Assessing yield stability of heat-tolerant lentil genotypes across multiple hotspot regions in Bangladesh

Assessing yield stability of heat-tolerant lentil genotypes across multiple hotspot regions in Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessing yield stability of heat-tolerant lentil genotypes across multiple hotspot regions in Bangladesh Md. Aktar-Uz-Zaman, Mohammad Mahmudul Hasan Khan, Md. Rafiqul Islam, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7736936/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Terminal heat stress is a major abiotic constraint affecting lentil cultivation worldwide, presenting a significant challenge for plant breeders working to develop heat-tolerant genotypes. In this study, six heat-tolerant lentil genotypes BLX 09015, BLX 05002-3, LRIL-21-1-1-1-1, LRIL-21-1-1-1-1-6, BLX 05002-6 and a popular cultivated variety, BARI Mosur-8 (used as a check), were evaluated to identify more stable heat-tolerant genotypes across various agro-ecological zones in Bangladesh. Stability analysis was conducted based on grain yield data. The combined analysis of variance revealed significant differences among genotypes, environments, and genotype-environment interactions. Across the environment, BLX 05002-3 performed best in Barishal, BLX 05002-6 in Ishurdi, BARI Mosur-8 in the Barind region of Rajshahi, and LRIL-21-1-1-1-1-6 in Gazipur for improved yield. Among the four locations, Ishurdi proved to be the most stable for lentil cultivation, followed by Rajshahi, Barishal, and Gazipur, respectively. However, genotypes BLX 09015 and LRIL-21-1-1-1-1 showed instability in yield performance across the four environments. Overall, based on AMMI stability parameters, GGE biplots, Principal Component Analysis, Multitrait Stability Index (MTSI), and Y×WAASB biplot the genotype BLX 05002-3 was identified as the most stable across environments for yield traits and grain yield under terminal heat stress conditions. Biological sciences/Genetics Biological sciences/Plant sciences AMMI GGE Multitrait Stability Index terminal heat stress lentil Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The lentil ( Lens culinaris subsp. culinaris Medik) is a significant food legume in the sub-family Papilionoideae under the Fabaceae family. It is a diploid (2n = 14), self-pollinated crop with a haploid genome size of 4,063 Mbp 1 . Lentils are an affordable source of protein, especially valuable in low-income populations in developing regions where they serve as a substitute for animal protein due to their low cost and high nutritional content. They contain approximately 23% protein, 59% carbohydrates, 1.8% oils, and 0.2% ash, along with essential minerals like iron, zinc, calcium, phosphorus, magnesium, selenium, and vitamins A and B 2, 3 . Additionally, lentil straw and seed coats are widely used as animal feed in regions including West Asia, North and South America, Australia, North Africa, and the Indian subcontinent, especially in Bangladesh, Nepal, and India 4 , 5 . Lentils also contribute to soil health through symbiotic nitrogen fixation (annually fixing around 107 kg N ha⁻¹) 6 , conserving soil moisture, and preventing erosion 7 . They can tolerate drought and grow across a wide range of soil types, from light to heavy, with a pH between 5.5 and 9.0. Globally, lentils are cultivated on approximately 5.46 million hectares with a total production of 5.64 million tons, yielding an average of 1,033 kg per hectare 8 . Major lentil-producing countries include Turkey, Canada, India, Australia, Nepal, Bangladesh, China, the United States, Ethiopia, and Syria 8 . In Bangladesh, the cultivable area for lentils is 146,000 hectares, with a production of 186,000 tons and an average yield of 1,274 kg per hectare 8 . Lentil cultivation faces challenges from both biotic and abiotic stresses. Key abiotic stresses include cold, drought, heat, salinity, nutrient deficiency, and toxicity, with drought and heat being the primary constraints worldwide 9 . Climate change has intensified terminal heat stress as a significant abiotic factor affecting lentil production. While lentils can germinate at freezing temperatures, optimal germination occurs between 18–21°C, with growth and yield favored at temperatures above 24°C. However, temperatures exceeding 27°C can severely impede growth and yield 10 . In the southern and northwestern regions of Bangladesh, delayed sowing due to the late harvest of T-aman rice, as well as early vegetable production, further exacerbates terminal heat stress during the lentil reproductive stage. Consequently, lentil productivity has seen a significant decline. The selection of early-maturing, high-yielding lentil genotypes with stable performance within rice-fallow cropping systems in the Barind region has become crucial for lentil breeders in Bangladesh to improve self-sufficiency in lentil production. Currently, no research has recommended specific lentil genotypes for the rice-fallow cropping pattern, particularly those with stability across multiple traits. Recently, some promising lines were identified from heat tolerance screening programs under the PhD research of Aktar-Uz-Zaman et al. (2022) 11 . Evaluating these heat-tolerant genotypes in targeted environments could provide essential insights for selecting suitable cultivars 12 . Prior to releasing these heat-tolerant varieties, multi-locational yield trials are necessary across various hotspot regions in Bangladesh to assess adaptability. Generally, multi-locational or multi-environmental trials (MLT or MET) are used by plant breeders to evaluate advanced genotypes across different test environments before releasing them as commercial varieties. Usually plant breeders are used GEI and stability analysis for selection of stable genotypes in an easily understandable way. In a broad sense, parametric and non-parametric stability methods are used for stability analysis based on yield performance 13 . Parametric analysis is the most commonly used reported by several research for highlighted significant interactions between environments and genotypes for grain yield in annual crops such as groundnut 14–16 , pearl millet 17 , soybean 18 , 19 , wheat 20 ,21 , and maize 22 . Genotype × environment (GE) interaction is often assessed using non-parametric methods like the AMMI model, Multi-Trait Stability Index (MTSI), and the Y × WAASB biplot, which help to identify the superior genotype based on performance and stability 23 – 25 . Priority is given to lines that exhibit high yield potential, heat tolerance, and resistance to pests, especially when compared to the existing late-sowing variety, BARI Masur-8, used as a check variety. Therefore, this study aims to identify the superior terminal heat-tolerant genotypes with the highest yield potential across locations. Results and Discussion The ANOVA results revealed significant variation among the genotypes, environments, and genotype-environment interactions at a 0.001% probability level (Table 1 ). The significant G × E interaction indicates that grain yield among genotypes varied across environments, highlighting the influence of environmental effects within the G × E interaction. This further suggests that while genotypes are genetically diverse, some also respond differently to varying environmental conditions 26 . The sum of squares for genotypes was smaller than for G × E, underscoring differences in genotypic responses across environments. The interaction term was further analyzed using the AMMI model, decomposing the GE interaction into principal components, with three significant AMMI terms identified through F-statistics (p < 0.001). The first two AMMI principal components explained 92% of the total interaction effect, with PC1 and PC2 accounting for 76.3% and 15.7%, respectively (Table 1 ). The significance of the G × E interaction for grain yield indicates that AMMI analysis is effective in selecting promising genotypes for specific locations or environmental conditions 26 . Table 1 Analysis of variance for grain yield (kg ha − 1 ) in 6 genotypes of Lens culinaris Medik evaluated over 4 environments in Bangladesh. Source Degree of Freedom Sum of Square Mean of Square F value Probability level (> F) Proportion Environment 3 5544831.90 1848277.298 189.405808 9.092627e-08 NA Rep(Env) 8 78066.34 9758.293 3.082264 8.398451e-03 NA Genotypes 5 1164480.82 232896.164 73.562818 4.319171e-19 NA Genotypes:Environment 15 4770018.97 318001.264 100.444201 6.981189e-27 NA PC1 7 3640890.37 520127.196 164.290000 0.000000e + 00 76.3 PC2 5 747171.96 149434.392 47.200000 0.000000e + 00 15.7 PC3 3 381956.63 127318.877 40.220000 0.000000e + 00 8.0 Residuals 40 126637.98 3165.949 NA NA NA Total 86 16454054.97 191326.221 NA NA NA Note: PC1; Principal Component1, PC2; Principal Component2, PC3; Principal Component3. Biplot pattern for elucidation of multivariate analysis The main effects of genotype (G) and genotype-by-environment (G×E) interactions are the primary sources of variation in assessing genotype stability in multi-environment trials (MET), as outlined by Yan et al. (2000) 27 . Yan et al.16 proposed three key components for evaluating MET such as (a) “representativeness and discriminating “which used for the assessment of test environment, (b) “stability vs mean” Used to evaluate genotype performance across environments, and (c) “which-won-where” pattern or METis an effective approach for visualization the pattern of GEI based on the correlation between G and E. The combined effect of genotype (G) and genotype-by-environment interaction (G×E) variation accounted for 90.25% of yield per hectare (Fig. 1 , Patterns a, b, c). To identify the most suitable testing environment, the 'discriminativeness vs. representativeness' pattern in the GGE biplot is essential for effective breeding and the selection of superior genotypes. Discriminativeness refers to an environment's ability to differentiate among genotypes, while representativeness describes its capacity to typify all other evaluated environments, reflecting the ideal testing conditions 27 . In the 'discriminativeness vs. representativeness' biplot, the lines connecting the test environments are known as environment vectors. In this study, the biplot indicated that four test environments exhibited acute angles with each other (Fig. 2 a), suggesting a close relationship among them 28 . These four environments were positively correlated, showing a strong similarity. The genotypes G-2 and G-6 showed acute angles between their vectors, as did G-6 and G-5, indicating a similar response. This study concludes that among the four environments, ISH (Ishurdi) is the most suitable testing environment for lentil production, followed by Rajshahi (RAJ). Evaluation of genotypes across the environments based on mean vs. stability: The mean performance and stability of each genotype across locations were graphically represented through the Average Environment Coordination (AEC) view of the yield per hectare biplot (Fig. 1 b). The AEC view, which is genotype-metric-preserving (SVP = 3), allowed for a visual assessment of genotype discrimination based on combined performance. The first two principal components (PCs) explained 90.25% of the variation for yield traits, supporting previous reports 29 , 30 on the adequacy of this biplot analysis. Genotypes closer to the vector length are more stable, while those farther away are less so. In this study, based on six genotypes and four environments, the GGE biplot divided the genotypes into distinct clockwise fan-shaped sections (1, 1, 1, and 3, respectively). Genotypes G-2, G-5, and G-6 demonstrated strong multi-trait performance, whereas genotype G-1, positioned at the far left of the AEC ordinate, showed poor performance. Identification of which-won-where: The 'which-won-where' feature of