Genotype selection from the segregating BC3 population of Passiflora spp. for fruit production and quality using the REML/BLUP methodology

Genotype selection from the segregating BC3 population of Passiflora spp. for fruit production and quality using the REML/BLUP methodology | 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 Genotype selection from the segregating BC3 population of Passiflora spp. for fruit production and quality using the REML/BLUP methodology Luis Carlos Loose Coelho, Alexandre Pio Viana, Natan Ramos Cavalcante, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6710617/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Oct, 2025 Read the published version in Euphytica → Version 1 posted 7 You are reading this latest preprint version Abstract Passion fruit is important due to its high yield and economic relevance, but its production is limited by the fruit woodiness disease, caused by the CABMV virus. The objective of this study was to evaluate the production and quality of fruits of BC 3 passion fruit genotypes using the REML/BLUP methodology. The experiment was conducted in randomized blocks, with six replicates and five full-sib families, in addition to the parents P. setacea and UENF Rio Dourado ( P. edulis ). The following traits were evaluated: fruit production per plant, number of fruits, average fruit weight, fruit weight (FW), pulp weight (PW), pulp yield (PY), longitudinal diameter, transverse diameter, peel thickness, and soluble solids content. Analysis of deviance, genetic parameter estimation, and genotype selection were performed, revealing significant differences between genotypes. Heritability ranged from 0.17 to 0.81, with emphasis on FW and PY, which showed high selection potential. Progeny analysis indicated greater efficiency in selection by families, with heritability of 0.99 for FW and PW. Selection accuracy was high (> 0.94), ensuring reliable results. Genotypes 489 and 341 (BC 3 family 293) showed gains of 68.4% and 68.3% in production, respectively. Selection of genotypes 083 and 293 (BC 3 family 355) resulted in improvements in fruit quality. The results show significant genetic gains, highlighting the potential of mixed models and interspecific crosses in passion fruit breeding. Passion fruit Interspecific crosses Backcrossing Genetic parameters Genetic gain Figures Figure 1 Introduction The passion fruit ( Passiflora edulis Sims) is the most commercially important species within the Passifloraceae family and is widely cultivated in tropical regions due to its edible, medicinal, and ornamental values (Araya et al. 2017 ). In Brazil, where its cultivation predominates, the country stands as the world's largest producer, with a total production of 697,859 t in 2022 (IBGE 2024 ). The crop has high production potential, reaching up to 30 t per hectare annually (Hafle et al. 2009 ). However, the national average yield remains low, at approximately 15.303 t ha − 1 . Studies such as those by Santos et al. (2021) have shown that certain cultivars can exceed this average, with yields ranging between 17.16 t ha − 1 and 26.98 t ha − 1 . One of the main challenges to increasing passion fruit yield is the incidence of diseases, particularly fruit woodiness, caused by cowpea aphid-borne mosaic virus (CABMV). This virus significantly impairs fruit quality, rendering them unsuitable for the consumer market (Cavichioli et al. 2011 ). Severe mosaic disease on leaves presents symptoms such as chlorotic spots, roughness, blistering, and leaf distortion, while the virus-induced woodiness and deformation of the fruit lead to reduced pulp cavity size and seed number (Colariccio et al. 2018 ). Transmission occurs through aphids in non-persistent interactions, which makes chemical control ineffective (Fajardo and Nickel 2019 ). Given the absence of a specific control method, integrated pest management strategies are used, such as planting healthy seedlings grown in protected environments, performing careful pruning and thinning operations (Rodrigues et al. 2016 ), systematically eliminating symptomatic plants, and scheduling planting during periods of lower aphid incidence (Spadotti et al. 2019 ). Despite its significant economic importance, there are no CABMV-resistant cultivars available on the market, emphasizing the need for breeding programs aimed at developing resistant varieties. Wild species of Passiflora , such as P. setacea , are resistant to viruses and are promising sources of resistance genes for commercial passion fruit breeding (Sacoman et al. 2018 ). Research by Santos et al. ( 2015a ) demonstrated that backcrossed interspecific hybrids ( P. edulis × P. setacea ) exhibit resistance to CABMV and possess agronomic traits comparable to those of the commercial parent. The backcrossing strategy enables the transfer of resistance genes while preserving desirable agronomic characteristics (Mesquita et al. 2005 ). The Breeding Program at UENF has adopted this approach to develop superior cultivars (Santos et al. 2015a ). Selecting genotypes from allogamous populations presents additional challenges, as environmental factors heavily influence phenotypic expression (Viana and Resende 2014 ). Traits of agronomic interest typically involve complex gene-environment interactions, necessitating the application of concepts like heritability, which quantifies the proportion of phenotypic variation attributable to genetic factors (Borém and Miranda 2013 ). According to Jung et al. ( 2008 ), increased environmental variability complicates effective selection, requiring the use of tools to improve selective accuracy. Techniques such as REML/BLUP are essential for estimating variance components and predicting genotypic values in breeding programs (Viana and Resende 2014 ). These tools enable the identification of promising genotypes based on productivity and disease resistance, thereby optimizing selection outcomes. Additionally, multivariate analyses integrate multiple phenotypic and genotypic variables, helping classify genotypes according to their genetic diversity (Fonseca et al. 2004 ). Fruit quality is a critical factor in passion fruit breeding, particularly in the context of resistance to diseases such as fruit woodiness. Developing cultivars that combine both CABMV resistance and high fruit quality is essential to meet market demands and ensure the sustainability and profitability of the crop. Thus, the objective of this study was to evaluate the BC 3 population of Passiflora genotypes for resistance to cowpea aphid-borne mosaic virus, estimate the genetic parameters associated with key traits, and select superior genotypes for both fruit production and quality. Material and methods Origin of the BC 3 population The breeding program was carried out in several stages, as depicted in Fig. 1 . Starting with the initial cross, interspecific hybrids were selected based on prior studies. These hybrids were then successively backcrossed with P. edulis , resulting in the backcross generations (BC₁, BC₂, and BC₃), with selection at each stage aimed at improving desirable agronomic traits. The interspecific hybrids were obtained by Santos et al. ( 2015b ) through a biparental cross between P. edulis and P. setacea , and were evaluated in a competition assay. Resistant F 1 plants were selected and backcrossed with the P. edulis parent, generating BC 1 , the first backcross generation. The BC 1 individuals were evaluated by Preisigke et al. ( 2021 ). The genotypes selected in BC 1 were then backcrossed again with P. edulis , resulting in the BC 2 generation. BC 2 individuals were evaluated by Vidal et al. ( 2021 ), who selected the best genotypes for the third backcrossing cycle, forming the BC 3 generation. BC generation To obtain BC 3 , crosses were made between BC 2 and P. edulis . Initially, the flower buds of the female parents were emasculated before anthesis, and the flowers were protected with paper bags. The same procedure was applied to the male parents, with the exception of emasculation. Pollination was performed starting at 12:00, at the onset of anthesis of the parents' flowers. The anthers of the male parents were rubbed onto the stigma of the female flowers using tweezers. After pollination, the flowers were labeled and protected for up to 24 h. The fruits resulting from the crosses were kept protected with nylon netting until ripening. The seeds were then extracted, washed in water, dried, and stored in paper bags inside plastic bags, which were kept in a refrigerator at approximately 10 ºC in the Plant Breeding Laboratory (LMGV) at UENF. Planting and experimental setup The seeds of the BC 3 population were sown in the greenhouse of the UENF Research Support Unit (UAP) using 200-cell Styrofoam trays filled with commercial substrate. Once the seedlings developed two pairs of definitive leaves, they were individually transferred to polyethylene plastic bags containing commercial substrate. The plants were subsequently transplanted to the experimental area at the Antônio Sarlo Agricultural School, located in the northern region of Rio de Janeiro state (21º 74’ S, 41º 33’ W, 11.2 m above sea level), which has a typical AW climate—tropical with a dry winter (Köppen and Geiger 1928 )—and clayey-sandy soil. The region has an average annual temperature of 23.9°C and an average annual rainfall of 1112 mm, with precipitation concentrated from October to March (INMET 2022 ). A randomized block design with six replicates was used, consisting of five full-sib families (unbalanced for plants within families) and their parents— P. setacea (resistant parent) and the UENF Rio Dourado cultivar of P. edulis (recurrent and susceptible parent). Initially, each full-sib family comprised 22 genotypes, and the parents included four genotypes, for a total of 708 plants in the experiment. However, this number was adjusted due to seedling mortality. The plants were trained using a vertical trellis system, with 2.5 m high wooden posts spaced 4.0 m apart and number 12 wire positioned 1.80 m above the ground. The spacing between rows was 3.0 m, and the spacing between plants within rows was 2.0 m. Cultivation practices such as weed clearing, pruning, and disbudding were performed in accordance with the crop’s requirements (Manica 1981). For monthly fertilization, 80 g of potassium chloride and 100 g of urea were applied. A drip irrigation system was used to meet the plants' water requirements. Evaluated traits The fruits were collected weekly from each individual plant, allowing for the assessment of variables related to both fruit production and quality. To evaluate production, the following traits were considered: fruit production per plant (PROD, kg), number of fruits per plant (NF), determined by counting the fruits produced by each plant, average fruit weight (AFW, g), calculated as the ratio between the total production of the plant and the number of fruits, and fruit weight (FW, g). Fruit quality was assessed using the following variables: pulp weight (PW, g); pulp yield (PY, %), calculated as the ratio of pulp weight to fruit weight, multiplied by 100; longitudinal diameter (LD, mm); transverse diameter (TD, mm); peel thickness (PT, mm); and soluble solids content (SSC, °Brix), measured using an analog refractometer with a scale ranging from 0 to 32 °Brix. For these traits, five fruits from each plant were analyzed, and the final value was obtained as the arithmetic mean. All weight measurements were recorded using an analytical balance, while length and diameter measurements were taken with a digital caliper. Statistical analysis According to the model described in Viana and Resende ( 2014 ), the analysis of deviance (ANADEV) was determined by the following equation: D = − 2ln(L) (1) ln(L) = − 1/2ln|X´V − 1X|−1/2ln|V|−1/2(y − Xm)`V − 1(y − Xm) (2) where ln(L) is the maximum point of the restricted maximum likelihood logarithm (REML) function; y is the vector of the variable analyzed; m is the vector of the effects of the observations, assumed fixed; X is the incidence matrix of the fixed effects, and V is the variance-covariance matrix of y. The likelihood ratio test (LRT) was applied to test the significance of the effects, according to the equation: LRT: |−2ln(Lwe) + 2ln(Lfm) (3) where Lwe is the maximum point of the maximum likelihood function for the reduced model (without effects) and Lfm is the maximum point of the maximum likelihood function for the full model. The data related to fruit production and quality were analyzed using the Selegen REML/BLUP program, with statistical model 147 (Resende 2016 ). The analysis followed the statistical model: 𝑦 = 𝑋𝑟 + 𝑍𝑔 + 𝑊𝑝 + 𝑒 (4) where y = vector of data; r = vector of replicate effects (assumed to be fixed) added to the overall mean; g = vector of individual genotypic effects (assumed to be random); p = vector of plot effects (random); e = vector of errors or (random) residuals; and X , Z , and W = incidence matrices for the aforementioned effects. The following variance components were estimated: \(\:{\sigma\:}_{g}^{2}\) = genotypic variance between full-sib progenies, equivalent to 1/2 of the additive genetic variance plus 1/4 of the dominance genetic variance, ignoring epistasis; and \(\:{\sigma\:}_{p}^{2}\) = individual phenotypic variance. The following genetic parameters were also estimated: \(\:{h}_{a}^{2}\) = individual narrow-sense heritability, obtained by ignoring the fraction (1/4) of the dominance genetic variance; \(\:{\sigma\:}_{mp}^{2}\) = heritability of the progeny mean, assuming complete survival; Acprog = accuracy of progeny selection, assuming complete survival; \(\:{h}_{ad}^{2}\) = additive heritability within a plot, obtained by ignoring the fraction (1/4) of the dominance genetic variance and the overall mean of the experiment. Results and discussion Estimate of genetic parameters In the likelihood ratio test (LRT), the genotypes showed a significant difference at 1% for all traits evaluated, except for number of fruits, which was significant at 5% (Table 1 ). This result indicates that the effect associated with the genotype is statistically different from zero, confirming a significant relationship between the genotype and the variables analyzed. Thus, the findings suggest that the selection of genotypes has a high potential for success, reinforcing the feasibility of obtaining superior individuals through breeding. Table 1 Analysis of deviance for ten traits related to fruit production and quality evaluated in the BC 3 passion fruit population. Effect NF PROD AFW FW LD Dev LRT (X²) Dev LRT (X²) Dev LRT (X²) Dev LRT (X²) Dev LRT (X²) Genotype 3125.5 5.86* 1432.37 12.33** 3802.44 29.36** 4137.08 45.81** 2665.02 22.07** Full model 3119.64 1420.04 3773.08 4091.27 2642.95 Effect TD PW PT TSS PY Dev LRT (X²) Dev LRT (X²) Dev LRT (X²) Dev LRT (X²) Dev LRT (X²) Genotypes 2338.27 19.06** 3453.42 42.87** 646.17 14.4** 1064.8 16.42** 2323.21 15.22** Full model 2319.21 3410.55 631.77 1048.38 2307.99 * and ** significant at the 5% (3.84) and 1% (6.63) probability levels by the Chi-square test, respectively. Dev: deviance. LRT(X²): likelihood ratio test. PROD = fruit production; NF = number of fruits; AFW = average fruit weight; FW = fruit weight; LD = longitudinal diameter; TD = transverse diameter; PT = peel thickness; PW = pulp weight; PY = pulp yield; TSS = total soluble solids. Phenotypic variance ranged from 1.71 to 3400.48, demonstrating that each trait responds differently to environmental influence (Table 2 ). The traits with the highest phenotypic variance were fruit weight (3400.48), average fruit weight (1692.20), and pulp weight (803.99), respectively. The first two traits, although similar, were obtained differently. Fruit weight (FW) was determined from the average individual weight of five fruits per plant, while average fruit weight (AFW) was calculated as the ratio between total plant production and total number of fruits produced (AFW = PROD/NF). Pulp weight showed a strong positive correlation with fruit weight (r = 0.94), indicating that heavier fruits