Genetic and Agromorphological Characterization of Sesame (Sesamum indicum L.) Genotypes for Dehiscence-Related Traits and Grain Yield Performances

Abstract

Sesame is of global economic, nutritional, and health importance. However, capsule dehiscence remains a major constraint in sesame production, causing significant yield losses. Thus, the objectives of this study were to (1) determine the agromorphological and genetic variability of 160 sesame genotypes based on the grain yield and traits related to capsule dehiscence and (2) identify promising parental lines for developing non semi-dehiscent genotypes. The experiment was conducted at the International Institute of Tropical Agriculture (IITA) in the Benin Republic, using an alpha-lattice 32 × 5 design with three replications. Data were collected on 25 traits, including 19 quantitative and six qualitative traits contributing to dehiscence and yield performance. The phenological and agromorphological traits, yield, and yield components of sesame cultivars were evaluated, along with genetic parameters and clusters of different groups based on their dehiscence tolerance level. Capsule dehiscence was evaluated using the sun-drying and oven-drying methods at 60°C for 6 hours. The capsule split (ECS, %) and capsule open (ECO, %) were calculated to quantify dehiscence. Descriptive and inferential statistics were performed. After classification, three clusters were identified. The first group with less resistance to shattering had shorter sizes, lower yields, fewer capsules, and smaller capsular zones. However, they had better earliness. The highly resistant individuals with a low dehiscence rate exhibited a high plant height, capsule number, and capsule length. The intermediate group with normal dehiscence rates had moderate values for all attributes. Traits such as capsule split, capsule open, number of capsules per plant, and potential yield per genotype exhibited high genetic variability. ECS and ECO recorded the highest coefficients of genotypic and phenotypic variation. The results of this study provide valuable information for the development of indehiscent and semi-dehiscent sesame genotypes with good seed retention capacity at maturity, minimizing losses due to capsule dehiscence and ultimately benefiting sesame producers and the oilseed industry.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Oilseeds are the second most economically significant agricultural group after cereals. Sesame (Sesamum indicum L.) is widely cultivated due to its rich nutritional profile and associated health benefits [1]. Over the past 60 years, global sesame production has increased significantly, with Asia and Africa as the primary producers [2]. Major global producers include China, India, Myanmar, and African countries such as Sudan, Ethiopia, and Nigeria. In Africa, sesame is an important cash crop that supports the livelihoods of smallholder farmers, particularly in regions with limited rainfall where the crop’s drought resilience is advantageous [3]. In West Africa, Benin is emerging as an important sesame-producing country [4]. Sesame production in Benin continues to grow, with significant potential driven by the increasing global demand for sesame oil and seeds. Sesame cultivation is intense in the northern regions of Benin, where it is integrated into smallholder farming systems and typically grown in rotation with other crops. This crop diversification helps improve soil fertility and supports income generation for rural communities.

Sesame is valued not only for its economic role in local and international markets but also for its rich nutritional profile, which includes proteins, unsaturated fatty acids, and antioxidants. These characteristics make sesame highly sought after for oil production, food applications, and nutraceuticals. Sesame crop provides a source of income for smallholder farmers, supporting rural economies and contributing to rural development. The bioactive and nutritional components of sesame seeds have expanded the seed market to include pharmaceuticals, food, and, cosmetics [5]. Sesame’s role in biodiesel production has also expanded, enhancing its importance as a renewable energy source in addition to its other diverse applications. Seeds are a significant source of proteins, lipids, and lignans that contribute to their antioxidant, cholesterol-lowering, and anti-inflammatory properties [6]. Sesame oil contains sesamin, sesamol, and sesamolin, which have health-promoting properties and can increase the shelf life of foods by preventing cardiovascular and metabolic diseases [7].

Globally, the sesame unit yield remains low, averaging only 577.9 kg per hectare [8]. Despite significant demand, sesame productivity suffers due to certain wild characteristics in most genotypes, such as capsule dehiscence, indeterminate growth habits, and asynchronous capsule maturation, contributing to low crop yields [9–11]. Capsule dehiscence is a major constraint in sesame production, reducing farmers’ incomes and profitability. In many West African communities, sesame cultivation is a source of cash for many smallholder farmers. Yield losses due to dehiscence can range from 30% to 50%, severely reducing the volume of marketable produce [12, 13]. For smallholder farmers, who often operate on marginal incomes, this loss translates directly into reduced household earnings. With lower yields, sesame farmers face diminished bargaining power in local and international markets, limiting their ability to capitalize on sesame’s rising demand. For women, who play a significant role in sesame cultivation and postharvest processing, these losses can further deepen gender inequalities by limiting their economic independence. This income reduction hinders their capacity to invest in better agricultural inputs, such as improved seeds, fertilizers, and farming equipment, thereby perpetuating a cycle of low productivity and poverty.

Capsule dehiscence in sesame is a complex trait influenced by multiple genetic factors. Sesame capsules dehisce along preformed lignified suture lines (septa) when mature. The lignification of the septa and valve margins creates tension as the capsule dries, leading to explosive pod rupture [14, 15]. Histological studies reveal that shattering-resistant genotypes exhibit thicker parenchyma cell layers and reduced lignification in the capsule walls, delaying dehiscence [14, 16]. For instance, the indehiscent mutant cl1 shows altered adaxial/abaxial cell polarity, resulting in thicker capsule walls and delayed splitting [16]. Environmental stressors like humidity fluctuations and mechanical disturbance exacerbate shattering by altering capsule moisture content and lignification patterns [17, 18]. Paclobutrazol (PBZ), a gibberellin biosynthesis inhibitor, reduces shattering by modulating plant architecture and delaying senescence. The application of 450 mg L⁻¹ PBZ significantly lowered shattering losses in rainfed conditions by enhancing capsule integrity and synchronizing maturation [17]. SiCL1 gene, a homolog of KANADI1 (a transcription repressor), has been identified to control both leaf curling and capsule indehiscence in sesame. Mutations in SiCL1 lead to thickened capsule walls and reduced lignification in the dehiscence zone, resulting in indehiscent capsules [16]. High-density SNP maps identified a major shattering-related QTL on Chromosome 8 near marker S8_5062843, associated with the KAN1 homolog. This locus explains 78% of phenotypic variance in shattering, providing a genomic anchor for marker-assisted selection [18]. However, non-dehiscent mutants (e.g., cl1) developed through traditional breeding exhibit severe pleiotropic effects such as cupped leaves, twisted stems, short seed pods, semi-sterility, and low yield limiting their utility in breeding programs [13, 19]. Polygalacturonases (PGs), enzymes that degrade pectin in cell walls, are critical for dehiscence. Downregulation of PG genes via RNA interference (RNAi) in transgenic sesame delayed capsule opening by 99% and reduced cell wall degradation in the dehiscence zone [20].

Traditional methods aim to minimize capsule dehiscence, such as harvesting at physiological maturity and selecting extra-early cultivars. However, these methods are often limited. In traditional sesame systems, harvesting at physiological maturity led to yield reduction due to incomplete seed development in upper branches [21]. Mechanized harvesting poses challenges due to capsule dehiscence, necessitating widespread reliance on labor-intensive manual harvesting [15, 22]. Close-capsule genotypes have been explored but have limited adoption due to pleiotropic effects, including severe leaf curl, reduced plant height, stem twisting, and partial sterility [16, 23]. While non-dehiscent varieties obtained through mutation breeding have been developed to reduce seed loss, these face difficulties in threshing, which compromises seed quality [13, 23, 24]. Thus, developing semi-indehiscent genotypes with reliable seed retention at maturity could help minimize losses associated with capsule dehiscence and improve yield sustainability [15, 25].

While significant progress has been made in understanding the genetic basis of capsule dehiscence, there is still a need for comprehensive studies that integrate genetic, phenotypic, and environmental data to develop resilient, high-yielding sesame varieties. Understanding the phenotypic diversity and genetic variability associated with dehiscence traits and yield components is important for breeding resilient, high-yield sesame varieties. Therefore, this study aims to characterize the genetic and agromorphological variability of sesame genotypes, focusing on the grain yield and capsule dehiscence traits, to identify promising parental lines for developing semi-dehiscent varieties. Specifically, it is to (1) evaluate phenological and agromorphological traits, yield components, and dehiscence of sesame cultivars; (2) estimate genetic parameters (genotypic and phenotypic variances, genotypic and phenotypic coefficients of variation, broad-sense heritability, and genetic gain); and (3) categorize sesame varieties based on their dehiscence tolerance level.

