The development of transgressive segregant (TS) selection on convergent breeding populations of S4 maize is a concept that is rarely applied. Gene construction that focuses on the action of dominant genes and inbreeding depression are obstacles to this development. However, the development of TS is necessary to accelerate maize pipelines. Therefore, the objectives of this study were (1) to develop the concept of transgressive segregant selection and (2) to select S4 TS maize to be developed as hybrid cross parents. This study was also designed with an augmented design consisting of 6 blocks. The factors focused on maize genotypes were divided into two groups: unrepeated maize genotypes, 32 TS lines, and maize hybrid genotypes repeated in each block, namely, JH 37, NASA 29, BISI 18, and SINHAS 1. The combination of ratio analysis, path analysis, best linear unbiased prediction, relative fitness, and selection indices is a fair approach for assessing the genetic potential of the S4 TS. The selection index formed was 0.53 ear weight + 0.24 seed yield percentage + yield, which works on the fitness of BLUPs. The index selection resulted in 11 S4 transgressive segregant lines being further evaluated for their hybrid potential, with the TS line CB2.23.1 being the best. In addition, the three-way cross-hybrid evaluation results also recommended SG 3.35.12 × JH37 and CB 2.23.1 × JH37 as potential hybrid lines. However, these segregants are expected to focus on identifying and combining power and combinations of diallel crosses in the future.
Research Article
A New Approach for Evaluating Maize Transgressive Segregants and Their Three-Way Cross Potential in the S4 Convergent Breeding Population
https://doi.org/10.21203/rs.3.rs-5017223/v1
This work is licensed under a CC BY 4.0 License
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BLUP
multivariate analysis
systematic selection
segregative transgenics
Zea mays
Maize development has promising economic potential. This crop not only contributes carbohydrates for human nutritional intake. However, this crop has a high market value as a primary feed ingredient and raw material in various food industries [1, 2]. This makes the development of feed-based maize more favorable than its development as food [3, 4]. On the basis of data from Freddy et al. [5], the use of maize as feed in various parts of the world has reached 70% of Indonesia's maize demand. Therefore, farmers grow more feed maize than food maize. However, the selling price of food maize is higher than that of feed maize.
Farmers have yet to optimize the high demand from the feed sector. This can be attributed to the high import of maize feed ingredients, especially in Indonesia, so efforts to increase production are constantly being made to support this demand [5, 6]. In general, maize production in Indonesia increases by a small percentage annually. However, planted areas also dominate this increase [7]. This is risky because land conversion and population increases are increasing [8–10]. According to Tridakusumah et al. [11], the rate of paddy land conversion in Indonesia has reached 0.14 million hectares annually. According to Harewan et al. [12], the rate of land conversion in protected areas has reached 5.5%. In addition, changing climate dynamics present different challenges for increasing maize production in Indonesia [13–15]. Therefore, the development of plant breeding continues to be pursued to increase maize production in Indonesia.
Maize breeding development has focused more on the assembly of hybrid varieties. However, this crop can be developed by being open-pollinated. However, the development of hybrid varieties has greater potential for heterosis than open-pollinated varieties [6, 16–19]. This makes the market and selling value of hybrid maize varieties better than those of open-pollinated varieties [5, 6, 16]. The development of hybrid varieties of maize is critical for the development of pure-line elders [20, 21]. These elders result from self-pollination from a series of generations of lines whose potential is based on their base population. A greater contribution of genetic sources will correlate with greater diversity of the primary population formed [22, 23]. This increase in diversity can be applied through convergent breeding [24–27]. Convergent breeding can be performed by systematically crossing various superior hybrid varieties. Such combinations can capture the productivity potential of numerous hybrids, giving the resulting elders an excellent opportunity to form potential and adaptive hybrids [26, 27]. However, maize-pure pipelines still present some obstacles, especially in this process. In general, maize with inbreeding depression will experience a decrease in vigor in forming its lines [16, 20, 28]. This makes it difficult to predict the pipeline potential of maize. This concept differs from that of self-pollinated plants, where the potential and progress of pipelining selection can be seen from each generation [29–32]. In addition, the pipeline of pure lines generally takes a relatively long time, especially for complex genetic constructs such as this convergent breeding concept [32, 33]. Therefore, innovation in selfing methods is needed in the S4 population. One solution that can be applied is transgressive segregant selection.
Transgressive segregant (TS) selection is a concept of selection carried out in early generations [34–36]. This selection can generally reduce the channeling time faster than the conventional method can do [37, 38]. The concept of TS generally focuses on the potential for extreme outliers of segregating lines that have high productivity with low within-line variation against parents or check varieties [39–41]. The concept of TS selection has been widely reported by several researchers on various crops, such as Rostini et al. [42] and Anshori et al. [38] in chili; Koide et al. [34] and Pabuayon et al. [35] in rice; Putri et al. [43]; Reynolds and Braun [44] in tropical wheat; and Cazzola et al. [45] in pea plants. However, this concept is used mainly for self-pollinated plants. This is because the concept of breeding self-pollinated plants is more focused on the action of their additive genes [35, 37–39]. In contrast, the potential of cross-pollinated plants is more focused on the action of their dominant genes, so the prediction and control of TS will be more straightforward in self-pollinated plants than in cross-pollinated plants. In addition, the potential for inbreeding depression in maize increases the difficulty in selecting TS [37, 46]. However, the potential for TS in maize is needed for the efficiency and effectiveness of hybrid maize parent breeding. Several studies have also attempted to successfully develop this concept in maize [26, 47, 48]. Makmur et al. [26] reported the process of transgressive segregant selection on the S4 population from convergent breeding crosses. However, the selection still needs validation because the concept is still rough. On the other hand, Anshori et al. [38] developed the concept of predicting the TS in chili plants. The concept systematically considers the potential of lines and varieties against check varieties. The concept is considered quite effective and can be modified according to the potential of cross-pollinated plants. Therefore, the development of a transgressive segregant selection method for maize based on the concept of Anshori et al. [38] needs to be optimized.
The development of transgressive segregation selection needs to be further evaluated with cross-testing. In general, the evaluation of pure lines can use a line x tester, which is included in this study with transgressive segregant genotypes [47, 49, 50]. These tester genotypes can be derived from specific pure and random lines [30, 51, 52]. The two testers will form a specific hybrid cross (pure line) and randomly (open-pollinated) to S4 TS. Both concepts still do not reflect heterosis and more complex genetic interactions, so genetic complexity testing must include more complex testers, such as the F1 hybrid variety. This tester variety can form a three-way cross with selected TS lines [53]. Compared with single or open-pollinated crosses, such crosses result in more complex genetic interactions and heterosis [53, 54]. Therefore, evaluation testing of TS lines on a three-way cross must be performed. The objectives of this study are (1) to identify the effectiveness of the concept of transgressive segregant selection in S4 generation maize, (2) to select potential and validated TS S4 maize lines to be developed as hybrid crosses, and (3) to identify potential three-way crosses that can be developed.
