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O P EN A C C ESS
African Journal of
Plant Science
March 2021
ISSN 1996-0824
DOI: 10.5897/AJPS
www.academicjournals.org
About AJPS The African Journal of Plant Science (AJPS) is a peer reviewed open access journal. The journal commenced publication in September 2007. The African Journal of Plant Science covers all areas of plant science such as phytopathology, plant morphology, sustainable plant production, plant pathology and plant microbe biology. Indexing AgBiotechNet, Agricultural Economics Database, Agroforestry Abstracts, Animal Breeding Abstracts, Animal Production Database, Animal Science, Biofuels Abstracts, Botanical Pesticides, CAB Abstracts, CABI’s Global Health Database, Chemical Abstracts (CAS Source Index - CASSI), CNKI Scholar, Crop Physiology Abstracts, Crop Science Database, Environmental Impact, Environmental Science Database, Field Crop Abstracts, Forest Science Google Scholar, Grasslands and Forage Abstracts, Helminthological Abstracts, Horticultural Science, Horticultural Science Abstracts, Irrigation and Drainage Abstracts, Maize Abstracts, Microsoft Academic, Nematological Abstracts, Nutrition Abstracts and Reviews Series A: Human and Experimental, Nutrition Abstracts and Reviews Series B: Livestock Feeds and Feeding, Nutrition and Food Sciences, Ornamental Horticulture, Parasitology Database, Plant Breeding Abstracts, Plant Genetic Resources Abstracts, Plant Genetics and Breeding Database, Plant Growth Regulator Abstracts, Plant Protection Database, Potato Abstracts, Poultry Abstracts, Protozoological Abstracts, Rice Abstracts, Rural Development Abstracts, Seed Abstracts, Soil Science Database, Soils and Fertilizers Abstracts, Soybean Abstracts, Sugar Industry Abstracts, The Essential Electronic Agricultural Library (TEEAL), Veterinary Science Database, VetMed Resource, Weed Abstracts, Wheat, Barley and Triticale Abstracts, World Agricultural Economics and Rural Sociology Abstracts Open Access Policy Open Access is a publication model that enables the dissemination of research articles to the global community without restriction through the internet. All articles published under open access can be accessed by anyone with internet connection. The African Journal of Plant Science is an Open Access journal. Abstracts and full texts of all articles published in this journal are freely accessible to everyone immediately after publication without any form of restriction. Article License All articles published by African Journal of Plant Science are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone to copy, redistribute, remix, transmit and adapt the work provided the original work and source is appropriately cited. Citation should include the article DOI. The article license is displayed on the abstract page the following statement:
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Editors Prof. Amarendra Narayan Misra Center for Life Sciences School of Natural Sciences Central University of Jharkhand Jharkhand, India. Prof. H. Özkan Sivritepe Faculty of Agriculture and Natural Sciences, Konya Food and Agriculture University, Dede Korkut Mah. Beyşehir Cad. No.9 Meram, Konya, 42080 Turkey. Editorial Board Members Dr. Feng Lin Department of Plant, Soil and Microbial Sciences Michigan State University USA. Prof. Roger O. Anderson Biology Department Columbia University Lamont-Doherty Earth Observatory USA. Dr. Alexandre Bosco de Oliveira Plant Science, Federal University of Ceará, Brazi. Dr. Mohamed Mousa Biology, UAE University, UAE. Dr. Aysegul Koroglu Pharmaceutical Botany, Ankara University, Ankara.
Table of Content Grain yield and stability of selected early and medium duration cowpea in Ghana 71 Theophilus Kwabla Tengey, Emmanuel Yaw Owusu, Francis Kusi, George Yakubu Mahama, Frederick Justice Awuku, Emmanuel Kofi Sei, Ophelia Asirifi Amoako and Mohammed Haruna The multi-level table and circular diagnostic chart as alternative taxonomic key formats for plant identification 82 Adepoju Tunde Joseph OGUNKUNLE
Vol. 15(3), pp. 71-81, March 2021
DOI: 10.5897/AJPS2020.2125
Article Number: 1AE20DF66367
ISSN 1996-0824
Copyright © 2021
Author(s) retain the copyright of this article African Journal of Plant Science
http://www.academicjournals.org/AJPS
Full Length Research Paper
Grain yield and stability of selected early and medium
duration cowpea in Ghana
Theophilus Kwabla Tengey*, Emmanuel Yaw Owusu, Francis Kusi, George Yakubu Mahama,
Frederick Justice Awuku, Emmanuel Kofi Sei, Ophelia Asirifi Amoako and
Mohammed Haruna
Cowpea Improvement Program, CSIR-Savanna Agricultural Research Institute, P. O. Box 52, Nyankpala, Ghana.
Received 20 December, 2020; Accepted 10 February, 2021
Changes in climate are a major driver for climate -smart crops with short duration on-field and
adaptation to diverse growing conditions. This study evaluated the performance of nine early duration
and 10 medium duration cowpea genotypes at six locations within the Guinea and Sudan savanna
zones of Ghana. Genotypes for each maturity group were laid out in a Randomized Complete Block
Design with three replications for each location. There were significant (p72 Afr. J. Plant Sci.
(Dakora and Belane, 2019). Cowpea can derive a MATERIALS AND METHODS
substantial amount of its nitrogen nutrition from symbiotic
Experim ental m aterials and location of study
nitrogen fixation (Pule-Meulenberg et al., 2010) and N-
fixation in cowpea has been reported to vary from 16.6- A total of 19 cow pea advanced breeding lines and tw o checks w ere
23.0 kg/ha in Northern Ghana (Naab et al., 2009). used in this study. This includes nine early duration lines w ith
The yield of cowpea in Ghana is, however, still the Kirkhouse Benga as a check variety, and 10 medium duration
lowest in the world averaging 0.5 t/ha despite the genotypes w ith Padituya as check variety (Table 1). Evaluations
w ere conducted in the 2018 cropping season in six environments
numerous importance of this crop (Langyintuo et al., namely, Nyankpala, Yendi, Damongo, Manga, Wa, and Tumu.
