Department of Plant Breeding and Seed Technology, College of Plant Science and Crop Production, Federal University of Agriculture, Abeokuta
*Corresponding Author: Prof. Francis Abayomi Showemimo, Department of Plant Breeding and Seed Technology, College of Plant Science and Crop Production, Federal University of Agriculture, Abeokuta.
Received: April 13, 2023; Published: April 24, 2023
Abstract
Plant breeders select genotypes based on performances across varied environments and years for stability. Various methods are used to identify good performing genotypes, but performance index (PI), mean rank index (MRI) and scatter diagram (SD) methods are rapid, easy and practicable approach to selecting superior genotypes. This study assesses pod yield of nineteen okra accessions in replicated experiments for two years in three environments representing okra growing areas of south west, Nigeria. The pod yield/plot analysis revealed that NGB00469, NGB00467, NGB00371, NGB00514, NGB00328 and NGB00322 are high yielding across the 6environments. The PI was highest for NGB00469 (94), NGB00467 (83), NGB00371 (67), NGB00514 (56), NGB00328 (50) and NGB00322 (50). MRI was very low (≤6) for these six accessions. Scatter diagram identified and classified the six accessions as outstanding. The PI, MRI and SD methods are simple and easy alternative methods devoid of cumbersome calculation for identifying superior okra accessions.
Keywords: Okra accessions; Performance index; Rank; Classification; Adaptable
Introduction
Okra (Abelmoschus esculentus (L.) Moench) is native of West Africa, one of the commonly grown vegetable crops in the tropics due to warm climates. It is an important fruit vegetable crop with good source of calorie (4550Kcal/kg) for human consumption as could be boiled, fried or cooked (Reddy 2010, Benchasr 2012, Gemede et al. 2015). It contains 86% of water, 2.2% of protein, 10% of carbohydrate, 0.2% of fat and vitamins A, B and C (Christo and Onuh 2005, Vishnu 2018); Okra is grown for its tender fresh pods which are rich in vitamins, minerals and protein (Mbah et al. 2009, Gemede et al. 2015). The mature seeds can be roasted, ground and added to coffee as an adubiant, the okra leaves could be used as animal feeds, young leave can be consumed as salad and the dried leaves can be grounded into powder for storage (Prota 2004, Benchasr 2012). Okra mucilage is suitable for medicinal and industrial applications, the stem for extraction of fiber, ease of cultivation, bountiful returns and high export potential (FAO 1996, Reddy 2010, Vishnu 2018).
Germplasm is an indispensible material to plant breeders and it is plays key role for genetic improvement of any crop. Inbred line development is an important means of crop improvement before they are released as superior genotypes. Okra pod yield is a complex quantitative trait as it is governed by a large number of genes and considerably affected by the environments and years. Selection of okra lines based on individual location and could be faulty and misleading, thus the need for multiple location and year evaluations to identify and select superior and stable genotypes (Hammon and Van Sloten 1989, Ishaq and Falusi 2008, Kumar et al. 2010, Benchasr 2012).
Significant tests are used for discriminating between genotypic performances, thus, significantly similar or different means are assigned same or different alphabets as identifiers of similar or dissimilarity. This is a universally established procedure, but it tends to be cumbersome especially if the number of varieties increases, large data set and evaluation is done across several environments and years, thereby making easy and quick identification of superior genotypes very difficult and near impossible. This paper present an alternative, simple and practicable approach; performance index (PI), mean rank index (MRI) and scatter diagram (SD) to evaluate and identify superior genotypes among pod yield of nineteen okra breeding accessions.
Material and Methods
Nineteen okra accessions were obtained from National Centre of Genetic Resources and Biotechnology, Ibadan, Nigeria (NACGRAB) for this study. The nineteen accessions were grown for 2 years (2017 and 2018) and 3 locations; Teaching and Research Farm of the Federal University of Agriculture, Abeokuta (Rainforest, Latitude 7°27’N, Longitude 3°23’E, 638m ASL, Alfisol soil, average rainfall and temperature are 1130m and 28°C), research field of NACGRAB (Derived savannah, Latitude 7°40’N, Longitude 3°92’E, 230m ASL, Clayey loam soil, average rainfall and temperature are 1420mm and 25°C) and research field of College of Education, Ijanikin (Mangrove swamp, Latitude 6°52’N, Longitude 3°37’E, 41m ASL, Alluvial soil, average rainfall and temperature are 1508m and 22°C).