2.1. Experimental Design
This study was conducted at the Agricultural Standardization and Instrumentation Center for Cereal Crops in Lau Maros District, Maros Regency, South Sulawesi, at coordinates 04°59'51.9" S, 119°34'19.9" E, and an elevation of 60 masl. This study was performed from June to September 2023, using an augmented design of 6 blocks. The focus was on maize genotypes, which were divided into two groups: unrepeated maize genotypes (lines) and repeated maize genotypes (check varieties) within each block. Thirty-two genotypes of lines were tested, while the check varieties included three hybrid maize varieties (JH 37, Nasa 29, and Bisi 18) and one open-pollinated variety (Sinhas 1). Each block consisted of 5–6 genotypes and four check varieties, totaling 56 experimental units. Each experimental unit comprised 30 plants, with 15 plants per TS line to be crossed three-way with three hybrid check varieties (5 plants per comparison variety).
2.2. Research Procedure
This study followed the procedures outlined by Akfindarwan et al. [55] and Makmur et al. [26]. The research began with land preparation, clearing land, and plowing via a tractor. The experimental land was divided into six blocks measuring 3.5 m × 33 m and separated by 100 cm. Planting was conducted by creating individual holes with a spacing of 70 cm × 20 cm, with each row comprising 15 plants and two seeds per hole.
Maintenance included fertilization, watering, replacing dead or nongerminating seeds, thinning, weeding, hilling, and pest and disease control. Fertilization occurred thrice at 7, 35, and 50 days after planting (DAP) via urea, SP36, and Phonska fertilizers. Watering was conducted every ten days from planting until harvest, depending on weather conditions. Plants whose growth was poor or that died were replaced at 7 DAP. Thinning was performed at 10 DAP, leaving only one plant per well. Weeding was performed at 10 and 35 DAP to remove weeds around the maize. Hilling was conducted at 35 DAP to raise the mound and loosen the soil for better aeration. Pest control involves the use of insecticides to manage caterpillar infestations.
Natural pollination was conducted on ten maize plants per genotype for evaluation, while 15 genotypes were crossed with each comparison hybrid variety to form three-way cross lines. Additionally, five genotypes were self-pollinated. Cross-pollination and self-pollination were carried out by covering the ears with plastic before the silk appeared, trimming the silk to 1–1.5 cm, and transferring pollen from male flowers to female flowers according to the crossing concept. The female flowers were then covered again. Harvesting was adjusted according to the purpose of each of the 30 plants in each genotype line. The dried ears were pruned to separate the seeds from the earbeard. Moreover, the evaluation of transgressive lines focused on the open-pollinated pollination system.
2.3. Data Observation and Analysis
The observations focused on agronomic characteristics, including plant height (PH), number of leaves (NL), stem diameter (SD), days to male flowering (DMF), days at female flowering (DFF), anthesis-silking interval (ASI), ear height (EH), ear length (EL), ear diameter (ED), number of seed rows per ear (NSRE), number of seeds per row (NSR), ear weight (EW), 100-seed weight (100EW), seed yield percentage (SYP), and seed weight per ear (SWE). All the data were systematically analyzed.
The obtained data were first subjected to variance analysis. Characteristics significantly influenced by line diversity and line vs. check were analyzed via multivariate analysis. The multivariate analysis focused on correlation and path analysis via the RStudio software package Agricolae. Correlation analysis served as a preliminary approach to assess the relationship between a main character and supporting characters. This relationship was further elucidated by partitioning the correlation through path analysis to identify the direct influence of a characteristic on the main characteristic. However, if the independent variable exhibits no relationship pattern with the dependent variable in path analysis, the model will have high bias. Therefore, correlation analysis is necessary to exclude uncorrelated characters from path analysis. The results of the correlation and path analyses were followed by a ratio comparison, which explicitly focused on ear characteristics. Comparisons of ratio formation targeted characteristics representing the potential volume of the ear, such as ear diameter and length. The formula for ratio development is shown below:
$$\:ear\:ratio=\:\frac{yield\:component}{ear\:diameter\:or\:ear\:lenght}$$
Furthermore, the ratio values were converted back to standardized values by dividing the ratio values by the standard deviation of the lines. The formulation of the standardized value is as follows:
where X̅ represents the mean ear ratio and where S2 represents the variance in the line. The standardized values were analyzed via the concept of best linear unbiased prediction (BLUP) and a selection index. BLUP analysis was applied to the standardized value of the ratio per genotype via RStudio software with the nlme package. The resulting BLUP value served as the basis for forming the selection index. The selection index was constructed from the selection criteria and weightings obtained in the previous analysis. The index value of each line was compared with the index value of the check varieties. Lines with index values meeting the criteria were advanced to the evaluation stage for validating the TS in maize. The evaluation concept was based on the three-way cross method, which utilized the augmented BLUP. The evaluation results provide highlights and recommendations for determining transgressive lines for developing hybrid maize varieties.
Analysis of variance revealed low coefficient of variation (CV) values below 20% for all the characteristics, except for the anthesis‒silking interval. In terms of source diversity, check diversity significantly affected plant height, ear height, ear length, number of seeds per plant, number of seeds per row, ear weight, and seed weight per plot. In contrast, line diversity significantly influenced almost all the maize growth characteristics, except for male flowering age, anthesis‒silking interval, ear diameter, and number of rows of seeds per ear. Additionally, the check and line treatments comparisons revealed significant differences in all growth characteristics, except for the anthesis‒silking interval and 1000-grain weight.
