A New Approach for Evaluating Maize Transgressive Segregants and Their Three-Way Cross Potential in the S4 Convergent Breeding Population

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This study developed a selection index to identify S4 maize transgressive segregants with hybrid potential, recommending specific lines and three-way crosses for further evaluation.

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This preprint studied whether transgressive segregant (TS) selection can be applied to an S4 convergent breeding population of maize to identify lines with high productivity for use as hybrid cross parents. Using an augmented design across six blocks with 32 unrepeated TS lines and repeated check hybrids/open-pollinated varieties, the authors assessed genetic potential using ratio analysis, path analysis, BLUP-based prediction, relative fitness, and selection indices focused on traits including ear weight and seed yield percentage; they report selection index weights of 0.53 ear weight + 0.24 seed yield percentage + yield, yielding 11 TS lines for further hybrid testing. Among these, TS line CB2.23.1 performed best, and three-way cross evaluations prioritized SG 3.35.12 × JH37 and CB2.23.1 × JH37. The paper does not explicitly state a primary limitation in the provided text, but it frames three-way testing as future-focused for more complex genetic interactions. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract 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.
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A New Approach for Evaluating Maize Transgressive Segregants and Their Three-Way Cross Potential in the S4 Convergent Breeding Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A New Approach for Evaluating Maize Transgressive Segregants and Their Three-Way Cross Potential in the S4 Convergent Breeding Population Nuniek Widiayani, Muhammad Fuad Anshori, Nasaruddin Nasaruddin, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5017223/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jan, 2025 Read the published version in BMC Plant Biology → Version 1 posted 15 You are reading this latest preprint version Abstract 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. BLUP multivariate analysis systematic selection segregative transgenics Zea mays Figures Figure 1 Figure 2 1. Introduction 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. Materials and methods 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: $$\:ear\:ratio=\:\frac{yield\:component}{ear\:diameter\:or\:ear\:lenght}$$ 1. 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 S 2 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. 3. Results 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). Table 2 Path analysis of characteristics correlated with seed weight per ear of maize 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). Table 3 Standardized ratio and internal ratio values for the evaluation of transgressive segregant line S4 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. Table 5 Evaluation of three-way cross hybrids between S4 transgressive segregants and comparison of hybrid testers 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 4. Discussion 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. 5. Conclusion 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. Declarations 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. References Gwirtz JA, Garcia-Casal MN. (2014) Processing maize flour and corn meal food products. Ann N Y Acad Sci 1312(1):66–75. doi.10.1111/nyas.12299. Jiao Y, Chen HD, Han H, Chang Y. Evelopment and utilization of corn processing byproducts: A Review. Foods. 2022;11(22):3709. doi.org/10.3390/foods11223709 . Wadhawan N, Jain NK, Mudgal VD. Cronicon entrepreneurship development in maize processing. EC Nutr. 2019;15:01–7. Tanumihardjo SA, McCulley L, Roh R, Lopez-Ridaura S, Palacios-Rojas N, Gunaratna NS. Maize agro-food systems to ensure food and nutrition security in reference to the Sustainable Development Goals. Glob Food Sec. 2020;25:100327. doi.org/10.1016/j.gfs.2019.100327 . Freddy IM, Respatiadi H, Endy G, Gupta K. Reforming Trade Policy to Lower Maize Prices in Indonesia. Indonesia: Jakarta; 2018. Syahruddin K, Azrai M, Nur A, Abid M, Wu WZ. (2020) A review of maize production and breeding in Indonesia. In: IOP Conference Series: Earth and Environmental Science. Institute of Physics Publishing. p. 012040. Statistic of Indonesia. Luas Panen dan Produksi Jagung di Indonesia 2023 (Angka Sementara). Indonesia: Jakarta; 2023. Rondhi M, Pratiwi PA, Handini VT, Sunartomo AF, Budiman SA. Agricultural land conversion, land economic value, and sustainable agriculture: A case study in East Java. Indonesia Land. 2018;7(4):148. doi.org/10.3390/land7040148 . Fitton N, Alexander P, Arnell N, Bajzelj B, Calvin K, Doelman J, et al. The vulnerabilities of agricultural land and food production to future water scarcity. Glob Environ Chang. 2019;58:101944. doi.org/10.1016/j.gloenvcha.2019.101944 . Deng G, Jiang H, Zhu S, Wen Y, He C, Wang X et al. (2024) Projecting the response of ecological risk to land use/land cover change in ecologically fragile regions. Sci Total Environ 914:169908. doi.10.1016/j.scitotenv.2024.169908. Tridakusumah AC, Setiawan I, Nugraha ANA, Kurnia G, Sukayat Y. (2022) The relations between agricultural land conversion and urban farm workers livelihoods. In: E3S Web of Conferences. EDP Sciences. p. 3011. Harewan Y, Wurarah RN, Santoso B, Sabariah V. (2023) Analysis of land conversion to economic growth: the case of other purpose areas. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing. p. 12052. Apriyana Y, Surmaini E, Estiningtyas W, Pramudia A, Ramadhani F, Suciantini S, et al. The integrated cropping calendar information system: A coping mechanism to climate variability for sustainable agriculture in Indonesia. Sustain. 2021;13(11):6495. doi.org/10.3390/su13116495 . Farooq A, Farooq N, Akbar H, Hassan ZU, Gheewala SH. A Critical review of climate change impact at a global scale on cereal crop production. Agronomy. 2023;13(1):162. doi.org/10.3390/agronomy13010162 . Yuan X, Li S, Chen J, Yu H, Yang T, Wang C, et al. Impacts of global climate change on agricultural production: A comprehensive review. Agronomy. 2024;14(7):1360. doi.org/10.3390/agronomy14071360 . Fromme DD, Spivey TA, Grichar WJ. (2019) Agronomic response of corn ( Zea mays L. ) hybrids to plant populations. Int J Agron 2019(2):1–8. doi.10.1155/2019/3589768. Muntean L, Ona A, Berindean I, Racz I, Muntean S. Maize Breeding: From Domestication to Genomic Tools. Agronomy. 2022;12(10):2365. doi.org/10.3390/agronomy12102365 . Fikri M, Farid M, Musa Y, Anshori MF, Padjung R, Nur A. Multivariate analysis in the development of technology packages for corn cultivation by adding fertilizer to compost (2023). Chil J Agric Res 83 (4):471–83. doi.org/10.4067/S0718-58392023000400471 Abduh AD, Padjung R, Farid M, Bahrun AH, Anshori F, Ridwan I et al. (2021) Interaction of genetic and cultivation technology in maize prolific and productivity increase. Pak J Biol Sci 24(6):716–23. doi.10.3923/pjbs.2021.716.723. Kutka F. Open-pollinated vs. hybrid maize cultivars. Sustainability. 2011;3(9):1531–54. doi.org/10.3390/su3091531 . Sumalini K, Sravani D, Pradeep T, Rani UG, Bhaskar VA, Reddy UR, et al. A review on maize hybrid breeding—importance of multiple crosses in comparison with single crosses in present scenario. Environ Ecol. 2018;36(4):1079–82. Garot E, Joët T, Combes MC, Lashermes P. (2019) Genetic diversity and population divergences of an indigenous tree ( Coffea mauritiana ) in Reunion Island: role of climatic and geographical factors. Heredity (Edinb) 122 (6):833–847. doi.10.1038/s41437-018-0168-9. Kardos M, Armstrong EE, Fitzpatrick SW, Hauser S, Hedrick PW, Miller JM et al. (2021) Analyzed data. PNAS118(48): e2104642118. doi.10.1073/pnas.2104642118. Prakash SJ, Gayatonde V, Vennela PR. Convergence-divergence improvement in plant breeding. Indian J Agric Allied Sci. 2016;2(1):14–8. Würschum T, Zhu X, Zhao Y, Jiang Y, Reif JC, Maurer HP. Maximization through optimization? On the relationship between hybrid performance and parental genetic distance. Theor Appl Genet. 2023;136:186. doi.org/10.1007/s00122-023-04436-5 . Makmur FM, Ala A, Mandja K, Anshori MF, Fadhilah AN. The selection index of S3 corn convergent breeding population based on multivariate analysis. Biodiversitas J Biol Divers. 2024;25(3):1097–103. doi.org/10.13057/biodiv/d250324 . Firmansah H, Wahyu Y, Nur A, Tondok ET. (2024) The Response of advanced generation wheat lines derived from convergent breeding to biotic stress in high altitude area. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing. p. 12123. Samayoa LF, Olukolu BA, Yang CJ, Chen Q, Stetter MG, York AM, et al. Domestication reshaped the genetic basis of inbreeding depression in a maize landrace compared to its wild relative, teosinte. PLoS Genet. 2021;17:e100979. doi.org/10.1371/journal.pgen.1009797 . Collard BCY, Beredo JC, Lenaerts B, Mendoza R, Santelices R, Lopena V, et al. Revisiting rice breeding methods–evaluating the use of rapid generation advance (RGA) for routine rice breeding. Plant Prod Sci. 2017;20(4):337–52. doi.org/10.1080/1343943X.2017.1391705 . Labroo MR, Studer AJ, Rutkoski JE. (2021) Heterosis and hybrid crop breeding: A multidisciplinary review. Front Genet 24(12):643761. doi.10.3389/fgene.2021.643761. Sabadin F, DoVale JC, Platten JD, Fritsche-Neto R. Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets. Front Plant Sci. 2022;13:935885. doi.org/10.3389/fpls.2022.935885 . Syukur M. Teknik pemuliaan tanaman. Penebar Swadaya; 2018. Jeon D, Kang Y, Lee S, Choi S, Sung Y, Lee TH, et al. Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction. Front Plant Sci. 2023;14:1092584. doi.org/10.3389/fpls.2023.1092584 . Koide Y, Uchiyama T, Ota Y, Sakaguchi S, Tezuka A, Nagano AJ, et al. Genetic properties responsible for the transgressive segregation of days to heading in rice. Genes Genomics Genet. 2019;9(5):1655–62. doi.org/10.1534/g3.119.201011 . Pabuayon ILB, Sun Y, Guo W, Ritchie GL. High-throughput phenotyping in cotton: a review. J Cott Res. 2019;2:1–9. doi.org/10.1186/s42397-019-0035-0 . Swetha B, Devi HUN, Sankari A, Geethanjali S, Sudha M. (2023) Variability studies and genetic divergence in chilli ( Capsicum spp.) genotypes using multivariate analysis. Electron J Plant Breed 14(3):928–37. doi.10.37992/2023.1403.105. Mackay IJ, Cockram J, Howell P, Powell W. Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding. Plant Biotechnol J. 2021;19:26–34. doi.org/10.1111/pbi.13481 . Anshori MF, Musa Y, Dungga NE, Widiayani N, Arifin AS, Masniawati A, et al. A new approach for selection of transgressive segregants in F3 populations based on selection index and anthocyanin content in cayenne pepper. Front Sustain Food Syst. 2024;8:1288579. doi.org/10.3389/fsufs.2024.1288579 . de los Reyes BG. Genomic and epigenomic bases of transgressive segregation – New breeding paradigm for novel plant phenotypes. Plant Sci. 2019;288:110213. doi.org/10.1016/j.plantsci.2019.110213 . Nascimento MF, Rêgo ER, do, Nascimento NFF do, Leite PS da, Finger S, Bruckner FL et al. CH, (2019) Heritability of morpho-agronomic traits in ornamental pepper. Crop Breed Appl Biotechnol 19(3):253–61. doi.org/10.1590/1984-70332019v19n3a36 Maryono MY, Wirnas D, Human S. Analisis genetik dan seleksi segregan transgresif pada populasi F2 sorgum hasil persilangan B69× Numbu dan B69× Kawali. Indones J Agron. 2019;47(2):163–70. doi.org/10.24831/jai.v47i2.24991 . Rostini N, Yenny RF, Amien S. (2019) Inheritance pattern of capsaicin content of indonesian chili landraces ( Capsicum annum L .). In: IOP Conference Series: Earth and Environmental Science. IOP Publishing p. 12018. Putri NE, Sutjahjo SH, Nur A, Suwarno WB, Wahyu Y. Wheat transgressive segregants and their adaptation in the tropical region Sabrao. J Breed Genet. 2020;52(4):506–22. Reynolds MP, Braun HJ. Wheat Improvement: Food Security in a Changing Climate. Springer International Publishing; 2022. Cazzola F, Bermejo CJ, Cointry E. Transgressive segregations in two pea F2 populations and their respective F2:3 families. Pesqui Agropecu Bras. 2020;55:e01623. doi.org/10.1590/S1678-3921.pab2020.v55.01623 . Rehman AU, Dang T, Qamar S, Ilyas A, Fatema R, Kafle M, et al. Review revisiting plant heterosis—from field scale to molecules. Genes. 2021;12(11):1688. doi.org/10.3390/genes12111688 . Ka A, Hosamani J, Deshpande SK, Bhat JS, Kachapur RM, Mummigatti UV. Identification of transgressive segregants among newly derived F 4 inbred maize lines ( Zea mays L. ) for baby corn characteristics. J Pharmacogn Phytochem. 2021;10(2):404–11. Schoemaker DL, Qiu Y, de Leon N, Hirsch CN, Kaeppler SM. Genetic analysis of pericarp pigmentation variation in Corn Belt dent maize. G3 Genes. Genomes Genet. 2024;14(1):jkad256. doi.org/10.1093/g3journal/jkad256 . Kahriman F, Egesel CÖ, Orhun GE, Alaca B, Avci F. Comparison of graphical analyses for maize genetic experiments: Application of biplots and polar plot to line × tester design. Chil J Agric Res. 2016;76(3):285–93. doi.org/10.4067/S0718-58392016000300004 . Özdemir E, Sade B. Comparison of maize lines and their test crosses according to grain yield and some physiological properties. Turkish J Agric For. 2019;43(2):115–22. doi.org/10.3906/tar-1801-85 . Bourke PM, Evers JB, Bijma P, van Apeldoorn DF, Smulders MJM, Kuyper TW, et al. Breeding Beyond Monoculture: Putting the Intercrop Into Crops. Front Plant Sci. 2021;12:734167. doi.org/10.3389/fpls.2021.734167 . Maazou ARS, Adetimirin VO, Gedil M, Meseka S, Mengesha W, Menkir A. Suitability of testers to characterize provitamin a content and agronomic performance of tropical maize inbred lines. Front Genet. 2022;8(13):955420. 10.3389/fgene.2022.955420 . Tabu I, Lubobo K, Mbuya K, Kimuni N. Heterosis and line-by-tester combining ability analysis for grain yield and provitamin an in maize. Sabrao J Breed Genet. 2023;55(3):695–707. Sorsa Z, Mohammed W, Wegary D, Tarkegne A. Performances of three-way cross hybrids over their respective single crosses and related heterosis of maize ( Zea mays L. ) hybrids evaluated in Ethiopia. Heliyon. 2023;9(5):e15513. doi.org/10.1016/j.heliyon.2023.e15513 . Akfindarwan AK, Farid M, Syaiful SA, Anshori MF, Nur A. Selection criteria and index analysis for the S2 maize lines of doublecrosses. Biodiversitas J Biol Divers. 2023;24(1):191–9. Nur A, Riadi M, Yassi A, Farid M, Anshori MF, Akfindarwan AK. (2021) Selection and evaluation the corn lines from multiple-cross progeny based on targeted selection environment on acid soil. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing. p. 12016. doi.10.13057/biodiv/d240123Selection. Fadhilah AN, Farid M, Ridwan I, Anshori MF, Yassi A. Genetic parameters and selection index of high-yielding tomato F2 populations. Sabrao J Breed Genet. 2022;54(5):1026–36. Baye A, Berihun B, Bantayehu M, Derebe B. Genotypic and phenotypic correlation and path coefficient analysis for yield and yield-related traits in advanced bread wheat ( Triticum aestivum L .) lines. Cogent Food Agric. 2020;6:1752603. doi.org/10.1080/23311932.2020.1752603 . Thuy NP, Trai NN, Khoa BD, Thao NHX, Phong VT, Thi QVC. Correlation and path analysis of association among yield, micronutrients, and protein content in rice accessions grown under aerobic condition from Karnataka, India. Plant Breed Biotechnol. 11(2):117–129. doi.10.9787/PBB.2023.11.2.117. Reddy VR, Jabeen F. Narrow sense heritability, correlation and path analysis in maize ( Zea mays L ). Sabrao J Breed Genet. 2016;48(2):120–6. Aman J, Bantte K, Alamerew S, Sbhatu DB. correlation and path coefficient analysis of yield and yield components of quality protein maize ( Zea mays L .) hybrids at Jimma, Western Ethiopia. Int J Agron. 2020;2020:9651537. doi.org/10.1155/2020/9651537 . Mendes-Moreira PMR, Mendes-Moreira J, Fernandes A, Andrade E, Hallauer AR, Pêgo SE et al. (2014) Is ear value an effective indicator for maize yield evaluation? F Crop Res 161:75–86. doi.10.1016/j.fcr.2014.02.015. Sah RP, Chakraborty M, Prasad K, Pandit M, Tudu VK, Chakravarty MK, et al. Impact of water deficit stress in maize: Phenology and yield components. Sci Rep. 2020;10(1):2944. 10.1038/s41598-020-59689-7 . Mousavi SMN, Nagy J. (2021) Evaluation of plant characteristics related to grain yield of FAO410 and FAO340 hybrids using regression models. Cereal Res Commun 49:161–9. doi.10.1007/s42976-020-00076-3. Dermail A, Fuengtee A, Lertrat K, Suwarno WB, Lübberstedt T, Suriharn K. Simultaneous selection of sweet-waxy corn ideotypes appealing to hybrid seed producers, growers, and consumers in Thailand. Agronomy. 2022;12(1):87. doi.org/10.3390/agronomy12010087 . Baur H, Leuenberger C. (2011) Analysis of ratios in multivariate morphometry. Syst Biol. 60(6):813–25. doi.10.1093/sysbio/syr061. Judd LA, Jackson BE, Fonteno WC. (2015) Advancements in root growth measurement technologies and observation capabilities for container-grown plants. Plants 4(3):369–392. doi.10.3390/plants4030369. Gupta C, Tewari VK, Machavaram R, Shrivastava P. An image processing approach for measurement of chili plant height and width under field conditions. J Saudi Soc Agric Sci. 2022;21(3):171–9. doi.org/10.1016/j.jssas.2021.07.007 . Cooksey RW, Cooksey RW. Descriptive statistics for summarizing data. Springer; 2020. Anderson SF. (2020) Misinterpreting p: The discrepancy between p values and the probability the null hypothesis is true, the influence of multiple testing, and implications for the replication crisis. Psychol Methods 25(5):596. doi.10.1037/met0000248. Alves RS, de Carvalho Rocha JR do, AS, Teodoro PE, de Resende MDV, Henriques EP, Silva LA et al. (2018) Multiple-trait BLUP: a suitable strategy for genetic selection of Eucalyptus. Tree Genet Genomes 14(5):1–8. doi.10.1007/s11295-018-1292-7. Schmidt P, Hartung J, Bennewitz J, Piepho H-P. (2019) Heritability in plant breeding on a genotype-difference basis. Genetics 212(4):991–1008. doi.10.1534/genetics.119.302134. Olivoto T, Lúcio ADC, da Silva JAG, Sari BG, Diel MI. (2019) Mean performance and stability in multi-environment trials II: Selection on the basis of multiple traits. Agron J 111(6):2961–269. doi.10.2134/agronj2019.03.0221. Khanna A, Anumalla M, Catolos M, Bartholomé J, Fritsche-Neto R, Platten JD, et al. Genetic trends estimation in IRRIs rice drought breeding program and identification of high yielding drought-tolerant lines. Rice. 2022;15:14. doi.org/10.1186/s12284-022-00559-3 . Oliveira GHF, Buzinaro R, Revolti LTM, Giorgenon CHB, Charnai K, Resende D, et al. An accurate prediction of maize crosses using diallel analysis and best linear unbiased predictor (BLUP). Chil J Agric Res. 2016;76(3):294–9. doi.org/10.4067/S0718-58392016000300005 . Entringer GC, Vettorazzi JCF, Santos EA, Pereira MG, Viana AP. (2016) Genetic gain estimates and selection of S1 progenies based on selection indices and REML/BLUP in super sweet corn. Aust J Crop Sci 10(3):411–417. doi.10.21475/ajcs.2016.10.03.p7248. Zystro J, Peters T, Miller K, Tracy WF. (2021) Classical and genomic prediction of hybrid sweet corn performance in organic environments. Crop Sci 61(3):1698–708. doi.10.1002/csc2.20400. Rocha JR do AS, de Machado C, Carneiro JC. PCS (2018) Multitrait index based on factor analysis and ideotype-design: Proposal and application on elephant grass breeding for bioenergy. Gcb Bioenergy10:52–60. doi.org/10.1111/gcbb.12443 Olivoto T, Diel MI, Schmidt D, Lúcio AD. MGIDI: a powerful tool to analyze plant multivariate data. Plant Methods. 2022;18:121. You FM, Song Q, Jia G, Cheng Y, Duguid S, Booker H, et al. Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design. Crop J. 2016;4(2):107–18. doi.org/10.1016/j.cj.2016.01.003 . Burgueño J, Crossa J, Rodríguez F, Yeater KM, Glaz B, Yeater KM. (2018) Chap. 13: Augmented Designs-Experimental Designs in Which All Treatments are not Replicated. In: Applied statistics in agricultural, biological, and environmental sciences. pp. 345–69. Amaral L, de O, Miranda GV, Souza JdaS, Moitinho ACR, Cristeli DS, Silva HK et al. da, (2023) Application of Artificial neural networks to predict genotypic values of soybean derived from wide and restricted crosses for relative maturity groups. Agronomy 13(10):2476. doi.org/10.3390/agronomy13102476 Molenaar H, Boehm R, Piepho HP. Phenotypic selection in ornamental breeding: It’s better to have the BLUPs than to have the BLUEs. Front Plant Sci. 2018;9:1511. doi.org/10.3389/fpls.2018.01511 . Peternelli LA, Moreira ÉFA, Nascimento M, Cruz CD. Artificial neural networks and linear discriminant analysis in early selection among sugarcane families. Crop Breed Appl Biotechnol. 2017;17(4):299–305. doi.org/10.1590/1984-70332017v17n4a46 . Alsabah R, Purwoko BS, Dewi IS, Wahyu Y. Selection index for selecting promising doubled haploid lines of black rice. Sabrao J Breed Genet. 