ABSTRACT
The study was carried out to assess the yield stability of some taro (Colocasia esculenta (L.) Schott) genotypes commonly grown by farmers in Nigeria as influenced by some cropping system management practices (two locations in two years). The experiment was laid out as a split plot in a randomized complete block design with three replications. The six genotypes used were: NCe 001, NCe 002, NCe 003, NCe 004, NCe 005 and NCe 010. Data were collected on the growth characteristics and yield attributes. There was significant effect of crop management system on most of the growth and yield attributes measured. The genotypes varied significantly (p<0.001) in their performance for almost all the traits studied. Location had significant effect on taro yield with Ishiagu giving the best performance for growth and most of the measured yield on attributes. Genotypes had strong influence all the characters considered in this study with NCe 002 and NCe004 consistently giving the best performance in yield and few of the studied traits. This indicates that these genotypes show good adaptation to the soil and climatic conditions of these agro-ecosystems. AMMI showed high significance on the taro yield. Effects of genotype and environment were highly significant. The main effects (genotypes and environments) captured 62.9% of the total sum of squares (TSS) while the genotype by environment interaction (GEI) contained 8.2% of the TSS for population density. Under propagule the main effects (genotype and environment) captured 52.1% of the total sum of squares (TSS) while the GEI explained 17.2% of the total variation. For fertilizer rates, the main effects (genotypes and environments) captured 87.6% of the total sum of squares (TSS) while the GEI explained 4.5% of the TSS. The linear correlation (r) coefficients between the various attributes and the yield as affected by fertilizer rates showed that the correlation in number of secondary suckers and cormel number per plant was positively and significantly associated with yield (t/ha), r = 0.223** and r = 0.169*, respectively. However, plant height, number of leaves, leaf area, cormel length and cormel circumference were negatively but significantly associated with yield (t/ha). With regards to the linear correlation (r) coefficients between the various attributes and the yield per hectare as affected by propagule sizes, the cormel number per plant and cormel weight per plant were positively and significantly associated with yield (t/ha), r = 0.549** and r = 0.330*, respectively. However, plant height, number of leaves, number of secondary suckers and leaf area were negatively but significantly associated with yield (t/ha). In 2016, plant h==[-eight was the largest contributor to yield accounting for up to 18 % of the total yield (B = 0.421), while cormel number per plant was observed to be the largest contributor to yield accounting for up to 38 % of the total yield (B = 0.619; P<0.001) in 2017.
TABLE OF CONTENTS
Title Page i
Declaration ii
Certification iii
Dedication iv
Acknowledgements v
Table of Contents vi
List of Tables xi
List of Figures xvi
Abstract xvii
CHAPTER 1: INTRODUCTION 1
CHAPTER 2: LITERATURE REVIEW 7
2.1 Origin
and Domestication of Taro 7
2.2 Taxonomical Description of Taro 8
2.3 Botany
and Ecology of Taro 11
2.3.1 Classification of taro 11
2.4 Taro
Morphology and Anatomy 11
2.5 Growth Cycle and Developmental Stages of
Taro 14
2.5.1 Establishment 15
2.5.2 Vegetative growth and corm initiation 15
2.5.3 Corm bulking and maturation 16
2.6 Nutritional Content of Taro 16
2.7 Genetic Diversity Assessment of Taro 18
2.8 Methods of Assessing Genetic Diversity 20
2.8.1 Morphological markers 20
2.8.2 Genetic markers 21
2.9 Economic
Importance and Uses of Taro 23
2.10 Yield and Yield Attributes 25
2.11 Environmental
Requirements for the Cultivation of Taro 29
2.11.1 Water 29
2.11.2 Photoperiod and light intensity 30
2.11.3 Soils 31
2.12 Taro
Cultivation 31
2.13 Taro Planting 32
2.14 Planting Density 32
2.15 Factors
Influencing Taro Quality 33
2.15.1 Genetic factors 34
2.15.2 Planting date 35
2.15.3 Temperature 35
2.15.4 Rainfall 37
