH=plant height EPP=ears per plant; ER=ear rot; TEX=ear texture; SEN=leaf senescence
4.5.7 Line GCA effects for grain yield and other agronomic traits across environments
The inbred line GCA effects across sites for grain yield, anthesis days, anthesis-silking interval,
ears per plant, plant height, ear rot, texture, senescence, grey leaf spot and Exorhilium turcicum
are presented in the Table 4.14 below. The line with the best GCA effect for grain yield across
all environments was CL1215157 (0.43) and the poorest line had GCA effects of -0.62 which is
VL062656. The line with the highest negative GCA effects for anthesis days was CL1215157 (-
1.23) and the highest positive GCA effects was CL1215158 (0.97). GCA effects for the other
agronomic traits across environments are also presented in the Table 4.21a below. CL062656
47
(0.33) showed the highest positive GCA effects for anthesis-silking interval with CL1215157 (-
0.35) showing the highest negative GCA effects. For plant height, highest positive GCA effects
was shown by CL1215159 (4.77) and CL1215158 (-7.26) had negative GCA effects. Ears per
plant is another trait which is important for determining yield and VL062656 (0.04) had the
highest positive GCA effects with CL133480 (-0.07) having the highest negative GCA effects
for ears per plant. Across all environments, CL1215159 (-1.67) had shown the highest negative
GCA effects for ear rot with CL1215158 (1.34) showing the highest positive GCA effects for
that trait. The line with the highest negative GCA effects for ear texture was CL1215159 (-0.30)
with highest positive GCA effects recorded for CL133480 (0.17). Good GCA effects for diseases
is negative effects and the best lines were CL1215158 (-0.29) for Exorhilium turcicum and
CL1215157 and CL1215158 for grey leaf spot which have GCA effects of -0.16. Lines with poor
GCA effects for Exorhilium turcicum and grey leaf spot were CL1215157 (0.34) and CL133480
(0.13) respectively.
48
Table 4.14: Line general combining ability effects for grain yield and other agronomic
traits across environments
LINE GYD AD ASI PH EPP ER TEX ET GLS
CL1215159 0.24 -0.19 0.21 4.77 0.02 -1.67 -0.30 -0.28 0.09
CL1215157 0.43 -1.23 -0.35 -5.43 0.01 0.43 0.16 0.34 -0.16
CL133480 0.13 0.19 0.33 5.74 -0.07 -0.16 0.17 0.10 0.13
VL062656 -0.62 0.23 -0.07 2.18 0.04 0.08 0.08 0.13 0.11
CL1215158 -0.18 0.97 -0.12 -7.26 0.00 1.34 -0.12 -0.29 -0.16
GYD=grain yield; AD=anthesis days; ASI=anthesis silking interval; PH=plant height;
EPP=ears per plant; ER=ear rot; TEX=ear texture
4.5.8 Tester GCA effects for grain yield and other agronomic traits across environments
Testers with positive GCA effects for grain yield across environments were CML539, CML395,
CML444 and CML546 whilst testers CML442 and CML312 had negative GCA effects. The
tester with the best GCA effects was CML395 (0.29) whilst the tester with the poorest GCA
effect was CML312 (-0.59). The GCA effects for the secondary traits: anthesis days, anthesis
silking interval, plant height, ears per plant, ear rot, ear texture and senescence are presented in
Table 4.15 below. The tester with the least negative GCA effects for anthesis days was CML539
(-0.99) whist that for anthesis-silking interval was CML444 (-0.32). The highest positive GCA
effects for anthesis days was recorded for CML395 (1.67) and for anthesis-silking interval
CML442 (0.27). For plant height, the highest positive GCA was recorded for CML444 (6.24)
whilst the highest negative effect was recorded for CML539 (-7.38) and with ears per plant,
CML312 (0.05) had the highest positive GCA effect with the highest negative effect shown on
CML395 (-0.1). Tester with highest positive GCA effects for ear rot was CML312 (1.3) whilst
that with highest negative GCA effects for ear rot was CML546 (-0.90). The tester which
49
recorded positive GCA effects for ear texture was CML442 (0.6) whilst T3 (-0.36 had the
highest negative effect. For the diseases, CML442 showed the highest positive GCA effects for
Exorhilium turcicum and grey leaf spot with GCA effects of 0.16 and 0.2 respectively. Highest
negative GCA effects for Exorhilium turcicum was CML539 (-0.22) and for grey leaf spot it was
CML312 (-0.23).
Table 4.15: Tester general combining ability effects for grain yield and other agronomic
traits across environments
TESTER GYD AD ASI PH EPP ER TEX ET GLS
CML539 0.02 -0.99 -0.01 -7.38 -0.01 -0.30 -0.11 -0.22 -0.05
CML442 -0.22 -0.08 0.29 -1.71 0.02 0.55 0.60 0.16 0.20
CML312 -0.59 -0.72 0.18 0.59 0.05 1.30 -0.36 -0.04 -0.23
CML395 0.29 1.67 0.12 5.21 -0.10 -0.37 -0.23 0.02 0.15
CML444 0.23 0.49 -0.32 6.24 -0.01 -0.24 0.19 0.21 -0.15
CML546 0.28 -0.41 -0.26 -2.94 0.05 -0.90 -0.08 -0.13 0.10
GYD=grain yield; AD=anthesis days; ASI=anthesis silking interval; PH=plant height;
EPP=ears per plant; ER=ear rot; TEX=ear texture
4.6 Specific Combining Abilities
4.6.1 Specific combining ability effects for grain yield under optimum conditions
The best SCA effects were recorded for the CL133480 x CML312 (L3T3) combination with a
positive effect 2.02t/ha while the least SCA effects were recorded for the CL1215159 x
CML312 (L1T3) combination with a negative effect of -1.19t/ha (Table 4.16). CL1215159 had
positive SCA effects with four testers that are CML539, CML442, CML444 and CML546 whilst
the rest of the lines all had three positive combinations with different testers. A positive SCA
50
effect indicates that the line and tester belong to opposite heterotic groups and that a negative
SCA indicates that the line and the tester belong to same heterotic group.