2019;51(4):430–41. Swanson-Wagner R, Briskine R, Schaefer R, Hufford MB, Ross-Ibarra J, Myers CL et al. (2012) Reshaping of the maize transcriptome by domestication. Proc Natl Acad Sci 109(29):11878–83. doi.10.1073/pnas.1201961109. Katoch V, Rathour R, Sharma S, Rana SS, Sharma A. (2021) Studies on genetic parameters, correlation and path coefficient analysis in er2 introgressed garden pea genotypes. Legum Res Int J 44:621–626. doi.10.18805/LR-4142. Sabouri H, Rabiei B, Fazlalipour M. (2008)Use of selection indices based on multivariate analysis for improving grain yield in rice. Rice Sci 15(4):303–10. doi.10.1016/S1672-6308(09)60008-1. Ali M, Kuswanto, Kustanto H. Phenomenon of inbreeding depression on maize in perspective of the quran. Agrivita. 2019;41(2):385–93. doi.org/10.17503/agrivita.v41i2.2022 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5017223","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":360896013,"identity":"4c63b442-34e7-4752-b2db-cce47fc3ee3b","order_by":0,"name":"Nuniek Widiayani","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Nuniek","middleName":"","lastName":"Widiayani","suffix":""},{"id":360896014,"identity":"6f2e34a5-ea0f-47e8-942c-e6d06673a534","order_by":1,"name":"Muhammad Fuad Anshori","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBAC9gYGBgkQwwCID3xgYEiASkgwNuDQwnMAScvBGSRrYeZBaGHArUUi+eENhpo79ubsvQ8P27bdyePvP8D44QeDhSxuLWnGFgzHniXu7DlucDi37VmxxI0EZskeBgljXFrsJRLMJBjYDicY3EhjAGo5nNhwg4FBGujaRNy2pH+TYPh32B6sxRKoZf75A8y/8WvJMZNgbDvMuAGkBchI3HAggQ2/LTxvii0S+4AqzxxjONhz7nDixhuJbZY9Brj9wsOevvHGh29Ahx1vY/7wo+xw4rzzhw/f+FFRhzPEwCABlQuKEQN86kfBKBgFo2AUEAIAmMJdTHjB9NcAAAAASUVORK5CYII=","orcid":"","institution":"Hasanuddin University","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Fuad","lastName":"Anshori","suffix":""},{"id":360896015,"identity":"44874f3d-2436-48cd-bad0-8a58cbc82a40","order_by":2,"name":"Nasaruddin Nasaruddin","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Nasaruddin","middleName":"","lastName":"Nasaruddin","suffix":""},{"id":360896016,"identity":"a602a85f-9338-463f-afa8-61588c19df98","order_by":3,"name":"Muh Farid","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Muh","middleName":"","lastName":"Farid","suffix":""},{"id":360896017,"identity":"f4b5121e-e276-4b36-a63f-daf79e6f11cc","order_by":4,"name":"Ifayanti Ridwan","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Ifayanti","middleName":"","lastName":"Ridwan","suffix":""},{"id":360896019,"identity":"b972ae1a-1136-4c25-8f33-f1114aa6bcf6","order_by":5,"name":"Abd. Haris Bahrun","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Abd.","middleName":"Haris","lastName":"Bahrun","suffix":""},{"id":360896020,"identity":"3e63a915-e3fd-4e21-a4bf-a5c448c1d8cf","order_by":6,"name":"Muhammad Azrai","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Azrai","suffix":""},{"id":360896021,"identity":"c04a0b4b-cdc5-4b0f-b2f7-6c84d7f5c32a","order_by":7,"name":"Amin Nur","email":"","orcid":"","institution":"Indonesian Cereal Testing Instrument Standard Institute","correspondingAuthor":false,"prefix":"","firstName":"Amin","middleName":"","lastName":"Nur","suffix":""},{"id":360896023,"identity":"b585b8d9-d701-4ed7-ad54-57da6ccc8caa","order_by":8,"name":"Purnama Isti Khaerani","email":"","orcid":"","institution":"Hasanuddin University","correspondingAuthor":false,"prefix":"","firstName":"Purnama","middleName":"Isti","lastName":"Khaerani","suffix":""},{"id":360896024,"identity":"539c4cef-454c-4c49-8880-3478bc9c3b2e","order_by":9,"name":"Willy Bayuardi Suwarno","email":"","orcid":"","institution":"IPB University","correspondingAuthor":false,"prefix":"","firstName":"Willy","middleName":"Bayuardi","lastName":"Suwarno","suffix":""},{"id":360896025,"identity":"bc9baec1-a978-4775-a10e-1b73b3e95897","order_by":10,"name":"Karlina Syahruddin","email":"","orcid":"","institution":"National Research and Innovation Agency","correspondingAuthor":false,"prefix":"","firstName":"Karlina","middleName":"","lastName":"Syahruddin","suffix":""},{"id":360896026,"identity":"1d3e7c74-5bb0-4183-9512-3e9f948d6cb0","order_by":11,"name":"Naeem Khan","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Naeem","middleName":"","lastName":"Khan","suffix":""},{"id":360896027,"identity":"e6725561-a746-44a7-8917-822d005e4cc2","order_by":12,"name":"Majed A. Alotaibi","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Majed","middleName":"A.","lastName":"Alotaibi","suffix":""},{"id":360896028,"identity":"2c0e6037-a413-44a0-ad33-4303707fbaf4","order_by":13,"name":"Mahmoud F. Seleiman","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"F.","lastName":"Seleiman","suffix":""}],"badges":[],"createdAt":"2024-09-02 09:47:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5017223/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5017223/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-06103-x","type":"published","date":"2025-01-29T15:58:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66150671,"identity":"e183a421-3f51-4296-8105-7007ac69571d","added_by":"auto","created_at":"2024-10-08 07:50:33","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":673587,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation analysis of maize agronomic \u003cstrong\u003echaracteristics\u003c/strong\u003e 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)).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5017223/v1/166c2d54af1a0c732545b7b2.jpeg"},{"id":66150672,"identity":"7a3ef7b4-dff0-4e74-99e4-ce20496768e3","added_by":"auto","created_at":"2024-10-08 07:50:33","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160829,"visible":true,"origin":"","legend":"\u003cp\u003eRegression of the trangressive segregant index to the three-way cross index based on the F1 JH37 parent\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5017223/v1/cce832ad723fdd541064c2a1.jpeg"},{"id":75351361,"identity":"e9853d98-abe6-4568-a4aa-0fa0ef7a14bd","added_by":"auto","created_at":"2025-02-03 16:10:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2795592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5017223/v1/94b41203-9474-4d59-b806-da8833940509.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A New Approach for Evaluating Maize Transgressive Segregants and Their Three-Way Cross Potential in the S4 Convergent Breeding Population","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMaize 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 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This makes the development of feed-based maize more favorable than its development as food [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. On the basis of data from Freddy et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], 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.\u003c/p\u003e \u003cp\u003eFarmers 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 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In general, maize production in Indonesia increases by a small percentage annually. However, planted areas also dominate this increase [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This is risky because land conversion and population increases are increasing [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to Tridakusumah et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the rate of paddy land conversion in Indonesia has reached 0.14\u0026nbsp;million hectares annually. According to Harewan et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], 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 [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, the development of plant breeding continues to be pursued to increase maize production in Indonesia.\u003c/p\u003e \u003cp\u003eMaize 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 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This makes the market and selling value of hybrid maize varieties better than those of open-pollinated varieties [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The development of hybrid varieties of maize is critical for the development of pure-line elders [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This increase in diversity can be applied through convergent breeding [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, the pipeline of pure lines generally takes a relatively long time, especially for complex genetic constructs such as this convergent breeding concept [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, innovation in selfing methods is needed in the S4 population. One solution that can be applied is transgressive segregant selection.\u003c/p\u003e \u003cp\u003eTransgressive segregant (TS) selection is a concept of selection carried out in early generations [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This selection can generally reduce the channeling time faster than the conventional method can do [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The concept of TS selection has been widely reported by several researchers on various crops, such as Rostini et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and Anshori et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] in chili; Koide et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and Pabuayon et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] in rice; Putri et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]; Reynolds and Braun [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] in tropical wheat; and Cazzola et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] 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 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Makmur et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] 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. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] 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. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] needs to be optimized.\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These tester genotypes can be derived from specific pure and random lines [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Compared with single or open-pollinated crosses, such crosses result in more complex genetic interactions and heterosis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. 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.