The experiment was laid in a Randomized Complete Block Design, replicated three times for the two cropping seasons (2017 and 2018). The nineteen accessions were sown using 3 seeds/hole and later thinned to 2 plants/hole at a spacing of 0.30 x 0.60m intra and inter row, 6m long row and 4rows/plot. Recommended crop management practices were followed to raise successful okra as recommended by National Institute of Horticultural Research and Training (NIHORT 1986). Okra fruits were picked as fresh marketable fruits from the 2 central rows for each accession throughout the harvesting period. The okra fruits were weighed as yield per plot for each of the nineteen accessions.
Pod yield values per plot of the accessions were analyzed using analysis of variance procedure described by Panse and Sukhatme (1985). The Performance Index for each accession was computed using mean values of pod yield/plot (kg/plot). The accession means were arranged in descending order of magnitude based on year, location and year × location. The Least Significance Difference (LSD) value calculated from the analysis of variance for each year, location and year × location interaction were subtracted from the first mean. The result obtained was compared to the remaining accession means. The number of accession means that were less than this value indicated the number of accessions that were significantly inferior to the first variety mean. This number was designated as ‘m’. The procedure was repeated thereafter for the second and subsequent accession means. Thus, series of ‘m’ values is obtained for each year of study, locations and combination of year × location as suggested by Fasoulas (1983). The individual ‘m’ values were used to calculate the cultivar performance index (PI) as defined by Fasoulas (1983):
PI =      m      × 100
                   n − 1
Where m = the number of significantly inferior accessions, n = number of varieties tested.
In computing the mean rank index (MRI); the pod yield/plot of the accession tested in each of the 6 environments were ranked from 1 to 19, each accession was summed as rank total (RT) and divided by 6 to get the mean rank index (MRI). The best accession has the lowest value of MRI, preferably less than average mean total. Scatter diagram (SD) was plotted graphically by plotting PI across 6 environments against accession MRI and using mean values of PI and MRI as the divisors. The SD was used to identify and classify the nineteen accessions according to yield performance superiority.
Results and Discussion
Mean performance of okra accessions
Accessing the pod yield performance of okra in 2017, Table 1; NGB00469 had PI of 100 for both Abeokuta and Ijanikin and 89 for Ibadan, thus, it is outstandingly the highest yielding accession. Therefore, NGB00469 is superior to 18 accessions in Abeokuta and Ijanikin, but out yielded 89% of the accessions in Ibadan. Accessions NGB00469, NGB00467, NGB00371, NGB00514, NGB00328 and NGB00322 had PI above 40 in each of the locations in 2017, thus, they performed better than the rest accessions in 2017. While NGB00362 performed well at Ijanikin and Ibadan only with PI of 50 in 2017. Accessions with the same PI value indicate that the accessions performances were not significantly different. Accessions NGB00332, NGB00452 and NGB00335 had zero PI value for the 3 location in 2017 are not significantly better than any accession, while NGB00350 and NGB00375 are not better than any accession at Ibadan only for 2017 indicating some level of accession by location interaction interplay. These accessions show differential performance in the years and locations of evaluation, variability was also reported in okra by Sharma and Prasad (2015), Vishnu et al. (2018).
