Pure line selection for improved yield and early maturation in heterogeneous yellow flaxseed variety

Pure line selection for improved yield and early maturation in heterogeneous yellow flaxseed variety | 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 Pure line selection for improved yield and early maturation in heterogeneous yellow flaxseed variety Ana Caroline Basniak Konkol, Ana Carolina da Costa Lara Fioreze, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4831213/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Functional food markets have increased flaxseed ( Linum usitatissimum L.) demand, along with the need for competitive varieties. Flaxseed cultivated in Brazil results from natural hybridizations and mixtures of foreign genotypes. Given the unexplored genetic variability in heterogeneous Brazilian varieties, classical breeding methods like pure line selection can be effectively applied, especially when involving local producers in the breeding process. Therefore, the present study aimed to estimate the efficiency of participatory selection of superior yellow flaxseed lines from a heterogeneous variety through genetic gains obtained via the best linear unbiased predictor (BLUP). Individual plants were selected in a heterogeneous Brazilian crop in 2017, and the resulting lines were evaluated in 2018, 2019, and 2020 (across two environments). We evaluated the following traits: days to maturity (DM), number of capsules per plant (NCP), yield per area (GY), and yield per plant (GYP). Phenotypic data were used to estimate variance components via REML and genotypic values via BLUP. The efficiency of participatory plant breeding was measured by the genetic gain from selection (GS). The results reveal that the pure line selection method in a heterogeneous flaxseed variety was efficient, achieving gains of up to 5.37% for DM, 31% for NCP, 44.5% for GY, and 49.52% for GYP. Linum usistatissimum L pure line selection genetic gains BLUP Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The cultivation of flaxseed has been expanding globally (FAOSTAT 2024) in response to the growing market demand for functional foods, primarily due to the demand for edible oil sources rich in omega-3 (Cui et al. 2022 ). Flaxseed is a functional food because of its bioactive components, such as alpha-linolenic acid, lignans, and digestible fibers (Nowak and Jeziorek 2023 ). Reports on the functional foods market project an estimated growth of 8.44% from 2024 to 2031 (Verified Market Research 2024 ). This demand has been further driven by the COVID-19 pandemic, as consumers seek foods that promote health and boost immunity (Farzana et al. 2022 ). Functional foods like flaxseed are recognized for their health benefits due to the presence of vitamins, minerals, fibers, prebiotics, and probiotics (Nowak and Jeziorek 2023 ), further reinforcing their importance in the current market. In addition to the nutritional value of flaxseed, the economic exploitation of the plant also comes from the stem fiber used in the textile industry, oil extraction, the chemical and pharmaceutical industries, and flaxseed by-products in animal feed (Cui et al. 2022 ). This diversification of uses has contributed to market growth (Zhao et al., 2020 ), as reflected in the expansion of planted areas since 2008 (FAOSTAT 2024). This growth has been facilitated by increasing demand and advances in genetic breeding programs, which seek competitive productivity and quality in the products generated by the species (Gao 2020 ). Nevertheless, grain productivity remains a limiting factor for breeding programs, as does genetic diversity, since cultivated flaxseed shows less diversity when compared to its wild ancestors (Smykal et al. 2011 ). In Brazil, the progress of flaxseed cultivation was significant between the 1930s and 1970s due to the boost from the textile industry (Floss 1983a ). During this period, seeds from foreign cultivars were introduced, and the varieties most adapted to the southern region of the country were selected (Leal 1967 ; Floss 1983b ). However, there was a decline in flaxseed production with the rise of petroleum derivatives and synthetic fibers in the industry. Therefore, farmers began cultivating wheat and barley (Tomasini 1980 ). The cultivation of flaxseed continued in some regions of Brazil, but less significantly. Recently, flaxseed cultivation has been revitalized. The crop reveals potential for adaptation to edaphoclimatic conditions, especially in the south of Brazil (Bosco et al. 2020 ), and its ability to be integrated into existing farming systems for crop rotation and diversification has contributed to this resurgence. Despite the promising potential of flaxseed, Brazil encounters challenges in the availability of competitive varieties, stemming from insufficient Brazilian genetic studies on the crop for over four decades. However, this gap also presents an opportunity. It is possible to select pure lines considering that several Brazilian flaxseed producers use heterogeneous varieties. These varieties are derived from mixtures of populations multiplied over the harvests. This multiplication process promotes the expansion of natural genetic variability, caused by spontaneous mutations, occasional seed mixtures, and small rates of crossbreeding between plants. As a result, heterogeneous varieties are considered sources of genetic variability and contain alleles lost in breeding programs. An opportunity for selection is provided because these varieties have not been subjected to strong selection pressure. The pure line selection method was proposed by Louis de Vilmorin in 1856 (Allard 1999 ). The method assumes that the offspring of the best individuals selected from a population will be superior to those obtained from a random sample of the same population (Breseghello and Coelho 2013 ). This breeding approach is simple and effective for traits strongly influenced by the environment. It involves testing the offspring of selected individual plants in field trials across multiple years and diverse environments. Superior lines were obtained through the pure line selection method recently in wheat (Agorastos and Goulas, 2005 ), rice (Roy et al. 2017 ), lentils (Ninou et al. 2019 ), crambe (Lara-Fioreze et al. 2013 ), and soybeans (Priolli et al. 2013 ; Amaral et al. 2019 ). It is possible to measure the efficiency of a plant breeding program in severa ways (Ceccarelli 2015 ). The adoption level of a newly developed variety serves as a measure. The probability of adoption increases when the producer participates in the development of new technologies (Adesina and Baidu-Forson 1995 ; Sall et al. 2000 ), establishing a participatory selection and evaluation system, as carried out with flaxseed in countries like Ethiopia and India (Abebe et al. 2022 ; Wossen et al. 2016 ). In Brazil, the use of participatory breeding on flaxseed remains unknown. Mathematically, superior varieties potential and a breeding program performance can be measured through estimates of selection gains or responses to selection (Ceccarelli 2015 ; Walsh and Lynch 2018 ; Cobb et al. 2019 ). Genetic gain refers to the improvement of the genetic mean values in a population, or the phenotypic mean values resulting from selection within a population over several generations (Hazel and Lush 1942 ). The increased performance in response to selection can be achieved through an increment of selection differential or selection intensity, among other means (Xu et al. 2017 ). In the present study, we aimed to estimate the efficiency of participatory selection of superior yellow flaxseed lines from a heterogeneous variety through genetic gains obtained via the best linear unbiased predictor (BLUP). Materials and methods Field experiments The present study was initiated in 2017 in Zortéa municipality, Santa Catarina, Brazil. Our collaboration with the Agricultural Research and Extension Company of Santa Catarina (EPAGRI) commenced with the selection of an area where yellow flaxseed was being cultivated by a local farmer, which exhibited significant genetic variability. This area spanned 10,000 square meters. This particular area was selected based on its heterogeneity, evident from the observed phenotypic variations among the plants. Additionally, the crop originated from a manual mixture of seeds of unknown origin, which had been multiplied for several generations by the owner of the area. From this point on, we defined an ideotype of the expected flaxseed plant with the participation of farmers from the region, extension professionals, and researchers from EPAGRI and the Federal University of Santa Catarina (UFSC). We stipulated some premises such as high productivity and early maturation. After establishing the plant ideotype, we divided the area into 10 homogeneous plots and selected approximately 10 plants according to previously agreed standards. The collected samples were individually evaluated for the number of capsules per plant (NCP) and grain yield per plant (GYP). Accordingly, we established a ranking and selected plants with higher potential to continue the process as lines. To evaluate the performance of the 73 selected lines, in June 2018, a field experiment was implemented at the Agricultural Experimental Area of the Federal University of Santa Catarina, in Curitibanos (CBS), Santa Catarina, Brazil (latitude 27°16' S and longitude 50°30' W). We conducted the field trial using a randomized complete block design with three replications. The sowing was made manually in rows of 2 meters in length with a spacing of 0.34 meters, at a density of 148 plants m − 2 . Base and topdressing fertilization were performed according to crop recommendations, and weed control was carried out manually by weeding. We conducted evaluations of cycle and productive components to select lines with superior performance, according to the premises established previously. The evaluations involved counting the number of days to maturity (DM), the number of capsules per plant (NCP), weighing the grain yield per plant (GYP - g), and grain yield per area (GY - kg ha − 1 ). Based on these evaluations, 39 lines were selected for field evaluation in 2019 in Curitibanos municipality (CBS). The design adopted was randomized complete blocks