
Rapeseed and mustard occupies the third position in the world among the edible oils in terms of total production after soybean and palm oil (Statista, https://www.statista.com/statistics/263933/production-of-vegetable-oils-worldwide-since-2000/). Bangladesh imported over 2.9 million tons of oils and fats and spent about US $1.6 billion in recent years (Malaysia Palm Oil Council, http://mpoc.org.bd/2019/01/oils-and-fats-market-scenarios-of-the-country/). To reduce the import of edible oils, it is essential to increase the yield of the existing varieties or bring out new high yielding varieties. In Bangladesh, Boro-T-aman rice is the most popular cropping pattern that covers more than 60% of the total cultivated crop area (Zaman
Plant breeders in Bangladesh developed a few short duration rapeseed and mustard varieties with an average yield of around 1500 kg/ha (Das and Malek 2004). Bangladesh Agricultural Research Institute (BARI) and Bangladesh Institute of Nuclear Agriculture (BINA) have developed 17 and 10 improved rapeseed and mustard varieties, respectively (BARI 2018; DHCP, https://bsmrau.edu.bd/cbt/digital-herbarium-of-crop-plants/crop-plants/). Nevertheless, a lion share of the total mustard growing areas in Bangladesh is occupied by a low yielding but short duration variety, Tori-7, because other varieties fail to at-tract farmers (Miah
Genetic diversity is essential to meet the diverse goals in plant breeding including developing any short duration and high yielding variety (Nevo
Forty-eight rapeseed and mustard genotypes were selected in this study (Supplementary Table S1). The selected genotypes were planted in two large field plots. Each of those large field plots consisted of 48 individual plots and each of the individual plot was of 2.0 × 1.0 m2 in size (Fig. 1). Isolation distance between two adjacent plots was one meter. Seeds were sown in the individual plots in lines with 25 cm spacing between two lines. Thus, there were four lines per individual plots and each of the line was 2 m long (Fig. 1A). Data were collected from three randomly selected plants. Data on 19 variables named days to first flowering, days to 50% flowering, days to first siliqua formation, plant height (cm), length of primary branches, length of secondary branches, number of primary branches per plant, number of secondary branches per plant, number of tertiary branches per plant, number of siliqua per plant, number of siliqua in the main axis, number of siliqua in primary branches, number of siliqua in secondary branches, number of siliqua in tertiary branches, number of seeds per siliqua, length of siliqua (cm), 1000 seeds weight (γ) and seeds yield per plant (γ) were collected. Among them days to first flowering, days to 50% flowering and days to maturity variables were recorded in the field condition (Fig. 1B). We have also measured oil content and conducted fatty acid profiling of 10 selected early and high yielding genotypes of rapeseed and mustard among 48 genotypes (Supplementary Table S2). Seeds were cleaned, sundried, stored in a plastic container in room temperature before fatty acid profiling (Sharif
Oil content was determined using the methods (Soxhlet) described by Peña
Fatty acid methyl esters (FAME) of the selected 10 varieties were measured according to the method described by Goli
The data were subjected to statistical analysis. Genotypic and phenotypic variances were estimated according to the formula given by Johnson
Significant genotypic variations were observed among the lines for all traits studied except 1000 seeds weight (Table 1). Days to first flowering ranged from 26 to 32 (Table 2). Genotypes BD-7114, M-7118, BD-110455 and BD-7113 required 26 days for first flowering whereas the M-395, M-39-1 and Binasharisha-6 required 32 days for flowering (Table 2). Days to 50% flowering ranged from 28 to 38 (Table 2). Genotype BD-6951 and BD-6954 required the lowest days to 50% flowering while M-42 required the highest days. Days required for siliqua formation ranged from 29 to 36 days (Table 2). Genotype BD-6954 and BD-7113 took 29 days to first siliqua formation and M-119-5, M-397 and M-399 required 36 days. The mean days to maturity ranged from 79 to 95 (Table 2). Genotype M-362, BARI Sarisha-9, BD-110455 and BD-7113 reached at the early physiological maturity stage at 79 days whereas the genotypes NAP-0724-2, BD-7118 and BD-10109 were at delayed by 15-20 days.
Table 1 . Analysis of variances for 19 yield and yield at-tributing characters of 48 rapeseed and mustard genotypes.