Abeokuta   Ijanikin     Ibadan  
S/No. Accession Mean kg/plot m p Mean kg/plot m p Mean kg/plot M p
1 NGB00469 28.67 18 100 21.16 18 100 25.51 16 89
2 NGB00467 22.67 13 72 18.03 16 89 23.22 15 83
3 NGB00371 21.33 12 67 14.07 9 50 19.85 12 67
4 NGB00514 19.67 12 67 17.40 15 83 21.60 14 78
5 NGB00328 18.67 9 50 14.66 9 50 19.13 12 67
6 NGB00322 18.33 8 44 15.26 10 56 16.94 10 56
7 NGB00362 17.00 5 28 14.72 9 50 16.31 9 50
8 NGB00302 14.67 3 17 13.55 8 44 12.86 5 28
9 NGB00466 14.33 2 11 14.77 9 50 14.01 6 33
10 NGB00396 14.00 2 11 12.80 6 33 13.47 5 28
11 NGB00360 13.67 2 11 10.07 3 17 11.20 2 11
12 NGB00343 13.33 2 11 11.35 4 22 10.25 1 6
13 NGB00320 12.67 2 11 11.66 4 22 10.82 1 6
14 NGB00297 12.33 2 11 10.90 3 17 9.96 1 6
15 NGB00350 12.00 1 6 9.82 3 17 8.75 0 0
16 NGB00375 11.00 1 6 9.13 2 11 8.01 0 0
17 NGB00332 9.67 0 0 7.20 0 0 8.00 0 0
18 NGB00452 7.33 0 0 6.80 0 0 7.09 0 0
19 NGB00335 6.00 0 0 5.47 0 0 5.16 0 0
Location Mean 15.12 – – 12.57 – – 13.80 – –
Table 1: Performance of nineteen okra accessions according to mean and performance index (PI), 2017.
Similar trend was seen in 2018, Table 2; NGB00469 performed better than 17 accession at Abeokuta and Ibadan, and better than 16 accessions in Ijanikin. Accession NGB00467 performance was consistent in 2018, out yielding 89% of the accessions evaluated. The same accessions that were outstanding in 2017, performed significantly better in 2018; NGB00469, NGB00467, NGB00371, NGB00514, NGB00328 and NGB00322 had PI above 40 in each of the locations in 2018, except NGB00322 that had 39 at Abeokuta. These results indicate that these accessions were consistently stable across the 3locations of evaluation for 2018. Six accessions; NGB00297, NGB00350, NGB00375, NGB00332, NGB00452 and NGB00335 were not significantly different from each other and not better than any accession at the 3location in 2018. NGB00320 had zero value of PI at Abeokuta but better than 17% of the accessions in Ijanikin and 6% of the accessions in Ibadan for 2018. Location mean accession pod yield/plot was lower for Abeokuta and Ibadan in 2018 and slightly higher at Ijanikin in 2018 than 2017 Table 1 and 2. Ijanikin had the least location mean, while Abeokuta had the highest location mean for each year, thus, Ijanikin was low yielding and Abeokuta was high yielding environments. Kumar et al. (2010) and Singh et al. (2017) in assessing okra genotypes recorded low environmental mean yield due to poor performing genotypes.
Abeokuta   Ijanikin     Ibadan  
S/No. Accession Mean kg/plot m p Mean kg/plot M p Mean kg/plot M P
1 NGB00469 26.33 17 94 22.14 16 89 25.17 17 94
2 NGB00467 24.71 16 89 20.10 16 89 23.02 16 89
3 NGB00371 20.62 12 67 19.11 11 61 20.00 11 61
4 NGB00514 18.32 10 56 16.02 8 44 17.04 9 50
5 NGB00328 17.67 8 44 16.08 8 44 16.67 8 44
6 NGB00322 17.31 7 39 15.91 8 44 16.14 8 44
7 NGB00362 16.00 5 28 15.05 7 39 15.98 7 39
8 NGB00302 14.33 5 28 16.23 9 50 15.92 7 39
9 NGB00466 13.45 3 17 15.66 8 44 15.45 7 39
10 NGB00396 13.06 2 11 12.64 5 28 13.00 5 28
11 NGB00360 12.63 2 11 12.31 5 28 12.35 4 22
12 NGB00343 12.33 2 11 11.83 5 28 11.68 3 17
13 NGB00320 11.00 0 0 11.14 3 17 10.96 1 6
14 NGB00297 10.87 0 0 9.29 0 0 10.00 0 0
15 NGB00350 8.95 0 0 7.56 0 0 8.11 0 0
16 NGB00375 8.33 0 0 7.40 0 0 7.93 0 0
17 NGB00332 7.67 0 0 6.09 0 0 7.15 0 0
18 NGB00452 6.72 0 0 6.07 0 0 7.03 0 0
19 NGB00335 6.09 0 0 5.83 0 0 6.00 0 0
Location Mean 14.02 – – 12.97 – – 13.66 – –
Table 2: Performance of nineteen okra accessions according to mean and performance index (PI), 2018.