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Experimental Design\u003c/h2\u003e \u003cp\u003eThis study was conducted at the Agricultural Standardization and Instrumentation Center for Cereal Crops in Lau Maros District, Maros Regency, South Sulawesi, at coordinates 04\u0026deg;59'51.9\" S, 119\u0026deg;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\u0026ndash;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).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Research Procedure\u003c/h2\u003e \u003cp\u003eThis study followed the procedures outlined by Akfindarwan et al. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and Makmur et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. 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 \u0026times; 33 m and separated by 100 cm. Planting was conducted by creating individual holes with a spacing of 70 cm \u0026times; 20 cm, with each row comprising 15 plants and two seeds per hole.\u003c/p\u003e \u003cp\u003eMaintenance 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.\u003c/p\u003e \u003cp\u003eNatural 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\u0026ndash;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Observation and Analysis\u003c/h2\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003eThe 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:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:ear\\:ratio=\\:\\frac{yield\\:component}{ear\\:diameter\\:or\\:ear\\:lenght}$$\u003c/div\u003e\u003c/div\u003e1. \u003c/p\u003e \u003cp\u003eFurthermore, 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:\u003c/p\u003e \u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1728373298.png\" style=\"width: 144px;\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eX̅\u003c/em\u003e represents the mean ear ratio and where S\u003csup\u003e2\u003c/sup\u003e 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eAnalysis 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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-1.\u003c/b\u003e Analysis of Variance for the Validation of Transgressive Segregant S4 Maize Lines\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003echeck\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003echeck vs line\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0230\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0195\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0178\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0008\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0071\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0124\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0105\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0106\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0204\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0278\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100SW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0256\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0053\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *Significant effect at the 5% error level, **significant effect at the 1% error level, CV\u0026thinsp;=\u0026thinsp;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).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFig-1.\u003c/b\u003e Pearson correlation analysis of maize agronomic \u003cb\u003echaracteristics\u003c/b\u003e 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)).\u003c/p\u003e \u003cp\u003eAs 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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eThe results of the ratio analysis of the characteristics of cob weight, seed yield, and seed weight per cob are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. 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).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePath analysis of \u003cb\u003echaracteristics\u003c/b\u003e correlated \u003cb\u003ewith\u003c/b\u003e seed weight per ear of maize\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDirect Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e \u003cp\u003eIndirect effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDFF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNSRE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSYP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: 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).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized ratio and internal ratio values for the evaluation of transgressive segregant line S4\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eReal value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eInternal Ratio Standardized\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSYP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSWE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEW_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSYP_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSWE_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEW_r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSYP_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSWE_R\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.37.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e190.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e115.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB2.22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB2.23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB5.10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB5.2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e155.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB5.5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB5.7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB6.5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB7.1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG1.10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG2.16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e156.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG2.19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG2.25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG2.7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG2.7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG3. 18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e183.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e113.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG3. 35.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e128.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG3.10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG3.31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e169.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG3.32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG4.11.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e181.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e153.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG4.24.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG4.27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e170.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG4.36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG4.41.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG4.51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST3.8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBISI 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e158.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e17.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJH 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e207.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e173.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e15.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNASA 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e226.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e186.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSINHAS 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e205.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e170.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1236.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1172.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eNote: ear length (EL), ear weight (EW), seed yield percentage (SYP), and seed weight per ear (SWE), _R\u0026thinsp;=\u0026thinsp;ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe 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\u0026nbsp;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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable-4.\u003c/b\u003e Transgressive segregant index values in the evaluation of transgressive segregant lines of maize S4\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeno\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBLUP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eBLUP_standardized\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eBLUP Fitness Relative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndeks TS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBTK_r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRB_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBB_PT_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBTK_r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRB_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBB_PT_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBTK_r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRB_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBB_PT_R\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCB1.37.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e10.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-0.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB1.5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB2.22.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCB2.23.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB5.10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB5.2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB5.5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB5.7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB6.5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB7.1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG1.10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG2.16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSG2.19.