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GENETIC IMPROVEMENT OF COWPEA (Vigna unguiculata (L.) WALP)
FOR PHOSPHORUS USE EFFICIENCY
By
SABA BABA MOHAMMED
(10512766)
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN PARTIAL
FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF DOCTOR OF
PHILOSOPHY DEGREE IN PLANT BREEDING
WEST AFRICA CENTRE FOR CROP IMPROVEMENT
COLLEGE OF BASIC AND APPLIED SCIENCES
UNIVERSITY OF GHANA
LEGON
DECEMBER 2018
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DECLARATION
I hereby declare that except for references to works of other researchers, which have been duly cited,
this work is my original research and that neither part nor whole has been presented elsewhere for the
award of a degree.
..................................................
Saba Baba Mohammed
(Student)
..................................................
Prof. Pangirayi B. Tongoona
(Supervisor)
..................................................
Prof. Frank. K. Kumaga
(Supervisor)
..................................................
Dr Daniel K. DZIDZIENYO
(Supervisor)
.......20th May, 2019..................
Prof. Muhammad F. Ishiyaku
(Supervisor)
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ABSTRACT
Cowpea is an important grain legume crop for millions of humans, fodder for livestock and source of
income for all the value chain actors. Its productivity is constrained by several biotic and abiotic
stresses such as drought and poor soil fertility. Aligned with poor soil fertility in most growing areas,
this thesis describes phosphorus (P) use and acquisition of elite cowpea lines from different breeding
programmes. In the first chapter, a brief overview was provided about cowpea as an important multi-
purpose legume, constraints to its production including P deficiency and knowledge of farmers on
using P based fertilizers. A participatory survey of farmers was conducted across 36 villages of
cowpea growing areas in northern Nigeria, using a semi-structured questionnaire and focus group
discussions to determine farmers’ perceptions on phosphorus fertilization, prevailing cowpea
cropping systems, use of improved varieties and farmers’ perceived production constraints and
preferred traits. Results showed that farmers were aware of fertilizers as important for growth and
increased yield but did not know the major need of P for cowpeas. Intercropping with cereals was the
most popular cropping system. A little above 20% used improved varieties of cowpea. Farmers
identified insects and yield as the major constraint and preferred trait, respectively. Screening
experiments, both in the screenhouse and low P field, to identify and group elite cowpea lines based
on performance under low P and high soil P conditions using shoot dry weight and other parameters
were conducted. There was significant diversity among the elite lines for adaptation to low P and
response to applied P fertilizer. A few cowpea lines such as IT97K-556-6, IT84S-2246-4 and
IT89KD-288 produced above average yield under sub-optimal and high P conditions. The relative
reduction in yield as a result of soil P deficiency compared to high P performance was over 50%.
Cowpea lines with above average performance under the contrasting P soils were grouped as efficient
and responsive lines and are suitable for cultivation under limited and optimum P input systems.
There was a significant reduction in days to flowering and maturity among cowpea lines under high
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P conditions relative to low P. To understand the genetics underlying P use and uptake efficiency, a
quantitative trait loci (QTLs) mapping study was undertaken through marker-trait analysis with a
biparental recombinant inbred lines (RIL) mapping population. A total of 27 QTLs were detected
across 7 of 11 linkage groups of cowpea. These QTLs were different from the previously identified
ones, indicating they are new QTLs under varying P conditions. These genomic regions were
associated with flowering time, yield components and P use efficiency traits under low and high P
conditions. In addition, a genome-wide association mapping study (GWAS) using DArTseq derived
SNP markers on 400 diverse cowpea lines to identify QTLs and SNP markers based on historical
recombination events underlying the genetics of tolerance to low P and response to applied mineral
P fertilizer was conducted. The GWAS mapping resulted in the identification of over 60 SNP markers
significantly associated with adaptation to low P conditions and response to P application as measured
by differences in shoot dry weight, P use and uptake efficiency under two soil P conditions. In
conclusion, this research revealed that farmers did not use the recommended fertilizer types and rates
for cowpea production, use of mixed intercropping was the popular cropping system and cultivation
of landraces was most prominent over improved varieties. The screening of cowpea lines under
different P concentrations showed varied performance, and lines were grouped into efficient
responsive, efficient non-responsive, inefficient responsive and inefficient non-responsive classes
based on their performance in low and high P growth media. Marker-trait analysis with a biparental
RIL population led to the identification of QTLs and SNPs that will lay the foundation for marker-
assisted selection, that will fast-track the development of P efficient varieties. The study reported for
the first time on cowpea use of high-density SNP markers to identify QTLs and markers associated
with P traits under field conditions using genetic materials relevant to sub-Saharan African breeding
programmes. The validation of SNP markers identified from this study before their use in marker-
assisted selection is highly recommended.
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DEDICATION
This Thesis is dedicated to the memory of late Professor Balarabe Tanimu, the former Director (2008-
2012), Institute for Agricultural Research Ahmadu Bello University, Zaria Nigeria for his sincere
counselling that led me to Plant Breeding.
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ACKNOWLEDGEMENTS
My profound gratitude is due to the West Africa Agricultural Productivity Programme (WAAPP) for
funding my training and the Institute for Agricultural Research, Ahmadu Bello University
(IAR/ABU) Zaria for nominating me. The financial support from the Federal Ministry of Agriculture
and Rural Development - Nigeria and facilitation by Dr Sheu Salau of World Bank are acknowledged.
I am thankful to my supervisory team; Professors P. B. Tongoona, V. Gracen, F. Kumaga, M. F.
Ishiyaku and Dr D. K. Dzidzienyo for their support and guidance during the planning and conduct of
this work. I am grateful to Prof. Eric Danquah, the Director of West Africa Centre for Crop
Improvement (WACCI) for supporting me with funds to conduct parts of this work especially as
funding from WAAPP became irregular. My thanks to all Professors, Tutors, and staff at WACCI for
their support.
I am thankful to Prof. Tim Close of the University of California Riverside (UCR) for providing the
initial seed stock and genotypic data of the recombinant inbred lines used. I thank Drs. Maria, Bao-
Lam, Arsenio, and Sassoum of the UCR for their guidance in handling and analysis of genomic data.
Also, my gratitude goes to the Norman Borlaug LEAP for the award of visiting Fellowship to the
Pennsylvania State University (PSU) and International Institute of Tropical Agriculture (IITA). I
thank Prof. J. Lynch (PSU), Dr O. Boukar (IITA) and their team members: Drs Belko (IITA), Jimmy,
Anica, and Chris (PSU) for hosting me in their Labs. I extend my thanks to colleagues at WACCI;
Maryam, Ousmane, Obaiya, Moussa, Tchala, Ebenezer, Banla, Dede, Abu, Elizabeth, Tony, Dewa,
Godfrey and Michael for their good company.
Finally, I am indebted to my wife: Fatima Kolo and our children: Zayd, Abdurrahman, and Zubayr
for their support and patience during all the days of my absence from home.
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TABLE OF CONTENTS
DECLARATION .............................................................................................................................................................. I
ABSTRACT .................................................................................................................................................................... II
DEDICATION .............................................................................................................................................................. IV
ACKNOWLEDGEMENTS ............................................................................................................................................... V
TABLE OF CONTENTS ................................................................................................................................................. VI
LIST OF TABLES ............................................................................................................................................................X
LIST OF FIGURES ........................................................................................................................................................XII
LIST OF ABBREVIATIONS .......................................................................................................................................... XIV
CHAPTER ONE .............................................................................................................................................................. 1
1.0 GENERAL INTRODUCTION ...................................................................................................................................... 1
CHAPTER TWO ............................................................................................................................................................. 5
2.0 REVIEW OF LITERATURE ......................................................................................................................................... 5
2.1 BIOLOGY, ORIGIN AND DISTRIBUTION OF COWPEA ................................................................................................................ 5
2.2 COWPEA – A FOOD AND NUTRITION SECURITY CROP ............................................................................................................. 6
2.3 GENETIC GAINS AND CONSTRAINTS TO COWPEA PRODUCTION IN NIGERIA................................................................................. 7
2.4 BREEDING PHOSPHOROUS EFFICIENT COWPEA: CONSTRAINTS, ACCOMPLISHMENTS, AND PROSPECTS ............................................. 9
2.4.1 Phosphorus and cowpea production .............................................................................................................. 10
2.4.2 Screening cowpea for adaption to low P soils and response to mineral P addition ....................................... 12
2.4.3 Management of phosphorus deficiency and mechanism of low P adaptation in cowpea ............................. 14
2.5 PROSPECTS IN BREEDING P EFFICIENT COWPEA VARIETIES USING GENOMIC RESOURCES ............................................................. 17
2.6 FARMERS’ PREFERENCES AND KNOWLEDGE OF P DEFICIENCY ............................................................................................... 19
CHAPTER THREE ........................................................................................................................................................ 20
3.0 ASSESSING FARMERS’ KNOWLEDGE, PERCEPTIONS AND USE OF PHOSPHORUS FERTILIZATION FOR COWPEA
PRODUCTION IN NORTHERN GUINEA SAVANNAH OF NIGERIA.................................................................................. 20
3.1 INTRODUCTION ........................................................................................................................................................... 20
3.2 FARMERS’ PERCEPTION OF COWPEA PRODUCTIVITY AND YIELD CONSTRAINTS .......................................................................... 21
3.3 MATERIALS AND METHODS ........................................................................................................................................... 23
3.3.1. DESCRIPTION OF STUDY AREAS ................................................................................................................................... 23
3.3.2 Sampling procedure ....................................................................................................................................... 24
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3.3.3 Data acquisition and approach ...................................................................................................................... 24
3.3.4 Data analysis: ................................................................................................................................................. 26
3.4 RESULTS .................................................................................................................................................................... 30
3.4.1 Socio-Economic Metrics of the Respondents .................................................................................................. 30
3.4.2 Cowpea cropping systems and varieties under cultivation by farmers .......................................................... 33
3.4.3 Cowpea farmers’ knowledge and use of phosphorus-based fertilizers .......................................................... 36
3.4.4 Farmers’ perceptions of phosphorus fertilization on cowpea plants ............................................................. 38
3.4.5 Determinants for use and non-use of phosphorus-based fertilizers among cowpea farmers ....................... 41
3.4.6 Farmers’ production constraints and preference traits.................................................................................. 42
3.4.7 Access of farmers to field-days on cowpea and agricultural extension services ............................................ 45
3.5 DISCUSSION ............................................................................................................................................................... 46
3.6 CONCLUSIONS ............................................................................................................................................................ 51
CHAPTER FOUR .......................................................................................................................................................... 53
4.0 PHENOTYPING COWPEA FOR PHOSPHORUS EFFICIENCY AND RESPONSE IN LOW P ENVIRONMENTS ................. 53
4.1 INTRODUCTION ........................................................................................................................................................... 53
4.2 MATERIALS AND METHODS ........................................................................................................................................... 56
4.2.1 SCREENHOUSE EXPERIMENT ....................................................................................................................................... 56
4.2.1.1 Screening media .......................................................................................................................................... 57
4.2.1.2 PLANT MATERIALS ................................................................................................................................................. 57
4.2.1.3 NUTRIENT MEDIA AND PHOSPHORUS TREATMENTS ...................................................................................................... 57
4.2.1.4 EXPERIMENTAL PROCEDURE .................................................................................................................................... 58
4.2.1.5 DATA COLLECTION, PLANT ASSAYS AND ANALYSIS ......................................................................................................... 62
4.2.2 FIELD EXPERIMENT ................................................................................................................................................... 63
4.2.2.1 EXPERIMENTAL DESIGN .......................................................................................................................................... 63
4.2.2.2 DATA COLLECTION AND ANALYSIS ............................................................................................................................. 64
4.2.3 ASSESSING GENETIC DIVERSITY OF ROOT HAIR AND SEEDLING ROOT ARCHITECTURE TRAITS ................................................. 64
4.2.3.1 Experimental design .................................................................................................................................... 64
4.2.3.2 Seedling root architecture phenotyping ...................................................................................................... 65
4.2.3.3 Root hair phenotyping ................................................................................................................................ 65
4.3 RESULTS .................................................................................................................................................................... 66
4.3.1 Dry matter production of shoot, root, total plant biomass and P concentration ........................................... 67
4.3.2 Assessing the relationship between growth parameters and tissue P concentration.................................... 74
4.3.3 Grouping of cowpea lines based on performance in Low P & Response to P addition .................................. 75
4.3.4 RESULTS OF THE FIELD EXPERIMENT ............................................................................................................................. 77
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4.3.5 Grouping of cowpea lines based on Performance in Low P and Response to P under Field Conditions......... 82
4.3.6 ROOT HAIRS AND SEEDLING ROOT ARCHITECTURE ........................................................................................................... 84
4.4 DISCUSSION ........................................................................................................................................................... 86
4.5 CONCLUSIONS ............................................................................................................................................................ 87
CHAPTER FIVE ............................................................................................................................................................ 89
5.0 PHENOTYPIC EVALUATION AND QTL MAPPING FOR PHOSPHORUS USE EFFICIENCY AND YIELD IN RIL POPULATION
UNDER TWO PHOSPHORUS RATES ............................................................................................................................ 89
5.1 INTRODUCTION ........................................................................................................................................................... 89
5.2 MATERIALS AND METHODS ........................................................................................................................................... 90
5.2.1 Plant Materials ............................................................................................................................................... 90
5.2.2 Phenotyping sites and experimental design:.................................................................................................. 91
5.2.3 Phenotypic data collection and analysis: ....................................................................................................... 91
5.2.4 Genotypic data acquisition and genetic linkage map construction ............................................................... 92
5.2.5 QTL analysis and marker-trait association ..................................................................................................... 93
5.3 RESULTS .................................................................................................................................................................... 94
5.3.1 Phenotypic analysis of recombinant inbred lines population in contrasting P soils ....................................... 94
5.3.2 Phenotypic correlations of the RIL lines in contrasting soil P conditions ........................................................ 94
5.3.3 Linkage map and marker-trait association analysis ...................................................................................... 96
5.3.4.1 QTLs for phenological traits ........................................................................................................................ 97
5.3.4.2 QTLs for yield components .......................................................................................................................... 97
5.3.4.3 QTLs for P use efficiency traits .................................................................................................................... 98
5.4 DISCUSSION ............................................................................................................................................................. 104
5.5 CONCLUSIONS .......................................................................................................................................................... 106
CHAPTER SIX ............................................................................................................................................................ 108
6.0 GENOME-WIDE ASSOCIATION MAPPING OF COWPEA FOR ADAPTATION TO LOW PHOSPHORUS SOILS AND
RESPONSE TO PHOSPHATE FERTILIZER .................................................................................................................... 108
6.2 MATERIALS AND METHODS ......................................................................................................................................... 110
6.2.1 Plant materials ............................................................................................................................................. 110
6.2.2 Experimental design and phenotyping: ........................................................................................................ 110
6.2.3 Data collection and phenotypic analysis ...................................................................................................... 111
6.2.4 DNA isolation and genotyping ..................................................................................................................... 111
6.2.5 SNP calling and curation .............................................................................................................................. 112
6.2.6 Statistical analyses ....................................................................................................................................... 112
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6.2.6.1 Phenotypic data ........................................................................................................................................ 112
6.2.6.2 Population structure analysis and linkage disequilibrium ......................................................................... 113
6.2.6.3 Genome-wide association analysis ........................................................................................................... 113
6.3 RESULTS .................................................................................................................................................................. 114
6.3.1 Descriptive data of the phenotypes measured ............................................................................................. 114
6.3.2 Maker distribution, population structure and linkage disequilibrium .......................................................... 117
6.3.3 Genome-wide association mapping for Low P tolerance and response to P fertilization ............................ 118
6.4 DISCUSSION ............................................................................................................................................................. 126
6.5 CONCLUSIONS .......................................................................................................................................................... 128
CHAPTER SEVEN ...................................................................................................................................................... 129
GENERAL SUMMARY, CONCLUSIONS AND RECOMMENDATIONS ............................................................................ 129
LITERATURE CITED ................................................................................................................................................... 131
APPENDICES ............................................................................................................................................................ 154
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LIST OF TABLES
Table 3.1: List and description of variables used for binary logit model ........................................... 28
Table 3.2: Sex distribution of respondents ......................................................................................... 30
Table 3.3: Socio-economics attributes of cowpea farmers surveyed across study areas ................... 32
Table 3.4: Percentage of farmers reporting different cropping cowpea systems and use of local vis-a-
vis improved varieties ........................................................................................................................ 35
Table 3.5: Percent of farmers recognizing nutrient deficiency on cowpea, presence of nodules on
cowpea roots and knowledge of the role played by nodules in cowpea health .................................. 38
Table 3.6: Descriptive statistics on Likert items for the perception of farmers on cowpea phosphorus
fertilization, and sampling adequacy test ........................................................................................... 39
Table 3.7: Variables and their contribution to the total variance of the principal components ......... 40
Table 3.8: Binary logit outputs on variable influencing use of phosphorus fertilizers ...................... 41
Table 3.9: Farmers’ perceived production constraints identified during focus group discussion using
pair-wise ranking in Northwestern Nigeria in 2017 ........................................................................... 43
Table 3.10: Cowpea farmers’ preference traits identified during focus group discussion using pair-
wise ranking in Northwestern Nigeria in 2017 .................................................................................. 44
Table 3.11: Percentage of farmers with experience attending field-days and having contacts with
extension agents ................................................................................................................................. 45
Table 4.1: List of cowpea lines screened for both screenhouse and field experiments in 2016 and their
seed source-------------------------------------------------------------------------------------------------------61
Table 4.2: Nutrient salts, stock and final concentrations applied on cowpea lines in sand culture ... 62
Table 4.3: Physical and chemical properties of the river-sand used for pot experiment ................... 69
Table 4.4: Probabilities (p < 0.05) of F-test of the analysis of variance for the shoot, root, total biomass
and tissue P content of cowpea lines evaluated in the Screenhouse .................................................. 70
Table 4.5: Plant heights, shoot and root dry biomass of different cowpea lines evaluated under three
levels of phosphorus in a Screenhouse experiment ............................................................................ 71
Table 4.6: Total plant biomass and shoot to root ratio of different cowpea lines under three levels of
phosphorus in a Screenhouse experiment .......................................................................................... 72
Table 4.7: Physical and chemical properties of the low soil P of field experimental site .................. 78
Table 4.8: Probabilities (p < 0.05) of F-test for plant height, phenological traits, shoot dry weight, pod
yield, total biomass, and P concentrations of cowpea lines in the field ............................................. 79
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Table 4.9: Performance of cowpea lines under different P treatments plant height, and days to first
flowering and maturity evaluated in the field environment ............................................................... 80
Table 4.10: Shoot biomass and pod yield of cowpea lines under contrasting soil P in the field ....... 81
Table 4.11: Means, SD, ranges, F-test prob (0.05) and coefficient of variation (CV %) for cowpea
seed and seedling root traits measured. .............................................................................................. 85
Table 5.1: Descriptive statistics of parents and TVu-14676 x IT84S-2246-4 RIL lines evaluated under
contrasting soil P-------------------------------------------------------------------------------------------------95
Table 5.2. Quantitative trait loci for cowpea phenological traits and yield components mapped using
linear mixed model analysis ............................................................................................................... 99
Table 5.3. Quantitative trait loci for cowpea phosphorus use efficiency traits mapped using linear
mixed model analysis ....................................................................................................................... 100
Table 6.1: Summary statistics of cowpea lines under two phosphorus conditions--------------------115
Table 6.2: SNPs associated with shoot dry weight at high and low P conditions ............................ 123
Table 6.3: SNPs associated with P uptake efficiency at high and low P conditions ........................ 124
Table 6.4: SNPs associated with P use efficiency at the high and low P conditions ....................... 125
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LIST OF FIGURES
Figure 3.1: Map of study sites across three northern Nigerian States. Inset: Map of Nigerian States
............................................................................................................................................................ 29
Figure 3.2: Survey responses on fertilizer use in cowpea fields, X-axis is survey responses and Y-axis
is a percent of farmers using fertilizers .............................................................................................. 37
Figure 3.3: Percentage of cowpea farmers using different types of fertilizers, X-axis is fertilizer types
being used by farmers and Y-axis is a percent of farmers using fertilizers ....................................... 37
Figure 4.1: Quadrants of genotypes based on performance in low soil P & Response to applied P for
any measured traits (Mahamane, 2008)-----------------------------------------------------------------------55
Figure 4.2 An overview of the experimental plants in the Screenhouse ............................................ 60
Figure 4.3: Effect of P concentrations on a line (IAR-48). Left - High P plant, Middle-low P plant &
Right, No P plant. .............................................................................................................................. 60
Figure 4.4: Pictorial procedure of seed “Roll-Up” on germination paper and assessment of ................
seedling roots ...................................................................................................................................... 65
Figure 4.5: Root hair imaging and representative root hair contrast on some cowpea genotypes studied
............................................................................................................................................................ 66
Figure 4.6: Differential response of cowpea lines to varied phosphorus concentration evaluated in
Screenhouse using Hoagland Nutrient solution ................................................................................. 73
Figure 4.7: Pattern of the relationship between plant parameters and phosphorus contents in shoot and
root organs. Note: positive correlation increases with the intensity of greenness while negative
increases with increasing red colour .................................................................................................. 74
Figure 4.8: Biplot of cowpea shoot dry weight (g/plot) at low P and high P of Screenhouse experiment
............................................................................................................................................................ 76
Figure 4.9: Biplot of cowpea lines for pod yield at low and high P treatments under field conditions
............................................................................................................................................................ 83
Figure 4.10: Strong association of taproot hair and basal root hair length ........................................ 85
Figure 5.1: Phenotypic correlations of TV x IT RIL population under no-P and high P conditions.
Note: Positive correlation increases with the intensity of greenness while negative increases with
increasing red colour--------------------------------------------------------------------------------------------96
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Figure 5.2: QTL plots for days to flowering time. The X-axis indicates the chromosomes, the Y-axis
indicates the −log10P of the probability (p-values). The horizontal line indicates the significance
threshold at 0.05. .............................................................................................................................. 101
Figure 5.3: QTL plots for the grain yield. The X-axis indicates the chromosomes, the Y-axis indicates
the −log10P of the probability (p-values). The horizontal line indicates the significance threshold at
0.05. .................................................................................................................................................. 102
Figure 5.4. QTL plots for the physiological P use efficiency in cowpea. The X-axis indicates the
chromosomes, the Y-axis indicates the −log10P of the probability (p-values). The horizontal line
indicates the significance threshold at 0.05. ..................................................................................... 103
Figure 6.1 Bar charts showing the distribution of A) Response to P Fertilization, and B) Tolerance to
low phosphorus condition scores-----------------------------------------------------------------------------116
Figure 6.2: A three-dimensional PC view of the grouping of 400 cowpea lines ............................. 117
Figure 6.3: A Linkage disequilibrium decay plot of cowpea lines over distance ............................ 118
Figure 6.4: Manhattan plots and quantile-quantile plots for shoot dry weight at high and low P
conditions ......................................................................................................................................... 119
Figure 6.5: Manhattan plots and quantile-quantile plots for phosphorus uptake efficiency at high and
low P conditions ............................................................................................................................... 120
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LIST OF ABBREVIATIONS
ABU: Ahmad Bello University
APUE: Agronomic Phosphorus Use Efficiency
DArT: Diversity Array Technology
FAO: Food and Agricultural Organization
GAPIT: Genomic Analysis and Prediction Integrated Tool
GBS: Genotyping By Sequencing
IAR: Institute for Agricultural Research
ICRISAT: International Crop Research Institute for the Semi-Arid Tropics
IITA: International Institute for Tropical Agriculture
LME: Linear Mixed Effect
MLM: Mixed Linear Model
PCA: Principal Component Analysis
P: Phosphorus
PRA: Participatory Rural Appraisal
PUE: Phosphorus Use Efficiency
PuPE: Phosphorus Uptake Efficiency
SNP: Single Nucleotide Polymorphisms
UCR: University of California Riverside
WAAPP: West Africa Agricultural Productivity Programme
WACCI: West Africa Centre for Crop Improvement
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CHAPTER ONE
1.0 GENERAL INTRODUCTION
Cowpea (Vigna unguiculata (L.) Walp) is a grain legume that belongs to the family Fabaceae (Ehlers
& Hall, 1997; Singh et al., 2002). It is a diploid (2n = 2x = 22) species with a genome size of 620
Mbp and a self-pollinated species (Boukar et al., 2018). It is reported to have divergent domestication
with two major gene pools distributed across Western and Southern Africa with each pool related to
wild species found in those geographic regions (Huynh et al., 2013). Nigeria is the world’s largest
producer and consumer of cowpea grains (Coulibaly & Lowenberg-Deboer, 2002) and accounts for
over 65% of global production (Abate et al., 2012).
It is a multi-purpose grain legume rich in protein and provides food for humans and fodder for
livestock in sub-Saharan Africa (SSA). The crop contributes considerably to food security and
poverty reduction in the SSA. It has a potential of up to 3, 000 kg ha-1 (AATF, 2012). Cowpea is an
important component of farming systems in areas where soil fertility is limiting, where it is cultivated
as an intercrop with major cereals (Olufajo & Singh, 2002; Singh & Ajeigbe, 2007).
The crop’s yields in West and Central Africa are low (< 450 kg ha-1) (Abate et al., 2012), due to series
of biotic stresses like pests, diseases and parasitic weeds, and abiotic stresses such as drought, heat
stress, and poor soil fertility especially low nitrogen and phosphorus. Phosphorus (P) is a crucial
macronutrient required by cowpea for optimum growth and development. Legumes including cowpea
are only able to fix substantial amounts of N in the presence of sufficient available soil P (Armstrong
& Griffin, 1991; Hussain, 2017; Krasilnikoff et al., 2003). Unfortunately, soils of most cowpea
growing areas in West Africa are deficient in available P (Gyan-Ansah et al., 2016; Hussain, 2017),
where soils are mostly acidic (low pH values) and sandy. Sandy soils are generally poor, with low
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organic matter content, and deficient in major nutrients like N and P (Saidou et al., 2012; Sanginga
et al., 2000). P plays an important role in early root formation, crop quality, enhanced disease
tolerance, seed formation and several biochemical processes such as photosynthesis, respiration,
energy storage, and transfer, cell division & enlargement (Griffith, 2013; Johnston & Seyers, 2009;
Maharajan et al., 2017).
The use of P-formulated fertilizers appears to be a quick and easy fix for P deficiency of soils, but
that option has not been largely adopted by most smallholder cowpea growers due to several reasons.
The production of inorganic P-based fertilizers from rock phosphate reserves is expensive in most
developing countries, making P fertilizers not readily available especially in rural markets, and
relatively expensive for rural farmers when available (Mullins & Thomason, 2009; Olufowote &
Barnes-Mcconnell, 2002; Rothe et al., 2013).
Many local farmers also are unaware of the critical role played by P in plant nutrition and its resultant
effect on crop yield, so they apply low to no P fertilizers in cowpea fields (Nkaa et al., 2014; Karikari
et al., 2015). In addition, most of the P applied in the form of fertilizers and manure is easily bound
and becomes unavailable in the soil by reacting with complexes of Fe and Al in acidic soils, and Ca
in alkaline soils, making the amount of available P for plant use extremely low (Ho et al., 2005;
Lynch, 2011; Mullins & Thomason, 2009). Therefore, the most sustainable solution is to develop
cowpea varieties that yield well in low soil P conditions, and produce higher yield when P is applied
(Adusei et al., 2016; Nkaa et al., 2014; Wang et al., 2010).