Accession rating across year and location
Influence of environments on genotype is an essential consideration in any plant breeding programme, genotype by environment brings out genotypic performance as stable/adaptable genotypes to specific or across environments and years. Several methods are in use to study genotype by environment interaction and classify genotype according to their performance (Eberhart and Russell 1966, Perkins and Jinks 1968, Lin 1982). However, a simple, easy to understand and rapid alternative is the cultivar performance index approach. The differential genotypic performances as rating of nineteen okra accessions across locations, years and year × location used in this study are presented in Table 3; accession NGB00469, NGB00467, NGB00371, NGB00514 and NGB00328 had P value across years (Py) of 50 and above for each of the 3locations. Accession NGB00322 and NGB00362 with Py of 50 in Ijanikin and Ibadan only, while NGB00302 and NGB00466 are adapted to Ijanikin only by out yielding 50% of the accessions evaluated across 2years at the 3locations. Evaluation of the 19 accession across 3locations for 2 years revealed that accession NGB00469, NGB00467, NGB00371, NGB00514, NGB00328 and NGB00322 are superior to over and better than 40% of the accession in both years. Accession NGB00362 was superior to over 50% of the accessions in 2017 only, thus, expressed yearly effect on it performance. Evaluations across 2years and 3locations performance index (Pys) indicate that accession NGB00469, NGB00467, NGB00371, NGB00514, NGB00328 and NGB00322 were superior, consistent and better than other accessions evaluated. Accession NGB00350, NGB00375, NGB00332, NGB00452 and NGB00335 are poor performing throughout the evaluations, they are consistently low yielding accessions across the 2years and 3locations of evaluation. The 6 environments used in this study serve as good environmental discriminant in accession × location expression. Similarly, Alake and Ariyo (2012), Akotkar et al. (2014), Sanket et al. (2018) reported influence of environments on okra genotypic performance using different approach.
Locations across 2years (Py) Years across 3locations (Ps) Across 2 years & 3 locations (Pys)
S/No. Accession Abeokuta Ijanikin Ibadan 2017 2018  
1 NGB00469 94 94 94 100 94 94
2 NGB00467 83 83 89 83 89 83
3 NGB00371 67 56 67 67 61 67
4 NGB00514 61 56 67 67 50 56
5 NGB00328 50 50 56 61 44 50
6 NGB00322 39 50 50 50 44 50
7 NGB00362 28 50 50 50 39 39
8 NGB00302 17 50 33 22 39 28
9 NGB00466 17 50 33 28 33 28
10 NGB00396 11 27 28 22 28 22
11 NGB00360 11 17 17 11 22 17
12 NGB00343 11 22 6 11 17 17
13 NGB00320 6 22 6 11 6 11
14 NGB00297 0 17 6 11 0 6
15 NGB00350 0 6 0 6 0 0
16 NGB00375 0 0 0 6 0 0
17 NGB00332 0 0 0 0 0 0
18 NGB00452 0 0 0 0 0 0
19 NGB00335 0 0 0 0 0 0
Table 3: Rating of nineteen okra accessions using performance index (PI) approach.
Ranking of 19 okra accessions and mean yield are presented in Table 4; accession NGB00469, NGB00467 and NGB00371 had the first to third best pod yield respectively in 2017 at the 3 locations except NGB00371 that came fourth position in Ibadan. Accession NGB00469 in 2018 came first, second and third in Abeokuta, Ibadan and Ijanikin, while NGB00467 and NGB00371 came first in Ibadan and Ijanikin. Accession NGB00467 was second in both Abeokuta and Ijanikin, while NGB00371 came third in Abeokuta and Ibadan in the evaluation done in 2018. These accessions expressed their best pod yield in areas of good locational influences due to genotype x environment interaction. Similarly, accessions NGB00469, NGB00467, NGB00371, NGB00514, NGB00328 and NGB00322 occupy top 6 positions in each of the 3 locations and 2 years, these accessions had MRI less than 40, RM of less than 7 and their pod yield is higher than 16kg/plot, also higher than the overall mean pod yield of 13.69kg/plot. The top six accession average pod yield ranged from 24.83-16.65 kg/plot, thus, they are potential high yielding accessions. Accession NGB00350, NGB00375, NGB00332, NGB00452 and NGB00335 were consistently the last five accessions in the 2years and 3locations, and they had very high RT, MRI and pod yield of less than 10kg/plot.
