Determination of selection criteria for yield traits to improve okra (Abelmoschus esculentus L. Moench) breeding in IndonesiaAbstract
The Abelmoschus esculentus L. Moench, commonly known as okra, is increasingly cultivated in Indonesia due to its recognition as a functional food source. Current efforts in breeding new okra varieties are focused on high productivity, yet minimal information is available regarding selection criteria. This study aimed to identify key characteristics for selecting high-yield okra varieties. In-depth, this research aims to obtain selection characters in each type of okra (red and green okra). A randomized block design was used with 20 okra genotypes and three replicates, resulting in 60 experimental units, each containing 10 sample plants. Results indicated that green okra exhibited superior growth traits like stem diameter and number of nodes, but red okra showed higher overall productivity due to its resilience under biotic stress. However, both types showed similar selection characteristics related to high productivity, specifically the number of fruits and nodes. These findings provide essential insights for future breeding programs aimed at improving okra productivity in Indonesia. The way to determine the selection criteria is not fixated on correlation and path analysis, but can use correlation and stepwise regression as in this study. This method of determining selection criteria is more comprehensive than research on okra in Indonesia. However, it can be used externally to breed other commodity crops in various regions.
Keywords:
okra; productivity; selection criteria; correlation; regression analysis
Resumo
O Abelmoschus esculentus L. Moench, comumente conhecido como quiabo, é cada vez mais cultivado na Indonésia devido ao seu reconhecimento como fonte de alimento funcional. Os esforços atuais na criação de novas variedades de quiabo estão concentrados na alta produtividade, mas há poucas informações disponíveis sobre os critérios de seleção. Este estudo teve como objetivo identificar as principais características para a seleção de variedades de quiabo de alta produtividade. Especificamente, esta pesquisa buscou identificar caracteres de seleção em cada tipo de quiabo (quiabo vermelho e verde). Foi usado um delineamento de blocos aleatórios com 20 genótipos de quiabo e três réplicas, resultando em 60 unidades experimentais, cada uma contendo 10 plantas de amostra. Os resultados indicaram que o quiabo verde apresentou características de crescimento superiores, como diâmetro do caule e número de nós, enquanto o quiabo vermelho apresentou maior produtividade geral devido à sua resistência ao estresse biótico. Entretanto, os dois tipos apresentaram características de seleção semelhantes relacionadas à alta produtividade, especificamente o número de frutos e nós. Essas descobertas fornecem percepções essenciais para futuros programas de melhoramento genético destinados a aumentar a produtividade do quiabo na Indonésia. A maneira de determinar os critérios de seleção não se limita à correlação e à análise de caminho, mas pode usar a correlação e a regressão por etapas, como neste estudo. Esse método de determinação dos critérios de seleção é mais abrangente do que os estudos existentes sobre quiabo na Indonésia, podendo ser aplicado externamente para criar outras culturas de commodities em várias regiões.
Palavras-chave:
quiabo; produtividade; critérios de seleção; correlação; análise de regressão
1. Introduction
Abelmoschus esculentus L. Moench, widely known as the okra plant, is increasingly recognized as a valuable functional food and is being cultivated more extensively in Indonesia. Okra, belonging to the Malvaceae family, has traditionally been utilized as a vegetable and is occasionally prepared as infused water (Riyanti et al., 2018). The plant’s bioactive compounds, identified by Durazzo et al. (2018), contribute to its growing popularity due to its health benefits. Okra fruit is particularly notable for its high water content of 81.9% and a low-fat content of 0.066 g per 100 g of fresh weight. Additionally, it is rich in dietary fiber (8.16 g per 100 g fresh weight), carbohydrates (4.86 g per 100 g fresh weight), and protein (3.55 g per 100 g fresh weight) (Romdhane et al., 2020). Recent studies by Liwanda et al. (2024) have highlighted the presence of essential macrominerals in okra, such as potassium and magnesium, which are found in higher concentrations compared to iron. Furthermore, okra is rich in polyphenolic compounds, including carotene, folic acid, thiamin, riboflavin, niacin, and vitamin C, enhancing its nutritional value (Roy et al., 2014). The seeds of okra are a significant source of protein and fats, containing polyphenols like flavonoid derivatives, lysine, and fatty acids such as palmitate, oleate, and linoleate (Roy et al., 2014; Gemede et al., 2015; Graham et al., 2017). Additionally, okra roots are rich in carbohydrates and flavonol glycosides (Sunilson et al., 2008), while the leaves contain minerals, tannins, and flavonol glycosides (Caluete et al., 2015).
