Abstract
Winged bean (Psophocarpus tetragonolobus L.) is a nutrient-rich tropical legume with considerable potential to improve food security in tropical regions. However, breeding programs for purple-podded winged bean remain limited due to insufficient information on key genetic parameters. This study aimed to assess heritability, expected genetic gain, trait correlations, and path analysis in advanced purple-podded winged bean lines. Ten breeding lines derived from crosses of Indonesian landraces were evaluated using a randomized complete block design with three replications. Traits observed included days to flowering, days to harvest, fruit set, pod width, pod length, pod weight, pod weight per plant, number of seeds per pod, and yield potential. The results revealed high broad-sense heritability (>0.5) for all traits, with the highest values observed for pod length (0.96), pod width (0.95), and fruit set (0.90). Expected genetic gain was highest for fruit set (35.20%), pod weight per plant (34.44%), and yield potential (34.44%). Correlation analysis indicated strong positive relationships between days to flowering and days to harvest (r = 0.90), while yield potential showed a perfect correlation with pod weight per plant (r = 1.00). Path analysis revealed positive direct effects of fruit set (0.478) and pod weight (0.310) on pod weight per plant, whereas number of seeds per pod had a negative direct effect (-0.429). Genotypes MNN 1122 and PLB 2324 exhibited the highest yield potentials (8.58 and 7.63 t ha-1, respectively). These findings provide important genetic insights for the development of high-yielding purple-podded winged bean varieties.
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Key words: genetic gain, heritability, path analysis, winged bean
Introduction
Winged bean (
Psophocarpus tetragonolobus L.) is a tropical legume of high nutritional importance, characterized by its rich protein content, essential fatty acids, and vital minerals crucial for human health (
Mohanty et al. 2020). Its seeds, particularly those from purple-podded varieties, contain high protein levels ranging from 27.8% to 41.9%, along with essential micronutrients such as calcium, iron, and zinc (
Millar et al. 2019). In addition, the presence of anthocyanins in purple-podded winged bean confers antioxidant properties with potential benefits in reducing the risk of chronic diseases (
Bepary et al. 2023). These attributes position winged bean not only as an agronomically valuable crop but also as a strategic component for improving food security and public health in tropical regions affected by malnutrition.
Despite its substantial potential, genetic improvement of winged bean—particularly purple-podded types—has progressed slowly. Breeding efforts remain constrained, resulting in low productivity and underutilization of existing genetic variability. One of the major limitations is the lack of comprehensive information on key genetic parameters such as heritability, trait associations, and their contributions to yield, which are essential for designing effective selection strategies (
Rakhmad et al. 2021). Addressing this knowledge gap is critical for optimizing breeding programs and fully exploiting the crop’s genetic potential.
In this context, understanding genetic diversity and heritability becomes fundamental in plant breeding. Genetic diversity assessments provide insight into the extent of variability within germplasm, while heritability estimates indicate the relative contribution of genetic factors to trait expression and the expected response to selection (
Thapa et al. 2024). In winged bean, several yield-related traits have been reported to exhibit moderate to high heritability, including days to flowering, pod weight, pod weight per plant, pod length, pod width or diameter, and number of seeds per pod. Heritability estimates for pod weight and pod length ranging from 65-75% have been reported, indicating strong genetic control and good prospects for selection (
Handayani et al. 2016;
Shonde et al. 2023). High genetic variation in days to flowering, pod length, and pod weight per plant has also been observed, with these traits showing positive associations with total yield (
Yulianah et al. 2020). Moreover, early flowering has been recognized as an important trait contributing to yield uniformity and earlier crop maturity (
Akeem et al. 2019). In line with these findings,
Kuswanto et al. (2024) identified NSM2 as having ideal flowering time and pod length, PTL as possessing desirable pod shape, and MML and PBL as meeting consumer taste preferences. Since no single line combined all superior traits, crossing among selected lines is recommended to develop an optimal winged bean variety.