2017     2018        
S/No. Accession Abeokuta Ijanikin Ibadan Abeokuta Ijanikin Ibadan Rank Total Rank Mean Mean yield (kg/plot)
1 NGB00469 1 1 1 1 3 2 9 1.50 24.83a
2 NGB00467 2 2 2 2 2 1 11 1.83 21.96a
3 NGB00371 3 3 4 3 1 3 17 2.83 19.16ab
4 NGB00514 4 4 3 4 6 4 25 4.17 18.34ab
5 NGB00328 5 8 5 5 5 5 33 5.50 17.15ab
6 NGB00322 6 5 6 6 7 6 36 6.00 16.65ab
7 NGB00362 7 7 7 7 9 7 44 7.33 15.84bc
8 NGB00302 8 9 10 8 4 8 47 7.83 14.59bc
9 NGB00466 9 6 8 9 8 9 49 8.17 14.61bc
10 NGB00396 10 10 9 10 10 10 59 9.83 13.16c
11 NGB00360 11 11 11 11 11 11 66 11.00 12.04c
12 NGB00343 12 13 13 12 12 12 74 12.33 11.80c
13 NGB00320 13 12 12 13 13 13 76 12.67 11.38c
14 NGB00297 14 14 14 14 14 14 84 14.00 10.56c
15 NGB00350 15 15 15 15 15 15 90 15.00 9.20cd
16 NGB00375 16 16 16 16 16 16 96 16.00 8.63cd
17 NGB00332 17 17 `17 17 17 17 102 17.00 7.63d
18 NGB00452 18 18 18 18 18 18 108 18.00 6.84d
19 NGB00335 19 19 19 19 19 19 114 19.00 5.76d
              Mean Pod Yield 13.69
Table 4: Ranking of okra accession performances based on rank total.
Grouping of okra accession
The scatter diagram in Figure 1 classified the 19 accessions evaluated across 3 locations and 2 years, and using their RM to plot the diagram. The scatter diagram classify the accessions into four groups; A, B, C and D. Group A had 7 accessions; NGB00469, NGB00467, NGB00371, NGB00514, NGB00328, NGB00322 and NGB00362 that have high Pys and low RM with high and consistent Pod yield. The accessions in group A are valuable and highly desired as outstanding accessions. Group B has no accession, group C has 3 accessions, these accessions have low Pys and RM, thus, they could be of moderate breeding value due to their RM. Nine accessions have low Pys and high RM, they are grouped in quadrant C, all the 9 accessions have low pod yield for the 2 years and 3 locations of evaluations, thus, they are poor performing accessions and are not desirable for yield improvement.
View figure in the article PDF

Figure 1: Scatter diagram of okra accessions.
Conclusion
All the ranking methods used were consistent in identifying six accessions; NGB00469, NGB00467, NGB00371, NGB00514, NGB00328 and NGB00322 as superior accessions. The methods used in this study are better alternative approach without much calculation, ease of computation, interpretation and simple to identify top performing accessions. It is therefore concluded that the methods used here, particularly scatter diagram are valuable for plant breeders that need to make quick decision for selecting outstanding genotypes.
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Citation: Francis Abayomi Showemimo and Kudus Feranmi Oladele. (2023). Yield Analysis of okra (Abelmoschus Esculentus (L.) Moench) Accessions Using performance Index and Ranking Methods. Journal of Agriculture and Aquaculture 5(2).
Copyright: © 2023 Francis Abayomi Showemimo. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.