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSG2.25.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e6.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSG2.7.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG2.7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG3. 18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSG3. 35.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-1.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSG3.10.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG3.31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG3.32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSG4.11.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-1.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSG4.24.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-1.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSG4.27.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e5.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-1.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG4.36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSG4.41.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e9.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG4.51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST3.8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBISI 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJH 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNASA 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSINHAS 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: ear length (EL), ear weight (EW), seed yield percentage (SYP), and seed weight per ear (SWE), _R\u0026thinsp;=\u0026thinsp;ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAn evaluation of the three-way cross (TWC) hybrids in comparison with the F1 hybrid tester is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e. 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\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that nine three-way cross-maize hybrids presented positive index values. Notably, the TWC hybrids SG 3.35.12 \u0026times; JH37 (2.06) and CB 2.23.1 \u0026times; 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 \u0026times; JH37 (0.72), SG 2.19.6 \u0026times; JH37 (0.56), CB 2.23.1 \u0026times; BISI18 (0.51), SG 3.10.1 \u0026times; JH37 (0.39), and SG 4.41.4 \u0026times; 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.\u0026nbsp;2). The regression relationship between the TSI and TWCI has a good coefficient of determination (R\u0026sup2;) of 0.66, with the regression equation TWC\u0026thinsp;=\u0026thinsp;y = -11.298TSI2\u0026thinsp;+\u0026thinsp;10.782TSI \u0026minus;\u0026thinsp;1.593.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of three-way cross hybrids between S4 transgressive segregants and comparison of hybrid testers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBLUP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eBLUP standard\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eBLUP Fitness Relative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndeks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBTP_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRB_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBTP_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRB_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBTP_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRB_R\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eY_R\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB 2.23.1 X BISI18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 2.7.14 X NASA29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB 1.37.14 X NASA29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB 2.23.1 X JH37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJH37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 4.41.4 X NASA 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBISI 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 2.7.14 X JH37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 3.35.12 X JH37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 3.35.12 X BISI18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 2.19.6 X NASA29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNASA 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB 2.23.1 X NASA29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 3.10.1 X JH37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 3.35.12 X NASA29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 2.19.6 X JH37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 4.41.4 X JH37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCB 1.37.14 X JH37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-2.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG 4.41.4 X BISI 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-2.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.085046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.118614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eNote: ear length (EL), ear weight (EW), and seed yield percentage (SYP), seed weight per ear (SWE), _R\u0026thinsp;=\u0026thinsp;ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFig-2.\u003c/b\u003e Regression of the trangressive segregant index to the three-way cross index based on the F1 JH37 parent\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe augmented design concept, a methodology that emphasizes two primary assessments, the evaluation of line variance and the comparison between lines and checks [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], 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. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and Makmur et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] 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.\u003c/p\u003e \u003cp\u003eCorrelation and path analyses are critical for determining selection criteria and represent systematic techniques for identifying potential criteria that support production [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Various studies have documented this approach, including those by Baye et al. [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], Khan et al. (2022), Thuy et al. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and Anshori et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], with similar methodologies applied to maize [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. 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. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], Sah et al. [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], Mousavi and Nagy [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], and Dermail et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], highlighting their significant regression effects in determining yield potential [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Consequently, combining ear weight and seed yield with seed weight per ear was effective in the validation of S4 TS maize.\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eIn this study, the comparative ratio was focused primarily on ear length. Generally, ratio comparisons can be performed through volume measurements [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], 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 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Narrow variability within a line indicates that segregants may have reached their homozygous potential [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], 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 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The comparison of standardized values to simple ratios reveals significant differences, indicating that uncorrected mean values can lead to misinterpretations [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, the potential of TS was standardized and transformed via the best linear unbiased prediction and selection index methodologies\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan additionalcitationids=\"CR72 CR73\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], including applications in maize research [\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. The BLUP concept primarily accounts for potential random effects and the genetic variance of genotypes [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. The potential random effect is determined in augmented designs relative to the comparison variety [\u003cspan additionalcitationids=\"CR81\" citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. 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. [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], Burgue\u0026ntilde;o et al. [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], and Amaral et al. [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eDeveloping 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 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], Alsabah et al. [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], and Fadhilah et al. [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], including in maize [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the direct effect value must be corrected with the determination value. This results in the following relative fitness BLUP-based selection index:\u003c/p\u003e \u003cp\u003eIndeks\u0026thinsp;=\u0026thinsp;0.83*0.64*BTK\u0026thinsp;+\u0026thinsp;0.37*0,64*RB\u0026thinsp;+\u0026thinsp;Yield\u003c/p\u003e \u003cp\u003eIndeks\u0026thinsp;=\u0026thinsp;0.53 BTK\u0026thinsp;+\u0026thinsp;0.24 RB\u0026thinsp;+\u0026thinsp;yield\u003c/p\u003e \u003cp\u003eBased 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. [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] and Anshori et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], 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 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. 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.