with three replications, and the useful plot consisted of a row of 1 meter with a spacing of 0.34 meters (density of 294 plants m − 2 ). Soil preparation and cultivation practices followed the same procedures as previously described, and the same characteristics were evaluated. In 2020, 20 lines out of the 39 evaluated in the previous year progressed. These lines were subjected to field experiments in a randomized complete block design, with four replications in two locations: in the municipalities of Curitibanos (CBS) and Campos Novos (CNV). Soil preparation and cultivation practices remained the same as previously described, and the same characteristics were evaluated. All the steps conducted in the present study are detailed in Fig. 1 . Statistical analysis All statistical procedures were performed in the R Studio software system, version 4.2.2, using the package metan (Olivoto and Lúcio 2020 ) and the functions gamem() and gamem_met(). Mixed-effect model Individual analyses were performed for data obtained in 2018, 2019, and 2020, and a joint analysis for the data obtained in both environments in 2020. A mixed-effect model was considered for individual and joint analysis. For individual analysis, the model used was: $$\:\varvec{y}\:=\:\varvec{X}\varvec{b}\:+\:\varvec{Z}\varvec{g}\:+\:\varvec{e}$$ where \(\:\varvec{y}\) is the vector of phenotypic data; \(\:\varvec{b}\) is vector of unknown and unobservable fixed effects of blocks, which includes all repetitions; \(\:\varvec{g}\) is a vector of unknown and unobservable random effects of genotype \(\:[\) 𝑔 ∼ 𝑁(0, \(\:{\sigma\:}_{g}^{2}\) )], where \(\:{\sigma\:}_{g}^{2}\) is the genotypic variance; \(\:e\) is a vector of random errors [ \(\:\varvec{e}\) ∼ 𝑁(0, \(\:{\sigma\:}_{e}^{2}\) )], where \(\:{\sigma\:}_{e}^{2}\) is the residual variance; the letters \(\:\varvec{X}\) and \(\:\varvec{Z}\) mean the incidence matrices for the described effects. The model used in the joint analysis was: 𝑦 = 𝑋𝑟 + 𝑍𝑔 + 𝑊𝑔𝑒 + 𝑒 where \(\:\varvec{y}\) is the vector of phenotypic data; \(\:\varvec{b}\) is a vector of unknown and unobservable fixed effects of blocks, which includes all repetitions from each location; \(\:\varvec{g}\) is a vector of unknown and unobservable random effects of genotype \(\:\left[\varvec{g}\sim\:N\left(0,\:{\sigma\:}_{g}^{2}\right)\right]\) , where \(\:{\sigma\:}_{g}^{2}\) is the genotypic variance; \(\:\varvec{g}\varvec{e}\) is the vector of random effects of the G \(\:\times\:\) E interaction \(\:\left[\varvec{g}\varvec{e}\sim\:N\left(0,\:{\sigma\:}_{ge}^{2}\right)\right]\) , where \(\:{\sigma\:}_{ge}^{2}\) is the variance of the G \(\:\times\:\) E interaction; \(\:\varvec{e}\) is a vector of random errors \(\:\left[\varvec{e}\sim\:N\left(0,\:{\sigma\:}_{e}^{2}\right)\right]\) , where \(\:{\sigma\:}_{e}^{2}\) is the residual variance; the letters \(\:\varvec{X}\) , \(\:\varvec{Z}\) , and \(\:\varvec{W}\) mean the incidence matrices for the described effects. Variance components and genetic parameters To verify significant differences between lines, the data for each environment were subjected to a likelihood ratio test (LRT). Considering the mixed-effect model, we estimated variance components through the restricted maximum likelihood (REML) and the genotypic values through the best linear unbiased prediction (BLUP) methods. For the calculation of mean-based heritability ( \(\:{h}^{2}\) ), the following estimator was used: $$\:{h}^{2}=\:\frac{{\sigma\:}_{g}^{2}}{{\sigma\:}_{g}^{2}+\:\frac{{\sigma\:}_{ge}^{2}}{e}+\frac{{\sigma\:}_{e}^{2}}{\left(eb\right)}}$$ Where \(\:{\sigma\:}_{g}^{2}\) is the genotypic variance; \(\:{\sigma\:}_{ge}^{2}\) is the genotype by environment interaction variance; \(\:{\sigma\:}_{e}^{2}\) is the environment variance; e and b are the number of environments and blocks, respectively. Selective accuracy ( \(\:h\) ) was estimated by: $$\:h=\:\sqrt{{h}^{2}}$$ Genetic gains from selection The efficiency of the participatory plant breeding was evaluated by the genetic gain from selection (GS), calculated from the BLUP averages of the lines at five selection intensities (10, 20, 30, 40, and 50%). We estimate the percentage gain from selection (GS %) considering the following formula: $$\:GS\:\left(\%\right)=\:\frac{(\stackrel{-}{{BLUP}_{s}}-\:\underset{\_}{\stackrel{-}{{BLUP}_{O}}})\times\:100}{\stackrel{-}{y}}$$ Where \(\:\stackrel{-}{{BLUP}_{s}}\) is the BLUP average of the selected genotypes at five selection intensities (10, 20, 30, 40, and 50%), \(\:\stackrel{-}{{BLUP}_{O}}\) is the BLUP average of all genotypes, and \(\:\stackrel{-}{y}\) is the phenotypic average of genotypes. Results and discussion Genetic variability in the initial population The wide phenotypic variability among collected plants in 2017 (Zortéa, Santa Catarina, Brazil) was confirmed by yield components data (Fig. 2 ). Between 113 lines, the number of capsules per plant (NCP) ranged from 29 to 309, with an average value of 90. The average grain yield per plant was 3.02g, ranging from 0.92 to 11.05 g plant -1 . This wide variation in these traits has already been described under different growing conditions, with NCP ranging from 15 to 250 capsules per plant (Bibi et al. 2013 ; Paul et al. 2017 ; Patial et al. 2019 ; Dabalo et al. 2020 ) and GYP from 0.18 to 21.2 g plant -1 (Paul et al. 2017 ; Saroha et al. 2022 ; Sarwar et al. 2022 ). The superior performance of some flaxseed lines for these two traits (well above average), observed in the frequency distribution graphs, highlights the potential use of the selection of pure lines in working conditions such as these (Allard 1999 ). The occurrence of plants with below-average performance may explain the low yield performance of the crop in the field, justifying the selection of superior lines. Variance components and genetic parameters The validation of results obtained in field experiments can be quantified through accuracy estimation, which defines experimental precision. Accuracy estimation is a relevant index in plant breeding because it reflects the correlation between predicted and parametric values (Resende and Alves 2022 ). According to Resende and Duarte ( 2007 ), high accuracy values were observed for GY and GYP in 2018 crop season and for DM, NCP, and GYP in 2020 (Table 1 ). Null accuracy values for DM were observed in the 2019 and 2020 crop seasons, as well as in the joint analysis, reflecting the low genetic variation detected for this trait. We also observed high accuracy values for NCP and GYP data in joint analysis. Variation in the accuracy estimate is expected when traits are evaluated in different environments and years, as is the case in this study. Greater reliability is obtained by joint analysis, which considers a greater number of replications (Ramalho et al. 2012 ). In the 2018 crop season, the components of genetic variance between lines were significant, except for DM (Table 1 ). In 2019, NCP showed significant genetic variance. However, in the 2020 crop season, genetic variance was significant for all traits except GY across the two environments. For joint analysis, NCP and GY were significant for genetic variance. Plant selection over the years has led to a reduction in the number of lines, resulting in a natural reduction in genetic variance for grain yield. This is a consequence of the progress on the genetic improvement since grain yield was used as a selection parameter. However, it is important to note that the reduction in variance values does not imply the absence of genetic variability among the selected lines. DM was the most variable trait over the years and environments (Fig. 3 ). In the 2019 crop season, DM values were higher and revealed greater variation. The variation observed was a response to the increased accumulation of rainfall during flowering, which extended the growing cycle of plants. The median value for DM was lower in both crop environments in 2020. This is also an effect of the selection for early maturation plants. The selection made in 2018 led to increased GY in subsequent crop seasons and environments. For NCP and GYP, higher averages and variations were observed in CBS environment in the 2020 crop season. Although flaxseed is an autogamous plant, the observed genetic variability was already expected. This reflects the heterogenous variety profile used as initial population, which was a mixture of seeds multiplied by farmers. In this case, genetic variability can arise from the mechanical mixing of seeds, mutations, and natural hybridization (Ramalho et al. 2012 ), which can reach up to 4.85% (Gürbüz 1999 ). More importantly, the effectiveness of the pure line selection method is directly related to the percentage of genetic variance observed in heterogeneous varieties. Table 1 Variance components, significance of likelihood ratio test (LRT), and genetic parameters estimated to the variables evaluated in 2018, 2019, and 2020 environments. Parameters 2018 – CBS DM NCP GY GYP \(\:{\sigma\:}_{g}^{2}\) 2.59 ns 70.39 ** 5.34 \(\:\times\:\) 10 4 ** 0.17 ** \(\:{\sigma\:}_{e}^{2}\) 32.98 292.70 9.31 \(\:\times\:\) 10 4 0.33 \(\:{h}^{2}\) (%) 7.29 19.39 36.46 33.27 \(\:h\) (%) 43.70 64.74 79.53 77.41 Mean 146.7 52.69 848.16 1.32 Parameters 2019 – CBS DM NCP GY GYP \(\:{\sigma\:}_{g}^{2}\) 1.82 \(\:\times\:\) 10 − 12 ns 31.95 * 2.31 \(\:\times\:\) 10 3 ns 0.05 ns \(\:{\sigma\:}_{e}^{2}\) 45.63 130.85 2.21 \(\:\times\:\) 10 5 0.24 \(\:{h}^{2}\) (%) 0.00 19.63 1.03 17.53 \(\:h\) (%) 0.00 65.03 17.41 62.41 Mean 165.9 54.05 2,858.94 2.32 Parameters 2020 - CBS DM NCP GY GYP \(\:{\sigma\:}_{g}^{2}\) 2.85 ** 421.93 ** 9.74 \(\:\times\:\) 10 3 ns 0.82 ** \(\:{\sigma\:}_{e}^{2}\) 3.07 638.59 2.84 \(\:\times\:\) 10 5 1.28 \(\:{h}^{2}\) (%) 48.19 39.79 3.31 38.99 \(\:h\) (%) 88.78 85.18 34.72 84.78 Mean 142.3 96.58 2,795.57 4.35 Parameters 2020 - CNV DM NCP GY GYP \(\:{\sigma\:}_{g}^{2}\) 35.74 * 157.93 ** 5.08 \(\:\times\:\) 10 3ns 0.33 ** \(\:{\sigma\:}_{e}^{2}\) 97.25 347.94 1.90 \(\:\times\:\) 10 5 0.69 \(\:{h}^{2}\) (%) 26.87 31.22 2.60 32.53 \(\:h\) (%) 77.14 80.30 31.05 81.15 Mean 137.1 62.65 2,403.20 2.77 Parameters 2020 – CBS + CNV DM NCP GY GYP \(\:{\sigma\:}_{g}^{2}\) 0.00 ns 209.81 ** 1.51 \(\:\times\:\) 10 4 ns 0.49 ** \(\:{\sigma\:}_{ge}^{2}\) 19.30 ** 80.12 ns 0.00 ns 0.08 ns \(\:{\sigma\:}_{e}^{2}\) 50.16 