Traits | Genotypic | Error | F statistic | |
---|---|---|---|---|
FDF | 4.7 | 0.99 | 4.69 | <0.0001 |
D50%F | 12.8 | 1.27 | 10.1 | <0.0001 |
DSF | 4.3 | 1.31 | 3.17 | <0.0001 |
PlantHt | 595.01 | 94.2 | 6.29 | <0.0001 |
LPB | 578.1 | 153.9 | 3.72 | <0.0001 |
LSB | 372.3 | 120.7 | 3.39 | <0.0001 |
NoPB | 4.1 | 1.49 | 2.67 | <0.0001 |
NoSB | 47.2 | 17.1 | 2.76 | <0.0001 |
NoTB | 31.97 | 13.26 | 2.43 | <0.0001 |
TotalS | 23046 | 4118 | 5.59 | <0.0001 |
NoSMA | 193.5 | 102.5 | 1.89 | 0.004 |
NoSPB | 4314 | 1007 | 4.28 | <0.0001 |
NoSSB | 6588 | 1613 | 4.2 | <0.0001 |
NoSTB | 84.91 | 25.1 | 3.39 | <0.0001 |
NoSPS | 35.7 | 3.81 | 9.38 | <0.0001 |
LenS | 3.86 | 0.16 | 22.86 | <0.0001 |
Y/Plant | 15.6 | 4.41 | 3.5 | <0.0001 |
1000SW | 0.77 | 0.59 | 1.38 | 0.121 |
Maturity | 65.6 | 0.03 | 2361.2 | <0.0001 |
FDF: days to first flowering, D50%F: days to 50% flowering, DSF: days to siliqua formation, PlantHt: plant height, LPB: length of primary branches, LSB: length of secondary branches, NoPB: number of primary branches, NoSB: number of secondary branches, NoTB: number of tertiary branches, TotalS: total number of seeds, NoSMA: number of siliqua in main axis per plant, NoSPB: number of siliqua in primary branches, NoSSB: number of siliqua in secondary branches, NoSTB: number of siliqua in tertiary branches, NoSPS: number of seeds per siliqua, LenS: length of siliqua, Y/Plant: yield per plant, 1000SW: 1000 seeds weight, Maturity: days to maturity.
Table 2 . Estimation of mean, variance components, coeffiients of variability and heritability of the 19 characters.
Traits | Genotypic variance | Phenotypic variance | Mean | Minimum | Maximum | Heritability % | Genotypic coefficient of variance (GCV) (%) | Phenotypic coefficient of variance (PCV) (%) | Genetic advance (GA) | Genetic advance of mean (GAM) (%) |
---|---|---|---|---|---|---|---|---|---|---|
FDF | 1.24 | 2.23 | 28.7 | 26 | 32 | 55.5 | 3.88 | 5.21 | 1.71 | 5.9 |
D50%F | 3.84 | 5.11 | 33.4 | 28 | 38 | 75.1 | 5.87 | 6.78 | 3.5 | 10.5 |
DSF | 1.0 | 2.31 | 32.0 | 29 | 36 | 43.3 | 3.13 | 4.75 | 1.36 | 4.24 |
PlantHt | 166.9 | 261.1 | 94.0 | 53.5 | 138 | 63.9 | 13.7 | 17.2 | 21.3 | 22.6 |
LPB | 141.4 | 295.3 | 70.6 | 36.4 | 142.3 | 47.9 | 16.8 | 24.3 | 16.9 | 24 |
LSB | 83.9 | 204.6 | 30.4 | 0 | 74 | 41 | 30.1 | 47 | 12.1 | 39.7 |
NoPB | 0.86 | 2.36 | 4.9 | 2 | 10 | 36.7 | 19 | 31.4 | 1.16 | 23.7 |
NoSB | 10.01 | 27.14 | 8.79 | 0 | 31 | 36.9 | 36 | 59.3 | 3.96 | 45 |
NoTB | 6.24 | 19.5 | 2.07 | 0 | 34 | 32 | 120.7 | 213.4 | 2.91 | 140.6 |
TotalS | 6309.3 | 10427.3 | 165 | 35 | 642 | 60.5 | 48.2 | 61.9 | 127.3 | 77.2 |
NoSMA | 30.3 | 132.8 | 29.5 | 0 | 55 | 22.8 | 18.7 | 39.1 | 5.42 | 18.4 |
NoSPB | 1102.3 | 2109.3 | 89.2 | 21 | 256 | 52.3 | 37.2 | 51.5 | 49.4 | 55.5 |
NoSSB | 1658.3 | 3271.3 | 44.7 | 0 | 322 | 50.7 | 91.1 | 128 | 59.7 | 133.7 |
NoSTB | 19.9 | 45 | 1.63 | 0 | 55 | 44.3 | 273.7 | 411.1 | 6.13 | 375.9 |
NoSPS | 10.6 | 14.4 | 18.1 | 9 | 28 | 73.6 | 18 | 21 | 5.76 | 31.9 |
LenS | 1.23 | 1.39 | 5.76 | 3 | 9.7 | 88.5 | 19.3 | 20.5 | 2.15 | 37.3 |
Y/Plant | 3.72 | 8.12 | 4.56 | 0.26 | 14.2 | 45.8 | 42.3 | 62.5 | 2.69 | 58.9 |
1000SW | 0.06 | 0.65 | 2.44 | 0.31 | 9.86 | 9.6 | 10.2 | 33 | 0.16 | 6.5 |
Maturity | 21.9 | 21.9 | 88.7 | 79 | 95 | 99.9 | 5.27 | 5.27 | 9.62 | 10.9 |
High broad sense heritability was estimated for phenological traits indicating an increase of dependent variables (e.g. yield per plant) along with independent variables. High heritability was possessed by the days to first flowering (55.5%), days to 50% flowering (75.1%), plant height (63.9%), total number of siliqua (60.5%), number of siliqua in primary branches (52.3%), number of siliqua in secondary branches (50.6%), number of seeds per siliqua (73.6%), length of siliqua (88.5%) and days to maturity (99.9%) while days to first siliqua formation (43.3%), length of primary branches (47.9%), length of secondary branches (41%), number of siliqua