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003eSelection 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\u0026thinsp;+\u0026thinsp;0.24 RB BL\u0026thinsp;+\u0026thinsp;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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA short paragraph specifying their contributions must be provided for research articles by several authors. The following statements should be used: \u0026ldquo;Conceptualization: Nuniek Widiayani, Muhammad Fuad Anshori, Muh Farid, and Mahmoud F. Seleiman; Data curation: Nasaruddin Nasaruddin, \u0026nbsp;Muh Farid, Willy Bayuardi Suwarno, Muhammad Azrai, and Amin Nur; \u0026nbsp;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: \u0026nbsp;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\u0026mdash;original draft preparation: Nuniek Widiayani, Muhammad Fuad Anshori, and Willy Bayuardi Suwarno; Writing\u0026mdash;review and editing: all authors. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data is available within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGwirtz JA, Garcia-Casal MN. (2014) Processing maize flour and corn meal food products. Ann N Y Acad Sci 1312(1):66\u0026ndash;75. doi.10.1111/nyas.12299.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiao Y, Chen HD, Han H, Chang Y. Evelopment and utilization of corn processing byproducts: A Review. Foods. 2022;11(22):3709. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/foods11223709\u003c/span\u003e\u003cspan address=\"10.3390/foods11223709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWadhawan N, Jain NK, Mudgal VD. Cronicon entrepreneurship development in maize processing. EC Nutr. 2019;15:01\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanumihardjo SA, McCulley L, Roh R, Lopez-Ridaura S, Palacios-Rojas N, Gunaratna NS. Maize agro-food systems to ensure food and nutrition security in reference to the Sustainable Development Goals. Glob Food Sec. 2020;25:100327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.gfs.2019.100327\u003c/span\u003e\u003cspan address=\"10.1016/j.gfs.2019.100327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreddy IM, Respatiadi H, Endy G, Gupta K. Reforming Trade Policy to Lower Maize Prices in Indonesia. Indonesia: Jakarta; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSyahruddin K, Azrai M, Nur A, Abid M, Wu WZ. (2020) A review of maize production and breeding in Indonesia. In: IOP Conference Series: Earth and Environmental Science. Institute of Physics Publishing. p. 012040.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatistic of Indonesia. Luas Panen dan Produksi Jagung di Indonesia 2023 (Angka Sementara). Indonesia: Jakarta; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRondhi M, Pratiwi PA, Handini VT, Sunartomo AF, Budiman SA. Agricultural land conversion, land economic value, and sustainable agriculture: A case study in East Java. Indonesia Land. 2018;7(4):148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/land7040148\u003c/span\u003e\u003cspan address=\"10.3390/land7040148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitton N, Alexander P, Arnell N, Bajzelj B, Calvin K, Doelman J, et al. The vulnerabilities of agricultural land and food production to future water scarcity. Glob Environ Chang. 2019;58:101944. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.gloenvcha.2019.101944\u003c/span\u003e\u003cspan address=\"10.1016/j.gloenvcha.2019.101944\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng G, Jiang H, Zhu S, Wen Y, He C, Wang X et al. (2024) Projecting the response of ecological risk to land use/land cover change in ecologically fragile regions. Sci Total Environ 914:169908. doi.10.1016/j.scitotenv.2024.169908.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTridakusumah AC, Setiawan I, Nugraha ANA, Kurnia G, Sukayat Y. (2022) The relations between agricultural land conversion and urban farm workers livelihoods. In: E3S Web of Conferences. EDP Sciences. p. 3011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarewan Y, Wurarah RN, Santoso B, Sabariah V. (2023) Analysis of land conversion to economic growth: the case of other purpose areas. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing. p. 12052.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApriyana Y, Surmaini E, Estiningtyas W, Pramudia A, Ramadhani F, Suciantini S, et al. The integrated cropping calendar information system: A coping mechanism to climate variability for sustainable agriculture in Indonesia. Sustain. 2021;13(11):6495. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/su13116495\u003c/span\u003e\u003cspan address=\"10.3390/su13116495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarooq A, Farooq N, Akbar H, Hassan ZU, Gheewala SH. A Critical review of climate change impact at a global scale on cereal crop production. Agronomy. 2023;13(1):162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/agronomy13010162\u003c/span\u003e\u003cspan address=\"10.3390/agronomy13010162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan X, Li S, Chen J, Yu H, Yang T, Wang C, et al. Impacts of global climate change on agricultural production: A comprehensive review. Agronomy. 2024;14(7):1360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/agronomy14071360\u003c/span\u003e\u003cspan address=\"10.3390/agronomy14071360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFromme DD, Spivey TA, Grichar WJ. (2019) Agronomic response of corn (\u003cem\u003eZea mays L.\u003c/em\u003e) hybrids to plant populations. Int J Agron 2019(2):1\u0026ndash;8. doi.10.1155/2019/3589768.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuntean L, Ona A, Berindean I, Racz I, Muntean S. Maize Breeding: From Domestication to Genomic Tools. Agronomy. 2022;12(10):2365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/agronomy12102365\u003c/span\u003e\u003cspan address=\"10.3390/agronomy12102365\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFikri M, Farid M, Musa Y, Anshori MF, Padjung R, Nur A. Multivariate analysis in the development of technology packages for corn cultivation by adding fertilizer to compost (2023). Chil J Agric Res 83 (4):471\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.4067/S0718-58392023000400471\u003c/span\u003e\u003cspan address=\"10.4067/S0718-58392023000400471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbduh AD, Padjung R, Farid M, Bahrun AH, Anshori F, Ridwan I et al. (2021) Interaction of genetic and cultivation technology in maize prolific and productivity increase. Pak J Biol Sci 24(6):716\u0026ndash;23. doi.10.3923/pjbs.2021.716.723.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKutka F. Open-pollinated vs. hybrid maize cultivars. Sustainability. 2011;3(9):1531\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/su3091531\u003c/span\u003e\u003cspan address=\"10.3390/su3091531\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSumalini K, Sravani D, Pradeep T, Rani UG, Bhaskar VA, Reddy UR, et al. A review on maize hybrid breeding\u0026mdash;importance of multiple crosses in comparison with single crosses in present scenario. Environ Ecol. 2018;36(4):1079\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarot E, Jo\u0026euml;t T, Combes MC, Lashermes P. (2019) Genetic diversity and population divergences of an indigenous tree (\u003cem\u003eCoffea mauritiana\u003c/em\u003e) in Reunion Island: role of climatic and geographical factors. Heredity (Edinb) 122 (6):833\u0026ndash;847. doi.10.1038/s41437-018-0168-9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKardos M, Armstrong EE, Fitzpatrick SW, Hauser S, Hedrick PW, Miller JM et al. (2021) Analyzed data. PNAS118(48): e2104642118. doi.10.1073/pnas.2104642118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrakash SJ, Gayatonde V, Vennela PR. Convergence-divergence improvement in plant breeding. Indian J Agric Allied Sci. 2016;2(1):14\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW\u0026uuml;rschum T, Zhu X, Zhao Y, Jiang Y, Reif JC, Maurer HP. Maximization through optimization? On the relationship between hybrid performance and parental genetic distance. Theor Appl Genet. 2023;136:186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1007/s00122-023-04436-5\u003c/span\u003e\u003cspan address=\"10.1007/s00122-023-04436-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakmur FM, Ala A, Mandja K, Anshori MF, Fadhilah AN. The selection index of S3 corn convergent breeding population based on multivariate analysis. Biodiversitas J Biol Divers. 2024;25(3):1097\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.13057/biodiv/d250324\u003c/span\u003e\u003cspan address=\"10.13057/biodiv/d250324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFirmansah H, Wahyu Y, Nur A, Tondok ET. (2024) The Response of advanced generation wheat lines derived from convergent breeding to biotic stress in high altitude area. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing. p. 12123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamayoa LF, Olukolu BA, Yang CJ, Chen Q, Stetter MG, York AM, et al. Domestication reshaped the genetic basis of inbreeding depression in a maize landrace compared to its wild relative, teosinte. PLoS Genet. 2021;17:e100979. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1371/journal.pgen.1009797\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1009797\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollard BCY, Beredo JC, Lenaerts B, Mendoza R, Santelices R, Lopena V, et al. Revisiting rice breeding methods\u0026ndash;evaluating the use of rapid generation advance (RGA) for routine rice breeding. Plant Prod Sci. 2017;20(4):337\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1080/1343943X.2017.1391705\u003c/span\u003e\u003cspan address=\"10.1080/1343943X.2017.1391705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabroo MR, Studer AJ, Rutkoski JE. (2021) Heterosis and hybrid crop breeding: A multidisciplinary review. Front Genet 24(12):643761. doi.10.3389/fgene.2021.643761.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabadin F, DoVale JC, Platten JD, Fritsche-Neto R. Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets. Front Plant Sci. 2022;13:935885. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3389/fpls.2022.935885\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.935885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSyukur M. Teknik pemuliaan tanaman. Penebar Swadaya; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeon D, Kang Y, Lee S, Choi S, Sung Y, Lee TH, et al. Digitalizing breeding in plants: A new trend of next-generation breeding based on genomic prediction. Front Plant Sci. 2023;14:1092584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3389/fpls.2023.1092584\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1092584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoide Y, Uchiyama T, Ota Y, Sakaguchi S, Tezuka A, Nagano AJ, et al. Genetic properties responsible for the transgressive segregation of days to heading in rice. Genes Genomics Genet. 