Plant breeding plays a crucial role in enhancing crop traits, including those of okra, by introducing new genetic characteristics that improve the plant's performance (Swarup et al., 2020). The breeding process generally begins with the acquisition of germplasm to ensure a diverse genetic pool. This diversity is essential for identifying and selecting desirable traits, which are further enhanced through techniques such as crossing, mutation, and gene transformation. These processes lead to the selection and evaluation of new varieties that exhibit superior traits, such as higher yields, resistance to biotic and abiotic stresses, and improved nutritional profiles.
In Indonesia, okra cultivation has expanded, particularly within educational and private sectors, with efforts focused on developing new varieties that surpass existing ones in terms of productivity and stress resistance. The use of crossing techniques followed by careful selection is vital for creating okra cultivars with enhanced qualities (Afifah et al., 2021). Identifying key selection traits, especially those associated with yield potential, is fundamental to the success of breeding programs (Bernardo, 2020). However, selecting for yield potential is complex and depends on multiple contributing traits. So, the selection criteria play a crucial role in determining the development of diverse agricultural products (Seifu, 2018).Therefore, this study aims to identify critical characteristics that can be used as selection criteria to develop high-yielding okra varieties. As highlighted by Mustafa et al. (2018), achieving optimal selection outcomes requires a deep understanding of yield components, which can be facilitated by applying targeted selection criteria (Anisa et al., 2022).
In-depth, this research aims to obtain selection characters in each type of okra (red and green okra). If these two types of okra produce different selection characters, then the process of plant breeding in both kinds of okra will be more straightforward with the results of this study. So, this finding is very useful for okra plant breeders.
2. Materials and Methods
2.1. Experimental material
This study was conducted between May and August 2023 at the IPB Alam Sinarsari Residential Experimental Garden with coordinate 6°34'01”S 106°43'29”E. Twenty okra genotypes were used, all of which were sourced from the Plant Breeding Education Laboratory, Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University. These genetic materials align with those previously documented by Reswari et al. (2024)
2.2. Experimental design and treatments
The experiment was structured using a randomized complete block design (RCBD) with genotype as the sole factor, and three replications were conducted. Each replication included 10 plants per genotype, resulting in 60 experimental units and a total of 600 plants. The methodology for planting and field management followed the procedures outlined in prior studies (Reswari et al., 2024). The first task is sowing the seeds. Planting involves spacing the seeds 50×50 cm apart, using a double row planting system on each 5×1 m bed, all covered with silver-black plastic mulch. Maintenance activities include morning watering and weekly fertilizing with 250 mL of AB Mix fertilizer per plant. Pesticide spraying is done twice weekly using insecticide containing 2 mL/L of abamectin and agristick adhesive. The parameters measured included plant height (cm), stem diameter (mm), number of nodes, fruit count, segment length (cm), leaf length (cm), leaf width (cm), lobe depth (cm), petiole length (cm), fruit weight (g), fruit length (cm), fruit diameter (mm), number of locules, carpel thickness (mm), mature fruit length (cm), mature fruit diameter (mm), flower area (cm2), days to flowering (DAP), days to harvest (DAP), weight per plot (g), weight of 1000 seeds (g), and productivity (tons/ha).
2.3. Statistical analysis
Data analysis was performed using PKBT-STAT 3.1. An F-test was applied to assess significant differences among the genotypes, with a significance level set at 5%. When significant differences were found, further analysis was conducted using the BNJ (Newman-Keuls) test at the same significance level. Additionally, a comparative analysis between green and red okra varieties was carried out using SAS OnDemand for Academics. Correlation and stepwise regression analyses were performed using Minitab statistical software and Microsoft Excel 2019.