Table-1. Analysis of Variance for the Validation of Transgressive Segregant S4 Maize Lines
| Character | check | line | check vs line | CV (%) |
|---|---|---|---|---|
| PH | < .0001** | 0.0230* | < .0001** | 4.00 |
| NL | 0.7000 | 0.0195* | < .0001** | 3.22 |
| SD | 0.1103 | 0.0003** | < .0001** | 5.00 |
| DMF | 0.3950 | 0.0759 | < .0001** | 3.14 |
| DFF | 0.1743 | 0.0178* | < .0001** | 2.59 |
| ASI | 0.5803 | 0.9389 | 0.3443 | 27.78 |
| EH | < .0001** | 0.0003** | < .0001** | 4.69 |
| EL | 0.0008** | 0.0071** | < .0001** | 5.20 |
| ED | 0.2714 | 0.1561 | < .0001** | 5.59 |
| NSRE | 0.0124* | 0.3249 | < .0001** | 8.40 |
| NSR | 0.0105* | 0.0106* | < .0001** | 6.73 |
| EW | 0.0204* | 0.0278* | < .0001** | 9.63 |
| 100SW | 0.0858 | < .0001** | 0.0003 | 4.29 |
| SYP | 0.3475 | < .0001** | < .0001** | 1.48 |
| SWE | 0.0256* | 0.0053** | < .0001** | 10.35 |
| Note: *Significant effect at the 5% error level, **significant effect at the 1% error level, CV = coefficient of variance, plant height (PH), number of leaves (NL), stem diameter (SD), days to male flowering (DMF), days at female flowering (DFF), anthesis‒silking interval (ASI), ear height (EH), ear length (EL), ear diameter (ED), number of seed rows per ear (NSRE), number of seeds per row (NSR), ear weight (EW), 100-seed weight (100SW), seed yield percentage (SYP), and seed weight per ear (SWE). | ||||
As shown in Fig. 1, seed weight per ear was significantly correlated with stem diameter (0.59), number of leaves (0.43), age at female flowering (-0.58), ear height (0.47), ear length (0.53), number of seeds per row (0.58), ear weight (0.81), and seed yield (0.44). This correlation pattern is relatively similar to that of ear weight. However, the correlation values differ for some characters. The correlations of these characteristics with ear weight are as follows: plant height (0.57), stem diameter (0.70), number of leaves (0.64), age at female flowering (-0.42), ear height (0.50), ear length (0.74), and number of seeds per row (0.83). The results of this analysis need to be further examined with path studies.
Fig-1. Pearson correlation analysis of maize agronomic characteristics in transgressive segregant S4 (plant height (PH), number of leaves (NL), stem diameter (SD), days at female flowering (DFF), ear height (EH), ear length (EL), number of seeds per row (NSR), ear weight (EW), 100-seed weight (100SW), seed yield percentage (SYP), and seed weight per ear (SWE)).
As shown in the table, almost all the characteristics had a weak direct effect on the weight of the seeds per ear, except for the weight of the ears (Table 2). This characteristic is the dominant factor affecting seed weight, both through a direct effect of 0.83 and indirect effects on other traits. In addition to ear weight, seed yield (0.37) can also be considered when assessing the effectiveness of the direct effect on seed weight. However, this characteristic has a low indirect effect value.
The results of the ratio analysis of the characteristics of cob weight, seed yield, and seed weight per cob are shown in Table 3. According to the table, the average population ratios of the three characteristics were 9.59, 5.65, and 7.39. The lines with the highest average ratios for cob weight, seed yield, and seed weight were CB1.43.7 (12.46), SG4.36.1 (7.5), and CB1.43.7 (10.75), respectively. The check variety with the highest average ratio for the three characteristics was SINHAS 1, with a potential cob weight of 12.88, a seed yield of 5.23, and a seed weight of 10.66. However, based on the standardized values of within-line variance, the overall results differed from those of the original ratio. The lines with the greatest cob weight, seed yield, and seed weight were SG3.10.1 (9.25), SG2.7.14 (12.05), and SG3.10.1 (7.41), respectively. On the other hand, the check varieties with the greatest potential cob weight, seed yield, and seed weight were JH 37 (9.18), BISI 18 (17.70), and Sinhas 1 (8.78).
| Characters | Direct Effect | Indirect effect | Residual | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| SD | NL | DFF | EH | EL | NSRE | EW | SYP | |||
| SD | -0.04 | -0.01 | 0.1 | 0.04 | -0.02 | -0.04 | 0.58 | -0.03 | 0.13 | |
| NL | -0.01 | -0.02 | 0.02 | 0.04 | -0.03 | -0.05 | 0.53 | -0.04 | 0.13 | |
| DFF | -0.17 | 0.02 | 0 | -0.02 | 0 | 0.02 | -0.35 | -0.09 | 0.13 | |
| EH | 0.08 | -0.02 | -0.01 | 0.04 | -0.02 | -0.04 | 0.41 | 0.03 | 0.13 | |
| EL | -0.05 | -0.01 | -0.01 | 0 | 0.04 | -0.05 | 0.61 | 0.01 | 0.13 | |
| NSRE | -0.07 | -0.02 | -0.01 | 0.04 | 0.04 | -0.04 | 0.69 | -0.05 | 0.13 | |
| EW | 0.83 | -0.03 | -0.01 | 0.07 | 0.04 | -0.04 | -0.06 | 0 | 0.13 | |
| SYP | 0.37 | 0 | 0 | 0.04 | 0.01 | 0 | 0.01 | 0.01 | 0.13 | |