2.16 Fertilizer Rate 38
CHAPTER 3: MATERIALS AND METHODS 42
3.1 Experiment I: Genotypes by Planting
Density Interaction in Some Taro Genotypes 42
3.2 Experiment II: Effect of Different
Fertilizer Rates on the Yield Stability
of
Some Taro Genotypes 45
3.3 Experiment III: Effects of Size of
Propagules on Yield Stability of Some
Taro
Genotypes 46
CHAPTER 4: RESULTS 49
4.1 Soil Physicochemical
Properties of Experimental Sites 49
4.2 Agro-Meteorological
Data of the Experimental Sites 49
4.3 Effects of Planting Density on Growth,
Yield and Yield Stability of
Some
Taro Genotypes 54
4.3.1 Effects
of planting density on growth attributes 54
4.3.1.1. Effect of planting density on plant height
(cm) of some taro genotypes
at 8, 12, 16 and 20 WAP in two
locations 54
4.3.1.2.Effect of planting density on number of leaves of some taro genotypes
at
8, 12, 16 and 20 WAP in two locations 60
4.3.1.3 Effect of planting density on leaf area of
some taro genotypes at 12,
16 and 20 WAP in two locations 64
4.3.1.4 Effect of planting density on number of
secondary suckers of
some taro genotypes at 12, 16 and 20
WAP in two locations 69
4.3.2 Effect
of planting density on yield and yield associated traits 72
4.3.2.1 Effect of planting density on taro yield 72
4.3.2.2 Effect of planting density on cormel length 74
4.3.2.3 Effect of planting density on cormel
circumference 74
4.3.2.4 Effect of planting density on number of cormels
per plant 77
4.3.2.5 Effect of planting density on cormel weight per
plant as influenced by plant population
density 79
4.3.2.6 Effect of planting density on corm length 82
4.3.2.7 Effect of planting density on corm
circumference 84
4.3.2.8 Effect of planting density on corm weight per
plant 84
4.4 Effects of Different Fertilizer Rates on
Growth, Yield and Yield
Stability
of Some Taro Genotypes 86
4.4.1 Effects
of fertilizer rates on growth attributes 86
4.4.1.1 Effect of fertilizer rate on plant height
(cm) of some taro genotypes
at 8, 12, 16 and 20 WAP in two
locations 86
4.4.1.2 Effect of fertilizer rate on number of
leaves of some taro genotypes
at
8, 12, 16 and 20 WAP in two locations 92
4.4.1.3 Leaf area (cm2) at 8, 12, 16 and
20 WAP of some taro genotypes as
affected
by location and fertilizer rates. 98
4.4.1.4 Number of secondary suckers at 8, 12, 16 and
20 WAP of some taro
genotypes as influenced by location and fertilizer
rates. 104
4.4.2 Effect
of fertilizer rates on yield and its associated traits 110
4.4.2.1 Effect of fertilizer rates on yield per hectare
per tonne 110
4.4.2.2 Effect of fertilizer rates on corm circumference 112
4.4.2.3
Effect
of fertilizer rates on corms length 112
4.4.2.4 Effect of fertilizer rates on cormel length 112
4.4.2.5 Effect of fertilizer rate on corm weight per
plant 115
4.4.2.6 Effect of fertilizer rate on cormel
circumference 115
4.4.2.7 Effect of fertilizer rate on number of cormels
per plant 115
4.5 Effects of Propagule Size on Growth,
Yield and Yield Stability of
Some
Taro Genotypes 119
4.5.1 Effects
of propagule size on growth attributes 119
4.5.1.1 Effect of propagule size on plant height
(cm) of some taro genotypes
at 8, 12, 16 and 20 WAP in two
locations 119
4.5.1.2 Effect of propagule size on number of leaves
of some taro genotypes
at 8, 12 and 16
WAP in two locations 125
4.5.1.3 Effect of propagule weight on leaf area of
some taro genotypes at 8, 12,
16 and 20 WAP in
two locations 128
4.5.1.4 Effect of propagule weight on number of
secondary suckers of
some taro
genotypes at 8, 12, 16 and 20 WAP in two locations 133
4.5.2 Effect
of propagule size on yield and yield-associated traits 138
4.5.2.1 Effect of propagule size on yield per hectare 138
4.5.2.2 Effect propagule size on corm circumference 138
4.5.2.3 Effect propagule size on corm length 141
4.5.2.4 Effect propagule size on cormel circumference 141
4.5.2.5 Effect propagule size on cormel length 141
4.5.2.6 Effect propagule size on number of cormels per
plant 145
4.5.2.7 Effect propagule size on cormel weight per
plant 147
4.5.2.8 Effect propagule size on corm weight per plant 147
4.6 Yield Stability
Analyses 150
4.6.1 Population density 150
4.6.2 Propagule
size 159
4.6.3 Fertilizer rates 168
1.