2003; Ofosu-Budu et al., 2008). Yield is a quantitative
trait that is influenced by the environment. Cowpea
production is constrained by various field pests, disease Experim ental design and analysis
infestation, amount of rain, drought, and photoperiod and
The experiment w as laid out in a Randomized Complete Block
this varies from one agroecology to another. This
Design w ith three replications. Tw o separate experiments w ere set
differential environmental condition resulting in differing up for each location, one for early duration genotypes and another
yield responses of cowpea genotypes can be attributed to for medium duration genotypes. Experimental design consists of 4
genotype-by-environment interaction (Odeseye et al., row s of 4 m length w ith spacing 20 cm w ithin row s and 60 cm
2018). To identify high-yielding and well-adapted cowpea betw een row s. Crops w ere established under rainfed conditions
genotypes, multi-location evaluation of a large number of w ith no irrigation. Rainfall pattern during the grow ing period is
described in Table 2. Field pests w ere controlled using K-Optimal
diverse improved cowpea genotypes must be carried out (Cyhalothrin 15 g/L + Acetamiprid 20; EC) at the rate of 500 ml per
within the different agro-ecologies of the region. Multi- ha at vegetative, flow ering, and podding stages. Weeds w ere
environment evaluation trials of crop cultivars are manually controlled as, and w hen, necessary. Data on agronomic
essential as genotype performance will be determined performance such as days to 90 % maturity, grain yield and 100
across different environments. Through this, the seed w eight w ere taken for the various trials. Tw o middle row s w ere
genotype by environment analysis can be conducted to harvested (net plots) to estimate grain yield per plot and this w as
converted to t/ha
help identify high yielding and stable genotypes for the Data w ere subjected to analysis of variance (ANOVA) using the
test environments (Asio et al., 2009; Horn et al., 2018; GENSTAT 12th edition (Payne, 2002). Means w ere separated
Sousa et al., 2018). Since genotype-by-environment using LSD at 5 %. The combined analysis w as also conducted to
interaction (GEI) limits selection response there is a need test for the presence of genotype by environment interaction effect.
to investigate its presence. Previous studies have dwelt This led to genotype x environment interaction analysis using the
on using conventional methods of analysis to determine genotype-by-environment interaction (GGE) -biplot model (Yan et
al., 2000, 2007) done using GENSTAT.
the genotype x environment interaction and these
methods provide little information on the patterns in the
interaction (Kempton, 1984). A simple crop physiological RESULTS
model to study the yield basis and environmental effects
of cowpea genotypes was reported in a multilocational Days to 90% maturity, 100 seed weight, and grain
study (Marfo and Waliyar, 1997). Recently AMMI and yield
GG-Biplot analysis have been conducted on most crops
to understand the genotype x environment effects. The There were variations in days to 90 % maturity among
GG-Biplot however, offers more advantages, as it is early maturing genotypes (Table 3), and medium
much easier to understand, identify high performing and maturing genotypes (Table 4) performance within
stable genotypes, identify mega environments, and select locations and across locations. Among the early maturing
discriminative and representative environments for the category, SARI-5-5-5, SARI-6-2-9, SARI-2-50-80, and
traits of interest (Dos Santos et al., 2016; Sousa et al., SARI-1-3-90 attained 90 % maturity in less than 65 days
2018). The GG-Biplot methodology has, however, not after planting (DAP); while the check variety (Kirkhouse
been much exploited in identifying high-yielding and Benga) took 65 days (Table 3). All the nine medium
stable cowpea genotypes in Ghana. maturing lines evaluated reached mean maturity of 67
This study aims at evaluating cowpea genotypes DAP, which was much earlier than the check variety,
across six locations in the Guinea and Sudan savanna Padituya which reached maturity at 69 DAP (Table 4).
agroecological zones of Ghana, identifying high- Variations were also observed in 100 seed weight of
performing cowpea genotypes and making cowpea genotypes. SARI-2-50-80 had the highest seed
recommendations for its utilizations in either weight and the only genotype that outperformed the
hybridizations or future release. Specifically, this study check variety in terms of 100 seed weight (Table 5).
sought to: (i) Evaluate the genotype x environment Apart from the check variety with an average 100 seed
interaction, (ii) Identify high-yielding and stable cowpea weight of 20.86 g, IT10K-837-1 had the highest 100 seed
genotypes, and (iii) Identify mega-environments, as well weight among the other medium maturing lines with a
as discriminative and representative environments for weight of 18.4 g (Table 6). There were significant
cowpea grain yield in Northern Ghana. differences (PTengey et al. 73
Table 1. List of genotypes used for the study.
Genotype Source Maturity group Market class
IT99K-1122 IITA Early (60-70 DAP) Brown seeded
IT07K-298-15 IITA Early (60-70 DAP) White seeded
SARI-1-3-90 CSIR-SARI Early (60-70 DAP) Mottled
SARI-5-5-5 CSIR-SARI Early (60-70 DAP) White seeded
IT07K-299-6 IITA Early (60-70 DAP) Medium size, white seeded with black hilium
SARI-13-17-2 CSIR-SARI Early (60-70 DAP) White seeded
SARI-1-50-81 CSIR-SARI Early (60-70 DAP) Whites seeded
SARI-6-2-9 CSIR-SARI Early (60-70 DAP) White seeded with brown eye
SARI-3-11-80 CSIR-SARI Early (60-70 DAP) Large, white seeded with black eye
SARI-2-50-80 CSIR-SARI Early (60-70 DAP) Large, white seeded with black eye
Kirkhouse-Benga (check) CSIR-SARI Early (60-70 DAP) Large, white seeded with black eye
IT06K-137-1 IITA Medium (70-75 DAP) White seeded
IT07K-298-15 IITA Medium (70-75 DAP) White seeded
IT07K-299-69 IITA Medium (70-75 DAP) White seeded
IT08K-126-19 IITA Medium (70-75 DAP) White seeded
IT09K-456 IITA Medium (70-75 DAP) White seeded
IT10K-837-1 IITA Medium (70-75 DAP) Large, white seeded with black eye
IT86D-610 IITA Medium (70-75 DAP) Brown seeded
IT98K-628 IITA Medium (70-75 DAP) White seeded
SARI-6-2-6 CSIR-SARI Medium (70-75 DAP) Mottled
Padituya (check) CSIR-SARI Medium (70-75 DAP) Large, white seeded with black eye
Table 2. Description of trial location and rainfall pattern in 2018 cropping season.
Mean Precipitation (mm)
Environment Agroecology Latitude Longitude
July August September
Nyankpala Guinea savanna 09° 23' N 01° 00' W 150 190 220
Yendi Guinea savanna 09° 30' N 00° 01' W 180 220 250
Damongo Guinea savanna 09° 01' N 01° 36' W 160 220 250
Manga Sudan savanna 11° 01' N 00° 16' W 180 230 170
Tumu Guinea savanna 10° 54' N 01° 95' W 280 270 170
Wa Guinea savanna 10° 04' N 02° 30' W 140 190 195
cowpea genotypes grown at each of the six locations Genotype by environment effect and stability
(Table 7). Grain yield of SARI-2-50-80 (1.92 t/ha), SARI- analysis for grain yield
13-17-2 (1.91 t/ha), IT99K-1122 (1.84 t/ha), SARI-3-11-
80 (1.76 t/ha) and IT07K-299-6 (1.75 t/ha) were higher Environment, Genotype, and Genotype x Environment
than the check, Kirkhouse Benga (1.69 t/ha). The highest were significant (p74 Afr. J. Plant Sci.