3. Results
The contrast analysis conducted on growth characteristics revealed no significant differences between green and red okra in terms of plant height, internode length, leaf length, and lobe depth. However, significant differences were observed in stem diameter, number of nodes, leaf width, and petiole length, with green okra showing higher values than red okra for these traits. The mean values for the growth characteristics of the various green and red okra genotypes are presented in Table 1 and Table 2.
For yield components, the contrast analysis indicated that the number of fruits, fruit length, and fruit diameter did not differ significantly between green and red okra. In contrast, significant differences were found in fruit weight and the number of locules, with green okra exhibiting a higher average fruit weight (16.47±3.92 g) compared to red okra (14.56±2.89 g). While no significant differences were found in fruit weight among the red okra varieties, green okra varieties did show significant differences, with the Red Hill Country variety having the highest fruit weight. The mean values for the yield components across different green and red okra varieties are summarized in Tables 3, 4, 5, and 6.
When analyzing the characteristics of carpel thickness, the length and diameter of mature fruits, no significant differences were observed between green and red okra. However, flowering time and flower area showed significant variations, with red okra flowering earlier (29.85±4.42 days after planting) than green okra (33.62±4.12 days after planting). The flower area, calculated by multiplying the length and width of the flowers in each observed variety, was significantly larger in green okra (67.71±7.68 cm2) compared to red okra (51.00±12.30 cm2).
The contrast test results for harvesting time revealed that red okra was ready for harvest sooner (35.49±4.66 days after planting) compared to green okra (39.64±4.21 days after planting). This aligns with the earlier flowering time observed in red okra. In terms of fruit weight per plot, red okra produced a higher average fruit weight per plot (2297.9±416.67 g) compared to green okra (1964.6±387.18 g). No significant differences were found in the weight of 1000 seeds between the green and red okra varieties. However, the productivity analysis showed that red okra had higher productivity, with an average yield of 9.19±1.67 tons/ha compared to 7.85±1.55 tons/ha for green okra. This productivity was calculated by converting the fruit weight per plot, adjusted for land area and planting distance, into yield per hectare.
3.1. Correlation analysis of green okra
In green okra, several traits exhibited significant correlations. Plant height was positively and significantly correlated with internode length (r = 0.85). The number of nodes showed a strong positive correlation with both the number of fruits (r = 0.78) and productivity (r = 0.49). Additionally, leaf length was positively correlated with leaf width (r = 0.73) and petiole length (r = 0.54). Leaf width also correlated positively with lobe depth (r = 0.57) and petiole length (r = 0.61). Yield component traits, such as the number of fruits, were positively correlated with the length of mature fruits (r = 0.55), fruit weight per plot (r = 0.61), and overall productivity (r = 0.61). Fruit weight exhibited strong positive correlations with fruit diameter (r = 0.90), carpel thickness (r = 0.81), mature fruit diameter (r = 0.80), and the weight of 1000 seeds (r = 0.53). Fruit diameter was also positively correlated with carpel thickness (r = 0.95), mature fruit diameter (r = 0.91), but negatively correlated with mature fruit length (r = -0.74) and flower area (r = -0.63). Carpel thickness was positively correlated with mature fruit diameter (r = 0.88) but negatively correlated with mature fruit length (r = -0.73) and flower area (r = -0.55). Flowering time showed a very strong positive correlation with harvesting time (r = 0.98). Among the correlated traits in green okra, only the number of nodes and the number of fruits were significantly associated with productivity. The correlations between these traits in green okra are detailed in Table 7.
3.2. Correlation analysis of red okra
In red okra, the plant height trait was significantly correlated with internode length (r = 0.91). Stem diameter showed positive correlations with the number of nodes (r = 0.60), petiole length (r = 0.59), flowering time (r = 0.73), and harvesting time (r = 0.73), but was negatively correlated with lobe depth (r = -0.51) and mature fruit length (r = -0.56). The number of nodes was positively correlated with petiole length (r = 0.60), flowering age (r = 0.65), and harvest age (r = 0.69). The number of fruits was positively correlated with mature fruit length (r = 0.51), fruit weight per plot (r = 0.63), and overall productivity (r = 0.63). Additionally, fruit weight was positively correlated with fruit diameter (r = 0.72), carpel thickness (r = 0.55), and mature fruit diameter (r = 0.57). The fruit diameter also showed positive correlations with carpel thickness (r = 0.79) and mature fruit diameter (r = 0.70). Flowering time and harvesting time were highly correlated (r = 0.96). Of all these traits, only the number of fruits showed a significant correlation with productivity in red okra. The correlations between these traits in red okra are also presented in Table 7.