Beyond heritability, understanding correlations and direct trait effects is essential for identifying key yield determinants. Previous studies in winged bean reported positive correlations between days to flowering, pod width, pod length, pod weight, and overall yield (
Al-Mukhtar et al. 1981;
Yulianah et al. 2020). To further disentangle these relationships, path coefficient analysis has been widely applied and proven effective in partitioning correlation coefficients into direct and indirect effects, often yielding high coefficients of determination (
De Oliveira et al. 2018). In winged bean and related legume studies, path analysis consistently identified pod weight and pod length as traits with the strongest direct effects on yield, highlighting their importance as primary selection criteria in breeding programs. These findings confirm that correlation alone is insufficient and that path analysis provides a more robust framework for prioritizing traits with real genetic impact on yield (
Shubha et al. 2024;
Ton et al. 2021).
Considering the documented importance of heritability, trait correlations, and path analysis in winged bean improvement, the present study was designed to evaluate these genetic parameters in purple-podded winged bean genotypes. By estimating heritability, genetic progress, trait associations, and direct effects through path analysis, this research aims to identify key traits and superior genotypes to support more efficient and targeted breeding strategies, ultimately contributing to increased productivity and improved nutritional quality of winged bean in tropical agriculture.
Materials and Methods
Materials
The genetic materials used in this study comprised 10 purple-podded winged bean genotypes (
Table 1). These genotypes are advanced breeding lines selected from crosses involving local landraces from Sulawesi, Nusa Tenggara Barat (NTB), and Java. They were selected for their potential high yield and distinct purple pod characteristics, representing a pool of genetic resources for developing superior varieties. The field experiment utilized goat manure, urea, SP36, KCl, and a growing medium consisting of rice husk charcoal, soil, and manure.
This study used a Randomized Complete Block Design (RCBD) with a single factor consisting of 10 treatments, repeated 3 times, resulting in 30 experimental units. Each treatment consisted of 6 plants, with a total population of 180 plants. The estimated heritability (h2) was calculated using the broad-sense heritability formula derived from the analysis of variance.
The broad-sense heritability (h
2(bs)) was estimated using variance components derived from the Analysis of Variance (ANOVA) as described by
Wricke et al. (1986):
Where h2 represents broad-sense heritability, σg2 denotes the genotypic variance, σp2 is the phenotypic variance, and σe2 is the environmental variance. Heritability values are classified as low (h2 < 0.2), moderate (0.2 ≤ h2 ≤ 0.5), or high (h2 > 0.5).
Expected Genetic Gain (EGG) was calculated to predict the progress of selection using the formula by
Singh et al. (1978):
Here, EGG is the expected genetic gain, i is the selection intensity constant (1.76 at 10% selection intensity), h2 is the heritability, and σp is the phenotypic standard deviation. The percentage of Expected Genetic Gain (EGG%) was classified as low (<3.3%), moderately low (3.31-6.6%), fairly high (6.61-10%), or high (>10%).
Correlation analysis was performed using the Pearson correlation coefficient (r) formula to determine the relationship between traits (
Gomez et al. 1984).
Where rxy is the correlation coefficient between variables x and y, n is the number of paired observations, and x and y represent the individual values of the two traits.
The correlation coefficient (r) is calculated based on the sample size (n), with the significance level alpha (α) set at 5%. If the obtained significance value is lower than α, the influence between variables is considered significant. Conversely, if the value is higher than α, the influence between variables is considered not significant (
Obilor et al. 2018). Path coefficient analysis was performed to partition the correlation coefficients into direct and indirect effects of yield-related traits on yield, following standard biometrical procedures, in order to identify traits with the greatest direct contribution to yield.
Results
Analysis of Variance
The analysis of variance (ANOVA;
Table 2) revealed that genotype effects were highly significant (
p<0.01) for all evaluated traits, including days to flowering, days to harvest, fruit set, pod width, pod weight, pod weight per plant, pod length, number of seeds per pod, and yield potential. In contrast, replication effects were not significant, indicating uniform experimental conditions across blocks. These results confirm the presence of substantial genetic variability among purple-podded winged bean genotypes and validate the suitability of the data for subsequent heritability, correlation, and path coefficient analyses.