Table 3. Days to 90 % maturity of early maturing lines.
Days to 90 % maturity
Genotype
Damongo Nyankpala Yendi Wa Tumu Manga Mean
IT99K-1122 65.67 bc 64.33 62.67 ab 68.33 e 70.33 cd 60 65.22
IT07K-298-15 66bc 64.67 64.33 de 61.67 abc 69cd 59.67 64.22
SARI-1-3-90 67.67 bc 64 62.33 a 62.33 abc 66.33 a 59 63.61
SARI-5-5-5 61a 64 63.33bc 63 bc 71d 60 63.72
IT07K-299-6 65.33 bc 65 65e 63.33 bc 70.33 cd 59.33 64.72
SARI-13-17-2 64.33 b 64.33 66f 67.33 de 69.67 cd 59.33 65.17
SARI-1-50-81 68.67 c 64 65e 64 cd 68.67 bc 59 64.89
SARI-6-2-9 66bc 64.33 63.33 bc 59 a 67ab 59 63.11
bc f abc cd
SARI-3-11-80 67.33 65 66.67 62.33 69 59 64.88
b cd
SARI-2-50-80 65 65 63.67 59.67 ab 69.33 cd 59 63.61
bc cd de cd
Kirkhouse-Benga 65.67 64.67 63.67 67.33 70.33 59.33 65.17
Mean 65.7 64.485 64.182 63.48 69.18 59.33
cv% 2.7 0.9 0.8 3.2 1.5 1.2
CV: Coefficient of variation. Genotypes w ith different letters are significantly different at PTengey et al. 75
Table 5. 100 seed w eight of early maturing cow pea lines.
100 seed weight (g)
Genotype Mean
Damongo Nyankpala Yendi Wa Tumu Manga
IT99K-1122 12.00 a 13a 10.67 aa 13.5 a 10.67 a 14.33 ab 12.36
IT07K-298-15 15.33 bc 12.67 a 14b 17.5 de 14b 14a 14.58
SARI-1-3-90 15.33 bc 14.67 b 14.67 bc 15 abc 14.67 bc 14.67 abc 14.84
SARI-5-5-5 14.33 b 14.67 b 15bcd 14.33 ab 15bcd 15.67 abcd 14.83
IT07K-299-6 15.33bc 14.67 b 15.33 cde 14.6 abc 15.33 cde 15.33 abc 15.10
SARI-13-17-2 17d 17.33 c 16de 14.8 abc 16de 16.33 bcde 16.24
SARI-1-50-81 16.67 d 15.67 b 16.33 ef 15.67 bcd 16.33 ef 15.67 abcd 16.06
SARI-6-2-9 16cd 15b 16.33 ef 15.5 bc 16.33 ef 16.67 cde 15.97
ef cd gh cd gh cde
SARI-3-11-80 18.67 17.33 18 16.33 18 16.67 17.5
SARI-2-50-80 19.67 f 18.67 ce 18.67 h 17.43 de 18.67 h 18.33 e 18.57
e e fg e fg de
Kirkhouse-Benga (check) 18.33 18.67 17.33 18.73 17.33 17.67 18.01
Mean 15.24 15.67 15.67 15.76 15.67 15.94
cv% 3.6 4.7 4.5 6.5 4.5 7.5
Table 6. 100 seed w eight of medium maturing lines.
100 seed weight (g)
Genotype Mean
Damongo Nyankpala Yendi Wa Tumu Manga
IT06K-137-1 18.33 de 18.33 bc 17.33 c 18.33 d 17.33 d 15.7 b 17.56
IT07K-298-15 15.33 ab 16.33 ab 15.67 b 17.47 cd 16.03 c 15ab 15.97
13.67 a 16 ab b
IT07K-299-69 15 15.63 b 15.83 bc 14.4 a 15.09
17.33 cd 16.67 ab b
IT08K-126-19 15.67 17.5 cd 17.1 d 17c 16.88
16.33 bc 15 a a
IT09K-456 13.67 15.67 b 15.67 bc 14.33 a 15.11
19.33 ef 18.33 bc c
IT10K-837-1 17.67 19.53 e 18.03 e 17.67 c 18.43
14 a 17.33 ab a
IT86D-610 12.67 15.23 ab 15.23 b 14a 14.74
14.67 ab 15 a b
IT98K-628 15 14.33 a 14.13 a 15ab 14.69
17.33 cd 17 ab b
SARI-6-2-6 15.67 17.13 c 16.9 d 17c 16.84
Padituya (check) 21f 20.33 c 21.67 d 21.87f 19.97f 20.33 d 20.86
Mean 16.73 17.03 16 17.27 16.623 16.04
CV % 5.9 8.6 4 3.5 2.1 3.6
610 were the most stable among the high-performing Tumu were grouped into another mega environment with
genotypes. Padituya being the best performing variety.
Four environments, namely, Nyankpala, Yendi,
Damongo, and Manga had relatively longer vectors and
Best environments for genotypes therefore discriminated the grain yield of early duration
cowpea. Tumu has the least vector length, followed by
The results of the biplot who-wins-where showed three Wa. Vector angles between Nyankpala and Wa, Tumu
mega-environments for grain yield of early duration and Yendi, Damongo and Manga were less than 90º and
cowpea. This includes Nyankpala and Wa; Tumu and are said to be correlated with each other (Figure 5a).
Yendi; and Damongo and Manga with the best average Five out of six environments well discriminated the
performers for these environments being IT99K-1122, grain yield of the medium duration cowpea (Figure 5b).
SARI-13-17-2, and SARI-2-50-80, respectively (Figure 2). Wa had the least vector length, all the other environments
Two mega environments were identified for grain yield of had longer vectors. Vectors angles between Nyankpala,
medium duration cowpea (Figure 4). Wa, Nyankpala, Wa, Manga, and Damongo is less than 90º and can be
Manga, and Damongo formed one mega environment said to be correlated together. Vector angle between
with IT86D-610 being the best performing line. Yendi and Tumu and Yendi are also less than 90º and are also76 Afr. J. Plant Sci.
Table 7. Grain yield of early maturing cow pea advanced breeding lines in six locations.