the GGE biplot is crucial for identifying both the best-performing genotypes and the winning genotypes for specific test environments. This feature provides a graphical representation of genotypes or winning genotypes within the test environments. A polygon is first drawn around the genotypes, positioning all genotypes at the outermost points from the biplot origin, or within the polygon 31 . The genotypes located at the vertices of the polygon are either the best or poorest performers in one or more environments. Perpendicular lines, called equality lines 31 , are then drawn from the biplot’s origin to each side of the polygon, dividing it into several sectors. Each sector contains one or more genotypes at the polygon's vertex. In our study, the equality lines divided the graph into five sectors, with all four environments falling within a single sector (Fig. 2 c). Among the six genotypes, G-1 was positioned at a vertex, indicating it as an unstable genotype across the study locations. In contrast, genotypes G-2, G-5, G-6, G-3, and G-4 showed the most promise within the study environments. This approach has been used widely for identifying mega-environments in various crops 32 , 33 . Figure 2 was generated using the first two Principal Components (PC1 and PC2) for the average grain yield per hectare of six lentil genotypes across four environments. This biplot illustrates the ranking of environments using a concentric circle, a method frequently employed in GGE biplot analysis to visually identify the most favorable environment for yield production 34 . In this model, the ideal environment is conceptually placed at the center of the concentric circle, with other environments positioned according to their correlation and proximity to this ideal genotype. In the present study, ISH (Ishurdi) is located at the center of the concentric circle, indicating its high suitability as a testing environment. This positioning suggests that Ishurdi provides conditions that are both representative of other environments and conducive to maximizing genotype performance. Following ISH, BSL (Barishal), RAJ (Rajshahi), and JOY (Joydebpur) are positioned closely around the circle. Their proximity to the center suggests that these environments also provide suitable, though slightly less ideal, conditions for lentil production. Similar results have been reported in previous studies, where environments positioned near the center of the concentric circle demonstrated strong stability and yield performance, making them optimal for crop trials 26 . Therefore, based on these results, Ishurdi is recommended as the most suitable environment for lentil production, followed closely by Barishal, Rajshahi, and Joydebpur. Similarly Fig. 3 illustrates the ranking of genotypes by their positions within a concentric circle, where the ideal genotype is conceptually located at or near the center. In this study, genotype G-2 is closest to the center, followed by G-6 and G-5, indicating these genotypes' relative stability and suitability across environments. Genotypes G-3 and G-4 are positioned further from the center, while G-1 is the most distant, suggesting a higher level of instability. The positioning of G-2 and G-6 near the center indicates their minimal variation across locations, which signifies consistent performance in yield and stability across diverse environments. This finding aligns with prior research, where stable genotypes generally remain close to the concentric center due to uniform adaptability 26 . Studies in lentil by Chatterjee et al. (2023) 29 and in rice by Hasan et al. (2022) 35 and Akter et al. (2015) 36 similarly observed that genotypes positioned near the ideal point are often characterized by superior yield stability across multiple environments. In genotype evaluation studies, proximity to the ideal genotype is widely used as an indicator of a genotype's performance consistency and adaptability, as reflected in GGE biplot analyses. The clear distinction in G-2’s position as the most stable genotype across locations, followed by G-6 and G-5, suggests its suitability as a candidate for broad adaptation. Conversely, G-1’s position further from the center highlights its performance instability, making it less ideal for environments with variable conditions. The stable performance of G-2 and G-6, combined with low variance, highlighting the utility of multi-environment trials to identify high-performing genotypes across diverse settings, as seen in similar research on crop stability and adaptability 35 – 37 . S election of Superior Genotypes based on Multitrait Stability Index (MTSI) and Y× WAASB biplot The exploration of Genotype-Environment Interaction (GEI) is critical in plant breeding, particularly for developing crop varieties that perform consistently across diverse environments. Tools like AMMI, GGE, and Y × WAASB biplot were effectively utilized to explore Genotype-Environment Interaction (GEI) in field crops across multiple locations and years 25 , 38 . The Multi-Trait Stability Index (MTSI) is a selection index commonly used by plant breeders to identify superior genotypes for multi-environment trials (MET) based on the mean performance and stability of genotypes across locations 39 . This index evaluates both desirable and undesirable traits by analyzing each ideotype’s factorial scores. A spatial probability is then computed based on the distance between an accession and the ideotype, enabling the ranking of accessions 40 . Consequently, the genotype with the lowest MTSI is closest to the ideotype and demonstrates optimal mean performance and stability across the analyzed variables 41 . In our study, Fig. 4 (A) displays the ranking of six lentil genotypes according to MTSI. Among them, the red-colored genotype, G-2, was identified as the superior and most stable genotype, achieving the highest mean performance in the multi-trait analysis. Additionally, Fig. 4 (B) shows that genotype G-2, with PC1 scores near zero and along the center lines of the Y × WAASB biplot, demonstrated the high mean performance and stability for grain yield across all environments. This finding aligns with previous research by Murphy et al. (2009) 42 , which supports the use of biplot analysis in revealing stable, high-performing genotypes across multiple traits and environments. Overall, these analytical tools collectively enhance the efficiency of genotype selection in MET, aiding breeders in identifying resilient and high-yielding varieties adapted to specific environmental conditions. Heatmap analysis for genotypes on based yield and yield contributing traits across the environments A heatmap is a data visualization technique that represents the intensity of a phenomenon using color in two dimensions. The variation in color, either through hue or intensity, provides readers with clear visual insights into how the phenomenon is distributed or grouped across different environments. It highlights relative patterns of high-abundance features against a background of features with low abundance or absence. Heatmap analysis was conducted on yield-contributing traits and the yield performance of six lentil genotypes across four environments, offering a chromatic evaluation of these genotypes (Fig. 5). This analysis included the construction of double dendrograms. The first dendrogram, oriented horizontally, and arranged the yield and yield-contributing traits while the second dendrogram oriented vertically influencing the lentil genotypes and clustered at different caterogies (Fig. 5). The first dendrogram categorized the traits into three major groups viz, group (a) lined to traits SY, GY, FPP, DF, DM, BPP, and PPP, and the group (b) lined to trait PH and group (c) linked to traits HSW and Dsc indicating a degree of diversity among the genotypes. Similarly, the second dendrogram grouped the genotypes into five distinct clusters viz, group (a) consisting two genotypes G-2 and G-4 with better yield performance, straw yield and final plant stand; group (b) consisting one genotype G-3 with maximum days flowering and days to maturity, and the group (c) consisting the genotype G-6 with maximum pods per plant, branches per plant, plant height and days to flowering, group (d) consisting the genotype G-4 with better resistance against Stemphylium blight disease and group (e) consisting the genotype G-1 with maximum grain size. The clustering and characterization of these genotypes serve as crucial criteria for selecting and identifying the best materials for hybridization programs in plant breeding 43 . The heatmap analysis effectively illustrated the relationships among genotypes based on their morphological traits and comparative yield performance across different environment 44 . Disease reaction against root rot and Stemphylium blight disease of lentil Lentil genotypes were screened under natural field conditions to assess their response to root rot and Stemphylium blight diseases, which are globally recognized as the most devastating diseases of lentil, occurring at the seedling stage and from flowering initiation to the podding stage, respectively. The studied lentil genotypes exhibited significant differential responses to these diseases during the 2023-24 crop season (Table 2 ). Among the tested genotypes, BLX 05002-3 exhibited a resistant reaction, while two genotypes, BLX 05002-6 and the check variety BARI Masur-8, were found to be moderately resistant. Conversely, the genotypes BLX 09015 and LRIL 21-1-1-1-1 were categorized as susceptible, with LRIL 21-1-1-1-1-6 being the only genotype showing high susceptibility to root rot under natural epiphytic condition. These findings support with similar studies by Kharte et al. (2023) 45 , who screened 90 lentil genotypes under natural field conditions, and Mohammadi et al. (2012) 46 , who also evaluated 55 advanced lentil lines for resistance to Fusarium wilt caused by Fusarium oxysporum f. sp. under both natural field and greenhouse-controlled condition. The study suggests that the resistant genotypes, such as BLX 05002-3, BLX 05002-6, and BARI Masur-8, may produce higher levels of antifungal compounds, such as phenolics, compared to susceptible genotypes. This mechanism potentially provides resistance against root rot pathogens like Sclerotium rolfsii or Fusarium oxysporum f. sp. , as reported by Iftikhar et al. (2005) 47 , Jamil et al. (1996) 48 , and Sahi et al. (2000) 49 . Regarding susceptibility to Stemphylium blight, genotype G-3 exhibited the least infection (disease score: 2.25) with a moderately resistant reaction, followed by G-5 (2.50) and G-2 (2.75) across four environments. The highest disease rating score (3.75) was recorded in LRIL 21-1-1-1-1-6, indicating moderate susceptibility, followed by BLX 09015 (3.58) and BARI Masur-8 (3.25) under natural field conditions (Table 2 ). Similar findings were reported by Aktar-Uz-Zaman et al. (2025) 50 who screened 60 lentil genotypes against Stemphylium blight disease under natural epiphytic and artificial inoculum conditions during the 2019–20 and 2020-21 crop seasons. Table 2 Disease reaction of the genotypes against root rot disease (based on 1–9 disease rating scale) and Stemphylim blight disease (based on 0–5 disease rating scale) at natural field condition during 2023-24. Genotype Disease rating score of root rot Disease rating scale of Stemphylium blight Rating score Reaction Rating score Reaction BLX 09015 47.059a S 3.58ab MS BLX 05002-3 9.468d MR 2.75c MR LRIL 21-1-1-1-1 23.834c S 2.25d MR LRIL 21-1-1-1-1-6 50.774a HS 3.75a MS BLX 05002-6 37.111b MS 2.50cd MR BARI Masur-8 38.814b MS 3.25b MS CV% 9.74 - 16.49 - LSD 3.9680** - 0.41** - Note: **Significant at 0.05 level of probability. Mean performance and comparison of the genotypes All genotypes exhibited significant variation across observed parameters, including