Table 4.16: Specific combining ability effects for grain yield under optimum conditions
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 0.55 0.17 -1.19 -0.83 0.48 0.82
CL1215157 -0.28 0.96 0.20 -0.05 -0.97 0.15
CL133480 0.00 0.03 2.02 0.29 -1.12 -1.21
VL062656 -0.72 -0.71 -0.21 0.07 0.64 0.93
CL1215158 0.46 -0.45 -0.83 0.52 0.98 -0.68
4.6.1.1 Specific Combining Ability Effects for anthesis days under optimum conditions
VL062656 and CML444 produced a cross with the highest negative SCA effects (-1.8) for
anthesis days, which is an indication of earliness. CL1215157 had a negative GCA effect (-0.78)
for anthesis days whilst CML539 also had a negative GCA effect (-0.86) for anthesis days.
VL062656 and CML442 produced a cross that has the highest positive SCA effects (1.51), which
is an indication of lateness. CL1215158 had a positive GCA effect (1) for anthesis days whilst
CML395 also had a positive GCA effect (1.53) for anthesis days.
51
Table 4.17: Specific Combining Ability Effects for anthesis days under optimum conditions
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 -0.62 -0.52 -0.47 0.49 0.80 -0.13
CL1215157 0.71 -0.48 -1.58 -0.03 0.16 0.91
CL133480 -0.64 0.51 0.15 -0.17 0.12 0.12
VL062656 0.32 1.51 0.15 -0.45 -1.80 0.22
CL1215158 0.10 -1.25 1.42 0.20 0.51 -1.11
4.6.1.2 SCA effects for anthesis-silking interval evaluated under optimum conditions
SCA effects for anthesis silking interval under optimum conditions are presented in Table 4.18
below. Crosses that produced negative SCA effects are ideal for this trait. The cross with the best
SCA effect (-1.49) was between CL133480 and CML442. The poorest cross (1.53) was between
VL062656 and CML444. The second best cross (-1.11) was between CL1215158 and CML539
with the second poorest cross (1.21) being between CL133480 and CML395.
Table 4.18: SCA effects for anthesis-silking interval recorded under optimum conditions
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 0.10 0.80 0.05 -0.75 -0.68 0.48
CL1215157 0.89 0.72 0.47 -0.46 -0.76 -0.86
CL133480 0.43 -1.49 1.01 1.21 -0.59 -0.57
VL062656 -0.32 -0.87 -0.99 -0.17 1.53 0.81
CL1215158 -1.11 0.84 -0.53 0.17 0.49 0.14
52
4.6.2 SCA effects for grain yield evaluated under sandy soil conditions
The best SCA effects for grain yield evaluated under sandy soil conditions were recorded for the
CL133480 x CML395 (L3T4) combination with a positive effect of 1.65t/ha while the least SCA
effects were recorded for the CL1215157 x CML444 (L2T5) combination with a negative SCA
effect of -2.136t/ha (Table 4.19). A positive SCA effect indicated that the line and tester belong
to opposite heterotic groups and that a negative SCA indicated that the line and the tester belong
to same heterotic group.
Table 4.19: SCA effects for grain yield evaluated under sandy soils
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 0.18 0.50 -0.39 -0.56 0.33 -0.07
CL1215157 -1.00 -0.84 1.55 -1.77 1.20 0.86
CL133480 -0.06 1.63 0.32 1.65 -2.14 -1.41
VL062656 -0.77 0.12 -0.66 -0.06 0.81 0.56
CL1215158 1.65 -1.41 -0.83 0.74 -0.21 0.05
4.6.2.1 SCA effects for anthesis days recorded under sandy soils
CL133480 and CML442 produced a cross with the highest negative SCA effects (-2.12) for
anthesis days, which is an indication of earliness. CL1215159 had a negative GCA effect (-1.25)
for anthesis days whilst CML312 also had a negative GCA effect (-1.6) for anthesis days (Table
4.20). VL062656 and CML312 produced a cross that had the highest positive SCA effects (3.68),
which is an indication of lateness. CL1215158 had a positive GCA effect (2.17) for anthesis days
whilst CML444 also had a positive GCA effect (2.8) for anthesis days
53
Table 4.20: SCA effects for anthesis days recorded under sandy soils
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 0.95 -0.45 -2.15 1.55 -1.05 1.15
CL1215157 0.45 2.05 -1.15 -0.45 -0.55 -0.35
CL133480 -1.22 -2.12 0.68 -0.62 2.28 0.98
VL062656 -0.22 0.38 3.68 -0.12 -1.72 -2.02
CL1215158 0.03 0.13 -1.07 -0.37 1.03 0.23
4.6.2.2 SCA effects for anthesis-silking interval measured under sandy soil conditions
SCA effects for anthesis silking interval under sandy soil conditions are presented in Table 4.21
below. Crosses that produced the negative SCA effects are good combiners for this trait. The
cross with the best SCA effect (-2.32) was between CL133480 and CML444. The poorest cross
(0.93) was between CL1215157 and CML442. The second best cross (-1.41) was between
CL1215159 and CML442 with the second poorest cross (0.8) being between CL1215159 and
CML539.