Grain yield (t/ha)
Genotype
Damongo Nyankpala Yendi Wa Tumu Manga Mean
IT99K-1122 2.23 cd 1.9 e 1.53 bc 1.9 cde 1.5 bc 2 bc 1.84
IT07K-298-15 2.23 cd 1.37 bcd 1.57 bc 1.23 a 1.33 bc 1.77 b 1.58
SARI-1-3-90 1.63 b 1.2 bc 1.47 bc 1.56 b 1.1 ab 1.53 ab 1.42
SARI-5-5-5 1.07 a 1 ab 0.53 a 1.2 a 0.7 a 1.03 a 0.92
IT07K-299-6 2.4 d 1.37 bcd 1.57 bc 2.01 de 1.47 bc 1.67 b 1.75
SARI-13-17-2 2.2 cd 1.57 cde 2.13 d 2.07 e 1.37 bc 2.1 bc 1.91
SARI-1-50-81 2.13 cd 0.73 a 1.13 b 1.55 b 1.13 ab 1.53 ab 1.37
SARI-6-2-9 2.23 cd 1.03 ab 1.33 bc 1.53 b 1.17 ab 1.47 ab 1.46
d bcde cd cd bc b
SARI-3-11-80 2.4 1.47 1.73 1.81 1.4 1.73 1.76
2.43 d 1.73 de 1.67 cd 1.72 bc 1.47 bc c
SARI-2-50-80 2.5 1.92
c bcd cd b c b
Kirkhouse-Benga (check) 2 1.33 1.7 1.57 1.83 1.7 1.69
Mean 2.09 1.34 1.49 1.65 1.32 1.73
cv% 7.4 18.3 17.8 7.2 19.9 19.2
CV: Coefficient of variation. Genotypes w ith different letters are significantly different at PTengey et al. 77
Table 9. ANOVA results for grain yield of early maturing genotypes.
Source of variation d.f. s.s. s.s. (%) m.s. v.r. F pr.
Environment 5 27.05132 45.91128 5.41026 97.2678 Afr. J. Plant Sci.
Table 10. ANOVA results of grain yield for medium maturing genotypes.
Source of variation d.f. s.s. s.s (%) m.s. v.r. F pr.
Environment 5 3.000177 10.02452 0.600035 73.54Tengey et al. 79
Figure 3. Grain yield and stability of medium maturing cow pea genotypes across six environments.
groupings were discriminative of the grain yield of attributed to the region experiencing the least rainfall
cowpea. Environments within each mega- environment during the growing season of the crop (Table 2). There is,
can be said to be correlated with each other as the angle therefore, evidence that cowpea with different maturity
between the vectors as shown in Figure 5 are less than groups differs in their response to water stress. Too much
90°. The lower the angle between two vectors than 90º rain will have a more pronounced effect on early duration
the better the correlation (Yan and Tinker, 2006). Similar cowpea than medium duration cowpea and too little
occurrences of positive correlation of encompassing rainfall will have a negative impact on grain yield of
environments have been reported in other studies (Dos medium duration cowpea than early duration cowpea.
Santos et al., 2016; Sousa et al., 2018). The length of the Some genotypes originated from the vertices of the
vector can also be used to tell which of the environments polygon but contain no clustered environment. Examples
are most discriminating of the target trait. Apart from of such genotypes are SARI-5-5-5 and SARI-1-50-81
Tumu which was the least discriminating environment for (Figure 2), IT07K-298-15, IT09K-456, IT98K-628, and
grain yield of early duration cowpea (Figure 5a) and Wa IT06K-137-1 (Figure 4). These genotypes can be said to
which was the least discriminating environment for be unfavorable to the test environments due to their low
medium duration cowpea (Figure 5b), the rest of the grain yields and are therefore not recommended. This
environments well discriminated the genotypes and can similar situation has been reported by other authors (Dos
be used to select superior genotypes. Tumu being the Santos et al., 2016; Karimizadeh et al., 2013). The
least performing environment for the early maturing identification of suitable genotypes and an ideal
cowpea could be attributed to it receiving the highest environment using the GGE-Biplot method could help
mean rainfall. Wa being the least performing environment future evaluation studies as genotypes will be tested in
among the medium duration, genotypes could also be environments with greater GxE.80 Afr. J. Plant Sci.
Figure 4. A w hich w on graph and mega environments classification of medium maturing cow pea
genotype.
Figure 5. Discrimination and representativeness of grain yield of early duration cow pea genotypes (left) and medium duration
cow pea genotypes (right).Tengey et al. 81
Conclusion Langyintuo AS, Low enberg-Deboer J, Faye M, Lambert D, Ibro G,
Moussa, B, Kergna A, Kushw aha S, Musa S, Ntoukam G (2003).
Cow pea supply and demand in West and Central Africa. Field Crops
Grain yield has consistently been high for Damongo and Research 82(2-3):215-231.
Yendi for the early maturing and medium maturity cowpea Marfo KO, Waliyar F (1997). Genotype x environment interaction effects
genotypes that were evaluated. For early maturing lines, on some physiological yield determinants in cow pea (Vigna
unguiculata (L.) Walp. Ghana Journal of Agricultural Science
Tumu was the least performing environment; while Wa
30(1):15-21.
was a poor performing environment for medium maturing Naab JB, Chimphango SMB, Dakora FD (2009). N2 fixation in cow pea
lines. Notwithstanding that, high yielding and stable early plants grow n in farmers’ fields in the Upper West Region of Ghana,
maturing lines (such as SARI-2-50-80, SARI-13-17-2, measured using 15N natural abundance. Symbiosis 48(1-3):37-46.
Odeseye AO, Amusa NA, Ijagbone IF, Aladele SE, Ogunkanmi LA
IT99K-1122, SARI-3-11-80, and IT07K-299-6) (2018). Genotype by environment interactions of tw enty accessions
outperformed the check (Kirkhouse Benga); and SARI-2- of cow pea [Vigna unguiculata (L.) Walp.] across tw o locations in
50-80 had the highest 100 seed weight. Medium maturing Nigeria. Annals of Agrarian Science 16(4):481-489.
lines (such as SARI-6-2-6, IT86D-610, and IT10K-837-1) Ofosu-Budu KG, Obeng-Ofori D, Afreh-Nuamah K, Annobill R (2008).
Effect of phospho-compost on grow th and yield of cow pea (Vigna
could also be selected as candidate lines, because they unguiculata). Ghana Journal of Agricultural Science 40(2):169-176.
matured earlier than the check and have yields Ow usu EY, Amegbor IK, Mohammed H, Kusi F, Atopkle I, Sie EK,
comparable to the check. In terms of 100 seed weight, Ishahku M, Zakaria M, Iddrisu S, Kendey HA, Boukar O, Fatokun C,
however, only IT10K-837-1 came close to the check. Nutsugah SK (2020). Genotype × environment interactions of yield of
cow pea (Vigna unguiculata (L.) Walp) inbred lines in the Guinea and
These selected genotypes can further be evaluated on-
Sudan Savanna ecologies of Ghana. Journal of Crop Science and
farms and released as varieties in the future. Biotechnology 23(5):453-460.