2. Methodology

2.1. Plant Materials and Field Experimental Trials

The experiment was conducted at the International Institute of Tropical Agriculture (IITA) (6250 30 ′N, 2190 460 ′E) situated in the township of Abomey-Calavi, in the southern part of Benin Republic. The climate is subequatorial, with two rainy seasons, a long one from April to July and a short one from September to November, and two dry seasons, a long one from December to March and a short one from August to September [26]. The experiment was conducted during the two distinct rainy seasons in the study area. The first experiment was conducted during the major rainy season (April to July 2023), while the second experiment was carried out during the minor rainy season (September–November 2023). The area is located at an altitude of 17 m and is dominated by ferralitic soils [27]. The relative humidity ranged from 82% to 86%. The climatic conditions during the trials are presented in Table 1 [28]. The study involved 160 sesame genotypes (Supporting Table S1), which were tested for yield and dehiscence characteristics. The 160 sesame genotypes used in this study originate mainly from Benin, Niger, and Senegal. The genotypes include local seeds collected from farmers in Benin and Niger [4, 29], as well as recognized varieties provided by gene banks from ISRA_CERAAS, the University of Abomey-Calavi (GbioS), and Abdou Moumouni University of Niger. The field experimental layout was set up in an alpha-lattice 32 ${}^{\mathrm{\ast }}$ 5 with three replications. The experiment was set up using an alpha-lattice block design [30]. The alpha-lattice design was selected due to its superior ability to control spatial variability, accommodate large numbers of genotypes, and improve statistical precision under heterogeneous field conditions [31, 32]. Alpha-lattice design is used in plant breeding and is recommended to be the most appropriate for variety yield trials [33]. The use of alpha-lattice designs is highly recommended in agricultural research, particularly for trials involving a large number of genotypes or treatments. [32, 34, 35]. The experimental layout was generated in R using the FielDHub package ensuring a random distribution of genotypes [36]. A total of three replications were conducted. Each replication included 32 blocks, with each block subdivided into five plots, resulting in 160 plots per replication (32 × 5 = 160). Thus, the experiment comprised a total of 480 plots (3 replications × 32 blocks × 5 plots). Each genotype was randomly assigned to a plot within each replication, ensuring that each genotype was repeated three times (once per replication). Each elementary plot measured 2 m in length by 1.5 m in width. The plot contained 15 plants arranged in three rows. The spacing between rows was set at 0.5 m, while the spacing between individual plants within each row was maintained at 0.5 m [29, 37]. The spacing between blocks was 0.6 and 2 m between replications. Thinning of seedlings was carried out 2 weeks after sowing, leaving one seedling in each hole.

Table 1

Experimental site climatic conditions during the experiments.

Month	Total rainfall (mm)	Temperature (°C) min–max	Relative humidity (%)	Saison
April	101.00	26.00–30.70	82	Major rainy season
May	179.00	25.2–29.70	84
June	226.00	24.1–28.44	87
July	137.00	23.5–27.30	85
August	69.50	23.11–27.85	85
September	239.00	23.5–27.80	86	Minor rainy season
October	159.00	24.88–28.82	86
November	86.00	25.90–29.90	83

Note: Source [28].

2.2. Data Collection

A total of 25 traits, 19 quantitative and 6 qualitative traits contributing to dehiscence and yield assessment, were recorded using standard procedures described for sesame (IPGRI and NBPGR, 2004) on five plants in the middle rows for each experimental unit. The data were collected during the first rainy season and the second rain season. The yield and yield components include days to first flower initiation (InF), days to 50% flowering (HalFl), days to the first capsule (InC), days to maturity (MP), plant height (Haut) in cm, height of the first branch (HIPB) in cm, internode length (LEN) in cm, height of the first capsule (HIPC) in cm, number of capsules per plant (NCP), length of capsule-bearing zone (ZoC) in cm, number of capsules per leaf axile (NCA), 1000 seed weight (P1000G) in g, potential yield per genotype (Rdt), capsule length (Lonc) in mm, and capsule width (LaC) in mm. For the capsule dehiscence-related traits, the capsule split before drying of capsules in mm (CS-1), capsule split after drying of capsules in mm (CS-2), the capsule open before drying of capsules in mm (CO-1), and capsule open after drying of capsules in mm (CO-2) were recorded. The qualitative traits were growth type (indeterminate, determinate); leaf arrangement (alternate, opposite, ternate, mixed); lodging score (none, low, moderate, severe); growth habit (erect, prostrate, semi-erect); capsule arrangement (monocapsular, multicapsular); and leaf shape (elliptic, lanceolate, linear, narrowly cordate, ovate).

2.3. Evaluation of Capsule Dehiscence

Capsule dehiscence was assessed by collecting capsules at the maturity stage between physiological maturity when approximately three-quarters of the seeds in the capsule are fully mature but still attached to the plant and harvest maturity, marked by the first dry capsules. Controlled drying treatments were applied to standardize the assessment of capsule resistance to shattering. While sun-drying is a natural method compared to field conditions, it is influenced by environmental variability, leading to inconsistencies in drying rates and dehiscence measurements. Meanwhile, oven-drying at a constant temperature was also employed to accelerate dehiscence under controlled conditions, ensuring uniformity and comparability of genotype performance. Oven-drying at 40°C–60°C has been shown to preserve seed quality while providing consistent data for shattering assessments [38]. Capsules were dried at a constant temperature of 60°C for 6 hours in an oven to accelerate dehiscence and provide a controlled comparison of capsule resistance to shattering [39–42].

Observations were recorded on capsule split and capsule opening as seeds dried [43]. Three plants per genotype were harvested randomly, and 15 matured capsules of each genotype were placed in khaki envelopes for 2 days at room temperature to allow their seed moisture content to equilibrate [41, 44]. The Capsule Split (ECS, %) was determined using CS-1 and CS-2 measurements. ECS represents the percentage increase in the split between carpels after drying compared to before drying, quantifying how much the capsule structure separates along the suture. CS-1 was measured before oven-drying as the extent of the split between carpels exposing the membrane but not the seed. It was recorded from the base to the top of the seed chamber along the suture. CS-2 was measured as the distance (in mm) between carpels exposing the membrane but not the seed after oven-drying it. This was measured from the base to the top of the seed chamber along the suture for capsules sampled for CS-1. The capsule open (ECO, %) was calculated using CO-1 and CO-2 measurements. ECO represents the percentage increase in the opening between carpels after drying compared to its initial state before drying, measuring how much the capsule exposes the seed chamber post-drying. CO-1 and CO-2 were recorded before and after oven-drying as the distance between carpels where the membranes had opened enough to expose the seed or seed chamber. This was measured from the base to the top of the seed chamber along the placenta in the same sampled capsules. Capsule dehiscence parameters were recorded based on the methods of Haibru and Dash [43, 44], which were used to calculate the ECS (I) and ECO (II).

I. Capsule Split (ECS%) = $CS-2-CS-1/CL\times 100$,

II. Capsule Open (ECO%) = $\text{CO}2-CO-1/CL\times 100$.

2.4. Data Analysis

R version 4.3.3 software was used for the data analysis. The data were collected during two rainy seasons, and their means were used for data analysis. Quantitative data were explored to identify and exclude outlier variables using the R package “outliers” [45, 46]. The psych package [47] was used to calculate descriptive statistical analysis (frequency, mean, standard deviation, minimum, maximum, variance, and coefficients of variation). Analysis of variance (ANOVA) was performed to assess whether the variations observed at the variety level for all quantitative traits were significant. According to the Student–Newman–Keuls (SNK) test at the 5% confidence level, ranking of means was performed in case of significant difference. The relationships between the different variables were determined using the Pearson correlation coefficients. Their significance was tested with the Pearson test using the package Hmisc. Then, the principal component analysis (PCA) was used to determine relationships between genotypes on a limited number of orthogonal axes. The hierarchical ascending classification (HAC) grouped genotypes into more or less homogeneous classes. After hierarchical classification, five genotypes per class were selected to provide a representative view of the general trends observed among the 160 genotypes studied. Tables 2, 3, and 4 present a subset of genotypes to illustrate the most significant variations in the traits measured. The dendrogram was plotted using Ward’s aggregation method (minimization of the intraclass variance). These multivariate analyses were performed using the “FactoMineR” and “Factoshiny” packages [48]. Genetic parameters were estimated from the components of the ANOVA. Genotypic and phenotypic variances, genotypic and phenotypic coefficients of variation, broad heritability, and expected genetic gain were calculated according to the formulas used by Abate [49], Chowdhury [50], and Singh [51]. To estimate the diversity among the genotypes via qualitative traits, the Shannon–Weaver diversity index (H^′) was computed using the phenotypic frequencies to assess the phenotypic diversity for each character for all accessions. The Shannon diversity index ($H^{\prime }$) was calculated using the Shannon–Weaver equation reported by Hennink & Zeven [52],$$\begin{array}{}\text{(3)}& H=-{\displaystyle \sum }_{i=1}^{n}pi\ln pi,\end{array}$$where n is the number of phenotypic classes for a character and pi is the proportion of the total number of entries consisting of the ith class. To express the values of $H^{\prime }$ in the range of 0–1, each value of $H^{\prime }$ was divided by ln n (where ln n represents the natural logarithm of (n)),$$\begin{array}{}\text{(4)}& H=\frac{H}{H\max }\text{\hspace{0.17em}and\hspace{0.17em}}H\max =\ln N.\end{array}$$

Table 2

ANOVA for phenological traits.