final plant population (FPP) per square meter, days to 50% flowering, days to maturity (DM), plant height (PH), branches per plant (BPP), pods per plant (PPP), hundred seed weight (HSW), grain yield (GY) in kg per hectare, and straw yield (SY) in kg per hectare (Table 3 ). Plant populations ranged from 36.2 (G-1) to 232 (G-2), with an average of 117.12 ± 5.23. Genotypes G-1 and G-4 showed earlier flowering and maturity at 44.50 and 85.92 days, and 44.82 and 88.42 days, respectively. Plant height (PH) was lowest in G-1 at 27 cm and highest in G-2 at 43.4 cm, with a mean value of 34.83 ± 0.53 cm. BPP varied from 1.8 (G-1) to 4 (G-4), averaging 2.84 ± 0.07. Genotype G-6 had the highest number of pods per plant (PPP) at 74.47, followed by G-2 (62.65) and G-5 (61.55), with an average of 56.92 ± 3.92; the lowest PPP was recorded in G-1 (40.84). The maximum grain yield (GY) was produced by G-5 at 1281.97 kg per hectare across all environments, followed by G-2 at 1259.04 kg per hectare, with an average of 1148.95 ± 48 kg per hectare; the lowest yield was from G-1 (902.23 kg per hectare). HSW ranged from 1.8 (G-2) to 3.6 (G-1), with an average of 2.44 ± 0.04 across the four study locations in Bangladesh. The highest straw yield (SY) was recorded in G-5 at 1508.79 kg per hectare, followed closely by G-4 at 1500 kg per hectare, with an average SY of 1397.89 ± 57.77 kg per hectare, while the lowest SY was recorded in G-1 at 1106.13 kg per hectare. According to Chowdhury et al. (2019) 51 , grain yield per plant correlates positively with primary branches per plant, pods per plant, hundred seed weight, and seeds per plant. Studies by Vanave et al. (2019) 52 , Sharma et al. (2018) 53 , and Pandey et al. (2015) 54 further confirm significant positive correlations between yield per hectare and these yield-contributing traits, except for days to 50% flowering and days to maturity 55 . Plant breeders therefore prioritize these traits when selecting superior lentil genotypes. Table 3 The mean performance of phenological duration, yield-contributing traits, and yield across six Lens culinaris Medik genotypes under four different environments during 2023-24. Genotypes FPP DF DM PH BPP PPP HSW GY SY G-1 98.48 44.50 85.92 35.37 2.45 40.84 2.88 902.23 1106.13 G-2 140.13 47.67 89.75 34.91 2.83 62.65 2.35 1259.04 1388.08 G-3 123.79 51.17 91.67 34.18 2.75 54.17 2.26 1085.16 1443.42 G-4 96.33 44.83 88.42 33.78 2.82 47.83 2.38 1185.15 1500.83 G-5 122.57 50.33 91.08 34.98 3.00 61.55 2.38 1281.97 1508.79 G-6 121.44 51.42 91.42 35.75 3.17 74.47 2.38 1180.16 1440.08 Mean 117.12 48.32 89.71 34.83 2.84 56.92 2.44 1148.95 1397.89 SE 5.23 1.43 0.66 0.54 0.07 3.92 0.04 48 56.77 Std. Dev 44.08 12.06 5.57 4.56 0.58 33.05 0.37 403 478.32 CV 37.9 25.14 6.25 13.2 20.68 58.48 15.13 35 34.46 Min 36.2 (G-1 in BSL) 31 (G-3 in BSL) 80 (G-1 in JOY) 27 (G-4 in RAJ) 1.8 (G-2 in BSL) 35.2 (G-3 in BSL) 1.8 (G-2 in BSL) 321 (G-1 in ISH) 386 (G-1 in ISH) Max 223 (G-2 in ISH) 66 (G-3 in RAJ) 99 (G-6 in ISH) 43.4 (G-2 in BSL) 4 (G-4 in JOY) 136 (G-6 in ISH) 3.6 (G-1 in JOY) 1980 (G-6 in RAJ) 2250 (G-6 in RAJ) MinENV BSL (60.93) BSL (33.78) JOY (84.44) RAJ (31) JOY (2.56) JOY (32.33) ISH (2.21) ISH (943.69) ISH (1144.06) MaxENV ISH (160.72) RAJ (64.39) ISH (95.28) BSL (38.6) RAJ (3.24) ISH (107.68) JOY (2.61) RAJ (1625.56) RAJ (1930) MinGEN G-4 (96.33) G-1 (44.5) G-1 (85.92) G-4 (33.77) G-1 (2.45) G-1 (40.84) G-3 (2.27) G-1 (902.22) G-1 (1106.12) MaxGEN G-2 (140.13) G-6 (51.42) G-3 (91.67) G-6 (35.75) G-6 (3.17) G-6 (74.47) G-1 (2.88) G-5 (1281.97) G-5 (1508.79) Note: G-1(BLX 09015), G-2(BLX 05002-3), G-3(LRIL21-1-1-1-1), G-4(LRIL21-1-1-1-1-6), G-5(BLX 05002-6), G-6(BARI Masur-8), FPP final plant population per square meter, DF days to 50% flowering(days), DM days to maturity (days), PH plant height (cm), 3BPP branches per plant, PPP pods per plant, HSW hundred seed weight (g), GY yield (kg/ha), SY straw yield (kg/ha), DSc disease score, CV co-efficient of variation, SE. standard error, Std. Dev. standard deviation, Max. maximum, Min. minimum. ENV environment, GEN genotype with LSD test at p > 0.05. Conclusion The primary goal of this multi-location study was to evaluate six lentil genotypes based on mean performance across a range of environments to select superior genotype. Multi-environment trials (MET) of lentil genotypes also provide valuable insights into genotype adaptability and stability in various environmental conditions. However, before a selected genotype can be proposed as a commercial variety, it is essential to assess its susceptibility to genotype-by-environment interaction (GEI). Based on multivariate statistical analyses using GGE, AMMI biplots and hierarchical clustering heatmap, the tested genotypes were grouped into four main categories. The first group includes genotypes with high stability and yield potential, notably genotype G-2 (BLX-05002-3), which showed strong adaptability across diverse environments. The second category consists of genotypes with high yield but lower stability, each performing best in specific environments. For instance, the genotypes G-6 (BARI Masur-8) and G-3 (LRIL-21-1-1-1-1) performed best in Rajshahi. The third category consisted the genotype G-4 (LRIL-21-1-1-1-1-6) performed better in Joydebpur. The four group consisted the genotype G-1 (BLX-09015), which exhibited lower yields across all test locations in terms of mean performance, stability, and yield. Considering the yield stability across the all environments the genotypes G-5 (BLX-05002-6) and G-2 (BLX-05002-3) were selected as suitable genotypes for targeted environments. The Multi-Trait Stability Index (MTSI) was also used to select a superior genotype with both high performance and stability across locations. As a result, genotype G-2 (BLX-05002-3) was identified as the superior genotype among the six studied, demonstrating high performance and stability across multiple traits, including days to flowering, days to maturity, plant height, pods per plant, 100-seed weight, resistance to Stemphylium blight, grain yield per hectare, and straw yield per hectare. Materials and Methods Plant Materials and Experimental sites Six heat-tolerant lentil genotypes viz; BLX 09015, BLX 05002-3, LRIL 21-1-1-1-1, LRIL-21-1-1-1-1-6, and BLX 05002-6 were used in this study. These genotypes were identified through field and laboratory screening for yield, yield-contributing traits, and physiological parameters associated with heat stress tolerance 11 . Among these genotypes, BLX 09015, BLX 05002-3, and BLX 05002-6 are advanced breeding lines developed by the Pulses Research Centre (PRC), BARI. The genotypes LRIL 21-1-1-1-1 and LRIL-21-1-1-1-1-6 were obtained from the International Centre for Agricultural Research in Dry Areas (ICARDA) in Morocco, as part of a research collaboration between ICARDA and BARI, following international guidelines and legislation for germplasm exchange. BARI Masur-8, a variety released by PRC, BARI, Ishurdi, Pabna, Bangladesh, was used as a check variety. The pedigree information for the plant materials is provided in Table 4 . Four distinct environments were selected as hotspots for conducting this experiment: Barind, Rajshahi; Regional Agricultural Research Station, Bangladesh Agricultural Research Institute (BARI), Rahmatpur, Barishal; Pulses Research Centre, BARI, Ishurdi, Pabna; and Pulses Research Substation, BARI, Gazipur. These environments differ significantly due to variations in their Agro-Ecological Zones (Fig. 6 ), edaphic factors, and other soil characteristics ( Table 5 ). Weather data for each experimental site were recorded during the trial period and are presented in Table 5 . Table 4 List of genotypes with their pedigree and present status SL. No. Name of the genotypes Pedigree/Source Status 01. BLX 09015 PRC, Bangladesh Heat tolerant 02. BLX 05002-3 PRC, Bangladesh Heat tolerant 03. LRIL 21-1-1-1-1 ICARDA, Morocco Germplasm 04. LRIL-21-1-1-1-1-6 ICARDA, Morocco Germplasm 05. BLX 05002-6 PRC, Bangladesh Heat tolerant 06. BARI Masur-8 (Check) PRC, Bangladesh Released variety Bangladesh (Source: Bangladesh Agricultural Research Council). Table 5 Salient features of the experimental sites along with weather data (monthly average maximum, minimum temperature, relative humidity, sunshine hours in every day and total rainfall) during crop season 2023-24 at four locations in Bangladesh. Location Weather variable AEZ Altitude (m) Geographical Position Soil Texture Soil fertility level and soil pH Monthly average maximum, minimum temperature, relative humidity, sunshine hours in very dayand at total rainfall (mm)/month during crop season 2023-24 at different locations Weather parameter November December January February March April 2023 2023 2024 2024 2024 2024 Ishurdi 11 16.00 24◦030 N 89◦050 E CL Low 7.36 Tmax ( 0 C) Tmim ( 0 C) RH(%) Sunshine hours Rainfall(mm) 30.10 18.60 88.97 6.95 0.00 26.10 14.00 90.16 6.70 3.80 22.20 10.80 89.18 5.12 0.00 27.40 13.80 82.10 7.22 1.30 31.90 17.80 66.61 7.69 27.70 39.20 24.50 63.57 8.37 0.00 Barishal 13 2.10 22◦480 N 90◦370 E SC High 7.30 Tmax ( 0 C) Tmim ( 0 C) RH(%) Sunshine hours Rainfall(mm) 23.11 16.09 59.37 4.84 80.80 25.29 16.23 77.20 5.01 4.00 22.36 12.97 76.54 4.39 11.oo 27.45 16.71 73.77 5.77 54.30 31.40 20.10 73.79 6.54 21.00 34.79 26.61 75.07 7.42 24.00 Gazipur 28 14.00 22◦460 0 N 90◦390 0 E SCL Low 7.50 Tmax ( 0 C) Tmim ( 0 C) RH(%) Sunshine hours Rainfall(mm) 31.17 19.88 79.50 7.92 28.00 26.88 16.19 86.00 4.80 48.00 21.73 13.72 78.00 3.71 438.00 28.09 16.08 73.50 6.57 8.07 31.56 18.09 65.5 5.67 10.70 36.89 24.07 63.00 8.05 17.00 Barind, Rajshahi 11 16.00 24.170 0 N 89.140 0 E VCL, terrace Low Tmax ( 0 C) Tmim ( 0 C) RH(%) Sunshine hours Rainfall(mm) 30.34 18.72 90.47 6.91 3.40 26.25 18.72 90.24 5.53 37.50 21.99 10.78 88.92 3.35 0.00 27.44 14.11 81.19 6.45 14.70 71.79 17.90 75.98 7.23 23.20 39.07 24.00 66.45 8.19 0.20 Note: CL = Clay loam; SC = Silty clay; SL = Silty loam; SCL = Silty clay loam; VCL = Very clay loam; Tmax( 0 C) = Maximum Temperature in degree Celsius; Tmim ( 0 C) = Minimum Temperature in degree Celsius; RH(%) = Relative Humidity in percentage, Rainfall(mm) = Rainfall in millimeter. Trial management and Seed sowing The tested genotypes were arranged in a Randomized Complete Block Design with three replications. The study aimed to assess yield stability across different agro-ecological zones in Bangladesh. Seeds were sown at various times according to location (Table 6 ). Lentil seeds were hand-sown continuously in rows at a depth of 3 cm, at a rate of 30 kg ha⁻¹, with 30 cm between rows, and then covered with soil. Prior to sowing, seeds were treated with Provax-200 WP at 2.5 g per kg of seeds to control root rot disease. Each genotype was sown in eight rows, each 4 m long. Flood irrigation was applied immediately after sowing to ensure uniform germination and seedling establishment in the experimental plots. One spading and two hand weedings were carried out at 15, 25, and 35 days after sowing to keep the plots weed-free and support robust crop establishment. Additionally, three scheduled sprays of Rovral 50 WP at 1.0 mL per liter of water were applied at seven-day intervals, starting at the onset of flowering, to control Stemphylium blight, a major disease affecting lentils. Table 6 Sowing time of the different locations of the experiment during 2023-24 Locations Date of sowing Barind, Rajshahi 11 December, 2023 RARS, BARI, Rahmatpur, Barishal 06 December, 2023 PRC, BARI, Ishurdi, Pabna 17 November, 2023 PRSS, BARI, Gazipur 10 December, 2023 Fertilization The recommended blanket doses of N, P, K, S, and B fertilizers were applied at rates of 21, 16, 17.5, 9.5, and 1.36 kg ha⁻¹, respectively, in the form of urea (46% N), triple super phosphate (TSP, 50% P₂O₅), muriate of potash (MP, 60% K₂O), gypsum (18% S), and boric acid (17% B) 56 . The full doses of