493.26 2.29 \(\:\times\:\) 10 5 0.99 \(\:{h}^{2}\) (%) 0.00 26.79 6.20 31.62 \(\:h\) (%) 0.00 82.06 58.83 86.62 Mean 139.7 79.61 2,599,38 3.56 DM: days to maturity; NCP: number of capsules per plant; GY: grain yield per area; GYP: grain yield per plant. CBS: Curitibanos/SC; CNV: Campos Novos/SC . ** Significant at 1% probability by the maximum likelihood ratio test; * Significant at 5% probability by the maximum likelihood ratio test; ns non-significant . \(\:{\sigma\:}_{g}^{2}\) = genotypic variance; \(\:{\sigma\:}_{e}^{2}\) = environmental variance; \(\:{\sigma\:}_{ge}^{2}\) = genotype by environment interaction variance; \(\:{h}^{2}\) = mean-based heritability; \(\:h\) = accuracy of selection . Environmental variance was expressive for all variables (Table 1 and Fig. 3 ), as expected for quantitative traits. DM, which indirectly affects the yield components, exhibited the highest environmental variance due to the indeterminate growth of the flaxseed plants, whose duration is strongly affected by climatic conditions (rainfall and temperature). These climatic conditions, can vary widely between years and affect the results (Allard and Bradshaw 1964 ). The 2020 joint analysis identified that a portion of the variance for DM is attributed to the G \(\:\times\:\) E interaction, suggesting differing responses of lines between environments. Similar results were observed for NCP and GY, though not significant by the LRT test (Table 1 ). Low (> 30%) and moderate (30–60%) heritability estimates (Paul et al. 2017 ), were observed for all the traits evaluated in all tested environments. Lower values were observed for DM, as expected due to its low genetic variance and high environmental variance (except in 2020). NCP and GYP revealed viable heritability values for selection. The estimate of broad sense heritability is crucial to evaluate the effectiveness of using genetic variability intrinsic to the experiment (Lush 1949 ). However, determining heritability and genetic gains together is more effective in predicting the impact of selection on phenotypic expression (Johnson et al. 1955 ). Estimates of expected individual gain for five selection intensities (10, 20, 30, 40, and 50%) are shown in Table 2 and Fig. 5 . These estimates varied according to selection intensity. Expected gains decreased as the number of selected individuals increased, despite the maintenance of genetic variability. This pattern was observed across all evaluated traits. Additionally, genetic gains were achieved in the desired direction for all traits. A negative gain from selection was observed for DM due to selection aimed at reducing the plant cycle. In the 2020 CNV environment, a 7-day reduction in the plant cycle would be achieved with a selection intensity of 10%. In the 2019 crop season, as well as in the 2020 joint analysis, no genetic gain from selection was observed for DM, due to the absence of genetic variability detected and the high environmental influence. The genetic gain for NCP ranged from 5.30–31.06% (Table 2 ). Substantial genetic gains from selection were observed in 2020 for both environments and in the joint analysis. These genetic gains represent an increase of 7 to 28 capsules per plant (on average). The selection gains for NCP were reflected in GYP (Fig. 5 ), given the positive correlation between the traits (Bibi et al. 2013 ; Dabalo et al. 2020 ). Genetic gain for GYP ranged from 11.96 to 32.23% for the individual and joint analyses for 2020, similar to that for NCP. In the 2018 crop season, almost a 50% increase in GYP (0.65 g plant -1 ) was achieved on average with a 10% selection intensity. Great genetic variability in 2018 allowed high genetic gains for GYP and GY in this crop season. Moreover, higher genetic gains from selection are observed when the population mean is low. For example, the GY average was 848.16 kg ha -1 in 2018, and it was 2,858.94 kg ha -1 in 2019. For GYP, the average was 1.32 g plant -1 in 2018 and 2.32 g plant -1 in 2019. The selection of almost 50% of the best lines in 2018 raised the population average in 2019 and reduced variability. In this way, expected gains for 2019 were lower for all traits (Fig. 5 ). Table 2 Estimates of expected gains (EG) from selection of yellow flaxseed lines at different selection intensities (10, 20, 30, 40, and 50%) in 2018, 2019, and 2020 crop seasons. DM Gain (%) EG (10%) EG (20%) EG (30%) EG (40%) EG (50%) 2018 – CBS -0.94 -0.71 -0.58 -0.48 -0.38 2019 – CBS 0.00 0.00 0.00 0.00 0.00 2020 – CBS -2.04 -1.73 -1.30 -0.95 -0.71 2020 – CNV -5.37 -3.83 -3.22 -2.82 -2.55 2020 – CBS + CNV 0.00 0.00 0.00 0.00 0.00 Gain (unit) EG (10%) EG (20%) EG (30%) EG (40%) EG (50%) 2018 – CBS -0.94 -0.71 -0.58 -0.48 -0.38 2019 – CBS 0.00 0.00 0.00 0.00 0.00 2020 – CBS -2.04 -1.73 -1.30 -0.95 -0.71 2020 – CNV -5.37 -3.83 -3.22 -2.82 -2.55 2020 – CBS + CNV 0.00 0.00 0.00 0.00 0.00 NCP Gain (%) EG (10%) EG (20%) EG (30%) EG (40%) EG (50%) 2018 – CBS 19.02 14.79 12.17 10.13 8.17 2019 – CBS 13.43 10.39 8.21 6.63 5.30 2020 – CBS 29.72 25.59 21.41 17.41 13.88 2020 – CNV 31.06 24.94 19.92 15.56 11.39 2020 – CBS + CNV 27.27 21.48 16.94 13.99 11.57 Gain (unit) EG (10%) EG (20%) EG (30%) EG (40%) EG (50%) 2018 – CBS 10.02 7.79 6.41 5.34 4.30 2019 – CBS 7.26 5.61 4.44 3.58 2.86 2020 – CBS 28.70 24.71 20.67 16.81 13.41 2020 – CNV 19.46 15.62 12.48 9.75 7.13 2020 – CBS + CNV 21.71 17.10 13.49 11.14 9.21 GY Gain (%) EG (10%) EG (20%) EG (30%) EG (40%) EG (50%) 2018 – CBS 44.50 33.68 26.11 20.98 16.30 2019 – CBS 0.53 0.40 0.32 0.27 0.22 2020 – CBS 2.54 1.90 1.40 1.09 0.83 2020 – CNV 1.61 1.37 1.08 0.90 0.71 2020 – CBS + CNV 4.84 4.21 3.53 2.77 2.06 Gain (unit) EG (10%) EG (20%) EG (30%) EG (40%) EG (50%) 2018 – CBS 377.41 285.70 221.45 177.98 138.24 2019 – CBS 15.24 11.38 9.16 7.66 6.18 2020 – CBS 70.91 53.09 39.20 30.36 23.17 2020 – CNV 38.60 32.90 25.98 21.61 17.14 2020 – CBS + CNV 125.92 109.39 91.79 72.12 53.57 GYP Gain (%) EG (10%) EG (20%) EG (30%) EG (40%) EG (50%) 2018 – CBS 49.52 35.88 27.98 22.86 17.59 2019 – CBS 11.76 9.38 7.51 5.95 4.67 2020 – CBS 31.40 24.38 20.12 16.59 13.61 2020 – CNV 32.23 25.79 21.19 16.28 11.96 2020 – CBS + CNV 31.91 23.38 19.12 15.88 13.04 Gain (unit) EG (10%) EG (20%) EG (30%) EG (40%) EG (50%) 2018 – CBS 0.65 0.47 0.37 0.30 0.23 2019 – CBS 0.27 0.22 0.17 0.14 0.11 2020 – CBS 1.37 1.06 0.88 0.72 0.59 2020 – CNV 0.89 0.72 0.59 0.45 0.33 2020 – CBS + CNV 1.14 0.83 0.68 0.57 0.46 DM: days to maturity; NCP: number of capsules per plant; GY: grain yield per area; GYP: grain yield per plant. CBS: Curitibanos/SC; CNV: Campos Novos/SC . The 2020 joint analysis indicated more significant genetic gains for the yield components (NCP and GYP). Among the lines that have progressed since 2017, LINPG49 is the most promising, as it reveals the highest values for yield components (Fig. 6 ), although it has a longer cycle than the population average. This response is due to the positive correlation between DM and GY (Bibi et al. 2013 ; Patial et al. 2019 ), which makes it challenging to improve these traits simultaneously. Early-maturation lines present a challenge for breeding due to their high interaction with the environment. Additionally, the positive correlation between cycle length and grain yield makes simultaneous selection for both traits difficult. BLUP data indicate that selecting pure lines over the years has been effective in increasing the population average for yield components (Fig. 6 ). The average yield of the lines in 2018 was 848.16 kg ha -1 (Table 1 ). After two selection cycles, the average yield increased to 2,599.38 kg ha -1 (three times more than the original population). The genetic gain in GY is evident, despite environmental effects. Among the 19 selected lines, it is also possible to verify the range of variation between the BLUPge values (Fig. 6 ). Although the selection of plants into heterogeneous varieties was reported in other crops, (Carvalho et al. 1952; Ramalho et al. 1982 ; Rangel et al. 1998; Yokoyama et al. 1999 ; Lara-Fioreze et al. 2013 ; Amaral et al. 2019 ), this is the first scientific report on flaxseed cultivation. Flaxseed cultivated in Brazil is a result of natural hybridizations and mixtures of Canadian, French, and Dutch genotypes introduced in Brazil over the years (Leal 1967 ). As a result, flaxseed crops exhibit wide genetic variability. In this context, although it is an ancient breeding method (Ramalho et al. 2012 ), the selection of pure lines proved to be effective in selecting superior flaxseed lines, with better agronomic performance. Conclusion The genetic gains and the best linear unbiased predictors (BLUP) of the lines prove the efficiency of participatory selection of superior yellow flaxseed lines from a heterogeneous variety. Additional genetic gains can be obtained by considering the range of variation between the BLUPs of the lines. Declarations Acknowledgements: The authors would like to thank the Agricultural Research and Rural Extension Company of Santa Catarina (EPAGRI) for their support and contribution to the development of this paper. Funding: This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) from the Brazilian Government. Conflict of interest: The authors declare that they have no conflict of interest. Author contributions: All authors contributed to the study conception and design. Data collection (experimental design, sowing, phenotypic evaluations, and harvesting) was performed by Ana Caroline Basniak Konkol, Ana Carolina da Costa Lara Fioreze, Nicole Orsi, Karol Anne Krassmann, Clarice Elisabete Antunes, Samuel Luiz Fioreze and Círio Parizotto. The analysis of data was performed by Ana Caroline Basniak Konkol and Ana Carolina da Costa Lara Fioreze. First draft of the manuscript was written by Ana Caroline Basniak Konkol and Ana Carolina da Costa Lara Fioreze. The manuscript was edited and reviewed by Ana Caroline Basniak Konkol, Ana Carolina da Costa Lara Fioreze, and Samuel Luiz Fioreze. Data availability: No datasets were generated or analyzed during the current study. 