54
Table 4.21: SCA effects for anthesis-silking interval measured under sandy soil conditions
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 0.79 -1.41 0.29 0.29 0.58 -0.51
CL1215157 -0.37 0.93 -0.87 0.13 0.42 -0.17
CL133480 -0.11 0.19 0.39 -0.11 -2.32 0.59
VL062656 -0.37 -0.07 0.13 0.13 0.42 -0.17
CL1215158 0.04 0.34 0.04 -0.46 -0.17 0.24
4.6.3 SCA effects for grain yield measured under managed drought conditions
The best SCA effects were recorded for the CL1215158 x CML395 (L5T4) combination with a
positive effect 2.8t/ha while the least SCA effects were recorded for the CL1215158 x CML312
(L5T3) combination with a negative effect of -1.8t/ha (Table 4.22). VL062656 had positive
SCA effects with four testers that are CML539, CML442, CML395 and CML546 whilst lines
CML1215159, CL133480 and CL1215158 all had three positive combinations with different
testers. CL1215157 has however shown to be the poorest specific combiner with only two
positive combinations with CML539 and CML312. A positive SCA effect indicates that the line
and tester belong to opposite heterotic groups and that a negative SCA indicates that the line and
the tester belong to same heterotic group.
55
Table 4.22: SCA effects for grain yield measured under managed drought conditions
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 0.38 0.19 -0.33 -1.02 1.02 -0.25
CL1215157 0.33 -0.62 2.56 -0.40 -1.11 -0.76
CL133480 -0.30 -0.95 1.39 -1.40 -0.92 2.17
VL062656 1.10 0.88 -1.83 0.02 -0.25 0.08
CL1215158 -1.50 0.49 -1.80 2.80 1.25 -1.24
4.6.3.1 SCA effects for anthesis days recorded under managed drought conditions
CL1215157 and CML395 produced a cross with the highest negative SCA effects (-3.50) for
anthesis days, which is an indication of earliness. CL1215157 had a negative GCA effect (-1.18)
for anthesis days whilst CML539 also had a negative GCA effect (-3.23) for anthesis days.
CL1215157 and CML444 produced a cross that has the highest positive SCA effects (3.51),
which is an indication of lateness. VL062656 had a positive GCA effect (1.56) for anthesis days
whilst CML444 also had a positive GCA effect (4.76) for anthesis days.
56
Table 4.23: SCA effects for anthesis days measured under managed drought conditions
TESTER
LINE
CML53
9 CML442 CML312 CML395 CML444 CML546
CL1215159 -1.94 -0.54 0.66 1.06 -0.43 -0.24
CL1215157 1.50 0.40 -2.40 -3.50 3.51 3.20
CL133480 0.91 0.31 -1.49 2.91 -0.58 -1.39
VL062656 0.14 -1.46 2.74 -0.36 -0.34 -2.16
CL1215158 -0.59 1.31 0.51 -0.09 -1.58 0.61
4.6.3.2 SCA effects for anthesis-silking interval measured under managed drought
conditions
SCA effects for anthesis silking interval under managed drought conditions are presented in
Table 4.24 below. Crosses that produced the negative SCA effects are good combiners for this
trait. The cross with the best SCA effect (-2.36) was between VL062656 and CML539. The
poorest cross (8.27) was between VL062656 and CML312. The second best cross (-1.86) was
between L2 and CML312 with the second poorest cross (2.16) being between CL1215159 and
CML442.
57
Table 4.24: SCA effects for anthesis-silking interval measured under managed drought
conditions.
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 1.56 2.16 -1.82 -0.82 -1.04 -0.14
CL1215157 0.52 0.12 -1.86 0.14 0.92 0.32
CL133480 -0.67 0.93 -1.05 1.95 -0.77 -0.37
VL062656 -2.36 -0.76 8.27 -0.73 0.54 -0.56
CL1215158 1.08 -2.32 0.70 -0.80 0.48 0.88
4.6.4 SCA effects for grain yield measured across environments
The best SCA effects were recorded for the CL133480 x CML312 (L3T3) combination with a
positive effect 1.56t/ha while the least SCA effects were recorded for the CL1215159 x CML312
(L1T3) combination with a negative effect of -1t/ha (Table 4.25). CL1215159 had positive SCA
effects with four testers that are CML539, CML442, CML444 and CML546 whilst lines
CL1215157, CL133480 and CL1215159 all had three positive combinations with different
testers. CL1215159 has however shown to be the poorest specific combiner with only two
positive combinations with CML444 and CML546. A positive SCA effect indicates that the line
and tester belong to opposite heterotic groups and that a negative SCA indicates that the line and
the tester belong to same heterotic group (Vasal et al., 1992).
58
Table 4.25: SCA effects for grain yield evaluated across sites
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 0.59 0.08 -1.00 -0.72 0.63 0.42
CL1215157 -0.23 0.35 0.98 -0.53 -0.71 0.13
CL133480 -0.26 0.30 1.56 0.26 -1.39 -0.46
VL062656 -0.49 -0.06 -0.59 -0.03 0.59 0.60
CL1215158 0.40 -0.66 -0.95 1.03 0.87 -0.69
4.6.4.1 SCA effects for anthesis days evaluated across sites
CL1215157 and CML312 produced a cross with the highest negative SCA effects (-1.27) for
anthesis days, which is an indication of earliness. CL1215157 had the highest negative GCA
effect (-1.23) for anthesis days whilst CML539 also had the highest negative GCA effect (-
0.9901) for anthesis days. CL1215159 and CML444 produced a cross that has the highest
positive SCA effects (1.48), which is an indication of lateness. CL1215158 had a positive GCA
effect (0.97) for anthesis days whilst CML395 also had a positive GCA effect (1.67) for anthesis
days.