Payne RW (2002). Design and analysis of experiments. in Payne RW
(ed.) Guide to GenStat release 6.1. Part 2. Statistics (RW Payne
(ed.)). VSN International.
CONFLICT OF INTERESTS https://repository.rothamsted.ac.uk/item/88z56/design-and-analysis-
of-experiments.
The authors have not declared any conflict of interests. Pule-Meulenberg F, Belane AK, Krasova-Wade T, Dakora FD (2010).
Symbiotic functioning and bradyrhizobial biodiversity of cow pea
(Vigna unguiculata L. Walp.) in Africa. BMC Microbiology 10(1):89.
Quaye W, Adofo K, Buckman ES, Frempong G, Jongerden J,
ACKNOWLEDGMENTS Ruivenkamp G (2011). A socio-economic assessment of cow pea
diversity on the Ghanaian market: implications for breeding.
This work was supported by the IITA/Bayer, Tropical International Journal of Consumer Studies 35(6):679-687.
Silva RR, Benin G (2012). Análises biplot: Conceitos, interpretações e
Legumes III, Accelerated Varietal development and Seed
aplicações Biplot analysis: concepts, interpretations and uses
delivery of Legumes and Cereals in Africa (Grant number Raphael. Ciencia Rural 42(8):1404-1412.
OPP1198373). ICRISAT and IITA through which we got Sousa MBE, Damasceno-Silva KJ, Rocha MDM, Menezes JJD, Lima
the AVISA funds are duly acknowledged. We thank Dr. LRL (2018). Genotype by environment interaction in cow pea lines
using GGE Biplot method. Revista Caatinga 31(1):64-71.
Osmane Boukar for providing cowpea genotypes from Yan W (2001). GGEbiplot — A Window s Application for Graphical
IITA. Many thanks to colleagues and staff of CSIR- Analysis of Multienvironment Trial Data and Other Types of Tw o-Way
Savanna Agricultural Research Institute. Data. Agronomy Journal 1118(2000):1111-1118.
Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000). Cultivar evaluation and
mega-environment investigation based on the GGE biplot. Crop
Science 40(3):597-605.
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103(1):123-135.Vol. 15(3), pp. 82-93, March 2021
DOI: 10.5897/AJPS2020.2101
Article Number: 4EB8D1166400
ISSN 1996-0824
Copyright © 2021
Author(s) retain the copyright of this article African Journal of Plant Science
http://www.academicjournals.org/AJPS
Full Length Research Paper
The multi-level table and circular diagnostic chart as
alternative taxonomic key formats for plant
identification
Adepoju Tunde Joseph OGUNKUNLE
Department of Pure and Applied Biology, Ladoke Akintola University of Technology, P. M. B. 4000, Ogbomoso,
Oyo State, Nigeria.
Received 11 November, 2020; Accepted 29 January, 2021
Correct identification of plants is a prerequisite to achieving desirable results in health care delivery,
sustainable food production and housing, forest resources management and environmental protection.
However, many of the paper-based/printable taxonomic key formats available to the taxonomist for this
important responsibility are fraught with inadequacies some of which include fixed sequence of plant
identification steps, non- or hardly-susceptible to computerisation, lack of provision for confirmation of
suspected plant identity and indeterminable character states, and tedious construction and navigation
procedures. This paper with the aim of making the practice of plant taxonomy more attractive, less
laborious and dreaded, proposes two new key formats with highlights of their design/features,
construction procedures and usage. These alternative key formats, with varying capacities to
circumvent some of the enumerated challenges are multi-level table of identification and multi-layer
circular diagnostic chart. The status of each of the proposed key formats is discussed with reference to
the inadequacies observed in the dichotomous key format with which most taxonomists are familiar.
Based on their structural features and functionality attributes, it is conclusive that the two alternative
key formats constitute useful templates upon which reliable plant diagnostic tools can be based.
Key words: Automated plant identification, computerised key, diagnostic key, dichotomous key, multi-access
key, plant identification, single-access key, taxonomic key.
INTRODUCTION
The basic terminology in systematics is „taxon‟, a formal unit at a particular level in the taxonomic hierarchy, and
category of living things, or a „taxonomic group‟ sufficiently different from other groups of the same rank
recognised by having certain characteristics in common to be treated separately from them (Radford et al., 1974).
which we take as evidence of genetic relationship, and Going by these regular definitions, each taxon (except
possessed some degree of objective reality (Rickett, the highest), such as a species belongs to one and only
1958). It is a group of one or more individuals, or of lower one taxon of the next higher rank, such as a genus,
taxa judged sufficiently similar to each other to be treated implying each individual belongs to exactly one species
together formally as a single evolutionary or informational (and has one name) in any particular taxonomic
E-mail: tjogunkunle@lautech.edu.ng.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International LicenseOgunkule 83
treatment of its group. Taxonomists frequently present tediousness of taxonomic practices along with obsolete
organised written descriptions of the characteristics of tools for identification (Stagg and Donkin, 2013) and the
similar taxa such as species or genera, etc., to facilitate attendant declining interest in botany (Drea, 2011),
identification or recognition of unknown organisms. These especially plant taxonomy (The Conservation, 2020). The
organised descriptions are referred to as diagnostic keys, number of botany specialists is reducing by the day. In
which come in different formats and styles such as in the United States of America, the number of
Hopkins and Stanfield (1966), Lowe and Stanfield (1974), undergraduate degrees earned in botany is said to have
Payne et al. (1974), Payne and Preece (1980), Jones et decreased by 50% since the late 1980s (Bidwell, 2013), a
al. (1998) and Javatpoint (2018), each with its merits and trend that should never be taken lightly. The
demerits. aforementioned challenges therefore formed the basis for
Outside the understanding of the terminology from the conceptualising this study to make a contribution towards
systematics (strict) point of view, and for all practical ameliorating the declining interest in plant taxonomy.