Even though P is important for increased yield of cowpea, there is limited information about
perceptions of its use in cowpea production and possible reasons for low to no use of mineral P
fertilizers in growing areas by smallholder farmers. Earlier reports have indicated the existence of
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genetic variability of P uptake and have suggested exploiting this variation to develop superior high
yielding varieties with ability to fix N under sub-optimal soil P (Sanginga et al., 2000). This aligned
well with major goals of cowpea improvement programmes that include pyramiding genes for
tolerance to biotic and abiotic traits like low P soil conditions (Timko et al., 2008). Despite numerous
reports of genetic variability for P use and uptake among cowpea lines, there has been no effort to
identify parental lines for P use and acquisition efficiency among biparental and multi-parent
advanced generation inter-cross (MAGIC) recombinant inbred lines (RIL) populations genotyped by
the Cowpea Team at the University of California, Riverside USA as part of outputs of the Tropical
Legume II Project (Huynh et al., 2013).
Conventional breeding programmes have made substantial progress in the past few decades, but
progress has been limited in improving certain quantitative traits. Marker-assisted breeding could
increase efficiency in developing low P tolerant varieties. However, the number of useful QTLs and
other markers identified for important quantitative traits in cowpea are limited (Timko et al., 2008;
Agbicodo, 2009) when compared to crops like maize, soybean, and wheat.
There are few studies on QTLs and markers for P efficiency traits in cowpea using biparental mapping
populations (Fonji, 2015; Rothe, 2014) or genome-wide association mapping studies of molecular
markers for low P tolerance and response to applied mineral P fertilizers (Ravelombola et al., 2017).
Therefore, there is a need to identify additional QTLs and markers associated with genomic regions
controlling the inheritance of P efficiency traits for cowpea improvement. Absence of significant
QTLs and information on useful markers on P traits have limited ability to adapt molecular breeding
to improve the locally adapted farmer preferred lines, which are low yielding and cultivated under
low soil fertility conditions. The low yield levels of cowpea lead to deficits in places like Nigeria
(AATF, 2012) and because of such deficits, over 518, 400 metric tons of cowpea grains have to be
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imported into Nigeria annually from the neighbouring countries (Coulibaly & Lowenberg-Deboer,
2002). More recent statistics are not available. However, considering Nigeria (with a population of
over 180 million people) and as the largest consumer of cowpea in the world, this figure is likely to
have increased substantially over the last 17 years.
Aligned with these above-mentioned challenges, this research was designed to initiate the genetic
improvement of cowpea for adaptation to low soil phosphorus tolerance and efficient use of applied
phosphorus. The specific objectives were to:
• assess smallholder cowpea farmers’ knowledge, perception on P fertilization of cowpea,
perceived constraints and preference traits in major growing areas in Nigeria,
• phenotype parents of biparental and multi-parent advanced generation inter-cross recombinant
inbred lines populations for P use and acquisition efficiency,
• map cowpea genomic regions and identify SNP markers associated with P use and acquisition
efficiency, and
• conduct a genome-wide association mapping for adaptation to low P tolerance and response
to applied P fertilizer.
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CHAPTER TWO
2.0 REVIEW OF LITERATURE
2.1 Biology, origin and distribution of cowpea
Cowpea is a diploid and self-pollinating crop with 11 chromosomes and genome size of about 620
million base pairs. The crop is commonly known as beans in Nigeria and black-eye pea in the United
States (Ehlers & Hall, 1997; Xu et al., 2011). It is an herbaceous plant that belongs to the family
Fabaceae and the only cultivated subspecies of the genus Vigna while other subspecies namely;
dekindtiana, stenophylla, and tenuis are wild types (Padulosi & Ng, 1997). The cultivated cowpea is
sub-divided into five cultivar groups; Unguiculata, Sesquipedalis (yard-long-bean), Textilis, Biflora,
and Melanophthalmus (Timko & Singh, 2008a).
Cultivated cowpeas are believed to have originated from Africa, as its wild relatives are only found
in the continent (Padulosi & Ng, 1997). There are several speculations as to the precise centre of
origin of the crop in Africa. However, West and Central Africa encompassing Nigeria, southern parts
of Republic of Niger, northern Benin, Togo, northwestern Cameroon and parts of Burkina Faso are
the areas with most diversity of cultivated cowpeas, while wild species are mostly found in southern
Africa encompassing Namibia, Zambia, and Zimbabwe (Padulosi & Ng, 1997). Recent molecular
studies have supported these conclusions, as two gene pools were attributed to Western and Southern
Africa regions (Huynh et al., 2013). Cowpeas are grown across tropics and sub-tropic areas of the
world (Olajide & Ilori, 2017). The main production areas of the crop are in sub-Saharan Africa, with
Nigeria being the world’s largest producer followed by the Niger Republic. Cowpea was introduced
to the Indian subcontinent from Africa approximately over 3000 years ago and moved from Asia to
southern Europe. It is believed that cowpea reached the USA in the 1700 BC from West Indies (Ehlers
& Hall, 1997).
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2.2 Cowpea – a food and nutrition security crop
Cowpea is a multi-functional, important grain legume with diverse uses and benefits (Ehlers & Hall,
1997; Singh & Singh, 2016; Timko et al., 2007) as food, feed and source of income when sold raw
or as processed products for millions of its value chain actors (Boukar et al., 2018; Coulibaly &
Lowenberg-Deboer, 2002; ICRISAT, 2017a; Langyintuo et al., 2003). The crop is an integral
component of traditional cropping systems in the semi-arid tropics where it is mainly cultivated in
mixed intercrops of cereals like sorghum, pearl millet and maize in some areas (Ewansiha et al., 2014;
Olufajo & Singh, 2002; Singh & Ajeigbe, 2007; Singh et al., 2003). The crop is relatively tolerant to
limited soil moisture conditions making it suitable for cultivation in marginal areas of Sahelian agro-
ecologies of arid and semi-arid areas known to be prone to drought (Agbicodo et al., 2009; Ehlers &
Hall, 1997; Muchero et al., 2013; Muchero et al., 2009) where cereals like maize are unable to thrive
or are less resilient.
Cowpea haulms serve as a nutritious fodder for livestock due to their high content of lysine and
tryptophan, the two most limiting amino acids in cereals, thereby supporting integration of crop-
livestock farming in the main producing areas like northern parts of Nigeria (Ehlers & Hall, 1997;
Ewansiha et al., 2014; Kingley & Vernon, 2015; Samireddypalle et al., 2017; Singh et al., 2003;
Tipilda et al., 2005). Its ability to fix atmospheric nitrogen through biological nitrogen fixation with
Bradyrhizobium spp. makes it a good soil fertility replenisher, contributing as much as in 50 - 100 kg
N ha-1 (Beaver et al., 2003; Kyei-Boahen et al., 2017; Sanginga et al., 2000; Santos et al., 2011). The
spreading types of cowpea varieties with semi-determinate to indeterminate growth habit have been
used as a weed control tool due to their weed suppressive effects and in the control of Striga
hermonthica of cereals through a phenomenon known as suicidal germination (Ehlers & Hall, 1997;
Hall et al., 2003; Matsui & Singh, 2003).
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It is a cheaper source of quality protein especially for households that cannot afford animal-based
proteins, thereby making it an essential resource for addressing nutritional insecurity in poor nations
by complementing calorie rich cereals, roots and tuber crops (Boukar et al., 2018; Philips et al., 2003;
Ojiewo et al., 2018; Singh et al., 2003). Improved productivity, development and deployment of high
yielding varieties with tolerance to biotic and abiotic stresses would enable cowpea growers to
produce more grains for consumption and sell, thereby contributing to food security and a better
standard of living.
2.3 Genetic gains and constraints to cowpea production in Nigeria
The global area under cowpea cultivation is over 12 million hectares with an approximate production
of 7 million tonnes of grain (FAOSTAT, 2016), with Africa accounting for 95% of the global
production and Nigeria, Niger and Burkina Faso being leading producers in that order. The average
yield of the crop is less than 600 kg ha-1 compared with genetic potential of 2,500 - 3,000 kg ha-1
(AATF, 2010; ICRISAT, 2017a) due to several biotic and abiotic factors including insect pests,
diseases, parasitic weeds, drought, heat, poor soil fertility and poor agronomic practices (Boukar et
al., 2018; Ehlers & Hall, 1997; ICRISAT, 2017b; Ojiewo et al., 2018; Timko et al., 2007). The
cultivation is largely under rainfed conditions (Hall et al., 2003; Olufajo & Singh, 2002) with little
off-season cultivation under mixed intercrop of major cereals using limited to no inputs like improved
seeds, fertilizers and chemicals (Ajeigbe et al., 2010; Ewansiha et al., 2014; Singh & Ajeigbe, 2007).
Cowpea research began in the early 1960s in Nigeria and many other countries in sub-Saharan Africa
with most starting cowpea research programmes around 1980. International research organizations
became involved in cowpea research in the 1970s with the involvement of Canada’s International
Development Research Centre and the International Institute of Tropical Agriculture (IITA) - Semi-
Arid Food Grains Research and Development (SAFGRAD) in 1977 (Boukar et al., 2018). A major
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goal in breeding programmes for the crop is stacking of resistance to biotic and abiotic stresses.
Research organizations both at the national and international level are at the forefront of genetic
improvement of cowpea for biotic and abiotic stress tolerance (Timko et al., 2008). Over the past six
decades, considerable progress has been made towards genetic improvement of cowpea for biotic and
abiotic stresses that constrained its productivity (Ehlers & Hall, 1997; Singh et al., 2002; Timko et
al., 2007) by the IITA and African National Agricultural Systems (NARS) largely using conventional
approaches (Huynh et al., 2013). Detailed review on milestones in different areas of breeding cowpea
has been reported (Blade et al., 1997; Ehlers & Hall, 1997) and more recently (Boukar et al., 2018).
Early breeding was focused on the collection of germplasm, screening for resistance to insect pests,
diseases, early maturity, plant architecture, and seed quality. These procedures have led to the varietal
release in the past, they are, however, relatively time consuming and expensive, with low efficiency
as it takes over 8 years to develop varieties (Ehlers & Hall, 1997; Huynh et al., 2013). In addition,
most of the earlier efforts have studied above-ground shoot traits (Hammond et al., 2009; Lynch,
2011; Lynch & Brown, 2012; Lynch, 2013) while root traits have received little attention in most
breeding programmes due to the underground nature and limited high-throughput phenotyping tools
for root systems (Burridge et al., 2016; Canto et al., 2018; Lynch, 2013; Paez-Garcia et al., 2015).
Selection based on genomic loci in tight linkage with molecular markers and traits of interest is
expected to increase progress, precision and reduce the duration of cycles of phenotypic evaluation.
Marker-assisted selection is expected to accelerate the rate of genetic gain and potentially reduce the
cost of phenotypic evaluations (Boopathi, 2013; Collard et al., 2005; Batieno et al., 2016; Kelly et
al., 2003; Maharajan et al., 2017). The success of MAS in breeding is dependent on the quality of
phenotypic and genotypic data for marker-trait association studies.
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2.4 Breeding phosphorous efficient cowpea: constraints, accomplishments, and prospects
A serious challenge in this century is providing enough food and energy for the world, with nearly a
billion people facing hunger and malnutrition. Current projections show that the trend is expected to
worsen with the prediction of the world population reaching 9 billion by the year 2050 (Godfray et
al., 2010; Nelson et al., 2012). Explosive population growth, varying climates especially for moisture
deficit and poor soil fertility are challenges for global agriculture. There is an urgent need for
increased food production to meet the increased demand of a growing world, and this must come from
efficient input use such as fertilizer and water due to the impact of climate change on the available
lands. The development of crop varieties with high yield under low nutrient conditions and limited
water supply has become a priority for breeding programmes (Vinod & Heuer, 2012).
Soils of sub-Saharan are inherently low in organic matter and nutrients like nitrogen and phosphorus,
the two most essential macronutrients for plants (Bishopp & Lynch, 2015; Ho et al., 2005; Lynch,
2011; York et al., 2013). P is an integral part of all cellular activity and required as a component of
cell bio-energy (in the form of ATP), nucleic acids (DNA & RNA), NADPH, phospholipids, and
phosphoproteins (Vinod & Heuer, 2012).
Cowpeas are mostly cultivated on small plots by smallholder farmers with limited access to credit
facilities, mechanization and synthetic fertilizer. They rely on P in the soil for efficient N fixation,
early maturity, tolerance to pests and diseases, and optimum yield (Ankomah et al., 1996; Hussain,
2017; Sanginga et al., 2000; Santos et al., 2011). Therefore, improving the genetic make-up of cowpea
for P adaptation and response to P fertilization is the best and most sustainable strategy for low and
high inputs systems because P is a problem in all inputs system today. In high input agriculture in
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developed nations, excessive P fertilization leads to acidification, and eutrophication via P runoff to
water bodies, while low P in low input systems has resulted in low yield and reduced income of
farmers.
2.4.1 Phosphorus and cowpea production
P deficiency is a major yield-limiting constraint in most agro-ecosystems in the world especially in
West Africa (MacDonald et al., 2011). Most of the world’s agricultural production occurs on low P
soils which affect over 50% of global arable land and about 75% of cultivable land in Africa (Lynch,
2011). Rock phosphate, a major resource for producing inorganic P fertilizers, is a non-renewable
natural resource and pure grade of P deposits are limited and being depleted rapidly (Cordell et al.,
2009). The distribution of P reserves globally is uneven, with the majority of the world rock phosphate
being in Morocco, USA and China, in that order. There are concerns in certain quarters that the global
reserves of rock phosphate would be exhausted in the next 3 - 4 centuries (Kauwenbergh, 2010).
Cowpea productivity is highly associated with the amount of P fertilization, as several reports have
shown increased yield and performance of the crop as P input increases (Adusei et al., 2016; Gyan-
Ansah et al., 2016; Karikari et al., 2015; Kolawole et al., 2008; Kyei-Boahen et al., 2017; Oladiran
et al., 2012; Saidou et al., 2012). Although empirical evidence has shown strong cowpea response to
P fertilization, the use of synthetic P fertilizer is not well adopted among smallholder farmers due to
poor availability especially in rural areas, low awareness on need for P fertilizer and high cost (Adusei
et al., 2015; FanWay, 2015; Mahamane, 2008). Therefore, development and deployment of varieties
that are efficient in uptake and use of P will improve the productivity of the crop and benefit varied
cropping systems, ensure cleaner environment that would come from reduced P application and
increased yield in low P soils (Mahamane et al., 2006; Rothe et al., 2013).
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P is the main limiting nutrient for cowpea cultivation in tropical soils of West Africa due to the acidic
and high sand content of the soils (Abdou, 2018; Mahamane, 2008; Saidou et al., 2011). This is due
to P fixing properties of those soils leading to formation of complexes with Fe and Al oxides that
immobilize P, thereby making it unavailable for plants even when supplied in sufficient quantity
(Vinod & Heuer, 2012), and low to no application of P as inorganic fertilizer (Adusei et al., 2016;
Singh & Ajeigbe, 2007) is another challenge. P uptake and efficient use from growth media in crop
plants are governed by a combination of factors such as soil properties, genotypic differences of crop
plants and exudation of chemical compounds (Richardson et al., 2011; Johnson et al., 1996).
The level of nutrients beyond which an increase in such nutrient does not result in yield increase is
referred to as a critical level (Mullins & Thomason, 2009). Critical level of soil P for grain legumes
have been reported as 10 mg kg-1 (Adeoye & Agboola, 1985), conversely the rate of P depletion in
soils of Africa is above 10 kg ha-1 year-1 and this shows that tropical soils are low in available P
(Henao & Baanante, 1999; FAO, 2000). Therefore, ability of genotypes to give optimal yield under
limiting P conditions calls for concerted efforts to develop cowpea varieties with enhanced ability in
acquiring limited soil P and efficient utilization of P applied. Effective use of nutrients especially
available soil P, tolerance to drought stress, and micronutrient deficiency will increase adaptation and
yield of cowpea in low input farming systems on marginal lands. There is a difference between
elemental P and phosphate (P2O5) in fertilizers. Most soil test outputs are elemental P, while
commercial fertilizers are formulated as phosphate. One unit of elemental P (e.g. 1 lb) is little more
than two units of phosphate (2.29 Ibs) and fertilizers are recommended as phosphate. P is available
for plant uptake in soil solution (dissolved in soil water) as negatively charged orthophosphate anions
(H - 2-2PO4 , HPO4 ) (Mullins & Thomason, 2009).
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Application of nitrogen fertilizer is not necessary on cowpea fields which are efficiently nodulated,
even on infertile soil. However, most growers do not inoculate cowpea seeds before planting.
Moisture deficit and high temperature could hinder the efficiency of inoculation (Mullen et al., 2003).
In growing areas in Nigeria, suitable strains of Rhizobia species for cowpea have not been identified
to produce commercial Rhizobia inoculant, hence; growers do not inoculate cowpea seeds before
planting in most areas in Nigeria.
Due to lack of availability and the high cost of inorganic fertilizers, the direct use of indigenous rock
phosphate has been shown as an alternative to the water soluble fertilizer (Adusei et al., 2015), but
this has not been adopted by most growers of cowpea. In soils deficient in available P, P should be
provided at the rate of atleast 10 kg P ha-1. This is the same as applying about 120 kg single super
phosphate (SSP) ha-1 and providing 12 kg of sulfur ha-1. Application of molybdenum may be required
for cowpea on some acid soils while deficiency of Zinc can occur in alkaline clay soils. Liming of
acid soils may be used to improve the availability of fixed soil P in soils with P fixing properties
(Mullen et al., 2003).
2.4.2 Screening cowpea for adaption to low P soils and response to mineral P addition
Several procedures have been employed in phenotyping crop plants for P use. The use of hydroponic
solutions supplemented with different P concentrations coupled with imaging of root system
architecture has been used for cowpea (Rothe et al., 2013). Plants in pots in sandy soils and natural
low P sandy field soils have been used (Saidou et al., 2007). Digital imaging of root traits has been
demonstrated for dicots including cowpea and monocots root phenotyping (Das et al., 2015). A
review of high-throughput imaging technique including visible imaging, imaging spectroscopy,
thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging suitable for
phenotyping plants for biotic or abiotic stress (disease, insects, drought and salinity) have been
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documented (Li et al. 2014). More recently, manual excavation followed by visual scoring of root
numbers and angles, termed Shovelomics, have been used for crops like soybean, maize, common
bean and cowpea (Burridge et al., 2016; Colombi et al., 2015; Trachsel et al., 2011) and this has
enabled high throughput phenotyping of maize root architecture under field environments (Abiven et
al., 2015).
Using these screening approaches, genetic diversity for P uptake and use have been found to exist in
the cowpea gene pool, such as Sanginga et al. (2000) who reported differences in P absorption of
cowpea genotypes as a result variation in the root system architecture. Using low P field soils in
screening in pots, two popular lines from Nigeria; IT89KD-288 and Danila were identified as good
performers for grain yield under no or minimal P addition (Adusei et al., 2015). In another screening
work with white silica sand supplemented with Hoagland nutrient solution varied in P concentration,
four cowpea genotypes with tolerance to low P (Big John, IT97K-1069-6, IT98K-476-8, and TX2028-
1-3-1), and (CB46, and Golden-Eye Cream) with partial low P tolerance via high seed P content were
reported (Rothe et al., 2013). Contrary to the report of Adusei et al. (2015) and Saidou et al. (2012),
a cowpea landrace (Danila), earlier found to be adapted to low P conditions was reported to have poor
tolerance to low soil P (Rothe, 2014). This discrepancy could be due to using different genetic
materials bearing the same name, a problem widespread with landraces among farmers. Several other
workers have reported similar findings (Kolawole et al., 2008; Mahamane, 2008; Ojo et al., 2006;
Oladiran et al., 2012; Saidou et al., 2011).
Different traits have been used as determinants of low P tolerance in cowpea; for example, tolerance
to low soil P was determined at harvest, using plant height, shoot and root dry weights, and shoot P-
content, and shoot-to-root ratios as indices (Kakamega et al.,, 2011; Mahamane, 2008; Rothe et al.,
2013; Saidou et al., 2012). Seed P concentration and grain yield per plant as indices for P tolerance
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in cowpea from rock phosphate fertilization (Gyan-Ansah, 2012; Ojo et al., 2006) and shoot biomass
yield has been extensively used (Kugblenu et al., 2014; Rothe et al., 2013; Sanginga et al., 2000).
The dry shoot biomass appears to be the most potent criteria for assessing tolerance to low P, since P
deficiency slows down utilization of carbohydrate by plants (IAEA, 2013).
2.4.3 Management of phosphorus deficiency and mechanism of low P adaptation in cowpea
Phosphorus use efficiency (PUE) refers to the ability of crop plants to grow and yield substantially
in soils with sub-optimal available P (Hammond et al., 2009; Leiser et al., 2015; Pask et al., 2012).
Developing PUE crops will improve food sufficiency of poor nations and ensures the sustainability
of agriculture in high input systems of rich nations (Bishopp & Lynch, 2015). For soils with low
available P or depleted P from continuous cultivation without replenishment, the use of P fertilizers
to supplement low soil P is recommended to avoid soil mining. However, application of P is not a
common practice in low input cropping systems due to the high cost of P fertilizer and inadequate
supply (Agwu, 2004; Bationo et al., 2002; FanWay, 2015; Horn et al., 2014).
Indeed, the use of inorganic P fertilizers as long term solution to low soil P has both economic and
environmental trade-offs, since the global reserves of rock phosphate, a critical ingredient for making
P fertilizer are unevenly distributed and fast depleting (Kauwenbergh, 2010; Wrage et al., 2010).
There is also an imbalance in P use with some rich nations using too much, while others using too
little P (MacDonald et al., 2011; Provin & Pitt, 2002). Excessive use of P is associated with
eutrophication of lakes and water bodies resulting from leaching and P runoff (Delgado-Baquerizo et
al., 2013).
Deficiency of P needs to be recognized before applying management practices. Symptoms of P stress
in most plants appear on leaves (stunted, reduced expansion, surface area, and numbers), as delayed
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flowering & maturity, reduced growth, and purplish colouration along the margin of young leaves
especially in maize and tomatoes (Beegle & Durst, 2002) and on some cowpea genotypes
(Mahamane, 2008). Discolouration symptoms are not common phenomena on all plants and seldom
appears under field conditions. Reduced fruit size and quality, decreased tolerance to diseases and
low yield (Armstrong & Griffin, 1991) are also prevalent. Symptoms are often more pronounced on
the shoot than the root system, thereby leading to low shoot to root dry biomass and on older lower
leaves than young active meristematic ones because of mobilization and subsequent translocation
from older tissues to active growing ones (Armstrong, 1999). P deficiency impacts root growth and
development, as rooting depth and vigour are greatly reduced. Certain plants produced dark green
leaves from P deficiency due to the accumulation of carbohydrate resulting from continuous
production from photosynthesis and slow utilization. P deficiency may be a secondary problem in
some acid soils, such as those with high levels of iron and aluminium, which could restrict root
development (Ismail et al., 2007).
The effectiveness of P applied as fertilizer or manure is usually low ranging from 5 - 10% (Beegle &
Durst, 2002; Lynch, 2011), due to P fixation in soils. Response to soil P by crop plants is dependent
on its availability in the soil solution and genetic potential of the crop to take it up. P uptake from soil
solution depends on a number of factors such as enhanced root system architecture, since P moves
largely by diffusion in the root zone, this makes root traits like root hairs, and lateral branching very
important for P acquisition (Bates & Lynch, 2001; Bishopp & Lynch, 2015; Chimungu et al., 2014;
Krasilnikoff et al., 2003). Soil resources are stratified with P reserves concentrated mainly in the top
layer (0 - 15cm), while water and nitrate tend to be more available in the lower strata, hence genotypes
with topsoil foraging ability via expanded lateral roots are better equipped to access limited soil P
(Burridge et al., 2016; Lynch, 2013; Paez-Garcia et al., 2015; Ramaekers et al., 2010), while nitrate
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and water are better accessed by deep rooting genotypes (Ho et al., 2005; Miguel et al., 2015; Trachsel
et al., 2013).
Strategies for the management of P fixations include application of high quality P fertilizers to provide
enough plant available P, good timing and method of application, conducting soil test (Mullen et al.,
2003), application of rock phosphate in soils with high P fixation (Mahamane, 2008) and breeding
varieties that can access P reserves in these soils, especially those with well-developed root
architecture (Mehra et al., 2015; Nnadi & Mohammed-Saleem, 1996; Snapp et al., 2018).
Plants have adapted to P deficiency using various strategies. Root exudates such as sugars,
oligosaccharides, organic acids are secreted by some roots, and these are capable of inducing soil
microbial activity within the rhizosphere to release immobilized P fixed in complex forms (Simpson
et al., 2011; Wu et al., 2015). Interestingly, a major QTL identified in rice called phosphorus uptake
(Pup1) for tolerance to low P deficiency has been associated with root growth (Gamuyao et al., 2012).
The larger root system has been identified in maize for low P tolerance (Li et al., 2009).
High seed P content and large root surface area have been found in cowpea to be responsible for
tolerance to low P conditions (Rothe, 2014). Rothe (2014) also author reported inheritance of P
tolerance to be governed by additive genes with high narrow-sense heritability. P uptake is enhanced
in crops with large root surface area as a result of long root hairs and branched root systems which
permit the plant to explore wider topsoil areas and thereby provide better access to P reserves
(Bishopp & Lynch, 2015; Lynch & Brown, 2012). The transport of P to plants is believed to be via
root plasma membrane by P transporters, therefore, larger root systems would be beneficial for plants
in gaining access to limited P reserves (Paszkowski et al., 2002). Furthermore, the use of soil
amendments with Biochar has been associated with increased uptake of immobile soil nutrients like
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P in unfertile soils and drought prone areas (Abiven et al., 2015). Association formed between soil
mycorrhizal fungi such as arbuscular and vesicular mycorrhizal and cowpea root system in some
genotypes is known to enhance uptake of nutrients and water (Mullen et al., 2003; Saidou et al., 2012)
in poor soils. The fungal hyphae serve as extensions of the root architecture that extends the volume
of soil exploration and thereby increasing efficient soil resource of plants (Islam et al., 1980). Certain
soil bacterial genera such as Pseudomonas, Enterobacter, and Bacillus are able to dissolve the
insoluble P forms in the soil (FAO, 2000), thereby making more P available for plant use.
2.5 Prospects in breeding P efficient cowpea varieties using genomic resources
For a successful implementation of modern breeding programmes in cowpea, certain genomic tools
and resources are required. These include high throughput genotyping facilities for accurate
fingerprinting of parents and progenies resulting from hybridization. SNP based genotyping platforms
such as Illumina GoldenGate (Muchero et al., 2009), and 60K Iselect SNP Infinium (Justin, 2014)
are now available for cowpea programmes. The cowpea GoldenGate Assay can genotype effectively
96 DNA samples over 1,536 SNP loci, and the genotypic data is usually provided via ‘GenomeStudio’
software by Illumina Inc, which provides data visualization and primary summarization (Boukar et
al., 2016; Muñoz-Amatriaín et al., 2017).
Different mapping populations have been developed for cowpea; such as biparental recombinant
inbred lines (Andargie et al., 2013; Andargie et al., 2011, 2014; Huynh et al., 2013; 2016; Lucas et
al., 2012; Muchero et al., 2013; Omo-Ikerodah et al., 2008), and more recently an eight-parent multi-
parent advanced generation intercross population (MAGIC) recombinant inbred lines (Huynh et al.,
2018). Molecular markers are used in the construction of genetic linkage maps used for the detection
of genomic regions containing genes controlling the expression of agronomic traits (Sánchez-Sevilla
et al., 2015).