59
Table 4.26: Specific Combining Ability Effects for anthesis days across sites
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 -0.77 -0.68 -0.68 0.31 1.48 0.01
CL1215157 1.09 0.45 -1.27 -0.65 -0.76 1.01
CL133480 -0.06 -0.07 -0.02 0.92 -0.63 0.00
VL062656 -0.28 0.30 0.78 -0.69 0.42 -0.50
CL1215158 -0.03 -0.02 0.95 0.23 -0.59 -0.53
4.6.1.2. Specific Combining Ability Effects for anthesis-silking interval across environments
SCA effects for anthesis silking interval across environments are presented in table 4.27 below.
Crosses that produced the negative SCA effects are ideal combiners for this trait. The cross with
the best SCA effect (-0.82) was between CL133480 and CML444. The poorest cross (1.3) was
between VL062656 and CML444. The second best cross (-0.8) was between CL133480 and
CML442 with the second poorest cross (1.12) being between CL133480 and CML395.
Table 4.27: Specific combining ability effects for anthesis-silking interval across sites
TESTER
LINE CML539 CML442 CML312 CML395 CML444 CML546
CL1215159 0.45 0.65 -0.24 -0.52 -0.57 0.20
CL1215157 0.60 0.63 -0.17 -0.29 -0.24 -0.57
CL133480 0.17 -0.79 0.57 1.12 -0.82 -0.33
VL062656 -0.68 -0.73 0.07 -0.24 1.30 0.40
CL1215158 -0.54 0.24 -0.23 -0.10 0.35 0.29
60
4.7 SCA Effects: Heterotic Groups As Determined by Testers CML312 and CML444
In CIMMYT maize breeding programs in southern Africa, two heterotic groups are used that is
group A and B. The inbred lines known to belong in group A and B are CML312 and CML444
respectively. Lines which show positive SCA effects with CML312 are lines which belong to
heterotic group B and that lines which shows positive SCA effects with CML444 belong to
heterotic group A. From the SCA effects for grain yield, CL1215157 (0.98) and CL133480
(1.56) had positive SCA effects with CML312 and lines CL1215159 (0.63), VL062656 (0.59)
and CL1215158 (0.87) all had positive SCA effects with CML444.
61
CHAPTER 5
5.0 Discussion
5.1 Grain yield and its components
5.1.1 Grain yield and its components under optimum conditions
Grain yield for the single cross hybrids under optimum conditions averaged 6.73t/ha. There was
significant difference observed amongst the entries for grain yield which showed that the
genotypes were significantly different from each other thus best performing single cross testers
can be identified. There were hybrids that were produced from parental lines with positive GCAs
and were the ones that were amongst the best yielding having high yields. Amongst the thirty
single crosses evaluated, cross combinations L2T2 (7.23t/ha) and L1T6 (7.13t/ha) were some of
the best yielding crosses from lines with positive GCA effects. Amongst the testcrosses, the line
with the best mean was L2 (6.48t/ha) and the tester with best mean was T4 (6.45t/ha). The
difference which was shown for grain yield on hybrids was attributed to additive gene action due
to males and also to non-additive gene action with additive gene action contributing more to the
variation.
There were highly significant differences for hybrids on anthesis days under optimum
conditions. Line, tester and line x tester interaction also showed significant differences. This
showed that the variation among genotypes was attributed to both additive and non-additive gene
action. For additive gene action, the males contributed more to the variation than the females.
Anthesis-silking interval mean squares for the genotypes were not significantly different from
each other under optimum environment. This could be explained by the fact that under optimal
conditions, the emergence of silks occurs almost the same time with that of pollen shedding thus
genotypes do not show much difference for this trait. This was supported by Banziger and Lafitte
62
(1997) who showed that silk emergence occurred at almost the same time with that of pollen
shedding. The figures obtained are smaller and near zero. The reverse is true under stress
conditions as ASI is longer and genotypes that are stress tolerant exhibits a shorter ASI.
The genotypes had shown to have significantly different mean squares for plant height. The
additive gene action was responsible for the variation as both line and tester showed significantly
different mean squares with additive gene action due to females contributing more to the
variation. Under these conditions, ears per plant, ear rots and ear texture had significantly
different mean squares with both additive and non-additive gene action contributing to the
variation. The mean squares of the genotypes showed significant difference for foliar disease
resistance with both additive and non-additive gene action being responsible for the variation.
5.1.2 Grain yield and its components under sandy soil conditions
Grain yield for the single cross hybrids under sandy soil conditions averaged 3.81t/ha. There was
significant difference observed amongst the entries for grain yield which showed that the hybrids
were significantly different from each other thus best performing single cross testers can be
identified. There were hybrids that were produced from parental lines with positive GCAs which
were L3T4 (5.97t/ha), L2T6 (5.50t/ha) and L3T2 (5.52t/ha) which were the best yielding
hybrids. Amongst the hybrids, the line with the best mean was L2 (4.32t/ha) and tester with the
best mean was T4 (4.25t/ha). The significantly different mean squares for grain yield showed
that variation was contributed to non-additive gene action.
Significantly different mean squares for anthesis days were observed on line and tester meaning
that additive gene action contributed to the variation. For anthesis-silking interval, mean squares
for the genotypes were significantly different and this variation was attributed to non-additive
63
gene action. Under this environment, genotypic differences on ears per plant and ear rot were not
expressed. The mean squares for ear texture were significantly different from each other with
line and tester being also significant indicating that variation was due to additive gene action.
5.1.3 Grain yield and its components under managed stress conditions
For the single cross hybrids under managed stress conditions, grain yields were on average of
2.66t/ha. Tolerant genotypes can therefore be selected as they are not affected much by the
stress. Under stress conditions, Bolanos and Edmeades (1996) determined that average grain
yields should be in the range of 20% to 30% of that average grain yield obtained under optimum
management in that same environment such that genotypes that show general good performance
under both conditions can be selected. The best cross combinations from line and tester with
positive GCA effects was L3T6 (5.14t/ha) under managed drought. There was however no
significant differences for grain yield mean squares under managed drought conditions. This was
as a result that genotypes were exposed to severe stress thus the environment fails to discriminate
the genotypes for this trait. This is supported by findings from Edmeades et al. (1993) where the
managed drought environment failed to discriminate genotypes for grain yield.