GEN	InF	InC	HalFl	MP
GEN120	34.63 ± 4.1b	39.75 ± 0.55d	47.04 ± 1.52d	96.67 ± 4.04cdefg
GEN14	36.55 ± 1.92b	38.22 ± 0.11d	47.01 ± 1.07d	85.67 ± 9.29fg
GEN115	35.55 ± 2.17b	38.39 ± 0.14d	46.88 ± 0.69d	94.67 ± 1.53defg
GEN17	30.55 ± 0.19b	33.48 ± 0.23e	48.34 ± 0.7d	82.33 ± 5.77g
GEN106	34.88 ± 3.72b	39.52 ± 0.12d	48.65 ± 0.62d	90 ± 1.73efg
GEN46	45.98 ± 0.75a	48.31 ± 1.82c	55.86 ± 3.03c	100.49 ± 1.71cdef
GEN132	46.75 ± 0.94a	49.81 ± 1c	53.77 ± 1.15c	103.87 ± 0.35bcde
GEN64	48.65 ± 1.46a	51.75 ± 1.3bc	62.5 ± 2.65b	104.36 ± 3.26bcd
GEN104	45.74 ± 1.41a	54.12 ± 0.7ab	67.44 ± 2.65a	101.97 ± 4.95bcde
GEN140	45.88 ± 1.68a	55.44 ± 1.17ab	61.77 ± 1.53b	106.5 ± 1.8bc
GEN152	52.23 ± 2.66a	55.15 ± 1.9ab	69.72 ± 1.07a	128.76 ± 1.08a
GEN49	53.07 ± 3.2a	56.38 ± 2.17a	68.86 ± 1.27a	113.08 ± 8.59b
GEN13	53.39 ± 0.55a	57.78 ± 0.6a	70.41 ± 3.63a	123.74 ± 1.1a
GEN125	52.55 ± 7.12a	56 ± 5.2ab	54.63 ± 3.87c	106.82 ± 2.05bc
GEN99	50.52 ± 5.37a	54.16 ± 1.01ab	71.44 ± 1.71a	112.02 ± 5.13b

Note: Means followed by the same alphabetic letters are not significantly different at the 0.05 level according to the SNK test. Days to first flower initiation (InF), days to 50% flowering (HalFl), days to the first capsule (InC), days to maturity (MP).

Table 3

ANOVA for yield and related trait.

GEN	Haut	HIPB	LEN	HIPC	NCP	ZoC	P1000G	Rdt
GEN120	95.13 ± 3.84d	45.5 ± 1.32a	7.72 ± 0.35a	53.71 ± 2.85a	53.33 ± 6.55h	35 ± 3.48cde	1.19 ± 0.3e	257.1 ± 8.26d
GEN14	104.17 ± 3.34d	37.04 ± 1.92bc	4.67 ± 0.61de	44.14 ± 1.73bc	56.22 ± 3.33gh	30.54 ± 0.7e	2.19 ± 0.23d	259.43 ± 35.69d
GEN115	104.54 ± 4.35d	44.39 ± 4.45ab	6.16 ± 1.34bc	46.95 ± 1.24abc	46.22 ± 4.41h	34.43 ± 1cde	2.84 ± 0.14bcd	257.32 ± 37.19d
GEN17	98.41 ± 3.45d	43.72 ± 3.43ab	7.08 ± 0.98ab	48.24 ± 2.18abc	47.29 ± 8.77h	34.44 ± 3.75cde	2.53 ± 0.38cd	308.12 ± 46.77d
GEN106	99.03 ± 1.26d	41.84 ± 2.53ab	6.63 ± 0.99abc	45.86 ± 2.78abc	48.78 ± 1.09h	31.56 ± 1.02de	3.49 ± 1.04abc	322.57 ± 22.42d
GEN46	167.17 ± 1.53a	18.66 ± 1.4ef	4.12 ± 0.2e	43.72 ± 1.51c	129.33 ± 2.17c	58.32 ± 1.18a	3.69 ± 0.37abc	745.63 ± 4.11a
GEN132	175.01 ± 9.69a	18.61 ± 1.27ef	4.43 ± 0.36de	33.51 ± 3.75d	155.71 ± 4.04a	54.5 ± 3.17a	3.93 ± 0.32ab	725.17 ± 27.63a
GEN64	165.22 ± 1.17a	16.6 ± 2.1f	4.43 ± 0.29de	36.8 ± 3.89d	158.56 ± 3.75a	47.1 ± 1.61b	3.27 ± 0.02abcd	744.98 ± 2.11a
GEN104	170.51 ± 5.93a	24.44 ± 3.39de	4.38 ± 0.11de	46.89 ± 0.19abc	126.45 ± 2.15c	49.32 ± 1.51b	4.04 ± 0.86ab	727.41 ± 11.54a
GEN140	172.63 ± 4.31a	25.17 ± 3.88de	4.57 ± 0.2de	46.45 ± 3.74abc	146.47 ± 3.3b	55.55 ± 0.69a	3.86 ± 0.71ab	740.73 ± 31.19a
GEN152	103.4 ± 6.99d	32.25 ± 2.23cd	5.85 ± 0.53bcd	46.77 ± 1.19abc	66.6 ± 2.14fg	35.33 ± 1.76cde	2.54 ± 0.41cd	436.62 ± 112.6c
GEN49	150.58 ± 9.83b	25.76 ± 0.87de	4.35 ± 0.28de	35.75 ± 1.6d	117.56 ± 4.85d	47.2 ± 1.37b	4.31 ± 0.29a	574.9 ± 58.55b
GEN13	125.25 ± 3.83c	32.59 ± 7.73cd	6.11 ± 0.63bc	52.91 ± 7.33ab	77.29 ± 10.55e	38.22 ± 0.69cd	3.58 ± 0.32abc	424 ± 52.49c
GEN125	154.33 ± 4.17b	26.48 ± 0.71de	6.49 ± 0.18abc	45.33 ± 2.33abc	64.78 ± 2.52fg	34.75 ± 3.95cde	3.39 ± 0.43abcd	481.2 ± 7.57c
GEN99	168.5 ± 7.02a	29.09 ± 4.91d	5.27 ± 0.49cde	43.5 ± 5.31c	75 ± 4.34ef	39.87 ± 7.47c	4.02 ± 0.51ab	435.8 ± 60.31c

Note: Means followed by the same alphabetic letters are not significantly different at the 0.05 level according to the SNK test. Plant height (Haut) in cm, Height of the first branch (HIPB) in cm, internode length (LEN) in cm, length of the first capsule (HIPC) in cm, number of capsules per plant (NCP), length of capsule-bearing zone (ZoC) in cm, number of capsules per leaf axile (NCA), 1000 seed weight (P1000G) in g, potential yield per genotype (Rdt).

Table 4

ANOVA for dehiscence traits.

GEN	Lonc	LaC	Cs1	Cs2	ECS	CO2	CO1	ECO
GEN120	16.24 ± 0.62f	5.56 ± 0.34a	2.59 ± 0.26a	7.57 ± 0.34a	30.76 ± 4.41a	14.21 ± 0.37c	8.38 ± 0.43d	36.03 ± 5.74b
GEN14	18.67 ± 0.44ef	5.09 ± 0.48a	2.59 ± 0.26a	7.57 ± 0.34a	30.76 ± 4.41a	16.5 ± 0.08abc	8.65 ± 0.46d	42.01 ± 2.15a
GEN115	18.24 ± 1.9ef	5.29 ± 0.55a	2.68 ± 0.12a	6.37 ± 0.34 b	23.23 ± 1.54b	13.98 ± 0.62c	8.86 ± 0.47d	33.28 ± 0.61b
GEN17	18.14 ± 1.54ef	6.36 ± 0.54a	2.74 ± 0.19a	6.46 ± 0.08b	20.64 ± 2.42bc	13.9 ± 0.31c	8.12 ± 0.86d	33.83 ± 2.16b
GEN106	16.29 ± 0.31f	5.57 ± 0.18a	2.62 ± 0.09a	6.27 ± 0.81b	22.48 ± 5.2b	14.56 ± 1.09bc	7.25 ± 1.19d	44.9 ± 0.98a
GEN46	28.83 ± 0.54 ab	6.03 ± 0.45a	1.51 ± 0.17b	3.37 ± 0.29e	6 ± 0.14d	17.68 ± 0.31a	16.32 ± 0.22a	4.5 ± 0.67e
GEN132	30.69 ± 2.57a	5.68 ± 0.52a	1.58 ± 0.15b	3.4 ± 0.06e	5.41 ± 0.05d	17.45 ± 0.27ab	15.33 ± 0.36a	6.41 ± 0.11e
GEN64	28.98 ± 1.87ab	5.28 ± 0.53a	1.8 ± 0.21b	3.4 ± 0.26e	5.26 ± 0.96d	17.33 ± 0.01ab	15.12 ± 0.83a	7.29 ± 0.25e
GEN104	30.75 ± 4.38a	6.12 ± 0.59a	1.5 ± 0.15b	3.53 ± 0.16e	6.55 ± 1.28d	16.68 ± 1.07abc	14.57 ± 0.96ab	6.77 ± 1.04e
GEN140	25.95 ± 0.9bc	5.17 ± 0.5a	1.62 ± 0.26b	3.41 ± 0.06e	6.91 ± 0.87d	17.67 ± 0.84a	16.1 ± 1.08a	6.01 ± 1.75e
GEN152	19.92 ± 1.61def	5.67 ± 0.41a	2.64 ± 0.05a	4.85 ± 0.22d	14.5 ± 1.63c	14.54 ± 2.14bc	11.35 ± 2.01c	17.63 ± 1.24c
GEN49	26.05 ± 0.71bc	5.05 ± 0.42a	1.64 ± 0.21b	5.48 ± 0.16cd	14.75 ± 1.15c	18.35 ± 1.54a	14.46 ± 0.58ab	18.2 ± 0.47c
GEN13	24.36 ± 1.82c	5.56 ± 0.6a	2.6 ± 0.06a	5.44 ± 0.8cd	17.59 ± 4.32bc	14.61 ± 0.47bc	12.72 ± 1.02bc	11.95 ± 3.91d
GEN125	21.97 ± 3.32cde	5.62 ± 0.05a	2.4 ± 0.14a	5.85 ± 0.45bc	19.24 ± 1.53bc	17.24 ± 2.22ab	12.54 ± 1.81bc	17.24 ± 0.63c
GEN99	23.48 ± 1.29cd	5.42 ± 0.43a	2.73 ± 0.15a	6.42 ± 0.39b	20.03 ± 2.32bc	16.04 ± 1.59abc	12.29 ± 1.26bc	17.61 ± 0.42c