all fertilizers were thoroughly incorporated into the soil as a basal application during the final land preparation. Root rot and Stemphylium blight disease scoring of lentil Root rot and Stemphylium blight are among the most devastating diseases affecting lentil production in Bangladesh. Root rot typically occurs during the seedling stage, while Stemphylium blight infects plants from the flowering stage to the podding stage. Although the experiment was conducted across four locations, root rot disease data were collected from only two locations (Ishurdi and Barishal) due to the high severity of infection in these areas. The incidence of root rot was calculated based on the total number of infected plants relative to the total number of plants per square meter, using the following formula: Disease incidence (%) = \(\:\frac{\text{N}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{i}\text{n}\text{f}\text{e}\text{c}\text{t}\text{e}\text{d}\:\text{p}\text{l}\text{a}\text{n}\text{t}\text{s}\:}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{o}\text{b}\text{s}\text{e}\text{r}\text{v}\text{e}\text{d}\:\text{p}\text{l}\text{a}\text{n}\text{t}\text{s}}\times\:100\) The disease reaction score, indicating the resistance or susceptibility of each genotype, was determined using a modified 1–9 disease rating scale based on the method described by Arya and Kushwaha (2019) 57 ( Table 7 ). In contrast, Stemphylium blight disease scoring was conducted across four locations in the experimental plot, following the 0–5 rating scale developed by Bakr and Ahmed (1992) 58 ( Table 8 ). Table 7 Rating scale for reaction of lentil genotypes against root rot disease. Rating scale Incidence percent of root rot Reaction 1 1% or less mortality of plants Resistant (R) 3 2–10% mortality of plants Moderately Resistant (MR) 5 11–20% mortality of plants Moderately Susceptible (MS) 7 21–50% mortality of plants Susceptible (S) 9 Above 50% mortality of plants Highly Susceptible (HS) Table 8 Rating scale for reaction of lentil genotypes against Stemphylium blight disease. Rating scale Incidence percent of root rot Reaction 0 No infection Immune 1 Few scattered leaves infected and no twig blighted Resistant (R) 2 5–10% leaflets infected and/or scattered 1% twig blighted Moderately Resistant (MR) 3 11–20% leaflets infected and/or 1–5% twig blighted Moderately Susceptible (MS) 4 21–50% leaflets infected and/or 6–10% twig blighted Susceptible (S) 5 Above 50% leaflets infected /or more than 10% twig blighted Highly Susceptible (HS) Data collection and statistically analysis In this study, data were collected for eight quantitative traits viz., final plant population per square meter (FPP m⁻²), days to 50% flowering (DF), days to 80% maturity (DM), plant height in centimeters (PH cm), branches per plant (BPP), pods per plant (PPP), 100-seed weight in grams (HSW g), straw yield per hectare (SY kg ha⁻¹), disease score (DS) as a yield-contributing trait, and grain yield (GY kg ha⁻¹). Each entry was monitored from seedling to harvest and compared with the check varieties, with data recorded according to the classification and descriptors for Lens culinaris subspecies by IPGRI and ICARDA. Yield-related data were gathered from 10 randomly selected plants in the middle rows of each plot and replication, at various growth stages in the field and research station lab post-harvest. At maturity, individual lines were selected based on earliness, disease resistance, and yield potential. Grain yield was calculated from the entire plot and then converted to kilograms per hectare (kg ha⁻¹). The statistical analysis was done using R statistical computer-based program version R.4.4.1 59 . Stability were analyzed based on yield performance over locations using R software 59 . Superior genotypes were selected using Multritrait Stability Index (MSI) and Weighted average of the absolute scores (Y×WAAB) which were estimated by R 4.4.1 version software based on the all yield and yield contributing traits of the tested genotypes of lentil over the locations 59 . Declarations Weather data The weather data of the four growing locations were collected from the respective Meteorological Department, of that locations. Data availability The data that has been generated in this study are attached in the supplementary files in the manuscript uploading system. Acknowledgements This research has been financed by Bangladesh Agricultural Research Institute, Gazipur, Bangladesh. The authors would like to acknowledge the support of Bangladesh Agricultural Research Institute, Gazipur. Funding The publication of this article in Open Access mode and finding provided by the Regional Coordinator of South Asia and China, ICARDA, New Delhi. India. Conflict of interest The authors declare no conflict of interest. Consent for publication All authors have approved the manuscript and agree with its submission to Journal of Scientific Report’s. References Schaefer, H. et al. Systematics, biogeography, and character evolution of the legume tribe Fabeae with special focus on the middle-Atlantic island lineages. BMC Evol. Biol. 12 , 250 (2012). Zafar, M., Maqsood, M., Anser, M. R. & Ali, Z. Growth and yield of lentil as affected by phosphorus. Int. J. Agric. Biol. 5 , 98–100 (2003). Nutritionvalue.org. Nutrition Value: Find Nutritional Value of a Product. Available online: April (2023). https://www.nutritionvalue.org/ (accessed on 19. Webb, C. & Hawtin, G. Lentils. Commonwealth Bureau, Page Bros, Norwich (1981). Erskine, W. et al. Global production, supply and demand//The lentil: botany, production and uses. In: W Erskine (Editors), United Kingdom. pp. 4–13 (1997). Saxena, M. C. & Wassimi, N. Effect of fertilizer application and inoculation on the performance of lentil and subsequent wheat crop. LENS 7 , 52–53 (1980). Muehlbauer, F. J., Summerfield, R. J., Kaiser, W. J., Boerboom, C. M. & Welsh-Maddux, M. M. Principles and practice of lentil production. Electronic publication at http://www.ars.usda.gov/is/np/lentis/lentils.htm FAOSTAT. World Food and Agriculture, Statistical Yearbook 2021, Rome. (1992). https://doi.org/10.4060/cb4477en (2021). Turner, N. C., Wright, G. C. & Siddique, K. H. M. Adaptation of rain legumes (Pulses) to water-limited environments. Advan Agron. 71 , 193–231 (2001). Muehlbauer, F. J., Kaiser, W. J., Hannan, R. M. & Tullu, A. Lentil crop production manual (1998). Aktar-Uz-Zaman, M. et al. Selection of lentil ( Lens Culinaris (Medik.)) genotypes suitable for high-temperature conditions based on stress tolerance indices and Principal Component Analysis. Life 12 , 1719. https://doi.org/10.3390/life12111719 (2022). Yan, W. & Tinker, N. A. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant. Sci. 86 (3), 623–645 (2006). Lin, C. S., Binns, M. R. & Lefkovitch, L. P. Stability analysis: Where do we stand? Crop Sci. 26 , 894–900. https://doi.org/10.2135/cropsci1986.0011183X002600050012x (1986). Navrood, F. F., Zakaria, R. A., Rad, M. M., Zare, N. & Ahrabi, M. M. Stability analysis of groundnut ( Arachis hypogaea L.) genotypes using AMMI and GGE biplot models and ideal genotype selection indicator. Indian J. Genet. Plant. Breed. 83 (4), 518–525 (2023). Lal, C., Ajay, B. C. & Rupapara, K. V. AMMI and GGE models indicating seasonal variations as major source of variations for nodulation related characters in peanut. Indian J. Genet. Plant. Breed. 81 (2), 277–288. 10.31742/IJGPB.81.2.10 (2021). Minde, A. S., Kamble, M. S. & Pawar, R. M. Stability analysis for pod yield and its component traits in groundnut ( Arachis hypogaea L). Asian J. Bio Sci. 12 , 15–20. https://doi.org/10.15740/HAS/AJBS/12.1/15-20 (2017). Reddy, P. S. et al. Athoni B. K. GGE biplot analysis for identification of ideal cultivars and testing locations of pearl millet ( Pennisetum glaucum L.R. Br.) for peninsular India. Indian J. Genet. Plant. Breed. 82 (2), 167–176. 10.31742/IJGPB.82.2.5 (2022). Cubukcu, P. et al. Stability analysis of some soybean genotypes using parametric and non-parametric methods in multienvironments. Turk. J. Field Crops . 26 , 262–271. https://doi.org/10.17557/tjfc.1033363 (2021). Goksoy, A. T. et al. The parametric and non-parametric stability analysis for interpreting genotype by environment interaction of some soybean genotypes. Turk. J. Field Crops . 24 , 28–38. https://doi.org/10.17557/tjfc.562637 (2019). Wardofa, G. A. & Ararsa, A. D. Evaluation of grain yield stability analysis in bread wheat (Triticum aestivum L.) genotypes using parametricmethods. Am. J. Life Sci. 8 , 189–195. 10.11648/j.ajls.20200806.12 (2020). Kaya, Y. & Sahin, M. Non-parametric stability analyses of dough properties in wheat. Food Sci. Technol. 35 , 509–515. http://dx.doi.org/10.1590/1678-457X.6642 (2015). Shojaei, S. H. et al. Yield stability analysis of maize (Zea mays L.) hybrids using parametric and AMMI methods. Scientifica https://doi.org/10.1155/2021/5576691 (2021). Article ID 5576691. Hasan, M. J. et al. Assessment of GGE, AMMI, Regression, and Its Deviation Model to Identify Stable Rice Hybrids in Bangladesh. Plants 11 , 2336. https://doi.org/10.3390/plants11182336 (2022). Akter, A. et al. GGE Biplot analysis for yield stability in multi-environment trials of promising hybrid rice ( Oryza sativa L). Bangladesh Rice J. 19 (1), 1–8 (2015). Singamsetti, A. et al. Genotype × environment interaction and selection of maize ( Zea mays L.) hybrids across moisture regimes. Field Crops Res. , 270, 1 August 108224. (2021). Islam, M. R. et al. Yield stability and genotype environment interaction of water deficit stress tolerant mung bean ( Vigna radiata L. Wilczak) genotypes of Bangladesh. Agronomy 11 (11), 2136. https://doi.org/10.3390/agronomy11112136 (2021). Yan, W., Hunt, L. A., Sheng, Q. & Szlavnics, Z. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40 , 597–605 (2000). Yan, W. & Tinker, N. A. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant. Sci. 86 (3), 623–645 (2006). Chatterjee, S. et al. Identification of suitable genotypes of lentil (Lens culinaris) for improved adaptation to rice fallow areas of Gangetic Alluvial Zone. Indian J. Agril Sci. 93 (8), 862–867. https://doi.org/10.56093/ijas.v93i8.125345 (2023). Yan, W., Kang, M. S., Ma, B., Woods, S. & Cornelius, P. L. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47 , 643–655 (2007). Yan, W. & Kang, M. S. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists p. 71 (CRC, 2002). Singh, B. et al. Delineation of genotype-by-environment interactions for identification and validation of resistant genotypes in mungbean to root-knot nematode ( Meloidogyne incognita ) using GGE biplot. Sci. Rep. 10 , 4108 (2020). Gore, P. G. et al. Understanding G × E interaction for nutritional and antinutritional factors in a diverse panel of Vigna stipulacea (Lam.) Kuntz germplasm tested over the locations. Fron Plant. Sci. 12 , 766645 (2021). Oladosu, Y. et al. Genotype × Environment Interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia. Acta Agric. Sci. B Soil. Plant. Sci. 67 , 590–606 (2017). Karimizadeh, R. et al. F., GGE Biplot Analysis of Yield Stability in Multi-environment Trials of Lentil Genotypes under Rainfed Condition. Not. Sc. Biol , 5(2), 256–262 (2013). (2013). 10.15835/nsb.5.2.9067 Hasan, M. J. et al. Assessment of GGE, AMMI, Regression, and Its Deviation Model to Identify Stable Rice Hybrids in Bangladesh. Plants 11 , 2336. https://doi.org/10.3390/plants11182336 (2022). Akter, A. et al. GGE Biplot analysis for yield stability in multi-environment trials of promising hybrid rice ( Oryza sativa L). Bangladesh Rice J. 19 (1), 1–8 (2015). Nataraj, V. et al. WAASB-based stability analysis and simultaneous selection for grain yield and early maturity in soybean. Agronomy J First published . 29. https://doi.org/10.1002/agj2.20750 (2021). Authrapun, J., Lertsuchatavanich, U. & Kang, D. Selection for improving field resistance to capsicum chlorosis virus and yield-related traits using selection indices in peanut Breeding. Acta Sci. Agric. 