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Markers that are tightly linked to important genes can be deployed in marker-assisted breeding
(MAB). Until recently, the number of molecular markers for cowpea has been limited. In addition,
consensus genetic maps formed from merging multiple individual linkage maps have been
constructed. Such maps are very useful for breeders as they serve as an important resource in
analyzing traits inheritance and marker-trait associations (Muchero et al., 2009; Muñoz-Amatriaín et
al., 2017; Lucas et al., 2011).
Several QTLs have been mapped using different marker systems, such as AFLP; QTLs for cowpea
golden mosaic virus (Rodrigues et al., 2012), Striga resistance (Boukar et al., 2004), drought-induced
senescence (Muchero et al., 2009), charcoal rot resistance (Muchero et al., 2011) and flower bud thrip
resistance (Omo-Ikerodah et al., 2008) were mapped. SCAR markers were used by Boukar et al.,
(2004) to map Striga resistance QTLs. QTLs for seed weight were mapped by Fatokun et al., (1992)
using RFLP markers. Linkage and QTL mapping with SSRs have been conducted for the following
traits; pod fibre layer thickness (Andargie et al., 2011), pod length and domestication-related traits
(Kongjaimun et al., 2012), time of flower opening and days to flowering (Andargie et al., 2013), pod
number per plant (Xu et al., 2013), floral scent compounds (Andargie et al., 2014), and pod tenderness
(Kongjaimun et al., 2012).
Furthermore, SNP markers were employed by several workers to map QTLs for cowpea traits. These
include; cowpea bacterial blight resistance (Agbicodo et al., 2010), foliar thrip tolerance (Lucas et
al., 2012), hastate leaf shape (Pottorff et al., 2012), charcoal rot resistance (Muchero et al., 2011),
flower and seed coat colour (Xu et al., 2011), days to flower, nodes to first flower, leaf senescence
(Xu et al., 2013), heat tolerance and seed size (Lucas et al., 2013), Fusarium wilt resistance (Fot race
3) (Pottorff et al., 2012), and Fusarium wilt resistance (Fot race 4) (Pottorff et al., 2014).
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Understanding the genetic architecture of quantitative traits variation is one of the major foci of
modern biology (Mackay et al., 2009). QTLs and molecular markers are potential candidates for
marker-assisted selection in cowpea. An excellent review on available cowpea molecular markers
ranging from AFLP, RFLP, SCAR, SSR, and SNP has been reported (Boukar et al., 2016; Huynh et
al., 2013; Muñoz-Amatriaín et al., 2017; Ndeve, 2017; Varshney et al., 2009; Varshney et al., 2013).
2.6 Farmers’ Preferences and Knowledge of P deficiency
Low phosphorus (P) is a wide-spread abiotic factor constraining productivity of cowpea in farmers’
field. This is aggravated by continuous cultivation of land without application of recommended
fertilization and unsustainable practices such as removal of crop residues to feed livestock and bush
burning that continuously deplete soil nutrients and organic matter content (Abdou, 2018). Fertilizer
application among farmers has been little to none due to several reasons, such as lack of awareness
on the importance of P for cowpeas, while high cost and poor accessibility to phosphate fertilizers
has hindered those with awareness (Bationo et al., 2002).
It is important to understand farmers’ perception on phosphate fertilization on cowpea fields
especially that there is widespread misinformation among some farmers that cowpeas do not need
fertilizers, such information will help in designing programmes targeting increase productivity of
cowpeas and soil management practices (Marenya et al., 2008). Knowledge of farmers on soil types
and using it to identify soils with poor fertility has been reported (Kome et al., 2018). Many farmers
are aware that low crop yield, the prevalence of Striga and yellowing of leaves are indicators of poor
soil fertility especially for major cereals (Souhore et al., 2017). Recent studies have documented
farmers preferred traits and perceptions on different constraints militating cowpea productivity
(Lawan, 2014). There are little to no documentation on knowledge and perceptions of farmers on
recommended fertilizers for cowpea especially in the major growing areas in Nigeria.
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CHAPTER THREE
3.0 Assessing Farmers’ Knowledge, Perceptions and Use of Phosphorus
Fertilization for Cowpea Production in Northern Guinea Savannah of Nigeria
3.1 Introduction
Cowpea is a popular leguminous crop in Nigeria and other countries in sub-Saharan Africa and
provides food for over two hundred million people (AATF, 2010). Cowpea supplies a substantial
amount of daily protein needs of most people (Lowenberg-DeBoer & Ibro, 2008) in the growing
areas. The yield of cowpeas grown by local farmers is low, less than 600 kg ha-1, compared to the
yield potential of 1,500 – 2,500 kg ha-1 (AATF, 2012; ICRISAT, 2017a). The low yield is due to
many biotic (insect pests, diseases, parasitic weeds) and abiotic constraints (low soil fertility
especially phosphorus, drought, heat) (ICRISAT, 2017a). Other important factors resulting in low
yield are low plant density due to intercropping and wide intra-plant spacing (Olufajo & Singh, 2002;
Ewansiha et al., 2014). There is also a major price fluctuation due to the volatility of markets, lack of
quality control and standards (Abate et al., 2012) such that prices are low at harvest and go up when
most farmers have finished selling their stocks. Such price instability hinders farmers from adopting
improved technologies.
Among abiotic factors responsible for low yield of cowpea are poor soil fertility, especially nitrogen
(N) and phosphorus (P) (Bationo et al., 2002). Cowpeas can fix a considerable amount of N in the
presence of adequate P, however, it is poor in accessing soil available P (Bationo et al., 2002). For
soils very low in P and those with P fixing properties, it is desirable that P be applied as inorganic
fertilizer or manure (Bationo et al., 2002; Mahamane, 2008; Saidou et al., 2012). Most farmers in
poor nations are unaware of P as a yield-boosting factor that needs to be applied (Horn et al., 2014).
The non-use of synthetic P fertilizer by many smallholder farmers are compounded by the high cost
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of fertilizers (Bationo et al., 2002), and non-availability (Olufowote & Barnes-Mcconnell, 2002).
There is limited information on the level and practice of P fertilization in cowpea growing areas,
especially in northern Nigeria. The low level of adoption of improved management practices such as
(phosphate fertilization, improved cropping systems, improved seeds, planting spacings, and insect
control measures) by smallholder farmers (Horn et al., 2014), who account for most food production
in developing nations, has been partly due to lack of involvement of product end-users in planning
and design of such practices, thereby leading to low levels of adoption, since these technologies are
mostly results of researchers’ conceived problems and were developed under optimum conditions of
research institutions.
3.2 Farmers’ perception of cowpea productivity and yield constraints
Cowpeas are mainly produced in the northern parts of Nigeria, while the southern part of the country
provides a market for grains from the north (Agwu, 2004; Boukar et al., 2018; Lowenberg-DeBoer
& Ibro, 2008). For a new technology such as improved variety to have a high level of adoption by
farmers, it must incorporate inputs and thinking of all stakeholders in the product development plan
(Persley & Anthony, 2017). Understanding the knowledge and preferences of farmers and consumers
and taking them into account when designing a new product is critical to the successful adoption of
the product (Kushwaha et al, 2004). For instance, using a “person-on-the-street” approach, the level
of awareness and acceptance of genetically modified (GM) cowpea was investigated in northern
Nigerian States of Gombe, Adamawa, Jigawa, and Kano, and it was revealed that 90% of consumers
had knowledge of biotechnology-derived crops and showed willingness to adopt such products when
commercialized in Nigeria, while 10% expressed certain ethical concerns and disapproved of the
technology (Kushwaha et al., 2004).
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Appropriate understanding of end-users’ perception and needs is important before release of new
products, since adoption of new products and technologies does not guarantee increase in productivity
(Chambers, 1994), as was clearly demonstrated in the early 2000s in some famine-hit Southern Africa
countries, especially Zambia, when GM corn grains donated by the US were rejected by the recipients
due to poor perception and ethical concerns for GM crops
(http://news.bbc.co.uk/2/hi/africa/2371675.stm). Studies on consumers’ preferred traits like grain
size, texture, seed coat colour, cooking time, ease of hilum and testa removal are important for market
development but are often not taken into account with high priority in breeding programmes.
Consumers surveyed in Ghana, Mali, and Nigeria would pay high prices for large cowpea grains and
rejected grains with exit holes created by storage weevils (Mishili et al., 2007). Traits like these are
crucial for key stakeholders in the cowpea value chain and should be considered in designing and
deploying products that fit the needs of the end-users (Mishili et al., 2007).
Cultivation of landraces, especially photoperiod sensitive types with low yield potential, is still very
popular with cowpea growers despite years of research by national, international research institutions
and development partners (Mishili et al., 2007). The popularity of landraces among farmers is partly
due to their large grains preferred by many consumers (Huynh et al., 2013; Lucas et al., 2015; Lo et
al., 2018). Knowledge of farmers and consumers preferences will rapidly drive demand-led breeding
and facilitate uptake and use of improved varieties, thereby leading to increased income for farmers
and other cowpea value-chain actors such as grain merchants, and processors (Lowenberg-DeBoer &
Ibro, 2008). Understanding farmers knowledge of production technologies and farming systems is
critical to increasing the level of crop productivity and adoption of new farming technologies
(Hoffmann et al., 2007). In Nigeria, especially in the northern parts where there is a substantial
amount of cowpea production, farmers cultivate cowpea in intercrops with maize, sorghum and pearl
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millet (Agwu, 2004). In areas where research organizations like IITA and NARS have been in touch
with farming communities, sole cropping of cowpea and other improved cereal-legume intercropping
has been advocated but the level of adoption has not been well documented. Farmers’ knowledge and
perception of phosphorus and other inorganic fertilizers in cowpea fields have not been investigated.
Thus, there is little information about farmers’ knowledge of P fertilization, level of adoption of sole
cropping, perceived production constraints, and preferences of cowpea traits in northern parts of
Nigeria.
The objectives of this research were to assess farmers knowledge of phosphorus fertilization, identify
prevailing popular cowpea cropping systems, determine farmers’ perceived production constraints,
preferred varieties, reasons for preference of cultivated varieties, and management strategies for
production constraints.
3.3 Materials and Methods
3.3.1. Description of study areas
The study was carried out across 36 villages in twelve local government areas (LGA) of three States
in the northern guinea savannah agro-ecological zone of Nigeria (Fig. 3.1). These States; Kano,
Kaduna, and Katsina, are known for a significant level of cowpea production in the region and have
been previously surveyed (Kormawa et al., 2002; Langyintuo et al., 2003; Lowenberg-DeBoer &
Ibro, 2008; USAID, 2015). These States fall under “Northern Guinea Savannah”, one of the three
agro-vegetational regions of Nigerian savannah zones. Kano State is the second most populous State
in Nigeria after Lagos, with a population of over 11 million (NBS, 2012). It lies mostly in the Sudan
savannah with pockets of other types of savannah ecologies giving it a wider range of growing period
with an annual rainfall of 500 -1000 mm. Cowpea is grown by most farmers in Kano State and serves
as an important source of income and food (Kormawa et al., 2002; Lowenberg-DeBoer & Ibro, 2008).
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Kano is home to the biggest international cowpea grain market (Dawanau-Market) in West Africa,
serving as the main importing and exporting terminal for cowpea grains (Lowenberg-DeBoer & Ibro,
2008). The four districts surveyed in Kano were Albasu, Bunkure, Tsanyawa and Minjibir LGAs. The
second State for the survey was Kaduna State. It is in the central part of northern Nigeria, with a
population of over 7 million (NBS, 2012) and constitutes one of the main cowpea producing and
consuming areas in Nigeria. Kaduna’s annual rainfall is 1000 - 1500 mm (NAERLS et al., 2017).
Four LGAs were visited and surveyed; Birnin-Gwari, Giwa, Kajuru, and Makarfi. The third surveyed
State was Katsina State; located in northwestern Nigeria, with a population of over 6 million. It is
also a major producing zone of the crop and has three main agro-ecologies; Sudan, Sahel and northern
Guinea savannah. The four LGAs surveyed were; Matazu, Kaita, Danja and Dandume. The annual
rainfall of the State is within 1000 – 1600 mm (NAERLS et al., 2017).
3.3.2 Sampling procedure
A two-step sampling procedure was undertaken to select cowpea farmers for the study. The first was
to identify major production areas in Nigeria from review of literature (Kormawa et al., 2002;
Lowenberg-DeBoer & Ibro, 2008; USAID, 2015) and random selection of three States in the northern
‘cowpea belt’, a term used to describe major cowpea production zones (Lowenberg-DeBoer & Ibro,
2008). The second step comprised a random selection of 420 households involved in cowpea
cultivation across the selected study sites with the help of village extension agents for the various
studied areas. Each of the randomly selected farmers was based on criteria, that she/he have grown
cowpea in the previous season.
3.3.3 Data acquisition and approach
Semi-structured questionnaires were administrated to an average of 10 farmers per site in each of the
LGAs visited using stratified random sampling procedure and data were collected from a total of 420
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farmers in these villages (an average of 35 farmers from 4 sites in a LGA, that is 35*4 LGA*3 States).
Data was collected on socio-economic characteristics, the experience of cowpea production, use of
fertilizers, knowledge of phosphorus-based fertilizers in cowpea fields, cropping system, varietal
preferences, preferred traits, constraints of cowpea production and mitigation strategies being used.
The questionnaires were administered by trained enumerators while household data, cropping system,
varietal use, production constraints, and preference traits were validated from information obtained
via focus group discussion sessions with key informants and personal observations during the survey.
Focus group discussion (FGD) sessions were conducted between January through March 2016. One
group per every site, with each comprising average of 8 - 10 participants, was interviewed for a
detailed insight into some of the questions stated in the questionnaire. The criteria for inclusion in the
FGD was having grown cowpea in the previous season. The groups included young and old male and
female growers except for Kano State, where separate groups of men (young and old) and women
(young and old) were formed due to cultural norms. The assistance of village extension agents from
the Agricultural Development Programme (ADPs) of the target States facilitated the formation and
discussion sessions. The FGD was managed by the principal researcher and in some areas facilitators
who usually introduced the topics for discussion, and guided members of the group towards effective
participation. Pair-wise comparison charts were used to rank constraints and preferences identified
by farmers from the FGD exercise.
To further understand the thinking of farmers on the use of phosphorus specific fertilizers on cowpea
fields, a five-point Likert scale was used with seven questions to investigate the perception of the
respondents. Responses to Likert items were coded on a five-point scale, where 1 = strongly disagree,
2 = disagree, 3 = undecided, 4 = agree and 5 = strongly agree. Factor analysis was used to help
determine important variables influencing farmers’ perceptions of P-based fertilizers. Factor analysis
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was executed on (SPSS v.20.0). It is important to test the adequacy of sampled respondents for factor
analysis to be valid, therefore Kaiser Meyer-Olkin measure of sampling adequacy (KMO - MSA) was
estimated. The strength of the relationship between perception variables was measured using
Bartlett’s test of sphericity.
A pair-wise comparison chart (PCC) was used during the FGD held in each of the 36 sites to identify
cowpea production constraints and preferred traits in varieties. Constraints and preferences were
identified through FGD and ranked per site. PCC helped to identify the most important constraint
faced by farmers. Identified constraints were arranged in a matrix of rows and columns on a
whiteboard and ranked in a pairwise manner, such that when two constraints were ranked, the most
important receives 1 and the less important gets 0.
3.3.4 Data analysis:
Data collected from questionnaires and FGD were coded and analyzed using SPSS v.20.0 and Nlogit
4.0 software. Results were summarized and presented with descriptive statistics and factor analysis
outputs, while binary logit model was used to study farmers’ choices of using synthetic fertilizers on
cowpea or not, as two mutually exclusive events such that when a farmer used synthetic P fertilizers
as a chosen option, the other is by default not taken (Horn et al., 2014). The use of P containing
fertilizer for cowpea was modelled as a dependent variable with a binary choice taking 1 if the farmer
uses P based fertilizer in cowpea fields and 0 as otherwise. The logit model was appropriate for a
response (s) with two possible outcomes such as 1 or 0, yes or no, or present and absent (Mendesil et
al., 2016; Naziri et al., 2014). The use of P containing fertilizers by cowpea farmers (use and non-
use) served as dependent variable while sex, marital status, age, household size, level of formal
education, experience in growing cowpea, ability to detect nutrient deficiency symptoms, cropping
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systems, attendance of field days, and contacts with extension agents served as independent
(explanatory) variables (Table 3.1) for factor analysis in Nlogit software.
The original data set included information on different levels of formal education (primary, secondary
and tertiary) but these levels of formal education were categorized as formal vs no formal education
to arrive at a simple distinction between formal education and informal education where formal is 1
and the latter is 0, thereby measured as a dummy variate. The main aim of this analysis was to describe
the way in which the use of P or P containing fertilizers was influenced by explanatory variables
mentioned above. Empirically, the model for estimating the determinants of the probability of
farmers' using P containing fertilizer was as described as follows (Verbeek, 2004):
𝑃
ln [ 𝑥 ] = 𝛽𝑜 + ∑ 𝛽𝑖 𝑋𝑖 1−𝑃𝑥
where Px is the probability of an event occurring (1) if the farmer uses P containing fertilizer and (0)
is otherwise; βo is a constant term; βi is a coefficient associated with the independent variate xi, and
xi is the independent variate. Several independent variables likely to influence the choice of farmers
using synthetic P based fertilizers on cowpea were identified and used in the model. These are
presented in the table below;
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Table 3.1: List and description of variables used for binary logit model
Variable Description Variable type Units
Dependent variable
Use P Fertilizers Farmers’ use of P on cowpea Dummy 1= yes, 0 = no
Independent variable
Sex Sex of the farmer Dummy 1= male, 0 = female
Status Marital status of the farmer Dummy 1 = married, 0 = single
Age Age of the farmer Years Continuous
Household-size Size of farmers’ household Persons Continuous
Education Formal education of a farmer Dummy 1 = formal, 0 = informal
Experience Experience in cowpea cultivation Years Continuous
Def-Symptoms Ability to know nutrient deficiency symptoms Dummy 1 = yes, 0 = no
Cropping-System System of cowpea cultivation Dummy 1 = sole, 0 = mixed
Field-day Attendance of cowpea field day Dummy 1 = yes, 0 = no
Access to cowpea production information Dummy 1 = yes, 0 = no
Extension-Contact from extension agents
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Figure 3.1: Map of study sites across three northern Nigerian States. Inset: Map of Nigerian States
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3.4 Results
3.4.1 Socio-Economic Metrics of the Respondents
The majority (85%) of cowpea farmers across the three States of the study were male (Table 3.2).
There was an interesting trend in certain areas with a good representation of female cowpea
producers: Birnin-Gwari, Bunkure, Kajuru, Makarfi, and Tsanyawa (Table 3.2). In these areas,
female cowpea farmers had good contacts with extension agents and were involved in cooperative
activities. At Tsanyawa, most of the women farmers had post-secondary education and used hired
labour for the management of their farms. Table 3.3 shows that most of the farmers were married
(96%), indicating the establishment of the family is an important cultural norm of these farming
communities.
Table 3.2: Sex distribution of respondents
Gender of Responder Percent by gender
Total
Local Government Areas Male Female interviewed Male Female
Albasu 35 3 38 92 8
Birnin-Gwari 30 5 35 86 14
Bunkure 25 10 35 71 29
Dandume 29 0 29 100 0
Danja 36 0 36 100 0
Giwa 37 5 42 88 12
Kaita 30 0 30 100 0
Kajuru 24 17 41 59 41
Makarfi 19 11 30 63 37
Matazu 32 3 35 91 9
Minjibir 35 0 35 100 0
Tsanyawa 24 10 34 71 29
Total 356 64 420 85 15
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The age of farmers cultivating cowpea varied widely: most farmers were between 30 - 50 years
old across all sites. Our surveys showed that the level of formal education among the respondents
was generally low, as farmers without any form of formal education constituted over 37% (Table
3.3). Approximately, 21% of the respondents had received some formal education: at least the six
years of primary, secondary education. The post-secondary qualification - mostly two-year
certificate courses were reported in areas close to urban centres like Albasu, Dandume, Matazu,
and Tsanyawa. Most farmers in some areas had informal (Qur’anic) education (37%) and were
able to read and write literature in the local language (Hausa) using Arabic alphabets, a system
known as “Ajami”.
Half of the cowpea farmers interviewed (50%) had more than ten years’ experience of cowpea
cultivation, 27% had up to 20 years’ experience, while approximately 2% have been growing
cowpeas for up to 40 years. The various years of experience in growing cowpeas imply that these
farmers should be aware of the traditional cowpea production practices (Table 3.3).
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Table 3.3: Socio-economics attributes of cowpea farmers surveyed across study areas
Attributes of the Respondents Frequency (420) Percent (%)
Marital Status of Responder
Married 402 95.7
Single 18 4.3
Age distribution of Responder 11 - 20 9 2.1
21 - 30 56 13.3
31 - 40 127 30.2
41 - 50 108 25.7
51 - 60 91 21.7
> 60 29 6.9
Level of Responder's Education Primary 88 21.0
Secondary 89 21.2
Tertiary 88 21.0
Informal 155 36.9
Years of cowpea production 1-10 years 211 50.2
11-20 years 113 26.9
21-30 years 66 15.7
31-40 years 23 5.5
Above 40 years 7 1.7
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3.4.2 Cowpea cropping systems and varieties under cultivation by farmers
There were varied cropping systems depending on the aim of the grower, type of variety planted
(early or late maturing), level of education, contact with extension agents and sex of the farmer.
Intercropping of cowpea with major cereals like maize, sorghum, and pearl millet was the major
cropping system in most areas. On the average for the 12 LGAs studied, 42% of respondents
planted cowpea in a mixed intercropping fashion (Table 3.4). Sole cropping of cowpea was
adopted by 25% of the farmers and was more popular in Albasu, Bunkure, Kajuru, Minjibr, and
Tsanyawa over other studied areas. In-depth discussions with these farmers revealed that sole
cropping in these areas was popular among farmers with basic formal education, contacts with
extension, and those that participated in the demonstration programmes such as the USAID-
supported cowpea upscaling project, and IITA’s varietal demonstration programmes in these areas.
Relay cropping was another system found in a few localities (9%), where cowpea cultivation
followed the harvest of main cereals like maize. Usually, such farmers would grow early maturing
varieties of maize and follow it with cowpea varieties, mostly medium to late maturing types. A
good number of farmers (23%) had both sole and mixed-intercropped fields (Table 3.4). Farmers
grew cowpea in mixed intercrop to maximize the use of available land and resources especially as
they believe cowpeas benefit from residual fertilization of maize fields. In most mixed
intercropped fields, separate application of fertilizer was usually not carried out for cowpea.
Farmers planted the cowpeas late so that most crops would have been harvested by the time
cowpeas were ready for harvesting, thereby making more labour available and also that gave the
crop sufficient time to dry in the field reducing the stress of sun drying fresh or undried pods
associated with early maturing varieties that mature and get harvested in the middle of the rainy
season.
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Cultivation of landraces vis-a-vis improved varieties was found to be still very popular among
farmers. These landraces are characterized by low yield potential compared to improved cowpea
varieties. On average across the study areas, 79% of the respondents’ planted landraces and only
6% planted improved varieties. Improved varieties were used more in Albasu, Minjibir, Bunkure,
and Tsanyawa and a good number of farmers cultivated both landraces and improved varieties
(15%) on different plots (Table 3.4), thereby making total cultivation of improved varieties 21%.
Some of the landraces being used might be improved varieties introduced to farmers by different
intervention programmes and projects, whose names were changed to local names over time,
making it difficult to distinguish them from local ones. For instance; some of these varieties were
given names of the projects or programmes that introduced them like Dan-Project (translated as
son of project), Dan Research (son of research), Kwankwaso (introduced by former administration
of Kano State Governor called Kwankwaso), Dan-OC (son of officer-in-charge), Dan-
KATARDA, and Dan-KNARDA among others. Other local cultivars with suspicious names were
Dan-Acre, Dan-Gombe, Dan-Sokoto, and Dan-Arbain. These might all be improved varieties that
were introduced to farmers at different times. No efforts were made to clearly ascertain these
varieties as improved ones and they were grouped as landraces. So, any study aimed at determining
the level of adoption of improved cowpea varieties should ask farmers when and where these
varieties were obtained originally, and will need to use more precise tools to identify improved
varieties from landraces.
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Table 3.4: Percentage of farmers reporting different cropping cowpea systems and use of local vis-a-vis improved varieties
Local government areas (LGAs)
Cropping Systems
Albasu Birnin_Gwari Bunkure Dandume Danja Giwa Kaita Kajuru Makarfi Matazu Minjibir Tsanyawa Mean
Sole cowpea 42 17 51 0 0 19 3 71 10 9 51 26 25
Mixed cropping 50 17 14 90 61 50 57 27 43 46 31 24 42
Relay intercrop 0 54 3 0 14 19 0 0 7 3 0 9 9
Sole & Mixed plots 8 11 31 10 25 12 40 2 40 43 17 41 23
Varieties In-Use
Landraces 45 100 94 97 97 86 93 100 90 71 46 29 79
Improved Varieties 3 0 0 3 0 0 0 0 3 3 49 6 6
Local & Improved 53 0 6 0 3 14 7 0 7 26 6 65 15
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3.4.3 Cowpea farmers’ knowledge and use of phosphorus-based fertilizers
Most farmers used some forms of fertilization indicating that farmers were aware of the importance
of fertilizers in crop productivity. Most (82%) farmers across all studied areas (Figure 3.2) used
some forms of fertilizers on their fields. Several kinds of fertilizers and combinations were used
including nitrogen phosphorus potassium (NPK), single super phosphate (SSP), Urea, farmyard
manure (FYM), NPK + Urea, SSP + NPK, NPK + FYM, FYM + SSP, FYM + Urea, NPK, Urea
+ FYM (Figure 3.3). FYM is a combination of animal droppings like poultry, cow dung, ashes,
and refuse dumps. Most farmers did not use phosphorus specific fertilization to grow cowpea.
Only 10% used SSP; a pure phosphorus-based commercial fertilizer, 11% used a combination of
SSP+NPK, 0.5% used FYM plus SSP and 17% added no fertilizers. Many farmers believed
cowpea does not require fertilizers and hence did not systematically add recommended fertilizers
to cowpea fields.
Over 81% of farmers knew when cowpea plants were suffering from inadequate nutrient in the
soil (Table 3.5). Most farmers (88.2%) reported having noticed the presence of nodules on roots
(Table 3.5). The identification of root nodules was facilitated during the interview with
photographs of cowpea roots with nodules attached. However, the majority were unaware (72%)
of roles played by nodules (Table 3.5) and 6% perceived nodules to be harmful (perceived as Striga
attachments) to the health of the plant.
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Figure 3.2: Survey responses on fertilizer use in cowpea fields, X-axis is survey responses and Y-
axis is a percent of farmers using fertilizers
Figure 3.3: Percentage of cowpea farmers using different types of fertilizers, X-axis is fertilizer
types being used by farmers and Y-axis is a percent of farmers using fertilizers
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Table 3.5: Percent of farmers recognizing nutrient deficiency on cowpea, presence of nodules on
cowpea roots and knowledge of the role played by nodules in cowpea health
Frequency Percent
Deficiency Recognition
Yes 337 80.2
No 83 19.8
Knowledge of Nodules
Yes 370 88.2
No 50 11.8
Percentage reporting role of nodules for cowpea
Yes 92 22
No 302 72
Harmful 26 6
3.4.4 Farmers’ perceptions of phosphorus fertilization on cowpea plants
Descriptive statistics of the Likert items showed that the Likert item “Phosphorus increases cowpea
yield”, accounted for most of the variation with the highest mean of 4.4. The result of KMO test
showed a value of 0.620 (Table 3.6), which falls within the range of acceptable values for
satisfactory factor analysis (Kaiser & Rice, 1974). The Bartlett's test of sphericity was highly
significant (Table 3.6) and therefore, the null hypothesis was rejected. Several Likert items were
influential in determining farmers’ perception on the use of P-based fertilizers (Table 3.7). The
first three principal components were retained for further interpretation since they explained over
half of the variation (61.1%) in the dataset (Table 3.7).