Note: Means followed by the same alphabetic letters are not significantly different at the 0.05 level according to the SNK test. Capsule length (Lonc) in mm, capsule width (LaC) in mm. Capsule split before drying of capsules in mm (CS-1), capsule split after drying of capsules in mm (CS-2), capsule open before drying of capsules in mm (CO-1), and capsule open after drying of capsules in mm (CO-2).

The qualitative traits were designed in classes by using the descriptors for sesame (IPGRI and NBPGR, 2004).

3. Result

3.1. Agromorphological Characterization

3.1.1. Phenological Traits

The basic descriptive statistics recorded for the quantitative traits in sesame are presented in Table 5. Earliness traits show relatively low coefficients of variation (< 20%). In sesame genotypes, there was a low level of variability in days to flowering and capsule dates. The largest variation was observed for days to 50% flowering and days to maturity. The days to maturity of the sesame genotypes ranged from 79 to 130 days. GEN17, GEN120, and GEN106 were the earliest maturing genotypes, with 80 days on average. On the other hand, genotypes GEN13, GEN49, and GEN125 show the longest time to mature, with 130 days on average (Table 2). High significant differences (p < 0.001) were observed between the different genotypes for flowering dates (Table 6). The first inflorescences appeared 30 days after sowing, and 58 days later for the late genotypes. There was no significant difference (p < 0.1) between blocks and replications for the days to flowering, capsule initiation, and days to maturity. A significant (p < 0.001) difference was observed between genotypes for capsule initiation, ranging between 33 and 79 days. The 50% flowering date showed a highly significant difference between genotypes (p < 0.001), with an overall mean of 57.76 days. Maturity dates also showed a significant difference among genotypes (p < 0.001). GEN152 (128.76 ± 1.08), GEN13 (123.74 ± 1.1), and GEN49 (113.08 ± 8.59) show late maturity, while GEN17 (82.33 ± 5.77), GEN14 (85.67 ± 9.29), and GEN106 (90 ± 1.73) show early maturity (Table 2).

Table 5

Basic descriptive statistics recorded for the yield traits in sesame.

	Mean	Min	Max	sd	Variance	Cv (%)
InF	45.4	30	58	6.53	42.59	14.37
InC	49.49	33	79	7.01	49.14	14.17
HalFl	58.61	40	79	11.4	129.99	19.45
MP	106.48	79	130	11.21	125.72	10.53
Haut	147.01	83.28	179.67	31.40	986.00	21.36
NCA	1.18	1	3	0.46	0.21	38.59
NCP	86.27	28	182.67	35.63	1269.52	21.83
HIPB	28.77	14	64	10.72	114.89	10.16
HIPC	45.4	17	86	9.06	82.09	19.95
ZoC	45.4	21.67	63.68	11.15	124.40	24.57
LEN	5.24	2.33	11	1.63	2.64	31.03
P1000G	3.61	1.02	5.02	0.71	0.66	19.65
Rdt	560.12	250.5	740.73	203.19	41,287.59	36.28
Lonc	23.92	14.16	29.71	5.63	31.66	23.52
LaC	5.69	4.00	6.99	0.74	0.55	13.04
Cs1	3.01	1.32	15.08	2.73	7.47	30.74
Cs2	5.25	2.05	10.41	3.4	24.00	32.55
ECS	19.33	1.84	77.07	17.26	297.92	31.18
Co1	7.6	1.12	13.25	2.98	11.50	18.21
Co2	12.2	7.23	17.28	1.9	4.06	15.60
ECO	20.02	7.63	69.09	11.87	140.99	37.22

Table 6

Analysis of variance of all traits.

	Genotype	BLOCK	REP	BLOCK $\mathrm{\ast }$ REP
	ddl = 159	ddl = 4	ddl = 2	ddl = 8
	F value	F	F	F
InF	${30.65}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	2.22.	0.40	1.15
InC	${33.29}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	1.99.	0.72	1.30
HalFl	${36.82}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.88	2.73.	1.11
MP	${35.51}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	1.61	0.25	1.75.
Haut	${232.707}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.152	0.587	0.873
HIPB	${49.827}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.113	0.128	0.750
LEN	${18.467}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	2.017.	0.291	1.875.
HIPC	${42.778}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.835	2.964.	0.926.
NCP	${472.977}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	1.218	0.065	0.799
ZoC	${24.620}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.962	0.170	1.385
NCA	${192.269}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	1.386	0.985	0.812
P1000G	${10.656}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	1.006	2.183	${2.378}^{\mathrm{\ast }}$
Rdt	${124.072}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.523	0.192	0.809
Lonc	${45.030}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.456	2.998.	1.202
LaC	1.080	1.581	${13.332}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	${5.835}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$
Cs1	${14.204}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.752	0.055	1.056
Cs2	${47.587}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.388	0.865	0.878
ECS	${5.044}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.037	0.937	1.335
Co2	${39.247}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	1.188	0.911	0.937
Co1	${80.279}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	1.625	0.364	1.180
ECO	${83.057}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$	0.839	2.634.	1.116

Note: Signif. codes: “${}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$” (p < 0.001); “${}^{\mathrm{\ast }\mathrm{\ast }}$” (p < 0.01), “${}^{\mathrm{\ast }}$” (p < 0.05), “.” (p < 0.1).

3.1.2. Yield and Related Traits

There was a high variation observed among different genotypes for the plant height, number of capsules per plant, and seed yield per plant (Table 5). The variances for these traits were 986.00, 1269.52, and 41287,59, respectively. The study revealed significant variations in traits across different genotypes (Table 6). The height of the plant ranged from 83.2 to 179.67 cm, with an overall mean of 147 cm. GEN132 (175.01 ± 9.69), GEN140 (172.63 ± 4.31), and GEN46 (167.17 ± 1.53) were the tallest. The height of insertion of the first branch and the first capsule also differed significantly between the genotypes. The mean values recorded were 28.67 and 41.79, respectively. The number of capsules per plant significantly varied among the genotypes. GEN64 (158.56 ± 3.75), GEN132 (155.71 ± 4.04), GEN140 (146.47 ± 3.3) recorded the highest number of capsules, and GEN17 (47.29 ± 8.77), GEN106 (48.78 ± 1.09), and GEN 115 (46.22 ± 4.41) showed the lowest. The mean internode length was 5.28 cm, while the capsular zone averaged 44.55 cm. The capsule zone and number of capsules per leaf axil were also significant among the genotypes. The yield significantly differed among the genotypes, ranging from 214.2 kg to 740.73 kg/hectare, with an average of 560.12 kg/ha (Table 5). GEN46 (745.63 ± 4.11), GEN64 (744.98 ± 2.11), and GEN140 (740.73 ± 31.19) recorded the highest yield, and GEN115 (257.32 ± 37.19), GEN14 (259.43 ± 35.69), and GEN 17 (308.12 ± 46.77) had the lowest yield (Table 3). However, the effects of block and repetition were less notable, with few traits exhibiting significance (internode length and height of insertion of the first branch). Interaction effects between the block and repetition (BLOCK ${}^{\mathrm{\ast }}$ REP) were observed for P1000G (p < 0.05). All of these traits had moderate coefficients of variation (< 45%).