The evaluation of ten purple-podded winged bean genotypes (
Table 3) revealed significant genetic variation for all measured traits, as indicated by the statistical grouping and coefficients of variation (CV). Genotypes MNN 1122 and PLB 2324 were identified as the highest yielders, MNN 1122 produced the highest pod weight per plant (PWP = 1046.17 g) and consequently the highest yield potential (YP = 8.58 t ha
-1), PLB 2324 was the next best performer (PWP = 930.91 g. YP = 7.63 t ha
-1). The superior yield of these two genotypes was primarily driven by their high pod weight per plant, as their PWG (individual pod) was competitive but not the highest. Interestingly, MNN 1122 combined this high yield with a relatively earlier harvest (DH = 98.00 days) compared to the latest-flowering genotype, PLB 2324 (DH = 106.67 days). In contrast, several genotypes from the PTL and MML series (e.g., PTL 2113 and MML 1415) formed the low-yielding group, demonstrating a potential trade-off between earliness (e.g., MML 1414 flowered in 77 days) and ultimate productivity in this germplasm set. The high CV values for key yield components like PWP (9.6%) and YP (9.6%) confirm ample genetic diversity for effective selection.
Based on
Table 3 genotype MNN 1122 exhibited the highest pod yield per plant (1046.17 g), followed by PLB 2324 (930.91 g). The high yield in these genotypes was largely attributed to their superior fruit set percentage and overall pod weight per plant, with PLB 2324 notably having the highest fruit set (FS = 31.35%).
Table 4 presents the heritability and expected genetic advance (EGG) for various quantitative traits. Heritability (h
2) estimates indicate the proportion of genetic influence on trait variation. with higher values suggesting stronger genetic control. The expected genetic advance (EGG) and its percentage (%EGG) provide insights into the potential for trait improvement through selection.
Table 4 presents the heritability and expected genetic gain estimates for key quantitative traits in purple-podded winged bean, providing crucial information for breeding strategy. The analysis reveals that all measured traits exhibit high heritability (h
2 > 0.5), indicating a strong genetic influence on their phenotypic variation. This suggests that selection for these traits will be effective, as the observed performance is highly likely to be transmitted to the next generation.
Among the traits, Pod Length (h2 = 0.96), Pod Width (h2 = 0.95), and Fruit Set (h2 = 0.90) show the highest heritability, implying they are almost entirely under genetic control with minimal environmental influence. The critical yield parameters, Pod Weight per Plant (h2 = 0.83) and Yield Potential (h2 = 0.83), also demonstrate very high heritability.
The Expected Genetic Gain (EGG) provides a practical forecast of improvement under selection. Traits with both high heritability and a high or fairly high percentage of genetic gain (%EGG) are the most promising for rapid genetic improvement. Fruit Set (%EGG = 35.20%), Pod Weight per Plant (%EGG = 34.44%), and Yield Potential (%EGG = 34.44%) stand out in this regard, indicating a strong potential for significant yield enhancement through direct selection. Similarly, Pod Length (%EGG = 24.29%) and Number of Seeds per Pod (%EGG = 18.99%) show high potential for gain. In contrast, Days to Flowering shows a high heritability but only a slightly low predicted gain (%EGG = 5.00%), suggesting that while genetically controlled, progress in making plants flower earlier may be slower. In conclusion, the concurrent high heritability and high expected genetic advance for major yield components confirm that direct phenotypic selection for traits like Pod Weight per Plant, Fruit Set, and Yield Potential will be highly effective in improving the overall productivity of winged bean.
Correlation Analysis
Correlation analysis is a statistical method used to measure the relationship between two variables. A high correlation between certain agronomic traits can serve as an indicator for selecting superior plants.
Fig. 1 illustrates the results of the correlation analysis of traits in purple winged bean in this study.
The heatmap of the correlation coefficients reveals several strong relationships among the quantitative traits of winged bean plants. There is a very high positive correlation between Days to Flowering (DF) and Days to Harvest (DH; r = 0.90), indicating that as the flowering period increases, the harvest time also becomes later. These two time-related traits also show a strong positive correlation with the Number of Seeds per Pod (NS; DF-NS: r = 0.70; DH-NS: r = 0.84). This suggests that a longer growth period is associated with more seeds being formed in each pod.