3.3. Stepwise regression analysis
Stepwise regression analysis indicated that green okra productivity could be predicted based on the number of fruits, petiole length, and fruit diameter, with a determination coefficient (R2) of 63.09%. The number of fruits in green okra could be estimated from the number of nodes, carpel thickness, and flower area, with a determination coefficient of 77.70%. In red okra, productivity could be predicted from the number of fruits, with a determination coefficient of 39.74%. The number of fruits in red okra could be estimated based on plant height, stem diameter, number of nodes, and fruit diameter, with a determination coefficient of 87.84%. The stepwise regression coefficients for green and red okra are provided in Table 8.
4. Discussion
The contrast analysis between green and red okra was conducted to identify key differences in characteristics that could guide breeders in establishing selection criteria and determining the direction for developing new okra varieties. Given that differences between green and red okra traits may necessitate distinct selection criteria, it is crucial to understand the relationship between growth traits, yield components, and overall productivity (Al-Juboori, 2021). The study revealed that several growth characteristics, including stem diameter, number of nodes, leaf width, and petiole length, were significantly greater in green okra compared to red okra.
Fruit weight emerged as a critical trait influencing productivity, with variations observed among green okra varieties, while red okra varieties showed no significant differences. Notably, the Red Hill Country variety displayed a substantially higher fruit weight compared to other varieties, though it had the lowest fruit count. This finding suggests that in green okra, fruit weight may significantly impact productivity, whereas in red okra, fruit weight alone does not appear to be a determining factor for productivity. To avoid biased conclusions, it is important to perform a thorough correlation analysis.
Red okra varieties were found to mature earlier than green okra, as evidenced by the flowering and harvesting time characteristics. This correlation between flowering time and harvest time aligns with previous studies, which have demonstrated that a shorter flowering time results in a shorter generative phase, leading to earlier harvests (Chandrasari and Nasrullah, 2012; Sharma et al., 2021). Despite green okra showing higher fruit weight as a yield component, red okra outperformed green okra in fruit weight per plot and overall productivity. This observation underscores the influence of plant survival rates in the field, as biotic stressors, including pest and disease attacks, impacted green okra more severely, leading to higher mortality and reduced productivity. Conversely, red okra demonstrated greater resilience, maintaining higher productivity under stress conditions. These findings highlight the importance of selecting traits that influence productivity in okra, warranting further analysis through correlation and regression to deepen the understanding of these relationships. Hastini et al. (2019) stated that correlation and regression analysis were the standard techniques to elucidate the relationship between two quantitative variables.
Correlation analysis plays a crucial role in determining the relationships between traits and productivity. It provides valuable insights into the associations between yield components, enabling the identification of superior genotypes from diverse genetic populations (Singla et al., 2018). The current study's correlation analysis confirmed that nearly all traits selected as selection criteria were relevant (Amas et al., 2023). Understanding the relationships between various quantitative traits is essential for developing effective selection methods to improve yield components (Sravanthi et al., 2021). Correlations can be either positive or negative, with positive correlations indicating that an increase in one trait corresponds to an increase in another, while negative correlations suggest an inverse relationship.
In both green and red okra, plant height was positively correlated with internode length, indicating that taller plants tend to have longer internodes. This finding is consistent with the work of Vinod and Gaibriyal (2023), who reported a significant positive correlation between plant height and internode length at both phenotypic and genotypic levels. However, the correlation patterns differed between green and red okra in terms of the number of nodes. For green okra, the number of nodes was positively correlated with the number of fruits, aligning with the findings of Thulasiram et al. (2017), who observed a significant positive correlation between these traits. In red okra, however, the number of nodes was correlated with leaf petiole length, flowering time, and harvest time.