3.1.3. Capsule and Dehiscence Traits

Significant differences (p < 0.001) in the capsule length were observed among genotypes, ranging from 14.17 to 33.9 mm, with an average of 24.16 mm. Capsule split (ECS) ranged from 1.89% to 75.51% with an overall mean of 19.33%. GEN132, GEN64, and GEN104 show a less value of ECS (Table 4). GEN120 (30.76 ± 4.41), GEN115 (23.23 ± 1.54), and GEN 99 (20.03 ± 2.32) recorded the highest values of ECS. Capsule opening (ECO) ranged from 1.95% to 61.00%. Genotypes GEN14, GEN106, and GEN 120 exhibit high values of ECS, while GEN 46, GEN 104, and GEN 132 show low values (Table 4). Capsule split values before drying capsules (CS-1) ranged from 1.32 mm to 15.08 mm, while CS-2 ranged from 2.05 to 10.41 mm. Capsules open before oven-drying (CO-1) ranged from 1.12 to 13.25 mm, with an overall mean of 7.6 mm (Table 5). CO-2 ranged from 7.23 to 17.28 mm. Except for capsule width, all traits showed a significant effect among genotypes. Capsule length and capsule opening showed no significant effects of blocks and replications. Capsule width demonstrated a significant effect (p < 0.001) for the replication and the combined effect of block and replication (Table 6).

3.2. Analysis of Relationships Between Descriptors

The correlation analysis reveals significant relationships among phenological, morphological, and yield components (Figure 1). Flowering-related traits show strong positive correlations, with days to first flower initiation (InF) strongly correlated with days to 50% flowering (HalFl) (r = ${0.83}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$) and days to maturity (MP) (r = ${0.87}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$). The phenological traits also demonstrate positive correlations with plant height (Haut) (r = ${0.55}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$ to ${0.59}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$) and yield (Rdt) (r = ${0.50}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$ to ${0.60}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$). Height of the first branch (HIPB) shows positive correlations with internode length (LEN) (r = ${0.74}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$) and height of the first capsule (HIPC) (r = ${0.60}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$). Number of capsules per plant (NCP) shows positive correlations with capsule-bearing zone (ZoC) (r = ${0.71}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$) and length of capsule (Lonc) (r = ${0.75}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$). The 1000-seed weight (P1000G) exhibits positive correlations with yield (Rdt) (r = ${0.71}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$) and capsule size (Lonc: r = ${0.62}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$, LaC: r = ${0.25}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$). Dehiscence-related traits (ECO and ECS) show consistent negative correlations with most yield components. ECO (capsule split) displayed strong negative correlations not only with capsule length (Lonc, r = −${0.78}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$) and potential yield (Rdt, r = −${0.82}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$), but also with the 1000-seed weight (P1000G, r = −${0.64}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$). Similarly, ECS (capsule opening) exhibited robust negative relationships with capsule length (Lonc, r = −${0.76}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$), potential yield (Rdt, r = −${0.76}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$), and 1000-seed weight (P1000G, r = −${0.56}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$). ECO and ECS also showed moderate negative correlations with the capsule-bearing zone length (ZoC, r = −${0.64}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$ and r = −${0.68}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$, respectively) and number of capsules per plant (NCP, r = −${0.69}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$ and r = −${0.66}^{\mathrm{\ast }\mathrm{\ast }\mathrm{\ast }}$, respectively).

[figure(s) omitted; refer to PDF]

3.3. PCA

The PCA revealed three primary dimensions that collectively explain 79.20% of the total variability observed in the dataset (Table 7). The first dimension, explaining 59.9% of the total variability, demonstrates a positive correlation with various yield-related traits, including genotype yield potential, plant height at maturity, number of capsules per plant, length of capsule-bearing zone, and capsule length. This dimension also shows a negative correlation with traits such as the height of insertion of the first branch, length of internodes, capsule split, and capsule open. This axis can be interpreted as representing yield and dehiscence traits. In contrast, the second dimension, explaining 13.6% of the total variation, mostly includes phenological traits such as flowering date, capsular initiation date, 50% flowering date, and maturity dates. The traits associated with Axes 1 and 2 contributed significantly to the germplasm phenotypic variation, accounting for over 73.02% (Figure 2). The third axis complements Axis 1, with the insertion height of the first capsule as the only variable contributing to its formation.

Table 7

Coordinate, contribution, and eigenvalues of variables with the different PCA dimension.

	Eigenvalues
	Dim.1		Dim.2		Dim.3
Variance	9.58		2.17		0.90
% of var.	59.93		13.60		5.66
Cumulative % var.	59.93		73.53		79.20
	Eigenvectors
	Coord	ctr	Coord	ctr	Coord	ctr
InF	0.77	6.28	0.57	14.99	−0.09	0.99
InC	0.80	6.77	0.52	12.60	−0.04	0.17
HalFl	0.70	5.16	0.50	11.48	−0.09	0.96
MP	0.62	4.01	0.66	20.03	−0.16	3.10
Haut	0.89	8.36	−0.26	3.31	0.06	0.46
HIPB	−0.81	6.89	0.13	0.89	0.16	3.16
LEN	−0.88	8.14	0.18	1.60	−0.11	1.56
HIPC	−0.50	2.65	0.22	2.41	0.75	62.50
NCP	0.83	7.27	−0.23	2.56	−0.05	0.31
ZoC	0.78	6.46	−0.24	2.65	0.29	9.27
P1000G	0.78	6.40	0.03	0.00	0.07	0.56
Rdt	0.92	8.85	−0.28	3.62	0.03	0.11
Lonc	0.85	7.65	−0.32	4.85	0.09	1.05
LaC	0.26	0.74	0.60	17.05	0.34	12.78
ECS	−0.80	6.83	0.16	1.29	−0.13	2.13
ECO	−0.84	7.47	0.11	0.62	−0.08	0.80

[figure(s) omitted; refer to PDF]

3.4. Classification and Group Characterization

According to the HAC, there are three distinct groups. The cluster dendrogram and cluster plot (Figures 3 and 4) show these 03 groups on the Factorial Plane 1 and 2. Table 8 provides the summary of clusters based on tolerance to dehiscence and yield characteristics.

[figure(s) omitted; refer to PDF]

Table 8

Summary of clusters based on tolerance to dehiscence and yield characteristics.

	Cluster 1	Cluster 2	Cluster 3
	Sensible	Resistant	Intermediate
	N = 48	N = 11	N = 101
InF	36.77 ± 3.66	46.79 ± 4.57	53.94 ± 2.05
InC	39.84 ± 3.70	51.14 ± 4.56	57.85 ± 1.76
HalFl	46.75 ± 4.02	61.74 ± 10.14	69.16 ± 5.23
MP	95.95 ± 5.73	108.06 ± 8.44	121.91 ± 8.02
Haut	103.33 ± 9.25	170.28 ± 6.04	138.77 ± 7.21
HIPB	39.54 ± 6.45	23.31 ± 5.86	30.53 ± 6.22
LEN	6.97 ± 0.99	4.40 ± 0.55	6.06 ± 0.47
HIPC	47.69 ± 5.91	38.50 ± 8.84	47.42 ± 9.06
NCP	47.01 ± 4.64	106.84 ± 5.33	69.11 ± 6.61
ZoC	33.05 ± 7.30	51.11 ± 7.05	34.56 ± 3.54
P1000G	2.73 ± 0.68	3.91 ± 0.44	3.60 ± 0.51
Rdt	239.31 ± 7.22	711.48 ± 2.22	457.08 ± 11.66
Lonc	17.34 ± 1.81	28.22 ± 3.19	25.22 ± 3.19
LaC	5.56 ± 0.40	5.68 ± 0.36	6.30 ± 0.32
ECS	24.49 ± 9.91	8.02 ± 3.66	18.75 ± 5.04
ECO	32.74 ± 11.03	8.00 ± 3.16	18.44 ± 9.03

Cluster 1, comprising 48 genotypes, is characterized by its good earliness but high susceptibility to dehiscence. These genotypes demonstrated a higher capsule split (ECS = 24.49 ± 9.91%) and capsule open (ECO = 32.74 ± 11.03%), indicating their vulnerability to capsule shattering. They showed a moderate number of capsules per plant (NCP = 47.01 ± 4.64) and exhibited the shortest plant heights (103.33 ± 9.25 cm) among the clusters. Despite their sensitivity, these genotypes display short maturing dates of 95.95 ± 5.73 days, with an average flowering date of 36.77 ± 3.66 days. The capsules from this cluster are characterized by moderate lengths (17.34 ± 1.81 mm) and widths (5.56 ± 0.40 mm). However, these genotypes feature a higher insertion height of the first branch (39.54 ± 6.45 cm) and capsule (47.69 ± 5.91 cm), indicating a distinctive combination of early development and susceptibility to dehiscence.