Next, Pod Weight (PWG) shows a strong positive correlation with Pod Length (PL; r = 0.69) and with NS (r = 0.68). implying that heavier pods tend to be longer and contain more seeds. The most striking result is the perfect positive correlation between Yield Potential (YP) and Pod Weight per Plant (PWP; r = 1.00). This reinforces the idea that PWP is the main direct determinant of yield in this dataset, meaning that selection for increased pod weight per plant will directly increase yield potential.
However, an interesting pattern emerges in the relationship between YP and NS. Although YP is highly correlated with PWP, it actually shows a moderate negative correlation with NS (r = -0.50). A similar pattern is seen where PWP is also negatively correlated with NS (r = -0.50). This suggests a trade-off between the number of seeds per pod and pod weight per plant and yield potential. In other words, increasing the number of seeds may not necessarily increase yield and may even decrease it, perhaps because the plant allocates resources to form more, smaller seeds rather than increasing overall weight.
A moderate positive correlation is also observed between Fruit Set (FS) and PWP and YP (r = 0.54 each), highlighting the importance of having many formed pods to achieve high yield. Meanwhile, Pod Width (PWD) tends to have weak or negative correlations with most other traits, such as with PL (r = -0.06) and NS (r = -0.62).
Overall, these correlation patterns provide valuable guidance for winged bean breeding. To increase yield potential, selection should focus on improving Pod Weight per Plant (PWP) and Fruit Set (FS), while carefully considering the trade-offs with the Number of Seeds per Pod (NS).
Path Analysis
Path analysis is employed as an advanced method to decompose the direct and indirect contributions of various traits to the yield of purple winged bean. This approach enables the identification of key factors influencing yield. The results of the path analysis are presented in
Table 5.
Path analysis was employed to evaluate the direct and indirect effects of various traits on the yield of purple winged bean. The analysis identified key traits that significantly contribute to yield potential. The results show that Days to Flowering (DF) and Days to Harvest (DH) have strong positive direct effects on each other (r = 0.90), and both traits positively influence the Number of Seeds per Pod (NS), with DH showing an especially strong correlation (r = 0.84). Additionally, Pod Weight per Plant (PWP) was found to be the most critical direct determinant of yield potential, with a perfect correlation (r = 1.00). This indicates that higher pod weight directly leads to an increase in yield. However, there was a negative correlation between PWP and NS (r = -0.50), suggesting a trade-off where increasing pod weight per plant results in fewer seeds per pod. Fruit Set (FS) also showed moderate positive direct effects on both PWP and Yield Potential (YP), highlighting the importance of fruit formation for higher yields. Interestingly, while traits like Pod Length (PL) and Pod Width (PWD) influenced some other traits, their direct effects on yield were less significant. The indirect effects revealed that the relationships between DF, NS, and YP, and between DH, PL, NS, and YP, were negative, suggesting that increases in seed number or certain pod dimensions may not always translate into higher yield. Overall, this analysis underscores the importance of focusing on PWP for improving yield while considering the trade-offs with seed number and other pod characteristics.
Discussion
Heritability and Genetic Gain
The results indicate high heritability (ranging from 0.51 to 0.96) for most agronomic traits in these purple-podded winged bean genotypes, underscoring the dominant role of genetic factors in trait inheritance and the efficacy of selection-based breeding. These genotypes, as advanced breeding lines from targeted crosses of local landraces, exhibit substantial genetic variability, making them valuable for developing high-yielding varieties adapted to tropical conditions. Traits with the highest heritability, such as pod length (0.96), pod width (0.95), and fruit set (0.90), are particularly amenable to genetic improvement, as they are minimally influenced by environmental factors and can significantly enhance yield and quality. This aligns with recent findings in winged bean, where high heritability for pod-related traits (e.g., pod length at 96.84% and seeds per pod at 98.98%) facilitates rapid selection for productivity (
Thapa et al. 2024). Similarly, in related legumes like soybean, high heritability for pod length and width supports genomic selection for yield enhancement (
Chen et al. 2023).