purposes, it is useful to operationally define „taxon‟ as A taxonomic key is derived from a data matrix of a
one or more objects recognized by sharing certain given number of „objects‟, and it is usually possible to
characteristics, and representing a group or category, the contrive a large number of different key formats for one
members of which may or may not be related set of objects such as plant species, but the keys will vary
taxonomically. Such objects will include tangible items as in their usability (Pankhurst, 1970). Dichotomous key is
plant specimens of one or more taxonomic groups (e.g. widely acknowledged as the most popular type of
species, sub-genus, genus, etc.), plant organs (e.g. root, identification key (Sinh et al., 2017), and had been a
flower, seed, fruit, etc.), or intangibles and other forms of clever means of organising taxonomic information before
categorisation such as plant diseases, colours, sounds, the age of computers (Godfray et al., 2007). The use of
odours and so on (Amante and Norton, 2003). So, in the this key format is known to have contributed to increasing
context of this study, taxon is more widely viewed beyond the quality and durability of knowledge of plant
its conventional usage as „taxonomic group‟ or classification acquired in comparison to traditional
assemblage of plants or animals that are genetically teaching techniques (Andic et al., 2019) and an
related, or so related to the best of our knowledge established method for teaching plant identification skills
(Rickett, 1958) to mean a unit of classification (taxonomic (Stagg and Donkin, 2013). However, a number of
or otherwise) of objects (tangible or intangible), seemingly demoralising weaknesses are associated with
recognised by a set of features that distinguish (or dichotomous key format, including: being tedious to
diagnose) the group from such other groups. This construct (Lobanov, 2003), having fixed point of entry and
position equally recognises both the relationship of daunting path of navigation (Hagedorn et al., 2010), the
inclusion between levels and of complementarity within problem of unanswerable couplet (Rambold and
levels as aptly described by Price (1967) regarding the Martellos, 2010), being unusable for confirmation of
objects classified into groups under groups. That is, suspected identity, and non-readily amenable to
given a large group of taxa (or objects), and based on the automation (Yin et al., 2016). So, invention of new key
relationships among the objects, recursive classification formats shall continue to be a welcome development in
or compartmentalisation of the group members into taxa taxonomy. In providing a way out of the challenges
of lower categories/ranks is possible until the entire group enumerated earlier, the present study aimed at making
is resolved into the smallest manageable clusters of taxa, the practice of plant taxonomy more attractive, less
or each taxon as it were. laborious and dreaded, and so, the objectives are to
Correct identification of plants is important in health propose two new taxonomic key formats with highlights of
care delivery (Upton and Romm, 2010), sustainable food their features, construction procedures and usage that
production (Amante and Norton, 2003) and housing, should possibly make them desirable, either as
criminal justice (Bock and Norris, 2016), forest resources alternative or complementary tools for plant identification.
management and environmental protection. Medicinal
plants misidentification and misrepresentation are two
MATERIALS AND METHODS
known root causes of herb adulteration or substitution,
which in turn, is the basic cause of serious health Adoption of heuristic approach to addressing the weaknesses
problems to consumers of herbal medicinal products in dichotomous key format
(Panter et al., 2014), and a motivation for bad publicity
The first step taken in actualising the objectives of this study was to
and legal burdens sometimes faced by the align with the thoughts of Pankhurst (1970) on the two
pharmaceutical industry (Dukes, 2006). For these complementary problems in taxonomy, which are still valid till date.
reasons, there is a huge responsibility on the shoulders Firstly, given a set of objects (e.g., plants), examine their
of plant taxonomists, who, unfortunately, often have to characteristics in order to find a classification, that is, group the
contend with a number of challenges including the objects into subsets (or taxa), and assign names to the subsets;
and secondly, given a classification and an object, identify that
intricate nature and complexity of plant life, and variability
object. In other words, given a list of the characteristics of named
in their characteristics (Tilling, 1987), perceived subsets which are known to exist, and an additional object, decide84 Afr. J. Plant Sci.
which subset the object belongs (that is, recognise it, or find its conventional table of results, the characters are arranged in tiers or
name). Accepting that the taxonomists‟ diagnostic key was an levels: primary, secondary, tertiary, quaternary, quinary, senary,
important tool in the process of identification, the format and styles septenary, octonary, nonary denary, etc., representing first,
of the frequently used single-access diagnostic keys along with the second, third, …tenth level, respectively; indicating the levels of
challenges associated with their features, construction and successive classification/compartmentalisation of the plant group
application were critically examined (Walter and Winterton, 2007). using the characters. Characters of the first tier/level (that is, of the
In an effort to address some of these inadequacies, consideration primary classification of the taxa) are listed in the first row on top of
was also given to selection criteria for construction of efficient the table while the next row is used to display all the names of the
diagnostic keys (Payne, 1981, 1988). Information obtained from taxa (that is, a cluster of plants) as defined by each character or
the steps highlighted earlier were integrated into a thought to characters in the first/primary level; characters of the second
develop two alternative key formats, namely: the multi-level table tier/level are listed in the third row of the table while the fourth row is
and multi-layer circular diagnostic chart, each with far reaching used to display the clusters of taxa as defined by each character or
desirable qualities in terms of design/features, construction, characters in the second level, etc., thus rows of lists of characters
navigation efficiency and possibility of automation. alternate with rows of lists of taxa, and at the end of each
successive level, the number of taxa in a cluster progressively
reduces down the line.
Data procurement for purpose of illustration
Wood anatomical data on five medicinal herbs marketed as plant Construction procedure of multi-level table of identification
roots in Ogbomoso township, south western Nigeria were sourced
for the purpose of illustration from the 2019 compilation of The essential activities in building the multi-level table of
unpublished results at the medicinal plants research laboratory in identification consist of the following steps:
the Department of Pure and Applied Biology, Ladoke Akintola
University of Technology, Ogbomoso, Nigeria. The species are (1) constructing a conventional table of character comparison for
Aristolochia ringens, Calliandra haematocephala, Parquetina the plant group under study, displaying characters in rows and the
nigrescens, Sarcocephalus latifolius and Zanthoxylum taxa in columns as in Tables 1 and 2;
zanthoxyloides. The data items were obtained in accordance with (2) selecting one, or few characters as character combinations,
the standard procedures: tissue sectioning/maceration (Schoch et which is/are considered as being of primary importance for
al., 2004), staining, dehydration (Ogunkunle and Oladele, 2014), classifying the group under study into few clusters of taxa that
mounting (Arx et al., 2016), and microscopic observations (de may be mutually or non-mutually exclusive, and stating those
Parnia and Miller, 1991), while the terminology and descriptions of characters in the first row of the table; meanwhile, maximum
observed features followed those of the International Association of number of clusters should be four to avoid the key being unwieldy;
Wood Anatomists (IAWA Committee, 1989). Twenty-five (3) enumerating the taxa in each of the few clusters in the next
characters, consisting of ten qualitative (Table 1) and fifteen row of the table as defined by the characters or character
quantitative features (Table 2), all of which were diagnostic of the combinations in the previous row;
species were compiled. Staining was done in 1% alcoholic safranin, (4) considering the clusters of taxa obtained from primary level of
mounting was carried out in Canada balsam and observations classification, one at a time, selecting another character or
made using Olympus biological microscope CH20i Model with character combination as being of secondary importance to further
binocular facility. Quantitative characters were considered circumscribe the taxa in the primary cluster into smaller clusters
diagnostic of the species only if the means of the replicated values (again, mutually-exclusive or not), and enumerating such taxa as a
were statistically significant at α = 5 following One-Way Analysis of subset of that cluster in the next row;
Variance, and Duncan multiple range classification of the means (5) considering again, each cluster of taxa obtained from second
(Landau and Everitt, 2004). tier of characters, selecting another, or few characters as being of
tertiary importance to further circumscribe the taxa therein and
enumerating such taxa as a subset of the secondary cluster in the
Conceptualisation of procedures for constructing and applying next row;
the two alternative key formats (6) recursively selecting one or few characters (of quaternary,
quinary, senary, etc., importance) for subsequent circumscription of
For a given number of taxa with certain observable characters, the the taxa as earlier described until every taxon in each cluster of
features as well as the procedures for constructing and navigating a taxa has been sorted/keyed out separately towards the bottom of
multi-level table of identification on one hand, and the multi-layer their respective columns in the table;
circular diagnostic chart on the other hand were heuristically (7) noting that if mutually exclusive clusters of taxa were possible
conceptualised as recursive or repetitive process of „divide and and achievable throughout the recursive classifications of the
conquer‟ algorithms (Hagedorn et al., 2010). Further to the group in steps „2‟ to „6‟, each taxon would be keyed out only once.