5 , 22–31 (2021). Sellami, M. H., Pulvento, C. & Lavini, A. Selection of suitable genotypes of lentil ( Lens culinaris Medik.) under rainfed conditions in south italy using multi-trait stability index (MTSI). Agronomy 11 , 1807. https://doi.org/10.3390/agronomy11091807 (2021). Olivoto, T., Lúcio, A. D., da Silva, J. A., Sari, B. G. & Diel, M. I. Mean performance and stability in multi-environment trials II: Selection based on multiple traits. J. Agron. 111 , 2961–2969 (2019). Murphy, S. E. et al. Genotype × Environment interaction and stability for iso favone content in soybean. Crop Sci. 49 , 1313–1321 (2009). Souza, E. & Sorrells, M. E. Relationships among 70 American oat germplasm. I. Cluster analysis using quantitative characters. Crop Sci. 31 , 599–605. https://doi.org/10.2135/cropsci1991.0011183X003100030010x (1991). Virga, G. et al. Agro-morphological characterization of sicilian chili pepper accessions for ornamental purposes. Plants 9 (10), 1400 (2020). Kharte, S. et al. Field Screening of Lentil Germplasm against Wilt of lentil caused by Fusarium oxysporum f. sp . lentis. Biol. Forum-An Intern. J. 15 (1), 88–91 (2023). Mohammadi, N., Puralibaba, H., Mohammadi, E., Goltapeh, Ahari, A. B. & Sardrood, B. P. Advanced lentil lines screened for resistance to Fusarium oxysporum f. sp. lentis under greenhouse and field conditions. Phytoparasitica 40 , 69–76 (2012). Iftikhar, A., Khan, S., Sarwar, Alman, Haq, A. & Jabbar, A. Biochemistry of resistance in chickpea against wilt disease caused by Fusarium oxysporum f. sp. ciceris . Pakistan J. Bot. 37 , 97–104 (2005). Jamil, F. F., Bashir, N., Haq, I., Rath, J. & Barz, W. Accumulation of phytoalexins and other defense related compounds in chickpea against Ascochyta rabiei . Pakistan J. Phytopathol. 9 , 1–4 (1996). Sahi, S. T., Randhawa, M. A., Sarwar, N. & Khan, S. M. Biochemical basis of resistance in lentil ( Lens culinaris Medik.) against Ascochyta blight: 1. Phenols. Pakistan J. Biol. Sci. 3 , 1141–1143 (2000). Aktar-Uz-Zaman, M. et al. Screening of lentil genotypes against stemphylium blight disease and molecular identification of causal organism. Sci. Rep. 15 , 32318. https://doi.org/10.1038/s41598-025-15587-4 (2025). Chowdhury, M. M., Haque, M. A., Malek, M. A., Rasel, M. & Ahamed, K. U. Genetic variability, correlation, and path coefficient analysis for yield and yield components of selected lentil ( Lens culinaris M.) Genotypes. Fund Appl. Agril . 4 (2), 769–776. 10.5455/faa.21740 (2019). Vanave, P. B. et al. Correlation and path analysis studies in lentil ( Lens culinaris Medic). Electron. J. Plant. Breed. 10 (3), 1322–1328. 10.5958/0975-928X.2019.00169.8 (2019). Sharma, S. R., Singh, S., Gill, R. K., Kumar, R. & Parihar, A. K. Selection of promising genotypes of lentil ( Lens culinaris Medik.) by deciphering genetic diversity and trait association. Legume Res. Print. ISSN . 0250–5371. 10.18805/LR-4056 (2018). /Online ISSN:0976 – 0571. Pandey, S., Bhatore, A. & Babbar, A. Studies on genetic variability, interrelationships association and path analysis in indigenous germplasm of Lentil in Madhya Pradesh, India. Electron. J. Plant. Breed. 6 (2), 592–599 (2015). Sakthivel, G. et al. Genetic variability, correlation and path analysis in lentil germplasm ( Lens culinaris Medik). Pharma Innov. J. 8 (6), 417–420 (2019). Alam, M. J., Shahinuzzaman, M., Azam, M. G. & Islam, M. A. Ali, M. O. Production technology of BARI Masur-8. Leaflets (Bengali), Pulses Research Centre, BARI, Ishurdi, Pabna-6620 (2016). Arya, A. & Kushwaha, K. P. S. Management of Lentil Wilt through Host Resistance. Int. J. Curr. Microbiol. Appl. Sci. 8 (03), 438–444 (2019). Bakr, M. A. & Ahmed, F. Development of Stemphylium blight of lentil and its chemical control. Bangladesh J. Plant. Pathol. 8 , 39–40 (1992). Team, R. C. & Austria, U. R. L. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, (2017). https://www.R-project.org/ Additional Declarations No competing interests reported. Supplementary Files AllTables.docx MLYTofLentilRawdata.xlsx data.xlsx rankingGenotypes.jpeg GGEGYHeatmap.jpeg meanvsstability.jpeg GGEbiplotGY.jpeg GYvsWAASBplot.jpeg MTSI.jpeg GGEbiplottype1.jpeg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7736936","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":539118417,"identity":"8ac5da17-aa1d-4ee3-8195-e437e48596d2","order_by":0,"name":"Md. Aktar-Uz-Zaman","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"","lastName":"Aktar-Uz-Zaman","suffix":""},{"id":539118418,"identity":"00dd687b-14a6-42b0-b190-2b3ec56bd577","order_by":1,"name":"Mohammad Mahmudul Hasan Khan","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Mahmudul Hasan","lastName":"Khan","suffix":""},{"id":539118419,"identity":"8f84212b-bd99-416a-a788-db3103c9504e","order_by":2,"name":"Md. Rafiqul Islam","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Rafiqul","lastName":"Islam","suffix":""},{"id":539118420,"identity":"6c33923d-9308-4565-b2a2-89b4d2d4d661","order_by":3,"name":"Debashish Sarkar","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Debashish","middleName":"","lastName":"Sarkar","suffix":""},{"id":539118421,"identity":"faa4e0f1-d19e-43cd-af62-f67775630025","order_by":4,"name":"Md. Shahin Iqbal","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Shahin","lastName":"Iqbal","suffix":""},{"id":539118422,"identity":"b13dd9dc-2a47-4d30-8ba0-e5e4adea6a05","order_by":5,"name":"Md. Shahinuzzaman","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"","lastName":"Shahinuzzaman","suffix":""},{"id":539118423,"identity":"f59b8eb7-f161-4a92-ab54-0beadd5d5390","order_by":6,"name":"Md. Shakhawat Hossain","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Shakhawat","lastName":"Hossain","suffix":""},{"id":539118424,"identity":"facf2f8a-e1db-41ef-b822-b3bcfd48cfd6","order_by":7,"name":"Raz Uddin","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Raz","middleName":"","lastName":"Uddin","suffix":""},{"id":539118425,"identity":"e0aff44d-1c12-4aed-9057-2dc0d61457b8","order_by":8,"name":"Nazmun Nahar","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Nazmun","middleName":"","lastName":"Nahar","suffix":""},{"id":539118426,"identity":"023e60a7-a6e2-41d8-a7db-424092e20fd4","order_by":9,"name":"Md. Motiar Rohman","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Motiar","lastName":"Rohman","suffix":""},{"id":539118427,"identity":"6dc49dd5-af65-4c2c-accb-537ebb3e7998","order_by":10,"name":"Shamima Akhter","email":"","orcid":"","institution":"Bangladesh Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Shamima","middleName":"","lastName":"Akhter","suffix":""},{"id":539118428,"identity":"cb6be3be-a348-4d79-bdfd-8816a6b9c67e","order_by":11,"name":"Ashutosh Sarker","email":"","orcid":"","institution":"Food Legume Breeder and Ex-Regional Coordinator, International Centre for Agricultural Research in Dry Areas","correspondingAuthor":false,"prefix":"","firstName":"Ashutosh","middleName":"","lastName":"Sarker","suffix":""},{"id":539118429,"identity":"207d1191-220e-411e-9957-ffcd04d06900","order_by":12,"name":"Shiv Kumar Agarwall","email":"","orcid":"","institution":"Regional Coordinator, International Centre for Agricultural Research in Dry Areas","correspondingAuthor":false,"prefix":"","firstName":"Shiv","middleName":"Kumar","lastName":"Agarwall","suffix":""},{"id":539118430,"identity":"727f986c-c329-489a-9cc4-404b8fa95722","order_by":13,"name":"Md. Ashraful Haque","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYFACHiC2AWIJ5gMHEgxsEmCCCQwH8GlJA2lhS3zwoSKNJC08xoYzzhwmrIW//+yxDz8SGOy2SzeYSfO2nc/jn5HA+OBtG0MeHw4tEjfykmf2JDAk75xzIA2o5XaxxI0EZsO5bQzFkrgcdoPHmIH3B0OywY2EYyAtiQ03EtiADIbEDTi0yJ8/Y8z4JwGkJbENqPJc4vwbCey/8WkxOJBjzMwD9IvBjWRmoPcPJG4A2sKMT4vhDaAWmQSGBMs5xxiBgZxcbHjmYbPknHMSOP0iB3LYmwQGe3Pp/g/AqLTLkzuefPDDmzIbnCEGBf8TNyA4jA1AQgKvehCwNyCoZBSMglEwCkYsAAC17WXgVg1JVgAAAABJRU5ErkJggg==","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Ashraful","lastName":"Haque","suffix":""}],"badges":[],"createdAt":"2025-09-29 01:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7736936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7736936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95228513,"identity":"6299c61b-9846-4d62-bcfe-4f3d73ebeed8","added_by":"auto","created_at":"2025-11-05 16:33:51","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63692,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/514eab2f7be6502fb39238e1.jpg"},{"id":95228738,"identity":"2a256c86-0a8b-4ee0-8c1e-e69c32ab971a","added_by":"auto","created_at":"2025-11-05 16:34:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1021244,"visible":true,"origin":"","legend":"","description":"","filename":"FinalManuscriptforsubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/70587edb9bd1a2684cac33c9.docx"},{"id":95228118,"identity":"6a24ac05-b6d9-4f5e-baa0-c1816a07adc2","added_by":"auto","created_at":"2025-11-05 16:33:24","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40276,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/843491b4cb4620da2de426ac.jpg"},{"id":95227697,"identity":"10eb2e05-b9eb-4f64-bc7e-4575bb2cd81d","added_by":"auto","created_at":"2025-11-05 16:32:45","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33596,"visible":true,"origin":"","legend":"","description":"","filename":"AllTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/656771f14acf923fa04bea6c.docx"},{"id":95228548,"identity":"4c462a3e-0cc4-4ba7-995a-0225daf1b4d9","added_by":"auto","created_at":"2025-11-05 16:33:55","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33378,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/7134b7e049d331c7a62c5417.jpg"},{"id":95200971,"identity":"11245f4d-0e65-482f-b9fe-ae663cd18bf2","added_by":"auto","created_at":"2025-11-05 12:16:13","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32490,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/3fdabf4db07d3072d0ab6a14.jpg"},{"id":95200920,"identity":"92d8d7a8-8eef-4528-a3a8-4ba3cf69475a","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80640,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/4f6925324991bbaad7349a44.jpg"},{"id":95200923,"identity":"c596da18-378a-4f46-980f-b1ef55c7ed93","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123098,"visible":true,"origin":"","legend":"","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/4ce7a34e5648a93dc9c380ff.jpg"},{"id":95228341,"identity":"ec92728c-4be6-4eea-91c3-f909cc902b87","added_by":"auto","created_at":"2025-11-05 16:33:38","extension":"json","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14115,"visible":true,"origin":"","legend":"","description":"","filename":"8e880dee77a047758ad8a3fa30fe5f44.json","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/3fbc00e10f6c714f5fb10cad.json"},{"id":95200931,"identity":"2ca6bd51-ea27-48e8-8337-8e754bed2c94","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65436,"visible":true,"origin":"","legend":"","description":"","filename":"GGEGYHeatmap.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/8b00753a3f1ab1489886b91d.jpeg"},{"id":95200929,"identity":"609b6af1-1e13-435f-bbf1-df6d30b16c27","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"jpeg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75401,"visible":true,"origin":"","legend":"","description":"","filename":"GGEbiplotGY.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/e4546b64bb17d355885f91ae.jpeg"},{"id":95228602,"identity":"b3f833be-7f95-4380-8434-848c241383ba","added_by":"auto","created_at":"2025-11-05 16:33:59","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73996,"visible":true,"origin":"","legend":"","description":"","filename":"GGEbiplottype1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/b684e7a9e3b4ba25a9d9949a.jpeg"},{"id":95228530,"identity":"57a37975-1219-4cff-a54e-1a5afe005961","added_by":"auto","created_at":"2025-11-05 16:33:53","extension":"jpeg","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73837,"visible":true,"origin":"","legend":"","description":"","filename":"GYvsWAASBplot.