The expected genetic gain (%EGG) further highlights strong improvement potential, with fruit set (35.20%), pod weight per plant (34.44%), and yield potential (34.44%) showing high gains, indicating that targeted selection could boost overall productivity by over 30% in one generation. For instance, genotypes like MNN 1122 and PLB 2324, with yield potentials of 8.58 t ha
-1 and 7.63 t ha
-1 respectively, demonstrate how high heritability translates to practical gains in pod weight per plant (1046.17 g and 930.91 g), driven by superior fruit set (up to 31.35%). This high %EGG for yield components is consistent with meta-analyses in bread wheat and legumes, where traits like seed weight exhibit sustainable gains through breeding (
Halladakeri et al. 2021;
Thungo et al. 2021). In contrast, days to flowering shows moderate heritability (0.51) and low %EGG (5.04%), suggesting greater environmental sensitivity, as observed in pigeonpea where flowering traits require multi-environment testing for reliable gains (
Siddanagouda et al. 2024). Overall, these parameters confirm that direct selection on high-heritability yield traits in these breeding lines can accelerate variety development, addressing malnutrition in tropical regions by prioritizing genotypes with elevated yield data.
Correlation analysis reveals intricate relationships among traits that directly impact yield in these winged bean breeding lines, providing insights for multi-trait selection strategies. Days to harvest (DH) exhibits a strong positive correlation with days to flowering (DF; r = 0.90), implying that earlier-flowering genotypes mature faster, potentially enabling multiple cropping cycles in tropical agriculture (
Mondal et al. 2011). Pod weight per plant (PWP) shows a significant positive correlation with fruit set (FS; r = 0.54), emphasizing that improving fruit set can amplify total biomass and yield potential, as seen in high performers like PLB 2324 (FS = 31.35%, yield = 7.63 t ha
-1). This supports findings in okra, where fruit set correlates positively with pod yield, guiding selection for productivity (
Komolafe et al. 2022).
However, a notable trade-off emerges with the number of seeds per pod (NS) showing a moderate negative correlation with PWP and yield potential (r = -0.50 each), suggesting resource allocation limits where more seeds per pod may reduce overall pod biomass and yield. This negative association is evident in low-yielding genotypes like PTL 2113 and MML 1415, which prioritize earliness but compromise on yield (e.g., yields below 5 t ha
-1). Such trade-offs are reported in greengram and pigeonpea, where seed number negatively correlates with yield under resource constraints, necessitating balanced breeding (
Sandhiya et al. 2018;
Siddanagouda et al. 2024). Additionally, NS positively correlates with DH (r = 0.84) and pod length (PL; r = 0.68). indicating that longer growth periods favor seed formation, consistent with mungbean studies (
Mondal et al. 2011). Pod width (PWD) displays weak or negative correlations (e.g., with NS at r = -0.62), highlighting its lesser role in yield but potential in quality traits.
Pod weight (PWG) positively correlates with PL (r = 0.69) and NS (r = 0.68), showing that heavier single pods are longer and seedier, contributing to higher PWP in top genotypes like MNN 1122 (PWG competitive at ~20 g pod
-1, PWP = 1046.17 g). Distinguishing PWG from PWP is crucial: PWG reflects component quality, while PWP aggregates plant-level output, directly determining yield (perfect r = 1.00 with yield potential). This distinction aids in breeding, as seen in peanut where pod weight components drive total yield (
Luo et al. 2018). Overall, focusing selection on PWP and FS while managing NS trade-offs could optimize yields in these lines, aligning with legume breeding for enhanced agronomic performance (
Ton et al. 2021).
Path analysis decomposes direct and indirect effects on pod weight per plant (PWP), a key yield determinant in these winged bean breeding lines, revealing priorities for genetic improvement. NS exerts a strong negative direct effect on PWP (-0.429), indicating that higher seed counts per pod diminish total plant biomass, likely due to photosynthetic resource limitations during seed filling this trade-off is pronounced in lower-yielding genotypes and mirrors findings in melon where seed allocation reduces yield components (
Wang et al. 2021). Conversely, fruit set (FS; 0.478) and pod weight (PWG; 0.310) show positive direct effects, underscoring their role in boosting productivity; for example, in high-yielders like MNN 1122, elevated FS and PWG drive PWP to 1046.17 g and yield to 8.58 t ha
-1.