2019;9(5):1655\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1534/g3.119.201011\u003c/span\u003e\u003cspan address=\"10.1534/g3.119.201011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePabuayon ILB, Sun Y, Guo W, Ritchie GL. High-throughput phenotyping in cotton: a review. J Cott Res. 2019;2:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1186/s42397-019-0035-0\u003c/span\u003e\u003cspan address=\"10.1186/s42397-019-0035-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwetha B, Devi HUN, Sankari A, Geethanjali S, Sudha M. (2023) Variability studies and genetic divergence in chilli (\u003cem\u003eCapsicum\u003c/em\u003e spp.) genotypes using multivariate analysis. Electron J Plant Breed 14(3):928\u0026ndash;37. doi.10.37992/2023.1403.105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMackay IJ, Cockram J, Howell P, Powell W. Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding. Plant Biotechnol J. 2021;19:26\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/pbi.13481\u003c/span\u003e\u003cspan address=\"10.1111/pbi.13481\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnshori MF, Musa Y, Dungga NE, Widiayani N, Arifin AS, Masniawati A, et al. A new approach for selection of transgressive segregants in F3 populations based on selection index and anthocyanin content in cayenne pepper. Front Sustain Food Syst. 2024;8:1288579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3389/fsufs.2024.1288579\u003c/span\u003e\u003cspan address=\"10.3389/fsufs.2024.1288579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede los Reyes BG. Genomic and epigenomic bases of transgressive segregation \u0026ndash; New breeding paradigm for novel plant phenotypes. Plant Sci. 2019;288:110213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.plantsci.2019.110213\u003c/span\u003e\u003cspan address=\"10.1016/j.plantsci.2019.110213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNascimento MF, R\u0026ecirc;go ER, do, Nascimento NFF do, Leite PS da, Finger S, Bruckner FL et al. CH, (2019) Heritability of morpho-agronomic traits in ornamental pepper. Crop Breed Appl Biotechnol 19(3):253\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1590/1984-70332019v19n3a36\u003c/span\u003e\u003cspan address=\"10.1590/1984-70332019v19n3a36\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaryono MY, Wirnas D, Human S. Analisis genetik dan seleksi segregan transgresif pada populasi F2 sorgum hasil persilangan B69\u0026times; Numbu dan B69\u0026times; Kawali. Indones J Agron. 2019;47(2):163\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.24831/jai.v47i2.24991\u003c/span\u003e\u003cspan address=\"10.24831/jai.v47i2.24991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRostini N, Yenny RF, Amien S. (2019) Inheritance pattern of capsaicin content of indonesian chili landraces (\u003cem\u003eCapsicum annum L\u003c/em\u003e.). In: IOP Conference Series: Earth and Environmental Science. IOP Publishing p. 12018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePutri NE, Sutjahjo SH, Nur A, Suwarno WB, Wahyu Y. Wheat transgressive segregants and their adaptation in the tropical region Sabrao. J Breed Genet. 2020;52(4):506\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReynolds MP, Braun HJ. Wheat Improvement: Food Security in a Changing Climate. Springer International Publishing; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCazzola F, Bermejo CJ, Cointry E. Transgressive segregations in two pea F2 populations and their respective F2:3 families. Pesqui Agropecu Bras. 2020;55:e01623. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1590/S1678-3921.pab2020.v55.01623\u003c/span\u003e\u003cspan address=\"10.1590/S1678-3921.pab2020.v55.01623\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehman AU, Dang T, Qamar S, Ilyas A, Fatema R, Kafle M, et al. Review revisiting plant heterosis\u0026mdash;from field scale to molecules. Genes. 2021;12(11):1688. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/genes12111688\u003c/span\u003e\u003cspan address=\"10.3390/genes12111688\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKa A, Hosamani J, Deshpande SK, Bhat JS, Kachapur RM, Mummigatti UV. Identification of transgressive segregants among newly derived F 4 inbred maize lines (\u003cem\u003eZea mays L.\u003c/em\u003e) for baby corn characteristics. J Pharmacogn Phytochem. 2021;10(2):404\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoemaker DL, Qiu Y, de Leon N, Hirsch CN, Kaeppler SM. Genetic analysis of pericarp pigmentation variation in Corn Belt dent maize. G3 Genes. Genomes Genet. 2024;14(1):jkad256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1093/g3journal/jkad256\u003c/span\u003e\u003cspan address=\"10.1093/g3journal/jkad256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahriman F, Egesel C\u0026Ouml;, Orhun GE, Alaca B, Avci F. Comparison of graphical analyses for maize genetic experiments: Application of biplots and polar plot to line \u0026times; tester design. Chil J Agric Res. 2016;76(3):285\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.4067/S0718-58392016000300004\u003c/span\u003e\u003cspan address=\"10.4067/S0718-58392016000300004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;zdemir E, Sade B. Comparison of maize lines and their test crosses according to grain yield and some physiological properties. Turkish J Agric For. 2019;43(2):115\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3906/tar-1801-85\u003c/span\u003e\u003cspan address=\"10.3906/tar-1801-85\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourke PM, Evers JB, Bijma P, van Apeldoorn DF, Smulders MJM, Kuyper TW, et al. Breeding Beyond Monoculture: Putting the Intercrop Into Crops. Front Plant Sci. 2021;12:734167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3389/fpls.2021.734167\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2021.734167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaazou ARS, Adetimirin VO, Gedil M, Meseka S, Mengesha W, Menkir A. Suitability of testers to characterize provitamin a content and agronomic performance of tropical maize inbred lines. Front Genet. 2022;8(13):955420. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fgene.2022.955420\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2022.955420\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabu I, Lubobo K, Mbuya K, Kimuni N. Heterosis and line-by-tester combining ability analysis for grain yield and provitamin an in maize. Sabrao J Breed Genet. 2023;55(3):695\u0026ndash;707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSorsa Z, Mohammed W, Wegary D, Tarkegne A. Performances of three-way cross hybrids over their respective single crosses and related heterosis of maize (\u003cem\u003eZea mays L.\u003c/em\u003e) hybrids evaluated in Ethiopia. Heliyon. 2023;9(5):e15513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.heliyon.2023.e15513\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e15513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkfindarwan AK, Farid M, Syaiful SA, Anshori MF, Nur A. Selection criteria and index analysis for the S2 maize lines of doublecrosses. Biodiversitas J Biol Divers. 2023;24(1):191\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNur A, Riadi M, Yassi A, Farid M, Anshori MF, Akfindarwan AK. (2021) Selection and evaluation the corn lines from multiple-cross progeny based on targeted selection environment on acid soil. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing. p. 12016. doi.10.13057/biodiv/d240123Selection.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFadhilah AN, Farid M, Ridwan I, Anshori MF, Yassi A. Genetic parameters and selection index of high-yielding tomato F2 populations. Sabrao J Breed Genet. 2022;54(5):1026\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaye A, Berihun B, Bantayehu M, Derebe B. Genotypic and phenotypic correlation and path coefficient analysis for yield and yield-related traits in advanced bread wheat (\u003cem\u003eTriticum aestivum L\u003c/em\u003e.) lines. Cogent Food Agric. 2020;6:1752603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1080/23311932.2020.1752603\u003c/span\u003e\u003cspan address=\"10.1080/23311932.2020.1752603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThuy NP, Trai NN, Khoa BD, Thao NHX, Phong VT, Thi QVC. Correlation and path analysis of association among yield, micronutrients, and protein content in rice accessions grown under aerobic condition from Karnataka, India. Plant Breed Biotechnol. 11(2):117\u0026ndash;129. doi.10.9787/PBB.2023.11.2.117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReddy VR, Jabeen F. Narrow sense heritability, correlation and path analysis in maize (\u003cem\u003eZea mays L\u003c/em\u003e). Sabrao J Breed Genet. 2016;48(2):120\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAman J, Bantte K, Alamerew S, Sbhatu DB. correlation and path coefficient analysis of yield and yield components of quality protein maize (\u003cem\u003eZea mays L\u003c/em\u003e.) hybrids at Jimma, Western Ethiopia. Int J Agron. 2020;2020:9651537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1155/2020/9651537\u003c/span\u003e\u003cspan address=\"10.1155/2020/9651537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendes-Moreira PMR, Mendes-Moreira J, Fernandes A, Andrade E, Hallauer AR, P\u0026ecirc;go SE et al. (2014) Is ear value an effective indicator for maize yield evaluation? F Crop Res 161:75\u0026ndash;86. doi.10.1016/j.fcr.2014.02.015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSah RP, Chakraborty M, Prasad K, Pandit M, Tudu VK, Chakravarty MK, et al. Impact of water deficit stress in maize: Phenology and yield components. Sci Rep. 2020;10(1):2944. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-59689-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-59689-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMousavi SMN, Nagy J. (2021) Evaluation of plant characteristics related to grain yield of FAO410 and FAO340 hybrids using regression models. Cereal Res Commun 49:161\u0026ndash;9. doi.10.1007/s42976-020-00076-3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDermail A, Fuengtee A, Lertrat K, Suwarno WB, L\u0026uuml;bberstedt T, Suriharn K. Simultaneous selection of sweet-waxy corn ideotypes appealing to hybrid seed producers, growers, and consumers in Thailand. Agronomy. 