achievement of the objectives of this study, the algorithm in each However, if mutual exclusivity of the clusters was not achievable
case was systematically executed, and is here, being proposed as throughout the entire classifications, that is, if one or more non-
a number of logical steps. mutually exclusive clusters were involved, at least one taxon would
be keyed out more than once in the table.
Design and statement of the features of multi-level table of
identification Application of multi-level table of identification
The multi-level table of identification was conceived as a diagnostic In order to apply the multi- level table of identification, the following
tool having features similar to the conventional table of results steps were formulated, adopted and are herein proposed:
displaying characters and plant taxa in columns and rows. Unlike in
a conventional table of results, the characters in the key are stated (1) Evaluate the plant material based on the provisions of the first
either as unit characters or combinations of two or more features level of character combinations and decide which of the few
observable in either a taxon or in clusters of taxa. Also unlike in the clusters of taxa in the next row the plant belongs;Ogunkule 85
Table 1. Some wood anatomical descriptive features in the roots of five medicinal herbs marketed in Ogbomoso, Nigeria.
S/N Diagnostic features ARRI CAHA PANI SALA ZAZA
Vessels
1 Pore type Diffuse-porous Diffuse-porous Ring-porous Diffuse-porous Diffuse-porous
Solitary***; Radial chains Solitary**; Radial Solitary***; Radial Solitary*; Radial
2 Occurrence Solitary
of 2***; Clusters of 3* chains of 2-7**** chains of 2-3*** chains of 2-8****
Oblique***; Oblique**; Oblique***; Oblique***;
3 End walls Oblique
Truncate**** Truncate**** Truncate*** Truncate****
Wood fibres
Aggregates and Aggregates; non- Aggregates; non-
4 Occurrence Aggregates Diffuse; non-storied
diffuse; non-storied storied storied
5 Frequency/relative abundance high*** high*** Low** Low** Low**
Wood parenchyma cells (WPC)
Type of WPC in transverse Apotracheal (diffuse- Apotracheal (diffuse- Paratracheal (scanty**;
6 Apotracheal (diffuse) Apotracheal (diffuse)
section aggregate) aggregate) vasicentric**; aliform**)
Wood rays (WRY)
Shape of WRY in Transverse Square**; Square***; Square**;
7 Square*; procumbent**** Procumbent
Section procumbent**** procumbent*** procumbent****
Uniseriate****; Uniseriate****; Uniseriate***;
Width of WRY in Tangential Biseriate**;
8 Uniseriate biseriate**; biseriate**; biseriate**;
Longitudinal Section multiseriate****
multiseriate* ultiseriate* multiseriate**
Composition of WRY in
Homocellular****; Homocellular****; Homocellular***;
9 Tangential Longitudinal Homocellular Heterocellular
heterocellular* heterocellular** heterocellular***
Section
General shape of WRY in linear**; Mono- Mono-convex**;
Bi-convex****; Mono-convex**;
10 Tangential Longitudinal Linear convex**; bi- linear****;
Section convex**** dumb-bell* bi-convex****
dumb-bell**
ARRI= Aristolochia ringens; CAHA= Calliandra haematocephala; PANI= Parquetina nigrescens; SALA= Sarcocephalus latifolius; ZAZA=Zanthoxylum zanthoxyloides;****(very
frequent/usually observed/very high frequency , that is 60-99% occurrence); ***(frequent/averagely observed/high frequency, that is,40-59% occurrence); **(less frequent/sometimes
observed/low frequency, that is, 10-39% occurrence); *(seldom frequent/rarely observed/very low frequency, that is, 1-9% occurrence).
Source: Extract from the 2019 unpublished data compiled at the medicinal plants research laboratory, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
(2) Once a cluster of taxa has been selected in the row as (3) Re-evaluate the plant on the basis of the provisions of features do not match the stated character combinations in
the likely group in which the plant belongs, the few other the next level of character combinations to decide which of the row, refrain from making a selection, but proceed to
clusters of taxa and the columns in which they fall should the clusters of taxa in the next row the plant belongs; and if evaluate the unknown plant material on the basis of the
no longer be considered in subsequent steps; a decision is difficult or impossible because the plant characters in the next level;86 Afr. J. Plant Sci.
Table 2. Mean quantitative wood anatomical characteristics of some types of cells in the roots of five medicinal herbs marketed in Ogbomoso, Nigeria.