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/139a229fbc834349a7837d38.jpeg"},{"id":95200930,"identity":"0f3a2464-d887-401a-a10f-6149e833c1ad","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14377,"visible":true,"origin":"","legend":"","description":"","filename":"MLYTofLentilRawdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/1f15803e17b7ee57d295277f.xlsx"},{"id":95200935,"identity":"79ead56d-2c32-4294-96ab-89e1c55d3648","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"jpeg","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74906,"visible":true,"origin":"","legend":"","description":"","filename":"MTSI.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/64412d77068b9547a0eb04d5.jpeg"},{"id":95200945,"identity":"f1c92636-f735-49b9-8818-4507276612a1","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22215,"visible":true,"origin":"","legend":"","description":"","filename":"data.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/ea30e2aa8e85e75bdf2a9445.xlsx"},{"id":95227707,"identity":"3d6c8736-8d68-4058-bb31-3f8fefae6ab0","added_by":"auto","created_at":"2025-11-05 16:32:46","extension":"jpeg","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83105,"visible":true,"origin":"","legend":"","description":"","filename":"meanvsstability.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/cc9b9396e3039d29c7dd0a8e.jpeg"},{"id":95227403,"identity":"045a1d41-b8a6-4f15-a3db-31625ab1a1ef","added_by":"auto","created_at":"2025-11-05 16:32:27","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94478,"visible":true,"origin":"","legend":"","description":"","filename":"rankingGenotypes.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/89b2af42f35405c6ccdd0bcc.jpeg"},{"id":95200939,"identity":"decf6e3f-529a-4bf9-961a-f12a4a5a12d5","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":234742,"visible":true,"origin":"","legend":"","description":"","filename":"8e880dee77a047758ad8a3fa30fe5f441enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/25ebf54e98b2b0af01452c2e.xml"},{"id":95227444,"identity":"4d95724c-20e7-49db-8c48-aa27bc32d408","added_by":"auto","created_at":"2025-11-05 16:32:30","extension":"jpg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":63692,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/d001225a3fedd29618e7cd65.jpg"},{"id":95228697,"identity":"2a759fa6-b147-4e28-8ce5-6e1800d47a2e","added_by":"auto","created_at":"2025-11-05 16:34:04","extension":"jpg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40276,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/c9b7cc453fef35469157b040.jpg"},{"id":95228459,"identity":"fd711999-6061-4b83-a1a2-4400d8c20c71","added_by":"auto","created_at":"2025-11-05 16:33:46","extension":"jpg","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33378,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/6fa6541a82a49fee3f2a8f94.jpg"},{"id":95227778,"identity":"e39ff00e-3489-430f-a2b6-f86ce3cb6549","added_by":"auto","created_at":"2025-11-05 16:32:56","extension":"jpg","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32490,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/23736de67346f9c87e86f49b.jpg"},{"id":95229056,"identity":"f5769ac1-1f30-457e-86d7-715f5adc3a96","added_by":"auto","created_at":"2025-11-05 16:34:24","extension":"jpg","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80640,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/c8fddaa92255488ab7d55235.jpg"},{"id":95229201,"identity":"e9476ec0-45d2-4007-a856-a0abbfe02c3e","added_by":"auto","created_at":"2025-11-05 16:34:36","extension":"jpg","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123098,"visible":true,"origin":"","legend":"","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/4c26e9edbe844c8a5e7e1700.jpg"},{"id":95200933,"identity":"a0d04920-4d8a-4557-9483-44b52f0375bd","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"jpeg","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":42951,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/5b97fbee5c40b50ac9b5c9e6.jpeg"},{"id":95227216,"identity":"c07ee50a-da30-44b3-9789-cc43125ee79b","added_by":"auto","created_at":"2025-11-05 16:32:16","extension":"jpeg","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41658,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/3f318988a3137dcdcf5df094.jpeg"},{"id":95200964,"identity":"c9df145c-b053-415b-9310-133cd5619b2f","added_by":"auto","created_at":"2025-11-05 12:16:13","extension":"jpeg","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":164153,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/63252485f7b0f4f696deb292.jpeg"},{"id":95200961,"identity":"f0ab7275-c2e5-434b-a538-3d093d096405","added_by":"auto","created_at":"2025-11-05 12:16:13","extension":"jpeg","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":204720,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/fc646a0ab79291d311bfe8c9.jpeg"},{"id":95227849,"identity":"2b309827-5666-4415-9441-90e48a52f571","added_by":"auto","created_at":"2025-11-05 16:33:01","extension":"jpeg","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":216676,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/73ae4ab56f00a17145d1bf23.jpeg"},{"id":95200949,"identity":"9b0825f6-0f0c-46ef-ad9c-c9c534908e78","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":530149,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/a3f86bd6572fe67d1a839fa0.png"},{"id":95200974,"identity":"67881ed4-463c-40eb-8b0c-33ba10adad79","added_by":"auto","created_at":"2025-11-05 12:16:17","extension":"jpeg","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":103224,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/1aef03f923fbe8e363a14d00.jpeg"},{"id":95200968,"identity":"55b30ee5-2ae9-46ee-ae85-13d333fad5d4","added_by":"auto","created_at":"2025-11-05 12:16:13","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31423,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/e7e15dd4a0c5a3101d1f874e.png"},{"id":95200944,"identity":"e4bea2b0-aea9-4a26-8c72-d84ef5b169ea","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12266,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/36cc31fa03e95f6394fd7d72.png"},{"id":95229221,"identity":"03d80f3e-2b4c-45f3-891b-0c7d7584fe1e","added_by":"auto","created_at":"2025-11-05 16:34:37","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10312,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/a723aee3ee40f27d8fdcfdd8.png"},{"id":95229222,"identity":"5cbf2a8b-e9a9-4cfe-b839-214fdd05b351","added_by":"auto","created_at":"2025-11-05 16:34:37","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10164,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/2dbe341535a716c2888e7ae2.png"},{"id":95227844,"identity":"0de3ae52-e0e8-43b1-bcfb-9cce473f880a","added_by":"auto","created_at":"2025-11-05 16:33:01","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30823,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/7da4bbf744f0c3a7bfce35c0.png"},{"id":95228249,"identity":"b346ba85-501a-4894-a5a8-514cfd251031","added_by":"auto","created_at":"2025-11-05 16:33:32","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70586,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/74148a9e7eb09dc31ecb8dba.png"},{"id":95200941,"identity":"0ed92e1d-ec76-4560-988b-bad2012d0655","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"png","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13782,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/ca9261bc8f473afdbbde11f2.png"},{"id":95227361,"identity":"84300801-696b-4d35-9cfb-6506dde954fb","added_by":"auto","created_at":"2025-11-05 16:32:25","extension":"png","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8639,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/b073aa2dbea28f20a1b5f115.png"},{"id":95200959,"identity":"f8054423-f1f1-4e26-8d57-ed2e23a527bd","added_by":"auto","created_at":"2025-11-05 12:16:13","extension":"png","order_by":40,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31872,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/6b4d76c86f83d3f68270136f.png"},{"id":95200956,"identity":"7d5480d1-c2b6-4b85-9401-0a9c0c58b428","added_by":"auto","created_at":"2025-11-05 12:16:13","extension":"png","order_by":41,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43432,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/ca418071d3528f03fb37372d.png"},{"id":95200967,"identity":"beaac54e-7cf1-48fb-9d47-8a9ef9bea772","added_by":"auto","created_at":"2025-11-05 12:16:13","extension":"png","order_by":42,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":45151,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/3295e1828e0c1973732bf0a2.png"},{"id":95200958,"identity":"e5298d37-757b-4ca5-9552-e1c3381cbf1c","added_by":"auto","created_at":"2025-11-05 12:16:13","extension":"png","order_by":43,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70282,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/4ff6f8211f558750d766ffe7.png"},{"id":95200951,"identity":"19fe5e63-6d91-4c43-a9be-1cb39205c108","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"png","order_by":44,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57707,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/b72f7f300b65207f67e5b050.png"},{"id":95200954,"identity":"ff9a8e8c-f429-48ef-84ad-35de0e4a8bb9","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"xml","order_by":45,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":232112,"visible":true,"origin":"","legend":"","description":"","filename":"8e880dee77a047758ad8a3fa30fe5f441structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/be4bd51fc31c8ef32cb7d812.xml"},{"id":95200973,"identity":"079a96a4-dc76-4f7d-8798-5330d3992dde","added_by":"auto","created_at":"2025-11-05 12:16:13","extension":"html","order_by":46,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":250502,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/674114358f7c05611d9b619c.html"},{"id":95200911,"identity":"9016053a-85bb-40eb-a530-3e7dfd78fb00","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63692,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/738f2d55a4dc46a3eb149762.jpg"},{"id":95200932,"identity":"44478be5-7a74-4f9e-97a4-39c4327cafb2","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40276,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/e17e70e6a25e028944b06a4e.jpg"},{"id":95200914,"identity":"20215f41-7f2c-43f1-b351-1ad4c4a7c0d4","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33378,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/4bd40dc42358ab70cb3e3a79.jpg"},{"id":95228615,"identity":"62382863-6e81-4a57-b058-5ba1f4d5f1c5","added_by":"auto","created_at":"2025-11-05 16:34:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32490,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/d4a87904cc2671a4b076a695.jpg"},{"id":95200943,"identity":"fc105d10-8bfd-4136-a96a-3a507a1e0e30","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":80640,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/320af215df5d24470efdd878.jpg"},{"id":95312150,"identity":"34877f9a-6c10-4b1c-891c-fbce6bb9a64d","added_by":"auto","created_at":"2025-11-06 15:47:36","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":123098,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/39f01d9602186551321d1986.jpg"},{"id":96364820,"identity":"48a7e4e7-9efd-4cb9-a609-14e0defbc6ec","added_by":"auto","created_at":"2025-11-20 10:09:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1811512,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/86cb14f7-1777-499b-8fa3-0d15e581ebc6.pdf"},{"id":95200912,"identity":"23505b08-2825-4ee2-be5f-e928c2a77de7","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":33596,"visible":true,"origin":"","legend":"","description":"","filename":"AllTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/1e069ccb4da1d465d8ed3da3.docx"},{"id":95200917,"identity":"fc041304-4c58-4a2a-8971-09c3764bc201","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14377,"visible":true,"origin":"","legend":"","description":"","filename":"MLYTofLentilRawdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/dcb0e84db8456260cbc4ff21.xlsx"},{"id":95228595,"identity":"be81f8a2-120a-4307-9802-089110b3212a","added_by":"auto","created_at":"2025-11-05 16:33:58","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22215,"visible":true,"origin":"","legend":"","description":"","filename":"data.