| Note: number of leaves (NL), stem diameter (SD), days at female flowering (DFF), ear height (EH), ear length (EL), number of seed rows per ear (NSRE), ear weight (EW), and seed yield percentage (SYP). | ||||||||||
| Genotype | Real value | Ratio | Internal Ratio Standardized | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| EL | EW | SYP | SWE | EW_R | SYP_R | SWE_R | EW_r | SYP_R | SWE_R | ||
| CB1.37.14 | 12.82 | 123.58 | 84.85 | 109.04 | 9.52 | 6.67 | 8.23 | 5.96 | 5.08 | 5.87 | |
| CB1.42.5 | 12.96 | 109.58 | 80.49 | 90.95 | 7.91 | 7.17 | 6.57 | 2.34 | 1.78 | 2.17 | |
| CB1.43.7 | 15.33 | 190.93 | 85.18 | 163.95 | 12.58 | 5.66 | 10.75 | 3.96 | 4.86 | 3.82 | |
| CB1.5.2 | 12.48 | 112.38 | 84.88 | 99.85 | 8.83 | 6.81 | 7.67 | 3.16 | 4.71 | 3.11 | |
| CB1.5.4 | 12.48 | 73.84 | 84.29 | 67.93 | 5.98 | 6.74 | 5.32 | 1.25 | 5.29 | 1.25 | |
| CB1.5.7 | 14.14 | 146.58 | 76.2 | 115.18 | 10.46 | 5.4 | 7.94 | 3.90 | 6.42 | 4.36 | |
| CB2.22.4 | 14.51 | 136.53 | 79.55 | 108.48 | 9.32 | 5.44 | 7.35 | 2.04 | 1.96 | 1.70 | |
| CB2.23.1 | 15.53 | 142.53 | 84.81 | 124.18 | 9.28 | 5.49 | 8.03 | 6.59 | 6.60 | 6.29 | |
| CB5.10.4 | 15.31 | 145.73 | 83.88 | 124.36 | 9.81 | 5.53 | 8.31 | 2.76 | 5.80 | 2.91 | |
| CB5.2.6 | 16.87 | 187.73 | 82.1 | 155.69 | 11.11 | 4.91 | 9.21 | 4.31 | 7.30 | 4.33 | |
| CB5.5.8 | 12.21 | 136.23 | 79.85 | 110.7 | 10.4 | 7.02 | 8.34 | 3.87 | 2.51 | 4.06 | |
| CB5.7.15 | 13.73 | 149.63 | 74.04 | 109.07 | 10.66 | 5.52 | 7.93 | 6.84 | 3.00 | 3.86 | |
| CB6.5.5 | 14.89 | 149.43 | 82.4 | 124.89 | 9.87 | 5.72 | 8.21 | 5.16 | 4.35 | 4.93 | |
| CB7.1.2 | 12.59 | 123.2 | 79.12 | 103.43 | 9.18 | 6.31 | 7.61 | 2.24 | 6.52 | 2.10 | |
| SG1.10.6 | 14.77 | 116.18 | 80.35 | 91.91 | 7.11 | 5.95 | 5.59 | 3.44 | 5.25 | 2.79 | |
| SG2.16.6 | 16.11 | 156.78 | 79.77 | 125.84 | 8.71 | 5.37 | 6.89 | 2.81 | 4.15 | 2.45 | |
| SG2.19.6 | 18.37 | 136.38 | 76.52 | 101.79 | 6.57 | 4.08 | 4.73 | 5.32 | 10.14 | 5.53 | |
| SG2.25.6 | 16.47 | 141.78 | 76.19 | 26.74 | 8.02 | 4.8 | 0.37 | 7.74 | 5.42 | 6.78 | |
| SG2.7.14 | 15.61 | 126.38 | 77.93 | 96.43 | 7.54 | 5.21 | 5.65 | 7.74 | 12.05 | 6.00 | |
| SG2.7.2 | 16.03 | 119.98 | 76.46 | 90 | 6.5 | 5.07 | 4.73 | 2.94 | 4.97 | 2.72 | |
| SG3. 18.8 | 14.68 | 183.11 | 68 | 113.96 | 12.46 | 4.73 | 7.84 | 4.38 | 2.17 | 2.34 | |
| SG3. 35.12 | 13.06 | 128.71 | 81.02 | 103.25 | 9.8 | 6.75 | 7.9 | 5.63 | 2.93 | 4.58 | |
| SG3.10.1 | 14.02 | 140.91 | 78.47 | 108.7 | 10.14 | 5.73 | 7.89 | 9.25 | 7.80 | 7.41 | |
| SG3.31.4 | 15.46 | 169.31 | 37.5 | 63.44 | 10.69 | 2.5 | 4.03 | 4.29 | 6.90 | 2.09 | |
| SG3.32.8 | 15.26 | 122.51 | 75.96 | 91 | 7.97 | 5.11 | 5.98 | 3.83 | 5.51 | 3.01 | |
| SG4.11.12 | 15.7 | 181.91 | 84.13 | 153.19 | 11.5 | 5.43 | 9.71 | 4.81 | 10.43 | 4.28 | |
| SG4.24.13 | 13.66 | 144.76 | 83.21 | 117.77 | 10.41 | 6.27 | 8.61 | 5.64 | 4.64 | 5.83 | |
| SG4.27.5 | 16.02 | 170.56 | 81.75 | 138.53 | 10.78 | 5.15 | 8.83 | 3.79 | 10.39 | 3.51 | |
| SG4.36.1 | 10.76 | 102.56 | 77.52 | 76.44 | 9.73 | 7.5 | 7.53 | 2.94 | 4.01 | 2.79 | |
| SG4.41.4 | 12.76 | 111.36 | 75.9 | 80.85 | 8.85 | 6.17 | 6.63 | 6.61 | 4.35 | 6.20 | |
| SG4.51.4 | 10.66 | 82.16 | 69.22 | 58.07 | 7.16 | 6.68 | 5.1 | 1.80 | 5.95 | 1.37 | |
| ST3.8.6 | 11.51 | 103.03 | 82.18 | 87.61 | 8.1 | 7.08 | 6.79 | 1.65 | 6.37 | 1.64 | |
| BISI 18 | 16.91 | 193.03 | 81.79 | 158.15 | 11.47 | 4.87 | 9.39 | 6.10 | 17.70 | 5.51 | |
| JH 37 | 18.41 | 207.62 | 83.05 | 173.01 | 11.32 | 4.57 | 9.44 | 9.18 | 15.49 | 8.46 | |
| NASA 29 | 17.69 | 226.55 | 82.32 | 186.04 | 12.72 | 4.76 | 10.46 | 7.38 | 11.80 | 7.75 | |
| SINHAS 1 | 16.01 | 205.73 | 82.61 | 170.24 | 12.88 | 5.23 | 10.66 | 8.76 | 10.73 | 8.78 | |
| mean | 14.6 | 144.42 | 78.71 | 111.68 | 9.59 | 5.65 | 7.39 | 4.73 | 6.54 | 4.24 | |
| variance | 3.84 | 1236.71 | 65.13 | 1172.28 | 3.25 | 0.98 | 4.2 | 4.73 | 13.13 | 4.17 | |
| Note: ear length (EL), ear weight (EW), seed yield percentage (SYP), and seed weight per ear (SWE), _R = ratio | |||||||||||
The transgressive segregant index (TSI) analysis revealed that all the check varieties presented relatively superior index values compared with those of the TS lines, especially when the indices were evaluated against hybrid maize varieties (Table 4). Notably, the hybrid variety JH 37 presented the highest TSI value of 1.64, whereas SINHAS 1 represented the comparison variety with the lowest TSI value of 0.32. Among the hybrid check varieties, NASA 29 demonstrated the lowest TSI value of 0.60. Interestingly, four transgressive segregants, namely, SG2.25.6 (0.52), SG2.7.14 (0.48), SG3.10.1 (0.74), and SG4.24.13 (0.38), presented TSI values exceeding those of the SINHAS 1 variety. Furthermore, since a positive index value, lines CB1.37.14 (0.04), CB2.23.1 (0.18), SG2.19.6 (0.27), SG3.35.12 (0.18), SG4.11.12 (0.22), SG4.27.5 (0.05), and SG4.41.4 (0.13) could be considered promising TSs in this study.