S/N Diagnostic features ARRI CAHA PANI SALA ZAZA
Vessels (VS)
2 d e b a c
1 Density/mm in transverse section (TS) 37 ±1.20 74 ±2.73 10 ±0.72 5 ±0.22 31 ±1.06
2 % frequency of VS shapes in TS Round(50); Oval(50) Round(54); Oval(46) Round(58); Oval(42) Round(47); Oval(53) Round(53); Oval(47)
b a e d c
3 VS diameter (µm) 101.97 ±5.54 63.81 ±1.85 231.94 ±11.77 197.03 ±10.01 146.86 ±3.65
b a e d c
4 VS lumen width (µm) 89.51 ±5.34 53.25 ±1.76 208.13 ±11.45 181.47 ±5.48 132.95 ±3.73
a c d b bc
5 Length of VS member (µm) 194.25 ±7.47 605.01 ±39.40 804.52 ±52.86 499.78 ±24.26 528.04 ±11.84
Wood fibres (FB)
2 c c a b b
6 Density/mm 289 ±14.76 270 ±23.11 63 ±5.05 120 ±6.03 127 ±11.09
b a d d c
7 Diameter (µm) 20.82 ±1.07 14.59 ±0.81 34.05 ±0.92 31.57 ±1.15 25.43 ±1.09
a a c c b
8 Lumen width (µm) 11.95 ±0.99 9.64 ±0.67 26.03 ±0.95 25.17 ±0.89 20.14 ±1.09
a b a c c
9 FB length (µm) 514.72 ±17.45 856.06 ±40.63 604.50 ±14.38 1091.58 ±67.40 1059.02 ±31.31
Wood parenchyma cells
2 b d c a b
10 Density/mm in transverse section 59 ±4.05 256 ±19.64 197 ±11.14 32 ±1.57 83 ±7.25
WRY in TLS
2 d b d c a
11 Density/mm in TLS 19 ±0.48 11 ±0.43 20 ± 0.70 13 ±0.37 6 ± 0.27
a b b b c
12 Number of cells across WRY width in TLS 1 ±0.00 (1-cell) 2 ±0.09 (1-3 cells) 2 ± 0.10 (1-3 cells) 2 ±0.14 (1-3 cells) 3 ± 0.07 (2-3 cells)
a a a a a
13 WRY thickness in TLS (µm) 12.97 ±0.84 29.02 ±1.78 31.40 ±1.55 55.30 ±2.93 86.02 ±2.89
a b b c d
14 Number of cells in WRY height (TLS) 6 ±0.30 13 ±0.89 13 ± 1.63 25 ±2.02 34 ±1.71
a ab b c c
15 WRY height in TLS (µm) 187.39 ±6.21 308.57 ±19.93 435.20 ± 49.82 1022.50 ±73.98 882.35 ±42.87
ARRI= Aristolochia ringens; CAHA= Calliandra haematocephala; PANI= Parquetina nigrescens; SALA= Sarcocephalus latifolius; ZAZA=Zanthoxylum zanthoxyloides. TS= transverse section, TLS=
tangential longitudinal section. The mean values of data in a row with the same superscripts are not significantly different (p>=0.05) while those with different superscripts are significantly different
(pOgunkule 87
into „cells‟, boxes or compartments, and each sector is assigned a and both of these types of key are single-access devices.
name of taxon at the circumference; While Table 3 and Figure 1 were products of
(5) The compartments in the circle are labeled using information in
the appropriate table of identification by following a number of sub-
classifications of the five taxa into two mutually exclusive
steps: groups, Table 4 and Figure 2 are the results of
classification of the taxa into three non-mutually exclusive
(a) Consider the first taxon in the circular diagram, proceed to the groups.
first level of characters in the appropriate table, examine one
column in the table after the other, and take note of every number
(earlier assigned in step 2 above) attached to the character
combinations 1, 2, 3, etc., that are contributors to the classification
DISCUSSION
of the taxon being considered;
(b) Transfer those numbers attached to the relevant character Narrowing the lines of demarcation between the
combinations for the identification of the taxon into the major components of taxonomy
compartments in the circle in reverse order, that is, the appropriate
number at the first (topmost) level in the table is inserted in the last
In constructing a taxonomic key, an important suggestion
(innermost) compartment for the taxon; the next appropriate
number at the next relevant level of characters down the table is by Radford et al. (1974) is to “identify all groups to be
inserted in the next upper compartment in the diagram and so on. included in the key and prepare a description of each
Once the taxon is observed to have been keyed out in the table, taxon”. This position has not changed till date. Morse
subsequent transfer of numbers attached to character combinations (1971) explained that in preparing a key, one usually
for the taxon should be done in parentheses, indicating such divides the initial group of taxa by a character couplet into
characters to be of secondary importance, that is, although they
are, or may be diagnostic of the taxon or a cluster of taxa, such
two subgroups, each of which is independently divided
characters need not be observable for taxa recognition to occur; into further subgroups, and so forth, until every taxon is
(c) Consider the next taxon in the diagram and repeat sub-steps „a‟ distinguished from all others. Again, this procedure is
and „b‟; valid till date, more so with a recent consenting
(d) Remove any extra, entirely empty rings of compartments at the publication (Hagedorn et al., 2010) regarding keys as
periphery of the circle, and the first part of the key would have been „divide and conquer‟ search algorithms that reduce the
achieved;
(e) Compile, as an attachment to the circular chart, a list of all the
result set recursively until the remainder is small enough
characters/character combinations in the chronological order of to be solved by direct comparison. These submissions
numbering in the appropriate table, that is, 1, 2, 3, etc., thereby point to the fact that although, identification is a separate
achieving the second part of the key. activity in systematics, but in practice, it involves the
other three major components of taxonomy namely
classification, description and nomenclature (Radford et
Application of multi-layer circular diagnostic chart
al., 1974). This scenario played out in the course of this
In order to apply the multi-layer circular diagnostic chart for study because the three activities were brought to bear in
identification, the user enters the key at the centre and proceeds developing two new key formats for the purpose of
centrifugally (toward the circumference) by selecting the characters identification.
observable on/applicable to the unknown plant specimen at the
successive rings of compartments. This process progressively
narrows down on the choice of the possible identities (that is,
sectors) of the unknown specimen until only one choice is Examining beliefs and opinions on identification keys
achievable, which represents its identity.
One implication of adopting the above-stated suggestion
by Radford et al. (1974) is that the author of a key should
Illustrative execution of the proposed procedures or will not, ordinarily require same key to identify any of
The proposed steps for constructing and using the two new key
the taxa included in it. Ab-initio, he identified all the taxa
formats developed from this study were executed using the wood and created the key. If one views this scenario on the
anatomical data in Tables 1 and 2 to obtain two single-entry surface, along with the general belief that the use of
diagnostic keys usable for identifying five medicinal herbs sold as identification keys requires intensive training and
roots in Ogbomoso, Nigeria. experience, which only few individuals do have
(Waldchen et al., 2018), one will agree with Lobanov
(2003) that “keys are compiled by those who do not need
RESULTS them for those who cannot use them”. However, on a
closer examination of the two pillars on which Lobanov‟s
Tables 3 and 4 and Figures 1 and 2 are the results conjecture reclines, one would tend to disagree with him.
obtained following execution of the proposed Firstly, a taxonomist is not expected to have worked on
procedures for constructing and using two alternative key all plant groups, nor is he obliged to keep in mind
formats. Construction of a multi-level table of separately the names and diagnostic features of those
identification (Tables 3 and 4) is a major step in making taxa included in all the keys he has authored. Therefore,
the multi-layer circular diagnostic chart (Figures 1 and 2), as a specialist, he not only uses keys created by hisYou can also read