in tertiary branches (44.3%) and yield per plant (45.8%) showed moderate heritability (Table 2). Days to maturity showed the similar phenotypic coefficient of variance (PCV) and genotypic coefficient of variance (GCV) values (5.3) while days of first flowering (3.9 and 5.2), days to 50% flowering (5.9 and 6.8), days to first siliqua formation (3.13 and 4.75), plant height (13.7 and 17.2), length of primary branches (16.8 and 24.3), total number of siliqua (48.15 and 61.90), number of seeds per siliqua (18.02 and 21.01) and length of siliqua (19.3 and 20.5) possessed almost similar values (Table 2). On the contrary, length of secondary and tertiary branches; number of primary, secondary and tertiary branches; number of siliqua in main axis, primary branches, secondary branches and tertiary branches; yield per plant and days to maturity observed more than 15% differences between GCV and PCV value (Table 2). Days to first flowering (5.96%), days to siliqua formation (4.2%) and 1000 seeds weight (6.5%) accounted for lower genetic advance of mean (GAM). While the days to 50% flowering (10.5%), number of siliqua in main axis (18.4%) and days to maturity 10.9%) recorded a comparatively moderate GAM (Table 2). Other variables showed a high GAM and accounted for low to moderate heritability along with more than 15% difference between GCV and PCV (Table 2).
The first three principal components (PC) explained about 60% of total variation of the dataset where PC1, PC2 and PC3 individually explained 36.6%, 15.1% and 7.7% data variation respectively, which are statistically significant for varieties (Table 3). PC4 and PC5 individually explained 7% and 6% data variations, respectively and these two PCs did not explain any varietal variations. PC1 accounted positive coefficients for all the traits but plant height, length of primary branches, length of secondary branches, total number of siliqua, number of siliqua in primary and secondary branches and yield per plant had comparatively higher coefficients (Table 3). On the other hand, PC2 showed genotypic variation for the positive coefficients of the days to first flowering, days to 50% flowering, days to first siliqua formation, plant height, length of primary branches, number of primary branches, number of siliqua in main axis and primary branches, number of seeds per siliqua, length of siliqua, weight of 1000 seeds, days to maturity and for the negative coefficients of the length of secondary branches, number of secondary and tertiary branches, total number of siliqua, number of siliqua in secondary and tertiary branches, yield per plant (Table 3). PC3 accounted for large negative coefficient for the number of primary and secondary branches, number of siliqua in primary branches, number of seeds per siliqua, length of siliqua and for the positive coefficient of 1000 seed weight (Table 3). Variables which gave positive and negative coefficient for the PC1 and PC2 are distributed in the loading plot (Fig. 2). Loading plot exhibited that the PC1 that explained the largest amount of varietal variation showed separation due to greater coefficient of total of seeds, number of siliqua in primary and secondary branches, length of primary and secondary branches, number of secondary branches, plant height and yield per plant compared to lower coefficient of 1000s seed weight, length of siliqua and number of seeds per siliqua (Fig. 2A). The PC scores of the genotypes separated them from each other due to variability in mor-phological traits along PC1 and PC2 (Fig. 2B). PC2 se-parated short duration genotypes for their negative PC scores coupled with negative coefficients of days to first flowering, days to 50% flowering, days to first siliqua formation and days to maturity compared to long duration genotypes for their contrasting features (Fig. 2B).
Table 3 . Coefficient of PCs of different traits of the 48 rapeseed and mustard genotypes.