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Table 3.6: Descriptive statistics on Likert items for the perception of farmers on cowpea
phosphorus fertilization, and sampling adequacy test
Variables (Perception) Mean Std. Deviation
Phosphorus reduces growth & vigour
1.9 1.3
Phosphorus increases cowpea yield
4.4 1.1
Phosphorus use increases the cost of production
3.0 2.9
Phosphorus use is labour intensive and time-consuming
2.7 1.3
I do not use P because of no prior knowledge on its use
2.9 1.5
I do not use P because it is expensive to purchase
2.8 1.4
I do not use P because it is unavailable in the market
2.9 1.5
Sampling adequacy and strength of relationship among variables
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.620
Bartlett's test of sphericity Approx. Chi-Square 268.093
DF 21
Significance. 0.000
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Table 3.7: Variables and their contribution to the total variance of the principal components
Principal Components (PC)
Variables (Factors) 1 2 3
Phosphorus reduces growth & vigour NA 0.790 NA
Phosphorus increases cowpea yield NA -0.799 NA
Phosphorus use increases the cost of production 0.713 NA NA
Phosphorus use is labour intensive and time-consuming 0.648 NA NA
I do not use P because of no prior knowledge on its use NA NA NA
I do not use P because it is expensive to purchase 0.775 NA NA
I do not use P because it is unavailable in the market NA NA 0.869
Variance (%) explained by PC 24.6 21.1 15.2
Cumulative variance (%) explained by PC 24.6 45.9 61.1
NA = no contribution to the component by the variable.
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3.4.5 Determinants for use and non-use of phosphorus-based fertilizers among cowpea
farmers
A binary logit model was used to model factors influencing use and no-use of phosphorus
fertilizers among cowpea farmers, taking use or no-use as the dependent variable. The model
showed the positive and significant prediction of use of P on cowpea by farmers being influenced
by three independent variables; knowledge of nutrient deficiency symptoms, attendance of field
day and contacts with extension agents (Table 3.8).
Table 3.8: Binary logit outputs on variable influencing use of phosphorus fertilizers
Variable1 Coefficient SE b/St. Er. P[|Z|>z] Mean of X
Constant 2.895** 1.041 2.772 0.007 NA
Sex -0.741 0.312 -2.374 0.018 1.15
Status -0.370 0.539 -0.686 0.493 1.04
Age 0.066 0.118 0.557 0.577 4.72
Household-size 0.006 0.014 0.408 0.683 13.49
Education -0.162 0.982 -1.649 0.099 2.74
Experience-cowpea -0.282 0.133 0.212 0.832 1.81
Def-symptoms -0.986** 0.274 -3.598 0.0003 1.19
Cropping-system -0.105 0.107 -0.977 0.329 2.28
Field-day 0.453* 0.206 2.193 0.028 0.50
Extension-contacts 0.380* 0.164 2.324 0.020 0.78
Definition of variables are given in Table, *P<0.05, **P<0.01, N = 420, log likelihood = -
242.6675, LR x2 (11) = 53.39966, Prob > x2 = 0.0000, McFadden pseudo-R2 = 0.0991205, NA =
not applicable
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3.4.6 Farmers’ production constraints and preference traits
The following constraints were common to most sites; insect pests, aphid infestation, limited
access to improved seeds, Striga, Maruca, and drought varied across location (Table 3.9). Other
common constraints identified were adulterated chemicals, limited access to improved seeds, pod
sucking bugs, and fluctuation in market prices. It is interesting to note that, farmers in some areas
specifically mentioned aphid, Maruca, pod sucking bugs, and termites as their constraints rather
than mentioned the generic term of insect pests as was used by some farmers.
Farmers identified and listed different traits as preferred traits in cowpea varieties. Across all
locations, farmers ranked high grain yield as the most preferred trait in a variety. Other preferences
varied between the studied areas, such as the appealing look of grains termed as good market value
and large-seeded were ranked second and third most preferred traits in most of the locations (Table
3.10).
Other qualities identified as preferred traits in most of the sites were resistance to pests, early
maturity, and good fodder quality. Interestingly, small-seeded grains were mentioned as an
important characteristic in two LGAs, this was preferred by local food vendors for making steam-
cowpea paste called “moi-moi”, while large-seeded types were preferred by those interested in
using cowpea grains for direct consumption in local dishes such as rice plus cowpea. Reasons
given for early maturity was that they provided quick food and money to take care of other crops
in the field while those with good fodder types provided feed for their livestock.
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Table 3.9: Farmers’ perceived production constraints identified during focus group discussion using pair-wise ranking in
Northwestern Nigeria in 2017
Local Government Areas
Constraints Albasu Bunkure Tsanyawa Minjibir Matazu Kaita Danja Dandume Birnin-Gwari Giwa Kajuru Makarfi
Adulterated chemicals - 4 3 - - - - 4 - 3 - -
Aphid 1 2 - - 3 2 2 3 2 2
Diseases - 7 - - - - - - - - 4 -
Drought 5 - - 3 2 4 2 1 5 5
Flower abortion - 10 - - - - - - - - 3 -
Flower thrips 6 - - - - - - - - - - -
Fluctuation in price - 11 6 - - - - - - - - -
High cost of chemicals - - 5 - - - - - - - - -
Insect pests 2 1 - 2 1 1 1 1 5 3 1
Late maturity 9 - - - - - - - 6 - -
Limited access to seeds - 3 1 - - - 2 3 - - - 3
Maruca 4 5 - - - - 1 2 6 4
Pod shattering 8 - - - - - - - - - - -
Pod sucking bugs - 9 - - - 3 - - 3 3 6 5
Poor access to fertilizers - 8 2 - 3 - 3 - - - - -
Poor soil fertility - - - 4 - - - - - - - -
Poor storage facility - 10 4 - - - - - - - - -
Seed quality 7 - - - - - - - - - - -
Seed viability 3 - - - - - - - - - - -
Striga 2 6 - 1 2 - - - 4 1 -
Termites 9 - - - - - - - 6 - -
Weeds - - - - - - - - 6 - -
*Numbers indicates Ranks
1 = most important constraint,
11 = least important constraint
-denotes constraint was not reported in the area
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Table 3.10: Cowpea farmers’ preference traits identified during focus group discussion using pair-wise ranking in Northwestern
Nigeria in 2017
Local Government Areas
Preferences Albasu Bunkure Tsanyawa Minjibir Matazu Kaita Danja Dandume Birnin-Gwari Giwa Kajuru Makarfi
High yield 1 1 1 1 - 1 3 1 1 1 1 1
Good market value 2 2 2 - - - - - 2 - 2 -
Large-seeded grains 3 3 3 3 - - 2 - - 3 4 2
Access to P-fertilizers - - - - 2 - - - - - - -
Early maturity - - 2 5 1 2 - 2 - - - 4
Good fodder quality 7 - 3 4 - 3 - - - -
Resistance to pests 4 4 - 2 3 3 3 - - - -
Good taste 8 6 - - - - - - - 5 4 -
Non-shattering - - - - - - - - - - -
Pod quality 9 - - - - - - - - - - -
Purity of seeds 8 - 3 - - - - - - 4 4 -
Seed colour 5 - - - - - - - - - - -
Seed quality 6 5 - - - - - - 3 2 3 -
Small-seeded grains - - 4 - - - - - - - 3
1 = most important preference,
9 = less important preference
- denotes traits was not reported in the area as a preference
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3.4.7 Access of farmers to field-days on cowpea and agricultural extension services
Over half of the farmers (60%) reported no attendance at a field-day on cowpea cultivation and
only about 10% attended more than five. The trend was similar with contacts with agricultural
extension agents; 44.3% reported no contact, 33.1% had one to five contacts, and only 23% had
more than five contacts with extension personnel that educate and guide them on cowpea
production practices (Table 3.11). Some of the farmers that reported attendances of field-days were
those that participated in the recent USAID-sponsored upscaling project at Matazu, Minjibir, and
Albasu areas, whilst others were farmers used by IITA as out growers’ programme at Giwa area.
Table 3.11: Percentage of farmers with experience attending field-days and having contacts with
extension agents
Attendance of field day Contact with Extension Agents
Frequency Percentage Frequency Percentage
None 251 59.8 186 44.3
1 - 5 129 30.7 139 33.1
> 5 40 9.5 95 22.6
N 420 100 420 100
N = total number of respondents
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3.5 Discussion
The study interviewed a total of 420 farmers using semi-structured questionnaires and focus group
discussion (FGD) across 36 sites visited. There were more FGD sessions for men because they
were more involved in cowpea production in the studied areas. The KMO-MSA value of 0.620 in
this study was above the accepted minimum value (0.50), implying the adequacy of sampled
respondents for the study. Field (2009) has established that over 300 respondents are adequate for
sampling analysis, therefore, the 420-sample size used was more than adequate to investigate
research questions at hand.
In the present study, the majority of cowpea growers in the northern parts of Nigeria were men.
This is probably because of the religious and cultural background of the respondents, as most
women do not engage in direct crop cultivation activities like land preparation, planting, weeding,
and field management. Women are mostly responsible for post-harvest processing, threshing, and
winnowing. Contrary to the practice in some southern and west African countries like Zambia and
Burkina Faso where women have been reported as dominant producers of cowpea (Gómez, 2004;
Nkongolo et al., 2009). The results of this study corroborate findings in northern Ghana (Akpalu
et al., 2014) and northeast of Nigeria (Iya & Kwaghe, 2007) that more men are involved in cowpea
farming than women, and women are involved in post-harvest operations like threshing and
winnowing of grains. Most of the respondents did not have a formal education. This is similar to
what Akpalu et al. (2014) reported that over half (57%) of cowpea farmers in northern Ghana had
no formal education. The poor participation in formal education could be among the main limiting
factors for the use of improved technologies among these farmers. Farmers’ education is very key
to the success of any agricultural development programme, thus there is an urgent need to upscale
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the level of awareness of smallholder farmers to new farming technologies; availability of
improved seeds, use of P fertilizers, and other improved technologies.
Results revealed that cowpea farmers were aware of the important roles fertilizers play in having
normal and healthy plants in the field, but most did not use P-specific fertilizer formulations. There
were several reasons advanced by farmers for non-use of P-based fertilizers on cowpea plants.
Most farmers claimed to be unaware of the need to use P specific fertilizers like SSP for cowpea
cultivation, while those with knowledge of its use gave reasons like lack of availability in rural
markets and high cost as reasons for not using it. FanWay (2015) pointed out that Nigerian farmers
were constrained by inadequate technical knowledge, research, and dissemination of new findings.
Similarly, it has been noted elsewhere that most farmers in developing countries do not have
sufficient access to phosphate fertilizers in their communities (Magani et al., 2009; Chimungu &
Lynch, 2014) and similar findings were reported among cowpea growers in Namibia (Horn et al.,
2014). In addition, most cowpea farmers believed that cowpeas did not require fertilizers and hence
did not systematically apply the required amount of recommended fertilizers, this is in
concordance with the perception of cowpea farmers in northern Namibia (Horn et al., 2014).
Most of the Nigerian arable land are poor in soil fertility, especially low organic matter content,
severe N deficiency (< 0.1% N), 75% P deficiency (< 10 mg P/kg) and 60% (< 25mg K/kg)
(FanWay, 2015), this poor soil fertility is mainly due to low use of fertilizers and other unhealthy
practices like bush burning, and removal of plant residues after harvest for feeding animals and
roofing/constructing local structures (Bationo et al., 2002). Low usage of fertilizers in the region
is primarily due to the high cost of fertilizers, especially in countries like Nigeria, where most of
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the fertilizers and its raw materials are imported (FanWay, 2015) and untimely availability of the
products. This underscores the need to create and sustain platforms to continuously educate, and
guide farmers on sustainable agronomic and management practices. From interviews conducted
and focus group discussion sessions, it was established that the use of P-based fertilizer like SSP
for cowpea production was only common among growers with some levels of formal education
and those with contacts with projects like IITA programmes, and USAID-sponsored cowpea
upscaling project in some northern Nigeria states of Kano, Katsina, and Sokoto (Adetonah et al.,
2016; ICRISAT, 2017b).
The most popular cropping system for cowpea in the study areas was intercropping with major
cereals like maize, sorghum, and pearl millet. This is similar to what was reported in northern
Namibia (Horn et al., 2014), northern Ghana (Akpalu et al., 2014), and earlier in Nigeria (Olufajo
& Singh, 2002; Ewansiha et al., 2014). Despite the huge benefits associated with sole cropping of
cowpeas, only about 25% reported cultivation of cowpea as a sole crop. The traditional approach
of intercropping cowpea with cereals is associated with low grain and fodder yield due to low plant
density per ha, as this practice is associated with wider intra-row spacing, shading of cowpeas by
cereals, little or minimal fertilization for cowpea and poor level of pest management (Olufajo &
Singh, 2002; Ewansiha et al., 2014).
Farmers seem to be more comfortable with intercropping cowpeas because intercropping provides
insurance in case of failure one of the crops, multiple benefits, and ease of pest management
(Mawo et al., 2016; Olufajo & Singh, 2002). There is need to create awareness among cowpea
growers to adopt intercropping of improved cereal-legume cropping systems such as the 2 rows
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maize – 4 rows cowpea planting system that produced higher yield per unit area than the traditional
practices (Ajeigbe et al., 2010; Singh & Ajeigbe, 2007). Many farmers seem to be unaware of this
improved intercropping style (2: 4 maize – cowpea system), as this planting system is rarely seen
in farmers’ fields. Incorporating farmers in technology design and development, testing and
validation will greatly enhance the speed of dissemination and adoption by the end-users. Based
on this premise, some researchers have advocated development of cowpea varieties with genetic
potential that fit into mixed intercropping systems popular with farmers because the current
popular intercropping system is faced with problems of low plant population, low yield, insect and
disease incidence, shading of cowpeas, drought and low soil fertility (Olufajo & Singh, 2002).
However, others have argued that there is no need for separate breeding programmes for sole and
mixed cowpea, since most varieties that do well in the sole system also do well in mixed intercrop
(Singh et al., 2002).
Information during FGD showed that the average cowpea yield among the respondents ranged
from 300 to 1000 kg/ha compared with the genetic potential of 1500 to 2500 kg ha-1 for the pure
sole cropping (Nkongolo et al., 2009). Some farmers asserted during one FGD that, improved
cowpea varieties were not used mostly because they did not grow well in intercrop scenario while
farmers were more interested in planting cowpea as an intercrop. Use of landraces over improved
varieties were reported by farmers. This indicates there is poor adoption of improved varieties and
this may be due to non-involvement of end-users in the process of development and deployment
of cowpea varieties (Nkongolo et al., 2009). Horn et al. (2014) reported over 70% cowpea farmers
in northern Namibia used local landraces instead of improved varieties, and 76% in northern Ghana
used landraces (Akpalu et al., 2014).
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The results of binary logit analysis revealed that use of P-specific fertilizer was strongly associated
with farmers’ ability to determine nutrient deficiency, attendance of field-days and contacts with
agricultural extension agents. This is consistent with the opinion that farmers’ education is
important for successful adoption of farming technologies and recommendations. The model used
to understand factors determining the use of P fertilizers for cowpea, has been used previously to
estimate factors influencing knowledge of Napier stunt disease (Khan et al., 2014), farmers’
knowledge of pea weevils (Mendesil et al., 2016) and decision to use pesticides in vegetable crops
(Sharma et al., 2015).
Cowpea farmers in this study had poor exposure to production guidance and did not have enough
contacts with extension agents. This is probably due to the low farmer to extension ratio (1:10,000)
in Nigeria (Haruna & Abdullahi, 2013; NAN, 2016) as against the recommendation of 1:800 -
1000 extension agents to farm families ratio by the World Bank. This clearly demonstrates the
need to provide farmers with training and education on cowpea practices to achieve sustainable
cowpea food system. Contacts with extension agents provide an opportunity for agricultural
information exchange including better crop management practices and soil fertility improvement
strategies, as such information results in farmers having better knowledge about yield increasing
factors. Agricultural input use and advisory guides might be important to educate smallholder
farmers on knowledge about crop management practices (Belt et al., 2015).
Farmers indicated their major production constraints as insect pests and the most preferred trait
they wanted to see in a variety as yield. Most farmers did not know the name of chemicals they
used in controlling cowpea pests, while some even used adulterated chemicals and others used
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non-recommended chemicals or dosages. Some respondents indicated that certain insecticides
used did not protect their fields from pest damage. This might be due to various factors as
highlighted above. Pesticides application and associated problems for farmers in developing
countries have been documented by earlier reports (Khan et al., 2015; Pretty & Bharucha, 2015).
Use of non-recommended dosages and adulterated pesticides leads to poor pest control and expose
farmers to health hazards and pollution of the environment (Pretty & Bharucha, 2015). These
findings underscore the need to educate farmers to avoid problems associated with dosage and
unhealthy exposure to chemicals. Based on the market demand, farmers produced cowpeas with
different seed coat colours in the surveyed area. These findings corroborate earlier reports that
market demand constitutes an important decision making factor for farmers to adopt and produce
certain varieties (Coulibaly & Lowenberg-Deboer, 2002; Lowenberg-DeBoer & Ibro, 2008;
Mishili et al., 2007).
3.6 Conclusions
Cowpea growers do not use recommended fertilizer types and rates for cowpea, thereby
contributing to low grain yield that has characterized African agriculture. Most of the 420 farmers
that participated in the study were aware of fertilizers being important for crop growth and healthy
development but did not know the appropriate fertilizer recommendations for cowpeas. Those who
were aware of the need to use P fertilization on cowpea complained of high cost as reasons for not
using P fertilizers. The Use of P was strongly predicted by farmers’ knowledge of nutrient
deficiency symptoms, exposure to training and guidance from field-day events and interaction with
extension agents. Cowpea is still cultivated in the traditional intercropping with cereals. Farmers’
practices the intercropping to avoid the risk of crop failure and maximize benefits from the limited
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land available to them. It is imperative to train farmers in the areas included in this study on the
need for P fertilization and advantages of sole cropping of cowpeas and or improved 2: 4 cereal-
cowpea planting system to improve yields.
The use of improved varieties was low as most growers used landraces that have low yield
potential. Many farmers claimed ignorance of improved cowpea varieties and this calls for the
need to increase advocacy and dissemination of available improved technologies. Insect pests,
especially (aphids, Maruca, pod sucking bugs), weeds (Striga), drought and lack of availability of
fertilizers at the time needed were the major production constraints identified by farmers. Farmers
expressed willingness to adopt improved varieties and use SSP if provided to them or made
available in the rural markets at subsidized prices. Most farmers indicated high yield was the most
important trait they wanted in new varieties. Breeding for high yield should remain the most
important priority of cowpea breeding programmes. It is important to incorporate farmers’
knowledge and perception when designing new varieties as this will greatly facilitate the diffusion
and adoption of new varieties among farmers.
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CHAPTER FOUR
4.0 Phenotyping Cowpea for Phosphorus Efficiency and Response in Low P
Environments
4.1 Introduction
Production of inorganic P fertilizers from rock phosphate reserves is costly (Kauwenbergh, 2010)
thereby making them not readily available, and expensive in cowpea growing regions (Kolawole
et al., 2002). Global rock phosphate reserves, a key ingredient for making inorganic P are limited,
unevenly distributed and predicted to end in the next few decades (Cordell & White, 2009b).
Applied P could be fixed into forms not readily available for plant use (Korkmaz & Altıntaş, 2016).
Furthermore, over 70% of P applied via fertilizers are not utilized in the current year of application,
resulting in about 10 - 30% uptake while the uptake in the succeeding years decreases, thereby
making the uptake of P by plants below optimum (Reynolds et al., 2012).
Excessive use of P fertilizers as often practised in high inputs system raises the risk of
environmental degradation, eutrophication of water bodies and water pollution due to P runoff
(Bishopp & Lynch, 2015). In low input systems, most local farmers are not aware of the
importance of P as a yield-determining factor for cowpea, and thereby use little to zero inorganic
P (ICRISAT, 2017a). Due to the foregoing, the use of P fertilizers cannot be a sustainable option
due to its attendant economic, and environmental cost (Lynch & Brown, 2012) and the most
sustainable solution is to develop varieties that can give good yield under low soil P, and optimum
yield when P is applied (Hammond et al., 2009).
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To understand the role played by P in the growth and development of crop plants, several
approaches have been advocated; such as the use of nutrient solutions and inert media like sand or
gravel cultures (Hoagland & Arnon, 1950). Several modifications of Hoagland and Arnon (1950)
have been made and used in hydroponic, and sand culture medium to study nutrient deficiency in
plants. An example of such modification was Johnson et al. (1994) on the effect of P stress on
white lupin and a slight modification to Johnson et al. (1994) nutrient formula to investigate the P
use efficiency (PUE) of cowpea lines (Rothe, 2014). Genotypic differences, level of P in the
growth media and soil type contribute significantly to the P uptake of plants. There is limited
information on mechanisms governing the differential response of cowpea under low or high P
supply, especially on root traits. An understanding of these mechanisms would help in designing
breeding methods and selection of appropriate varieties for specific environments like areas with
limited use of P due to cost or high soil fixation of P.
The total soil P pool is in most cases over 100 times more than plant-available soil P, therefore the
main idea of P efficient and responsive genotypes is to identify individuals that can access P not
usually available to most genotypes under suboptimal soil P (P efficiency), but in addition respond
to external P supply (P responsiveness) (Reynolds et al., 2012). As such cowpea lines were
classified based on shoot dry matter (DM) yield as efficient responsive (ER), inefficient responsive
(IER), efficient non-responsive (ENR) and inefficient non-responsive genotypes (IENR) (Gyan-
Ansah, 2012; Fonji, 2015; Hammond et al., 2009; Saidou, 2005; Korkmaz et al., 2009; Mahamane,
2008; Pask et al., 2012; Zapata & Roy, 2004), see Figure 4.1. This classification takes into
consideration the performance of genotypes under nutrient stressed (efficient vs inefficient) and
optimum nutrient (responsive vs non-responsiveness) conditions (Caradus et al., 1980; Gerloff,
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1987). It has been used in CIMMYT wheat breeding (Pask et al., 2012). The ER group produced
a higher yield than others under low P and responds positively to the external supply of P. ENR
group produced an above-average yield in low P supply and below average yield when P is
supplied. IER group produced below average yield in low P conditions but responded positively
to P addition while IENR group produced below average yield in low and high P conditions (Pask
et al., 2012). Such grouping would permit selection of varieties with adaptation to specific soil
nutrient conditions. The ER and ENR classes are the most desirable genotypes for both high and
low inputs systems.
I = Non-Efficient but P Responsive II= Efficient and Responsive to P
(IER) (ER)
III = Non-Efficient and No Responsive to P IV=Efficient and No-responsive to P
(IENR) (ENR)
Adaptation to low soil available phosphorus
Figure 4.1: Quadrants of genotypes based on performance in low soil P & Response to applied P
for any measured traits (Mahamane, 2008).
In the present study, parental lines used for the development of two discovery populations, namely;
biparental and multi-parent advanced generation inter-cross population recombinant inbred lines
(RIL) were screened for P utilization efficiency (PUE) and P acquisition efficiency (PAE) using
nutrient sand culture combination, and under natural low soil P field. Many of the lines used are
parents in some biparental RIL populations previously described (Huynh et al., 2018; Huynh et
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Phosphorus
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al., 2013; Muñoz-Amatriaín et al., 2017). The RILs were developed for use by the international
cowpea community, and have been previously phenotyped for several important biotic and abiotic
stresses such as tolerance to aphids, drought, macrophomina, Striga, foliar thrips, heat and other
phenological attributes like leaf morphology and maturity (Huynh et al., 2013). PUE is the ability
of plants to produce high yield per unit of P in plant or supplied, while P acquisition efficiency
(PAE) is the ability to take up more P from low soil P pool (Reynolds et al., 2012). PUE and PAE
are important indices for abiotic stress, and these inbred lines have not been previously
characterized for these traits, so there was the need to use a high-throughput phenotyping strategy
to establish the PUE and PAE of these lines for further use in breeding programmes. The central
goal of this research was to determine genetic variability in cowpea for P acquisition and response
to P addition. The specific objectives were therefore to investigate;
• the response of cowpea lines to various levels of P fertility,
• the relationship between growth parameters and tissue P concentration,
• the level of genetic diversity of root hair length and density, and
• differences in seedling root architectural traits.
4.2 Materials and Methods
4.2.1 Screenhouse experiment
The experiment was conducted at the Institute for Agricultural Research, Ahmadu Bello University
(IAR/ABU) Zaria Nigeria. The aim was to evaluate genetic variability among cowpea lines under
various concentrations of P in the growth medium.
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4.2.1.1 Screening media
The soil used was river-sand from a local stream called Rafin Kudungi at the Ahmadu Bello
University (N11009’49.6’’ E007037’13.8’’ on 668m elevation). River-sand has been used in an
earlier P-response study (Saidou, 2005), due to its low available P and other physicochemical
properties (Table 4.3). The river-sand was sieved with < 2 mm sieve to remove debris, air-dried
for 24 overnight and acid-washed by soaking in 1% HCl for 24 hours and rinsed several times with
tap water until the pH was between 5.5 to 6.5. The soil physical and chemical properties were
determined at the Department of Soil Science, Ahmadu Bello University Zaria – Nigeria. Pots (24
cm x 24 cm diameter by height) were filled with 5 kg of acid-washed river-sand (< 2 mm). Prior
to filling the pots with sand, they were lined with a damp-proof membrane cut into 47 cm x 47 cm,
to prevent the sandy soil from escaping via the perforated holes of the pots and to reduce the level
of water loss from pot drains. The linings were later perforated gently with needles to ensure free
flow out of water and nutrients.
4.2.1.2 Plant materials
Plant materials were thirty (30) cowpea lines, of which 20 were parental lines for 12 biparental
RILs, and eight - parents MAGIC RILs, and 10 popular Nigerian lines. The RIL parents were from
the University of California, Riverside (UCR), USA (Table 4.1).
4.2.1.3 Nutrient media and phosphorus treatments
A modified Hoagland nutrient solution used on white lupin (Johnson et al., 1996) and on cowpea
lines with little modifications on P concentration was adapted (Rothe, 2014). Stock solutions were
prepared for each of the salts (Table 4.2). Defined quantities of each stock solution were then
measured into a 20-litre bucket, and reverse osmosis water (RO) was used to make up the required
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volume for various P treatments with the pH adjusted to 6.5 with NaOH or HC1. The RO was used
as diluent due to its low content of dissolved solutes especially calcium and magnesium since the
P source was calcium phosphate. Therefore, using water sources with high Ca could potentially
increase Ca content of the growth medium and that could likely make P not readily available for
plants uptake. P was supplied as Ca(H2PO4)2.H20 at 0 mg, 1.5 mg and 30 mg for the zero, low and
high P treatments, which were equivalent to 0 M, 25.0 µM and 0.5 mM of Ca(H2PO4)2.H2O in the
solution.
4.2.1.4 Experimental procedure
The 30 cowpea lines were planted in a total of 450 pots in a factorial arrangement using 3 P
concentrations; 0 mg, 1.5 mg and 30 mg P kg-1, with cowpea lines and P levels as treatments and
arranged in a randomized complete block design in five replications. All pots received the
following; 3.0 mM KNO3, 2.5 mM Ca(NO3)2, 1.0 mM MgSO4, 12.0 µM FeEDTA, 4.0 µM MnCl2,
22.0 µM H3BO3, 0.4 µM ZnSO4, 0.05 µM NaMoO4, 1.6 µM CuSO4 except Ca(H2PO4)2.H2O that
was applied to low and high P pots (Johnson et al., 1996; Rothe, 2014).