Anthesis days had significantly different mean squares for the genotypes. Line, tester and line x
tester interaction mean squares were also significantly different thus highlighting that variation
was due to additive and non- additive gene action with much contribution coming from additive
gene action due to males. Hybrids that had positive grain yield tend to have a lower value for
ASI. Significant differences for anthesis-silking interval mean squares were observed with
variation attributed to additive gene action due to females and also non-additive gene action. The
genotypes also show significantly different mean squares for ear rot and ear texture. The
64
environment also failed to distinguish genotypes for ears per plant and senescence which are
very crucial secondary traits used to aid selection of genotypes tolerant to stress.
Drought and heat stress accelerates leaf senescence thus genotypes with these stress tolerance
tend to show delayed senescence. Blum (1988) stated that the lack of transpirational cooling
resulting in heating of all or parts of the leaf from elevating temperatures results in premature
leaf senescence. Single cross hybrids with low senescence values were L5T2 and L3T3 with
grain yields of 3.34t/ha and 5.18t/ha which are all above the mean grain yield of 2.66t/ha.
5.1.4 Grain yield and its components across environments
It is very essential to know whether G x E interactions was significant such that stable genotypes
can be selected. The stable genotypes are genotypes that do not give a huge yield penalty when
grown under stress environments. There was no signification variation among hybrids for grain
yield, anthesis-silking interval, plant height and ears per plant but significant differences were
observed for anthesis days, ear rot and ear texture. Therefore, hybrids did not show substantial
environmental and genotypic differences across environments. However, the lines that proved to
be stable across environments were CL1215159 (5.18t/ha) and CL133480 (5.13t/ha) and stable
testers were CML395 (5.23t/ha) and CML444 (5.18t/ha).
Non-significant interactions of genotypes with environment for grain yield, anthesis-silking
interval, plant height and ears per plant showed that genotypes that performed well under
optimum conditions were the ones that performed well on the other environments. Therefore
performance of genotypes did not change with change in environments. This shows that the
hybrids did not differ on how they expressed their traits under different environments. Therefore,
it implies that variation amongst the hybrids was a result of other causes. Significantly different
65
mean squares for anthesis days were observed for genotypes across environments. The variation
was due to additive gene action both due to females and males with non-additive gene action also
contributing to the variation.
Hybrids that do well across environments are able to combine tolerance to stress and yield
potential in tropical maize (Betran et al., 2003). Single cross hybrids are known to be sensitive to
environmental changes (Hallauer et al., 1988). However it was not so in this study as there was
no significant interaction of genotypes with the environment for most traits. Breeders thus tend
to target to produce three way hybrids which are stable and have a broad genetic base for
marginalized environments.
Yield generally tend to decrease under stressful conditions and this is shown from the mean
yields under the different environments with 6.73t/ha under optimum conditions, 3.81t/ha under
sandy soil conditions and 2.66t/ha under managed stress conditions. A yield reduction of up to
60% of that under optimum conditions was recorded. This is supported by findings from
Banziger et al. (2000) who reported that when severe stress levels which affect both flowering
and grain filling stage is exposed to the genotypes, a yield reduction of 30-60% of that realized
under optimum conditions is expected.
5.2 GCA effects for grain yield and its components
The line that has the best GCA effects for grain yield was CL1215157 both under optimum
environment and sandy soils with GCA effects of 0.49 and 0.51 respectively. This line under
managed stress conditions however had a negative GCA effect of -0.15 with CL1215159 being
the line with best GCA effects (0.51) under that environment. Across sites, CL1215157 tend to
be again the line with best GCA effects for grain yield, with GCA effect of 0.43. The line that
66
showed poor GCA effects under optimum conditions, sandy soils and across environments was
VL062656 with GCA effects of -0.50, and -0.59. The tester with best GCA effects for optimum
conditions, sandy soils and across environments was CML395 whilst CML539 was the best for
grain yield performance under managed stress. The poorest tester for other environments and
across environments except managed stress environment was CML312 with the poorest tester
under managed stress being CML444.
For anthesis days CL1215157 had shown good GCA effects for earliness and had also show
good GCA effects for anthesis-silking interval across environments. Lines with negative GCA
effects for anthesis days and anthesis-silking interval are desirable for stress breeding. Delay of
silking whilst pollen is shed leads to a long anthesis-silking interval which correlate highly to
setting of kernels ((Edemeades et al., 2000). Negative GCA effects for ASI indicated that those
lines had better synchronization to their genotypes VL062656 which performed poorly for grain
yield had the poorest GCA effects for ears per plant and senescence which points to its poor
GCA effects for grain yield. Across environments, CL1215159 and CL1215157 tend to have
GCA effects for earliness whilst CL133480, VL062656 and CL1215158 tend to have GCA
effects for lateness.
The poor tester for grain yield which is CML312 had shown positive GCA effects for ears per
plant. Under managed drought, CML312 had shown the best GCA effects for senescence. The
best general combiner for grain yield that is CML539 under managed stress conditions had
shown good GCA effects also for ears per plant whilst the worst general combiner for grain yield
that is CML444 had also shown poor GCA effects for ears per plant and senescence.