2.
3.
4.
4.7
Correlation Analysis 176
4.7.1 Correlation
analysis of the growth and yield attributes of taro as
affected by the fertilizer rates in the first planting
year (2016) 176
4.7.2 Correlation analysis of the growth and yield
attributes of taro as affected
by
the fertilizer rates in the second
planting year (2017) 178
4.7.3 Correlation
analysis of the growth and yield attributes of taro as affected
by the propagule size in the first planting year 180
4.7.4 Correlation
analysis of the growth and yield attributes of taro as affected
by the propagule size in the second planting year 182
4.8
Multiple
Regression Studies 184
CHAPTER 5: DISCUSSION AND CONCLUSION 183
References
LIST
OF TABLES
4.1: Soil
physico-chemical characteristics of the study site for 2016
and 2017 cropping season. 50
4.2: Agro-meteorogical
data of the experimental sites 52
4.3: Agro-meteorogical
data of the experimental sites 2017 53
4.4: Plant
height (cm) of some taro genotypes at 8 WAP across two locations
in
two planting seasons 56
4.5: Plant
height (cm) of some taro genotypes at 12 WAP across two
locations
in two planting seasons 57
4.6: Plant
height (cm) of some taro genotypes at 16 WAP across two
locations
in two planting seasons 58
4.7: Plant
height (cm) of some taro genotypes at 20 WAP across two locations
in
two planting seasons 59
4.8: Number
of leaves of some taro genotypes at 12 WAP across two
locations in two planting seasons 61
4.9: Number
of leaves at 16 WAP across locations in two planting seasons 62
4.10: Number
of leaves of some taro genotypes at 20 WAP across two
locations in 2016 planting season 63
4.11:
Leaf area of some taro genotypes at 8
WAP across locations in two
planting
seasons 65
4.12: Leaf
area of some taro genotypes at 12 WAP across locations in two
planting
seasons 66
4.13: Leaf
area of some taro genotypes at 16 WAP across locations in two
planting
seasons 67
4.14: Leaf
area of some taro genotypes at 20 WAP across locations in 2016
planting
season 68
4.15: Number
of secondary suckers of some taro genotypes at 8 WAP across
locations
in 2016 planting seasons 70
4.16: Number
of secondary suckers of some taro genotypes at 12 WAP across
locations
in 2016 planting seasons 70
4.17: Number
of secondary suckers of some taro genotypes at 16 WAP across
locations in 2017 planting season 71
4.18: Number
of secondary suckers of some taro genotypes at 20 WAP across
locations
in two planting seasons 71
4.19: Yield
(t/ha) of some taro genotypes across two locations in two
planting seasons 73
4.20: Cormel
length (cm) of some taro genotypes across locations in two
planting
seasons 75
4.21: Cormel
circumference (cm) of some taro genotypes in two locations in
2016
and 2017 76
4.22: Number
of cormels per plant of some taro genotypes across two locations
in two planting seasons 77
4.23:
Cormel weight (kg plant-1)
of some taro genotype in 2016 planting
season
80
4.24: Cormel
weight per plant (kg plant-1) of some taro genotypes in locations 81
4.25: Corms
length (cm) of some taro genotypes across locations in two
planting
seasons 85
4.26: Corms
circumference (cm) of some taro genotypes across two locations in
2017
planting season 85
4.27: Corms
weight (kg) per plant of some taro genotypes in two locations as
influenced
by plant population density 85
4.28: Plant
height (cm) of some taro genotypes as affected by location and
fertilizer in 2016 and 2017 planting
seasons at 8 WAP 88
4.29: Plant
height (cm) of some taro genotypes as affected by location and
fertilizer
in 2016 and 2017 planting seasons at 12 WAP 89
4.30: Plant
height (cm) of some taro genotypes as affected by location and
fertilizer
in 2016 and 2017 planting seasons at 16 WAP 90
4.31: Plant
height (cm) of some taro genotypes as affected by location and
fertilizer
in 2016 and 2017 planting seasons at 20 WAP 91
4.32: Number
of leaves at 8 WAP of some taro genotypes as affected by
locations
and fertilizer rates across locations in two years 94
4.33: Number
of leaves at 12 WAP of some taro genotypes as affected by
locations
and fertilizer rates across locations in two years 95
4.34: Number
of leaves at 16 WAP of some taro genotypes as affected by
locations
and fertilizer rates across locations in two years 96