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(2019) Morphological characterization and genetic diversity of linseed ( Linum usitatissimum L.). J Oilseeds Res 36:8-16. https://doi.org/10.56739/jor.v36i1.126048 Paul S, Kumar N, Chopra P (2017) Genetic variation and characterization of different linseed genotypes ( Linum usitatissimum L.) for agro-morphological traits. J Appl Nat Sci 9:754-762. https://doi.org/10.31018/jans.v9i2.1268 Priolli RHG et al. (2013) Genetic structure and a selected core set of Brazilian soybean cultivars. Genet Mol Biol, 36:382-390. https://doi.org/10.1590/S1415-47572013005000034 Ramalho MAP et al. (1982) Avaliação de amostras de cultivares de feijão roxo e seleção de progênies. In: Reunião nacional de pesquisa de feijão 1. Embrapa, Brasília, p. 20-21. Ramalho MAP et al. (2012) Aplicações da genética quantitativa no melhoramento de plantas autógamas. Lavras: UFLA, 522 p. Resende MDV and Alves RS (2022) Statistical significance, selection accuracy, and experimental precision in plant breeding. Crop Breed Appl Biotechnol 22:e42712238. https://doi.org/10.1590/1984-70332022v22n3a31 Resende MDV and Duarte JB (2007) Precision and quality control in variety trials. Pesq Agropec Trop 37:182-194. https://doi.org/10.5216/pat.v37i3.1867 Roy PS et al. (2017) Participatory and molecular marker-assisted pure line selection for refinement of three premium rice landraces of Koraput, India. Agroecol Sust Food 41:167-185. https://doi.org/10.1080/21683565.2016.1258607 Sall S, Norman D, and Featherstone AM (2000). Quantitative assessment of improved rice variety adoption: the farmer’s perspective. Agric Syst, 66:129-144. https://doi.org/10.1016/S0308-521X(00)00040-8 Saroha A et al. (2022) Agro-morphological variability and genetic diversity in linseed ( Linum usitatissimum L.) germplasm accessions with emphasis on flowering and maturity time. Genet Resour Crop Evol 69:315-333. https://doi.org/10.1007/s10722-021-01231-3 Sarwar AG et al. (2022) Agro-morphological characterization and genetic dissection of linseed ( Linum usitatissimum L.) genotypes. Phyton - Int J Exp Bot 91:1721-1742. https://doi.org/10.32604/phyton.2022.021069 Smykal P et al. (2011) Genetic diversity of cultivated flax ( Linum usitatissimum L.) germplasm assessed by retrotransposon-based markers. Theor Appl Genet 122:1385–1397. https://doi.org/10.1007/s00122-011-1539-2 Tomasini RG (1980) Linho: resultado de pesquisas. EMBRAPA, Passo Fundo. Verified Market Research (2024) Functional Foods Market Valuation – 2024-2031. https://www.verifiedmarketresearch.com/product/global-functional-foods-market/. Accessed on 15 July 2024. Walsh B, Lynch M (2018) Evolution and selection of quantitative traits. 1st ed. Oxford Univ. Press, Oxford, UK. doi:10.1093/ oso/9780198830870.001.0001 Wossen T, Anley W and Habtie F (2016) Participatory evaluation of improved varieties of linseed in Dabat District, Northwest highland of Ethiopia. International J Life Sci Res 4:100-105. (não acho o DOI - TRABALHO ESTRANHO) Xu Y et al. (2017) Enhancing genetic gain in the era of molecular breeding. J Exp Bot 68:2641-2666. https://doi.org/10.1093/jxb/erx135 Yokoyama LP et al. (1999) Nível de aceitabilidade da cultivar de feijão “Pérola”: avaliação preliminar. Embrapa Arroz e Feijão, Santo Antônio de Goiás, 20p. (Documento 98). Zhao B et al. (2020) Varied previous crops on improving oilseed flax productivity in semiarid Loess Plateau in China. Oil Crop Sci 5:187-193. https://doi.org/10.1016/j.ocsci.2020.12.002 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4831213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338528412,"identity":"14858f3d-2b68-4fd8-949f-bf355951b833","order_by":0,"name":"Ana Caroline Basniak 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Brazil","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Carolina da Costa Lara","lastName":"Fioreze","suffix":""},{"id":338528414,"identity":"49e8be6c-3f88-44ea-80ea-cd874025488e","order_by":2,"name":"Nicole Orsi","email":"","orcid":"","institution":"Universidade Federal de Santa Catarina, Rodovia Ulysses Gaboardi","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Orsi","suffix":""},{"id":338528415,"identity":"1d3a8243-e507-4274-b94b-d3850d4acd5b","order_by":3,"name":"Karol Anne Krassmann","email":"","orcid":"","institution":"Universidade Federal de Santa Catarina, Rodovia Ulysses Gaboardi","correspondingAuthor":false,"prefix":"","firstName":"Karol","middleName":"Anne","lastName":"Krassmann","suffix":""},{"id":338528416,"identity":"d2c16d06-84cc-4f99-a782-d5fe2fe7dbc1","order_by":4,"name":"Clarice Elisabete Antunes","email":"","orcid":"","institution":"Universidade Federal de Santa Catarina, Rodovia Ulysses 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21:31:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4831213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4831213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63601572,"identity":"1fcd0c06-ed73-4d46-b5d1-d61ccb3f2868","added_by":"auto","created_at":"2024-08-30 05:29:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":336574,"visible":true,"origin":"","legend":"\u003cp\u003eTimeline and steps of participatory plant breeding of yellow flaxseed.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4831213/v1/3613be2a931e9ccf633c7505.png"},{"id":63599886,"identity":"6dd87814-a075-4d08-82cd-27facc3edd99","added_by":"auto","created_at":"2024-08-30 05:05:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42357,"visible":true,"origin":"","legend":"\u003cp\u003eVariation observed for number of capsules per plant (NCP) and grain yield per plant (GYP), in 2017.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4831213/v1/c010acb53b8c451f6d4505a1.png"},{"id":63600752,"identity":"7072bd51-842d-4525-87bb-71caa17c92fb","added_by":"auto","created_at":"2024-08-30 05:21:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49697,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots representing the traits variation in 2018, 2019 and 2020 environments.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4831213/v1/f323a55ade32cbc4d815c820.png"},{"id":63599882,"identity":"4b7c552c-d2b6-4f6f-a398-657a36331775","added_by":"auto","created_at":"2024-08-30 05:05:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136379,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of observed variance components for the traits evaluated in 2018, 2019, and 2020 environments.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4831213/v1/e3c911fed0f2488aa98161f3.png"},{"id":63600753,"identity":"ce6b1701-8824-4097-aa1b-03d702fa47a9","added_by":"auto","created_at":"2024-08-30 05:21:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46622,"visible":true,"origin":"","legend":"\u003cp\u003eEstimates of expected gains in percentage at different selection intensities (10, 20, 30, 40 and 50%).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4831213/v1/27a7524090801b84382b7473.png"},{"id":63599887,"identity":"d59e482d-272e-4063-a6ff-7da861649c53","added_by":"auto","created_at":"2024-08-30 05:05:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":151168,"visible":true,"origin":"","legend":"\u003cp\u003eBLUPge values for traits evaluated in 2020 in Curitibanos (CBS) and Campos Novos (CNV) municipalities.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4831213/v1/535d207e2a1be7db85791e9d.png"},{"id":76163837,"identity":"fb0cbda0-f193-4b33-ab00-6a0d087e58ce","added_by":"auto","created_at":"2025-02-13 03:46:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1832517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4831213/v1/70356343-a2ca-4079-9a27-73f492badb0f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pure line selection for improved yield and early maturation in heterogeneous yellow flaxseed variety","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe cultivation of flaxseed has been expanding globally (FAOSTAT 2024) in response to the growing market demand for functional foods, primarily due to the demand for edible oil sources rich in omega-3 (Cui et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Flaxseed is a functional food because of its bioactive components, such as alpha-linolenic acid, lignans, and digestible fibers (Nowak and Jeziorek \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Reports on the functional foods market project an estimated growth of 8.44% from 2024 to 2031 (Verified Market Research \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This demand has been further driven by the COVID-19 pandemic, as consumers seek foods that promote health and boost immunity (Farzana et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Functional foods like flaxseed are recognized for their health benefits due to the presence of vitamins, minerals, fibers, prebiotics, and probiotics (Nowak and Jeziorek \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), further reinforcing their importance in the current market.\u003c/p\u003e \u003cp\u003eIn addition to the nutritional value of flaxseed, the economic exploitation of the plant also comes from the stem fiber used in the textile industry, oil extraction, the chemical and pharmaceutical industries, and flaxseed by-products in animal feed (Cui et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This diversification of uses has contributed to market growth (Zhao et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as reflected in the expansion of planted areas since 2008 (FAOSTAT 2024). This growth has been facilitated by increasing demand and advances in genetic breeding programs, which seek competitive productivity and quality in the products generated by the species (Gao \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, grain productivity remains a limiting factor for breeding programs, as does genetic diversity, since cultivated flaxseed shows less diversity when compared to its wild ancestors (Smykal et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Brazil, the progress of flaxseed cultivation was significant between the 1930s and 1970s due to the boost from the textile industry (Floss \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1983a\u003c/span\u003e). During this period, seeds from foreign cultivars were introduced, and the varieties most adapted to the southern region of the country were selected (Leal \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Floss \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1983b\u003c/span\u003e). However, there was a decline in flaxseed production with the rise of petroleum derivatives and synthetic fibers in the industry. Therefore, farmers began cultivating wheat and barley (Tomasini \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). The cultivation of flaxseed continued in some regions of Brazil, but less significantly. Recently, flaxseed cultivation has been revitalized. The crop reveals potential for adaptation to edaphoclimatic conditions, especially in the south of Brazil (Bosco et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and its ability to be integrated into existing farming systems for crop rotation and diversification has contributed to this resurgence.