Variable | PC1 | PC2 | PC3 |
---|---|---|---|
FDF | 0.14 | 0.42 | 0.23 |
D50%F | 0.13 | 0.46 | 0.17 |
DSF | 0.14 | 0.44 | 0.17 |
PlantHt | 0.26 | 0.23 | 0.06 |
LPB | 0.29 | 0.04 | 0.08 |
LSB | 0.25 | ‒0.25 | 0.13 |
NoPB | 0.21 | 0.00 | ‒0.31 |
NoSB | 0.28 | ‒0.12 | ‒0.12 |
NoTB | 0.23 | ‒0.28 | 0.07 |
TotalS | 0.36 | ‒0.13 | 0.03 |
NoSMA | 0.18 | 0.07 | 0.09 |
NoSPB | 0.32 | 0.03 | ‒0.10 |
NoSSB | 0.32 | ‒0.23 | 0.10 |
NoSTB | 0.20 | ‒0.23 | 0.10 |
NoSPS | 0.09 | 0.15 | ‒0.56 |
LenS | 0.11 | 0.19 | ‒0.50 |
Y/Plant | 0.31 | ‒0.04 | 0.02 |
1000SW | 0.02 | 0.07 | 0.36 |
Maturity | 0.16 | 0.17 | ‒0.14 |
% variation | 36.6 | 15.1 | 7.7 |
<0.001 | <0.001 | <0.001 |
In this study, yield per plant exhibited significant and direct relationship with days to first flowering, days to 50% flowering, days to first siliqua formation, plant height, length of primary branches, length of secondary branches, number of primary branches, number of secondary branches, number of tertiary branches, total number of siliqua, number of siliqua in main axis, number of siliqua in primary, secondary and tertiary branches, length of siliqua and days to maturity but the relationship was non-significant with 1000 seed weight (Supplementary Table S3).
In the research work, the mean performance of different clusters calculated for different traits revealed a wide range of differences among clusters concerning these traits (Supplementary Table S4). The cluster analysis for 19 studied traits in 48 genotypes distributed the traits into 3 clusters. Cluster III represented the hierarchal mean values whereas the cluster II represented the highest mean values for most of the variables (Supplementary Table S4). Cluster I represented the traits which are responsible for the earliness of any genotypes, i.e. days to first flowering, days to 50% flowering, days to siliqua formation, 1000 seeds weight and days to maturity (Fig. 3). Cluster II represented the genetic composition of traits such as plant height, length of primary and secondary branches, number of tertiary branches, total number of seeds, number of siliqua in main axis per plant, number of siliqua in secondary and tertiary branches and yield per plant (Fig. 3).
Oil content ranged from 35.1 to 41.2% with an average of 37.7 where genotype M-119-5 significantly (
Table 4 . Percentage of different oil content (%) and fatty acid content (%) in the 10 selected rapeseed and mustard genotypes.
Genotype | Oil content | Palmetic | Stearic | Oleic | Linoleic | Linolenic | Arachidic | Myristic | Palmitolic | Hepa | Alpha | Ecosa | Behenic | Ligniceric | Trico |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
M-119-5 | 41.1a | 2.89 d | 1.42 de | 16.9 bc | 14.4 cd | 7.15 α-d | 7.95 cd | 0.11 e | 0.14 e | 0.12 d | 0.77 ab | 0.32 c | 0.46 a | 0.52 d | 0.23 a |
M-395 | 40.1 b | 2.87 de | 1.39 de | 17.8 abc | 14.4 cd | 7.46 abc | 9.67 a | 0.07 f | 0.16 cde | 0.12 d | 0.81 a | 0.45 ab | 0.28 d | 0.57 d | 0.21 ab |
M-262 | 37.9 c | 2.36 e | 1.23 e | 16.8 bc | 14.2 d | 7.8 a | 9.2 ab | 0.04 f | 0.15 de | 0.12 d | 0.66 bc | 0.3 c | 0.16 f | 0.8 b | 0.2 b |
Tori-7 | 37.9 c | 2.59 de | 1.23 e | 15.5 cd | 15.5 bcd | 7.62 ab | 8.1 bc | 0.08 ef | 0.17 cde | 0.19 a | 0.79 a | 0.47 a | 0.37 bc | 0.69 c | 0.18 c |
BARI Sarisha -14 | 40.1 b | 2.77 de | 1.38 de | 14.2 d | 14.6 cd | 6.71 bcd | 6.76 d | 0.11 e | 0.11 f | 0.11 d | 0.73 abc | 0.31 c | 0.33 c | 0.99 a | 0.14 d |
BD-6953 | 35.3 d | 4.56 b | 2.35 a | 19.5 a | 17.3 ab | 6.32 de | 7.86 cd | 0.5 b | 0.19 c | 0.18 abc | 0.76 ab | 0.4 b | 0.28 d | 0.61 cd | 0 e |
BD-6954 | 35.9 d | 3.88 c | 1.79 bc | 18.3 ab | 16.6 bc | 5.62 e | 8.83 abc | 0.17 d | 0.23 b | 0.16 bc | 0.77 ab | 0.45 ab | 0.38 bc | 0.52 d | 0 e |
BD-10112 | 38.3 c | 3.9 c | 1.81 b | 16.3 cd | 16.7 bc | 7.87 a | 7.86 cd | 0.39 c | 0.16 de | 0.15 c | 0.62 c | 0.47 a | 0.25 de | 0.84 b | 0 e |
BD-10455 | 35.3 d | 5.66 a | 2.45 a | 18.7 ab | 19.4 a | 6.53 cde | 8.1 bc | 0.6 a | 0.43 a | 0.18 ab | 0.69 bc | 0.41 b | 0.23 e | 0.56 d | 0 e |
BD-7113 | 35.5 d | 3.61 c | 1.56 cd | 18.8 ab | 16.6 bc | 7.52 abc | 9.29 ab | 0.12 e | 0.17 cd | 0.17 abc | 0.71 abc | 0.47 a | 0.39 b | 0.68 c | 0 se |