Table-4. Transgressive segregant index values in the evaluation of transgressive segregant lines of maize S4
| Geno | BLUP value | BLUP_standardized | BLUP Fitness Relative | Indeks TS | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| BTK_r | RB_R | BB_PT_R | BTK_r | RB_R | BB_PT_R | BTK_r | RB_R | BB_PT_R | ||
| CB1.37.14 | 5.06 | 10.01 | 4.23 | 0.16 | 0.88 | -0.25 | 0.05 | 0.43 | -0.09 | 0.04 |
| CB1.42.5 | 4.13 | 9.59 | 3.33 | -1.14 | 0.70 | -1.38 | -0.36 | 0.34 | -0.50 | -0.61 |
| CB1.43.7 | 4.53 | 10.13 | 3.67 | -0.58 | 0.93 | -0.95 | -0.18 | 0.45 | -0.35 | -0.34 |
| CB1.5.2 | 4.34 | 9.97 | 3.56 | -0.84 | 0.86 | -1.09 | -0.26 | 0.42 | -0.40 | -0.44 |
| CB1.5.4 | 3.86 | 10.04 | 3.10 | -1.53 | 0.90 | -1.66 | -0.48 | 0.44 | -0.61 | -0.76 |
| CB1.5.7 | 4.59 | 9.11 | 4.10 | -0.49 | 0.49 | -0.42 | -0.15 | 0.24 | -0.15 | -0.18 |
| CB2.22.4 | 4.10 | 8.67 | 3.39 | -1.18 | 0.30 | -1.30 | -0.37 | 0.15 | -0.47 | -0.64 |
| CB2.23.1 | 5.26 | 9.27 | 4.51 | 0.45 | 0.56 | 0.10 | 0.14 | 0.27 | 0.04 | 0.18 |
| CB5.10.4 | 4.29 | 9.16 | 3.69 | -0.92 | 0.52 | -0.93 | -0.29 | 0.25 | -0.34 | -0.43 |
| CB5.2.6 | 4.68 | 9.36 | 4.03 | -0.36 | 0.60 | -0.50 | -0.11 | 0.29 | -0.18 | -0.17 |
| CB5.5.8 | 4.55 | 8.88 | 3.91 | -0.54 | 0.39 | -0.65 | -0.17 | 0.19 | -0.24 | -0.28 |
| CB5.7.15 | 5.37 | 7.86 | 4.10 | 0.61 | -0.05 | -0.41 | 0.19 | -0.03 | -0.15 | -0.06 |
| CB6.5.5 | 4.94 | 8.03 | 4.36 | 0.01 | 0.02 | -0.09 | 0.00 | 0.01 | -0.03 | -0.03 |
| CB7.1.2 | 4.20 | 8.32 | 3.67 | -1.04 | 0.15 | -0.95 | -0.33 | 0.07 | -0.35 | -0.50 |
| SG1.10.6 | 4.65 | 6.36 | 4.36 | -0.41 | -0.70 | -0.08 | -0.13 | -0.34 | -0.03 | -0.18 |
| SG2.16.6 | 4.49 | 6.22 | 4.28 | -0.64 | -0.76 | -0.19 | -0.20 | -0.37 | -0.07 | -0.26 |
| SG2.19.6 | 5.12 | 7.00 | 5.03 | 0.26 | -0.43 | 0.75 | 0.08 | -0.21 | 0.27 | 0.27 |
| SG2.25.6 | 5.74 | 6.38 | 5.34 | 1.13 | -0.69 | 1.13 | 0.35 | -0.34 | 0.41 | 0.52 |
| SG2.7.14 | 5.74 | 7.25 | 5.15 | 1.13 | -0.32 | 0.89 | 0.35 | -0.15 | 0.33 | 0.48 |
| SG2.7.2 | 4.58 | 5.24 | 4.58 | -0.51 | -1.19 | 0.19 | -0.16 | -0.58 | 0.07 | -0.15 |
| SG3. 18.8 | 4.93 | 5.02 | 4.43 | -0.01 | -1.29 | 0.00 | 0.00 | -0.62 | 0.00 | -0.15 |
| SG3. 35.12 | 5.25 | 5.12 | 4.98 | 0.44 | -1.24 | 0.69 | 0.14 | -0.60 | 0.25 | 0.18 |
| SG3.10.1 | 6.17 | 5.75 | 5.67 | 1.73 | -0.97 | 1.55 | 0.54 | -0.47 | 0.57 | 0.74 |
| SG3.31.4 | 4.91 | 5.63 | 4.37 | -0.04 | -1.02 | -0.07 | -0.01 | -0.49 | -0.03 | -0.15 |
| SG3.32.8 | 4.85 | 4.37 | 4.83 | -0.12 | -1.57 | 0.50 | -0.04 | -0.76 | 0.18 | -0.02 |
| SG4.11.12 | 5.10 | 5.01 | 5.14 | 0.23 | -1.29 | 0.89 | 0.07 | -0.63 | 0.33 | 0.22 |
| SG4.24.13 | 5.30 | 4.40 | 5.46 | 0.51 | -1.56 | 1.29 | 0.16 | -0.76 | 0.47 | 0.38 |
| SG4.27.5 | 4.83 | 5.14 | 4.90 | -0.16 | -1.23 | 0.58 | -0.05 | -0.60 | 0.21 | 0.05 |
| SG4.36.1 | 4.61 | 4.31 | 4.72 | -0.46 | -1.59 | 0.37 | -0.14 | -0.77 | 0.13 | -0.13 |
| SG4.41.4 | 5.24 | 9.78 | 4.37 | 0.42 | 0.78 | -0.08 | 0.13 | 0.38 | -0.03 | 0.13 |
| SG4.51.4 | 4.01 | 9.99 | 3.19 | -1.31 | 0.87 | -1.55 | -0.41 | 0.42 | -0.57 | -0.68 |
| ST3.8.6 | 3.93 | 10.46 | 3.08 | -1.43 | 1.08 | -1.68 | -0.45 | 0.52 | -0.62 | -0.73 |
| BISI 18 | 6.45 | 12.72 | 5.66 | 2.13 | 2.06 | 1.54 | 0.67 | 1.00 | 0.56 | 1.15 |
| JH 37 | 7.20 | 10.17 | 6.62 | 3.19 | 0.95 | 2.73 | 1.00 | 0.46 | 1.00 | 1.64 |
| NASA 29 | 5.47 | 10.63 | 5.17 | 0.74 | 1.15 | 0.93 | 0.23 | 0.56 | 0.34 | 0.60 |
| SINHAS 1 | 5.34 | 11.86 | 4.50 | 0.57 | 1.68 | 0.08 | 0.18 | 0.82 | 0.03 | 0.32 |
| means | 4.94 | 7.98 | 4.43 | |||||||
| Sd | 0.71 | 2.30 | 0.80 | |||||||
| Note: ear length (EL), ear weight (EW), seed yield percentage (SYP), and seed weight per ear (SWE), _R = ratio. | ||||||||||
An evaluation of the three-way cross (TWC) hybrids in comparison with the F1 hybrid tester is presented in Table 5. The table illustrates index values derived from the BLUP fitness relative formulation, following the same conceptual framework applied to TS. Owing to the technical challenges associated with TWC breeding, not all lines were fully represented; however, the number of TWCs included in the analysis was deemed sufficiently large to assess the utility of the transgressive segregant index effectively in maize. Table 5 shows that nine three-way cross-maize hybrids presented positive index values. Notably, the TWC hybrids SG 3.35.12 × JH37 (2.06) and CB 2.23.1 × JH37 (1.94) surpassed the index value of P27 (1.39), the highest-ranking F1 hybrid comparator. Additionally, compared with the F1 BISI18 variety, three other three-way cross hybrids, SG 2.7.14 × JH37 (0.72), SG 2.19.6 × JH37 (0.56), CB 2.23.1 × BISI18 (0.51), SG 3.10.1 × JH37 (0.39), and SG 4.41.4 × JH37 (0.30), also presented superior index values. Furthermore, regression analysis of the transgressive segregant index (TSI) to the TWC index revealed a quadratic response in the scatterplot distribution (Fig. 2). The regression relationship between the TSI and TWCI has a good coefficient of determination (R²) of 0.66, with the regression equation TWC = y = -11.298TSI2 + 10.782TSI − 1.593.