The average daily temperature during the growth period was ± 27oC and the relative humidity was
70 - 80%, with 13/11 hours of day/night length (monitored with a Digital Thermometer). Seeds
were treated with a commercial fungicide (AllStar containing 20% w/w thiamethoxam, 20% w/w
metalaxyl-M and 2% w/w difenoconazole, Syngenta Crop Protection AG, Basel, Switzerland) at
a rate of four kilograms to a sachet of 10 g before planting based on manufacturer’s
recommendation.
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Prior to planting, pots were watered to field capacity with 1000 ml of RO water, two seeds were
planted per pot and later thinned to one plant per pot at ten days after sowing (DAS). Pots were
watered with the dilute nutrient solution described in 4.2.1.3 Nutrient media and phosphorus
treatments as follows; 300 ml per pot at planting, and subsequently 300 ml was applied at 3 DAS,
6 DAS, 9 DAS, 16 DAS, 23 DAS, 30 DAS, 37 DAS, 44 DAS, and 51 DAS. The appropriate
quantities of nutrient solution were dispensed using graduated beakers. Pots were periodically
supplied with RO water to prevent wilting and prevent the accumulation of salts in the soil. Plants
were protected against insect pests by spraying with Karate (50 g/l lambda-cyhalothrin, Syngenta
Crop Protection AG, Basel, Switzerland) and applied at a rate of 1.0 l ha-1 as at when due. Figure
4.2 shows the layout of the screening experiment and Fig. 4.3. shows cowpea genotypes with
differential responses to P nutrition. Right pot: high P, middle pot: low P while left pot: no-P
plant (stunted, and defoliated leaves and poor growth).
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Figure 4.2. An overview of the experimental plants in the Screenhouse
High P Low P No added P
Figure 4.3: Effect of P concentrations on a line (IAR-48). Left - High P plant, Middle-low P plant
& Right, No P plant.
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Table 4.1: List of cowpea lines screened for both screenhouse and field experiments in 2016 and their seed source
# Genotype Source of Seeds # Genotype Source of Seeds
1 UCR 779 UC Riverside, USA 16 Aloka-local IITA Kano, Nigeria
2 Yacine UC Riverside, USA 17 B301 IITA Kano, Nigeria
3 58-77 UC Riverside, USA 18 Tvu-14676 IITA Ibadan, Nigeria
4 CB 27 UC Riverside, USA 19 Kanannado IAR Samaru, Nigeria
5 CB 46 UC Riverside, USA 20 IT86D-1010 IAR Samaru, Nigeria
6 Danila UC Riverside, USA 21 SuVita2 UC Riverside, USA
7 TVU-7778 UC Riverside, USA 22 IT00K-1263 UC Riverside, USA
8 IT82E-18 UC Riverside, USA 23 24-125B-1 UC Riverside, USA
9 IT97K-556-6 UC Riverside, USA 24 Vita7 UC Riverside, USA
10 IT93K-503-1 UC Riverside, USA 25 524B UC Riverside, USA
11 IT89KD-288 UC Riverside, USA 26 IAR-48 IAR Samaru, Nigeria
12 IT84S-2246 UC Riverside, USA 27 IT90K-277-2 IAR Samaru, Nigeria
13 IT97K-499-35 UC Riverside, USA 28 DanMisra IAR Samaru, Nigeria
14 IT84S-2049 UC Riverside, USA 29 SAMPEA-17 IAR Samaru, Nigeria
15 Sanzi UC Riverside, USA 30 UAM-1055-6 UAM Benue, Nigeria
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Table 4.2: Nutrient salts, stock and final concentrations applied on cowpea lines in sand culture
# MW/FW Stock Stock Element
(g/mol) Conc. Mass Supplied Molar Conc.
(g/L) of Final
Nutrient Salt Solution/L
1 KNO3 101.10 1.0M 101.10 K, N 3.0 mM
2 Ca(NO3)2.4H2O 236.15 1.0M 236.15 Ca, N 2.5 mM
3 MgSO4 120.37 1.0M 120.37 Mg, S 1.0 mM
4 Fe EDTA 367.05 1.0mM 0.367 Fe 12.0 µM
5 MnCl2 125.84 1mM 0.126 Mn, Cl 4.0 µM
6 H BO 61.83 1mM 0.062 B 3 3 22.0 µM
7 ZnSO4.H2O 179.47 1mM 0.179 Zn, S 0.4 µM
8 NaMoO4.2H2O 241.95 1mM 0.242 Na, Mo 0.05 µM
9 CuSO4 159.61 1mM 0.160 Cu, S 1.6 µM
10 Ca(H2PO4)2.H2O (Low P) 252.07 1mM 0.252 P 25.0 µM
11 Ca(H2PO4)2.H2O (High P) 252.07 0.5M 126.04 P 0.5 mM
Courtesy: Johnson et al., 1996, modified in the present form by Rothe, 2014
4.2.1.5 Data collection, plant assays and analysis
At eight weeks after sowing (WAS), plant height was measured, and the experiment was
terminated. All the lines were uprooted, the shoots were detached from the roots above soil surface
using secateurs. Roots were cleaned by repeated washing under a running tap to remove soils
under a 1 mm mesh opening. Fresh shoot and root samples were dried for 24 hours in the
screenhouse under ambient temperature, and later moved to an incubator (Percival, Boone IOWA
50036) set at 60-65oC for 36 hours until the stable dry weight was attained and weighed using a
digital scale (Scouttm pro SPU202, Ohaus Corporation).
The following parameters were recorded; shoot dry biomass (g), root dry biomass (g), and shoot
to root biomass ratio computed. Prior to weighing, roots were checked for any adhering soil
particles, which were carefully removed when found. Dried shoot and root samples were ground
and passed through a 60-mesh size sieve and analyzed for P concentration using Vanadate-
molybdate method (Kitson & Mellon, 1944) at the Department of Soil Science, Ahmadu Bello
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University Zaria-Nigeria. Data were analyzed for differences in the parameters recorded with the
general linear model, and means were generated from SAS Proc GLM (SAS 9.4, licensed to
http://www.wacci.ug.edu.gh/). Graphical representation of the results made with R and XLSTAT
packages.
4.2.2 Field experiment
A field experiment was conducted at the research farm of the Institute for Agricultural Research,
Ahmadu Bello University (IAR/ABU) Samaru, Nigeria during 2016 growing season. Samaru
(N110 10’31.7’’, E 007036’43.9’’ on 709 m elevation, (Garmin GPSmap 78s) is in the northern
guinea savannah of Nigeria in West Africa and has a unimodal rainfall pattern with an annual
rainfall of about 1000 – 1200 mm (NAERLS et al., 2017). The experimental area was 45 m x 13
m (585m2), the land used had been left fallow for several years, extensive soil sampling was
conducted, and samples were tested for available P content, which was consistently found to be
low (4 - 6 mg P kg-1) (Table 4.7). The land was then cleared of shrubs, roots and stubble, sprayed
with glyphosate (Round-up) at the rate of 4 l ha-1, then ploughed, harrowed twice and ridged.
4.2.2.1 Experimental design
A strip-plot design with two replications and two factors (three P levels, and thirty cowpea lines)
was used. Table 4.1 shows the list of the lines screened. The cowpea lines were vertical factors on
the row while P levels served as horizontal factors in the design. Commercial single super
phosphate (SSP) fertilizer was the source of P applied at 0, 10 and 60 kg P ha-1 at 5 days after
sowing (DAS) for zero, low and high P treatments.
Prior to sowing, seeds were treated with a broad-spectrum commercial fungicide (AllStar
containing 20% w/w thiamethoxam, 20% w/w metalaxyl-M and 2% w/w difenoconazole,
Syngenta Crop Protection AG, Basel, Switzerland) at a rate of 4 kg to a sachet of 10 g based on
manufacturer’s recommendation. Sowing was by hand at an intra and inter-row spacing of 0.20 m
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and 0.75 m. Plots were one row of 2 m each with a 1 m unplanted walkway between plots. Plants
were protected against insect pests and weeds using recommended insecticides and hoe weeding.
4.2.2.2 Data collection and analysis
Data on plant height, days to flowering and maturity, shoot dry weight, and pod yield were
collected at physiological maturity. Two plants were uprooted from each strip plot using shovels
for shoot dry weight measurement. Shoot samples were first air-dried in the screenhouse under
ambient temperature and later moved to an Incubator (Percival, Boone IOWA 50036) for drying
at 60 - 65oC for 2 days until a stable weight was attained and weighed with a digital scale (Kerro
BL20001). Dried pods were hand threshed and weighed, while P concentration of shoot samples
were determined using Vanadate-molybdate approach (Kitson & Mellon, 1944) at the Department
of Soil Science, IAR/ABU Nigeria.
4.2.3 Assessing genetic diversity of root hair and seedling root architecture traits
A total of 50 lines including 20 used for phenotyping under the screenhouse and field
environments in this study were used to assess diversity in cowpea’s root hair and seedling root
architecture traits at the Root Lab, Pennsylvania State University, USA.
4.2.3.1 Experimental design
The cigar-roll method of seedling phenotyping was used (Burridge et al., 2016; Miguel et al.,
2015). Cowpea seeds were surface sterilized with 0.5 % sodium hypochlorite (NaOCl) for 1 min
and placed onto brown germination paper (Anchor Paper, St. Paul, MN, USA) saturated with 0.5
mM CaSO4 (Plate 1). Further cleaning steps were applied, which involved dipping seeds in a
0.1% copper solution (Captan, 50%WP) for 1 min before placing them on germination paper and
rolling the paper into a “cigar-roll” configuration to reduce the incidence of fungus growth.
Five seeds of each genotype were placed 2 cm from the top of a 20 cm long piece of germination
paper, rolled into a moderately tight cigar-roll configuration and placed in a 2-litre beaker
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containing 0.5 mM CaSO4. Each roll-up constituted a replicate and was composed of 4 - 5
seedlings in an individual cigar-roll configuration. Five to nine replications were made for each
of the genotypes. Each of the beakers was filled with 10 – 16 rolls and later placed in an incubator
chamber for 48 - 72 hours set at 32oC and then moved to a light chamber set at 28oC with a
photoperiod of 16/8 hours (light/darkness). Three representative seedlings of each genotype from
replicated roll sets were taken for data collection.
4.2.3.2 Seedling root architecture phenotyping
Cowpea genotypes were evaluated for primary root length (PRL in cm), basal root number defined
as the number of first-order lateral roots within 1 cm of the base of the hypocotyl (BRN), number
of first-order lateral roots on the primary root between 2-5 cm from the base of the hypocotyl
(TBD5) and number of lateral roots on taproot between 5 and 10 cm from the base of the hypocotyl
(TBD10) of the taproot length. Sampled seedlings were spread on a flat tray surface and the
measurements defined above were taken (Figure 4.4).
Cowpea seedling
Germinating seedlings prior being assessed for
Sterilized seeds on Seeds rolled on germination and
placed in 2 L container containing to assessment
root architecture
soaked traits after 10 days
germination paper CaSO4 of cigar-roll
Figure 4.4: Pictorial procedure of seed “Roll-Up” on germination paper and assessment of
seedling roots
4.2.3.3 Root hair phenotyping
Two cm root sections of basal, taproot and lateral roots on taproot were taken from 14 days old
seedlings. In each replicate, root sections with root hairs were imaged for their root hair length
and density using a Nikon Camera (Nikon Digital Sight DS-Fi1, Nikon Corporation Japan)
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mounted on a dissecting microscope (Nikon SMZ 1500, C-DSS115, Nikon Corporation
Japan) set at 30x magnification using an imaging software (NIS-Elements F4.30.01.64-bit)
(Figure 4.5). A detailed description of this procedure has been provided in Hanlon et al. (2018).
Root hair lengths and densities were measured using an open-access image processing
software ImageJ (https://imagej.nih.gov/ij/) to count along the edge for length and middle of
the root section for density. The length of 10 root hairs was traced with the ImageJ tool per
picture, and a total of 3 - 4 pictures per replicate and 12 pictures for each line, making a total
of 240 root hairs per line were measured for root length hair and density per mm2.
Longer basal RH on Tvu-7778
Short and dense RH on Tap root of IT89KD-288
Fewer and short basal RH on IT89KD-288
Figure 4.5: Root hair imaging and representative root hair contrast on some cowpea genotypes
studied
4.3 Results
There was significant genetic variation among cowpea lines tested under contrasting P levels both
at screenhouse using river-sand-Hoagland nutrient solution and natural low P field environment,
where SSP was used as a source of P-fertilizer. Shoot dry biomass, root dry biomass and plant
height measured in the screenhouse experiment increased with the increasing concentration of P
in the different treatments. The river-sand used had low to very low physical and chemical
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properties such as pH in H2O (1:1 water: soil mixture), pH in 0.01M CaCl2 was acidic, with mean
available P content of 3.70 mg kg-1 (Bray 1 method), total N and organic carbon were very low
and as well as other physicochemical properties (Table 4.3).
4.3.1 Dry matter production of shoot, root, total plant biomass and P concentration
There was highly significant genetic variation among the lines for all the parameters measured for
the three P levels (Table 4.4). The addition of P in nutrient solution had positive effects on plant
height, shoot dry weight (SDW), root dry weight (RDW) and total plant biomass (TPB) and shoot
to root ratio (Table 4.5 - 4.6). The effect of P was more pronounced for shoot dry matter and total
plant biomass than root dry matter yield. Genotypes TVu-7778, 58-77, B301, Aloka-local, IT84S-
2049, Kanannado, TVu-14676, and Sanzi were poor performing for SDW yield while IT97K-556-
6, CB46, Yacine, CB27, IT93K-503-1, IT89KD-288, IT84S-2246-4 and Danila had good
performance under no-external P (OP) media. Similar patterns were observed in RDW and TPB
for the lines in low P (LP) media.
The parental lines IT84S-2246-4, Yacine and TVu-14676, 58-77 which are parents for two RIL
populations had contrasting yields in shoot dry weight, root dry weight and total biomass yield.
Furthermore, lines with above average performance in their SDW, RDW and TPB in OP media
were generally taller in height than lines with poor yields (below average) in OP media, indicating
the importance of P in maintaining good plant height. All lines responded positively to the P
addition in (LP media), as the SDW, RDW, TPB and PHT were higher compared to the
performance of same lines in OP due to the impact of P supplied in the growth media. Genotypes
CB46, IT93K-503-1, IT97K-556-6, CB27, IT89KD-288, IT84S-2246, IT86D-1010 and IT97K-
499-35 were more efficient in converting P applied in the LP medium to biomass yields (SDW &
RDW) than TVu-7778, 58-77, B301, Aloka-Local, Kanannado and IT82E-18 that had low
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biomass yield, except that RDW of TVu-14676 was slightly higher as well as Danila gave
comparable SDW yield in LP with best-performing lines (Table 4.5).
The effect of P in nutrient solution was more vivid in the HP medium, as the SDW, RDW, TPB,
and PHT were higher compared to the performance of the same lines in OP and LP medium.
IT93K-503-1, IT97K-556-6, CB27, CB46, and IT82E-18, IT89KD-288, IT84S-2246-4, TVu-
14676, were more efficient in translating P in the medium to shoot yield while Tvu-7778, Danila,
UCR 779, 58-77, and B301 were less efficient in HP medium. Similar response patterns followed
for RDW of the lines. IT93K-503-1 and IT97K-556-6 were the best performing lines in total
biomass (SDW + RDW) with Danila and TVu-7778 being the lowest in total biomass production
in HP. Effect of HP was also more apparent on plant height, as IT93K-503-1 (37 cm) and IT97K-
556-6 (21.4 cm), were tallest in response to HP addition compared to Tvu-7778 (12.10 cm), Danila
(14 cm), UCR 779 (about 12 cm) which were not very good in making use of P in growing in
height.
The differential response of all lines to varying P rates revealed that total biomass generally
increased from OP to HP, with IT89KD-288, IT84S-2246, and IT00K-1263 having the highest
biomass while lines such as 58-77, B301 and Tvu-7778 had low total biomass yield in OP.
Similarly, Vita7, I89KD-288, IT84S-2246-4, IT00K-1263 had superior performance when P was
optimum over Tvu-7778, Danila and B301 (Figure 4.6).
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Table 4.3: Physical and chemical properties of the river-sand used for pot experiment
Analysis Results (River sand) Unit Rating
Composite Composite Composite
Sample 1 Sample 2 Sample 3 Mean
6.4 5.3 6.1 5.9 NA Acidic
pH (H2O)
5.5 4.9 5.6 5.3 NA Acidic
pH (0.01M CaCl2)
1.30 0.06 1.10 0.82 dsm
ECE (dsm)
0.24 0.24 0.24 0.24 g kg soil-1 Very low
Organic Carbon
0.11 0.07 0.14 0.11 g kg soil-1 Very low
Total Nitrogen
4.2 3.4 3.3 3.7 mg kg soil-1 Very low
Available P
Calcium++
1.84 1.45 1.25 1.51 Cmol/kg Very low
++ 0.28 0.32 0.35 0.32 Cmol/kg Very low Magnesium
0.09 0.09 0.06 0.08 Cmol/kg Very low
Potassium
1.71 1.59 0.28 1.19 Cmol/kg Very low
Sodium
+ 0.4 0.8 0.6 0.60 Cmol/kg Very low H Al
4.32 4.25 2.54 3.70 Cmol/kg) Low
CEC
Clay 12 8 8 9.33 %
Silt 6 6 4 5.33 %
Sand 82 86 88 85.33 %
Loamy Loamy Loamy Loamy
Texture Sand Sand Sand Sand
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Table 4.4: Probabilities (p < 0.05) of F-test of the analysis of variance for the shoot, root, total
biomass and tissue P content of cowpea lines evaluated in the Screenhouse
Source PHT SDW RDW TPB SR ShootPCont RootPCont
Lines < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
Phosphorus < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
Line x P < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0037 0.189
Mean 16.93 1.04 0.70 1.74 1.33 1230.41 779.95
CV 39.60 50.24 36.46 42.03 27.76 86.05 89.91
PHT = plant height, SDW = shoot dry weight, RDW = root dry weight, SR = shoot to root ratio,
ShootPCont = shoot P concentration and RootPCont = root P concentration
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Table 4.5: Plant heights, shoot and root dry biomass of different cowpea lines evaluated under
three levels of phosphorus in a Screenhouse experiment
Plant Height (cm) Shoot Dry Weight (g) Root Dry Weight (g)
Lines OP LP HP Mean OP LP HP Mean OP LP HP Mean
24-125B-1 13.9 11.9 18.7 14.9 0.4 0.5 2.8 1.2 0.4 0.4 1.8 0.9
524B 14.5 14.3 17.3 15.4 0.6 0.6 2.4 1.2 0.7 0.6 1.1 0.8
58-77 11.7 12.8 16.8 13.8 0.2 0.3 1.5 0.7 0.2 0.3 0.8 0.4
Aloka-local 15.2 15.4 24.9 18.5 0.3 0.4 2.2 1.0 0.2 0.3 0.8 0.4
B301 10.7 11.8 41.0 21.2 0.2 0.2 1.4 0.6 0.2 0.2 0.8 0.4
CB27 16.7 16.1 18.0 16.9 0.5 0.5 1.7 0.9 0.5 0.5 0.9 0.6
CB46 15.4 15.6 16.4 15.8 0.5 0.6 1.7 0.9 0.5 0.6 1.0 0.7
Danila 12.6 12.6 13.7 13.0 0.5 0.6 1.1 0.7 0.3 0.4 0.6 0.5
DanMisra 10.0 10.2 13.9 11.4 0.3 0.3 2.2 1.0 0.3 0.3 1.3 0.6
IAR-48 12.7 12.6 16.4 13.9 0.4 0.4 2.5 1.1 0.5 0.4 1.8 0.9
IT00K-1263 14.2 15.4 19.4 16.4 0.6 1.1 3.8 1.8 0.7 0.9 2.1 1.2
IT82E-18 11.9 11.1 13.9 12.3 0.3 0.3 1.6 0.8 0.7 0.5 1.6 1.0
IT84S-2049 19.4 18.3 24.7 20.8 0.3 0.4 2.3 1.0 0.3 0.4 1.0 0.5
IT84S-2246-4 23.2 23.8 25.0 24.0 0.9 0.7 3.6 1.7 0.5 0.5 1.0 0.7
IT86D-1010 16.7 17.0 35.4 23.0 0.3 0.5 2.3 1.0 0.3 0.4 1.0 0.6
IT89KD-288 26.6 25.8 41.3 31.2 1.1 1.1 3.2 1.8 0.6 0.6 1.2 0.8
IT90K-277-2 11.1 9.7 16.5 12.4 0.2 0.2 2.5 1.0 0.3 0.2 1.3 0.6
IT93K-503-1 20.7 20.5 36.6 25.9 0.5 0.5 2.3 1.1 0.4 0.6 1.2 0.7
IT97K-499-35 16.1 15.9 19.3 17.1 0.4 0.5 2.7 1.2 0.4 0.5 1.2 0.7
IT97K-556-6 19.9 14.4 21.4 18.6 0.5 0.5 2.0 1.0 0.7 0.6 1.4 0.9
Kanannado 16.2 15.9 36.6 22.9 0.3 0.4 1.9 0.8 0.2 0.3 1.1 0.6
SAMPEA-17 10.5 12.8 16.3 13.2 0.4 0.5 3.4 1.4 0.5 0.6 1.7 0.9
Sanzi 14.6 15.0 52.6 27.4 0.3 0.4 2.5 1.1 0.3 0.3 0.9 0.5
SuVita2 11.3 12.5 12.8 12.2 0.5 0.5 2.7 1.2 0.4 0.4 1.2 0.7
Tvu-14676 8.5 8.5 16.5 11.2 0.2 0.3 2.1 0.9 0.4 0.4 1.4 0.7
Tvu-7778 11.5 12.1 12.1 11.9 0.1 0.3 1.0 0.5 0.2 0.3 0.7 0.4
UAM-1055-6 12.2 12.0 13.5 12.6 0.4 0.4 1.4 0.7 0.3 0.3 0.9 0.5
UCR-779 9.2 9.7 11.6 10.1 0.3 0.5 1.4 0.7 0.5 0.7 1.5 0.9
Vita7 13.9 11.2 21.4 15.5 0.5 0.7 3.3 1.5 0.7 0.7 2.2 1.2
Yacine 14.4 14.2 16.0 14.9 0.4 0.5 1.4 0.8 0.6 0.6 1.3 0.8
Min 8.5 8.5 11.6 10.1 0.1 0.2 1 0.5 0.2 0.2 0.6 0.4
Max 26.6 25.8 52.6 31.2 1.1 1.1 3.8 1.8 0.7 0.9 2.2 1.2
Mean 14.5 14.3 22.0 16.9 0.4 0.5 2.2 1.0 0.4 0.5 1.2 0.7
OP = no-P application, LP= 1.5 mg P kg-1 soil, HP = 30 mg P kg-1 River sand
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Table 4.6: Total plant biomass and shoot to root ratio of different cowpea lines under three levels
of phosphorus in a Screenhouse experiment
Total Plant Biomass (g) Shoot to Root Biomass Ratio
Lines OP LP HP Mean OP LP HP Mean
24-125B-1 0.8 0.9 4.6 2.1 0.9 0.9 1.6 1.1
524B 1.2 1.3 3.5 2.0 0.9 1.0 2.0 1.3
58-77 0.4 0.6 2.3 1.1 0.9 1.0 1.8 1.2
Aloka-local 0.5 0.7 3.0 1.4 1.3 1.4 2.8 1.8
B301 0.4 0.4 2.2 1.0 0.9 1.0 1.6 1.1
CB27 0.9 1.0 2.7 1.5 0.9 1.2 1.8 1.3
CB46 1.1 1.3 2.7 1.7 1.0 0.9 1.7 1.2
Danila 0.8 1.0 1.7 1.2 1.4 1.6 1.7 1.6
DanMisra 0.6 0.6 3.6 1.6 1.0 1.3 1.7 1.3
IAR-48 0.9 0.7 4.3 2.0 0.9 1.0 1.4 1.1
IT00K-1263 1.3 1.9 5.9 3.0 1.0 1.3 1.9 1.4
IT82E-18 1.0 0.9 3.3 1.7 0.5 0.6 1.0 0.7
IT84S-2049 0.6 0.8 3.3 1.6 1.2 1.2 2.4 1.6
IT84S-2246-4 1.4 1.2 4.6 2.4 1.8 1.7 3.5 2.3
IT86D-1010 0.7 0.9 3.3 1.6 1.1 1.2 2.2 1.5
IT89KD-288 1.7 1.6 4.5 2.6 1.9 1.9 2.6 2.1
IT90K-277-2 0.5 0.4 3.8 1.6 0.9 1.1 1.9 1.3
IT93K-503-1 0.9 1.1 3.5 1.8 1.3 1.0 1.8 1.4
IT97K-499-35 0.8 0.9 3.9 1.9 1.1 1.1 2.2 1.5
IT97K-556-6 1.2 1.1 3.4 1.9 0.7 0.8 1.5 1.0
Kanannado 0.5 0.7 3.0 1.4 1.4 1.2 1.6 1.4
SAMPEA-17 0.9 1.1 5.0 2.4 0.8 0.8 2.0 1.2
Sanzi 0.6 0.7 3.4 1.6 1.1 1.2 2.7 1.7
SuVita2 0.9 0.9 4.0 1.9 1.3 1.3 2.3 1.6
Tvu-14676 0.6 0.7 3.4 1.6 0.7 0.7 1.4 0.9
Tvu-7778 0.3 0.5 1.7 0.8 0.6 1.2 1.6 1.1
UAM-1055-6 0.7 0.7 2.4 1.2 1.5 1.1 1.4 1.4
UCR-779 0.8 1.2 2.8 1.6 0.6 0.8 1.0 0.8
Vita7 1.1 1.4 5.5 2.7 0.7 0.9 1.5 1.0
Yacine 1.1 1.1 2.6 1.6 0.7 0.9 1.1 0.9
Min 0.3 0.4 1.7 0.8 0.5 0.6 1.0 0.7
Max 1.7 1.9 5.9 3.0 1.9 1.9 3.5 2.3
Mean 0.8 0.9 3.5 1.8 1.0 1.1 1.9 1.3
OP = no-P application, LP= 1.5 mg P kg-1 soil, HP= 30 mg P kg-1 River sand
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Total Plant Biomass Production (g/pot)
7.00
6.00
5.00
4.00
3.00 OP
LP
2.00
HP
1.00
0.00
Cowpea lines tested under different Phosphorus concentration
Figure 4.6: Differential response of cowpea lines to varied phosphorus concentration evaluated in Screenhouse using Hoagland Nutrient solution
(OP = No application of P, LP = low P and HP = high P)
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4.3.2 Assessing the relationship between growth parameters and tissue P concentration
Phosphorus element added in the nutrient media had significant effects on P content of cowpea
lines and led to a significant correlation between several pairs of parameters (Fig. 4.7). There
were high positive correlations between shoot dry weight and shoot P content at OP (r = 0.8)
and HP (r = 0.9). Shoot dry weight at OP and HP were moderately correlated (r = 0.6), likewise
the root dry weight at OP and HP (r = 0.6). Shoot: root ratio at HP and OP were negatively
correlated (r = -0.2, r = -0.3) with root dry weight at OP. As expected, shoot and root dry
weights at all P conditions had a positive significant association with total plant biomass,
likewise shoot and root P concentraion were associated with the shoot and root dry weights.