4.35: Number
of leaves at 20 WAP of some taro genotypes as affected by
locations and fertilizer rates across
locations in two years 97
4.36: Leaf area (cm2) of some taro
genotypes at 8 WAP across locations in
two
years 100
4.37:
Leaf area (cm2) of
some taro genotypes at 12 WAP across locations in
two years 101
4.38: Leaf
area (cm2) of some taro genotypes
at 16 WAP across locations
in two years 102
4.39:
Leaf area (cm2) of some taro genotypes at 20 WAP across
locations
in two years 103
4.40:
Number of secondary suckers (NSS) of some taro genotypes
at
8 WAP across locations in two years 106
4.41:
Number of secondary suckers (NSS) of some taro genotypes
at
12 WAP across locations in two years 107
4.42:
Number of secondary suckers (NSS) of some taro genotypes
at 16 WAP across locations in two years 108
4.43:
Number of secondary suckers (NSS) of some taro genotypes
at 20 WAP across locations in two years 109
4.44: Yield
(t/ha) of some taro genotypes across location in two years 111
4.45: Corm
circumference of some taro genotypes across locations in two years 113
4.46: Corms
length (cm) of some taro genotypes across locations in 2016 114
4.47: Cormel
length (cm) of some taro genotypes across location in 2017 114
4.48:
Corms weight (kg) per plant of some taro genotypes across locations in
2017
116
4.49: Cormel
circumference (cm) of some taro genotypes across location in
2017 117
4.50: Cormel
number/plant of some taro genotypes across location in 2017 118
4.51: Plant height (cm) of some taro genotypes at
8WAP across location
in two years 121
4.52:
Plant height (cm) of some taro genotypes at 12WAP across location
in
two years 122
4.53:
Plant height (cm) of some taro genotypes at 16WAP across location
in two years 123
4.54:
Plant height (cm) of some taro genotypes at 20WAP across two locations
in two years 124
4.55:
Number of leaves of some taro genotypes at 8WAP across locations
in
two years 126
4.56:
Number of leaves of some taro genotypes at 12WAP across locations
in 2017 127
4.57: Number
of leaves of some taro genotypes at 16WAP across locations
in two years 127
4.58:
Leaf area (cm) of some taro genotypes at 8WAP across
location in two years 129
4.59: Leaf
area (cm) of some taro genotypes at 12WAP across locations
in 2016 130
4.60: Leaf
area (cm) of some taro genotypes at 16 WAP across
location in two years 131
4.61: Leaf area (cm) of some taro genotypes at
20WAP across location
in two years 132
4.62: Number
of secondary suckers (NSS) of some taro genotypes
at
8WAP across locations in 2017 134
4.63: Number
of secondary suckers (NSS) of some taro genotypes
at
12WAP across locations in 2016 135
4.64:
Number of secondary suckers (NSS) of some taro genotypes
at 16WAP across location in two years 136
4.65: Number
of secondary suckers (NSS) of some taro genotypes
at 20WAP across locations in two years 137
4.66: Yield
(t/ha) of some genotypes at across locations in two years 139
4.67: Corm
circumference of some genotypes at across locations in two years 140
4.68: Corm
length of some genotypes across locations in two years 142
4.69: Cormel
circumference (cm) of some genotypes
across location in two years. 143
4.70: Cormel
length (cm) of some genotypes across locations in 2016 144
4.71: Number
of cormels/plant of some genotypes across locations in two years 146
4.72:
Cormel weight (g) per plant of some
genotypes across locations in 2017 148
4.73: Corms
weight per plant of some genotypes at across locations in 2017 149
4.74: AMMI analysis of
variance for the yield (tha-1) of some taro genotypes
grown at 12 environments
(combination of 2 locations, two years
and 3
population density levels) 152
4.75: AMMI analysis of variance for the yield (tha-1)
of some taro genotypes
grown at 16 environments
(combination of 2 locations, two years
and 4
propagule size levels) 161
4.76: AMMI analysis of variance for the yield (tha-1)
of some taro genotypes
grown at 24 environments
(combination of 2 locations, two years and 6
fertilizer
rates) 169
4.77: Correlation
analysis of the growth and yield attributes of taro as
affected
by the fertilizer rates in the first planting season (2016) 177
4.78: Correlation
analysis of the growth and yield attributes of taro as affected
by