tend to produce a greater amount of pulp, as reported by Morgado et al. (2010). The highest estimate of genotypic variance was observed for fruit weight (1371.93), while the lowest was recorded for peel thickness (0.35). Genotypic variance is a statistical parameter that quantifies the genetic variability present in a population for a given trait, disregarding environmental effects (Cruz et al. 2014 ). The results of this study indicate that the BC 3 population exhibited low genetic variability for the traits peel thickness (PT), soluble solids content (TSS), and production (PROD). Consequently, genetic gain for these traits may be slower in future selection cycles. Yield is one of the most relevant parameters in passion fruit breeding and is widely used as a criterion for selecting parents in this population. In this context, it is possible to infer that, with the advancement of generations, there has been a reduction in the genetic variability of this trait. On the other hand, the high genotypic variance observed for fruit weight (FW) suggests that there is still considerable genetic variation in this population, which may favor the selection of superior individuals for this trait. Table 2 Estimates of genetic parameters in BC 3 genotypes, obtained by the REML procedure for ten passion fruit traits. Estimate PROD (kg) NF AFW (g) FW (g) LD (mm) TD (mm) PT (mm) FW (g) PY (%) TSS (ºBrix) \(\:{\sigma\:}_{g}^{2}\) 1.47 26.04 634.58 1371.93 40.11 6.23 0.35 327.34 13.44 1.44 \(\:{\sigma\:}_{p}^{2}\) 8.69 296.95 1692.20 3400.48 135.84 53.59 1.71 803.99 61.2 4.85 \(\:{h}_{a}^{2}\) 0.34 0.17 0.75 0.81 0.59 0.23 0.41 0.81 0.44 0.59 \(\:{h}_{mp}^{2}\) 0.95 0.89 0.98 0.99 0.96 0.94 0.93 0.99 0.94 0.94 Acprog 0.97 0.94 0.99 0.99 0.98 0.97 0.96 0.99 0.97 0.97 \(\:{h}_{ad}^{2}\) 0.21 0.10 0.63 0.68 0.44 0.13 0.28 0.69 0.30 0.48 \(\:{\sigma\:}_{g}^{2}\) = genotypic variance; \(\:{\sigma\:}_{p}^{2}\) = individual phenotypic variance; \(\:{h}_{a}^{2}\) = individual narrow-sense heritability; \(\:{h}_{mp}^{2}\) = heritability of the progeny mean; Acprog = accuracy of progeny selection; \(\:{h}_{ad}^{2}\) = additive heritability within plot; PROD = production; NF = number of fruits; AFW = average fruit weight; FW = fruit weight; LD = longitudinal diameter; TD = transverse diameter; PT = peel thickness; PW = pulp weight; PY = pulp yield; TSS = total soluble solids. The estimates of narrow-sense individual heritability ranged from 0.17 to 0.81 (Table 2 ). According to the classification proposed by Resende ( 2002 ), heritability can be categorized into three levels: low ( \(\:{h}_{a}^{2}\) < 0.15), medium (0.15 < \(\:{h}_{a}^{2}\) < 0.50), and high (0.15 < \(\:{h}_{a}^{2}\) < 0.50). None of the evaluated traits exhibited low-magnitude heritability. The traits NF, TD, PROD, PT, and PY displayed medium-magnitude heritability, while LD, TSS, AFW, FW, and PW showed high-magnitude heritability. These results demonstrate significant potential for the selection of most traits of interest in passion fruit breeding. The estimated heritability of 0.34 for PROD in this study was lower than that reported by Vidal et al. ( 2021 ), who obtained 0.56, but higher than the 0.21 reported by Cavalcante et al. ( 2019 ). Although classified as medium-magnitude heritability, it is still possible to achieve significant gains in production through the application of efficient selection methods, aiming to maximize genetic progress in the short term (Freitas et al. 2011 ). Number of fruits (NF) and average fruit weight (AFW) are traits directly related to passion fruit production and are essential targets in crop breeding programs. In this study, the heritability estimates for these traits were 0.17 and 0.75, respectively (Table 2 ). Ferreira et al. ( 2016 ) reported heritability values of 0.65 for both traits. Compared to the present results, the heritability estimated for NF was lower, while for AFW it was higher. The authors emphasize that experimental factors such as the number of replicates, the number of plants per plot, and the application of uniform cultivation practices can directly influence heritability estimates. This highlights the importance of conducting specific studies for each context to ensure greater precision in selecting promising genotypes for passion fruit breeding. The traits transverse diameter (TD) and longitudinal diameter (LD) showed heritabilities of 0.59 and 0.23, respectively (Table 2 ). Assunção et al. ( 2015 ), using the REML methodology, reported heritability estimates of 0.65 for TD and 0.37 for LD. Although TD and LD are correlated traits, in both studies the heritability estimate for TD was higher than for LD, suggesting greater genetic stability for this trait. Assunção et al. ( 2015 ) also reported the same heritability estimate for PT (0.41) as observed in the present study. According to Coelho et al. ( 2011 ), variations in passion fruit peel thickness can occur within the same species, within the same orchard, and between different growing regions, and these variations can be exploited by selecting superior genotypes. Melleti (2011) emphasizes that cultivars intended for agroindustry should possess higher pulp yield, higher soluble solids content, more intensely colored pulp, and thinner peel, characteristics desirable for both the industry and the consumer market. This reinforces the importance of selecting genotypes with these attributes to add value to the final product. Pulp weight was the trait with the highest heritability among those analyzed, indicating that selection for this variable can be highly efficient (Table 2 ). Pulp yield (PY) showed a heritability of 0.44, a value lower than that reported by Freitas et al. ( 2011 ), but higher than that observed by Oliveira et al. ( 2008 ). Fruits with a greater amount of pulp indicate higher productive efficiency, reflecting desirable genetic characteristics that directly impact both quality and marketability of the final product. The heritability of the progeny mean was higher than the narrow-sense individual heritability for all evaluated traits, ranging from 0.89 to 0.99 (Table 2 ). This finding suggests greater variability between families than within them, making family-based selection more effective. A similar pattern was observed in other studies with passion fruit (Santos et al. 2011 ; Assunção et al. 2015 ; Cavalcante et al. 2019 ; Vidal et al. 2021 ) and in various other crops such as açaí (Farias Neto et al. 2008 ), peach palm (Farias Neto et al. 2013 ), Caesalpinia ebano (Torres et al. 2021 ), and pine (Silva et al. 2011). According to Costa et al. ( 2010 ), high progeny mean heritability ( \(\:{h}_{mp}^{2}\) ) indicates that selection based on family performance increases accuracy in identifying superior genotypes for breeding. The traits with the highest \(\:{h}_{mp}^{2}\) were FW and PW, both with values of 0.99, demonstrating great potential for genetic gain (Table 2 ). PROD also showed high \(\:{h}_{mp}^{2}\) (0.95), indicating strong selection potential. Given its importance for productive efficiency, it is advantageous to prioritize family-based selection to maximize production, rather than focusing on individual plants. However, this strategy may not be ideal for traits such as resistance to CABMV. The UENF passion fruit breeding program aims to increase productivity while also selecting for virus resistance, which requires a strategy that simultaneously considers both traits. Krause et al. ( 2021 ) analyzed several passion fruit traits using the REML/BLUP methodology and reported \(\:{h}_{mp}^{2}\) values ranging from 0.13 to 0.71, considerably lower than those found in this study. The highest heritability values in their study were observed for fruit ratio, length, and shape. The authors suggested that, in view of the high heritability of these traits, selection using progeny information may be more effective, as such traits are directly related to agro-industrial value and market acceptance. The accuracy of progeny selection was high (> 0.94) for all traits (Table 2 ), indicating excellent experimental precision. According to Pereira et al. ( 2013 ), this metric reflects the precision of selection, representing the correlation between predicted and true genetic values. The precision of these estimates can be further improved through more robust experimentation, including an increased number of measurements per plant, as recommended by Resende ( 2002 ). These results confirm that the present experiment was conducted with adequate rigor, generating reliable outcomes. This is particularly important for breeders, as it allows greater confidence when discarding unselected progenies and advancing to subsequent selection cycles. Genetic gains from selection Significant genetic gains were observed for traits directly influencing passion fruit yield (Table 3 ). Gains in PROD ranged from 52.1–68.4%, demonstrating the effectiveness of the breeding program, even with a heritability of medium magnitude (0.34). These findings highlight the potential of using mixed models in the breeding of perennial species, enabling substantial gains in traits of agronomic importance. Genotypes 489 and 341 (BC 3 family 293), as well as genotype 306 (BC 3 family 355), stood out for achieving the greatest genetic gains in production, with an estimated new mean of 6.4 kg, representing a relevant advance in the selection of high-yielding plants. The average production of the population in this study was 3.78 kg (Table 3 ). Aguiar et al. ( 2015 ) evaluated 13 passion fruit hybrids that produced between 84.0 and 104.6 kg over two cycles. However, these hybrids resulted from crosses involving already established cultivars, and the adopted spacing was 6.0 m between plants in the row and 4.0 m between rows, corresponding to 417 plants ha − 1 . In contrast, the present experiment used a spacing of 2.0 m between plants and 3.0 m between rows, resulting in 1666 plants ha − 1 . Under this configuration, the orchard would have a production potential of 6.1 t ha − 1 . Despite challenges posed by the virus, which has negatively affected productivity in some regions, this yield can be considered viable, especially when combined with appropriate management practices such as manual pollination, which can enhance both yield and fruit quality. It is important to note that pollination in the BC 3 orchard was carried out exclusively by the local bumblebee population. This may have contributed to the lower average yield, as fewer flowers may have been pollinated and the available pollen may have been insufficient. Additionally, the BC 3 population includes genotypes with characteristics that differ considerably from those of commercial cultivars, which resulted in fruits of inferior quality and lower productivity. These genotypes were retained throughout the selection cycles in order to preserve genetic variability, particularly regarding resistance to CABMV. In a previous study, Vidal et al. ( 2021 ) evaluated the BC 2 population of the UENF passion fruit breeding program and reported an average production of 2.65 kg, which was lower than that observed in this study (Table 3 ), representing a 39.0% increase in one selection cycle. It is expected that continued selection will further increase productivity, reaching more satisfactory levels over time. Table 3 Ranking of the 30 best genotypes, genetic gain, and predicted new mean for production-related traits in BC 3 full-sib families of Passiflora . PROD (kg) NF AFW (g) FW (g) Rank Fam/Gen Gain (%) New mean Fam/Gen Gain (%) New mean Fam/Gen Gain (%) New mean Fam/Gen Gain (%) New mean 1 489/293 68.4 6.4 306/355 36.1 33.8 611/355 68.3 248.9 293/355 78.5 307.1 2 341/293 68.3 6.4 614/501 34.9 33.5 082/355 62.9 240.8 083/355 75.8 302.4 3 306/355 68.2 6.4 341/293 34.4 33.3 351/293 60.0 236.5 461/355 72.6 296.9 4 365/501 67.4 6.3 617/501 33.7 33.2 602/355 57.6 233.1 611/355 70.5 293.4 5 617/501 66.5 6.3 489/293 33.1 33.0 292/355 55.5 229.9 351/293 67.6 288.4 6 510/355 65.6 6.3 446/293 32.7 32.9 508/355 53.7 227.3 515/355 65.5 284.8 7 443/293 64.7 6.2 364/501 32.3 32.8 290/355 52.5 225.4 469/355 63.8 281.8 8 304/355 63.8 6.2 231/501 32.0 32.7 461/355 51.3 223.6 508/355 62.5 279.6 9 308/355 62.9 6.2 288/501 31.7 32.7 460/355 50.3 222.2 181/293 61.3 277.5 10 349/293 62.0 6.1 365/501 31.4 32.6 495/355 49.4 220.9 82/355 60.2 275.6 11 614/501 61.0 6.1 530/501 31.1 32.5 496/355 48.6 219.8 349/293 59.3 274.0 12 261/153 60.2 6.1 510/355 30.8 32.5 348/293 47.8 218.6 162/355 58.4 272.5 13 495/355 59.4 6.0 443/293 30.5 32.4 304/355 47.2 217.6 294/355 57.7 271.3 14 178/293 58.7 6.0 344/293 30.1 32.3 294/355 46.4 216.5 308/355 57.0 270.2 15 469/355 58.0 6.0 060/501 29.8 32.2 469/355 45.8 215.5 080/355 56.5 269.2 16 060/501 57.4 5.9 175/293 29.5 32.1 595/355 45.2 214.7 028/293 56.0 268.4 17 344/293 56.8 5.9 283/501 29.2 32.0 597/355 44.7 213.9 595/355 55.5 267.6 18 467/355 56.3 5.9 433/293 28.9 32.0 147/355 44.2 213.2 456/355 55.1 266.9 19 530/501 55.8 5.9 522/501 28.6 31.9 667/293 43.7 212.5 235/501 54.8 266.3 20 516/355 55.4 5.9 333/293 28.4 31.9 594/355 43.3 211.9 488/293 54.4 265.6 21 511/355 55.0 5.9 173/293 28.1 31.8 697/153 42.9 211.3 307/355 54.0 264.9 22 164/355 54.6 5.8 182/293 27.9 31.7 349/293 42.5 210.7 601/355 53.5 264.2 23 573/153 54.2 5.8 227/501 27.6 31.7 473/293 42.1 210.1 092/355 53.1 263.5 24 606/355 53.9 5.8 178/293 27.4 31.6 083/355 41.7 209.6 596/355 52.8 262.8 25 456/355 53.6 5.8 192/293 27.2 31.6 308/355 41.4 209.0 487/293 52.4 262.2 26 182/293 53.2 5.8 672/293 27.0 31.5 305/355 41.0 208.5 495/355 52.0 261.6 27 579/153 52.9 5.8 349/293 26.8 31.5 088/355 40.7 208.0 669/293 51.7 260.9 28 173/293 52.7 5.8 492/293 26.6 31.4 583/153 40.3 207.5 093/355 51.3 260.3 29 283/501 52.4 5.8 271/501 26.4 31.4 084/355 40.0 207.0 583/153 51.0 259.8 30 461/355 52.1 5.8 429/293 26.2 31.3 291/355 39.7 206.5 292/355 50.7 259.2 Overall mean 3.78 24.81 147.86 172.1 Fam/Gen = family/generation; PROD = production per plant; NF = number of fruits per plant; AFW = average fruit weight; FW = fruit weight. Genetic gains related to the number of fruits were lower compared to production, ranging from 26.2–36.1% relative to the overall mean of 24.81 fruits plant − 1 . The new estimated means varied from 31.3 to 33.8 fruits plant − 1 , indicating that the genotypes in the ranking are capable of producing seven to nine more fruits than the population mean. Genotype 306 (BC 3 family 355) ranked first, with an estimated new mean of 33.8 fruits plant − 1 . In a study by Ferreira et al. ( 2016 ), who evaluated 27 half-sib progenies of passion fruit, the highest genetic gain for NF was 27.0%. The authors observed that 12 progenies (i.e., 44.4% of those evaluated) performed above the mean for this trait, denoting positive values for the additive genetic effect (Table 3 ). Regarding the AFW trait, genetic gains ranged from 39.7–68.3%, with the overall mean being 147.86 g (Table 3 ). This result is quite significant and promising, considering that the population mean was well below the market standard (> 200 g). Based on the ranking, the selected genotypes showed new means ranging from 206.5 g to 248.9 g, all exceeding the commercialization threshold. This indicates that these genotypes, even when cultivated in an orchard affected by the fruit woodiness virus, produced fruits of commercially acceptable weight. In comparison, Gomes et al. ( 2022 ) evaluated a passion fruit population infected by CABMV, in which fruit weights ranged from 85.55 g to 175.31 g—much lower than the values found in this study. Fruit weight, a variable closely related to AFW, showed genetic gains of 50.7–78.5% in relation to the overall mean of 172.05 g (Table 3 ). The top three genotypes were 293, 83, and 461, all from BC 3 family 355, with new means of 307.1 g, 302.4 g, and 296.9 g, respectively. It is worth noting that FW was based on the average weight of five fruits per plant, representing a sample of each plant’s production. Although this approach resulted in greater genetic gains, it would be more advisable to use the total fruit weight per plant, as