The moderate coefficient for PWG (~0.3) suggests a supportive rather than dominant influence interpreted as indirect amplification through interactions with FS and PL, making it actionable in breeding by combining with high-heritability traits for cumulative gains. This is supported by path analyses in lablab bean, where pod weight positively affects yield with similar moderate coefficients, emphasizing multi-trait integration (
Shubha et al. 2024). Pod width (PWD) and length (PL) have minor effects, indicating dimensional traits contribute less directly to PWP compared to formation and weight factors, as in soybean where pod size QTLs indirectly influence yield (
Chen et al. 2023;
Li et al. 2023).
Indirect effects further highlight complexities: DF and DH positively influence NS but negatively affect yield via PWP, suggesting prolonged growth may not always enhance output in resource-limited tropics. The model's R
2 of 0.717 explains 71.7% of PWP variation, leaving room for unaccounted genetic or environmental factors, consistent with soybean studies (
He et al. 2023). For these breeding lines, path insights advocate prioritizing FS and PWG for yield gains while mitigating NS trade-offs, integrating with genomic tools for marker-assisted selection (
Ho et al. 2024). This deepens phenotypic data into actionable breeding strategies, enhancing winged bean's potential as a nutrient-rich crop (
Bhadmus et al. 2023;
Shonde et al. 2023).
Acknowledgments
The authors wish to acknowledge the critical financial assistance provided by the “Hibah Doktor Non Lektor Kepala 2023” from the Faculty of Agriculture, Universitas Brawijaya. This funding was essential for the successful execution of the research presented herein.
Fig. 1Pearson correlation heatmap among agronomic and yield-related traits of purple-podded winged bean genotypes. Red indicates positive correlations and blue indicates negative correlations.
Table 1Information and Origin of 10 purple-podded winged bean genotypes.
Table 1
|
No |
Genotypes |
Seed color |
Origin |
|
1 |
MML 1.4.1.4 |
Purplish black |
Luwu Timur, Sulawesi Tenggara |
|
2 |
MML 1.4.1.5 |
Blackish brown |
Luwu Timur, Sulawesi Tenggara |
|
3 |
PTL 2.1.1.3 |
Purplish black |
Lombok Utara, Nusa Tenggara Barat (NTB) |
|
4 |
PTL 2.1.1.2 |
Black |
Lombok Utara, Nusa Tenggara Barat (NTB) |
|
5 |
PTL 2.1.1.1 |
Black |
Lombok Utara, Nusa Tenggara Barat (NTB) |
|
6 |
KePM 2.2.1.4 |
Purplish black |
Malang, Jawa Timur |
|
7 |
MDM 1.2 |
Black |
Malang, Jawa Timur |
|
8 |
MNN 1.1.2.2 |
Purplish black |
Nganjuk, Jawa Timur |
|
9 |
MNN 1.1.2.3 |
Blackish brown |
Nganjuk, Jawa Timur |
|
10 |
PLB 2.3.2.4 |
Blackish brown |
Brebes, Jawa Tengah |
Table 2Analysis of variance (ANOVA) of purple-podded winged bean.
Table 2
|
SV /MS |
DF |
DH |
FS |
PWD |
PWG |
PWP |
PL |
NS |
YP |
|
Treatment |
44,77**
|
97,71**
|
204,6**
|
0,11**
|
5,97**
|
82149**
|
37,93**
|
5,97**
|
5,52**
|
|
Replication |
17,50ns
|
26,43ns
|
23,11ns
|
0,00ns
|
2,94ns
|
14291ns
|
1,08ns
|
2,94ns
|
0,96ns
|
|
Error (MS) |
11,02 |
12,51 |
7,52 |
0,00 |
0,82 |
5144 |
0,55 |
0,82 |
0,35 |
Table 3Mean performance of ten purple-podded winged bean genotypes for pod yield and yield components.