67
Across environments, though the tester CML395 proved to be the best combiner for grain yield it
did not have good GCA effects for anthesis-silking interval and ears per plant but had shown
good GCA effects on ear rot and texture. The worst general combiner was CML312 but it
showed good GCA effects on ears per plant and ear texture but also having poor GCA effects on
anthesis-silking interval and ear rot. From the best and worst general combiners for grain yield, it
shows that ears per plant does not always directly translate to best GCAs for grain yield. The
tester GCA effects across environments for anthesis days showed that CML539, CML442,
CML312 and CML546 had GCA effects for earliness and CML444 and CML395 had GCA
effects for lateness.
5.3 SCA effects for grain yield
The single cross SCA effects for grain yield ranged from -1.39 to 1.56. The crosses that had
positive SCA effects implied that the parental lines are genetically distant and in terms of
heterotic groups, it meant they belong to different heterotic groups. Cross combinations out of
parents from diverse genetic background results in positive SCA effects with high performance
(Betran et al., 2003). The further away from zero the values are, it means the more distantly
related the lines are.
The best SCA effects for grain yield under optimum conditions was from cross combinations of
CL133480 x CML312 (2.02t/ha) and the least SCA effects was between CL1215159 x CML312
(-1.16t/ha). Under these conditions, CL1215159 showed to be the best specific combiner for
grain yield. For sandy soils, the best cross combination was from CL133480 x CML395
(1.65t/ha), CL1215158 x CML395 (2.98t/ha) was the best cross combination under managed
stress conditions with across environments having the best cross combination of CL133480 x
CML312 (1.56t/ha). A parental line that has good GCA effects does not always produce better
68
hybrids. Poor general combiners can produce good hybrids with the testers. It was true with
CML312 which has poor GCA effects for grain yield but proved to be the best specific combiner
with CL133480 for grain yield across environments.
It is because of the dominance effect where non-additive genes contributed towards expression
of grain yield. This is very useful in breeding whereby lines should be selected basing on effects
of GCA and SCA. For this study, best performing single cross hybrids were not of much
importance as the study sought to identify potential single cross testers and determining heterotic
relations. Potential single crosses were identified both from heterotic group A and B which were
CL1215159 x CML312 and CL133480 X CML444 respectively.
69
CHAPTER 6
6.1 Conclusion The North Carolina Design II was effective for the identification process of lines with good GCA
effects thus further enabled the identification of potential single cross testers. Significant
differences for GCA and SCA mean squares for grain yield indicated that variation was due to
additive and non-additive gene action with much contribution coming from additive gene action
due to females. This highlighted the importance of maternal effects’ influence for this trait. The
significant GCA effects enabled identification of testers based on GCA effects of grain yield to
be possible. Lines and testers that showed stability and good GCA effects for grain yield were
identified as good testers and these were CL1215159, CL133480, CML395 and CML444. From
heterotic group A, CL1215159 x CML312 and from heterotic group B, CL133480 X CML444
was identified as possible single cross testers.
The study further managed to identify heterotic groups of the lines under study in relation to
CIMMYT’s A and B heterotic groups by using CML312 and CML444 as testers. From SCA
effects with CML312 (heterotic group A) and CML444 (heterotic group B), lines that showed
positive SCA effects with these lines belong to the opposite group. Lines that showed positive
SCA effects with CML312 were CL1215157 and CL133480 resulting in these lines falling into
heterotic group B. Lines that showed positive SCA effects with CML444 were CL1215159,
VL062656 and CL1215158 resulting in them falling into heterotic group A. The environments
under study failed to discriminate the genotypes for grain yield and the secondary traits.
6.2 Recommendations There is need to do further trials to confirm the performance of the identified single cross testers.
Vasal et al. (1992) supported this in which he stressed the need to have more than one evaluation
70
to identify those good lines and good testers in the tropical maize germplasm. The single crosses
can be used as testers while further evaluations are being undertaken to confirm how suitable
they are testers.
71
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APPENDICES
Appendix 1: SCA Effects For PH Under Optimum Conditions.
TESTER
LINE 1 2 3 4 5 6 MEAN
1 208.75 212.5 223.13 223.75 231.88 215 219.17
2 205 215.63 205 216.88 208.75 211.25 210.42
3 220.63 216.25 228.13 225.63 217.5 213.13 220.21
4 215.63 218.75 213.75 225.63 229.38 216.88 220
5 198.75 206.25 200.63 212.5 214.38 205 206.25
MEAN 209.75 213.88 214.13 220.88 220.38 212.25 215.21
Appendix 2: SCA Effects For PH Under Sandy Soil Conditions.