Figure 4.7: Pattern of the relationship between plant parameters and phosphorus contents in
shoot and root organs. Note: positive correlation increases with the intensity of greenness while
negative increases with increasing red colour
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4.3.3 Grouping of cowpea lines based on performance in Low P & Response to P addition
The plant materials used in this work were grouped into four groups based on their shoot dry
weight, as the most reliable criteria for accessing P use for this study. The following cowpea
lines with shoot yield above mean OP (0.41 g) and HP (2.22 g); IT89KD-288, IT84S-2246-4,
IT00K-1263, SAMPEA-17, Vita7, IT97K-499-35, 524B, IT93K-503-1, IT97K-556-6, IAR-48
and SuVita2 were grouped as efficient responsive (ER) lines. Efficient non-responsive (ENR)
lines included CB27, CB46, Yacine, and Danila that had shoot yield of above average in OP
(0.41 g) and below (2.22 g) in HP. Inefficient responsive (IER) consisted of IT90K-277-2,
Sanzi, IT84S-2049, Tvu-14676, Aloka-local, Dan-Misra, and IT86D-10-10 with shoot yield
below mean yield in OP (0.41 g) and above average in HP (2.22 g) while the inefficient non-
responsive (IENR) lines; 58-77, Tvu-7778, B301, UCR779, Kanannado, IT82E-18 and UAM-
1055-6 were those with shoot yield below average in OP (0.41 g) and HP (2.22 g) media (Figure
4.8).
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IER ER
IENR ENR
Figure 4.8: Biplot of cowpea shoot dry weight (g/plot) at low P and high P of Screenhouse experiment
ER= efficient responsive, ENR = efficient non-responsive, IER = inefficient responsive, IENR =inefficient non-responsive
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4.3.4 Results of the field experiment
The field soil had low to very low physical and chemical properties such as pH in H2O (1:1
water: soil mixture) and pH in 0.01M CaCl2 was acidic, mean available P content was 3.70 mg
kg-1 (Bray 1 method), total N and organic carbon were very low and other physicochemical
properties (Table 4.7). The trend from the field experiment was similar to performance of plants
from the pots experiment.
There was significant variation among the lines in response to P in the growth environment for
all measured parameters; plant height, days to first flowering, days to maturity, shoot dry
weight, pod yield, total plant biomass, and P concentrations in shoot and root tissue (Table 4.8).
Generally, the performance increased with increasing P concentration. Phosphorus treatments
lead to a reduction in the number of days to first flowering and maturity of the lines evaluated
under medium and high P treatments. Delayed flowering and maturity were observed for lines
under no-external P application (Table 4.9). Like the screenhouse results, the pattern in shoot
biomass and pod yields were smaller in the OP and LP treatments of all the lines compared to
HP outputs for the same lines (Table 4.10).
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Table 4.7: Physical and chemical properties of the low soil P of field experimental site
Analysis Results (Field soil) Unit Rating
Composite Composite Composite
Sample 1 Sample 2 Sample 3 Mean
pH (H2O) 6.37 6.4 6.27 6.34 NA Acidic
pH (0.01M CaCl2) 5.67 5.7 5.40 5.59 NA Acidic
ECE (dsm) 0.35 1.0 1.05 0.80 dsm
Organic Carbon 0.70 0.7 0.93 0.79 g kg soil-1 Very low
Total N 0.11 0.1 0.13 0.11 g kg soil-1 Very low
Available P 4.65 2.8 5.03 4.15 mg kg-1 Very low
Calcium++ 4.02 2.6 3.38 3.32 Cmol/kg Very low
Magnesium++ 1.37 0.6 0.91 0.97 Cmol/kg Very low
Potassium 1.20 0.6 0.28 0.68 Cmol/kg Very low
Sodium 1.73 1.6 1.58 1.63 Cmol/kg Very low
H+ Al 0.40 0.5 0.60 0.49 Cmol/kg Very low
CEC 8.72 5.8 6.75 7.08 Cmol/kg Very low
Clay 23.33 17.3 16.00 18.89 %
Silt 30.67 30.0 32.67 31.11 %
Sand 46.00 52.7 51.33 50.00 %
Sandy Sandy Sandy
Texture Clay Loam Loam loam Loam
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Table 4.8: Probabilities (p < 0.05) of F-test for plant height, phenological traits, shoot dry
weight, pod yield, total biomass, and P concentrations of cowpea lines in the field
SOURCE PHT DFF MAT SDW PodHa Tbiomass ShootPC RootPC
Lines < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0087 0.0002 < 0.0001 < 0.0001
Phosphorus < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.3348 0.1514
Line x P 0.2698 0.0042 0.4137 0.0138 0.2938 0.1091 0.2064 0.0177
Mean 23.18 53.96 82.41 107.93 1582.80 237.60 1159.1 1171.46
CV 33.16 8.27 5.53 43.73 53.23 46.02 16.21 20.95
PHT = plant height, DFF = days to first flowering, MAT = days to maturity, SDW = shoot dry
weight, PodHa = pod yield per ha, Tbiomass = total plant biomass, ShootPC = shoot P
concentration in mg kg-1, RootPC = root P concentration in mg kg-1
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Table 4.9: Performance of cowpea lines under different P treatments plant height, and days to
first flowering and maturity evaluated in the field environment
Plant Height (cm) Days to first flowering Days to maturity
Lines OP LP HP Mean OP LP HP Mean OP LP HP Mean
24-125B-1 17.9 19.4 23.7 20.3 67 53 48 56 87 96 80 87
524B 15.4 26.2 49.9 30.5 48 43 43 44 90 77 75 81
58-77 16.4 26.0 35.5 26.0 47 41 45 44 85 76 73 78
Aloka_Local 15.2 20.9 27.7 21.3 70 52 52 58 92 89 84 88
B301 11.0 16.7 18.9 15.5 57 49 45 50 82 77 74 78
CB27 24.4 26.4 39.2 30.0 48 49 44 47 81 81 82 81
CB46 17.5 34.6 63.8 38.6 48 45 44 46 83 81 80 81
Danila 11.1 17.9 25.4 18.1 85 53 49 62 93 92 87 91
DanMisra 17.8 23.0 25.5 22.1 75 75 66 72 90 89 88 89
IAR-48 15.1 24.4 36.2 25.2 64 61 53 59 91 89 86 88
IT00K-1263 16.0 18.0 28.4 20.8 58 51 49 52 88 77 74 80
IT82E-18 15.9 17.4 21.5 18.3 54 49 47 50 81 79 77 79
IT84S-2049 16.4 23.8 34.4 24.9 52 45 45 47 83 76 75 78
IT84S-2246-4 15.9 20.5 24.0 20.1 61 50 46 52 84 81 77 81
IT86D-1010 30.3 36.5 78.2 48.3 48 48 45 47 78 76 72 75
IT89KD-288 9.8 16.7 21.2 15.9 70 69 64 67 93 89 84 89
IT90K-277-2 14.9 20.0 28.9 21.2 78 68 68 71 94 91 91 92
IT93K-503-1 13.7 18.0 38.9 23.5 57 59 52 56 89 84 76 83
IT97K-499-35 18.8 22.8 25.3 22.3 51 46 48 48 87 81 79 82
IT97K-556-6 15.9 18.9 29.4 21.4 59 53 48 53 86 82 82 83
Kanannado 15.2 22.2 31.7 23.0 73 73 70 72 104 95 87 95
SAMPEA-17 13.7 25.5 29.9 23.0 67 54 49 56 89 86 80 85
Sanzi 15.9 33.4 29.2 26.1 50 45 41 45 76 69 64 70
SuVita2 13.0 21.3 22.7 19.0 55 49 44 49 88 76 60 75
Tvu-14676 11.7 18.2 26.8 18.9 60 50 50 53 86 86 81 84
TVU-7778 18.3 23.1 36.7 26.0 51 47 46 48 81 76 76 77
UAM-1055-6 13.8 14.8 32.3 20.3 56 48 48 51 89 87 75 83
UCR 779 9.7 14.4 20.5 14.8 56 53 52 53 84 85 73 81
Vita7 15.8 23.0 33.4 24.0 77 53 49 59 90 85 79 85
Yacine 12.7 16.2 20.0 16.3 61 47 47 52 82 76 74 77
Min 9.7 14.4 18.9 14.8 47 41 41 44 76 69 60 70
Max 30.3 36.5 78.2 48.3 85 75 70 72 104 96 91 95
Mean 15.64 22.0 32.0 23.2 60 53 50 54 87 83 78 83
OP = no-P application, LP = 10 kg P kg-1 soil, HP = 60 kg P kg-1
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Table 4.10: Shoot biomass and pod yield of cowpea lines under contrasting soil P in the field
Shoot Dry Weight (g/plot) Pod yield (kg)/ha
Lines OP LP HP Mean OP LP HP Mean
24-125B-1 43.8 55.8 221.8 107.1 495.4 625.4 3328.1 1483.0
524B 15.8 39.5 70.5 41.9 215.5 643.2 1112.5 657.1
58-77 40.1 48.3 152.5 80.3 523.2 351.0 2202.8 1025.7
Aloka-local 20.8 57.0 96.2 58.0 479.9 964.8 1571.8 1005.5
B301 52.9 103.8 102.2 86.3 792.6 1891.8 1927.9 1537.4
CB27 65.6 40.9 68.9 58.5 731.8 646.1 1009.9 795.9
CB46 26.9 79.3 105.5 70.5 328.8 990.6 1477.7 932.4
Danila 37.6 75.8 188.7 100.7 35 775.8 2242.9 1007.4
DanMisra 82.2 83.6 382.8 182.8 1022 1179.7 5263.7 2488.5
IAR-48 85.7 81.9 222.6 130.0 971.8 1127.4 2578.5 1559.2
IT00K-1263 21.8 91.0 277.7 130.2 449.3 703.7 3451.4 1534.8
IT82E-18 74.2 89.6 169.5 111.1 1531.8 1204.2 2409.4 1715.1
IT84S-2049 18.5 80.9 148.8 82.7 340.5 1141.4 2601.6 1361.1
IT84S-2246-4 37.0 85.4 195.9 106.1 967.5 811.5 3240.9 1673.3
IT86D-1010 46.8 123.8 213.4 128.0 727.9 2345.0 3929.0 2334.0
IT89KD-288 14.0 68.0 194.1 92.0 311.6 1238.0 2831.5 1460.4
IT90K-277-2 106.1 134.8 345.9 195.6 1120.8 1702.9 3246.4 2023.4
IT93K-503-1 23.2 132.6 248.6 134.8 548.4 1491.4 3707.2 1915.7
IT97K-499-35 71.0 120.5 190.0 127.2 1306.3 2312.8 3323.6 2314.2
IT97K-556-6 139.9 48.9 225.1 138.0 1988.2 700.8 3469.6 2052.9
Kanannado 100.6 178.5 147.8 142.3 764.3 3495.2 2761.5 2340.3
SAMPEA-17 31.4 122.0 255.4 136.3 443.2 1068.1 3631.3 1714.2
Sanzi 33.7 41.1 147.1 74.0 564.9 925.6 3910.7 1800.4
SuVita2 21.4 61.3 145.9 76.2 312.7 1055.3 2333.9 1234.0
Tvu-14676 22.1 69.0 125.9 72.3 426.8 854.0 2128.4 1136.4
TVU-7778 30.7 88.6 254.0 124.4 772.8 1413.4 3454.7 1880.3
UAM-1055-6 51.5 49.3 200.2 100.3 837.3 606.7 4222.5 1888.8
UCR-779 7.0 51.3 155.0 71.1 126.6 961.4 1995.1 1027.7
Vita7 95.1 152.3 362.5 203.3 944.8 1417.4 3944.0 2102.1
Yacine 46.1 78.0 103.8 76.0 478.2 1429.1 2541.6 1483.0
Min 7.0 39.5 68.9 41.9 35.0 351.0 1009.9 657.1
Max 139.9 178.5 382.8 203.3 1988.2 3495.2 5263.7 2488.5
Mean 48.8 84.4 190.6 107.9 685.3 1202.5 2861.7 1582.8
OP = no-P application, LP = 10 kg P kg-1 soil, HP = 60 kg P kg-1
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4.3.5 Grouping of cowpea lines based on Performance in Low P and Response to P under
Field Conditions
Based on their performance in OP and HP treatments, lines were categorized as efficient or
inefficient in low P soil and as responsive or non-responsive when P is applied using pod yield,
giving four classes as earlier stated in the results of the pots experiment.
The efficient responsive lines had an above average pod yield (684 kg/ha) in OP and HP (2,862
kg/ha), these include IT97K-556-6, IT84S-2246-4, IT97K-499-35, IT86D-1010, Vita7, IAR-
48, IT90K-277-2, DanMisra, and UAM-1055-6. The efficient non-responsive had an above-
average yield in the OP but below average in HP: these lines are CB27, IT82E-18, B301 and
Kanannado. The Inefficient responsive were low yielding in OP and higher yielding in HP;
they are IT93K-503-1, IT89KD-288, Sanzi, IT00K-1263, 24-125B-1 and SAMPEA-17 while
Inefficient non-responsive had lower yield in OP and HP; UCR 779, Yacine, 58-77, CB 46,
Danila, IT84S-2049, Aloka_local, Tvu-14676, Suvita2, and 524B (Figure 4.9).
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Biplot of Pod yield of HP vs OP under field condition
6000
IER
5500 ER
DanMisra
5000
4500
UAM-1055-6
Sanzi IT86D-1010 Vita7
4000
IT93K-503-1
Sampea-17
IT00K-1263 TVU-7778 IT97K-556-6
3500 24-125B-1 IT97K-499-35
IT84S-2246-4 IT90K-277-2
3000 IT89KD-288
Kanannado
IT84S-2049 Yacine IAR-48
IT82E-18
2500 SuVita2
Danila 58-77
Tvu-14676
UCR 779
B301
2000
ENR
IENR Aloka_Local
CB46
1500
524B
CB27
1000
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Pod yied in kg/ha at OP
Figure 4.9: Biplot of cowpea lines for pod yield at low and high P treatments under field conditions
ER = efficient responsive, ENR = efficient non-responsive, IER = inefficient responsive, IENR = inefficient non-responsive
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Pod yield in kg/ha at HP
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4.3.6 Root hairs and seedling root architecture
For all the traits measured, the mean, standard deviation, range, CV and probability of F-test
are summarized in Table 4.11. The result of the analysis of variance indicated significant
differences (p < 0.05) among the lines for all the traits assessed. There were wide ranges for
primary root length (PRL) from 18 - 43.5 cm, basal root number (BRN) from 7 - 18, taproot
branching density I (TBD5) from 17 - 37, taproot branching density II (TBD10) from 13 - 33,
the weight of 100 seeds from 9.2 - 29.1 g, basal root hair density (BRHD) from 32.6 - 135.3,
lateral root hair density (LRHD) from 53.7 - 155.9 and tap root hair density (TRHD) ranged
from 19.9 - 76.9. The CV was higher for the root hair traits 33.7 – 41.1% for lateral root hair
density (LRHD) and taproot hair density (TRHD) respectively (Table 4.11). Root hairs of the
lines varied from 0.2 to 1.3 mm in length and density from 20 to 156 root hairs/mm2. In
addition, correlation analysis revealed a strong association between root hairs on tap and basal
roots (r = 0.44) (Figure 4.10).
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1.4
1.2 R² = 0.4396
1.0
0.8
0.6
0.4
0.2
0.0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Basal Root Hair Length (mm)
Figure 4.10: Strong association of taproot hair and basal root hair length
Table 4.11: Means, SD, ranges, F-test prob (0.05) and coefficient of variation (CV %) for
cowpea seed and seedling root traits measured.
Variables Mean Std Min Max Range CV Prob
Dev (%) (0.05)
Primary Root Length (cm) 28.4 5.0 18.0 43.5 25.5 20.3 <.0001
Basal root number 10.9 2.0 7.0 18.0 11.0 24.0 <.0001
Tap root branching density 26.8 4.5 17.0 37.0 20.0 25.0 <.0001
Tap root branching density 23.7 4.4 13.0 33.0 20.0 26.0 <.0001
Weight of 100 seeds (gram) 18.1 4.4 9.2 29.1 19.9 0.6 <.0001
Basal root hair length (mm) 0.5 0.1 0.4 0.7 0.4 38.4 <.0001
Basal root hair density 82.3 19.7 32.6 135.3 102.7 39.3 <.0001
Tap root lateral root hair length (mm) 0.5 0.1 0.4 0.8 0.4 33.7 <.0001
Tap root lateral root hair density 89.8 20.0 53.7 155.9 102.2 38.5 0.0062
Tap root hair density 51.9 12.6 19.9 76.9 57.0 41.1 <.0001
Tap root hair length (mm) 0.8 0.2 0.2 1.3 1.1 33.9 <.0001
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Tap-Root Hair Length (mm)
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4.4 Discussion
Use of sand and nutrient solution to screen cowpea plants’ response to P nutrient has been
reported (Saidou, 2005). The pattern of variation observed in this study is comparable to reports
of earlier works on adaptation and response of cowpea to different P rates (Adusei et al., 2016;
Karikari et al., 2015; Kolawole et al., 2008; Mawo et al., 2016; Saidou et al., 2007; Sanginga
et al., 2000). These authors have shown that different concentrations of P on cowpea lead to a
significant increase in shoot and root biomass production, number and weight of nodules, and
P content. The differential performance among tested lines in this study is an indication that
selection for superior performing lines with the potential to yield well under low and high P is
possible and varieties can be developed to fit different edaphic conditions. Variation in shoot
and pod yield among lines treated with P fertilizers appeared to be genetically controlled and needs
to be transferred to adapted cowpea lines. Similar observations have been made by previous
workers (Krasilnikoff et al., 2003; Singh et al., 2002).
Cowpea lines were grouped based on their potential to produce above-average yield on low
and high P soil into four categories; as efficient and inefficient under low P condition and
responsive and non-responsive under high P condition. Such grouping will permit
recommendation of lines that fit into specific agro-ecologies and breeding programmes target
nutrient efficiency. This grouping method was earlier suggested by Gerloff (1987) and has been
used by several workers to group crop plants (Hammond et al., 2009; Mahamane, 2008; Zapata
& Roy, 2004). Lines with higher response to P application and higher performance under minimal
or low available P are most desirable. Some of the lines identified from this current work as P
efficient and good responders to applied P-fertilizer have been reported. These include IT90K-
277-2 (Kolawole et al., 2008) and IT84S-2246-4 (Krasilnikoff et al., 2003) while lines like
Danila, a landrace from Nigeria was found to be P efficient in the screenhouse but inefficient
from field results. Similar contradictory reports have been made about this landrace.
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Krasilnikoff et al. (2003) reported Danila to be good at P uptake and attributed that to its long
root hairs, while Rothe (2014) reported the same line to be poor P efficient. These
contradictions may be due to many variants of Danila seeds in existence, as there were several
landraces that farmers called Danila due to the similarity in their seed coat.
There was a high positive significant correlation between low and high P conditions, as most
lines that produced high shoot yield in OP were also higher yielder when P was supplied. This
finding contradicts the earlier report that lines with tolerance to limited P conditions were not
good at responding to added P condition (Caradus & Snaydon, 1986). Impact of P nutrition
was visible in other parameters measured such as phenology (flowering and maturity time) and
plant height. Root dry weight was not measured under field condition due to difficulty in
recovering whole root architecture from the soil in the field. There were 20% and 30%
reduction in root growth as a result of plant’s efforts to maintain shoot growth under limited P
condition, this further attests to the critical role played by P in cowpea growth and development.
Even though several traits could be used as indices for measuring P performance, use of shoot
dry weight and total plant biomass were more discriminating for adjudging P adaptation and
response. Shoot dry weights have been used in several studies to measure adaptation to low P
and response to P fertilization (Caradus et al., 1991; Hammond et al., 2009; Korkmaz et al.,
2009; Leiser et al., 2015).
4.5 Conclusions
Extensive genetic differences in cowpea lines for the uptake of P from deficient soils and
efficient use of P applied through fertilizers or P-containing nutrient solutions were observed.
Results from this study enabled classification of lines into efficient, inefficient, responsive and
non-responsive groups based on their performance in the low and high P growth media. This
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classification will help in identifying lines suitable for cultivation under different agro-
ecologies and for farmers with different levels of access to fertilizer inputs.
Since most of the lines used in this study were parents of biparental and multi-parent advanced
generation inter-cross RIL populations. RILs with contrasting adaptation for P use and response
to P fertilization were identified for further studies. Strong associations were found between
root hairs on tap and basal root hairs. This finding will help direct breeding programmes
objectives for P use efficiency.
There were high positive significant correlations between performance of cowpea lines
evaluated under low and high P conditions. Tissue P concentrations in shoot biomass were
positively correlated with biomass and grain yield. In addition, seedling root architecture traits
such as primary root length, basal root number, lateral root branching density and root hair
traits (length and density) were varied significantly between the lines tested.
P acquisition and use efficiency should be taken as complementary strategies to reduce the use
of chemical or synthetic fertilizers instead of complete replacement of chemical fertilization.
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CHAPTER FIVE
5.0 Phenotypic evaluation and QTL mapping for phosphorus use efficiency
and yield in RIL population under two phosphorus rates
5.1 Introduction
Several screening studies have revealed genetic variation for adaptation to low soil P and
response to applied P, indicating the possibility of developing varieties for different soil
conditions (Abdou, 2018; Gyan-Ansah et al., 2016; Timko & Singh, 2008). The quantity of P
available in soil solution for plant uptake is conditioned by several factors such as soil pH, soil
type, association with arbuscular mycorrhizal fungi (AMF), root architecture, and genotype of
the crop type (Niu et al., 2013; Richardson et al., 2011; Vandamme et al., 2013). Legumes like
cowpea can fix a considerable amount of N through biological N fixation in association with
Bradyrhizobium spp when there is adequate soil P, this is because the rhizobium found in root
nodules are able to reduce atmospheric N gas to ammonium for plant use (Diaz et al., 2017;
Kyei-Boahen et al., 2017; Zahran, 1999).
Indicators for P use and acquisition efficiency reported in cowpea include shoot dry biomass,
root dry biomass, shoot to root ratio, P concentration in shoot and root, grain yield and
phenological attributes (Rothe, 2013; Ravelombola et al., 2017; Saidou et al., 2012) and
computed P efficiency traits such as agronomic P use efficiency, physiological P use efficiency,
P efficiency ratio and P utilization efficiency (Fonji, 2015; Solomon Gyan-Ansah, 2012;
Hammond et al., 2009). There are few reports on QTLs and markers for P efficiency traits in
cowpea especially using high-density SNP markers. This has limited the capacity of breeders
to deploy markers in selection to increase precision and reduce the time taken to achieve genetic
gains in developing P efficient varieties. Few SSR markers and QTLs for P use efficiency using
shoot dry biomass and tissue P concentration have been reported (Rothe, 2014).
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A cowpea biparental recombinant inbred lines population (TVu-14676 x IT84S-2246-4) was
evaluated under high and low soil P conditions representing common conditions of most
farmers’ field. The RIL population used in this work have been investigated and used by
different workers to map QTLs for yield components, resistance to Striga, and nematode and
yield components (Huynh et al., 2013; Lucas et al., 2013; Muchero et al., 2009). Initial
screenings (unpublished yet) showed cowpea line; IT84S-2246-4 to be efficient in low P soil
and respond positively to applied P under high P environments, while TVu-14676 has
contrasting performance under low and high P conditions. In the present study, P efficiency,
phenology, and yield traits were evaluated to investigate performance in contrasting P
conditions, identify the pattern of relationship between traits and identify QTLs associated with
P efficiency traits in cowpea for future breeding work on marker-assisted selection targeting
varieties with potential to yield well in low P soils and respond to applied P.
5.2 Materials and Methods
5.2.1 Plant Materials
The biparental recombinant inbred lines (RIL) set of TVu-14676 x IT84S-2246-4 (TV x IT)
consisting of 130 RILs (Lucas et al., 2011; Muchero et al., 2009; Muñoz-Amatriaín et al.,
2017) were evaluated with their two parents. The RILs were F9 lines advanced by single seed
descent (SSD) (Huynh et al., 2013) and seeds were kindly provided by the Cowpea team of the
University of California Riverside (UCR), USA. The IT84S-2246-4 and Tvu-14676 were from
IITA’s worldwide collection and segregated for nematode and Striga resistance (Muchero et
al., 2009). In the initial screening experiments, these lines segregated for biomass and grain
yield under low and high P growth media (nutrient-sand solution and natural field conditions).
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5.2.2 Phenotyping sites and experimental design:
The field evaluation of RIL lines was undertaken at Zaria (N11009’49.6’’ E007037’13.8’’ at
668m elevation), located in the northern guinea savannah of Nigeria in 2017 and 2018 cropping
seasons, each with three replications in an alpha lattice design of 12 x 11. Prior to planting,
seeds were treated with a commercial fungicide (AllStar) at 10 g 4 kg-1 of seeds according to
the manufacturer’s recommendation. Phosphorus (P) was applied to high P (HP) plots using
the commercial single super phosphate (SSP) fertilizer at the rate of 60 kg ha-1 seven days after
sowing (DAS). The SSP used contained 20% total P2O5, 15% water soluble P2O5, 16.5% water
and citrate-soluble P2O5, 11% Sulphur, 18% Ca and 4% moisture (TAK-AGRO SSP 20%). Urea
(46% N) and muriate of potash (MOP) fertilizers were applied at 30 kg ha-1 for both low and
high treatments to avoid confounding effects of N and K. Plots were one row of 2 m length at
a 0.20 x 0.75 m intra and inter-row spacing consisting of 10 plants per row.
The Low P (LP) treatments did not receive an application of SSP fertilizer and plants in the LP
plots were maintained on the inherent soil P (6 mg kg-1), contrary to the high P (HP) treatments
that received 60 kg P ha−1. Soil available P was earlier measured at 0 - 0.2 m soil depth (mean
of 6 mg kg−1 of soil, Bray I method) before planting and other physical and chemical properties
of the field were determined.
5.2.3 Phenotypic data collection and analysis:
Plant parameters assessed were phenological traits (days to first flowering & days to maturity),
yield components and P efficiency traits as physiological P use efficiency (PPUE), P utilization
efficiency (PUtE), agronomic P use efficiency (APE), and P uptake efficiency (PUpE).
Phosphorus concentration was determined using Vanadate-molybdate (Yellow) method
(Kitson & Mellon, 1944) at the Phosphorus Lab of the Department of Soil Science, Ahmadu
Bello University Nigeria. Dried plant shoot and grain samples were ground to a fine powder
using grinding mill and 1g of each sample was weighed out using a digital balance and packed
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into small zip lock bags. Total P content of lines was calculated as a product of P concentration
(in dry shoot or grains) and dry weight of shoot and grain per line per replication. Yield and
yield components were measured as dry weight of pods, grains per plot, and shoot biomass.
Data were recorded with the aid of Field-Book App on an android tablet (Rife & Poland, 2014).
When all measurements were taken and recorded, harvesting was done on a plot basis, into
individually labelled bags. For each plot, pods were threshed, and seeds retained in a secured
and labelled bag to preserve the identity of the seed. Phenotypic data were analysed to generate
means of RIL lines over replications using the linear mixed model (R software). Phenotypic
traits correlation using ggcorrplot R package on the pooled means of RIL lines (Kassambara,
2017) was undertaken to identify the important pattern of relationship in the data set.