the fertilizer rates in the second
planting year (2017) 179
4.79: Correlation
analysis of the growth and yield attributes of taro as
affected
by the propagule size in the first planting year (2016) 181
4.80: Correlation
analysis of the growth and yield attributes of taro as
affected
by the propagule size in the second planting year 183
4.81: Multiple
regression (B, step wise) coefficient of determination (R2), R2
change (∆R2) between yield (t/ha) and other
attributes for some taro
genotypes using
different fertilizer rates in 2016 and 2017 185
4.82: Multiple
regression (B, step wise) coefficient of determination (R2), R2
change (∆R2) between yield (t/ha) and other
attributes for some taro
genotypes using different propagule sizes in 2016 and
2017 186
LIST
OF FIGURES
1: The AMMI biplot (IPCA1 vs mean) for the
yield (t/ha) of 6 taro
genotypes across 12 environments as influenced by population
density 153
2: Polygon
view of the GGE biplot showing which taro genotype won
in which
environment as
influenced by population density 155
3: Ranking
genotypes based on both mean and stability relative to an ideal
genotype as
influenced by population density 156
4: Ranking
environments based on both mean and stability relative to
an ideal
environment as
influenced by population density 157
5: The
discrimination and representativeness view of the GGE biplot 158
6: The AMMI
biplot (IPCA1 vs mean) for the yield (t/ha) of 6 taro
genotypes
across 16 environments (propagule size-based) 162
7: Polygon
view of the GGE biplot showing which taro genotype won
in which
environment (propagule
size-based) 164
8: Ranking
genotypes based on both mean and stability relative to an ideal
genotype (propagule
size-based) 165
9: Ranking
environments based on both mean and stability relative to an
ideal environment (propagule
size-based) 166
10: The
discrimination and representativeness view of the GGE biplot
to show the discriminating ability
and representativeness of the test
environments (propagule size-based) 167
11: The AMMI
biplot (IPCA1 vs mean) for the yield (t/ha) of 6 taro
genotypes
across 24 environments (fertilizer rate-based) 170
12: Polygon
view of the GGE biplot showing which taro genotype won
in which
environment (fertilizer
rate based) 172
13: Ranking
genotypes based on both mean and stability relative to an ideal
genotype (fertilizer
rate based) 173
14: Ranking
environments based on both mean and stability relative to
an ideal
environment (fertilizer
rate based) 174
15: The
discrimination and representativeness view of the GGE biplot
to show the discriminating ability
and representativeness of the test
environments (fertilizer rate based) 175
CHAPTER
1
INTRODUCTION
Cocoyam is the common name for two tuber crops Colocasia esculenta and Xanthosoma sagitifolum, Onyeka (2014).
Cocoyam is a stem tuber that is widely cultivated in the tropical regions of
the world and is a well known food plant which has a long history of
cultivation (Ojiaku et al.,
2007). Cocoyam is the third most
important staple root/tuber crop after yam and cassava in Nigeria
(Bandyopadhyay et al., 2011). The
crop provides a cheaper yam substitute especially during period of food
scarcity in many parts of Igbo land (Osawaru and Ogwu, 2014). The Nigerian
Academy of Sciences noted that cocoyam may not after all be a “poor man’s food”
or “a woman crop” but a crop of promising economic value (FAO, 2009). Nigeria
happens to be the largest producer of cocoyam in the world with the production
figure of about 1.8 million tonnes per annum (CBN, 2001) and the production is
being carried out by rural farmers who employ primitive technology and
traditional practices. In fact, Onwueme (1999) had observed that the overall
picture of cocoyam in Nigeria is that of a crop that is “casually produced and
consumed” while Ezedinma, (1987) had reported that and the totality of
published work on cocoyam is insignificant compared to those of rice, cassava
and cowpea. Although it has been for centuries the traditional staple in many
developing countries, it has received least attention by most national research
institutes, extension services and agricultural development planners, despite
its nutritional value and industrial uses.