this would more accurately reflect the population’s reality. The sample's representativeness may be questioned due to the difference observed compared to AFW, since sampling may not fully capture production variability. On the other hand, using samples increases the efficiency of experimentation by reducing time and labor requirements. In addition to gains in production, the BLUPs also indicated potential improvements in fruit quality (Table 4 ). The gains in LD were significant, ranging from 15.2% (genotype in position 30) to 69.3% (genotype in first position). Notably, 28 of the 30 ranked genotypes belong to BC 3 family 355, suggesting that this family tends to produce larger fruits. Furthermore, the variation observed within the ranking highlights the genetic variability within this family. The population’s overall mean was 86.76 mm, while genotype 370 (BC 3 family 501), ranked first, showed an estimated new mean of 146.90 mm. All ranked genotypes displayed new means equal to or greater than 100 mm, surpassing or matching the values of established cultivars such as BRS Gigante Amarelo (98 mm to 103 mm), BRS Sol do Cerrado (89 mm to 96 mm), and BRS Rubi do Cerrado (97 mm to 100 mm) (Jesus et al. 2017). Although gains in TD were also observed, they were less pronounced compared to LD. This difference can be attributed to the variation in heritability estimates for the two traits: LD had a heritability of 0.59, whereas TD showed a considerably lower value of 0.23 (Table 3 ), indicating that LD is more strongly influenced by genotype. The overall population mean for TD was 77.53 mm, and the observed gains ranged from 5.6–6.9%, with estimated new means between 81.90 mm and 82.90 mm. The gain related to PT was close to zero, ranging from 1.7–0.6% (Table 4 ). For this trait, negative gains are desirable, as the goal is to select fruits with thinner peel, which are more commercially advantageous. The genotypes ranked for this trait belong to BC 3 family 17, which exhibited unsatisfactory fruit production and quality characteristics, such as green and flat fruits, low pulp yield, low peel resistance, and reduced total soluble solids content. Given this performance, the selection of genotypes from this family for the PT trait is not recommended. Krause et al. ( 2012 ) reported a gain of 1.73% for the same trait, a value comparable to that observed in this study. Similarly, Grisi et al. ( 2021 ), when evaluating 11 progenies of multispecific hybrids, also found positive gains for PT, ranging from 8.00–1.71%. In their analysis of passion fruit traits, the authors observed a significant correlation of -0.88 between PT and PY and a correlation of 0.64 between PT and FW. This suggests that although direct selection for PT may be inefficient, it is possible to perform indirect selection based on related traits such as PY and FW. The PW trait showed the highest genetic gains, ranging from 72.6–139.1%, as reflected by the large difference between the overall mean (60.0 g) and the new means of the selected genotypes, which ranged from 103.6 g to 143.4 g (Table 4 ). This result reflects the high heritability of this trait (0.81), the highest estimated among all traits evaluated. Heritability represents the ratio between additive genetic variance and phenotypic variance, indicating the reliability with which phenotypic values reflect genetic values, i.e., how well the phenotype represents the genotype. The genotypes with the highest gains for PW were 083 and 293 (BC 3 family 355), which also showed the greatest gains for FW (Table 4 ). This reinforces the potential of these genotypes to contribute to improvements across multiple traits, making their selection a promising strategy for simultaneously enhancing both average fruit weight and fruit weight. Genetic gains for pulp yield (PY) ranged from 17.5–24.3%, with an overall mean of 33.0% (Table 4 ). Genotype 083 (BC 3 family 355) stood out once again with the greatest gain, displaying an estimated new mean of 41.0% PY. On the other hand, genotype 670 (BC 3 family 293) had the smallest gain in the ranking, with a new mean of 38.8%, still meeting the industry minimum standard of 33.0%. Assunção et al. ( 2015 ), using the Mulamba and Mock selection index for passion fruit genotypes, obtained a low genetic gain of 0.72% for PY. The authors suggested that this outcome may result from simultaneous selection for multiple traits, which can balance gains across traits and consequently reduce the gains for some, as individuals selected for the most desirable traits may not exhibit optimal performance in others. Nonetheless, the expectation is that subsequent selection cycles will improve the PY trait as the process becomes more refined. Regarding the TSS variable, genetic gains ranged from 15.2–21.8%, relative to the overall mean of 12.8 ºBrix (Table 4 ). Among the 30 ranked genotypes, 16 (53.0%) exhibited new means equal to or greater than 15 ºBrix. The two most promising genotypes in this ranking were 219 and 371 (BC 3 family 501), both with 15.6 ºBrix. For the industry, particularly the fresh fruit market, a high TSS concentration is a highly desirable trait. According to Nascimento et al. ( 2006 ), the production of juice at 50 ºBrix requires approximately 11 kg of fruits with TSS between 11% and 12% to yield 1 kg of concentrated juice. Therefore, the higher the TSS content, the fewer fruits are needed to achieve the desired concentration. Thus, in order to meet market standards, the TSS variable should be a key criterion when selecting genotypes, ensuring that the fruits produce more concentrated juices and thereby increase production efficiency. Conclusion The results of this study demonstrated strong potential for genetic gains in several traits of agronomic and commercial relevance in passion fruit breeding. Despite variations in heritability among traits such as PROD, NF, and fruit quality, the genetic gains observed were significant, indicating that even traits with moderate heritability can yield meaningful progress. The selected genotypes showed particular promise for AFW, PW, PY, and TSS, with gains exceeding market standards. Genotypes from BC 3 family 355 stood out, with high gains in multiple traits, including fruit weight and pulp yield, highlighting the potential of this family for improving both the quality and productivity in commercial cultivation. Additionally, the REML/BLUP methodology proved effective, generating precise and reliable estimates with high selection accuracy, especially in progeny evaluation. This study enhances understanding of the genetic improvement potential in passion fruit and highlights the importance of proper management, family-based selection, and pollination strategies, such as manual pollination, in the context of viral disease pressure. Declarations Conflict of Interest: The authors declare that they have no conflict of interest. This work was supported by project E-26/010.001454/2019. The author Viana AP received research support from the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro – FAPERJ. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Authors' contributions - A.P.V., L.C.L.C., N.R.C., F.A.S., J.A.V.S.O. conceptualized the study.; L.C.L.C., N.R.C., F.A.S., J.A.V.S.O. performed data collection.; A.P.V., L.C.L.C., N.R.C., F.A.S. methodology definition.; L.C.L.C., N.R.C., A.P.V., R.S.C. performed data analysis and wrote the first draft manuscript, and all authors reviewed the manuscript. References Aguiar RS, Zaccheo PVC, Stenzel NMC, Sera T, Neves CSVJ (2015) Yield and quality of fruits of hybrids of yellow passion fruit in northern Paraná. Rev Bras Frutic 37(1):130-137. https://doi.org/10.1590/0100-2945-012/14 Araya S, Martins AM, Junqueira NTV, Costa AM, Faleiroa FG, Ferreira ME (2017) Microsatellite marker development by partial sequencing of the sour passion fruit genome ( Passiflora edulis Sims). BMC Genomics 18:549. https://doi.org/10.1186/s12864-017-3881-5 Assunção MP, Krause W, Dallacort R, Santos PRJ, Neves LG (2015) Individual selection of plants as yellow passion fruit quality through REML/BLUP. Rev Caatinga 28(2):57-63. Borém A Miranda GV (2013) Melhoramento de Plantas . 6. ed. Viçosa: UFV, 523 p. Cavalcante NR, Viana AP, Almeida Filho JE, Pereira MG, Ambrósio M, Santos EA, Ribeiro RM, Rodrigues DL, Sousa CMB (2019) Novel selection strategy for half-sib families of sour passion fruit Passiflora edulis (Passifloraceae) under recurrent selection. Genet Mol Res 18(2):gmr18305. http://dx.doi.org/10.4238/gmr18305 Cavichioli JC, Correa LS, Narita N, Kasai FS (2011) Ocurrence and severity of mosaic vírus on passion fruit. Rev Bras Frutic 33(spe1):411-414. Coelho CA, Cenci SA, Resende ED (2011) Yields and wastes of yellow passion fruit juice as a result of fruit size and different storage harvesting spots. Rev Bras Prod Agroind 13(1):55-63. Costa RBD, Resende MDVD, Gonçalves PDS, Roa RAR, Feitosa KCDO (2010) Genetic parameters and values prediction for growth and latex production traits in rubber tree progenies. Bragantia 69(1):49-56. https://doi.org/10.1590/S0006-87052010000100007 Cruz CD, Carneiro PCS, Regazzi AJ, (2014) Modelos biométricos aplicados ao melhoramento genético. V2, 3° ed. Viçosa-UFV, 668p. Fajardo TVM, Nickel O (2019) Transmissão de vírus e controle de viroses em plantas. Embrapa Uva e Vinho-Documentos (INFOTECA-E), 25p. Farias Neto JT, Clement CR, Resende MDV (2013) Estimates of genetic parameters and selection gain for fruit production in open-pollinated progenies of peach palm in the State of Pará, Brazil. Bragantia 72(2):122-126. https://doi.org/10.1590/S0006-87052013000200002 Farias Neto JT, Resende MDVde, Oliveira MDSPde, Nogueira OL, Falcão PNB, Santos NSA (2008) Estimates of genetic parameters and selection gains in progenyes of open pollination of açai tree ( Euterpe oleracea ). Rev Bras Frutic 30(4):1051-1056. https://doi.org/10.1590/S0100-29452008000400035 Ferreira RT, Viana AP, Silva FHL, Santos EA, Santos FO (2016) Intrapopulation recurrent selection in sour passion fruit by mixed models. Rev Bras Frutic 38(1):158-166. https://doi.org/10.1590/0100-2945-260/14 Fonseca AFAD, Sediyama T, Cruz CD, Sakiyama NS, Ferrão RG, Ferrão MAG, Bragança SM (2004) Discriminant analysis for the classification and clustering of robusta coffee genotypes. Crop Breed Appl Biotechnol 4(3):285-289. https://doi.org/10.12702/1984-7033.v04n03a04 Freitas JPX, Oliveira EJde, Neto AJC, Santos LR (2011) Evaluation of genetic resources of yellow passion fruit, Pesqui Agropecu Bras 46(9):1013-1020. https://doi.org/10.1590/S0100-204X2011000900007 Gomes FR, Silva DFPD, Rodrigues CDM, Salazar AH, Assunção HF, Cruz SCS (2022) Evaluation of production and fruit quality of a yellow passion fruit cultivar infected with the cowpea aphid-borne mosaic virus. Rev Bras Frutic 44(3):e-259. https://doi.org/10.1590/0100-29452022259 Grisi MCDM, Junqueira NTV, Conceição LDHCS, Faleiro FG, Braga MF, Vilela MS (2021) Genotypic selection of multispecific hybrids obtained through crosses between commercial Passiflora edulis and wild passiflora species. Rev Bras Frutic 43(1):1-15. https://doi.org/10.1590/0100-29452021963 Hafle OM, Ramos JD, Lima LCO, Ferreira EA, Melo PC (2009) Productivity and quality of fruit in yellow passion fruit tree submitted to pruning of productive branches. Rev Bras Frutic 31(3):763-770. https://doi.org/10.1590/S0100-29452009000300020 IBGE. (2024) Instituto Brasileiro de Geografia e Estatística - Banco de dados agregados: Sistema IBGE de Recuperação Automática. Available at https://sidra.ibge.gov.br/Tabela/1613#resultado. Accessed on 26 Jun 2024. INMET (2022) Instituto Nacional de Meteorologia . Série Histórica - Dados Mensais . Available at https://portal.inmet.gov.br/servicos/bdmep-dados-hist%C3%B3ricos. Accessed on 27 Jun 2024. Jung MS, Vieira EA, Brancker A, Nodari RO (2008) Heritability and genetic gain in fruit of sweet passion fruit characteres. Rev Bras Frutic 30(1):209-214. https://doi.org/10.1590/S0100-29452008000100038 Köppen W, Geiger R (1928) Klimate der Erde. Gotha: Verlag Justus Perthes. Krause DP, Fachi LR, Dalbosco EZ, Campos TNV, Freitas AP, Lima KS, Krause W (2021). Estimates of genetic parameters and selection gains in progenies of passion fruit via methodology REML/BLUP. Sci Electron Arch 14(5):42-48. https://doi.org/10.36560/14520211268 Krause W, Souza RSD, Neves LG, Carvalho MLDS, Viana AP, Faleiro FG (2012) Selection gain in the intrapopulation genetic breeding of yellow passion fruit. Pesqu Agropecu Bras 47(1):51-57. https://doi.org/10.1590/S0100-204X2012000100008 Meletti LMM (2011) Avanços na cultura do maracujá no Brasil. Rev Bras Frutic 33(1): 83-91. https://doi.org/10.1590/S0100-29452011000500012 Mesquita AGG, Guimarães CT, Parentoni SN, Paiva E (2005) Recovery of recurrent genotype in maize using ssr marker-assisted backcross. Rev Bras Milh Sorg 4(3):275-285. https://doi.org/10.18512/1980-6477/rbms.v4n03p%p Colariccio A, Garcêz RM, Rodrigues LK, Eiras M, Peruch LAM, Chaves ALR (2018) Doenças causadas por vírus na cultura do maracujazeiro ( Passiflora edulis ). In: Peruchi LAM, Schoreder AL (eds). Maracujazeiro-azedo: polinização, pragas e doenças. Florianópolis: Epagri, 2018. pp171-201. Nascimento AVS, Santana EN, Braz ASK, Alfenas PF, Pio-Ribeiro G, Andrade GP, Murilo Zerbini, F (2006) Cowpea aphid-borne mosaic virus (CABMV) is widespread in passionfruit in Brazil and causes passionfruit woodiness disease. Arch virol 151(9):1797-1809. https://doi.org/10.1007/s00705-006-0755-6 Oliveira EJD, Santos VDS, Lima DSD, Machado MD, Lucena RS, Motta TBN, Castellen MDS (2008) Selection on yellow passion fruit progenies by multivariate índices. Pesqu Agropecu Bras 43(11):1543-1549. https://doi.org/10.1590/S0100-204X2008001100013 Pereira TB, Carvalho JPF, Botelho CE, Resende MDVD, Rezende JCD, Mendes ANG (2013) Selection efficiency of F4 coffee progenies by mixed model methodology (REML/BLUP). Bragantia 72(3):230-236. https://doi.org/10.1590/brag.2013.031 Preisigke SC, Viana AP, Santos EA, Santos PR, Cavalcante NR, Ambrósio M, Freitas JCO, Rodrigues R (2021) Backcrossing in passion fruit: generation advance and selection of genotypes resistant to Cowpea aphid-borne mosaic virus. Genet Mol Res 20(1):GMR18668. https://doi.org/10.4238/gmr18668 Resende MDV (2000) Análise estatística de modelos mistos via REML/BLUP na experimentação em melhoramento de plantas perenes. Colombo: Embrapa Florestas, 101 p. Resende MDV (2002) Genética biométrica e estatística no melhoramento de plantas perenes. Brasília: Embrapa Informação Tecnológica, 975p. Resende MDV (2016) Software Selegen-REML/BLUP: a useful tool for plant breeding. Crop Breed Appl Biotechnol 16(4):330-339. http://dx.doi.org/10.1590/1984-70332016v16n4a49. Rodrigues LK, Chaves ALR, Damatto Junior ER (2016) Epidemiological aspects of the transmission and management of Cowpea aphid-borne mosaic virus in a passion fruit orchard. J Plant Pathol 98(3):531-539. https://dx.doi.org/10.4454/JPP.V98I3.037. Sacoman NN, Viana AP, Carvalho VS, Santos EA, Rodrigues R (2018) Resistance to cowpea aphid-borne mosaic virus in in vitro germinated genotypes of Passiflora setacea . Rev Bras Frutic 40(1):e-607. https://doi.org/10.1590/0100-29452017607 Santos CEM, Bruckner CH, Cruz CD, Siqueira DL, Rosado LDS (2011) Additive and nonadditive genetic components in passion fruit. Pesq Agropec Bras 46(5):482-490. https://doi.org/10.1590/S0100-204X2011000500005 Santos EA, Viana AP, Oliveira Freitas JC, Rodrigues DL, Tavares RF, Paiva CL, Souza MM (2015a) Genotype selection by REML/BLUP methodology in a segregating population from an interspecific Passiflora spp. crossing. Euphytica 204:1–11. https://doi.org/10.1007/s10681-015-1367-6 Santos EA, Viana AP, Oliveira Freitas JC, Lima e Silva FH, Rodrigues R, Eiras M (2015b) Resistance to Cowpea aphid-borne mosaic virus in species and hybrids of Passiflora : advances for the control of the passion fruit woodiness disease in Brazil. Eur J Plant Pathol 143:85–98. https://doi.org/10.1007/s10658-015-0667-y Santos JLV, Resende ED, Cenci SA, Maldonado JFM (2023) Productivity and juice quality of different passion fruit cultivars grown in northwest region of Rio de Janeiro. Rev. Bras. Tecnol. Agroindustr 17(2): 4150-4169. https://doi.org/10.3895/rbta.v17n2.15725 Silva JMda, Aguiar AV, Mori ES, Teixeira de Moraes, M. L. (2011) Genetic variation and expected gain in selection of Pinus caribaea Morelet var. caribaea progenies in Selviria, MS. Sci For 39(90):241-252. Spadotti DMA, Favara GM, Novaes QS, Mello APOA, Freitas DMS, Molina JPE, Rezende JAM (2019) Long-lasting systematic roguing for effective management of CABMV in passionflower orchards through maintenance of separated plants. Plant Pathol 68(7):1259–1267. https://doi.org/10.1111/ppa.13054 . Torres LJC, Blanco-Fuentes RD, Espitia-Camacho MM, Cardona-Ayala C, Aramendiz-Tatis H (2021) Genetic parameters of fruit and seed biometric characteristics in Caesalpinia ebano (Fabaceae). Acta Biol Colomb 26(3):327-334. https://doi.org/10.15446/abc.v26n3.85718 Viana AP, Gonçalves GM (2005) Genética quantitativa aplicada ao melhoramento genético do maracujazeiro. In Faleiro FG, Junqueira NTV, Braga MF (eds) Maracujá: germoplasma e melhoramento genético . Planaltina: Embrapa Cerrados, pp 243-274. Viana AP, Resende MDV (2014) Genética Quantitativa no Melhoramento de Fruteiras. 