Cluster 2 had the most resistant genotypes with days to maturity of 108.06 ± 8.44 days. These plants had low insertion height of the first branch, the tallest plant height (170.28 ± 6.04 cm), and a substantially higher number of capsules per plant (106.84 ± 5.33), resulting in elevated yields (711.48 ± 2.22 kg/ha). The capsules of these plants measure 28.22 ± 3.19 mm in length and 5.68 ± 0.36 mm in width. These genotypes exhibit higher values for flowering initiation (46.79 ± 4.57), capsular initiation (51.14 ± 4.56), and flowering period (61.74 ± 10.14 days). Importantly, these genotypes exhibited lower levels of capsule splitting (8.02 ± 3.66%) and opening (8.00 ± 3.16%), indicating enhanced resistance to premature capsule shattering.

Cluster 3, consisting of 101 genotypes, represents an intermediate phenotype between sensitivity and resistance to dehiscence. These genotypes exhibited high values for flowering initiation (53.94 ± 2.05), capsular initiation (57.85 ± 1.76), and 50% flowering date (69.16 ± 5.23 days). They had moderate plant heights (138.77 ± 7.21 cm) and a moderate number of capsules per plant (69.11 ± 6.61), showing intermediate productivity of 457.08 ± 11.66 kg/ha. Also, they showed intermediate levels of capsule splitting (ECS = 18.75 ± 5.04%) and opening (ECO = 18.44 ± 9.03%).

3.5. Genetic Variability of Traits

Capsule split (ECS%), capsule open (ECO%), number of capsules per plant (NCP), and potential yield per genotype (Rdt) exhibited high genetic variability as shown in Table 9. Days to first capsule (InC), days to 50% flowering (HalFl), and days to maturity (MP) displayed moderate genetic variability. Capsule width (LaC) showed low heritability and genetic advance despite having a moderately low genotypic coefficient of variation. The genotypic variance ranged from 0.084 to 2247.57, while the phenotypic variance ranged from 0.14 to 3447.14. Potential yield per genotype (GV = 2247.57, PV = 3447.14) and number of capsules per plant (GV = 1887.51, PV = 2820.91) demonstrated the highest genetic and phenotypic variances. Traits such as plant height (Haut), capsule split (ECS%), and days to 50% flowering (HalFl) also showed significant genetic and phenotypic variances. Capsule width (GV = 0.33, PV = 0.66) and number of capsules per leaf axile (GV = 0.084, PV = 0.14) recorded the lowest values. Other traits, including 1000 seed weight (P1000G), capsule split before drying (Cs1), and internode length (LEN), also exhibited relatively low genetic and phenotypic variances. Capsule open (ECO%) (GCV = 30.91%, PCV = 35.56%) and capsule split (ECS%) (GCV = 21.89%, PCV = 26.11%) recorded the highest coefficients of genotypic and phenotypic variation. High GCV values (> 30%) were observed for capsule split after drying (Cs2), number of capsules per plant (NCP), and potential yield per genotype (Rdt). Moderate GCV values (10%–30%) were noted for the length of the capsule-bearing zone (ZoC), capsule length (Lonc), plant height (Haut), and days to 50% flowering (HalFl). Low GCV values (< 10%) were recorded for the capsule width (LaC) (GCV = 7.63%) and days to maturity (MP) (GCV = 4.65%). Heritability estimates were high ($H^2$ > 70%) for traits such as capsule open before drying (Co1) ($H^2$ = 80.05%), capsule split after drying (Cs2) ($H^2$ = 77.77%), and capsule open after drying (Co2) ($H^2$ = 76.47%). Number of capsules per plant (NCP) ($H^2$ = 66.83%) and potential yield per genotype (Rdt) ($H^2$ = 65.12%) also exhibited high heritability. The capsule width (LaC) ($H^2$ = 49.85%) had moderate heritability. Genetic advance as a percentage of the mean (GAM) was the highest for the number of capsules per plant (NCP) (GAM = 126.06%) and capsule open (ECO%) (GAM = 88.42%). Traits such as potential yield per genotype (Rdt) (GAM = 77.18%) and capsule split after drying (Cs2) (GAM = 70.67%) exhibited strong genetic advance, indicating the potential for substantial improvement through selection. Moderate GAM values (20%–70%) were recorded for traits such as length of capsule-bearing zone (ZoC) (GAM = 46.96%), capsule length (Lonc) (GAM = 49.53%), and internode length (LEN) (GAM = 48.47%). Low GAM values (< 20%) were observed for the capsule width (LaC) (GAM = 14.59%) and days to maturity (MP) (GAM = 18.74%).

Table 9

Genotypic and phenotypic coefficient of variation, genetic advance, and genetic advance.

Traits	GV	PV	GCV (%)	PCV (%)	$H^2$(%)	GA	GAM
InF	25.42	34.19	11.09	12.88	74.36	10.96	24.14
InC	28.51	48.49	10.48	14.01	58.78	12.24	24.74
HalFl	79.92	133.65	26.84	30.44	59.76	26.51	45.24
MP	39.68	66.92	4.65	6.28	59.29	19.96	18.74
Haut	600.89	920.04	8.34	10.53	65.29	40.85	27.78
NCA	0.084	0.14	8.23	8.71	60.32	0.75	63.56
NCP	1887.51	2820.91	28.27	30.23	66.83	108.72	126.06
HIPB	58.00	94.01	15.51	17.29	61.79	18.84	65.50
HIPC	53.38	87.76	21.66	22.42	60.81	18.01	39.74
ZoC	74.90	125.42	24.07	25.06	59.73	21.29	46.96
LEN	1.10	2.11	25.34	27.49	52.39	2.54	48.47
P1000G	0.28	0.66	20.04	23.04	43.07	1.27	35.21
Rdt	2247.57	3447.14	36.49	36.92	65.12	432.43	77.18
Lonc	29.23	37.72	24.61	25.42	77.40	11.85	49.53
LaC	0.33	0.66	7.63	10.80	49.85	0.83	14.59
Cs1	0.28	0.43	27.82	30.80	65.32	1.10	36.52
Cs2	2.85	3.67	37.31	38.47	77.77	2.70	70.67
ECS	17.94	25.54	21.89	26.11	70.27	7.10	36.72
Co1	9.26	11.55	25.11	25.58	80.05	6.74	88.42
Co2	3.12	4.08	11.75	12.20	76.47	3.86	31.57
ECO	38.24	50.66	30.91	35.56	75.53	11.62	48.03

Note: Days to the first flower initiation (inf), days to 50% flowering (HalFl), days to the first flower capsule (InC), days to maturity (mp), plant height (Haut) in cm, height of the first branch (HIPC) in cm, internode length (LEN) in cm, height of the first capsule (HIPC) in cm, number of capsules per plant (NCP), length of capsule-bearing zone (zoc) in cm, number of capsules per leaf axile (NCA), 1000 seed weight (p1000g) in g, potential yield per genotype (Rdt), capsule length (Lonc) in mm, capsule width (LaC) in mm. Capsule split before the drying of capsules in mm (CS-1), capsule split after the drying of capsules in mm (Cs-2), capsule open before drying of capsules in mm (CO-1), capsule open after drying of capsules in mm (CO-2), capsule split (ECS%), capsule open (ECO%), H²: Broad-sense heritability.

Abbreviations: GA, genetic advance; GAM, genetic advance as % of Mean; GCV, genotypic coefficient of variation; GV, genotypic variance; PCV, phenotypic coefficient of variation; PV, phenotypic variance.

3.6. Analysis of Qualitative Variables

The results indicate the significant diversity in the qualitative traits among the accessions (Table 10). The majority of accessions (90%) exhibited an indeterminate growth type. The diversity index of 0.46 indicates relatively low diversity in growth type. Only 10% of accessions were determinate. The diversity index of 0.99 indicates high diversity, as the accessions are more evenly distributed across the different leaf arrangement classes (Alternate, Opposite, Ternate, Mixed). The most common leaf arrangement was opposite (27.5%), followed by alternate (26.875%), ternate (25.625%), and mixed (20%). The diversity index of 0.85 suggests high diversity, with the Moderate lodging class being the most common. More than half of the genotype (51.875%) had a moderate lodging score. A smaller percentage showed severe lodging (17.5%) and low lodging (21.87%), while only a few accessions (8.75%) exhibited no lodging. The diversity index of 0.91 indicates high diversity in growth habits. The genotypes are predominantly erect (50%) and semierect (34.37%), indicating upright or slightly leaning growth. A smaller percentage (15.62%) exhibited a prostrate growth habit, growing along the ground. The majority of accessions produced single capsules per node. 74.37% had a monocapsular arrangement and the remaining (25.62%) were multicapsular, producing multiple capsules per node. The leaf shape shows a high diversity index of 0.97. Leaf shapes varied among the genotypes, with elliptic being the most common (28.12%), followed by lanceolate (24.37%) and ovate (20%). Linear (13.12%) and narrowly cordate (14.37%) leaf shapes were less common.

Table 10

Variation in qualitative traits in sesame.