TESTER
LINE 1 2 3 4 5 6 MEAN
1 167.5 172.5 180 167.5 182.5 175 174.17
2 165 140 185 147.5 187.5 165 165.00
3 162.5 172.5 177.5 175 177.5 155 170.00
4 170 145 180 170 170 165 166.67
5 147.5 150 147.5 172.5 170 152.5 156.67
MEAN 162.50 156.00 174.00 166.50 177.50 162.50 166.50
Appendix 3: SCA Effects For PH Under Managed Drought Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 215 229 233 240 256 233 234.33
2 200 220 240 219 210 215 217.33
3 230 235 260 235 238 243 240.17
4 231 229 195 230 228 225 223.00
5 178 261 220 265 243 221 231.33
MEAN 210.8 234.8 229.6 237.8 235 227.4 229.23
82
Appendix 4: SCA Effects For Plant Height Across Environments
TESTER
LINE 1 2 3 4 5 6 MEAN
1 202.92 208.58 217.58 217.08 227.67 211.33 214.19
2 197.50 203.75 207.50 205.67 205.42 204.17 204.00
3 212.50 212.08 225.00 218.75 214.25 208.42 215.17
4 210.58 208.17 205.00 217.08 219.25 209.58 211.61
5 186.75 206.00 195.00 214.58 211.75 198.92 202.17
MEAN 202.05 207.72 210.02 214.63 215.67 206.48 209.43
Appendix 5: SCA Effects For EPP Under Optimum Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 1.10 1.10 1.49 0.96 1.09 1.27 1.17
2 1.20 1.11 1.12 1.17 1.20 1.31 1.19
3 1.08 1.09 1.01 1.00 1.00 1.23 1.07
4 1.24 1.34 1.22 1.01 1.28 1.31 1.23
5 1.08 1.15 1.47 1.08 1.20 0.98 1.16
MEAN 1.14 1.16 1.26 1.04 1.15 1.22 1.16
Appendix 6: SCA Effects For EPP Under Sandy Soil Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 1.03 1.10 1.10 0.93 1.26 1.04 1.08
2 1.00 1.07 1.17 1.05 1.22 1.04 1.09
3 1.04 1.14 0.94 0.97 1.00 0.94 1.00
4 1.10 0.97 1.10 0.93 1.22 1.31 1.10
5 1.16 1.00 1.09 1.04 1.07 1.46 1.13
MEAN 1.07 1.06 1.08 0.98 1.15 1.16 1.08
83
Appendix 7: SCA Effects For EPP Under Managed Drought Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 0.85 1.62 0.71 0.77 0.84 0.82 0.93
2 0.84 0.77 0.88 0.88 0.63 0.76 0.79
3 0.88 0.77 0.81 0.76 0.81 0.88 0.82
4 0.83 0.82 0.43 0.73 0.79 0.73 0.72
5 0.73 1.00 0.86 0.91 0.60 0.76 0.81
MEAN 0.83 0.99 0.74 0.81 0.73 0.79 0.81
Appendix 8 SCA Effects For EPP Across Environments
TESTER
LINE 1 2 3 4 5 6 MEAN
1 1.05 1.18 1.30 0.92 1.07 1.16 1.11
2 1.11 1.04 1.09 1.10 1.11 1.17 1.10
3 1.04 1.05 0.97 0.95 0.97 1.12 1.02
4 1.15 1.19 1.07 0.95 1.19 1.22 1.13
5 1.03 1.10 1.29 1.04 1.08 1.02 1.09
MEAN 1.08 1.11 1.14 0.99 1.08 1.14 1.09
Appendix 9 SCA Effects For TEX Under Sandy Soil Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 2 2 1 1.5 1.75 1.5 1.63
2 2.25 3.5 2.25 1.5 2.5 2.5 2.42
3 3 3.25 2.5 2.5 1.75 2.25 2.54
4 2.5 3.5 2.5 2.5 2.5 1.75 2.54
5 2 2.5 2 1.5 1.5 1.75 1.88
MEAN 2.35 2.95 2.05 1.9 2 1.95 2.20
84
Appendix 10 SCA Effects For TEX Under Managed Drought Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 2.5 3.25 2.5 4 2.75 2.75 2.96
2 3.25 3.75 2.5 3 4 3 3.25
3 3 4 2.5 3.5 4.25 3 3.38
4 2.5 3.75 3.5 3 3.5 3 3.21
5 4 3.25 2.25 2 3 3.5 3.00
MEAN 3.05 3.6 2.65 3.1 3.5 3.05 3.16
Appendix 11 SCA Effects For TEX Across Environments
TESTER
LINE 1 2 3 4 5 6 MEAN
1 2.3 2.65 2.05 2.55 2.55 2.25 2.39
2 2.6 3.7 2.3 2.3 3.25 3 2.86
3 2.75 3.55 2.4 2.7 3.05 2.75 2.87
4 2.55 3.5 2.7 2.6 3 2.3 2.78
5 2.70 3.05 2.22 2.15 2.55 2.75 2.57
MEAN 2.58 3.29 2.33 2.46 2.88 2.61 2.69
Appendix 12 SCA Effects For ER Under Optimum Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 0.53 0.47 0.70 0.44 1.14 0.54 0.64
2 0.65 0.99 4.35 1.75 0.64 2.17 1.76
3 1.98 3.03 3.64 1.99 0.46 2.15 2.21
4 2.22 2.31 2.44 2.13 1.57 1.33 2.00
5 1.98 4.03 3.13 2.96 1.30 2.02 2.57
MEAN 1.47 2.17 2.85 1.85 1.02 1.64 1.83
85
Appendix 13 SCA Effects For ER Under Sandy Soil Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 0.00 0.00 0.00 0.00 1.79 0.00 0.30
2 1.47 12.13 4.17 4.17 0.00 1.67 3.93
3 1.79 2.09 0.00 0.00 0.00 1.67 0.92
4 0.00 0.00 8.34 0.00 1.93 0.00 1.71
5 2.50 0.00 3.57 0.00 3.85 0.00 1.65
MEAN 1.15 2.84 3.22 0.83 1.51 0.67 1.70
Appendix 14 SCA Effects For ER Under Managed Drought Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 0 3.85 0 0 1.8 0 0.94