Cocoyam
is grown as a root crop because of its edible corms and leaves, which are used
as spinach throughout the humid tropics. Okpul (2005) reported that the crop is
now accepted as a crop that can guarantee food security, because it is
relatively low-priced and could therefore feed many low income families.
Nutritionally, cocoyam is composed of 70– 80% water, 20–25% starch and 15–30%
protein (Enyinnaya, 1992). Its leaves are excellent sources of folic acid,
vitamin C, riboflavin and vitamin A. The flour produced from cocoyam is
comparable in properties and bread qualities to that from wheat. Lebot et al. (2006) reported also that cocoyam
corms and cormels are rich in mucilage, which can be utilized in the paper
industry, in medicinal tablet manufacturing and in traditional medicine
practice.
Taro is an important staple crop in rural African countries
but its contribution to food security is limited by lack of research on its
agronomy and commercialization (Marc, 2009). It has relatively small size
starch grains which are easily digestible and therefore acclaimed to be a very
good source of carbohydrate for diabetic patients (Vinning, 2003). The corms
may be cut up and boiled in curries or fried to make crispy. Leaves and
leafstalks can also be cooked and eaten like spinach. It is also used as
ornamental in Australia, Japan, Italy and elsewhere. Its ability to tolerate
salinity makes it suitable for localities where few other crops grow (Grubben
and Denton, 2004).
Although it is an important staple food crop in many
tropical countries, especially Nigeria, yield is still low as a result of poor
genetic improvement (Ogbonna et al.,
2015). There is therefore the need to enhance the production of cocoyam as this
will help to increase food security and alleviate poverty among rural people.
A successful cultivation of any crop or
cultivar in an agro-climatic location depends on its adaptability and its yield
stability. Gene by environment interaction (GEI) normally causes versions in
yield performance across environments. The perturbation of GEI is a critical
puzzle for plant breeders and unraveling this has brought about more ideas and
advances in knowledge of the factors that affect plant boom and improvement
(Xu, 2010).
Yield stability refers to a genotype's
ability to perform consistently at high or low yield throughout a wide range of
environments (Lin and Binns 1994). Assessing the yield and performance of crop
sorts across an extensive variety of environments are critical to permit a
plant breeder to choose high yielding and continuously performing varieties
(Nwangburuka et al., 2011).
Several statistical techniques for reading
GEI results were advanced (Eberhrat and Russe, 1966, Kang, 1990; Crossa, 1990;
Yan, 2001). Amongst these techniques, additive important outcomes and
multiplicative interaction (AMMI) (Gauch, 2006) and Genotype plus Genotype X environment
interactions (GGE, biplot) Yan et al.,
2007), models are generally used for multi-surroundings trial.
AMMI and GGE biplot analyses are
beneficial for easy graphical rationalization of complicated genotype through
surroundings and AMMI and GGE make use of important element analysis. GGE
biplot differs from AMMI based on how the 2 - way table G X E approach is
handled before appearing singular value decomposition (SVD). The AMMI applies
SVD to the records minus the genotype and surroundings method, while GGE biplot
applies SVD to the information minus the surroundings means simplest (Gauch,
2006). Following, the GGE biplot technique composed of ideas, the biplot idea
and GGE concept, these standards are normally used to examine results of web
page regression evaluation of data. GGE biplot better suits for mega
surroundings evaluation, genotype evaluation (mean vs. stability) and study
surroundings evaluation which presents discrimination elasticity or
representatives (Amira et al., 2013).
The popularity of GGE biplot is related to its versatility and ability to
investigate a range of facts with a two-way shape.