2022;12(1):87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/agronomy12010087\u003c/span\u003e\u003cspan address=\"10.3390/agronomy12010087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaur H, Leuenberger C. (2011) Analysis of ratios in multivariate morphometry. Syst Biol. 60(6):813\u0026ndash;25. doi.10.1093/sysbio/syr061.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJudd LA, Jackson BE, Fonteno WC. (2015) Advancements in root growth measurement technologies and observation capabilities for container-grown plants. Plants 4(3):369\u0026ndash;392. doi.10.3390/plants4030369.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta C, Tewari VK, Machavaram R, Shrivastava P. An image processing approach for measurement of chili plant height and width under field conditions. J Saudi Soc Agric Sci. 2022;21(3):171\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.jssas.2021.07.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jssas.2021.07.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooksey RW, Cooksey RW. Descriptive statistics for summarizing data. Springer; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson SF. (2020) Misinterpreting p: The discrepancy between p values and the probability the null hypothesis is true, the influence of multiple testing, and implications for the replication crisis. Psychol Methods 25(5):596. doi.10.1037/met0000248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlves RS, de Carvalho Rocha JR do, AS, Teodoro PE, de Resende MDV, Henriques EP, Silva LA et al. (2018) Multiple-trait BLUP: a suitable strategy for genetic selection of Eucalyptus. Tree Genet Genomes 14(5):1\u0026ndash;8. doi.10.1007/s11295-018-1292-7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt P, Hartung J, Bennewitz J, Piepho H-P. (2019) Heritability in plant breeding on a genotype-difference basis. Genetics 212(4):991\u0026ndash;1008. doi.10.1534/genetics.119.302134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlivoto T, L\u0026uacute;cio ADC, da Silva JAG, Sari BG, Diel MI. (2019) Mean performance and stability in multi-environment trials II: Selection on the basis of multiple traits. Agron J 111(6):2961\u0026ndash;269. doi.10.2134/agronj2019.03.0221.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanna A, Anumalla M, Catolos M, Bartholom\u0026eacute; J, Fritsche-Neto R, Platten JD, et al. Genetic trends estimation in IRRIs rice drought breeding program and identification of high yielding drought-tolerant lines. Rice. 2022;15:14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1186/s12284-022-00559-3\u003c/span\u003e\u003cspan address=\"10.1186/s12284-022-00559-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira GHF, Buzinaro R, Revolti LTM, Giorgenon CHB, Charnai K, Resende D, et al. An accurate prediction of maize crosses using diallel analysis and best linear unbiased predictor (BLUP). Chil J Agric Res. 2016;76(3):294\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.4067/S0718-58392016000300005\u003c/span\u003e\u003cspan address=\"10.4067/S0718-58392016000300005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEntringer GC, Vettorazzi JCF, Santos EA, Pereira MG, Viana AP. (2016) Genetic gain estimates and selection of S1 progenies based on selection indices and REML/BLUP in super sweet corn. Aust J Crop Sci 10(3):411\u0026ndash;417. doi.10.21475/ajcs.2016.10.03.p7248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZystro J, Peters T, Miller K, Tracy WF. (2021) Classical and genomic prediction of hybrid sweet corn performance in organic environments. Crop Sci 61(3):1698\u0026ndash;708. doi.10.1002/csc2.20400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRocha JR do AS, de Machado C, Carneiro JC. PCS (2018) Multitrait index based on factor analysis and ideotype-design: Proposal and application on elephant grass breeding for bioenergy. Gcb Bioenergy10:52\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/gcbb.12443\u003c/span\u003e\u003cspan address=\"10.1111/gcbb.12443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlivoto T, Diel MI, Schmidt D, L\u0026uacute;cio AD. MGIDI: a powerful tool to analyze plant multivariate data. Plant Methods. 2022;18:121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou FM, Song Q, Jia G, Cheng Y, Duguid S, Booker H, et al. Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design. Crop J. 2016;4(2):107\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.cj.2016.01.003\u003c/span\u003e\u003cspan address=\"10.1016/j.cj.2016.01.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgue\u0026ntilde;o J, Crossa J, Rodr\u0026iacute;guez F, Yeater KM, Glaz B, Yeater KM. (2018) Chap. 13: Augmented Designs-Experimental Designs in Which All Treatments are not Replicated. In: Applied statistics in agricultural, biological, and environmental sciences. pp. 345\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmaral L, de O, Miranda GV, Souza JdaS, Moitinho ACR, Cristeli DS, Silva HK et al. da, (2023) Application of Artificial neural networks to predict genotypic values of soybean derived from wide and restricted crosses for relative maturity groups. Agronomy 13(10):2476. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3390/agronomy13102476\u003c/span\u003e\u003cspan address=\"10.3390/agronomy13102476\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolenaar H, Boehm R, Piepho HP. Phenotypic selection in ornamental breeding: It\u0026rsquo;s better to have the BLUPs than to have the BLUEs. Front Plant Sci. 2018;9:1511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3389/fpls.2018.01511\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2018.01511\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeternelli LA, Moreira \u0026Eacute;FA, Nascimento M, Cruz CD. Artificial neural networks and linear discriminant analysis in early selection among sugarcane families. Crop Breed Appl Biotechnol. 2017;17(4):299\u0026ndash;305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1590/1984-70332017v17n4a46\u003c/span\u003e\u003cspan address=\"10.1590/1984-70332017v17n4a46\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlsabah R, Purwoko BS, Dewi IS, Wahyu Y. Selection index for selecting promising doubled haploid lines of black rice. Sabrao J Breed Genet. 2019;51(4):430\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwanson-Wagner R, Briskine R, Schaefer R, Hufford MB, Ross-Ibarra J, Myers CL et al. (2012) Reshaping of the maize transcriptome by domestication. Proc Natl Acad Sci 109(29):11878\u0026ndash;83. doi.10.1073/pnas.1201961109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatoch V, Rathour R, Sharma S, Rana SS, Sharma A. (2021) Studies on genetic parameters, correlation and path coefficient analysis in er2 introgressed garden pea genotypes. Legum Res Int J 44:621\u0026ndash;626. doi.10.18805/LR-4142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabouri H, Rabiei B, Fazlalipour M. (2008)Use of selection indices based on multivariate analysis for improving grain yield in rice. Rice Sci 15(4):303\u0026ndash;10. doi.10.1016/S1672-6308(09)60008-1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli M, Kuswanto, Kustanto H. Phenomenon of inbreeding depression on maize in perspective of the quran. Agrivita. 2019;41(2):385\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.17503/agrivita.v41i2.2022\u003c/span\u003e\u003cspan address=\"10.17503/agrivita.v41i2.2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BLUP, multivariate analysis, systematic selection, segregative transgenics, Zea mays","lastPublishedDoi":"10.21203/rs.3.rs-5017223/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5017223/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe 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\u0026thinsp;+\u0026thinsp;0.24 seed yield percentage\u0026thinsp;+\u0026thinsp;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 \u0026times; JH37 and CB 2.23.1 \u0026times; 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.\u003c/p\u003e","manuscriptTitle":"A New Approach for Evaluating Maize Transgressive Segregants and Their Three-Way Cross Potential in the S4 Convergent Breeding Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 07:50:28","doi":"10.21203/rs.3.rs-5017223/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-09T11:29:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-29T17:05:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-29T08:15:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-24T08:51:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316037388713589739255709614220190706958","date":"2024-09-19T10:16:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119036514254331727819262550366296842897","date":"2024-09-17T00:24:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336454049896138776973684114148456195381","date":"2024-09-16T05:53:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115469226903480145354684387786957917922","date":"2024-09-15T04:53:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264797535392816371390358582071573915232","date":"2024-09-15T04:50:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255640023185523179643546532925221149459","date":"2024-09-09T10:02:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-06T23:50:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-06T13:50:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-05T11:51:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-05T11:49:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-09-02T09:45:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b6dacb3-80ed-4668-8c50-b23dd06f1b14","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:03:57+00:00","versionOfRecord":{"articleIdentity":"rs-5017223","link":"https://doi.org/10.1186/s12870-025-06103-x","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-01-29 15:58:05","publishedOnDateReadable":"January 29th, 2025"},"versionCreatedAt":"2024-10-08 07:50:28","video":"","vorDoi":"10.1186/s12870-025-06103-x","vorDoiUrl":"https://doi.org/10.1186/s12870-025-06103-x","workflowStages":[]},"version":"v1","identity":"rs-5017223","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5017223","identity":"rs-5017223","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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