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/6a03a319fefbbfd296c1c445.xlsx"},{"id":95200916,"identity":"3f2258e6-f6fb-4839-ac15-b387bb0459e9","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":94478,"visible":true,"origin":"","legend":"","description":"","filename":"rankingGenotypes.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/54c2603f956b20822cec2f01.jpeg"},{"id":95229060,"identity":"c32eca14-6ae0-45fe-a2fa-a999b255b332","added_by":"auto","created_at":"2025-11-05 16:34:24","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":65436,"visible":true,"origin":"","legend":"","description":"","filename":"GGEGYHeatmap.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/240135d453c082d2eaaed68b.jpeg"},{"id":95200940,"identity":"28e5e316-7ef6-49be-b4de-553b540aa480","added_by":"auto","created_at":"2025-11-05 12:16:12","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":83105,"visible":true,"origin":"","legend":"","description":"","filename":"meanvsstability.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/7704c58547c6ce6975abbf57.jpeg"},{"id":95228088,"identity":"7ece6428-b513-4e6e-9596-f0ecbf95a068","added_by":"auto","created_at":"2025-11-05 16:33:21","extension":"jpeg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":75401,"visible":true,"origin":"","legend":"","description":"","filename":"GGEbiplotGY.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/f5023874eff11f2cbdcbff9a.jpeg"},{"id":95227337,"identity":"fd280b46-0857-434c-851e-971ed89c80f8","added_by":"auto","created_at":"2025-11-05 16:32:24","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":73837,"visible":true,"origin":"","legend":"","description":"","filename":"GYvsWAASBplot.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/772553967db1982206b137de.jpeg"},{"id":95228651,"identity":"a16ec211-3813-4604-8d57-c09eec78df82","added_by":"auto","created_at":"2025-11-05 16:34:01","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":74906,"visible":true,"origin":"","legend":"","description":"","filename":"MTSI.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/dc4874f48141b2c4a3a3dfc3.jpeg"},{"id":95200926,"identity":"0cadfd26-e3b0-4811-857c-0ffe4a0ee2a4","added_by":"auto","created_at":"2025-11-05 12:16:11","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":73996,"visible":true,"origin":"","legend":"","description":"","filename":"GGEbiplottype1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7736936/v1/9258e93694e305b0ab1c4ea0.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing yield stability of heat-tolerant lentil genotypes across multiple hotspot regions in Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe lentil (\u003cem\u003eLens culinaris\u003c/em\u003e subsp. \u003cem\u003eculinaris\u003c/em\u003e Medik) is a significant food legume in the sub-family \u003cem\u003ePapilionoideae\u003c/em\u003e under the \u003cem\u003eFabaceae\u003c/em\u003e family. It is a diploid (2n\u0026thinsp;=\u0026thinsp;14), self-pollinated crop with a haploid genome size of 4,063 Mbp\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Lentils are an affordable source of protein, especially valuable in low-income populations in developing regions where they serve as a substitute for animal protein due to their low cost and high nutritional content. They contain approximately 23% protein, 59% carbohydrates, 1.8% oils, and 0.2% ash, along with essential minerals like iron, zinc, calcium, phosphorus, magnesium, selenium, and vitamins A and B\u003csup\u003e2, 3\u003c/sup\u003e. Additionally, lentil straw and seed coats are widely used as animal feed in regions including West Asia, North and South America, Australia, North Africa, and the Indian subcontinent, especially in Bangladesh, Nepal, and India\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Lentils also contribute to soil health through symbiotic nitrogen fixation (annually fixing around 107 kg N ha⁻\u0026sup1;)\u003csup\u003e6\u003c/sup\u003e, conserving soil moisture, and preventing erosion\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. They can tolerate drought and grow across a wide range of soil types, from light to heavy, with a pH between 5.5 and 9.0. Globally, lentils are cultivated on approximately 5.46\u0026nbsp;million hectares with a total production of 5.64\u0026nbsp;million tons, yielding an average of 1,033 kg per hectare\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Major lentil-producing countries include Turkey, Canada, India, Australia, Nepal, Bangladesh, China, the United States, Ethiopia, and Syria\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In Bangladesh, the cultivable area for lentils is 146,000 hectares, with a production of 186,000 tons and an average yield of 1,274 kg per hectare\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLentil cultivation faces challenges from both biotic and abiotic stresses. Key abiotic stresses include cold, drought, heat, salinity, nutrient deficiency, and toxicity, with drought and heat being the primary constraints worldwide\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Climate change has intensified terminal heat stress as a significant abiotic factor affecting lentil production. While lentils can germinate at freezing temperatures, optimal germination occurs between 18\u0026ndash;21\u0026deg;C, with growth and yield favored at temperatures above 24\u0026deg;C. However, temperatures exceeding 27\u0026deg;C can severely impede growth and yield\u003csup\u003e10\u003c/sup\u003e. In the southern and northwestern regions of Bangladesh, delayed sowing due to the late harvest of T-aman rice, as well as early vegetable production, further exacerbates terminal heat stress during the lentil reproductive stage. Consequently, lentil productivity has seen a significant decline. The selection of early-maturing, high-yielding lentil genotypes with stable performance within rice-fallow cropping systems in the Barind region has become crucial for lentil breeders in Bangladesh to improve self-sufficiency in lentil production. Currently, no research has recommended specific lentil genotypes for the rice-fallow cropping pattern, particularly those with stability across multiple traits. Recently, some promising lines were identified from heat tolerance screening programs under the PhD research of Aktar-Uz-Zaman et al. (2022)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Evaluating these heat-tolerant genotypes in targeted environments could provide essential insights for selecting suitable cultivars\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Prior to releasing these heat-tolerant varieties, multi-locational yield trials are necessary across various hotspot regions in Bangladesh to assess adaptability. Generally, multi-locational or multi-environmental trials (MLT or MET) are used by plant breeders to evaluate advanced genotypes across different test environments before releasing them as commercial varieties. Usually plant breeders are used GEI and stability analysis for selection of stable genotypes in an easily understandable way. In a broad sense, parametric and non-parametric stability methods are used for stability analysis based on yield performance\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Parametric analysis is the most commonly used reported by several research for highlighted significant interactions between environments and genotypes for grain yield in annual crops such as groundnut\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e, pearl millet\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, soybean\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, wheat\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,21\u003c/sup\u003e, and maize\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Genotype \u0026times; environment (GE) interaction is often assessed using non-parametric methods like the AMMI model, Multi-Trait Stability Index (MTSI), and the Y \u0026times; WAASB biplot, which help to identify the superior genotype based on performance and stability\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Priority is given to lines that exhibit high yield potential, heat tolerance, and resistance to pests, especially when compared to the existing late-sowing variety, BARI Masur-8, used as a check variety. Therefore, this study aims to identify the superior terminal heat-tolerant genotypes with the highest yield potential across locations.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThe ANOVA results revealed significant variation among the genotypes, environments, and genotype-environment interactions at a 0.001% probability level (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The significant G \u0026times; E interaction indicates that grain yield among genotypes varied across environments, highlighting the influence of environmental effects within the G \u0026times; E interaction. This further suggests that while genotypes are genetically diverse, some also respond differently to varying environmental conditions\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The sum of squares for genotypes was smaller than for G \u0026times; E, underscoring differences in genotypic responses across environments. The interaction term was further analyzed using the AMMI model, decomposing the GE interaction into principal components, with three significant AMMI terms identified through F-statistics (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The first two AMMI principal components explained 92% of the total interaction effect, with PC1 and PC2 accounting for 76.3% and 15.7%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The significance of the G \u0026times; E interaction for grain yield indicates that AMMI analysis is effective in selecting promising genotypes for specific locations or environmental conditions\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of variance for grain yield (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in 6 genotypes of \u003cem\u003eLens culinaris\u003c/em\u003e Medik evaluated over 4 environments in Bangladesh.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDegree of\u003c/p\u003e\u003cp\u003eFreedom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean of Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProbability level (\u0026gt;\u0026thinsp;F)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5544831.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1848277.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e189.405808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.092627e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRep(Env)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78066.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9758.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.082264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.398451e-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1164480.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e232896.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.562818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.319171e-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes:Environment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4770018.