Interestingly, both green and red okra exhibited similar correlations for flowering age, fruit weight, and fruit diameter. Flowering age was positively correlated with harvest age, indicating that earlier flowering leads to earlier harvesting. In both types of okra, fruit weight showed a significant positive correlation with fruit diameter, carpel thickness, and mature fruit diameter. However, in green okra, fruit weight also correlated with the weight of 1000 seeds, a relationship not observed in red okra. Additionally, fruit diameter in green okra was negatively correlated with mature fruit length and flower area, relationships that were absent in red okra.
The most critical traits to consider are those that correlate directly with productivity. Correlation analysis evaluates the traits' relationship and association with yield (Akbar et al., 2019). Correlation analysis in this study identified that in green okra, the number of nodes and number of fruits were positively correlated with productivity. In contrast, in red okra, only the number of fruits showed a significant correlation with productivity. These findings suggest that selection criteria for high productivity should focus on the number of nodes and fruits in green okra, while in red okra, the focus should be on the number of fruits alone. To ensure the robustness of these recommendations, regression analysis is necessary to confirm that these traits can reliably predict productivity.
The stepwise regression analysis further supported the importance of the number of fruits in predicting productivity. In green okra, productivity was influenced by the number of fruits, petiole length, and fruit diameter, with a relatively high coefficient of determination indicating the model's effectiveness. In red okra, productivity was primarily influenced by the number of fruits, but the coefficient of determination was lower, suggesting that other factors may also play a role. Hannachi et al. (2013) emphasize that the coefficient of determination reflects the effectiveness of explanatory variables in predicting outcomes. According to Reswari et al. (2019), this coefficient indicates how well the independent variables explain the dependent variable and how other unaccounted factors might influence the outcome. The number of fruits was a key determinant of productivity for both green and red okra. Stepwise regression is a statistical method to describe the relationship among the traits. Mousavi and Nagy (2021) used a regression model that was significant at one percent, which showed morphological traits have a straight effect on the yield of maize. Salehi Sardoei et al. (2023) used stepwise regression to investigate four citrus cultivars' physiological and biochemical characteristics. The results of stepwise regression for all traits indicated the importance and critical role of the trait in the plant itself. So, it's valid for determining criteria selection in okra high productivity.
In this study, we have examined in depth the differences in characters between red okra and green okra as well as the selection characters produced in the two types of okra, this is not found in other articles. Komolafe et al. (2022) conducted a correlation analysis and path analysis of okra characters for productivity. Nayak et al. (2023) also did the same thing, but they needed to explain in detail the different correlations between green okra and red okra. Therefore, the results of this study provide new insights into plant breeding programs in okra, primarily on determining selection criteria to increase productivity. The way to determine the selection criteria is not fixated on correlation and path analysis, but can use correlation and stepwise regression as in this study. This method of determining selection criteria is more comprehensive than research on okra in Indonesia. However, it can be used externally to breed other commodity crops in various regions.
5. Conclusion
This study identified key traits for improving okra breeding in Indonesia. Green okra exhibited superior growth traits like stem diameter and number of nodes, but red okra showed higher overall productivity due to its resilience under biotic stress. The number of fruits and nodes in green okra and the number of fruits in red okra were positively correlated with productivity, suggesting these as crucial selection criteria. Stepwise regression analysis confirmed that focusing on these traits can enhance yield. Therefore, selecting for the number of fruits and nodes in green okra and the number of fruits in red okra is recommended to develop high-yielding varieties. These findings provide essential insights for future breeding programs aimed at improving okra productivity in Indonesia. The way to determine the selection criteria is not fixated on correlation and path analysis, but can use correlation and stepwise regression as in this study. This method of determining selection criteria is more comprehensive than research on okra in Indonesia. However, it can be used externally to breed other commodity crops in various regions.
Acknowledgements
The researchers would like to express their sincere gratitude to the Ministry of Education, Culture, Research, and Technology of Indonesia for funding this study through the postgraduate research and doctoral dissertation program (Grant No. 027/E5/PG.02.00.PL/2024), led by Muhamad Syukur. We also extend our heartfelt thanks to all individuals and organizations who assisted in the data collection and contributed to the successful completion of this project.
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