| Genotype | BLUP Value | BLUP standard | BLUP Fitness Relative | Indeks | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| BTP_R | RB_R | Y_R | BTP_R | RB_R | Y_R | BTP_R | RB_R | Y_R | ||
| CB 2.23.1 X BISI18 | 0.43 | 0.048 | 0.60 | 1.09 | 0.28 | 0.86 | 1.28 | 0.27 | 1.12 | 1.86 |
| SG 2.7.14 X NASA29 | 0.42 | 0.046 | 0.60 | 1.00 | -0.57 | 0.86 | 1.17 | -0.55 | 1.13 | 1.62 |
| P27 | 0.41 | 0.046 | 0.59 | 0.85 | -0.59 | 0.77 | 1.00 | -0.57 | 1.00 | 1.39 |
| CB 1.37.14 X NASA29 | 0.38 | 0.045 | 0.55 | 0.56 | -0.84 | 0.43 | 0.66 | -0.82 | 0.56 | 0.72 |
| CB 2.23.1 X JH37 | 0.34 | 0.050 | 0.54 | 0.02 | 0.87 | 0.33 | 0.02 | 0.84 | 0.43 | 0.64 |
| JH37 | 0.38 | 0.046 | 0.53 | 0.52 | -0.45 | 0.26 | 0.61 | -0.44 | 0.34 | 0.56 |
| SG 4.41.4 X NASA 29 | 0.34 | 0.047 | 0.51 | 0.09 | -0.10 | 0.13 | 0.11 | -0.10 | 0.17 | 0.20 |
| BISI 18 | 0.36 | 0.045 | 0.52 | 0.31 | -0.91 | 0.16 | 0.37 | -0.88 | 0.21 | 0.19 |
| SG 2.7.14 X JH37 | 0.32 | 0.049 | 0.51 | -0.17 | 0.76 | 0.08 | -0.20 | 0.73 | 0.10 | 0.17 |
| SG 3.35.12 X JH37 | 0.32 | 0.050 | 0.51 | -0.17 | 0.79 | 0.07 | -0.19 | 0.77 | 0.09 | 0.17 |
| SG 3.35.12 X BISI18 | 0.34 | 0.047 | 0.49 | 0.05 | -0.09 | -0.03 | 0.06 | -0.09 | -0.04 | -0.03 |
| SG 2.19.6 X NASA29 | 0.34 | 0.046 | 0.49 | 0.00 | -0.42 | -0.06 | 0.00 | -0.41 | -0.08 | -0.17 |
| NASA 29 | 0.36 | 0.044 | 0.49 | 0.25 | -1.23 | -0.04 | 0.29 | -1.19 | -0.06 | -0.19 |
| CB 2.23.1 X NASA29 | 0.30 | 0.050 | 0.48 | -0.42 | 0.88 | -0.11 | -0.49 | 0.85 | -0.14 | -0.20 |
| SG 3.10.1 X JH37 | 0.30 | 0.049 | 0.48 | -0.40 | 0.77 | -0.13 | -0.47 | 0.74 | -0.17 | -0.24 |
| SG 3.35.12 X NASA29 | 0.34 | 0.046 | 0.48 | 0.02 | -0.59 | -0.14 | 0.03 | -0.57 | -0.19 | -0.31 |
| SG 2.19.6 X JH37 | 0.31 | 0.048 | 0.46 | -0.33 | 0.10 | -0.31 | -0.39 | 0.09 | -0.41 | -0.59 |
| SG 4.41.4 X JH37 | 0.27 | 0.050 | 0.44 | -0.72 | 1.03 | -0.45 | -0.85 | 1.00 | -0.59 | -0.80 |
| CB 1.37.14 X JH37 | 0.23 | 0.047 | 0.34 | -1.28 | 0.07 | -1.31 | -1.50 | 0.06 | -1.71 | -2.49 |
| SG 4.41.4 X BISI 18 | 0.23 | 0.048 | 0.34 | -1.29 | 0.26 | -1.36 | -1.51 | 0.25 | -1.77 | -2.51 |
| Means | 0.34 | 0.05 | 0.50 | |||||||
| Stdev | 0.085046 | 0.002804 | 0.118614 | |||||||
| Note: ear length (EL), ear weight (EW), and seed yield percentage (SYP), seed weight per ear (SWE), _R = ratio. | ||||||||||
Fig-2. Regression of the trangressive segregant index to the three-way cross index based on the F1 JH37 parent
The augmented design concept, a methodology that emphasizes two primary assessments, the evaluation of line variance and the comparison between lines and checks [56, 57], is not just significant but highly effective. This approach is predicated on the expectation that the lines developed will exhibit substantial diversity and distinctiveness compared with the check varieties, thereby enhancing the effectiveness of line selection. The augmented design concept, in particular, allows for a comprehensive evaluation of the diversity and distinctiveness of the developed lines, which is crucial in the context of maize breeding and production. According to the results of this study, nearly all growth characteristics, except the male flowering age, anthesis‒silking interval (ASI), ear diameter, and number of rows of seeds per ear, satisfy the criteria for character assessment in the augmented design. These four characteristics exhibited insignificant responses to line diversity, a finding corroborated by Akfindarwan et al. [55] and Makmur et al. [26] in their evaluations of maize lines in the S2 and S3 generations. Both studies reported no significant effects of line diversity on these characteristics. This suggests that evaluating TS is sufficiently robust to exclude these four characteristics from further analysis. Therefore, all growth characteristics, except for male flowering age, ASI, ear diameter, and the number of seed rows per ear, can be incorporated into subsequent correlation and cross-sectional analyses.