5.2.4 Genotypic data acquisition and genetic linkage map construction
The genotypic data for the RILs were sourced from the Cowpea team at the UCR, USA. The
procedure used for the genotyping is briefly described. Genomic DNA from each RIL line and
parents were extracted from dried young seedling leaves using Plant DNeasy procedure, and
DNA was quantified with Qunat-IT dsDNA kit. The detailed explanation for DNA extraction,
quantification and purity check have been described (Lo et al., 2018; Muñoz-Amatriaín et al.,
2017). The RILs were genotyped at the University of Southern California (USA) using the
Cowpea Iselect Consortium Array with 51, 128 SNPs. The linkage map of IT x TV population
used in this study has been published (Muñoz-Amatriaín et al., 2017). It has 14,660 SNP
markers across 11 the linkage groups of cowpea. The map was constructed using MSTmap50
(http://www.mstmap.org/) with the below criteria; “grouping LOD criteria = 10; population
type = DH (doubled haploid); no mapping size threshold = 2; no mapping distance threshold:
10 cM; try to detect genotyping errors = no; and genetic mapping function = kosambi.” after
SNPs were called with Illumina GenomeStudio software, and curated by removing SNPs with
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> 20% missing data, heterozygous calls, and as well as removing individual lines with duplicate
or non-parental alleles, leaving SNPs that are polymorphic between parents and RILs with
minor allele frequencies of > 25%. The Centimorgan distances were later divided by two for
the RIL lines to correct for potential inflation of cM in the MSTmap50 that used double
haploids as population. The linkage groups in the current map of this population have been
oriented and numbered according to synteny with common bean (Muñoz-Amatriaín et al.,
2017).
5.2.5 QTL analysis and marker-trait association
The QTL identification was conducted with a linear mixed effect model that controls
polygenetic background effects using marker-inferred kinship matrix between the lines. The
detailed description of the model has been given (Xu, 2013) and implemented in R using an in-
house code (Personal communication, Sassoum Lo, UCR, USA) as used by Lo et al. (2018) in
mapping QTLs for domesticated related traits in cowpea.
In this model, the effect of the marker is fitted as a fixed factor and tested using the Wald test
statistic (squared effect divided by the variance of estimated effect). The Wald test follows a
chi-square distribution under the null model with one degree of freedom, from which a p-value
was calculated for each marker. SNP markers of the entire genome were scanned and a test
statistic, -log10P (P-value) profile was computed (Lo et al., 2018) and a window of ±2 cM
around the testing interval (marker) was excluded from kinship matrix when scanning for
putative QTL region. The thresholds for declaring QTLs as significant were determined with
adjusted Bonferroni correction using a trait specific “effective number of SNPs” as the
denominator (Wang et al., 2016). The percentage of phenotypic variance explained by each
SNP was calculated (Lo et al., 2018). QTL peaks were displayed using TIBCO Spotfire student
license (TIBCO Software Inc., Palo Alto, CA, USA).
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5.3 Results
5.3.1 Phenotypic analysis of recombinant inbred lines population in contrasting P soils
The phenology, yield components and phosphorus efficiency traits were investigated in low
and high phosphorus (P) soils in a TV x IT biparental RIL population. The low and high P
conditions applied (0 and 60 kg ha-1) using SSP, reflects the typical P fertility in farmers’ fields
and experimental conditions, respectively. The phenotypic data revealed that performance for
all traits were generally superior under high P compared to the low P conditions with some
lines having higher output in low over high P treatment. The two parents had contrasting
performance across all P conditions. IT84S-2246-4 was overall superior to Tvu-14676 for all
the traits measured under both P conditions. The parental lines maintained near mean values
for phenological traits (DFF, D50F and DTM) under OP condition (Table 5.1).
In the TV x IT population, application of SSP (HP) lead to increased shoot biomass yield (34
- 85 g plot-1), pod yield (287 - 664 kg ha-1), grain yield (172 - 454 kg ha-1) and P accumulation
in grain and shoot (Table 5.1). The performance was reduced by more than half in OP relative
to HP performance. Adequate soil P resulted in earlier flowering and maturity of parents and
RILs while P stress delayed flowering and maturity from 1 - 4 days among parents (Table 5.1).
5.3.2 Phenotypic correlations of the RIL lines in contrasting soil P conditions
Grain yield at both LP and HP were positively correlated with biomass and P use efficient traits
(grain APUE, grain PPUE and grain P content) in both P conditions. Shoot P content and
biomass at both P conditions were positively correlated while significant positive correlations
were observed between P efficiency traits (grain APE vs PPUE and grain P content vs PPUE)
at both P conditions (Figure 5.1).
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Table 5.1: Descriptive statistics of parents and TVu-14676 x IT84S-2246-4 RIL lines evaluated under contrasting soil P
Parents RIL Lines
Trait P-Level TVu-14676 IT84S-2246-4 SD Min Max Range SD Mean
Days to flowering OP 48 47 1 43 58 16 4 50
HP 48 44 2 40 58 18 4 49
Days to 50% flowering OP 53 52 0 46 66 20 4 55
HP 54 50 3 44 59 15 4 54
Days to maturity OP 65 64 1 60 72 12 2 66
HP 67 61 4 57 73 16 2 65
Shoot Biomass yield OP 24 43 14 6 104 98 17 34
HP 138 74 45 20 217 197 36 85
Pod yield(kgha-1) OP 145 439 208 41 908 867 164 287
HP 647 966 226 53 2024 1970 362 664
Grain yield(kgha-1) OP 70 300 162 11 609 598 114 172
HP 685 698 9 28 1257 1229 255 454
Grain P uptake (gkg-1) OP 124.8 466.5 242 9.8 1078.8 1069 183.4 269.5
HP 1044 1189.7 103 57.3 2558.9 2501.6 452.7 802.5
Shoot P uptake (gkg-1) OP 29.63 57.77 20 7.1 156.7 149.6 27.0 39.0
HP 163.53 67.9 68 18.17 252.2 234.0 45.0 102.8
OP = 0 kg SSPha-1, HP = 60kgha-1, P uptake = dry weight x P concentration of tissue, SD = standard deviation
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Figure 5.1: Phenotypic correlations of TV x IT RIL population under no-P and high P
conditions. Note: Positive correlation increases with the intensity of greenness while negative
increases with increasing red colour.
5.3.3 Linkage map and marker-trait association analysis
The linkage map of TV x IT used in this study had 14, 660 SNP markers over a distance of
812.90 cM mapped into 1216 bins on 11 linkage groups with an average of 12 SNPs per bin.
The linkage grouping in this map was based on reference genome sequence (cowpea
pseudomolecules) available on (www.phytozome.net), making the current numbering different
from the previously published map of this population.
The mixed model QTL mapping used, identified a total of 27 QTLs for 17 traits on seven out
of the 11 cowpea linkage groups (Table 5.2 – 5.3) with −log10 (p-value) ranging from 2.41 for
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shoot dry weight at LP to 6.10 for days to maturity at LP (Table 5.2). The percentage of
phenotypic variance explained (PVE) for the mapped QTLs ranged from 6.11 for shoot dry
weight at LP to 28.38 for days to flowering at LP. A region (65.07 cM) on chromosome Vu01
was identified showing a cluster of QTLs for days to flowering (LP), pod and grain yield at HP
and three grain P use efficiency traits (gPUpE, gPPUE, and gAPE).
5.3.4.1 QTLs for phenological traits
One major QTL for days to flowering was identified on chromosome Vu08 under HP
explaining 27.80% of the PVE with the allele conferred by IT84S-2246-4 and two QTLs were
mapped for the same trait under LP on Vu08 and Vu01 explaining 28.38% and 13.5% of PVE
respectively, with alleles for the trait contributed by IT84S-2246-4 on Vu08 and TVu-14676
on Vu01 (Figures 5.2, Tables 5.2). Two QTLs were detected on Vu06 and Vu08 for days to
maturity explaining 15.2% and 13.7% PVE under HP while one major QTL was detected on
Vu01 for the trait under LP explaining 17.34% PVE. Negative alleles for the trait for all the
QTLs were conferred by the female parent (TVu-14676) (Table 5.2, Fig. 5.2).
5.3.4.2 QTLs for yield components
A total of 8 QTLs were mapped for yield and its component traits at HP and LP conditions.
QTL analysis revealed the presence of a main QTL on Vu09 for shoot dry biomass at HP with
PVE of 21.0%, spanning within 39.89 - 40.27 cM region, the allele for the effect was
contributed by IT84S-2246-4. For shoot dry weight under LP, a minor QTL was located on
Vu07 explaining 6.1% of the PVE with the allele effect contributed by TVu-14676 at LP
condition (Table 5.2, Fig. 5.3).
Pod yield QTLs were identified on Vu01 and Vu05 for HP and LP conditions, the PVE for this
QTLs ranged from 11.8 – 14.6% with both parents contributing positive alleles. The region
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ranges from 52.2 - 65.07 cM on Vu01 and 12.4 - 14.6 cM on Vu01. A QTL governing grain
yield with 13.1% PVE was identified on Vu01 under both P conditions with the allelic effects
contributed by IT84S-2246-4. One additional region was mapped on Vu05 for grain yield under
LP condition explaining 12.0% of the PVE with the alleles conferred by TVu-14676. These
QTLs were found on the same region with QTLs identified for pod yield on Vu01. The peak
SNPs for pod and grain yield at HP were the same (2_07925 and 2_33992) while pod and grain
yields at LP had similar peak SNPs (2_26276 and 1_1013) (Table 5.2, Fig. 5.3).
5.3.4.3 QTLs for P use efficiency traits
QTLs for PUE traits were mapped on chromosomes; Vu01, Vu02, Vu05, Vu08 and Vu09 of
cowpea. One QTL for gAPE was mapped on Vu09 with PVE of 13.26% and spanning 1.12 cM
distance, with positive alleles conferred by the female parent of the RIL population. The P
uptake (content) of grains at HP and LP QTLs were identified on Vu01, Vu02 and Vu05 jointly
explaining 23% and 19% under HP and LP conditions, respectively. Both grain P content traits
whose QTLs were mapped on Vu01 had their alleles contributed by the two parents (Table
5.3).
One main QTL was mapped for gPPUE on HP environment on Vu01 with 14.3% PVE, gPPUE
on LP had two QTLs on Vu08 and Vu01 with 15% and 12.6% PVE and allele effects derived
from the female parent. Two QTLs on Vu01 and Vu05 were detected for gPUpE and explained
23.1% of PVE with both parents contributing positive alleles for the trait while gPUpE under
LP had two QTLs on Vu01 and Vu02 jointly explaining 20.3% PVE. Taken together, P use
efficiency traits had QTLs in five regions from both parents, with those on Vu02 only observed
in LP and VuG05 and Vu08 observed in HP conditions (Table 5.3, Fig 5.4).
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Table 5.2. Quantitative trait loci for cowpea phenological traits and yield components mapped using linear mixed model analysis
Env Trait QTL Peak SNP (s) ChrNo Position(cM) -Log10P QTL region (cM) PVE (%) Effect
HP Days to flowering_HP Cfthp8 2_00706, 2_00707, 2_03649 8 31.5 5.89 31.75-34.01 27.8 -2.00
LP Days to flowering_LP Cftlp8 2_06417, 2_53317, 2_03632 8 31.75 4.36 31.38-32.89 28.4 -1.86
Cftlp5 2_07925, 2_27671, 2_13572 5 65.07 3.85 64.70-65.07 13.3 1.28
HP Days to maturity _HP Cdtmhp6 2_06829, 2_53681, 2_13759 6 4.90 4.22 3.40-4.90 15.2 -0.96
Cdtmhp8 2_12561, 2_32956, 2_41633 8 26.13 3.33 25.38-26.13 13.7 -0.83
LP Days to maturity _LP Cdtmlp1 2_03564, 2_09007, 1_0082 1 28.97 6.16 28.97-29.35 17.34 -0.83
HP Shoot Dry Weight_Hp Csdwhp9 2_46682, 2_10636, 2_01022 9 39.89 5.81 39.52-40.27 21.02 -16.71
LP Shoot Dry Weight_LP Csdwhl7 2_00706, 2_07522 7 21.59 2.41 21.59-33.19 6.11 4.28
HP Grain yield_HP CgrainHP1 2_07925, 2_33992 1 65.07 4.10 64.7-65.07 13.12 -13.83
LP Grain yield_LP CgrainLP1 1_1013, 2_26276 1 53.35 3.19 52.22-54.10 13.12 -6.19
Cgrainlp5 2_03774 5 14.61 3.00 13.11-14.61 12.39 6.09
Hp Pod yield_hp Cpodyldhp1 2_07925, 2_33992 1 65.07 4.59 64.7-63.95 14.61 -138.34
Cpodyldhp5 2_00867 5 12.36 3.00 11.99-12.36 11.77 124.81
LP Pod yield_lp Cpodyldlp1 1_1013, 2_26679 1 53.35 3.55 52.22-54.10 14.13 -61.35
Cpodyldlp5 2_03774 5 14.61 3.31 13.11-14.61 13.62 60.91
Env. = Environment (P condition), HP =high phosphorus, LP = low phosphorus, PVE = percent of variance explained. QTL are designated as follow: “C” to
indicate cowpea, followed by the trait code, then followed by the chromosome number. Positive or negative effect alleles, for which a positive value indicates
allele of the TVu-14676 is present and a negative value indicates the allele of the IT84S-2246-4 is present.
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Table 5.3. Quantitative trait loci for cowpea phosphorus use efficiency traits mapped using linear mixed model analysis
- PVE
Env Trait QTL Peak SNP (s) ChrNo Position(cM) Log10P QTL region (cM) (%) Effect
HP&LP bAPE Capeb9 2_46682, 2_03151, 2_07790 9 39.89 4.43 38.77-39.89 13.26 -0.32
HP gPAE CgrainPhp5 2_00867 5 12.36 3.30 11.99-12.36 13.07 24.68
CgrainPhp1 2_07925 1 65.07 3.24 64.7-65.07 10.19 -21.68
LP gPAE CgrainPLp1 1_1013 1 53.35 2.72 52.22-53.35 10.96 -9.08
CgrainPLp2 2_27234 2 46.82 2.63 46.45-46.82 8.22 7.92
Hp gPPUE Cppue_ghp1 2_07925, 2_33992 1 65.07 4.47 64.7-65.83 14.28 -8.57
LP gPPUE Cppue_glp8 2_09958 8 33.26 3.49 31.75-33.26 14.98 4.63
Cppue_glp1 2_05224 1 55.22 3.28 52.22-53.35 12.61 -4.24
HP gPUpE CpUpe_ghp1 2_07925 1 65.07 3.30 64.7-65.83 10.45 -21.73
CpUpe_ghp5 2_00867 5 12.36 3.23 11.99-16.49 12.65 24.03
LP gPUpE CpUpe_glp1 1_1013 1 53.35 2.87 52.22-54.1 12.00 -9.18
CpUpe_glp2 2_27234 2 46.82 2.59 46.07-46.82 8.26 7.67
Env. = Environment (P condition), HP =high phosphorus, LP = low phosphorus, PVE = percent of variance explained, bAPE: agronomic P use efficiency for
shoot biomass, gPPUE= physiological P use efficiency for grain, gPUpE: P uptake efficiency for grain, gPAE: P accumulation efficiency for grain. QTL are
designated as follow: “C” to indicate cowpea, followed by the trait code, then followed by the chromosome number. Positive or negative effect alleles, for
which a positive value indicates allele of the TVu-14676 is present and a negative value indicates the allele of the IT84S-2246-4 is present.
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High P
1 2 3 4 5 6 7 8 9 10 11
Low P
Figure 5.2: QTL plots for days to flowering time. The X-axis indicates the chromosomes, the
Y-axis indicates the −log10P of the probability (p-values). The horizontal line indicates the
significance threshold at 0.05.
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Figure 5.3: QTL plots for the grain yield. The X-axis indicates the chromosomes, the Y-axis
indicates the −log10P of the probability (p-values). The horizontal line indicates the significance
threshold at 0.05.
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High P
Low P
Figure 5.4. QTL plots for the physiological P use efficiency in cowpea. The X-axis indicates
the chromosomes, the Y-axis indicates the −log10P of the probability (p-values). The horizontal
line indicates the significance threshold at 0.05.
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5.4 Discussion
This research investigated the phenological, yield components and P use efficiency traits of a
RIL population contrasting for P use and acquisition under low and high P soils. The levels of
P used as low and high was drastic but reflects what is found in farmers’ fields, where cowpeas
are grown under little to no P fertilization. Growing of cowpeas under limited P condition has
been documented (Belko et al., 2016; Kugblenu et al., 2014; Saidou et al., 2012). The HP
conditions provided plants with sufficient P, while LP conditions had sub-optimal P such that
yield was negatively impacted, similar reports have been made (Rothe et al., 2013; Saidou et
al., 2012). To avoid confounding effects of major nutrient deficiencies in the experiment, N,
K, Mg, and S were applied through the application of Urea and muriate of potash (MOP)
fertilizers. The use of 60 kg ha-1 SSP as high P was based on a previous recommendation in the
literature (Adusei et al., 2016; Boukar et al., 2018; Sanginga et al., 2000).
The increased performance was observed for IT84S-2246-4 parent under both LP and HP over
the TVu-14676 that would be expected by chance for most of the traits measured. This is
probably due to increased selection pressure for yield, an important component of PUE.
Numerous studies have established within species variation of PUE in cowpea and these point
to the fact that these traits are governed by quantitative trait loci. Variation in yield and PUE
traits found in cowpea is in line with previous studies on PUE in rice, Brassica and common
bean (Diaz et al., 2017; Hammond et al., 2009; Wang et al., 2014). The pattern of relationship
between traits showed high biomass and P efficiency traits were important performance
indicators for cowpea grown in low P soil. Results further revealed that cowpea lines under LP
had lower P concentration in biomass and grains relative to lines grown under high soil P.
Fodders were sampled at a similar growth stage; 8 weeks after sowing to observe the response
to contrasting P of the lines in both P conditions. Results revealed significant reduction in
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biomass and grain yield of LP while the performance of the same lines was generally superior
under HP relative to LP, though few RILs had a superior performance by producing higher
yields in LP than HP. To have a better understanding of the role of P nutrition in this study,
three categories of traits; phenology, yield components and P use efficiency were investigated.
Earlier studies have focused on estimating genetic variation and identifying lines with
adaptation to low P soils and those with good response to P fertilization.
A total of 27 QTLs over seven linkage groups were identified for TV x IT RIL population
under low and high P environments. For most traits, one or two major QTLs were detected.
QTLs were identified for days to flowering and maturity, important phenological traits for
adaptation of varieties to different agro-ecologies. In the present study, four QTLs were
associated with flowering and maturity on cowpea chromosomes Vu01, Vu05, Vu06 and Vu08.
Significant QTLs associated with yield and PUE traits were mapped on chromosomes Vu01,
2, 5, 8 and 9 and this provided information to advance breeding for improved PUE in cowpea.
Studies on cowpea P traits are scarce and comparisons are mostly made with related species.
This is the first major report of QTL mapping in cowpea with high-density SNP markers. The
earlier known report identified QTLs for shoot dry matter, a component of P use efficiency
with three SSR markers (Rothe, 2014) while another study targeted the effects of high and low
soil P on biological N fixation and yield using nodule number, nodule dry weight, shoot dry
weight, number of pods per plant, and hundred seed dry weight as indicators and identified
QTLs for N fixation traits (Fonji, 2015). However, there are reports of QTLs associated with P
use traits under varying P conditions in common bean (Blair et al., 2009; Cichy et al., 2009;
Diaz et al., 2017; Hong et al., 2004), a close relative of cowpea and other crops especially rice
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where P uptake efficiency has been mapped (Wang et al., 2014; Wissuwa et al., 1998), soybean
(Zhang et al., 2017) and Brassica spp (Hammond et al., 2009).
P use efficiency attributes used in this study were grain agronomic P use efficiency measured
as increased yield per unit of applied fertilizer (g DM g-1 P), grain P uptake efficiency (PUpE)
as increased amount of plant P content per unit of added P fertilizer measured in g P g-1 and P
utilization efficiency (PUtE) measured as increased yield resulting from increased in plant
content g DM g-1 P. Several measures of PUE have been used in the literature to study P use
and acquisition of crop plants (Leiser et al., 2015; Wang et al., 2014; Wissuwa & Ae, 2001).
Yield QTLs were discovered for grain yield under both low and high P conditions on Vu01,
Vu05, Vu07 and Vu09 chromosomes. These QTLs and SNP markers mapped in this study
would lay the foundation for marker-assisted selection for developing P efficient cowpea
varieties for cowpea breeding programmes. Mapping QTLs for same traits under different
stress conditions as undertaken in this study has been previously conducted. For instance, days
to flowering under long and short day for cowpea (Huynh et al., 2018), nitrogen fixation traits
and hundred seed weight of cowpea RILs grown under low and high P in the field soil and pot
(Fonji, 2015) and symbiotic N fixation and yield traits in common bean under low and moderate
soil P (Diaz et al., 2017).
5.5 Conclusions
There was significant phenotypic variation between RILs of TV x IT cross that would be
expected by chance. The wide range of variation as a result of differences in P contained in the
growth media indicates the traits conferring P use efficiency are quantitative in nature. QTLs
for phenology, yield and P use efficiency traits were mapped on seven out of the 11
chromosomes of cowpea. Several genomic regions of cowpea were associated with PUE traits
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and many of them co-localized on Vu08 chromosome including QTLs for flowering, maturity,
and yield with phenotypic variance explained by these QTLs ranging from 6 – 28% with
desirable alleles mainly contributed by parent 2 (IT84S-2246-4).
Further fine mapping of these genomic regions is required for higher resolution and
identification of candidate genes underlying the QTLs. Such information will allow utilization
of QTLs for P use efficiency in cowpea. Varieties with higher PUE will require less P-based
fertilizers, thereby reducing the cost of production on fertilizer inputs for cowpea growers and
will aid in having a cleaner environment especially in areas where P is heavily used.
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CHAPTER SIX
6.0 Genome-wide association mapping of cowpea for adaptation to low
phosphorus soils and response to phosphate fertilizer
Breeding efforts to develop cowpea varieties for adaption to low P soils and cowpea’s response
to applied P fertilizers have already begun and several reports have identified lines with
tolerance to low P soils and response to applied P (Gyan-Ansah, 2012; Belko et al., 2016).
These lines used a different type of mechanism such as P use efficiency, a situation where
cowpea can use and recycle internal P with less uptake from soil and there is also P uptake
efficiency in which cowpea is able to efficiently remove P from soil. However, most of these
efforts were achieved using conventional screening tools. With the advent of molecular
markers and next-generation sequencing technologies, several genomic resources including
SNP array genotyping platforms, consensus genetic, and publicly accessible reference genome
sequences are now available and are promising to be more efficient tools that will allow rapid
progress when combined with conventional breeding techniques (Bohra & Abhishek, 2013;
Boukar et al., 2016).
Several QTLs and SNP markers for abiotic and biotic factors from biparental mapping
populations have been identified in cowpea (Agbicodo et al., 2010; Huynh et al., 2015; Lo et
al., 2018; Lucas et al., 2013; Muchero et al., 2009; Pottorff et al., 2012; Santos et al., 2018)
and including QTLs for low P tolerance using SSR markers (Rothe, 2014) and adaptation to
low P tolerance and response to rock phosphate using diversity panel (Ravelombola et al.,
2017). These QTLs were identified across the 11 linkage groups of cowpea with some of them
transferred into several elite parents to improve their tolerance level through marker-assisted
backcrossing and recurrent selection process (Boukar et al., 2016; Batieno et al., 2016).
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However, it is a known fact that the resolution of identified QTLs from biparental mapping is
limited due to few recombination events of alleles from the two parents used for the population
development. QTL resolution can now be improved with available multiple parent population
like genome-wide association panel and multi-parent advanced generation inter-cross
population and next-generation sequencing platforms like diversity array technologies (Huynh
et al., 2018; Ravelombola et al., 2017). The genome-wide association approach takes
advantage of historical recombination of loci, making identified linked regions to be more
reliable and with high resolution as against rare recombination events exploited in bi-parental
mapping. The linkage disequilibrium decay is low in such a population. The availability of
multiple and relatively cheaper platforms like diversity array technologies (DArT) and other
NGS makes genome-wide association approach appealing especially as it does not require the
expensive, laborious and time-consuming procedure of developing a mapping population
(Korte & Farlow, 2013).
The genome-wide association studies (GWAS) has been extensively used in maize, rice,
sorghum, cassava, pearl millet and animal breeding programmes leading to simultaneous
discovery of useful diversity and identification of SNP markers associated with traits of
breeding importance and genomic prediction of quantitative traits (Akbari et al., 2006; Begum
et al., 2015; Kilian et al., 2012; Sánchez-Sevilla et al., 2015). There are few reports on the use
of GWAS to map QTLs and identify markers associated with traits of interests in cowpea
breeding (Burridge et al., 2017). With the availability of a link to cowpea reference genome, it
is now possible to conduct genotyping-by-sequencing (GBS) (Elshire et al., 2011) and GWAS
to identify regions and SNPs associated with biotic and abiotic traits with high resolution. The
objectives of the present work was to conduct a GWAS mapping on 400 cowpea lines for
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adaptation to low P soils and response to applied phosphate fertilizer based on GBS generated
markers on DArTseq platform.
6.2 Materials and Methods
6.2.1 Plant materials
The study used a collection of 400 cowpea lines consisting of entries from the worldwide
collection of IITA, 14 commercial varieties and several breeding lines from the of IAR/ABU,
Nigeria.
6.2.2 Experimental design and phenotyping:
The field trials were laid in 20 x 20 alpha lattice design with two replications under low and
high P conditions at Zaria (11o 11’N, 07o 38’E) in Nigeria for two years. The lines were sown
in mid-September and were later supplemented with irrigation when the rain ceased in October
2017 and replanted in 2018 between 5th June – 30th September. Plots comprised of one row of
1 m length with 1.5 m space between blocks. Seeds were sown at the rate of 10 stands at 0.2
m intervals within rows and 0.75 m between rows and later thinned to 5 stands per plot. Prior
to sowing, seeds were treated with a broad-spectrum commercial fungicide Apron star at a rate
of 4 kg seeds to a sachet of 10 g to reduce the incidence of fungal diseases. Plants were
protected against insect pests at all stages by spraying insecticides at a rate of 1.0 l ha-1.
The trial was kept weed-free by hand-hoe weeding. There were two factors, namely cowpea
lines and phosphorus (P) application rates. The P rates were low and high P, where SSP
fertilizer was applied to high P treatments at a rate of 60 kg ha-1 while no SSP was applied to
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low P treatments. All treatments received Urea and MOP at the rate of 30 kg ha-1 applied
between plant stands and buried in the soil a week after sowing.
6.2.3 Data collection and phenotypic analysis
Evaluation of cowpea lines for tolerance to low P conditions and response to applied P fertilizer
was assessed using shoot dry biomass, P uptake and P use efficiency. Fodder samples were
taken at the mid-vegetative stage by sampling two plants from each plot. Sampled shoot
biomass from the field was air-dried in the screenhouse till constant weight was maintained
and weighed with a digital scale (Kerro BL20001). The P concentration of shoot samples were
determined using Vanadate-molybdate method (Kitson & Mellon, 1944).
Cowpea’s tolerance to low P and response to P fertilization were ranked on a scale of 1 – 5
using dry shoot biomass, P uptake and P use efficiency data, where a score of 1 = most efficient,
2 = efficient, 3 = moderately efficient, 4 = inefficient, and 5 = most inefficient for adaptation
to low P condition, whereas response to applied P fertilizer was scored as 1 = highly
responsive, 2 = responsive, 3 = moderately responsive, 4 = poorly responsive, and 5 = least
responsive (GRIN, 2008; Ravelombola et al., 2017). The scoring was based on grouping
system Gerloff (1987) that group plants under nutrient stress as efficient or inefficient and
responsive or non-responsive for plants under high nutrient condition, as adopted previously
by earlier workers (Hammond et al., 2009; Mahamane et al., 2006; Saidou, 2005).