\u003c/p\u003e \u003cp\u003eDespite the promising potential of flaxseed, Brazil encounters challenges in the availability of competitive varieties, stemming from insufficient Brazilian genetic studies on the crop for over four decades. However, this gap also presents an opportunity. It is possible to select pure lines considering that several Brazilian flaxseed producers use heterogeneous varieties. These varieties are derived from mixtures of populations multiplied over the harvests. This multiplication process promotes the expansion of natural genetic variability, caused by spontaneous mutations, occasional seed mixtures, and small rates of crossbreeding between plants. As a result, heterogeneous varieties are considered sources of genetic variability and contain alleles lost in breeding programs. An opportunity for selection is provided because these varieties have not been subjected to strong selection pressure.\u003c/p\u003e \u003cp\u003eThe pure line selection method was proposed by Louis de Vilmorin in 1856 (Allard \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The method assumes that the offspring of the best individuals selected from a population will be superior to those obtained from a random sample of the same population (Breseghello and Coelho \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This breeding approach is simple and effective for traits strongly influenced by the environment. It involves testing the offspring of selected individual plants in field trials across multiple years and diverse environments. Superior lines were obtained through the pure line selection method recently in wheat (Agorastos and Goulas, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), rice (Roy et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), lentils (Ninou et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), crambe (Lara-Fioreze et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and soybeans (Priolli et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Amaral et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is possible to measure the efficiency of a plant breeding program in severa ways (Ceccarelli \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The adoption level of a newly developed variety serves as a measure. The probability of adoption increases when the producer participates in the development of new technologies (Adesina and Baidu-Forson \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Sall et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), establishing a participatory selection and evaluation system, as carried out with flaxseed in countries like Ethiopia and India (Abebe et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wossen et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In Brazil, the use of participatory breeding on flaxseed remains unknown.\u003c/p\u003e \u003cp\u003eMathematically, superior varieties potential and a breeding program performance can be measured through estimates of selection gains or responses to selection (Ceccarelli \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Walsh and Lynch \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cobb et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Genetic gain refers to the improvement of the genetic mean values in a population, or the phenotypic mean values resulting from selection within a population over several generations (Hazel and Lush \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1942\u003c/span\u003e). The increased performance in response to selection can be achieved through an increment of selection differential or selection intensity, among other means (Xu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the present study, we aimed to estimate the efficiency of participatory selection of superior yellow flaxseed lines from a heterogeneous variety through genetic gains obtained via the best linear unbiased predictor (BLUP).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eField experiments\u003c/p\u003e \u003cp\u003eThe present study was initiated in 2017 in Zort\u0026eacute;a municipality, Santa Catarina, Brazil. Our collaboration with the Agricultural Research and Extension Company of Santa Catarina (EPAGRI) commenced with the selection of an area where yellow flaxseed was being cultivated by a local farmer, which exhibited significant genetic variability. This area spanned 10,000 square meters. This particular area was selected based on its heterogeneity, evident from the observed phenotypic variations among the plants. Additionally, the crop originated from a manual mixture of seeds of unknown origin, which had been multiplied for several generations by the owner of the area.\u003c/p\u003e \u003cp\u003eFrom this point on, we defined an ideotype of the expected flaxseed plant with the participation of farmers from the region, extension professionals, and researchers from EPAGRI and the Federal University of Santa Catarina (UFSC). We stipulated some premises such as high productivity and early maturation. After establishing the plant ideotype, we divided the area into 10 homogeneous plots and selected approximately 10 plants according to previously agreed standards. The collected samples were individually evaluated for the number of capsules per plant (NCP) and grain yield per plant (GYP). Accordingly, we established a ranking and selected plants with higher potential to continue the process as lines.\u003c/p\u003e \u003cp\u003eTo evaluate the performance of the 73 selected lines, in June 2018, a field experiment was implemented at the Agricultural Experimental Area of the Federal University of Santa Catarina, in Curitibanos (CBS), Santa Catarina, Brazil (latitude 27\u0026deg;16' S and longitude 50\u0026deg;30' W). We conducted the field trial using a randomized complete block design with three replications. The sowing was made manually in rows of 2 meters in length with a spacing of 0.34 meters, at a density of 148 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e. Base and topdressing fertilization were performed according to crop recommendations, and weed control was carried out manually by weeding.\u003c/p\u003e \u003cp\u003eWe conducted evaluations of cycle and productive components to select lines with superior performance, according to the premises established previously. The evaluations involved counting the number of days to maturity (DM), the number of capsules per plant (NCP), weighing the grain yield per plant (GYP - g), and grain yield per area (GY - kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Based on these evaluations, 39 lines were selected for field evaluation in 2019 in Curitibanos municipality (CBS). The design adopted was randomized complete blocks with three replications, and the useful plot consisted of a row of 1 meter with a spacing of 0.34 meters (density of 294 plants m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). Soil preparation and cultivation practices followed the same procedures as previously described, and the same characteristics were evaluated.\u003c/p\u003e \u003cp\u003eIn 2020, 20 lines out of the 39 evaluated in the previous year progressed. These lines were subjected to field experiments in a randomized complete block design, with four replications in two locations: in the municipalities of Curitibanos (CBS) and Campos Novos (CNV). Soil preparation and cultivation practices remained the same as previously described, and the same characteristics were evaluated.\u003c/p\u003e \u003cp\u003eAll the steps conducted in the present study are detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical procedures were performed in the R Studio software system, version 4.2.2, using the package metan (Olivoto and L\u0026uacute;cio \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the functions gamem() and gamem_met().\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMixed-effect model\u003c/h2\u003e \u003cp\u003eIndividual analyses were performed for data obtained in 2018, 2019, and 2020, and a joint analysis for the data obtained in both environments in 2020. A mixed-effect model was considered for individual and joint analysis. For individual analysis, the model used was:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{y}\\:=\\:\\varvec{X}\\varvec{b}\\:+\\:\\varvec{Z}\\varvec{g}\\:+\\:\\varvec{e}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{y}\\)\u003c/span\u003e\u003c/span\u003e is the vector of phenotypic data; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{b}\\)\u003c/span\u003e\u003c/span\u003e is vector of unknown and unobservable fixed effects of blocks, which includes all repetitions; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{g}\\)\u003c/span\u003e\u003c/span\u003e is a vector of unknown and unobservable random effects of genotype \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:[\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e\u0026#119892;\u003c/b\u003e \u0026sim; \u0026#119873;(0,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e)], where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the genotypic variance; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:e\\)\u003c/span\u003e\u003c/span\u003e is a vector of random errors [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{e}\\)\u003c/span\u003e\u003c/span\u003e \u0026sim; \u0026#119873;(0, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e )], where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the residual variance; the letters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{X}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{Z}\\)\u003c/span\u003e\u003c/span\u003e mean the incidence matrices for the described effects.