Correlation and path analyses are critical for determining selection criteria and represent systematic techniques for identifying potential criteria that support production [19, 38]. Various studies have documented this approach, including those by Baye et al. [58], Khan et al. (2022), Thuy et al. [59], and Anshori et al. [38], with similar methodologies applied to maize [19, 60, 61]. The results of these analyses indicate that ear weight and grain yield serve as secondary selection criteria that complement the potential of seed weight per plant. The efficacy of these criteria in maize evaluation has been demonstrated by Mendes-Moreira et al. [62], Sah et al. [63], Mousavi and Nagy [64], and Dermail et al. [65], highlighting their significant regression effects in determining yield potential [62, 64]. Consequently, combining ear weight and seed yield with seed weight per ear was effective in the validation of S4 TS maize.
The evaluation of the TS against the established selection criteria involves a significant transformation. The three selection criteria were converted into a ratio, providing a methodological solution for comparisons on the basis of morphometric principles [66]. This ratio concept aligns with the comparative approach used in this study. Compared with self-pollinated transgressive lines, maize hybrid varieties generally exhibit superior growth potential [26, 37, 46]. This disparity renders direct comparisons between the two unfair. Hence, the three selection criteria were converted into a ratio representing the general potential of the ear.
In this study, the comparative ratio was focused primarily on ear length. Generally, ratio comparisons can be performed through volume measurements [67, 68], where the ear's length and diameter serve as the basis of the ratio. However, in this analysis, ear diameter exhibited an insignificant response to the segregant lines. In contrast, ear length showed a significant response, suggesting that ear length is the most appropriate metric for ratio comparisons among the tested genotypes. The results indicate that certain segregant lines demonstrated superior ratio potential compared with some comparator varieties. This contrasts with the performance of the three selection criteria before transformation, where the comparator varieties generally outperformed the lines regarding ear potential. However, it is crucial to note that relying solely on the ratio for assessment is ineffective without considering the variability within lines. This aligns with previous findings indicating that transgressive lines should be evaluated based on intraline variability [38]. Narrow variability within a line indicates that segregants may have reached their homozygous potential [39–41], necessitating the correction of the ratio comparison to a standardized value that accounts for this variation. A high standardized value can reflect strong performance potential and uniformity within a line [26, 38, 55, 65]. The comparison of standardized values to simple ratios reveals significant differences, indicating that uncorrected mean values can lead to misinterpretations [69, 70]. However, further refinement of the standardization process is needed to assess the potential of TS accurately, which could be recommended for developing hybrid maize varieties [38]. Therefore, the potential of TS was standardized and transformed via the best linear unbiased prediction and selection index methodologies
The development of best linear unbiased prediction (BLUP) analysis and selection indices represents a practical methodology for assessing channelization. This approach has been documented in various studies [71–74], including applications in maize research [75–77]. The BLUP concept primarily accounts for potential random effects and the genetic variance of genotypes [73, 78, 79]. The potential random effect is determined in augmented designs relative to the comparison variety [80–82]. This random effect serves as a correction factor for the performance potential of the tested lines within each block. The effectiveness of the BLUP approach in augmented designs has also been corroborated by Molenaar et al. [83], Burgueño et al. [81], and Amaral et al. [82]. In this study, a comprehensive correction was applied to the standardized values of each genotype relative to their best linear unbiased prediction (BLUP) values, enhancing the precision of the evaluation process. The BLUP-derived values were subsequently utilized with a selection index, which was the final component in evaluating the TS. However, before the development of the selection index, the BLUP values were also transformed into standardized values and relative fitness metrics to ensure accurate assessment.
Developing standardized and relative fitness values is crucial for equalizing dimensions across traits and refining the selection process. Although each parameter has been transformed into internal ratios and standardized values, this approach has been applied only within each genotype. Population-wide standardization has not yet been implemented. Such standardization would reveal the potential of a genotype relative to the overall population response, thereby identifying genotypes with greater selection potential than others in the population [38, 84, 85]. The selection process is further refined via roulette wheel selection or the relative fitness approach, which compares the potential of a genotype against the population's highest-performing genotype [86, 87]. However, in this study, the concept was modified using standardized values as fitness benchmarks so that relative fitness only pertains to comparisons between genotypes and the best comparator. This modification aligns with the inherent concept of TS, where TS in cross-pollinated plants is expected to exhibit equal or superior potential to that of their parents or comparators. Thus, the potential of each transgressive segregant line must be corrected relative to the maximum potential of the comparator variety [38–40]. This approach narrows the range of standardized values, making the selection process more stringent than relying on standardized values alone. Consequently, these two approaches serve as intermediate steps preceding index selection.