1 ed. Rio de Janeiro: Editora Interciência, 282p. Vidal RF, Viana AP, Preisigke SC, Cavalcante NR, Gonçalves Júnior DH, Mendes DS (2021). Evaluation of resistance to Cowpea aphid-borne mosaic virus in passion fruit backcrosses for recurrent selection and development of resistant cultivars. Genet Mol Res 20(1):gmr18687. http://dx.doi.org/10.4238/gmr18687 Table 4 Table 4 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table4.docx Cite Share Download PDF Status: Published Journal Publication published 07 Oct, 2025 Read the published version in Euphytica → Version 1 posted Editorial decision: Revision requested 30 Jun, 2025 Reviews received at journal 14 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers invited by journal 29 May, 2025 Editor assigned by journal 21 May, 2025 Submission checks completed at journal 21 May, 2025 First submitted to journal 20 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6710617","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463869449,"identity":"44abc512-d8cf-4d10-bdb1-3b3517ecb69b","order_by":0,"name":"Luis Carlos Loose Coelho","email":"","orcid":"","institution":"State University of Northern Rio de Janeiro (UENF)","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Carlos Loose","lastName":"Coelho","suffix":""},{"id":463869450,"identity":"220614c0-55e4-4580-842b-7913c8166666","order_by":1,"name":"Alexandre Pio Viana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDACZijNDyIqGA6QoEWyAUicIUoLDBgcIFaLOTvvw88FFffkjW8kH3twgOFOPkEtls3sxtIzzhQbbruRlg606ZllA0H3HGZjkOZtS2DcdiPHTPoDw2EDwl44zMb8G6jFfvOM/G8SB4jUwgayJXGDRA4bcVosm9nYrGecSUieceaZmcQBg2eEtZjzH2O+XVCRYNvfnvxM4kDFHSIcxoCITSiXRC2jYBSMglEwCrAAADrJOup4FwPJAAAAAElFTkSuQmCC","orcid":"","institution":"State University of Northern Rio de Janeiro (UENF)","correspondingAuthor":true,"prefix":"","firstName":"Alexandre","middleName":"Pio","lastName":"Viana","suffix":""},{"id":463869451,"identity":"8c71eb3c-c605-46dd-bb7d-db60c1508329","order_by":2,"name":"Natan Ramos Cavalcante","email":"","orcid":"","institution":"State University of Northern Rio de Janeiro (UENF)","correspondingAuthor":false,"prefix":"","firstName":"Natan","middleName":"Ramos","lastName":"Cavalcante","suffix":""},{"id":463869452,"identity":"71077bfb-75c4-4715-a51c-4983e396dae9","order_by":3,"name":"Renato Santa Catarina","email":"","orcid":"","institution":"State University of Northern Rio de Janeiro (UENF)","correspondingAuthor":false,"prefix":"","firstName":"Renato","middleName":"Santa","lastName":"Catarina","suffix":""},{"id":463869453,"identity":"e1096ac3-d5d0-4620-90a6-cffd191d8d3d","order_by":4,"name":"Flávia Alves da Silva","email":"","orcid":"","institution":"State University of Northern Rio de Janeiro (UENF)","correspondingAuthor":false,"prefix":"","firstName":"Flávia","middleName":"Alves da","lastName":"Silva","suffix":""},{"id":463869454,"identity":"e932fe6e-ba32-4dff-8e75-de1773c92d2e","order_by":5,"name":"Julie Anne Vieira Salgado Oliveira","email":"","orcid":"","institution":"State University of Northern Rio de Janeiro (UENF)","correspondingAuthor":false,"prefix":"","firstName":"Julie","middleName":"Anne Vieira Salgado","lastName":"Oliveira","suffix":""}],"badges":[],"createdAt":"2025-05-20 19:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6710617/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6710617/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10681-025-03612-6","type":"published","date":"2025-10-07T15:57:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83770868,"identity":"2f75c9fa-220b-4a6a-b9b4-7315c8db5ddd","added_by":"auto","created_at":"2025-06-02 12:25:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":162385,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the backcrossing process between \u003cem\u003ePassiflora setacea\u003c/em\u003e and \u003cem\u003ePassiflora edulis\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6710617/v1/ed966d7673b9c98dc44f06fd.png"},{"id":93419929,"identity":"284dffe6-da18-4d6b-937f-ea5e80670fdb","added_by":"auto","created_at":"2025-10-13 16:08:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1112050,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6710617/v1/3abe6df2-b070-4e63-aff0-9baadc46855f.pdf"},{"id":83771071,"identity":"0dceac1f-b3b5-47a4-85fc-e7572f46bd6e","added_by":"auto","created_at":"2025-06-02 12:33:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32397,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6710617/v1/8eded0df7c5d3df382f9acd7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genotype selection from the segregating BC3 population of Passiflora spp. for fruit production and quality using the REML/BLUP methodology","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe passion fruit (\u003cem\u003ePassiflora edulis\u003c/em\u003e Sims) is the most commercially important species within the \u003cem\u003ePassifloraceae\u003c/em\u003e family and is widely cultivated in tropical regions due to its edible, medicinal, and ornamental values (Araya et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In Brazil, where its cultivation predominates, the country stands as the world's largest producer, with a total production of 697,859 t in 2022 (IBGE \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The crop has high production potential, reaching up to 30 t per hectare annually (Hafle et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, the national average yield remains low, at approximately 15.303 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Studies such as those by Santos et al. (2021) have shown that certain cultivars can exceed this average, with yields ranging between 17.16 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 26.98 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne of the main challenges to increasing passion fruit yield is the incidence of diseases, particularly fruit woodiness, caused by cowpea aphid-borne mosaic virus (CABMV). This virus significantly impairs fruit quality, rendering them unsuitable for the consumer market (Cavichioli et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Severe mosaic disease on leaves presents symptoms such as chlorotic spots, roughness, blistering, and leaf distortion, while the virus-induced woodiness and deformation of the fruit lead to reduced pulp cavity size and seed number (Colariccio et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Transmission occurs through aphids in non-persistent interactions, which makes chemical control ineffective (Fajardo and Nickel \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the absence of a specific control method, integrated pest management strategies are used, such as planting healthy seedlings grown in protected environments, performing careful pruning and thinning operations (Rodrigues et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), systematically eliminating symptomatic plants, and scheduling planting during periods of lower aphid incidence (Spadotti et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its significant economic importance, there are no CABMV-resistant cultivars available on the market, emphasizing the need for breeding programs aimed at developing resistant varieties. Wild species of \u003cem\u003ePassiflora\u003c/em\u003e, such as \u003cem\u003eP. setacea\u003c/em\u003e, are resistant to viruses and are promising sources of resistance genes for commercial passion fruit breeding (Sacoman et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Research by Santos et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e) demonstrated that backcrossed interspecific hybrids (\u003cem\u003eP. edulis\u003c/em\u003e \u0026times; \u003cem\u003eP. setacea\u003c/em\u003e) exhibit resistance to CABMV and possess agronomic traits comparable to those of the commercial parent. The backcrossing strategy enables the transfer of resistance genes while preserving desirable agronomic characteristics (Mesquita et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The Breeding Program at UENF has adopted this approach to develop superior cultivars (Santos et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSelecting genotypes from allogamous populations presents additional challenges, as environmental factors heavily influence phenotypic expression (Viana and Resende \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Traits of agronomic interest typically involve complex gene-environment interactions, necessitating the application of concepts like heritability, which quantifies the proportion of phenotypic variation attributable to genetic factors (Bor\u0026eacute;m and Miranda \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to Jung et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), increased environmental variability complicates effective selection, requiring the use of tools to improve selective accuracy. Techniques such as REML/BLUP are essential for estimating variance components and predicting genotypic values in breeding programs (Viana and Resende \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These tools enable the identification of promising genotypes based on productivity and disease resistance, thereby optimizing selection outcomes. Additionally, multivariate analyses integrate multiple phenotypic and genotypic variables, helping classify genotypes according to their genetic diversity (Fonseca et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFruit quality is a critical factor in passion fruit breeding, particularly in the context of resistance to diseases such as fruit woodiness. Developing cultivars that combine both CABMV resistance and high fruit quality is essential to meet market demands and ensure the sustainability and profitability of the crop. Thus, the objective of this study was to evaluate the BC\u003csub\u003e3\u003c/sub\u003e population of \u003cem\u003ePassiflora\u003c/em\u003e genotypes for resistance to cowpea aphid-borne mosaic virus, estimate the genetic parameters associated with key traits, and select superior genotypes for both fruit production and quality.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOrigin of the BC\u003csub\u003e3\u003c/sub\u003e population\u003c/h2\u003e \u003cp\u003eThe breeding program was carried out in several stages, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStarting with the initial cross, interspecific hybrids were selected based on prior studies. These hybrids were then successively backcrossed with \u003cem\u003eP. edulis\u003c/em\u003e, resulting in the backcross generations (BC₁, BC₂, and BC₃), with selection at each stage aimed at improving desirable agronomic traits.\u003c/p\u003e \u003cp\u003eThe interspecific hybrids were obtained by Santos et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e) through a biparental cross between \u003cem\u003eP. edulis\u003c/em\u003e and \u003cem\u003eP. setacea\u003c/em\u003e, and were evaluated in a competition assay. Resistant F\u003csub\u003e1\u003c/sub\u003e plants were selected and backcrossed with the \u003cem\u003eP. edulis\u003c/em\u003e parent, generating BC\u003csub\u003e1\u003c/sub\u003e, the first backcross generation. The BC\u003csub\u003e1\u003c/sub\u003e individuals were evaluated by Preisigke et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The genotypes selected in BC\u003csub\u003e1\u003c/sub\u003e were then backcrossed again with \u003cem\u003eP. edulis\u003c/em\u003e, resulting in the BC\u003csub\u003e2\u003c/sub\u003e generation. BC\u003csub\u003e2\u003c/sub\u003e individuals were evaluated by Vidal et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who selected the best genotypes for the third backcrossing cycle, forming the BC\u003csub\u003e3\u003c/sub\u003e generation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBC generation\u003c/h3\u003e\n\u003cp\u003eTo obtain BC\u003csub\u003e3\u003c/sub\u003e, crosses were made between BC\u003csub\u003e2\u003c/sub\u003e and \u003cem\u003eP. edulis\u003c/em\u003e. Initially, the flower buds of the female parents were emasculated before anthesis, and the flowers were protected with paper bags. The same procedure was applied to the male parents, with the exception of emasculation. Pollination was performed starting at 12:00, at the onset of anthesis of the parents' flowers. The anthers of the male parents were rubbed onto the stigma of the female flowers using tweezers. After pollination, the flowers were labeled and protected for up to 24 h. The fruits resulting from the crosses were kept protected with nylon netting until ripening. The seeds were then extracted, washed in water, dried, and stored in paper bags inside plastic bags, which were kept in a refrigerator at approximately 10 \u0026ordm;C in the Plant Breeding Laboratory (LMGV) at UENF.\u003c/p\u003e\n\u003ch3\u003ePlanting and experimental setup\u003c/h3\u003e\n\u003cp\u003eThe seeds of the BC\u003csub\u003e3\u003c/sub\u003e population were sown in the greenhouse of the UENF Research Support Unit (UAP) using 200-cell Styrofoam trays filled with commercial substrate. Once the seedlings developed two pairs of definitive leaves, they were individually transferred to polyethylene plastic bags containing commercial substrate. The plants were subsequently transplanted to the experimental area at the Ant\u0026ocirc;nio Sarlo Agricultural School, located in the northern region of Rio de Janeiro state (21\u0026ordm; 74\u0026rsquo; S, 41\u0026ordm; 33\u0026rsquo; W, 11.2 m above sea level), which has a typical AW climate\u0026mdash;tropical with a dry winter (K\u0026ouml;ppen and Geiger \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1928\u003c/span\u003e)\u0026mdash;and clayey-sandy soil. The region has an average annual temperature of 23.9\u0026deg;C and an average annual rainfall of 1112 mm, with precipitation concentrated from October to March (INMET \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA randomized block design with six replicates was used, consisting of five full-sib families (unbalanced for plants within families) and their parents\u0026mdash;\u003cem\u003eP. setacea\u003c/em\u003e (resistant parent) and the UENF Rio Dourado cultivar of \u003cem\u003eP. edulis\u003c/em\u003e (recurrent and susceptible parent). Initially, each full-sib family comprised 22 genotypes, and the parents included four genotypes, for a total of 708 plants in the experiment. However, this number was adjusted due to seedling mortality.\u003c/p\u003e \u003cp\u003eThe plants were trained using a vertical trellis system, with 2.5 m high wooden posts spaced 4.0 m apart and number 12 wire positioned 1.80 m above the ground. The spacing between rows was 3.0 m, and the spacing between plants within rows was 2.0 m. Cultivation practices such as weed clearing, pruning, and disbudding were performed in accordance with the crop\u0026rsquo;s requirements (Manica 1981). For monthly fertilization, 80 g of potassium chloride and 100 g of urea were applied. A drip irrigation system was used to meet the plants' water requirements.