2 5.65 0 0 3.55 7.15 0 2.73
3 3.35 4.55 5.75 10 4.15 0 4.63
4 2.8 0 8.35 4.55 7.4 3.55 4.44
5 14.5 22.5 10.55 3.35 0 0 8.48
MEAN 5.26 6.18 4.93 4.29 4.1 0.71 4.25
Appendix 15 SCA Effects For ER Across Environments
TESTER
LINE 1 2 3 4 5 6 MEAN
1 0.00 0.77 0.00 0.00 0.98 0.00 0.29
2 1.42 2.62 4.73 2.07 2.08 1.40 2.39
3 1.62 2.35 2.40 2.63 0.83 0.99 1.80
4 1.24 0.93 5.05 1.60 2.28 1.17 2.05
5 4.01 5.89 4.20 1.66 2.42 1.73 3.32
MEAN 1.66 2.51 3.28 1.59 1.72 1.06 1.97
86
Appendix 16 SCA Effects For ASI Under Optimum Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 1.63 3.00 1.88 0.88 0.75 1.88 1.67
2 1.88 2.38 1.75 0.63 0.13 0.00 1.13
3 1.88 0.63 2.75 2.75 0.75 0.75 1.58
4 0.75 0.88 0.38 1.00 2.50 1.75 1.21
5 -0.25 2.38 0.63 1.13 1.25 0.88 1.00
MEAN 1.18 1.85 1.48 1.28 1.08 1.05 1.32
Appendix 17 SCA Effects For ASI Under Sandy Soil Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 2 2 -1 0 -1.5 -0.5 0.17
2 -0.5 -1.5 -2.5 -0.5 -1 -1.5 -1.25
3 0.5 1.5 0.5 3.5 -0.5 0 0.92
4 -2 -1 9 0 0 -1 0.83
5 2 -2 2 0.5 0.5 1 0.67
MEAN 0.4 -0.2 1.6 0.7 -0.5 -0.4 0.27
Appendix 18 SCA Effects For ASI Under Managed Drought Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 2.5 0 1.5 2 1.5 1 1.42
2 1.5 2.5 0.5 2 1.5 1.5 1.58
3 2 2 2 2 -1 2.5 1.58
4 1.5 1.5 1.5 2 1.5 1.5 1.58
5 2 2 1.5 1.5 1 2 1.67
MEAN 1.9 1.6 1.4 1.9 0.9 1.7 1.57
87
Appendix 19 SCA Effects For ASI Across Environments
TESTER
LINE 1 2 3 4 5 6 MEAN
1 1.83 2.33 1.33 1.00 0.50 1.33 1.39
2 1.42 1.75 0.83 0.67 0.27 0.00 0.82
3 1.67 1.00 2.25 2.75 0.36 0.92 1.49
4 0.42 0.67 1.36 1.00 2.09 1.25 1.13
5 0.50 1.58 1.00 1.08 1.08 1.08 1.06
MEAN 1.17 1.47 1.36 1.30 0.86 0.92 1.18
Appendix 20 SCA Effects For AD Under Optimum Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 66.00 66.29 66.57 69.50 69.38 66.57 67.38
2 66.71 65.71 64.86 68.38 68.13 67.00 66.80
3 66.29 67.63 67.50 69.14 69.00 67.13 67.78
4 66.88 68.25 67.13 68.50 66.71 66.86 67.39
5 67.88 66.71 69.63 70.38 70.25 66.75 68.60
MEAN 66.75 66.92 67.14 69.18 68.69 66.86 67.59
Appendix 21SCA Effects For AD Under Sandy Soil Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 65.5 65 62 68.5 67.5 66 65.75
2 64.5 67 62.5 66 67.5 64 65.25
3 66 66 67.5 69 73.5 68.5 68.42
4 65 66.5 68.5 67.5 67.5 63.5 66.42
5 68 69 66.5 70 73 68.5 69.17
MEAN 65.8 66.7 65.4 68.2 69.8 66.1 67.00
88
Appendix 22 SCA Effects For AD Under Managed Drought Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 95 100 98.5 103 104.5 100 100.17
2 98 100.5 95 98 108 103 100.42
3 99.5 102.5 98 106.5 106 100.5 102.17
4 100 102 103.5 104.5 107.5 101 103.08
5 99 104.5 101 104.5 106 103.5 103.08
MEAN 98.3 101.9 99.2 103.3 106.4 101.6 101.78
Appendix 23 SCA Effects For AD Across Environments
TESTER
LINE 1 2 3 4 5 6 MEAN
1 71.18 72.18 71.55 74.92 74.92 72.55 72.88
2 72.00 72.27 69.91 72.92 71.64 72.50 71.87
3 72.27 73.17 72.58 75.91 73.18 72.92 73.34
4 72.08 73.58 73.42 74.33 74.27 72.45 73.36
5 73.08 74.00 74.33 76.00 74.00 73.17 74.10
MEAN 72.12 73.04 72.36 74.82 73.60 72.72 73.11
Appendix 24 SCA Effects For GLS Under Optimum Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 2.00 2.00 1.50 1.75 1.50 2.00 1.79
2 1.50 1.25 2.00 1.50 1.50 1.50 1.54
3 1.75 2.00 1.25 2.25 1.75 2.00 1.83
4 1.50 2.50 1.38 2.00 1.75 1.75 1.81
5 1.50 1.75 1.25 1.75 1.25 1.75 1.54
MEAN 1.65 1.90 1.48 1.85 1.55 1.80 1.70
89
Appendix 25 SCA Effects For ET Under Optimum Conditions
Appendix 26 SCA Effects For SEN Under Managed Drought Conditions
TESTER
LINE 1 2 3 4 5 6 MEAN
1 5.25 5.4 4.5 5.4 4.65 5.45 5.11
2 5.35 6.15 4.1 6.15 6.4 6.65 5.80
3 4.7 4.6 3.65 6 5.6 4.5 4.84
4 4.15 5.3 4.85 6 6.75 6.15 5.53
5 7.25 3.95 4.9 4.4 5.5 6.5 5.42
MEAN 5.34 5.08 4.4 5.59 5.78 5.85 5.34
TESTER
LINE 1 2 3 4 5 6 MEAN
1 2.00 2.13 2.33 2.00 2.38 1.78 2.10
2 2.58 3.45 2.13 2.58 3.13 2.50 2.73
3 2.25 3.15 2.45 2.45 2.88 2.45 2.60
4 2.20 3.28 2.65 2.20 2.83 1.83 2.50
5 1.78 2.50 1.95 2.00 2.53 2.15 2.15
MEAN 2.16 2.90 2.30 2.25 2.75 2.14 2.42