The assessment based on the selection index concept is intricately linked to the weighting of each selection criterion involved. According to this study, the weight of each selection criterion can be estimated via the direct effect value derived from path analysis. The effectiveness of employing direct effect values as a basis for selection indices has been documented in studies by Sabouri [88], Alsabah et al. [85], and Fadhilah et al. [57], including in maize [18]. However, the direct effect value must be corrected with the determination value. This results in the following relative fitness BLUP-based selection index:
Indeks = 0.83*0.64*BTK + 0.37*0,64*RB + Yield
Indeks = 0.53 BTK + 0.24 RB + yield
Based on the index selection results, lines SG4.27.5, SG2.25.6, SG2.7.14, SG3.10.1, SG4.24.13, CB1.37.14, CB2.23.1, SG2.19.6, SG3.35.12, SG4.11.12, and SG4.41.4 have been identified as potential S4 maize TSs owing to their positive index values. This aligns with Paternelli et al. [84] and Anshori et al. [38], who suggested that a positive index value indicates potential in index-based selection. Among these, four lines exhibit superior potential to the SINHAS variety, an open-pollinated variety with generally lower heterosis than hybrid varieties [5, 6, 16, 20]. Thus, these four lines are anticipated to have substantial potential as hybrid parents. However, further investigation is needed to determine the effectiveness of the selection concept and the potential of the identified TS.
The results from the evaluation of three-way cross hybrids between TS and F1 testers showed promising potential, especially for crosses involving F1 JH 37. This finding indicates that JH 37 is a good tester for assessing the potential of TSs on the basis of the concept of three-way cross-analysis. In addition, the graph between the TSI and TWCI showed a quadratic response pattern with good determination. This is common in cross-pollinated plants, which focus on heterozygous and heterosis patterns and have the potential for inbreeding depression. The action of dominant genes strongly influences the concept of heterosis in cross-pollinated plants. This gene action causes the concept of the two indices' response to be not additive, so it does not follow a linear curve. This differs from self-pollinated plants, dominated by the concept of additive gene action, so the response pattern is relatively linear [16, 20, 30, 89]. On this basis, the concept of transgressive segregation developed in this study can be a good consideration when selecting maize hybrid elders. In addition, the maize crosses SG 3.35.12 X JH37 and CB 2.23.1 X JH37 can also be recommended as promising three-way crosses, and the transgressive segregant CB2.23.1 can be used as a potential parent in the maize hybrid assembly.
The development of maize selection concepts based on ratios, standard values, BUP, relative fitness, and selection indices has proven effective in estimating the potential of transgressive segregants (TS) in the S4 population. Integrating these concepts provides a comprehensive approach to assessing the genetic potential of TS, both in comparison to other lines and against comparator varieties. This combination ensures a standardized dimension of comparison between TS and comparative varieties. Additionally, using the best linear unbiased prediction (BLUP) based on relative fitness objectively assesses each S4 maize transgressive segregant line relative to its comparator varieties.
Selection criteria such as ear weight, grain yield, and yield characteristics are instrumental in formulating selection index values. These criteria are combined with the direct effect value to weight the index, resulting in an index formula of 0.53 BTK + 0.24 RB BL + yield. This index is applied to the relative fitness BLUP value. The index selection identified 11 potential S4 transgressive segregant lines (SG4.27.5, SG2.25.6, SG2.7.14, SG3.10.1, SG4.24.13, CB1.37.14, CB2.23.1, SG2.19.6, SG3.35.12, SG4.11.12, and SG4.41.4) for further evaluation of their hybrid potential. The three-way cross-hybrid potential test confirmed the effectiveness of the TSI for evaluating the three-way cross-index (TWCI) potential with a quadratic response. This approach is recommended for selecting potential TSs for hybrid maize variety development. In addition to the TSI evaluation, this assessment suggests potential parent lines and crosses for TWC variety assembly, specifically for the S4 TS line CB2.23.1. Moreover, SG 3.35.12 X JH37 and CB 2.23.1 X JH37 are recommended as TWC crosses for this study. Nevertheless, the potential of these segregants requires further evaluation in the context of single hybrids, particularly in assessing their combining ability. Future research should focus on determining the combining power and diallel cross combinations of the TS lines identified in this study.
Author Contributions
A short paragraph specifying their contributions must be provided for research articles by several authors. The following statements should be used: “Conceptualization: Nuniek Widiayani, Muhammad Fuad Anshori, Muh Farid, and Mahmoud F. Seleiman; Data curation: Nasaruddin Nasaruddin, Muh Farid, Willy Bayuardi Suwarno, Muhammad Azrai, and Amin Nur; Formal analysis: Muhammad Fuad Anshori, Purnama Isti Khaerani, Karlina Syahruddin and Willy Bayuardi Suwarno; Funding acquisition: Nuniek Widiayani, Nasaruddin Nasaruddin, Naeem Khan, Majed A. Alotaibi, and Mahmoud F. Seleiman; Investigation: Nuniek Widiayani and Purnama Isti Khaerani; Methodology: Nuniek Widiayani, Muhammad Fuad Anshori, Muh Farid; Resources: Nasaruddin Nasaruddin, Abd. Haris Bahrun, Muh Farid and Amin Nur; Software: Muhammad Fuad Anshori and Willy Bayuardi Suwarno; Supervision: Muh Farid, Ifayanti Ridwan, Abd. Haris Bahrun, and Mahmoud Seleiman; Validation: Ifayanti Ridwan, Muhammad Azrai, Naeem Khan, Majed A. Alotaibi, and Mahmoud Seleiman; Visualization: Willy Bayuardi Suwarno, Karlina Syahruddin and Muhammad Fuad Anshori; Writing—original draft preparation: Nuniek Widiayani, Muhammad Fuad Anshori, and Willy Bayuardi Suwarno; Writing—review and editing: all authors. All authors have read and agreed to the published version of the manuscript.
Consent for publication
Not Applicable
Availability of data and materials
All the data is available within the manuscript.
Competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper”.
Ethical Approval and Consent to Participate
not applicable
Funding
Researchers Supporting Project number (RSPD2024R751), King Saud University, Riyadh, Saudi Arabia, is acknowledged. In addition, the Faculty of Agriculture, Hasanuddin University, supported the work through Young Lecturer Research Funding to Nuniek Widiayani in 2023. Besides that, this work was also supported by the scheme of Penelitian Fundamental Kolaboratif UNHAS (UNHAS Fundamental Research Collaborative) to Nasaruddin Nasaruddin with grant Number: 00309/UN4.22/PT.01.03/2024.
Acknowledgments
Researchers Supporting Project number (RSPD2024R751), King Saud University, Riyadh, Saudi Arabia, is acknowledged. In addition, the Faculty of Agriculture, Hasanuddin University, supported the work through Young Lecturer Research Funding to Nuniek Widiayani in 2023. Besides that, this work was also supported by the scheme of Penelitian Fundamental Kolaboratif UNHAS (UNHAS Fundamental Research Collaborative) to Nasaruddin Nasaruddin with grant Number: 00309/UN4.22/PT.01.03/2024.
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No competing interests reported.