\u003c/p\u003e\n\u003ch3\u003eEvaluated traits\u003c/h3\u003e\n\u003cp\u003eThe fruits were collected weekly from each individual plant, allowing for the assessment of variables related to both fruit production and quality. To evaluate production, the following traits were considered: fruit production per plant (PROD, kg), number of fruits per plant (NF), determined by counting the fruits produced by each plant, average fruit weight (AFW, g), calculated as the ratio between the total production of the plant and the number of fruits, and fruit weight (FW, g).\u003c/p\u003e \u003cp\u003eFruit quality was assessed using the following variables: pulp weight (PW, g); pulp yield (PY, %), calculated as the ratio of pulp weight to fruit weight, multiplied by 100; longitudinal diameter (LD, mm); transverse diameter (TD, mm); peel thickness (PT, mm); and soluble solids content (SSC, \u0026deg;Brix), measured using an analog refractometer with a scale ranging from 0 to 32 \u0026deg;Brix. For these traits, five fruits from each plant were analyzed, and the final value was obtained as the arithmetic mean. All weight measurements were recorded using an analytical balance, while length and diameter measurements were taken with a digital caliper.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAccording to the model described in Viana and Resende (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the analysis of deviance (ANADEV) was determined by the following equation:\u003c/p\u003e \u003cp\u003eD\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2ln(L) (1)\u003c/p\u003e \u003cp\u003eln(L)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1/2ln|X\u0026acute;V\u0026thinsp;\u0026minus;\u0026thinsp;1X|\u0026minus;1/2ln|V|\u0026minus;1/2(y\u0026thinsp;\u0026minus;\u0026thinsp;Xm)`V\u0026thinsp;\u0026minus;\u0026thinsp;1(y\u0026thinsp;\u0026minus;\u0026thinsp;Xm) (2)\u003c/p\u003e \u003cp\u003ewhere ln(L) is the maximum point of the restricted maximum likelihood logarithm (REML) function; y is the vector of the variable analyzed; m is the vector of the effects of the observations, assumed fixed; X is the incidence matrix of the fixed effects, and V is the variance-covariance matrix of y.\u003c/p\u003e \u003cp\u003eThe likelihood ratio test (LRT) was applied to test the significance of the effects, according to the equation:\u003c/p\u003e \u003cp\u003eLRT: |\u0026minus;2ln(Lwe)\u0026thinsp;+\u0026thinsp;2ln(Lfm) (3)\u003c/p\u003e \u003cp\u003ewhere Lwe is the maximum point of the maximum likelihood function for the reduced model (without effects) and Lfm is the maximum point of the maximum likelihood function for the full model.\u003c/p\u003e \u003cp\u003eThe data related to fruit production and quality were analyzed using the Selegen REML/BLUP program, with statistical model 147 (Resende \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The analysis followed the statistical model:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u0026#119910; = \u0026#119883;\u0026#119903; + \u0026#119885;\u0026#119892; + \u0026#119882;\u0026#119901; + \u0026#119890; (4)\u003c/h2\u003e \u003cp\u003ewhere y\u0026thinsp;=\u0026thinsp;vector of data; r\u0026thinsp;=\u0026thinsp;vector of replicate effects (assumed to be fixed) added to the overall mean; g\u0026thinsp;=\u0026thinsp;vector of individual genotypic effects (assumed to be random); p\u0026thinsp;=\u0026thinsp;vector of plot effects (random); e\u0026thinsp;=\u0026thinsp;vector of errors or (random) residuals; and \u003cem\u003eX\u003c/em\u003e, \u003cem\u003eZ\u003c/em\u003e, and \u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;incidence matrices for the aforementioned effects.\u003c/p\u003e \u003cp\u003eThe following variance components were estimated: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e = genotypic variance between full-sib progenies, equivalent to 1/2 of the additive genetic variance plus 1/4 of the dominance genetic variance, ignoring epistasis; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e = individual phenotypic variance. The following genetic parameters were also estimated: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{a}^{2}\\)\u003c/span\u003e\u003c/span\u003e = individual narrow-sense heritability, obtained by ignoring the fraction (1/4) of the dominance genetic variance; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{mp}^{2}\\)\u003c/span\u003e\u003c/span\u003e = heritability of the progeny mean, assuming complete survival; Acprog = accuracy of progeny selection, assuming complete survival; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{ad}^{2}\\)\u003c/span\u003e\u003c/span\u003e = additive heritability within a plot, obtained by ignoring the fraction (1/4) of the dominance genetic variance and the overall mean of the experiment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEstimate of genetic parameters\u003c/h2\u003e \u003cp\u003eIn the likelihood ratio test (LRT), the genotypes showed a significant difference at 1% for all traits evaluated, except for number of fruits, which was significant at 5% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This result indicates that the effect associated with the genotype is statistically different from zero, confirming a significant relationship between the genotype and the variables analyzed. Thus, the findings suggest that the selection of genotypes has a high potential for success, reinforcing the feasibility of obtaining superior individuals through breeding.\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of deviance for ten traits related to fruit production and quality evaluated in the BC\u003csub\u003e3\u003c/sub\u003e passion fruit population.\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=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePROD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAFW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eFW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eLD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3125.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.86*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1432.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12.33**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3802.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e29.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4137.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e45.81**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2665.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e22.07**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3119.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1420.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3773.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4091.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2642.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ePY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLRT (X\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2338.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e19.06**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3453.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e42.87**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e646.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14.4**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1064.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e16.42**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2323.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e15.22**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2319.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3410.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e631.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1048.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2307.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* and ** significant at the 5% (3.84) and 1% (6.63) probability levels by the Chi-square test, respectively. Dev: deviance. LRT(X\u0026sup2;): likelihood ratio test. PROD\u0026thinsp;=\u0026thinsp;fruit production; NF\u0026thinsp;=\u0026thinsp;number of fruits; AFW\u0026thinsp;=\u0026thinsp;average fruit weight; FW\u0026thinsp;=\u0026thinsp;fruit weight; LD\u0026thinsp;=\u0026thinsp;longitudinal diameter; TD\u0026thinsp;=\u0026thinsp;transverse diameter; PT\u0026thinsp;=\u0026thinsp;peel thickness; PW\u0026thinsp;=\u0026thinsp;pulp weight; PY\u0026thinsp;=\u0026thinsp;pulp yield; TSS\u0026thinsp;=\u0026thinsp;total soluble solids.\u003c/p\u003e \u003cp\u003ePhenotypic variance ranged from 1.71 to 3400.48, demonstrating that each trait responds differently to environmental influence (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The traits with the highest phenotypic variance were fruit weight (3400.48), average fruit weight (1692.20), and pulp weight (803.99), respectively. The first two traits, although similar, were obtained differently. Fruit weight (FW) was determined from the average individual weight of five fruits per plant, while average fruit weight (AFW) was calculated as the ratio between total plant production and total number of fruits produced (AFW\u0026thinsp;=\u0026thinsp;PROD/NF). Pulp weight showed a strong positive correlation with fruit weight (r\u0026thinsp;=\u0026thinsp;0.94), indicating that heavier fruits tend to produce a greater amount of pulp, as reported by Morgado et al. (2010).\u003c/p\u003e \u003cp\u003eThe highest estimate of genotypic variance was observed for fruit weight (1371.93), while the lowest was recorded for peel thickness (0.35). Genotypic variance is a statistical parameter that quantifies the genetic variability present in a population for a given trait, disregarding environmental effects (Cruz et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The results of this study indicate that the BC\u003csub\u003e3\u003c/sub\u003e population exhibited low genetic variability for the traits peel thickness (PT), soluble solids content (TSS), and production (PROD). Consequently, genetic gain for these traits may be slower in future selection cycles. Yield is one of the most relevant parameters in passion fruit breeding and is widely used as a criterion for selecting parents in this population. In this context, it is possible to infer that, with the advancement of generations, there has been a reduction in the genetic variability of this trait. On the other hand, the high genotypic variance observed for fruit weight (FW) suggests that there is still considerable genetic variation in this population, which may favor the selection of superior individuals for this trait.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of genetic parameters in BC\u003csub\u003e3\u003c/sub\u003e genotypes, obtained by the REML procedure for ten passion fruit traits.\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\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePROD (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAFW (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFW (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLD (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTD (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePT (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFW (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePY (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTSS (\u0026ordm;Brix)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e634.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1371.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e327.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e296.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1692.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3400.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e135.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e803.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e61.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{a}^{2}\\)\u003c/span\u003e\u003c/span\u003e\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\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{mp}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcprog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{ad}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\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\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e \u003c/span\u003e= genotypic variance; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{p}^{2}\\)\u003c/span\u003e\u003c/span\u003e = individual phenotypic variance; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{a}^{2}\\)\u003c/span\u003e\u003c/span\u003e = individual narrow-sense heritability; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{mp}^{2}\\)\u003c/span\u003e\u003c/span\u003e = heritability of the progeny mean; Acprog = accuracy of progeny selection; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{ad}^{2}\\)\u003c/span\u003e\u003c/span\u003e= additive heritability within plot; PROD = production; NF = number of fruits; AFW = average fruit weight; FW = fruit weight; LD = longitudinal diameter; TD = transverse diameter; PT = peel thickness; PW = pulp weight; PY = pulp yield; TSS = total soluble solids.\u003c/p\u003e \u003cp\u003eThe estimates of narrow-sense individual heritability ranged from 0.17 to 0.81 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the classification proposed by Resende (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), heritability can be categorized into three levels: low (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{a}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 0.15), medium (0.15 \u0026lt; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{a}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 0.50), and high (0.15 \u0026lt; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{a}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 0.50). None of the evaluated traits exhibited low-magnitude heritability. The traits NF, TD, PROD, PT, and PY displayed medium-magnitude heritability, while LD, TSS, AFW, FW, and PW showed high-magnitude heritability. These results demonstrate significant potential for the selection of most traits of interest in passion fruit breeding. The estimated heritability of 0.34 for PROD in this study was lower than that reported by Vidal et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who obtained 0.56, but higher than the 0.21 reported by Cavalcante et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although classified as medium-magnitude heritability, it is still possible to achieve significant gains in production through the application of efficient selection methods, aiming to maximize genetic progress in the short term (Freitas et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumber of fruits (NF) and average fruit weight (AFW) are traits directly related to passion fruit production and are essential targets in crop breeding programs. In this study, the heritability estimates for these traits were 0.17 and 0.75, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Ferreira et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported heritability values of 0.65 for both traits. Compared to the present results, the heritability estimated for NF was lower, while for AFW it was higher. The authors emphasize that experimental factors such as the number of replicates, the number of plants per plot, and the application of uniform cultivation practices can directly influence heritability estimates. This highlights the importance of conducting specific studies for each context to ensure greater precision in selecting promising genotypes for passion fruit breeding.