\u003c/p\u003e \u003cp\u003eThe model used in the joint analysis was:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e𝑦 = 𝑋𝑟 + 𝑍𝑔 + 𝑊𝑔𝑒 + 𝑒\u003c/h3\u003e\n\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{y}\\)\u003c/span\u003e\u003c/span\u003e is the vector of phenotypic data; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{b}\\)\u003c/span\u003e\u003c/span\u003e is a vector of unknown and unobservable fixed effects of blocks, which includes all repetitions from each location; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{g}\\)\u003c/span\u003e\u003c/span\u003e is a vector of unknown and unobservable random effects of genotype \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left[\\varvec{g}\\sim\\:N\\left(0,\\:{\\sigma\\:}_{g}^{2}\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the genotypic variance; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{g}\\varvec{e}\\)\u003c/span\u003e\u003c/span\u003e is the vector of random effects of the G \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e E interaction \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left[\\varvec{g}\\varvec{e}\\sim\\:N\\left(0,\\:{\\sigma\\:}_{ge}^{2}\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{ge}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the variance of the G \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e E interaction; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{e}\\)\u003c/span\u003e\u003c/span\u003e is a vector of random errors \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left[\\varvec{e}\\sim\\:N\\left(0,\\:{\\sigma\\:}_{e}^{2}\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the residual variance; the letters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{X}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{Z}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{W}\\)\u003c/span\u003e\u003c/span\u003e mean the incidence matrices for the described effects.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVariance components and genetic parameters\u003c/h2\u003e \u003cp\u003eTo verify significant differences between lines, the data for each environment were subjected to a likelihood ratio test (LRT). Considering the mixed-effect model, we estimated variance components through the restricted maximum likelihood (REML) and the genotypic values through the best linear unbiased prediction (BLUP) methods.\u003c/p\u003e \u003cp\u003eFor the calculation of mean-based heritability (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}^{2}\\)\u003c/span\u003e\u003c/span\u003e), the following estimator was used:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{h}^{2}=\\:\\frac{{\\sigma\\:}_{g}^{2}}{{\\sigma\\:}_{g}^{2}+\\:\\frac{{\\sigma\\:}_{ge}^{2}}{e}+\\frac{{\\sigma\\:}_{e}^{2}}{\\left(eb\\right)}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the genotypic variance; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{ge}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the genotype by environment interaction variance; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the environment variance; \u003cb\u003ee\u003c/b\u003e and \u003cb\u003eb\u003c/b\u003e are the number of environments and blocks, respectively.\u003c/p\u003e \u003cp\u003eSelective accuracy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e) was estimated by:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:h=\\:\\sqrt{{h}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGenetic gains from selection\u003c/h2\u003e \u003cp\u003eThe efficiency of the participatory plant breeding was evaluated by the genetic gain from selection (GS), calculated from the BLUP averages of the lines at five selection intensities (10, 20, 30, 40, and 50%). We estimate the percentage gain from selection (GS %) considering the following formula:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:GS\\:\\left(\\%\\right)=\\:\\frac{(\\stackrel{-}{{BLUP}_{s}}-\\:\\underset{\\_}{\\stackrel{-}{{BLUP}_{O}}})\\times\\:100}{\\stackrel{-}{y}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{BLUP}_{s}}\\)\u003c/span\u003e\u003c/span\u003e is the BLUP average of the selected genotypes at five selection intensities (10, 20, 30, 40, and 50%), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{BLUP}_{O}}\\)\u003c/span\u003e\u003c/span\u003e is the BLUP average of all genotypes, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{y}\\)\u003c/span\u003e\u003c/span\u003e is the phenotypic average of genotypes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eGenetic variability in the initial population\u003c/p\u003e\n\u003cp\u003eThe wide phenotypic variability among collected plants in 2017 (Zort\u0026eacute;a, Santa Catarina, Brazil) was confirmed by yield components data (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Between 113 lines, the number of capsules per plant (NCP) ranged from 29 to 309, with an average value of 90. The average grain yield per plant was 3.02g, ranging from 0.92 to 11.05 g plant\u003csup\u003e-1\u003c/sup\u003e. This wide variation in these traits has already been described under different growing conditions, with NCP ranging from 15 to 250 capsules per plant (Bibi et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Paul et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Patial et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dabalo et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and GYP from 0.18 to 21.2 g plant\u003csup\u003e-1\u003c/sup\u003e (Paul et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saroha et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sarwar et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The superior performance of some flaxseed lines for these two traits (well above average), observed in the frequency distribution graphs, highlights the potential use of the selection of pure lines in working conditions such as these (Allard \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). The occurrence of plants with below-average performance may explain the low yield performance of the crop in the field, justifying the selection of superior lines.\u003c/p\u003e\n\u003cp\u003eVariance components and genetic parameters\u003c/p\u003e\n\u003cp\u003eThe validation of results obtained in field experiments can be quantified through accuracy estimation, which defines experimental precision. Accuracy estimation is a relevant index in plant breeding because it reflects the correlation between predicted and parametric values (Resende and Alves \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Resende and Duarte (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), high accuracy values were observed for GY and GYP in 2018 crop season and for DM, NCP, and GYP in 2020 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Null accuracy values for DM were observed in the 2019 and 2020 crop seasons, as well as in the joint analysis, reflecting the low genetic variation detected for this trait. We also observed high accuracy values for NCP and GYP data in joint analysis. Variation in the accuracy estimate is expected when traits are evaluated in different environments and years, as is the case in this study. Greater reliability is obtained by joint analysis, which considers a greater number of replications (Ramalho et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the 2018 crop season, the components of genetic variance between lines were significant, except for DM (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2019, NCP showed significant genetic variance. However, in the 2020 crop season, genetic variance was significant for all traits except GY across the two environments. For joint analysis, NCP and GY were significant for genetic variance. Plant selection over the years has led to a reduction in the number of lines, resulting in a natural reduction in genetic variance for grain yield. This is a consequence of the progress on the genetic improvement since grain yield was used as a selection parameter. However, it is important to note that the reduction in variance values does not imply the absence of genetic variability among the selected lines.\u003c/p\u003e\n\u003cp\u003eDM was the most variable trait over the years and environments (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In the 2019 crop season, DM values were higher and revealed greater variation. The variation observed was a response to the increased accumulation of rainfall during flowering, which extended the growing cycle of plants. The median value for DM was lower in both crop environments in 2020. This is also an effect of the selection for early maturation plants. The selection made in 2018 led to increased GY in subsequent crop seasons and environments. For NCP and GYP, higher averages and variations were observed in CBS environment in the 2020 crop season.\u003c/p\u003e\n\u003cp\u003eAlthough flaxseed is an autogamous plant, the observed genetic variability was already expected. This reflects the heterogenous variety profile used as initial population, which was a mixture of seeds multiplied by farmers. In this case, genetic variability can arise from the mechanical mixing of seeds, mutations, and natural hybridization (Ramalho et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), which can reach up to 4.85% (G\u0026uuml;rb\u0026uuml;z\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). More importantly, the effectiveness of the pure line selection method is directly related to the percentage of genetic variance observed in heterogeneous varieties.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariance components, significance of likelihood ratio test (LRT), and genetic parameters estimated to the variables evaluated in 2018, 2019, and 2020 environments.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e2018 \u0026ndash; CBS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNCP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGYP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.59\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.39\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.34 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e4 **\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.31 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}^{2}\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e848.