6.2.4 DNA isolation and genotyping
Young leaves from 2-3 weeks old cowpea seedlings were collected into LGC genomics sample
collection kits and shipped to the Integrated Genotyping Support Service (IGSS) facility at
BeCA-ILRI Kenya for DNA extraction using an in-house protocol available on
(https://ordering.igss-africa.org/files/DArT_DNA_isolation.pdf). The quality and quantity of
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the extracted DNA was checked on 0.8% agarose gel prior to sending genomic DNA to the
GBS service provider, ensuring the quality was at least 50 ng/ul but not exceeding 100 ng/ul
and the quantity is at least 30 ul but not exceeding 50 ul. DNA of at least 30 ul of each line was
sent to the Diversity Arrays Technology (DArT) facility based at Canberra, Australia
(https://www.diversityarrays.com/) for GBS as described by Elshire et al. (2011) using
DArTseq GBS (1.0) high-density SNP. Generated sequences were aligned to the cowpea
reference genome on Vunguiculata_469_v1.0 publicly accessible on Phytozome
(https://phytozome.jgi.doe.gov/pz/portal.html#!info?alias=Org_Vunguiculata_er).
6.2.5 SNP calling and curation
SNP markers derived from DArTseq sequencing were filtered to remove SNPs with more than
20% missing data or no calls (80% and above call rates retained) and greater than 30%
heterozygous calls. In addition, SNPs with < 5% minor allele frequencies (MAF) and those
missing in more than 20% of lines were pruned using TASSEL 5.1 (Bradbury et al., 2007)
leaving only 5, 621 informative SNPs from a total of 18, 056 called SNPs and 386 lines, out of
a total of 400 evaluated, which were used for further downstream analysis.
6.2.6 Statistical analyses
6.2.6.1 Phenotypic data
The phenotypic data were analyzed using a linear mixed effects (lme4) model that assumed
lines as a fixed factor while replications, years, and blocks within replications as random factors
to get the best linear unbiased prediction (BLUP) means of the lines using the R lme4 package.
A second model that assumed intercept as fixed while lines, reps, years and blocks with reps
as random was used to estimate the variance components (Bates et al., 2015). An estimate of
broad-sense heritability estimate was obtained as the proportion of the total variance explained
by the genetic variance.
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6.2.6.2 Population structure analysis and linkage disequilibrium
The level of genetic structure and relatedness among the cowpea lines used was investigated
using DArTseq derived SNP markers that passed the filtering test as described in 6.2.5 above.
The population structure, referred to as “Q” matrix as described (Pritchard et al., 2000) was
checked by conducting a principal component analysis (Zhao et al., 2007) with the R-GAPIT
package (Lipka et al., 2012). The DArT derived SNP markers were scored as 0, 1, and 2
representing major, minor, and heterozygous alleles while missing data was scored as “-”. The
compressed mixed linear model (CMLM) used assumes effects of individuals as random and
used this to determine the level of relatedness between individuals conveyed through the
Kinship “K” matrix as a variance-covariance matrix between individuals (Zhang et al., 2010).
Highly related individuals were assigned to the same group using a clustering algorithm that
measures the level of similarity in the cluster analysis using the average method.
Linkage disequilibrium (LD) analysis was performed with R-GAPIT package as a plot of R-
squared values between pairs of markers against their distance. LDs were calculated on a
sliding window with 100 adjacent genetic markers with a moving average of 10 markers for
adjacent markers (Lipka et al., 2012).
6.2.6.3 Genome-wide association analysis
The phenotypic data for shoot dry biomass, P uptake and use efficiency and curated DArT SNP
markers were used to conduct the analysis with GAPIT package implemented in R (Lipka et
al., 2012) using the basic scenario of the compressed mixed linear model (MLM). The MLM
method used compression approach to assign individual markers into groups for the analysis,
in other words, markers were not treated as an individual entity but considered as a group. The
MLM model used relatedness between individuals using Marker-inferred similarity between
lines (Kinship) generated via VanRanden method (VanRaden, 2008) and considers the
existence of potential population structure as described by Zhang et al. (2010). The marker
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inferred the relationship between individuals and the population structure helped to improve
the statistical power of the MLM model to detect a true association between traits and markers.
The MLM equation of the model used is represented as below;
𝑌 = 𝑋𝛽 + 𝑍𝑢 + 𝑒
Where Y is a vector of phenotype measured, β is unknown vector with fixed effects (including
SNP markers, population structure (Q) and the intercept); 𝑢 is an unknown vector of random
additive genetic effects such as effects of multiple QTL for individuals; X and Z are the known
design matrices; and e is the unobserved vector of residuals. The 𝑢 and 𝑒 vectors are assumed
to be normally distributed with a mean of zero and homogenous variance (Zhang et al., 2010).
The default critical threshold in GAPIT for declaring SNP significantly associated with traits
based on p-values of ≤ 0.0001 based on Bonferroni correction test for false positives at 5% in
the MLM model was too stringent for the traits in this study. Considering the MLM used has
accounted for population structure that may cause false positive associations between SNPs
and traits, a less stringent threshold was set at -log10P (p-values) > 3 by considering the bottom
0.1 percentile distribution of p-values as significant, as proposed by Chan et al. (2010) and
adapted by Pasam et al. (2012).
6.3 Results
6.3.1 Descriptive data of the phenotypes measured
Summary statistics from the linear mixed effects analysis and estimated broad-sense
heritability (h2) are presented in Table 6.1. The range revealed significant variation between
the cowpea lines used for the study. A plot of phenotypic data further showed that this variation
was normally distributed (Figure 6.1). The heritability for the traits was low to moderate for
shoot dry biomass and P use under the low and high P conditions.
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Table 6.1: Summary statistics of cowpea lines under two phosphorus conditions
Traits Min Max Range Mean SD h2
SDW-HP 4.7 89.6 84.9 33.0 10.2 0.11
SDW-LP 2.0 35.0 33.0 9.3 4.4 0.12
PuPE-HP 2.8 210.0 207.2 73.4 32.6 0.29
PuPE-LP 2.6 81.6 79.0 17.0 9.4 0.19
PUE-HP 1.8 123.7 121.9 17.4 10.3 0.45
PUE-LP 0.7 29.0 28.3 5.5 3.3 0.28
SDW-HP = shoot dry weight at HP, SDW-LP = shoot dry weight at LP, PuPE-HP = phosphorus
uptake efficiency at HP, PuPE-LP = phosphorus uptake efficiency at LP, PUE-HP =
phosphorus use efficiency at HP, PUE-LP = phosphorus use efficiency at LP
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N
o
o
f
L
i
n
e
s
1 2 3 4 5
A = Response to P-Fertilizer Groups
Figure 6.1 Bar charts showing the distribution of A) Response to P Fertilization, and B)
Tolerance to low phosphorus condition scores.
Where 1 = highly responsive, 2 = responsive, 3 = moderately responsive, 4 = poorly responsive,
and 5 = least responsive in (A) and a score of 1 = most efficient, 2 = efficient, 3 = moderately
efficient, 4 = inefficient, and 5 = most inefficient for adaptation to low P condition in (B)
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6.3.2 Maker distribution, population structure and linkage disequilibrium
A total of 5,621 cleaned SNPs that passed filtering were distributed across the 11 linkage
groups of cowpea with an average density of 511 SNPs per cowpea chromosome (Vu)/linkage
group. A total of 382 SNPs were placed on Vu01, 399 Vu02, 721 on Vu03, 564 on Vu04, 433
on Vu05, 528 on Vu06, 634 on Vu07, 459 on Vu08, 441 on Vu09, 540 on Vu10, and 510 on
Vu11. SNPs ranged from 382 being the lowest on Vu01 to 721 SNPs on Vu03. The 3D PCA
plots showed no obvious population structure between the lines. The average clustering
algorithm used grouped the lines into four sub-populations based on their inferred kinship with
the mean method (Figure 6.2). Linkage disequilibrium decay plot showed that LD ranged
between 0 and 0.81 and started to decay below 0.2 at about a distance of 1.0 – 2kb.
Figure 6.2: A three-dimensional PC view of the grouping of 400 cowpea lines
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Figure 6.3: A Linkage disequilibrium decay plot of cowpea lines over distance
6.3.3 Genome-wide association mapping for Low P tolerance and response to P
fertilization
The genome-wide association analysis conducted with 386 lines and 5, 621 DArT derived
SNPs using the compressed MLM in GAPIT resulted in the identification of significant SNPs
associated with adaptation to low P tolerance and response to applied mineral P fertilizer. The
threshold for declaring significance for SNPs was set at 10-3 for the traits assessed. The
quantile-quantile (QQ) plots showed that observed distribution was close to normal and the
model fit well between the observed and expected p-values with some outliers indicating
significant SNPs for shoot dry weight, P uptake and use efficiency (Figures 6.4 – 6.5)
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High P High P
Low P
Low P
Figure 6.4: Manhattan plots and quantile-quantile plots for shoot dry weight at high and low P conditions
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High P
High P
Low P Low P
Figure 6.5: Manhattan plots and quantile-quantile plots for phosphorus uptake efficiency at high and low P conditions
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High P High P
Low P
Low P
Figure 6.6: Manhattan plots and quantile-quantile plots for phosphorus use efficiency at high and low P conditions
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A total of 65 markers with percentage of phenotypic variance explained (PVE) of greater or equal to 3%
were associated with tolerance to low P and response to applied P fertilizer conditions measured via
shoot dry weight, P uptake and use efficiency (Table 6.2 - 6.4), the phenotypic variance explained (PVE)
(R-squared) ranged from 3 – 5%. Most of the markers detected for response to applied P fertilizer
measured as shoot dry weight were placed on chromosome Vu03 while markers for tolerance to low P
condition measured using the same trait were detected on Vu07 (Table 6.2).
Seven SNPs found on chromosome 3, 5 and 9 were significantly associated with response to P-
fertilization measured via P uptake efficiency with PVE ranging from 3 – 3.9% while tolerance to low
P condition measured as P uptake efficiency had 10 significant SNPs with PVE that ranged from 3.1 -
4.2% on chromosomes 2, 3, 4, 5, 7, 8, 10 and 11 (Table 6.3). The MLM revealed ten SNPs significantly
linked to response to P and tolerance to low P as measured from P use efficiency, these SNPs were found
on chromosomes 3, 4, 6, 7, 8, 9, 11 explaining on 3.0 - 3.7% and 4.1 – 5.1% respectively. SNPs with
the highest peak had both positive and negative allelic effects that increased and decreased the value of
the trait under both P conditions (Table 6.4).
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Table 6.2: SNPs associated with shoot dry weight at high and low P conditions
SNP Chrom Position P.value MAF R.square Allelic effect
HP Vu_100355825_7007 3 62253733 0.00037 0.07 0.05 -0.11
Vu_100155267_2554 3 60000957 0.00039 0.46 0.05 0.01
Vu_14077134_3423 3 60007556 0.00044 0.46 0.05 -0.01
Vu_100425302_10397 6 30604767 0.00052 0.11 0.05 0.06
Vu_100472254_13698 3 59985754 0.00096 0.38 0.05 0.00
Vu_14074546_15962 6 26647588 0.00120 0.35 0.04 0.01
Vu_14075813_6801 2 28324743 0.00124 0.39 0.04 0.01
Vu_14074035_12086 11 41268412 0.00127 0.08 0.04 -0.13
Vu_100158904_3719 3 63878878 0.00135 0.18 0.04 0.02
Vu_14082997_7772 3 59983984 0.00204 0.44 0.04 -0.12
Vu_14086785_17186 6 13767246 0.00239 0.23 0.04 0.05
Vu_14076927_7576 3 62279455 0.00250 0.07 0.04 -0.06
Vu_14082997_7771 3 59983984 0.00267 0.45 0.04 -0.13
Vu_14077501_7958 3 10167577 0.00283 0.11 0.04 -0.09
LP Vu_100149271_602 7 25039544 0.00038 0.14 0.05 0.06
Vu_100358211_7923 8 2169987 0.00090 0.16 0.05 0.09
Vu_14080179_16625 7 6377515 0.00091 0.32 0.05 -0.01
Vu_14075625_12451 4 40215362 0.00103 0.18 0.05 -0.17
Vu_14084227_14493 7 6284711 0.00106 0.32 0.04 -0.09
Vu_14074876_1600 8 2654522 0.00174 0.13 0.04 -0.02
Vu_100455158_12154 8 36719407 0.00246 0.45 0.04 -0.04
Vu_100354086_6297 3 40520235 0.00253 0.42 0.04 0.06
Vu_100483211_14785 4 3525842 0.00266 0.23 0.04 0.00
Vu_14074942_119 9 29574865 0.00282 0.37 0.04 -0.03
Vu_100454153_11739 1 39106476 0.00292 0.32 0.04 0.00
Vu_14083246_16909 7 6034243 0.00293 0.41 0.04 -0.11
Vu_14086622_17295 7 6031166 0.00298 0.40 0.04 -0.05
Vu_14073620_42 7 6137759 0.00305 0.32 0.04 0.00
HP = high P (representing response to applied P fertilizer), LP = low P (indicating tolerance to low P)
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Table 6.3: SNPs associated with P uptake efficiency at high and low P conditions
SNP Chrom Position P.value MAF R.square Allelic effect
HP Vu_100479597_14367 5 9820268 0.000112 0.188 0.039 -0.066
Vu_100454775_12003 9 23986375 0.00021 0.088 0.036 -0.145
Vu_100472254_13698 3 59985754 0.000358 0.381 0.033 0.000
Vu_14054709_5364 5 9784841 0.000533 0.141 0.031 0.078
Vu_100155267_2554 3 60000957 0.000536 0.460 0.031 0.004
Vu_14075593_8065 9 24261588 0.000563 0.098 0.031 -0.074
Vu_14082997_7772 3 59983984 0.000618 0.443 0.030 -0.007
LP Vu_100152691_1695 3 21914473 0.00047 0.127 0.042 -0.484
Vu_100423375_9585 8 18887233 0.000545 0.214 0.041 -0.324
Vu_100354086_6297 3 40520235 0.001747 0.416 0.035 -0.098
Vu_14087299_17924 4 39860454 0.002081 0.183 0.034 -0.137
Vu_14078738_11153 7 19499315 0.002479 0.259 0.034 -1.645
Vu_100352419_5612 5 47108004 0.002742 0.181 0.033 -0.135
Vu_14056133_14827 10 32654849 0.002879 0.168 0.033 -0.858
Vu_14085209_14149 2 23373744 0.002951 0.339 0.033 -0.061
Vu_100352225_5524 2 23273789 0.00314 0.222 0.032 0.067
Vu_14057854_12585 11 36681904 0.003758 0.155 0.031 -0.264
HP = high P (representing response to applied P fertilizer), LP = low P (indicating tolerance to low P)
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Table 6.4: SNPs associated with P use efficiency at the high and low P conditions
SNP Chrom Position P.value MAF R.square Allelic effect
Vu_100154399_2285 11 30941527 0.000708 0.196 0.037 -0.088
HP
Vu_100455625_12350 3 45363364 0.001018 0.172 0.035 -0.017
Vu_14082376_8140 4 1775753 0.001102 0.465 0.035 -0.076
Vu_14083701_14366 11 32477000 0.001275 0.282 0.034 0.035
Vu_14087168_14382 6 14581072 0.001276 0.439 0.034 0.013
Vu_100423814_9773 11 30951943 0.001616 0.214 0.033 0.124
Vu_14079476_13189 3 7149479 0.001906 0.184 0.032 -0.070
Vu_14082750_7332 3 15271540 0.002825 0.247 0.030 -0.098
Vu_14084028_8631 8 3995911 0.002904 0.201 0.030 -0.020
Vu_14086413_11353 6 22351425 0.00327 0.166 0.030 0.077
Vu_100149271_602 7 25039544 0.000372 0.136 0.051 0.110
LP
Vu_100358211_7923 8 2169987 0.001029 0.162 0.046 0.012
Vu_14083552_14308 4 16058911 0.001095 0.183 0.046 -0.117
Vu_14074942_119 9 29574865 0.001207 0.370 0.045 -0.026
Vu_14086780_18030 8 4631171 0.002004 0.078 0.043 -0.087
Vu_100160047_3946 3 59782784 0.002171 0.127 0.043 0.020
Vu_100455158_12154 8 36719407 0.002431 0.446 0.042 0.010
Vu_14076603_16728 3 58772456 0.002728 0.372 0.041 0.144
Vu_14075749_6769 7 28778480 0.003009 0.078 0.041 0.075
Vu_14081851_4855 8 37387039 0.003036 0.150 0.041 0.023
HP = high P (representing response to applied P fertilizer), LP = low P (indicating tolerance to low P)
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6.4 Discussion
In this study, differences in performance of cowpea under low P stress and response to synthetic P-
fertilizer were found among the 400-cowpea lines tested. Several authors have reported significant
differences in cowpea for adaptation to low P conditions and response to rock phosphate application or
synthetic P-fertilizers (Abdou, 2018; Karikari & Arkorful, 2015; Mawo et al., 2016). Diversity panels
serve as important sources of genes desired for improving cowpea for tolerance to biotic and abiotic
stresses such as poor soil fertility of P. The frequency distribution from scores of shoot dry weight used
to define tolerance to low P and response to applied P showed continuous distribution and further
indicated that the traits are quantitative, which agrees with previous findings (Mahamane et al., 2006;
Ravelombola et al., 2017).
Existence of potential population structure that could lead to the false marker-traits association was
investigated among the collection of lines used. There were small four sub-groups present among the
lines used, revealed with the aid of 3D principal component clustering. Absence of major structure in
the population used was good for accurate genome-wide mapping, as association mapping tends to be
skewed with the presence of obvious population structure (Lucas et al., 2013; Xiong et al., 2016).
Existence of two major gene pools have been identified in the worldwide collection of cowpea in a study
that used SNP markers to inferred relatedness and diversity between lines of cowpea, with one gene
pool representing materials from western Africa and the second for materials from eastern and southern
Africa (Huynh et al., 2013; Muñoz-Amatriaín et al., 2017).
Several DArT derived SNPs were associated with adaptation to low P condition and response to mineral
P fertilization. A total of 65 SNP markers were detected to be associated with cowpea adaptation to low
P tolerance and response to applied mineral P fertilizer across all the 11 chromosomes of cowpea except
chromosome 1. The number of SNPs used for association mapping in this study was 5,621 and resulted
in detection of 65 markers, this is over 5-fold more than 1,018 SNPs that lead to the detection of 18
markers by a similar study on cowpea (Ravelombola et al., 2017). A low percentage of phenotypic
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variance explained (PVE) was observed for these SNPs (3 - 5%) compared to PVE for SNPs mapped
from a biparental QTL mapping studies (Boukar et al., 2016; Lo et al., 2018), where PVE of 20 - 85%
have been reported for some traits like flowering, seed size and pod shattering. The low PVE reported
in this study are comparable with 2 - 7% PVE from recent studies at Texas A & M University, USA on
some 375 USDA cowpea accessions for low P tolerance and response to rock phosphate (Ravelombola
et al., 2017). The work by these authors is the first known report on SNP association with P traits in
cowpea. The present work is a complement and further improvement over the reports of Ravelombola
et al. (2017) for a few reasons; response to P fertilization in that study was investigated using rock
phosphate, a relatively less soluble form of P compared to synthetic and readily soluble P source (SSP)
used in the present study. Secondly, their work was conducted in the greenhouse, an artificial growth
environment where cowpea plant does not express full potential compared to field-grown cowpea plants
used in the present study.
Studies on the association of cowpea with P use efficiency traits are generally scarce. The first attempt
of using molecular markers to detect an association between cowpea and P use was made by Rothe
(2013), where four SSR markers using a biparental population were reported to be associated with P use
efficiency measured as shoot dry weight and the PVE explained by those SSR markers ranged from 6 –
15%. In addition, similar work demonstrating effects of low and high P soil conditions on N-fixation
traits identified QTLs for number of nodules, nodules dry weight, shoot dry weight, number of pods and
hundred seed dry weight in cowpea (Fonji, 2015).
However, there are more reports in common bean and soybean on P traits than in cowpea and such
reports can help explain the genetic architecture underlying P use and uptake in cowpea due to close
synteny between these crops (Lucas et al., 2011; Lucas et al., 2013). The close relationship between
cowpea and common bean was further strengthened by the recent consensus of cowpea community to
align the numbering of cowpea linkage groups with those of common bean (Lo et al., 2018). QTLs for
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P uptake have been identified to be associated with common bean’s performance in the low P field (Yan
et al., 2004) explaining about 14% of PVE. In another study by Beebe et al. (2006), 26 QTLs were
detected to be associated with P acquisition (same as uptake) in common bean using 86 biparental
recombinant inbred lines.
Results from the present study and from close relatives of cowpea will serve as an important source of
knowledge in mapping loci for P association in cowpea. It will also provide a basis for implementing
marker-assisted selection for cowpea improvement for adaptation to low P soils and response to external
P application. Marker-assisted selection is promising as an effective approach to screening and selection
with higher precision for quantitatively inherited traits and especially those difficult to measure like P
use and uptake efficiency (Asoro et al., 2013; Batieno et al., 2016; Lucas et al., 2013). The DArT GBS
technology used in the current work has been used successfully in other crops for genetic linkage
construction, QTL mapping in biparental and genome-wide association mapping, studying genetic
diversity, and genomic prediction (Sánchez-Sevilla et al., 2015).
6.5 Conclusions
An association panel consisting of 400 lines of cowpea was evaluated under two contrasting soil
phosphorus regimes for adaptation to low P tolerance and response to applied synthetic P fertilizers.
There were significant differences in the performance of the lines under the different P regimes. A total
of 65 DArT SNPs were detected to be associated with adaptation to low P tolerance and response to P
fertilizer in cowpea. The diversity in performance and SNPs association will provide a basis for selecting
superior lines for P improvement in cowpea. This is the first report on cowpea SNPs association with P
traits on cowpea grown under field conditions and using materials relevant to African breeding
programmes.
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CHAPTER SEVEN
General Summary, Conclusions and Recommendations
Cowpea is the major grain legume with widespread cultivation across the study areas. This research
investigated the level of phosphate-based fertilizer use among cowpea farmers and found that farmers
are aware that fertilizers were important for sustaining crop growth and increased yield, but few were
aware that cowpeas require P-based fertilizers to achieve increased yield. An investigation into
prevailing cowpea cropping systems revealed that the traditional approach of planting the crop in mixed
intercrop with cereals is still in place despite the availability of higher yielding planting patterns such as
2 - 4 cereal - cowpea intercropping system. The study found that farmers predominately cultivated
landraces and the use of improved varieties of cowpea was very low among the farming communities
studied. Higher grain yield was the most important trait cowpea farmers desired to have in a variety and
insect pests were identified as the top constraints impeding cowpea productivity across the study areas.
Thirty diverse cowpea lines were screened at varying levels of P nutrient under both nutrient-media and
field conditions. Results revealed significant differences in the performance of elite cowpea lines under
sub-optimal and high P conditions. Four different groups were identified among cowpea lines regarding
soil P conditions; these were efficient responsive, inefficient responsive, efficient non-responsive and
inefficient non-responsive lines. Classification based on ability to use inherent low soil P and response
to applied P is critical to identify lines suitable for cultivation in different agro-ecologies. As per
expectation, performance under high P was generally superior over low P condition.
A biparental RIL population differing in performance under low and high P was evaluated for
identification of QTLs and SNP markers associated with cowpea performance under varying soil P
conditions. The results revealed genomic regions and SNPs underlying P use and uptake in cowpea.
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Furthermore, a genome-wide association mapping study was employed in addition to biparental QTL
mapping to identify historic recombination events between important genomic loci and SNPs associated
with tolerance to low P and response to applied P fertilization.
There is a need for advocacy and extension outreach to educate farmers on the need to use P based
fertilizers on cowpea, implement higher yielding sole or intercropping systems and use available
improved varieties. Breeding programmes should take into account grain yield in addition to any
tolerance to biotic and abiotic tolerance that a new variety will possess to ensure adoption by farmers.
Cowpea varieties grouped as P efficient responsive should be used by resource-poor farmers. Further
studies are necessary to validate SNPs and QTLs identified in this study in different genetic backgrounds
and more environments before they could be used in marker-assisted selection for P use and acquisition
efficiency.
Findings would be relevant for breeding cowpea varieties with potential to produce good yield in soils
with sub-optimal P content for the benefits of smallholder cowpea farmers and consumers using marker-
assisted selection, this will facilitate the development of more efficient cowpea varieties with adaptation
to low soil P and desired yield qualities using a combination of conventional and molecular breeding
approaches.
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LITERATURE CITED
AATF. (2010). Cowpea Productivity Improvement - Guarding Against Insect Pests. Retrieved January
28, 2015, from http://cowpea.aatf-africa.org/cowpea-productivity-improvement-guarding-against-
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APPENDICES
Appendix 1: Villages/Sites Visited and their Waypoints
S/No Village Local Govt. Area State Lat. Long. Elev.(m)
1 Mando Birnin Gwari Kaduna 10.712086 6.566043 447.57
2 Ungwar Shitu Birnin Gwari Kaduna 10.661994 6.547136 438.274
3 Ungwar Shehu Rimi Birnin Gwari Kaduna 10.663703 6.426499 447.138
4 Giwa Town Giwa Kaduna 10.663753 6.426494 623.329
5 Gangara Giwa Kaduna 11.33893 7.384577 642.998
6 Ungwar Sarki Giwa Kaduna 11.240454 7.299287 665.741
7 Kallah Kajuru Kaduna 10.41908 7.802887 604.615
8 Rimau Kajuru Kaduna 10.411118 7.76336 642.366
9 Kasuwa Magani Kajuru Kaduna 10.401078 7.713589 692.842
10 Dan Guzuri Makarfi Kaduna 11.367094 7.830234 652.654
11 Tudun Wada Makarfi Kaduna 11.380765 7.885615 683.793
12 Angwar Bazai Makarfi Kaduna 11.231088 8.056419 666.302
13 Bataiya Albasu Kano 11.683822 9.031088 400.887
14 Panda Albasu Kano 11.605747 9.045033 453.9
15 Tsangaya Albasu Kano 11.612426 9.214769 476.157
16 Bunkure Bunkure Kano 11.698308 8.542983 439.993
17 Fallungu Bunkure Kano 11.735604 8.531023 467.997
18 Gurjiya Bunkure Kano 11.752846 8.560069 437.772
19 Yarganda Tsanyawa Kano 12.329073 8.075072 555.08
20 Harbau Tsanyawa Kano 12.326418 8.065062 565.122
21 Kabagiwa Tsanyawa Kano 12.310815 8.022003 573.567
22 Wasai Minjibir Kano 12.153197 8.67926 414.1
23 Zura Malam Lade Minjibir Kano 12.220525 8.574501 465.845
24 Dingin Minjibir Kano 12.171027 8.647709 450.027
25 Karaduwa Matazu Katsina 12.310354 7.685409 496.798
26 Sayaya Matazu Katsina 12.272257 7.644313 518.656
27 Matazu Town Matazu Katsina 12.242213 7.676314 533.293
28 Nasarawa Shadakotoma Kaita Katsina 13.087478 7.69949 445.824
29 Kafin Mashi Kaita Katsina 13.054806 7.700289 465.031
30 Yan Hoho Kaita Katsina 13.060463 7.734234 464.5
31 Tandama Danja Katsina 11.441281 7.439383 692.248
32 Kahutu Danja Katsina 11.37923 7.693465 678.526
33 Danja Town Danja Katsina 11.375105 7.565422 684.628
34 Mahuta A Dandume Katsina 11.444371 7.26894 716.186
35 Dantankari Dandume Katsina 11.444875 7.15717 719.654
36 Tumburkai Dandume Katsina 11.305977 7.231209 674.851
154