\u003c/p\u003e \u003cp\u003eThe traits transverse diameter (TD) and longitudinal diameter (LD) showed heritabilities of 0.59 and 0.23, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Assun\u0026ccedil;\u0026atilde;o et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), using the REML methodology, reported heritability estimates of 0.65 for TD and 0.37 for LD. Although TD and LD are correlated traits, in both studies the heritability estimate for TD was higher than for LD, suggesting greater genetic stability for this trait. Assun\u0026ccedil;\u0026atilde;o et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) also reported the same heritability estimate for PT (0.41) as observed in the present study. According to Coelho et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), variations in passion fruit peel thickness can occur within the same species, within the same orchard, and between different growing regions, and these variations can be exploited by selecting superior genotypes. Melleti (2011) emphasizes that cultivars intended for agroindustry should possess higher pulp yield, higher soluble solids content, more intensely colored pulp, and thinner peel, characteristics desirable for both the industry and the consumer market. This reinforces the importance of selecting genotypes with these attributes to add value to the final product.\u003c/p\u003e \u003cp\u003ePulp weight was the trait with the highest heritability among those analyzed, indicating that selection for this variable can be highly efficient (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Pulp yield (PY) showed a heritability of 0.44, a value lower than that reported by Freitas et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), but higher than that observed by Oliveira et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Fruits with a greater amount of pulp indicate higher productive efficiency, reflecting desirable genetic characteristics that directly impact both quality and marketability of the final product.\u003c/p\u003e \u003cp\u003eThe heritability of the progeny mean was higher than the narrow-sense individual heritability for all evaluated traits, ranging from 0.89 to 0.99 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This finding suggests greater variability between families than within them, making family-based selection more effective. A similar pattern was observed in other studies with passion fruit (Santos et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Assun\u0026ccedil;\u0026atilde;o et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cavalcante et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vidal et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and in various other crops such as a\u0026ccedil;a\u0026iacute; (Farias Neto et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), peach palm (Farias Neto et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u003cem\u003eCaesalpinia ebano\u003c/em\u003e (Torres et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and pine (Silva et al. 2011). According to Costa et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), high progeny mean heritability (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{mp}^{2}\\)\u003c/span\u003e\u003c/span\u003e) indicates that selection based on family performance increases accuracy in identifying superior genotypes for breeding.\u003c/p\u003e \u003cp\u003eThe traits with the highest \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{mp}^{2}\\)\u003c/span\u003e\u003c/span\u003e were FW and PW, both with values of 0.99, demonstrating great potential for genetic gain (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PROD also showed high \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{mp}^{2}\\)\u003c/span\u003e\u003c/span\u003e (0.95), indicating strong selection potential. Given its importance for productive efficiency, it is advantageous to prioritize family-based selection to maximize production, rather than focusing on individual plants. However, this strategy may not be ideal for traits such as resistance to CABMV. The UENF passion fruit breeding program aims to increase productivity while also selecting for virus resistance, which requires a strategy that simultaneously considers both traits.\u003c/p\u003e \u003cp\u003eKrause et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) analyzed several passion fruit traits using the REML/BLUP methodology and reported \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{mp}^{2}\\)\u003c/span\u003e\u003c/span\u003e values ranging from 0.13 to 0.71, considerably lower than those found in this study. The highest heritability values in their study were observed for fruit ratio, length, and shape. The authors suggested that, in view of the high heritability of these traits, selection using progeny information may be more effective, as such traits are directly related to agro-industrial value and market acceptance.\u003c/p\u003e \u003cp\u003eThe accuracy of progeny selection was high (\u0026gt;\u0026thinsp;0.94) for all traits (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating excellent experimental precision. According to Pereira et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), this metric reflects the precision of selection, representing the correlation between predicted and true genetic values. The precision of these estimates can be further improved through more robust experimentation, including an increased number of measurements per plant, as recommended by Resende (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). These results confirm that the present experiment was conducted with adequate rigor, generating reliable outcomes. This is particularly important for breeders, as it allows greater confidence when discarding unselected progenies and advancing to subsequent selection cycles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic gains from selection\u003c/h2\u003e \u003cp\u003eSignificant genetic gains were observed for traits directly influencing passion fruit yield (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Gains in PROD ranged from 52.1\u0026ndash;68.4%, demonstrating the effectiveness of the breeding program, even with a heritability of medium magnitude (0.34). These findings highlight the potential of using mixed models in the breeding of perennial species, enabling substantial gains in traits of agronomic importance. Genotypes 489 and 341 (BC\u003csub\u003e3\u003c/sub\u003e family 293), as well as genotype 306 (BC\u003csub\u003e3\u003c/sub\u003e family 355), stood out for achieving the greatest genetic gains in production, with an estimated new mean of 6.4 kg, representing a relevant advance in the selection of high-yielding plants.\u003c/p\u003e \u003cp\u003eThe average production of the population in this study was 3.78 kg (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Aguiar et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) evaluated 13 passion fruit hybrids that produced between 84.0 and 104.6 kg over two cycles. However, these hybrids resulted from crosses involving already established cultivars, and the adopted spacing was 6.0 m between plants in the row and 4.0 m between rows, corresponding to 417 plants ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In contrast, the present experiment used a spacing of 2.0 m between plants and 3.0 m between rows, resulting in 1666 plants ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Under this configuration, the orchard would have a production potential of 6.1 t ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Despite challenges posed by the virus, which has negatively affected productivity in some regions, this yield can be considered viable, especially when combined with appropriate management practices such as manual pollination, which can enhance both yield and fruit quality.\u003c/p\u003e \u003cp\u003eIt is important to note that pollination in the BC\u003csub\u003e3\u003c/sub\u003e orchard was carried out exclusively by the local bumblebee population. This may have contributed to the lower average yield, as fewer flowers may have been pollinated and the available pollen may have been insufficient. Additionally, the BC\u003csub\u003e3\u003c/sub\u003e population includes genotypes with characteristics that differ considerably from those of commercial cultivars, which resulted in fruits of inferior quality and lower productivity. These genotypes were retained throughout the selection cycles in order to preserve genetic variability, particularly regarding resistance to CABMV. In a previous study, Vidal et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) evaluated the BC\u003csub\u003e2\u003c/sub\u003e population of the UENF passion fruit breeding program and reported an average production of 2.65 kg, which was lower than that observed in this study (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), representing a 39.0% increase in one selection cycle. It is expected that continued selection will further increase productivity, reaching more satisfactory levels over time.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRanking of the 30 best genotypes, genetic gain, and predicted new mean for production-related traits in BC\u003csub\u003e3\u003c/sub\u003e full-sib families of \u003cem\u003ePassiflora\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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=\"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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePROD (kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eNF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eAFW (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eFW (g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFam/Gen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGain\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFam/Gen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGain\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFam/Gen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGain\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eFam/Gen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eGain\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNew\u003c/p\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e489/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e306/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e611/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e248.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e293/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e307.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e614/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e082/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e240.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e083/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e302.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e341/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e351/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e236.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e461/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e296.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e617/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e602/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e233.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e611/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e293.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e617/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e489/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e292/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e229.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e351/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e288.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e510/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e446/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e508/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e227.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e515/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e284.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e443/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e364/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e290/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e225.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e469/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e281.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e231/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e461/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e223.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e508/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e279.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e288/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e460/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e222.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e181/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e61.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e277.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e349/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e365/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e495/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e220.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e82/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e275.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e614/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e530/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e496/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e219.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e349/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e59.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e274.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e261/153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e510/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e348/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e218.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e162/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e58.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e272.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e495/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e443/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e304/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e217.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e294/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e57.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e271.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e344/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e294/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e216.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e308/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e57.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e270.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e469/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e060/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e469/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e215.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e080/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e269.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e060/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e595/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e214.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e028/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e268.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e344/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e597/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e213.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e595/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e267.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e467/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e433/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e147/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e213.