Qualitative traits	Level of traits	Class	Number of accessions	Frequency (%)	Diversity index (h’)
Growth type	Indeterminate	2	144	90	0.46
	Determinate		16	10
Leaf arrangement	Alternate	4	43	26.87	0.99
	Opposite		44	27.5
	Ternate		41	25.62
	Mixed		32	20.00
Lodging score	None	4	14	8.75	0.85
	Low		35	21.87
	Moderate		83	51.87
	Severe		28	17.50
Growth habit	Erect	3	80	50.00	0.91
	Prostrate		25	15.62
	Semierect		55	34.37
Capsule arrangement	Monocapsular	2	119	74.37	0.82
	Multicapsular		41	25.62
Leaf shape	Elliptic	5	45	28.12	0.97
	Lanceolate		39	24.37
	Linear		21	13.12
	Narrowly cordate		23	14.37
	Ovate		32	20.00

4. Discussion

Globally, sesame breeding has focused on developing varieties with higher yield potential, improved resistance to pests and diseases, and better seed retention at maturity. Several sesame varieties have been released in different regions, including varieties with enhanced resistance to capsule dehiscence, such as those from mutation breeding or hybridization programs. However, while some of these efforts have successfully reduced dehiscence, they often come with trade-offs, such as decreased reduced yield. The methods used in these breeding programs typically involve phenotypic selection and genetic improvement through cross-breeding, followed by multilocation trials to assess environmental adaptability. This study offers critical insights for sesame breeding by identifying genotypes and traits that contribute to high resistance to capsule dehiscence, particularly in the “highly resistant” cluster. These genotypes represent promising candidates for developing semi-dehiscent lines that balance high-yield potential with shatter resistance. Such advancements are particularly crucial in the context of climate change, where rising temperatures and erratic rainfall patterns can exacerbate dehiscence, leading to significant yield losses. High temperatures, for instance, are known to accelerate drying and weaken pod walls, increasing shattering in crops such as soybeans, rice, and sesame [53, 54]. By identifying traits such as capsule split and capsule open, which showed high genetic variability, this study lays a strong foundation for targeted breeding strategies to mitigate these challenges. Oven-drying treatment is a common scientific method to evaluate and manage plant dehiscence. The use of the oven-drying method to evaluate dehiscence aligns with established practices in crops like soybeans and Brassicaceae, providing robust data for selecting tolerant genotypes [42, 55–57]. Sun-drying and oven-drying offer a cost-effective balance between accuracy and practicality, unlike expensive methods like freeze-drying (Lyophilization) [38]. Although rapid, microwave-drying can cause uneven heating and thermal damage, leading to unreliable shattering data [58]. Other methods consist of placing the pods or capsules in airtight containers with silica gel, which absorbs moisture from the capsule. This method is useful for small-scale experiments or when preserving and maintaining the appearance and texture of materials is a priority, as it minimizes thermal stress [40, 59]. However, it has some limitations, including a slower drying process compared to oven-drying, the higher cost of silica gel, and limited scalability for large-scale studies. While this study provides a strong foundation for understanding and mitigating capsule dehiscence in sesame, further research is necessary to evaluate the performance of resistant genotypes across different agroecological zones. Multilocation trials, genetic diversity assessments, and breeding programs targeting specific climatic and soil conditions will enhance the global applicability of these findings. The integration of molecular tools for marker-assisted selection and genome editing could accelerate the development of indehiscent and semi-dehiscent sesame varieties, benefiting sesame producers worldwide.

4.1. Phenological Traits

Performance of sesame genotypes is affected by environmental factors and seasonal fluctuations [60, 61]. Earliness traits are important in sesame breeding to improve uniform ripening capsules, increase productivity, and better adapt to climate change [62, 63]. The flowering date plays a crucial role in sesame yield [64], and generally, it ranges between 30 and 40 days for early varieties and 70–80 days for late varieties after sowing [65]. In this study, the flowering date was around 45 days, which is similar to the results found by Hamissou [29] and Mohanty [66] who reported an average duration of 44 and 45 days. However, Abate [67] and Ranjithkumar [68] studies reported a longer flowering time of 68 days and a shorter time of 37 days, respectively. The 50% flowering date in this study is lower than that observed by Ahmed [69] and Pavani [70] who reported a duration of 38 and 39 days. But it is more similar to the 48 days reported by Dudhat [71]. Sesame is usually harvested at physiological maturity, which is generally observed between 91 and 106 days [65]. In this study, the early genotypes have a 79-day maturity. Varieties of early days to maturity are preferred for sesame, especially to reduce dehiscence [21]. Several authors have reported similar durations [29, 69, 72]. Sesame physiological maturity is influenced by growth type [73], so for indeterminate growth type, harvest dates ranged from 143 to 184 days after planting [6]. Late maturity is also important for plant breeding programs trying to adapt sesame germplasms to various ecological regions, as well as for research on photoperiod and thermo-sensitivity [74–76].

4.2. Yield and Related Traits

The height of sesame plants can vary greatly depending on various factors such as growth type [77, 78], row spacing [79, 80], and irrigation [81]. Sesame plants are typically tall 120–150 cm [82]. In this study, the height of the sesame plants was recorded to be higher than the collections from Ethiopia [67], Pakistan [83], and Turkey [84]. It also showed a wide range in this study as in the germplasm of Hamissou [29]. However, it is important to note that increasing plant height is not always the best approach for achieving higher seed yield in sesame. This is because excessively tall plants are more prone to lodging, which can ultimately lead to reduced yield and quality [8].

Another way to increase sesame yields is by selecting genotypes that have a high number of capsules per axil. Sesame farmers are more interested in cultivating varieties with a high number of axil capsules [4]. Yol and Uzun [85] and Gadri [86] demonstrated a positive correlation between the number of capsules per axil and sesame seed yield. While sesame typically produces only one capsule per axil, certain genotypes from China, Israel, and Turkey have been identified to produce three capsules per axil Yol and Uzun [85], Miao [82], and Rajkumar [87]. Similar to these reported, in this study, genotypes GEN54, GEN 77, and GEN126 produce between 2 and 3 capsules per axil. Pham [88] has reported genotypes that produce six and five capsules per axil. However, it is important to note that while this trait is heritable, it may not be uniform in the axils of all the leaves throughout the entire length of the plant due to variations in the genotype and environment [82, 87].

The number of capsules per plant is an important factor in sesame improvement. In this study, it was found to have a wide range of variability. On average, 87 capsules were observed per plant, which is comparatively higher than the collection from Ghana [89], Ethiopia [90], and India [91]. However, it is still lower than the collection evaluated by Akbar [83] and Pavani [70], which were 106 and 113, respectively. Some genotypes have been observed to produce up to 232 capsules per plant [88]. Capsule number and number of capsules per axis have been reported by several authors to be positively correlated with grain yield [85, 87, 92, 93]. However, it has been recorded by Pham [88] that varieties with high capsule density produced lower yields than those with low capsule density.

It is important to consider the height of the initial capsule in sesame production, especially for mechanized harvesting. Miao [82] has reported that the ideal height for manual harvesting is between 15 and 20 cm, while for machine harvesting, it is between 15 and 40 cm. The world average for the height of the first capsule is between 30 and 60 cm, indicating that shorter distances between capsules can potentially lead to a higher number of capsules and grain yield [8].

The insertion height of the first capsule is lower than what has been recorded by Bedawy and Mohamed [94] and Pavani [70]. The plant height to first branching (cm) is also lower than that observed by Teklu [95] and Gedifew [90], which were 39.8 cm and 34.65 cm, respectively. The length of the capsule-bearing zone is a very important to sesame breeders as it directly affects the seed yield. The length of this zone varies from 13.5 to 129.13 cm for the world collection [8]. In this study, it averaged 45.4 cm, which is similar to the findings of Tesfaye [96], Sintayehu [97] and Gedifew [90].

The average productivity of the germplasm is similar to the global average [8]. However, it is lower compared to the yield reported by Baraki [98] in Ethiopia (820.19 kg) or in China (1056.9 kg/ha) by Zhang [8]. However, it should be noted that in this study, no form of fertilization, disease, or insect management control was carried out. Only weeding was carried out.

4.3. Capsule and Dehiscence Traits

In sesame production, capsule dehiscence is a significant issue that leads to a massive loss of seeds, quality deterioration, and makes mechanical harvesting difficult. Therefore, it is crucial to breed sesame varieties that do not shatter and adopt appropriate agronomic practices to minimize capsule shattering. Sesame capsules can exhibit three different types of shattering: fully open and shattered (shattered capsules), partially open (semi-shattered capsules), and closed capsules (indehiscent) [10, 15]. The CS1 and CS2 values recorded in this study were higher than the values recorded by Haibru [43] but lower than Dash [44] findings. The CO1 and CO2 values recorded in this study are higher than Haibru [43] and Dash [44]. Capsule opening and capsule split are important characteristics of dehiscence evaluation in sesame [13]. Therefore, understanding the characteristics of capsule opening and split can provide valuable insights into developing non-shattering and semi-shattering sesame varieties. The capsule opening and capsule split values recorded in this study are lower than Haibru [43] but more similar to the values recorded by Dash [44]. This diversity recorded provides a baseline for developing semi-shattering sesame varieties. The results of this study demonstrate that sesame genotypes show considerable variability in both phenotypic traits related to dehiscence and yield components. The identification of distinct groups, such as the highly resistant group and the moderately resistant group, is crucial for understanding the genetic underpinnings of dehiscence. Genotypes with high resistance to capsule dehiscence also exhibited better yield components, including greater plant height and capsule number, suggesting that dehiscence resistance and yield potential are positively correlated in certain genotypes. These findings are significant as they open the door for the development of new sesame varieties with both high yield and improved seed shattering.