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e2019 \u0026ndash; CBS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGYP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e\u0026minus;\u0026thinsp;12 ns\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.95\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e3 ns\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.21 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}^{2}\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,858.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020 - CBS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGYP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.85\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421.93\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.74 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e3 ns\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e638.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.84 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}^{2}\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,795.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020 - CNV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGYP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.74\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157.93\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.08 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e3ns\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}^{2}\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,403.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e2020 \u0026ndash; CBS\u0026thinsp;+\u0026thinsp;CNV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGYP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209.81\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e4 ns\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{ge}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.12\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e493.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.29 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}^{2}\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,599,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003eDM: days to maturity; NCP: number of capsules per plant; GY: grain yield per area; GYP: grain yield per plant. CBS: Curitibanos/SC; CNV: Campos Novos/SC\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e** Significant at 1% probability by the maximum likelihood ratio test; * Significant at 5% probability by the maximum likelihood ratio test; ns non-significant\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003csup\u003e= genotypic variance;\u003c/sup\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003e= environmental variance;\u003c/sup\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{ge}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003e= genotype by environment interaction variance;\u003c/sup\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}^{2}\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003e= mean-based heritability;\u003c/sup\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e \u003csup\u003e= accuracy of selection\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEnvironmental variance was expressive for all variables (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), as expected for quantitative traits. DM, which indirectly affects the yield components, exhibited the highest environmental variance due to the indeterminate growth of the flaxseed plants, whose duration is strongly affected by climatic conditions (rainfall and temperature). These climatic conditions, can vary widely between years and affect the results (Allard and Bradshaw \u003cspan class=\"CitationRef\"\u003e1964\u003c/span\u003e). The 2020 joint analysis identified that a portion of the variance for DM is attributed to the G \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e E interaction, suggesting differing responses of lines between environments. Similar results were observed for NCP and GY, though not significant by the LRT test (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eLow (\u0026gt;\u0026thinsp;30%) and moderate (30\u0026ndash;60%) heritability estimates (Paul et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), were observed for all the traits evaluated in all tested environments. Lower values were observed for DM, as expected due to its low genetic variance and high environmental variance (except in 2020). NCP and GYP revealed viable heritability values for selection. The estimate of broad sense heritability is crucial to evaluate the effectiveness of using genetic variability intrinsic to the experiment (Lush \u003cspan class=\"CitationRef\"\u003e1949\u003c/span\u003e). However, determining heritability and genetic gains together is more effective in predicting the impact of selection on phenotypic expression (Johnson et al. \u003cspan class=\"CitationRef\"\u003e1955\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eEstimates of expected individual gain for five selection intensities (10, 20, 30, 40, and 50%) are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. These estimates varied according to selection intensity. Expected gains decreased as the number of selected individuals increased, despite the maintenance of genetic variability. This pattern was observed across all evaluated traits. Additionally, genetic gains were achieved in the desired direction for all traits. A negative gain from selection was observed for DM due to selection aimed at reducing the plant cycle. In the 2020 CNV environment, a 7-day reduction in the plant cycle would be achieved with a selection intensity of 10%. In the 2019 crop season, as well as in the 2020 joint analysis, no genetic gain from selection was observed for DM, due to the absence of genetic variability detected and the high environmental influence.\u003c/p\u003e\n\u003cp\u003eThe genetic gain for NCP ranged from 5.30\u0026ndash;31.06% (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Substantial genetic gains from selection were observed in 2020 for both environments and in the joint analysis. These genetic gains represent an increase of 7 to 28 capsules per plant (on average). The selection gains for NCP were reflected in GYP (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), given the positive correlation between the traits (Bibi et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dabalo et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Genetic gain for GYP ranged from 11.96 to 32.23% for the individual and joint analyses for 2020, similar to that for NCP. In the 2018 crop season, almost a 50% increase in GYP (0.65 g plant\u003csup\u003e-1\u003c/sup\u003e) was achieved on average with a 10% selection intensity. Great genetic variability in 2018 allowed high genetic gains for GYP and GY in this crop season. Moreover, higher genetic gains from selection are observed when the population mean is low. For example, the GY average was 848.16 kg ha\u003csup\u003e-1\u003c/sup\u003e in 2018, and it was 2,858.94 kg ha\u003csup\u003e-1\u003c/sup\u003e in 2019. For GYP, the average was 1.32 g plant\u003csup\u003e-1\u003c/sup\u003e in 2018 and 2.32 g plant\u003csup\u003e-1\u003c/sup\u003e in 2019. The selection of almost 50% of the best lines in 2018 raised the population average in 2019 and reduced variability. In this way, expected gains for 2019 were lower for all traits (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEstimates of expected gains (EG) from selection of yellow flaxseed lines at different selection intensities (10, 20, 30, 40, and 50%) in 2018, 2019, and 2020 crop seasons.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGain (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEG (10%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEG (20%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEG (30%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEG (40%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEG (50%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u0026thinsp;+\u0026thinsp;CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGain (unit)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (10%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (20%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (30%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (40%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (50%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u0026thinsp;+\u0026thinsp;CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGain (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (10%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (20%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (30%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (40%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (50%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u0026thinsp;+\u0026thinsp;CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGain (unit)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (10%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (20%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (30%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (40%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (50%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u0026thinsp;+\u0026thinsp;CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGain (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (10%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (20%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (30%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (40%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (50%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u0026thinsp;+\u0026thinsp;CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGain (unit)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (10%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (20%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (30%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (40%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (50%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e377.