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e456/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e266.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e530/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e522/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e667/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e212.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e235/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e266.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e516/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e333/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e594/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e211.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e488/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e265.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e511/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e173/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e697/153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e211.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e307/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e264.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e349/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e210.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e601/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e264.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e573/153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e473/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e210.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e092/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e263.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e606/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e178/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e083/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e209.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e596/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e52.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e262.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e308/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e209.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e487/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e52.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e262.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e672/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e305/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e208.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e495/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e261.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579/153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e349/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e088/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e208.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e669/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e260.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e492/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e583/153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e207.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e093/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e260.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e271/501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e084/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e207.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e583/153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e259.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e461/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e429/293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e291/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e206.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e292/355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e259.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOverall mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.78\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 \u003cp\u003e24.81\u003c/p\u003e \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 \u003cp\u003e147.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e172.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFam/Gen\u0026thinsp;=\u0026thinsp;family/generation; PROD\u0026thinsp;=\u0026thinsp;production per plant; NF\u0026thinsp;=\u0026thinsp;number of fruits per plant; AFW\u0026thinsp;=\u0026thinsp;average fruit weight; FW\u0026thinsp;=\u0026thinsp;fruit weight.\u003c/p\u003e \u003cp\u003eGenetic gains related to the number of fruits were lower compared to production, ranging from 26.2\u0026ndash;36.1% relative to the overall mean of 24.81 fruits plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The new estimated means varied from 31.3 to 33.8 fruits plant\u0026thinsp;\u0026minus;\u0026thinsp;\u003csup\u003e1\u003c/sup\u003e, indicating that the genotypes in the ranking are capable of producing seven to nine more fruits than the population mean. Genotype 306 (BC\u003csub\u003e3\u003c/sub\u003e family 355) ranked first, with an estimated new mean of 33.8 fruits plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In a study by Ferreira et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who evaluated 27 half-sib progenies of passion fruit, the highest genetic gain for NF was 27.0%. The authors observed that 12 progenies (i.e., 44.4% of those evaluated) performed above the mean for this trait, denoting positive values for the additive genetic effect (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the AFW trait, genetic gains ranged from 39.7\u0026ndash;68.3%, with the overall mean being 147.86 g (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This result is quite significant and promising, considering that the population mean was well below the market standard (\u0026gt;\u0026thinsp;200 g). Based on the ranking, the selected genotypes showed new means ranging from 206.5 g to 248.9 g, all exceeding the commercialization threshold. This indicates that these genotypes, even when cultivated in an orchard affected by the fruit woodiness virus, produced fruits of commercially acceptable weight. In comparison, Gomes et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) evaluated a passion fruit population infected by CABMV, in which fruit weights ranged from 85.55 g to 175.31 g\u0026mdash;much lower than the values found in this study.\u003c/p\u003e \u003cp\u003eFruit weight, a variable closely related to AFW, showed genetic gains of 50.7\u0026ndash;78.5% in relation to the overall mean of 172.05 g (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The top three genotypes were 293, 83, and 461, all from BC\u003csub\u003e3\u003c/sub\u003e family 355, with new means of 307.1 g, 302.4 g, and 296.9 g, respectively. It is worth noting that FW was based on the average weight of five fruits per plant, representing a sample of each plant\u0026rsquo;s production. Although this approach resulted in greater genetic gains, it would be more advisable to use the total fruit weight per plant, as this would more accurately reflect the population\u0026rsquo;s reality. The sample's representativeness may be questioned due to the difference observed compared to AFW, since sampling may not fully capture production variability. On the other hand, using samples increases the efficiency of experimentation by reducing time and labor requirements.\u003c/p\u003e \u003cp\u003eIn addition to gains in production, the BLUPs also indicated potential improvements in fruit quality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The gains in LD were significant, ranging from 15.2% (genotype in position 30) to 69.3% (genotype in first position). Notably, 28 of the 30 ranked genotypes belong to BC\u003csub\u003e3\u003c/sub\u003e family 355, suggesting that this family tends to produce larger fruits. Furthermore, the variation observed within the ranking highlights the genetic variability within this family. The population\u0026rsquo;s overall mean was 86.76 mm, while genotype 370 (BC\u003csub\u003e3\u003c/sub\u003e family 501), ranked first, showed an estimated new mean of 146.90 mm. All ranked genotypes displayed new means equal to or greater than 100 mm, surpassing or matching the values of established cultivars such as BRS Gigante Amarelo (98 mm to 103 mm), BRS Sol do Cerrado (89 mm to 96 mm), and BRS Rubi do Cerrado (97 mm to 100 mm) (Jesus et al. 2017).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003eAlthough gains in TD were also observed, they were less pronounced compared to LD. This difference can be attributed to the variation in heritability estimates for the two traits: LD had a heritability of 0.59, whereas TD showed a considerably lower value of 0.23 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating that LD is more strongly influenced by genotype. The overall population mean for TD was 77.53 mm, and the observed gains ranged from 5.6\u0026ndash;6.9%, with estimated new means between 81.90 mm and 82.90 mm.\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe gain related to PT was close to zero, ranging from 1.7\u0026ndash;0.6% (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). For this trait, negative gains are desirable, as the goal is to select fruits with thinner peel, which are more commercially advantageous. The genotypes ranked for this trait belong to BC\u003csub\u003e3\u003c/sub\u003e family 17, which exhibited unsatisfactory fruit production and quality characteristics, such as green and flat fruits, low pulp yield, low peel resistance, and reduced total soluble solids content. Given this performance, the selection of genotypes from this family for the PT trait is not recommended. Krause et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported a gain of 1.73% for the same trait, a value comparable to that observed in this study. Similarly, Grisi et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), when evaluating 11 progenies of multispecific hybrids, also found positive gains for PT, ranging from 8.00\u0026ndash;1.71%. In their analysis of passion fruit traits, the authors observed a significant correlation of -0.88 between PT and PY and a correlation of 0.64 between PT and FW. This suggests that although direct selection for PT may be inefficient, it is possible to perform indirect selection based on related traits such as PY and FW.\u003c/p\u003e\n\u003cp\u003eThe PW trait showed the highest genetic gains, ranging from 72.6\u0026ndash;139.1%, as reflected by the large difference between the overall mean (60.0 g) and the new means of the selected genotypes, which ranged from 103.6 g to 143.4 g (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This result reflects the high heritability of this trait (0.81), the highest estimated among all traits evaluated. Heritability represents the ratio between additive genetic variance and phenotypic variance, indicating the reliability with which phenotypic values reflect genetic values, i.e., how well the phenotype represents the genotype. The genotypes with the highest gains for PW were 083 and 293 (BC\u003csub\u003e3\u003c/sub\u003e family 355), which also showed the greatest gains for FW (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This reinforces the potential of these genotypes to contribute to improvements across multiple traits, making their selection a promising strategy for simultaneously enhancing both average fruit weight and fruit weight.\u003c/p\u003e\n\u003cp\u003eGenetic gains for pulp yield (PY) ranged from 17.5\u0026ndash;24.3%, with an overall mean of 33.0% (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Genotype 083 (BC\u003csub\u003e3\u003c/sub\u003e family 355) stood out once again with the greatest gain, displaying an estimated new mean of 41.0% PY. On the other hand, genotype 670 (BC\u003csub\u003e3\u003c/sub\u003e family 293) had the smallest gain in the ranking, with a new mean of 38.8%, still meeting the industry minimum standard of 33.0%. Assun\u0026ccedil;\u0026atilde;o et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), using the Mulamba and Mock selection index for passion fruit genotypes, obtained a low genetic gain of 0.72% for PY. The authors suggested that this outcome may result from simultaneous selection for multiple traits, which can balance gains across traits and consequently reduce the gains for some, as individuals selected for the most desirable traits may not exhibit optimal performance in others. Nonetheless, the expectation is that subsequent selection cycles will improve the PY trait as the process becomes more refined.\u003c/p\u003e\n\u003cp\u003eRegarding the TSS variable, genetic gains ranged from 15.2\u0026ndash;21.8%, relative to the overall mean of 12.8 \u0026ordm;Brix (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Among the 30 ranked genotypes, 16 (53.0%) exhibited new means equal to or greater than 15 \u0026ordm;Brix. The two most promising genotypes in this ranking were 219 and 371 (BC\u003csub\u003e3\u003c/sub\u003e family 501), both with 15.6 \u0026ordm;Brix. For the industry, particularly the fresh fruit market, a high TSS concentration is a highly desirable trait. According to Nascimento et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), the production of juice at 50 \u0026ordm;Brix requires approximately 11 kg of fruits with TSS between 11% and 12% to yield 1 kg of concentrated juice. Therefore, the higher the TSS content, the fewer fruits are needed to achieve the desired concentration. Thus, in order to meet market standards, the TSS variable should be a key criterion when selecting genotypes, ensuring that the fruits produce more concentrated juices and thereby increase production efficiency.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study demonstrated strong potential for genetic gains in several traits of agronomic and commercial relevance in passion fruit breeding. Despite variations in heritability among traits such as PROD, NF, and fruit quality, the genetic gains observed were significant, indicating that even traits with moderate heritability can yield meaningful progress. The selected genotypes showed particular promise for AFW, PW, PY, and TSS, with gains exceeding market standards. Genotypes from BC\u003csub\u003e3\u003c/sub\u003e family 355 stood out, with high gains in multiple traits, including fruit weight and pulp yield, highlighting the potential of this family for improving both the quality and productivity in commercial cultivation. Additionally, the REML/BLUP methodology proved effective, generating precise and reliable estimates with high selection accuracy, especially in progeny evaluation. This study enhances understanding of the genetic improvement potential in passion fruit and highlights the importance of proper management, family-based selection, and pollination strategies, such as manual pollination, in the context of viral disease pressure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\u003cp\u003eThis work was supported by project E-26/010.001454/2019. The author Viana AP received research\u003c/p\u003e\n\u003cp\u003esupport from the Funda\u0026ccedil;\u0026atilde;o Carlos Chagas Filho de Amparo \u0026agrave; Pesquisa do Estado do Rio de Janeiro \u0026ndash; FAPERJ.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e - A.P.V., L.C.L.C., N.R.C., F.A.S., J.A.V.S.O. conceptualized the study.; L.C.L.C., N.R.C., F.A.S., J.A.V.S.O. performed data collection.; A.P.V., L.C.L.C., N.R.C., F.A.S. methodology definition.; L.C.L.C., N.R.C., A.P.V., R.S.C. performed data analysis and wrote the first draft manuscript, and all authors reviewed the manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAguiar RS, Zaccheo PVC, Stenzel NMC, Sera T, Neves CSVJ (2015) Yield and quality of fruits of hybrids of yellow passion fruit in northern Paran\u0026aacute;. Rev Bras Frutic 37(1):130-137. https://doi.org/10.1590/0100-2945-012/14 \u003c/li\u003e\n\u003cli\u003eAraya S, Martins AM, Junqueira NTV, Costa AM, Faleiroa FG, Ferreira ME (2017) Microsatellite marker development by partial sequencing of the sour passion fruit genome (\u003cem\u003ePassiflora edulis\u003c/em\u003e Sims). BMC Genomics 18:549. https://doi.org/10.1186/s12864-017-3881-5 \u003c/li\u003e\n\u003cli\u003eAssun\u0026ccedil;\u0026atilde;o MP, Krause W, Dallacort R, Santos PRJ, Neves LG (2015) Individual selection of plants as yellow passion fruit quality through REML/BLUP. Rev Caatinga 28(2):57-63.