Genotype and environment interaction are when two
different genotypes respond to environmental variation in different ways. Genotype and environment
(G & E) interactions are of major concern to plant breeders. Purely
environmental effects, reflecting the different ecological potential of sites
and management conditions, are not of direct concern for the breeder or
recommendation of plant varieties. Genotypic main effects (i.e. differences in
yield between genotypes) provide the only relevant information when genotype x
environment (GE) interaction effects are absent or ignored (Eze et al., 2016). However, differences
between genotypes may vary widely among environments in the presence of GE
interaction effects as large as those reported in extensive investigations
(Delacy et al, 1990;
Annicchiarico, 1997).
Crop
management systems create novel systems that are resilient in the face of
changes in climate and rural demographics. Agriculture is constantly adapting
to change; consider the revolutions in agriculture due to irrigation;
fertilizer; planting density, propagule size, insect, and disease control; and
modern tillage systems. We will need to make similar changes to our cropping
systems as we face future changes in our climate and an increased need for
using resources efficiently (Apantaku, 2000)
The crop responses to planting density varies from
species to species and is highly dependent on such environmental conditions as
soil characteristics, biotic elements and climatic conditions of the area.
Plant spacing according to Baby et al.,
(2001) involves the growing of plants on a plot of land with sufficient space
between each of the plants so that they can develop their roots and shoots
fully. Zarate et al., (2007) have
reported that corm and cormel yield in taro were highest at a density of
120,000 plants/ha compared to yields obtained at 80,500 and 100,000 plants/ha
populations. However, cormel weight decreases with increase in plant population
(Osundare, 2007). Orji et al (2014)
also have reported that the highest total yield of taro was obtained with close
spacing which was contrary to the result obtained by Atiquzzaman et al., (2008). Ukpabi and Nwosu (2010)
reported cocoyam yield can be increased by increasing population but without
increasing amount of planting material.
Yield can be improved by increasing the individual potentials of each
plant or by increasing the yield per surface unit (through high planting
densities).
Cocoyam as an important tuber needs nutrients and
moisture for vigorous growth (Badaruddin et
al., 1999). Soils in this area lack essential nutrients especially those
that enhance growth and development in cocoyam.
Fertilizer requirement vary depending on the soil type, native
fertility, previous cropping, cultural practices and yield levels (FAO, 2013).
The use of inorganic fertilizers in cocoyam production is not common among the
rural farmers as they depend more on farmyard manure and household wastes but
the quantities of these materials available may not be enough for large scale
production. There is then the need to adopt the use of inorganic fertilizers by
these farmers. However, one of the problems facing rural farmers on fertilizers
usage is lack of information on fertilizer types and quantity. NPK fertilizers are an all –in –one source of
plant nutrients for the individual crops and soils. The use of NPK will provide
balanced nutrition and enhance soil fertility, maximize crop yield and
ultimately will boost the agricultural economy. Chukwu and Eteng (2014)
reported a higher yield of Xanthosoma
mafafa with application of 400kg/ha NPK fertilizer in Umudike. Onwueme
(1987) reported that cocoyam requires a lot of potassium which in the
traditional farming system is found in ash left after bush burning. Information
on N.P.K. fertilizer rates of cocoyam and its effects on the genotypes is
scanty in literature and hence needs to be worked on. Propagule size is another
factor that may affect the growth and yield of taro genotypes. Like most of the
tropical root crops, the planting material for taro is bulky, making it
expensive to transport over long distances (Mandal et al., 2013). The availability of planting materials is frequently
a limiting factor in taro production. With planting materials being so scarce,
it is not surprising that some farmers use whatever planting material they can
get. Establishing the effect of propagule size on taro may be a solution to
reduce the bulkiness of taro planting materials. The use of small set is a
means of rapid multiplication of limited quantities of planting materials in
the shortest possible time. Smaller sets are planted when the rains have become
regular and they are planted 25cm apart on well prepared ridges spaced one
metre apart (Ukpabi and Nwosu, 2010). There is limited research work on the
size of propagules of taro and research into various agronomic practices is
needed in order to improve its productivity and yield stability.
This
study was therefore aimed at the following objectives;
1.
To determine yield and yield stability of
some taro genotypes at different planting densities.
2.
To determine the effect of N.P.K.
fertilizer rates on the yield and yield stability of some taro genotypes.
3.
To determine the yield and yield stability
of taro planted with different propagule sizes.
4.
To determine inter-relationship between
yield and associated traits of some taro genotypes.
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