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u0026thinsp;+\u0026thinsp;CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGYP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGain (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (10%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (20%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (30%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (40%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (50%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u0026thinsp;+\u0026thinsp;CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGain (unit)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (10%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (20%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (30%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (40%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEG (50%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2019 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020 \u0026ndash; CBS\u0026thinsp;+\u0026thinsp;CNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003eDM: days to maturity; NCP: number of capsules per plant; GY: grain yield per area; GYP: grain yield per plant. CBS: Curitibanos/SC; CNV: Campos Novos/SC\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe 2020 joint analysis indicated more significant genetic gains for the yield components (NCP and GYP). Among the lines that have progressed since 2017, LINPG49 is the most promising, as it reveals the highest values for yield components (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), although it has a longer cycle than the population average. This response is due to the positive correlation between DM and GY (Bibi et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Patial et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), which makes it challenging to improve these traits simultaneously.\u003c/p\u003e\n\u003cp\u003eEarly-maturation lines present a challenge for breeding due to their high interaction with the environment. Additionally, the positive correlation between cycle length and grain yield makes simultaneous selection for both traits difficult. BLUP data indicate that selecting pure lines over the years has been effective in increasing the population average for yield components (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The average yield of the lines in 2018 was 848.16 kg ha\u003csup\u003e-1\u003c/sup\u003e (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). After two selection cycles, the average yield increased to 2,599.38 kg ha\u003csup\u003e-1\u003c/sup\u003e (three times more than the original population). The genetic gain in GY is evident, despite environmental effects. Among the 19 selected lines, it is also possible to verify the range of variation between the BLUPge values (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAlthough the selection of plants into heterogeneous varieties was reported in other crops, (Carvalho et al. 1952; Ramalho et al. \u003cspan class=\"CitationRef\"\u003e1982\u003c/span\u003e; Rangel et al. 1998; Yokoyama et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e; Lara-Fioreze et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Amaral et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), this is the first scientific report on flaxseed cultivation.\u003c/p\u003e\n\u003cp\u003eFlaxseed cultivated in Brazil is a result of natural hybridizations and mixtures of Canadian, French, and Dutch genotypes introduced in Brazil over the years (Leal \u003cspan class=\"CitationRef\"\u003e1967\u003c/span\u003e). As a result, flaxseed crops exhibit wide genetic variability. In this context, although it is an ancient breeding method (Ramalho et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), the selection of pure lines proved to be effective in selecting superior flaxseed lines, with better agronomic performance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe genetic gains and the best linear unbiased predictors (BLUP) of the lines prove the efficiency of participatory selection of superior yellow flaxseed lines from a heterogeneous variety. Additional genetic gains can be obtained by considering the range of variation between the BLUPs of the lines.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank the Agricultural Research and Rural Extension Company of Santa Catarina (EPAGRI) for their support and contribution to the development of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) from the Brazilian Government.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Data collection (experimental design, sowing, phenotypic evaluations, and harvesting) was performed by Ana Caroline Basniak Konkol, Ana Carolina da Costa Lara Fioreze, Nicole Orsi, Karol Anne Krassmann, Clarice Elisabete Antunes, Samuel Luiz Fioreze and Círio Parizotto. The analysis of data was performed by Ana Caroline Basniak Konkol and Ana Carolina da Costa Lara Fioreze. First draft of the manuscript was written by Ana Caroline Basniak Konkol and Ana Carolina da Costa Lara Fioreze. The manuscript was edited and reviewed by Ana Caroline Basniak Konkol, Ana Carolina da Costa Lara Fioreze, and Samuel Luiz Fioreze.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e No datasets were generated or analyzed during the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbebe S, Gichamo M, Doda A, Sime B (2022) Participatory evaluation of Linseed (\u003cem\u003eLinum usitatissimum\u003c/em\u003e L.) varieties under farmers training center at Dodola District of West Arsi Zone, Oromia Regional State, Ethiopia. Open J Plant Sci 7(1): 014-019. DOI: https://dx.doi.org/10.17352/ojps.000046\u003c/li\u003e\n\u003cli\u003eAdesina AA and Baidu-Forson J (1995) Farmers\u0026apos; perceptions and adoption of new agricultural technology: evidence from analysis in Burkina Faso and Guinea, West Africa. Agric Econ, 13:1-9. https://doi.org/10.1016/0169-5150(95)01142-8\u003c/li\u003e\n\u003cli\u003eAgorastos AG, Goulas CK (2005) Line selection for exploiting durum wheat (T. turgidum L. var. durum) local landraces in modern variety development program. Euphytica 146: 117-124. https://doi.org/10.1007/s10681-005-8495-3\u003c/li\u003e\n\u003cli\u003eAllard RW (1999) Principles of Plant Breeding, 2nd edn. John Willey and Sons Inc., New York.\u003c/li\u003e\n\u003cli\u003eAllard RW and Bradshaw AD (1964) Implications of genotype-environmental interactions in applied plant breeding. Crop Sci 4:503-508. https://doi.org/10.2135/cropsci1964.0011183X000400050021x\u003c/li\u003e\n\u003cli\u003eAmaral, LDO et al. (2019) Pure line selection in a heterogeneous soybean cultivar. Crop Breed Appl Biotechnol 19:277-284. https://doi.org/10.1590/1984-70332019v19n3a39\u003c/li\u003e\n\u003cli\u003eBibi T et al. (2013) Correlation studies of some yield related traits in linseed, \u003cem\u003eLinum usitatissimum\u003c/em\u003e. J Agric Res 51:121-132. https://doi.org/10.5555/20133241675\u003c/li\u003e\n\u003cli\u003eBosco LC, Becker DB, Stanck LT, Carducci CE, Harthmann OEL (2020) Linking meteorological conditions to linseed productivity and phenology in agroecosystems of Southern Brazil. Braz J Dev 6:24838\u0026ndash;24867. https://doi.org/10.34117/bjdv6n5-077\u003c/li\u003e\n\u003cli\u003eBreseghello F, Coelho ASG (2013) Traditional and modern plant breeding methods with examples in rice (Oryza sativa L.). J Agric Food Chem, 61:8277-8286. https://doi.org/10.1021/jf305531j\u003c/li\u003e\n\u003cli\u003eCarvalho A (1952) Melhoramento do cafeeiro: IV-Caf\u0026eacute; Mundo Novo. Bragantia 4: 97-130.\u003c/li\u003e\n\u003cli\u003eCeccarelli S (2015) Efficiency of plant breeding. Crop Sci 55:87-97. https://doi.org/10.2135/cropsci2014.02.0158\u003c/li\u003e\n\u003cli\u003eCobb JN et al. 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Oil Crop Sci 5:187-193. https://doi.org/10.1016/j.ocsci.2020.12.002\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Linum usistatissimum L, pure line selection, genetic gains, BLUP","lastPublishedDoi":"10.21203/rs.3.rs-4831213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4831213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFunctional food markets have increased flaxseed (\u003cem\u003eLinum usitatissimum\u003c/em\u003e L.) demand, along with the need for competitive varieties. Flaxseed cultivated in Brazil results from natural hybridizations and mixtures of foreign genotypes. Given the unexplored genetic variability in heterogeneous Brazilian varieties, classical breeding methods like pure line selection can be effectively applied, especially when involving local producers in the breeding process. Therefore, the present study aimed to estimate the efficiency of participatory selection of superior yellow flaxseed lines from a heterogeneous variety through genetic gains obtained via the best linear unbiased predictor (BLUP). Individual plants were selected in a heterogeneous Brazilian crop in 2017, and the resulting lines were evaluated in 2018, 2019, and 2020 (across two environments). We evaluated the following traits: days to maturity (DM), number of capsules per plant (NCP), yield per area (GY), and yield per plant (GYP). Phenotypic data were used to estimate variance components via REML and genotypic values via BLUP. The efficiency of participatory plant breeding was measured by the genetic gain from selection (GS). The results reveal that the pure line selection method in a heterogeneous flaxseed variety was efficient, achieving gains of up to 5.37% for DM, 31% for NCP, 44.5% for GY, and 49.52% for GYP.\u003c/p\u003e","manuscriptTitle":"Pure line selection for improved yield and early maturation in heterogeneous yellow flaxseed variety","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-30 05:05:12","doi":"10.21203/rs.3.rs-4831213/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6cbbfb60-6d81-42be-86ff-7bc778c96327","owner":[],"postedDate":"August 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-13T03:38:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-30 05:05:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4831213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4831213","identity":"rs-4831213","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}