\u003c/li\u003e\n\u003cli\u003eBor\u0026eacute;m A Miranda GV (2013) \u003cem\u003eMelhoramento de Plantas\u003c/em\u003e. 6. ed. Vi\u0026ccedil;osa: UFV, 523 p.\u003c/li\u003e\n\u003cli\u003eCavalcante NR, Viana AP, Almeida Filho JE, Pereira MG, Ambr\u0026oacute;sio M, Santos EA, Ribeiro RM, Rodrigues DL, Sousa CMB (2019) Novel selection strategy for half-sib families of sour passion fruit \u003cem\u003ePassiflora edulis\u003c/em\u003e (Passifloraceae) under recurrent selection. Genet Mol Res 18(2):gmr18305. http://dx.doi.org/10.4238/gmr18305 \u003c/li\u003e\n\u003cli\u003eCavichioli JC, Correa LS, Narita N, Kasai FS (2011) Ocurrence and severity of mosaic v\u0026iacute;rus on passion fruit. Rev Bras Frutic 33(spe1):411-414.\u003c/li\u003e\n\u003cli\u003eCoelho CA, Cenci SA, Resende ED (2011) Yields and wastes of yellow passion fruit juice as a result of fruit size and different storage harvesting spots. Rev Bras Prod Agroind 13(1):55-63.\u003c/li\u003e\n\u003cli\u003eCosta RBD, Resende MDVD, Gon\u0026ccedil;alves PDS, Roa RAR, Feitosa KCDO (2010) Genetic parameters and values prediction for growth and latex production traits in rubber tree progenies. Bragantia 69(1):49-56. https://doi.org/10.1590/S0006-87052010000100007 \u003c/li\u003e\n\u003cli\u003eCruz CD, Carneiro PCS, Regazzi AJ, (2014) Modelos biom\u0026eacute;tricos aplicados ao melhoramento gen\u0026eacute;tico. V2, 3\u0026deg; ed. Vi\u0026ccedil;osa-UFV, 668p.\u003c/li\u003e\n\u003cli\u003eFajardo TVM, Nickel O (2019) Transmiss\u0026atilde;o de v\u0026iacute;rus e controle de viroses em plantas. Embrapa Uva e Vinho-Documentos (INFOTECA-E), 25p.\u003c/li\u003e\n\u003cli\u003eFarias Neto JT, Clement CR, Resende MDV (2013) Estimates of genetic parameters and selection gain for fruit production in open-pollinated progenies of peach palm in the State of Par\u0026aacute;, Brazil. Bragantia 72(2):122-126. https://doi.org/10.1590/S0006-87052013000200002 \u003c/li\u003e\n\u003cli\u003eFarias Neto JT, Resende MDVde, Oliveira MDSPde, Nogueira OL, Falc\u0026atilde;o PNB, Santos NSA (2008) Estimates of genetic parameters and selection gains in progenyes of open pollination of a\u0026ccedil;ai tree (\u003cem\u003eEuterpe oleracea\u003c/em\u003e). Rev Bras Frutic 30(4):1051-1056. https://doi.org/10.1590/S0100-29452008000400035 \u003c/li\u003e\n\u003cli\u003eFerreira RT, Viana AP, Silva FHL, Santos EA, Santos FO (2016) Intrapopulation recurrent selection in sour passion fruit by mixed models. Rev Bras Frutic 38(1):158-166. https://doi.org/10.1590/0100-2945-260/14 \u003c/li\u003e\n\u003cli\u003eFonseca AFAD, Sediyama T, Cruz CD, Sakiyama NS, Ferr\u0026atilde;o RG, Ferr\u0026atilde;o MAG, Bragan\u0026ccedil;a SM (2004) Discriminant analysis for the classification and clustering of robusta coffee genotypes. Crop Breed Appl Biotechnol 4(3):285-289. https://doi.org/10.12702/1984-7033.v04n03a04 \u003c/li\u003e\n\u003cli\u003eFreitas JPX, Oliveira EJde, Neto AJC, Santos LR (2011) Evaluation of genetic resources of yellow passion fruit, Pesqui Agropecu Bras 46(9):1013-1020. https://doi.org/10.1590/S0100-204X2011000900007 \u003c/li\u003e\n\u003cli\u003eGomes FR, Silva DFPD, Rodrigues CDM, Salazar AH, Assun\u0026ccedil;\u0026atilde;o HF, Cruz SCS (2022) Evaluation of production and fruit quality of a yellow passion fruit cultivar infected with the cowpea aphid-borne mosaic virus. Rev Bras Frutic 44(3):e-259. https://doi.org/10.1590/0100-29452022259 \u003c/li\u003e\n\u003cli\u003eGrisi MCDM, Junqueira NTV, Concei\u0026ccedil;\u0026atilde;o LDHCS, Faleiro FG, Braga MF, Vilela MS (2021) Genotypic selection of multispecific hybrids obtained through crosses between commercial \u003cem\u003ePassiflora edulis \u003c/em\u003eand wild \u003cem\u003epassiflora \u003c/em\u003especies. Rev Bras Frutic 43(1):1-15. https://doi.org/10.1590/0100-29452021963 \u003c/li\u003e\n\u003cli\u003eHafle OM, Ramos JD, Lima LCO, Ferreira EA, Melo PC (2009) Productivity and quality of fruit in yellow passion fruit tree submitted to pruning of productive branches. Rev Bras Frutic 31(3):763-770. https://doi.org/10.1590/S0100-29452009000300020 \u003c/li\u003e\n\u003cli\u003eIBGE. (2024) Instituto Brasileiro de Geografia e Estat\u0026iacute;stica - Banco de dados agregados: Sistema IBGE de Recupera\u0026ccedil;\u0026atilde;o Autom\u0026aacute;tica. Available at https://sidra.ibge.gov.br/Tabela/1613#resultado. Accessed on 26 Jun 2024.\u003c/li\u003e\n\u003cli\u003eINMET (2022) Instituto Nacional de Meteorologia\u003cem\u003e. S\u0026eacute;rie Hist\u0026oacute;rica - Dados Mensais\u003c/em\u003e. Available at https://portal.inmet.gov.br/servicos/bdmep-dados-hist%C3%B3ricos. Accessed on 27 Jun 2024.\u003c/li\u003e\n\u003cli\u003eJung MS, Vieira EA, Brancker A, Nodari RO (2008) Heritability and genetic gain in fruit of sweet passion fruit characteres. Rev Bras Frutic 30(1):209-214. https://doi.org/10.1590/S0100-29452008000100038 \u003c/li\u003e\n\u003cli\u003eK\u0026ouml;ppen W, Geiger R (1928) Klimate der Erde. Gotha: Verlag Justus Perthes.\u003c/li\u003e\n\u003cli\u003eKrause DP, Fachi LR, Dalbosco EZ, Campos TNV, Freitas AP, Lima KS, Krause W (2021). Estimates of genetic parameters and selection gains in progenies of passion fruit via methodology REML/BLUP. Sci Electron Arch 14(5):42-48. https://doi.org/10.36560/14520211268 \u003c/li\u003e\n\u003cli\u003eKrause W, Souza RSD, Neves LG, Carvalho MLDS, Viana AP, Faleiro FG (2012) Selection gain in the intrapopulation genetic breeding of yellow passion fruit. Pesqu Agropecu Bras 47(1):51-57. https://doi.org/10.1590/S0100-204X2012000100008 \u003c/li\u003e\n\u003cli\u003eMeletti LMM (2011) Avan\u0026ccedil;os na cultura do maracuj\u0026aacute; no Brasil. Rev Bras Frutic 33(1): 83-91. https://doi.org/10.1590/S0100-29452011000500012 \u003c/li\u003e\n\u003cli\u003eMesquita AGG, Guimar\u0026atilde;es CT, Parentoni SN, Paiva E (2005) Recovery of recurrent genotype in maize using ssr marker-assisted backcross. Rev Bras Milh Sorg 4(3):275-285. https://doi.org/10.18512/1980-6477/rbms.v4n03p%p \u003c/li\u003e\n\u003cli\u003eColariccio A, Garc\u0026ecirc;z RM, Rodrigues LK, Eiras M, Peruch LAM, Chaves ALR (2018) Doen\u0026ccedil;as causadas por v\u0026iacute;rus na cultura do maracujazeiro (\u003cem\u003ePassiflora edulis\u003c/em\u003e). In: Peruchi LAM, Schoreder AL (eds). Maracujazeiro-azedo: poliniza\u0026ccedil;\u0026atilde;o, pragas e doen\u0026ccedil;as. Florian\u0026oacute;polis: Epagri, 2018. pp171-201.\u003c/li\u003e\n\u003cli\u003eNascimento AVS, Santana EN, Braz ASK, Alfenas PF, Pio-Ribeiro G, Andrade GP, Murilo Zerbini, F (2006) Cowpea aphid-borne mosaic virus (CABMV) is widespread in passionfruit in Brazil and causes passionfruit woodiness disease. Arch virol 151(9):1797-1809. https://doi.org/10.1007/s00705-006-0755-6 \u003c/li\u003e\n\u003cli\u003eOliveira EJD, Santos VDS, Lima DSD, Machado MD, Lucena RS, Motta TBN, Castellen MDS (2008) Selection on yellow passion fruit progenies by multivariate \u0026iacute;ndices. Pesqu Agropecu Bras 43(11):1543-1549. https://doi.org/10.1590/S0100-204X2008001100013 \u003c/li\u003e\n\u003cli\u003ePereira TB, Carvalho JPF, Botelho CE, Resende MDVD, Rezende JCD, Mendes ANG (2013) Selection efficiency of F4 coffee progenies by mixed model methodology (REML/BLUP). Bragantia 72(3):230-236. https://doi.org/10.1590/brag.2013.031 \u003c/li\u003e\n\u003cli\u003ePreisigke SC, Viana AP, Santos EA, Santos PR, Cavalcante NR, Ambr\u0026oacute;sio M, Freitas JCO, Rodrigues R (2021) Backcrossing in passion fruit: generation advance and selection of genotypes resistant to Cowpea aphid-borne mosaic virus. Genet Mol Res 20(1):GMR18668. https://doi.org/10.4238/gmr18668 \u003c/li\u003e\n\u003cli\u003eResende MDV (2000) An\u0026aacute;lise estat\u0026iacute;stica de modelos mistos via REML/BLUP na experimenta\u0026ccedil;\u0026atilde;o em melhoramento de plantas perenes. Colombo: Embrapa Florestas, 101 p.\u003c/li\u003e\n\u003cli\u003eResende MDV (2002) Gen\u0026eacute;tica biom\u0026eacute;trica e estat\u0026iacute;stica no melhoramento de plantas perenes. Bras\u0026iacute;lia: Embrapa Informa\u0026ccedil;\u0026atilde;o Tecnol\u0026oacute;gica, 975p.\u003c/li\u003e\n\u003cli\u003eResende MDV (2016) Software Selegen-REML/BLUP: a useful tool for plant breeding. Crop Breed Appl Biotechnol 16(4):330-339. http://dx.doi.org/10.1590/1984-70332016v16n4a49.\u003c/li\u003e\n\u003cli\u003eRodrigues LK, Chaves ALR, Damatto Junior ER (2016) Epidemiological aspects of the transmission and management of Cowpea aphid-borne mosaic virus in a passion fruit orchard. J Plant Pathol 98(3):531-539. https://dx.doi.org/10.4454/JPP.V98I3.037. \u003c/li\u003e\n\u003cli\u003eSacoman NN, Viana AP, Carvalho VS, Santos EA, Rodrigues R (2018) Resistance to cowpea aphid-borne mosaic virus in in vitro germinated genotypes of \u003cem\u003ePassiflora setacea\u003c/em\u003e. Rev Bras Frutic 40(1):e-607. https://doi.org/10.1590/0100-29452017607 \u003c/li\u003e\n\u003cli\u003eSantos CEM, Bruckner CH, Cruz CD, Siqueira DL, Rosado LDS (2011) Additive and nonadditive genetic components in passion fruit. Pesq Agropec Bras 46(5):482-490. https://doi.org/10.1590/S0100-204X2011000500005 \u003c/li\u003e\n\u003cli\u003eSantos EA, Viana AP, Oliveira Freitas JC, Rodrigues DL, Tavares RF, Paiva CL, Souza MM (2015a) Genotype selection by REML/BLUP methodology in a segregating population from an interspecific \u003cem\u003ePassiflora \u003c/em\u003espp. crossing. Euphytica 204:1\u0026ndash;11. https://doi.org/10.1007/s10681-015-1367-6 \u003c/li\u003e\n\u003cli\u003eSantos EA, Viana AP, Oliveira Freitas JC, Lima e Silva FH, Rodrigues R, Eiras M (2015b) Resistance to Cowpea aphid-borne mosaic virus in species and hybrids of \u003cem\u003ePassiflora\u003c/em\u003e: advances for the control of the passion fruit woodiness disease in Brazil. Eur J Plant Pathol 143:85\u0026ndash;98. https://doi.org/10.1007/s10658-015-0667-y \u003c/li\u003e\n\u003cli\u003eSantos JLV, Resende ED, Cenci SA, Maldonado JFM (2023) Productivity and juice quality of different passion fruit cultivars grown in northwest region of Rio de Janeiro. Rev. Bras. Tecnol. Agroindustr 17(2): 4150-4169. https://doi.org/10.3895/rbta.v17n2.15725 \u003c/li\u003e\n\u003cli\u003eSilva JMda, Aguiar AV, Mori ES, Teixeira de Moraes, M. L. (2011) Genetic variation and expected gain in selection of Pinus caribaea Morelet var. caribaea progenies in Selviria, MS. Sci For 39(90):241-252.\u003c/li\u003e\n\u003cli\u003eSpadotti DMA, Favara GM, Novaes QS, Mello APOA, Freitas DMS, Molina JPE, Rezende JAM (2019) Long-lasting systematic roguing for effective management of CABMV in passionflower orchards through maintenance of separated plants. Plant Pathol 68(7):1259\u0026ndash;1267. https://doi.org/10.1111/ppa.13054 . \u003c/li\u003e\n\u003cli\u003eTorres LJC, Blanco-Fuentes RD, Espitia-Camacho MM, Cardona-Ayala C, Aramendiz-Tatis H (2021) Genetic parameters of fruit and seed biometric characteristics in Caesalpinia ebano (Fabaceae). Acta Biol Colomb 26(3):327-334. https://doi.org/10.15446/abc.v26n3.85718 \u003c/li\u003e\n\u003cli\u003eViana AP, Gon\u0026ccedil;alves GM (2005) Gen\u0026eacute;tica quantitativa aplicada ao melhoramento gen\u0026eacute;tico do maracujazeiro. In Faleiro FG, Junqueira NTV, Braga MF (eds) \u003cem\u003eMaracuj\u0026aacute;: germoplasma e melhoramento gen\u0026eacute;tico\u003c/em\u003e. Planaltina: Embrapa Cerrados, pp 243-274.\u003c/li\u003e\n\u003cli\u003eViana AP, Resende MDV (2014) Gen\u0026eacute;tica Quantitativa no Melhoramento de Fruteiras. 1 ed. Rio de Janeiro: Editora Interci\u0026ecirc;ncia, 282p.\u003c/li\u003e\n\u003cli\u003eVidal RF, Viana AP, Preisigke SC, Cavalcante NR, Gon\u0026ccedil;alves J\u0026uacute;nior DH, Mendes DS (2021). Evaluation of resistance to Cowpea aphid-borne mosaic virus in passion fruit backcrosses for recurrent selection and development of resistant cultivars. Genet Mol Res 20(1):gmr18687. http://dx.doi.org/10.4238/gmr18687 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 4","content":"\u003cp\u003eTable 4 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"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":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Passion fruit, Interspecific crosses, Backcrossing, Genetic parameters, Genetic gain","lastPublishedDoi":"10.21203/rs.3.rs-6710617/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6710617/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePassion fruit is important due to its high yield and economic relevance, but its production is limited by the fruit woodiness disease, caused by the CABMV virus. The objective of this study was to evaluate the production and quality of fruits of BC\u003csub\u003e3\u003c/sub\u003e passion fruit genotypes using the REML/BLUP methodology. The experiment was conducted in randomized blocks, with six replicates and five full-sib families, in addition to the parents \u003cem\u003eP. setacea\u003c/em\u003e and UENF Rio Dourado (\u003cem\u003eP. edulis\u003c/em\u003e). The following traits were evaluated: fruit production per plant, number of fruits, average fruit weight, fruit weight (FW), pulp weight (PW), pulp yield (PY), longitudinal diameter, transverse diameter, peel thickness, and soluble solids content. Analysis of deviance, genetic parameter estimation, and genotype selection were performed, revealing significant differences between genotypes. Heritability ranged from 0.17 to 0.81, with emphasis on FW and PY, which showed high selection potential. Progeny analysis indicated greater efficiency in selection by families, with heritability of 0.99 for FW and PW. Selection accuracy was high (\u0026gt;\u0026thinsp;0.94), ensuring reliable results. Genotypes 489 and 341 (BC\u003csub\u003e3\u003c/sub\u003e family 293) showed gains of 68.4% and 68.3% in production, respectively. Selection of genotypes 083 and 293 (BC\u003csub\u003e3\u003c/sub\u003e family 355) resulted in improvements in fruit quality. The results show significant genetic gains, highlighting the potential of mixed models and interspecific crosses in passion fruit breeding.\u003c/p\u003e","manuscriptTitle":"Genotype selection from the segregating BC3 population of Passiflora spp. for fruit production and quality using the REML/BLUP methodology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 12:25:24","doi":"10.21203/rs.3.rs-6710617/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-30T04:49:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-14T07:05:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43027157980006102558218022542055691536","date":"2025-06-03T15:56:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-30T00:30:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-22T02:31:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-22T02:30:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Euphytica","date":"2025-05-20T19:47:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f37e8ae8-2001-4afc-b4a1-3c7a42596720","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:06:27+00:00","versionOfRecord":{"articleIdentity":"rs-6710617","link":"https://doi.org/10.1007/s10681-025-03612-6","journal":{"identity":"euphytica","isVorOnly":false,"title":"Euphytica"},"publishedOn":"2025-10-07 15:57:06","publishedOnDateReadable":"October 7th, 2025"},"versionCreatedAt":"2025-06-02 12:25:24","video":"","vorDoi":"10.1007/s10681-025-03612-6","vorDoiUrl":"https://doi.org/10.1007/s10681-025-03612-6","workflowStages":[]},"version":"v1","identity":"rs-6710617","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6710617","identity":"rs-6710617","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}