4.4. Classification and Group Characterization

Positive correlations were recorded between the seed yield plant height, number of capsules and capsule length, and 1000-seed weight in this study. Several studies found a significant positive correlation between the number of capsules per plant and seed yield [99, 100]. In this study, the number of capsules per plant is positively correlated with capsule-bearing zone and capsule length highlighting the importance of capsule number and size in the determining yield. In sesame, the seed yield was reported to be positively associated with plant height [101, 102]. In this study, taller plants are positively correlated with yield but negatively correlated with dehiscence, indicating a trade-off between height, yield, and shattering resistance. The height of the first branch shows strong positive correlations with internode length and height of the first capsule, reflecting the influence of plant architecture on capsule distribution and yield potential. The positive correlation between the capsule length and seed yield in sesame is noted in multiple studies, suggesting that it is an important yield component [99]. Positive correlations were also recorded with 1000 seed weight and seed yield when compared to other studies [103, 104]. Dehiscence is recognized to affect negatively the productivity of plants. Similarly, in this study dehiscence traits were negatively correlated with the yield. In sesame and other oilseed crops, premature dehiscence triggered by genetic, structural, or abiotic factors can lead to significant seed loss before harvest, directly reducing measurable yield. In soybeans, seed yield is closely correlated with the degree of pod-shattering [42, 55]. In cotton, boll dehiscence affects yield components and fiber properties [105, 106]. Silique dehiscence in Canola crops is a major yield loss [107]. Longer capsules are more prone to splitting due to mechanical stress during growth. The negative correlation between the capsule length and dehiscence trait suggests that structural integrity is compromised in elongated capsules, leading to higher shattering rates. Furthermore, genetic determinants influencing suture strength or lignin deposition in capsule walls may modulate dehiscence timing. Genotypes with weaker sutures or faster maturation of abscission layers are prone to early splitting, particularly under environmental stressors such as high humidity, wind, or abrupt temperature fluctuations, which accelerate tissue degradation [108]. Environmental factors further exacerbate this relationship. Shattering is increased by stresses such as heat, drought, and wind. Excessive heat stress during capsule maturation can deteriorate parenchyma cells, promoting dehiscence and seed dispersal [107]. Heat stress accelerates desiccation, exacerbating capsule splitting [109, 110] and amplifying the observed negative correlations. Further studies should focus on screening the highly resistant genotype under drought and heat stress conditions to identify lines where yield-dehiscence trade-offs are minimized. This underscores the need for breeding programs targeting dehiscence resistance in climate-resilient varieties.

The yield of sesame is affected by several factors such as plant height at maturity, the number of capsules per plant and length of internodes, the length of capsule-bearing zone, and the height of insertion of the first branch [86, 87, 97, 102]. The first dimension of PCA shows a positive correlation between these factors that contribute to seed yield. Although the length of the capsule contributed to the yield, the width of the capsule contributed poorly to the yield. The lowest variation was recorded in capsule width in this study. This was also observed by Haibru [43]. Breeding for shattering resistance of capsules is a key trait for improving sesame yield [8]. A genotype with low to medium capsule split and capsule opening along with medium capsule constriction is a desirable capsule character for developing semi-shattering sesame genotypes [43]. There are different levels of dehiscence observed in this study as revealed by the classification. Capsule split and capsule open exhibit a negative correlation with the yield. This demonstrates the implication of dehiscence in sesame yield reduction. Dehiscence-related traits, specifically capsule split (ECS) and capsule open (ECO), were instrumental in both the classification of genotypes and their overall evaluation. These traits played a central role in the clustering analysis, enabling the ranking of genotypes based on their resistance to capsule shattering and their potential yield. Dehiscence traits (ECO and ECS) exhibit complex relations with agronomic traits, where high resistance correlates with enhanced plant morphology, productivity, and long cycles.

In contrast, susceptibility is linked with early maturation but limited robustness. These genotypes prioritizing rapid seed maturation may invest less in capsule reinforcement, increasing shattering susceptibility. These correlations underline the importance of breeding efforts focused on balancing resistance to dehiscence with desirable agronomic traits to optimize sesame performance across varying environments.

The hierarchical classification identified three distinct groups. The first group, “Highly Resistant,” exhibited high resistance to capsule shattering, with a shattering rate of less than 10%. The second group, “Moderately Resistant” showed an intermediate resistance to shattering, with a shattering rate between 10% and 20%. The third group, “Susceptible,” was susceptible to dehiscence, with a shattering rate above 20%. The genotypes with less resistance to shattering, classified as “Susceptible,” had shorter sizes, lower yields, fewer capsules, and smaller capsular zones. However, they had better earliness as compared to “Highly Resistant” individuals with a low dehiscence rate but high plant height, capsule number, and capsule length. The individuals with “Moderately Resistant” had moderate values for all attributes, with moderate plant height, capsule number per plant, and moderate values for other attributes. Reducing the time sesame crops spend in the field can help minimize dehiscence losses. High humidity and capsule moisture are the main causes of shattering losses [21, 65]. By combining early-maturing genotypes with resistant genotypes, we can develop more resistant early genotypes. Additionally, we can cross genotypes with different internode lengths to create more diversity, such as combining reduced height genotypes with those having the longest and shortest internodes. The observed variability in germplasm indicates that selecting genotypes with semi-dehiscence attributes could improve dehiscence and yield in sesame through hybridization programs.

4.5. Genetic Variability

The results of this study demonstrate significant genetic variability in several phenotypic traits of sesame, which has important implications for breeding and improvement programs. Capsule split, capsule open, the number of capsules per plant, and potential yield per genotype exhibited high genetic variability, indicating that they are mainly controlled by genetic factors There is also high phenotypic variability for these traits, suggesting that these characteristics can be effectively improved through selection. This aligns with findings from studies of Kumar [92] and Patel [111], who also reported significant genetic variability and heritability in yield-related traits in sesame. On the other hand, traits such as days to the first capsule, days to 50% flowering, and days to maturity show moderate genetic variability. This moderate variability suggests that these traits are influenced by both genetic and environmental factors. Similar observations have been noted in sesame [112]. Capsule width stands out for its low heritability and genetic advance. The genetic and phenotypic variance for capsule width is also very low. Aye and Htwe [113] also found low heritability for capsule width, confirming that this trait is not ideal for breeding programs. The variation in the coefficients of genetic (GCV) and phenotypic (PCV) variation among the studied traits shows that capsule open and capsule split have the highest coefficients, indicating strong variability and therefore high potential for genetic improvement. In comparison, traits such as capsule length, the capsule-bearing zone, and the height of the first capsule show moderate GCV and PCV values, suggesting less pronounced but still significant genetic variability. These results are similar to those of Hika [114], who also reported high variability for the traits of capsule open and capsule split. For heritability, high values were observed for traits such as the number of capsules per plant, plant height, and potential yield per genotype, suggesting that these traits can be effectively selected for genetic improvement. Previous studies, corroborate these results, emphasizing the importance of these traits in breeding programs aimed at improving sesame yield [43, 69, 97].

5. Conclusion

Sesame (Sesamum indicum L.) is one of the oldest and most valuable oilseed crops, prized for its high oil content, nutritional benefits, and ability to grow in diverse climates. The results demonstrate significant variability in the sesame genotypes’ ability to resist capsule dehiscence and their overall growth characteristics. Three distinct clusters were identified, each exhibiting varying levels of tolerance to dehiscence, yield, and morphology. The first cluster was susceptible to dehiscence, the second was highly resistant to dehiscence, and the intermediate group was moderately resistant to dehiscence with balanced agronomic traits. The identification of highly resistant genotypes, with superior yield and capsule traits, provides clear candidates for integration into breeding programs to develop semi-dehiscent varieties with improved seed retention at maturity. To validate these findings and enhance their applicability across different agroecological zones, multilocation trials are planned as a follow-up to this study. Additionally, breeders are encouraged to prioritize genotypes from the “highly resistant” group for further testing and incorporation into breeding pipelines, focusing on traits like capsule split and capsule open that showed high genetic variability. These efforts aim to reduce harvest losses due to dehiscence, offering tangible benefits to farmers and the sesame industry in both productivity and economic returns. Further studies exploring the molecular mechanisms underlying dehiscence resistance and its association with yield components could provide more precise tools for selection in breeding programs, ultimately leading to the development of high-yielding, climate-resilient sesame varieties. By incorporating these insights into breeding strategies, it is possible to improve sesame productivity while maintaining resilience to environmental stresses, which is important for sustainable agriculture, food security, and meeting the nutritional demands of diverse global populations.

Funding

No funding was received for this research.

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