Table 3
|
Lines |
DF |
DH |
FS |
PWD |
PWG |
PWP |
PL |
NS |
YP |
|
PTL 2111 |
87.00 bc |
108.00 b |
22.36 a |
2.0 a |
19.75 bc |
677.22 ab |
26.6 a |
17.31 a |
5.55 a |
|
PTL 2112 |
85.67 abc |
108.33 b |
25.32 ab |
2.1 b |
20.90 c |
625.57 a |
28.3 ab |
17.64 ab |
5.13 a |
|
PTL 2113 |
86.00 abc |
108.00 b |
24.54 ab |
2.1 c |
20.73 c |
587.35 a |
26.9 abc |
18.09 abc |
4.82 a |
|
MML 1414 |
77.00 a |
93.33 a |
23.86 a |
2.2 cd |
17.71 ab |
628.43 a |
22.2 abc |
13.72 abc |
5.15 a |
|
MML 1415 |
83.33 abc |
100.00 ab |
25.10 ab |
2.1 cde |
16.65 a |
614.13 a |
20.6 bc |
13.31 abcd |
5.04 a |
|
PLB 2324 |
89.00 c |
106.67 b |
31.35 b |
2.0 cde |
20.54 c |
930.91 c |
22.6 cd |
15.73 bcd |
7.63 ab |
|
MNN 1122 |
80.67 abc |
98.00 ab |
25.78 ab |
2.1 cde |
18.97 abc |
1046.17 c |
23.6 de |
12.58 cd |
8.58 bc |
|
MNN 1123 |
78.00 ab |
95.00 a |
25.68 ab |
2.1 cde |
19.93 bc |
896.29 c |
22.6 e |
13.54 d |
7.35 c |
|
KePM 2214 |
83.67 abc |
101.00 ab |
23.13 a |
1.7 de |
19.17 abc |
624.43 a |
24.4 e |
16.81 d |
5.12 c |
|
MDM 12 |
82.67 abc |
106.00 b |
23.92 a |
1.6 e |
20.68 c |
841.85 bc |
32.5 f |
16.18 d |
6.90 c |
|
|
BNJ 5% |
9.72 |
10.35 |
7.2 |
0.13 |
2.65 |
209.99 |
2.18 |
3.40 |
1.72 |
|
CV % |
3.98 |
3.45 |
9.79 |
2.21 |
4.64 |
9.6 |
2.97 |
7.49 |
9.60 |
Table 4The heritability and expected genetic gain for quantitative traits.
Table 4
|
Trait |
h2
|
Category |
EGG |
EGG (%) |
Category |
|
Days to Flowering (DAP) |
0.51 |
High |
4.20 |
5.00 |
Slightly low |
|
Days to Harvest (DAP) |
0.69 |
High |
7.82 |
7.63 |
Fairly high |
|
Fruit Set (%) |
0.90 |
High |
13.51 |
35.20 |
High |
|
Pod Weight (g) |
0.68 |
High |
1.90 |
9.72 |
Fairly high |
|
Pod Weight per Plant (g) |
0.83 |
High |
257.3 |
34.44 |
High |
|
Pod Length (cm) |
0.96 |
High |
6.08 |
24.29 |
High |
|
Pod Width (cm) |
0.95 |
High |
0.33 |
16.59 |
High |
|
Number of Seed per Pod |
0.75 |
High |
2.95 |
18.99 |
High |
|
Yield Potential |
0.83 |
High |
2.11 |
34.44 |
High |
Table 5Path Analysis of Several Winged Bean Quantitative Traits
Table 5
|
Effect Type |
Path |
Effect Strength |
|
Direct |
DF → DH |
0.90 |
|
Direct |
DF → NS |
0.70 |
|
Direct |
DH → PWG |
0.66 |
|
Direct |
DH → PL |
0.64 |
|
Direct |
DH → NS |
0.84 |
|
Direct |
FS → PWP |
0.54 |
|
Direct |
FS → YP |
0.54 |
|
Direct |
PWG → PL |
0.69 |
|
Direct |
PWG → NS |
0.68 |
|
Direct |
PWP → NS |
-0.50 |
|
Direct |
PWP → YP |
1.00 |
|
Direct |
PL → PWD |
-0.62 |
|
Direct |
PL → NS |
0.64 |
|
Direct |
NS → YP |
-0.50 |
|
Indirect |
DF → NS → YP |
-0.35 |
|
Indirect |
DH → PL → NS → YP |
-0.21 |
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