a, b, c: significantly (
Yield is a complex trait being influenced by several interdependable quantitative characters. Thus selection for yield may not be effective unless the other yield components influencing it directly or indirectly are taken into consideration. Determination of correlation coefficients is an important statistical procedure to evaluate traits contributions to seed yield (Khan
Seed yield variable is complex along with the yield contributing characters those are considered as a collection of the defined gene pool. Genetic variability is measured for further improvement of desirable variety (Kempthorne 1957). Other than heritability, genotypic coefficient of var-iability and phenotypic coefficient of variability are found to be significant in the genetic advancement of the selected traits (Saini and Sharma 1995; Lekh
In general, PCA is applied to find out the association and contrast between traits which are responsible for creating variation among the genotypes (Belete 2011). In loading plot and biplot genotypes along with the associated traits having different locations perhaps accounted for variation in their genetic components (Fig. 2). PC1 separated BD-10112 from other genotypes (Fig. 2). From this, it can be commented on that this particular genotype bears a genetic constitution that favours higher yield and the higher yield is associated with total number of siliqua, number of secondary branches, number of siliqua in primary branches, number of siliqua in secondary branches per plant. PC2 differentiated short duration genotypes such as BD-10455, BD-7113, BD-6954 and BD-6953 from other genotypes as it mostly accounted for first day of flowering, days to 50% flowering, days to first siliqua formation and days to maturity which are considered as traits inducing earliness (Fig. 2).
From correlation analysis, maturity related traits and yield contributing characters showed a significant positive correlation among them indicating that increasing days to maturity enhance yield. Plant height, number of primary branches, number of siliqua per plant and 1000 seeds weight were positively correlated with yield per pant which exhibited resemblance with previous studies—Afrin
Besides, cluster analysis can be used as a suitable method for classifying the high yielding genotypes and component traits (Islam and Islam 2000). It is expected that traits that are under the same group will have similar behavior (Fig. 3). For example, traits related to growth duration were clustered in cluster I and the yield contributing traits were clustered in cluster II (Fig. 3).
The presence of genetic variation for fatty acid composition is found to be essential for improving the new cultivars with high oil quality (Murphy 1955; Ohlrogge 1994). A large number of plants have been evaluated for their seed oil content and fatty acids from which some have been introduced as new oilseed species (Velasco
A wide range of variation exhibited by the 48 rapeseed-mustard genotypes provides a large scope of selection for superior and desired genotypes. Considering both heritable and non-heritable components in the total variability—the following genotypes BD-6953, BD-6954, BD-10455, BD-10112 and BD-7113 have desirable criteria for exploiting them in future breeding programs for the development of nutritionally better as well as locally adaptable short duration cultivars.
The first author, MAUI, gratefully acknowledges his NST fellowship. The authors are grateful to the Bangladesh Agricultural Research Institute for giving access to gas chromatography facilities.
AHKR conceived the study. MAUI designed the experiments. MAUI and JAN conducted field experiments. MAUI analyzed the data and wrote the manuscript. AHKR critically revised the manuscript. All authors approved the final version of the manuscript.
This research was supported by the funding from Bangladesh Agricultural University Research Systems (Grant no. 2018/604/BAU).
No conflicts of interest exist.
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