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e318001.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.444201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.981189e-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3640890.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e520127.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e164.290000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000000e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e747171.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e149434.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.200000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000000e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e381956.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e127318.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.220000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000000e\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResiduals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126637.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3165.949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16454054.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e191326.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eNA\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: PC1; Principal Component1, PC2; Principal Component2, PC3; Principal Component3.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBiplot pattern for elucidation of multivariate analysis\u003c/h2\u003e\u003cp\u003eThe main effects of genotype (G) and genotype-by-environment (G\u0026times;E) interactions are the primary sources of variation in assessing genotype stability in multi-environment trials (MET), as outlined by Yan et al. (2000)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Yan et al.16 proposed three key components for evaluating MET such as (a) \u0026ldquo;representativeness and discriminating \u0026ldquo;which used for the assessment of test environment, (b) \u0026ldquo;stability vs mean\u0026rdquo; Used to evaluate genotype performance across environments, and (c) \u0026ldquo;which-won-where\u0026rdquo; pattern or METis an effective approach for visualization the pattern of GEI based on the correlation between G and E.\u003c/p\u003e\u003cp\u003eThe combined effect of genotype (G) and genotype-by-environment interaction (G\u0026times;E) variation accounted for 90.25% of yield per hectare (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Patterns a, b, c). To identify the most suitable testing environment, the 'discriminativeness vs. representativeness' pattern in the GGE biplot is essential for effective breeding and the selection of superior genotypes. Discriminativeness refers to an environment's ability to differentiate among genotypes, while representativeness describes its capacity to typify all other evaluated environments, reflecting the ideal testing conditions\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In the 'discriminativeness vs. representativeness' biplot, the lines connecting the test environments are known as environment vectors. In this study, the biplot indicated that four test environments exhibited acute angles with each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), suggesting a close relationship among them\u003csup\u003e28\u003c/sup\u003e. These four environments were positively correlated, showing a strong similarity. The genotypes G-2 and G-6 showed acute angles between their vectors, as did G-6 and G-5, indicating a similar response. This study concludes that among the four environments, ISH (Ishurdi) is the most suitable testing environment for lentil production, followed by Rajshahi (RAJ).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEvaluation of genotypes across the environments based on mean vs. stability:\u003c/h3\u003e\n\u003cp\u003eThe mean performance and stability of each genotype across locations were graphically represented through the Average Environment Coordination (AEC) view of the yield per hectare biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The AEC view, which is genotype-metric-preserving (SVP\u0026thinsp;=\u0026thinsp;3), allowed for a visual assessment of genotype discrimination based on combined performance. The first two principal components (PCs) explained 90.25% of the variation for yield traits, supporting previous reports\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e on the adequacy of this biplot analysis. Genotypes closer to the vector length are more stable, while those farther away are less so. In this study, based on six genotypes and four environments, the GGE biplot divided the genotypes into distinct clockwise fan-shaped sections (1, 1, 1, and 3, respectively). Genotypes G-2, G-5, and G-6 demonstrated strong multi-trait performance, whereas genotype G-1, positioned at the far left of the AEC ordinate, showed poor performance.\u003c/p\u003e\n\u003ch3\u003eIdentification of which-won-where:\u003c/h3\u003e\n\u003cp\u003eThe 'which-won-where' feature of the GGE biplot is crucial for identifying both the best-performing genotypes and the winning genotypes for specific test environments. This feature provides a graphical representation of genotypes or winning genotypes within the test environments. A polygon is first drawn around the genotypes, positioning all genotypes at the outermost points from the biplot origin, or within the polygon\u003csup\u003e31\u003c/sup\u003e. The genotypes located at the vertices of the polygon are either the best or poorest performers in one or more environments. Perpendicular lines, called equality lines\u003csup\u003e31\u003c/sup\u003e, are then drawn from the biplot\u0026rsquo;s origin to each side of the polygon, dividing it into several sectors. Each sector contains one or more genotypes at the polygon's vertex. In our study, the equality lines divided the graph into five sectors, with all four environments falling within a single sector (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Among the six genotypes, G-1 was positioned at a vertex, indicating it as an unstable genotype across the study locations. In contrast, genotypes G-2, G-5, G-6, G-3, and G-4 showed the most promise within the study environments. This approach has been used widely for identifying mega-environments in various crops\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was generated using the first two Principal Components (PC1 and PC2) for the average grain yield per hectare of six lentil genotypes across four environments. This biplot illustrates the ranking of environments using a concentric circle, a method frequently employed in GGE biplot analysis to visually identify the most favorable environment for yield production\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In this model, the ideal environment is conceptually placed at the center of the concentric circle, with other environments positioned according to their correlation and proximity to this ideal genotype. In the present study, ISH (Ishurdi) is located at the center of the concentric circle, indicating its high suitability as a testing environment. This positioning suggests that Ishurdi provides conditions that are both representative of other environments and conducive to maximizing genotype performance. Following ISH, BSL (Barishal), RAJ (Rajshahi), and JOY (Joydebpur) are positioned closely around the circle. Their proximity to the center suggests that these environments also provide suitable, though slightly less ideal, conditions for lentil production. Similar results have been reported in previous studies, where environments positioned near the center of the concentric circle demonstrated strong stability and yield performance, making them optimal for crop trials\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Therefore, based on these results, Ishurdi is recommended as the most suitable environment for lentil production, followed closely by Barishal, Rajshahi, and Joydebpur.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSimilarly Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the ranking of genotypes by their positions within a concentric circle, where the ideal genotype is conceptually located at or near the center. In this study, genotype G-2 is closest to the center, followed by G-6 and G-5, indicating these genotypes' relative stability and suitability across environments. Genotypes G-3 and G-4 are positioned further from the center, while G-1 is the most distant, suggesting a higher level of instability. The positioning of G-2 and G-6 near the center indicates their minimal variation across locations, which signifies consistent performance in yield and stability across diverse environments. This finding aligns with prior research, where stable genotypes generally remain close to the concentric center due to uniform adaptability\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Studies in lentil by Chatterjee et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and in rice by Hasan et al. (2022)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and Akter et al. (2015)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e similarly observed that genotypes positioned near the ideal point are often characterized by superior yield stability across multiple environments. In genotype evaluation studies, proximity to the ideal genotype is widely used as an indicator of a genotype's performance consistency and adaptability, as reflected in GGE biplot analyses. The clear distinction in G-2\u0026rsquo;s position as the most stable genotype across locations, followed by G-6 and G-5, suggests its suitability as a candidate for broad adaptation. Conversely, G-1\u0026rsquo;s position further from the center highlights its performance instability, making it less ideal for environments with variable conditions. The stable performance of G-2 and G-6, combined with low variance, highlighting the utility of multi-environment trials to identify high-performing genotypes across diverse settings, as seen in similar research on crop stability and adaptability\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eS\u003cb\u003eelection of Superior Genotypes based on Multitrait Stability Index (MTSI) and Y\u0026times; WAASB biplot\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe exploration of Genotype-Environment Interaction (GEI) is critical in plant breeding, particularly for developing crop varieties that perform consistently across diverse environments. Tools like AMMI, GGE, and Y \u0026times; WAASB biplot were effectively utilized to explore Genotype-Environment Interaction (GEI) in field crops across multiple locations and years\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The Multi-Trait Stability Index (MTSI) is a selection index commonly used by plant breeders to identify superior genotypes for multi-environment trials (MET) based on the mean performance and stability of genotypes across locations\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This index evaluates both desirable and undesirable traits by analyzing each ideotype\u0026rsquo;s factorial scores. A spatial probability is then computed based on the distance between an accession and the ideotype, enabling the ranking of accessions\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Consequently, the genotype with the lowest MTSI is closest to the ideotype and demonstrates optimal mean performance and stability across the analyzed variables\u003csup\u003e41\u003c/sup\u003e. In our study, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(A) displays the ranking of six lentil genotypes according to MTSI. Among them, the red-colored genotype, G-2, was identified as the superior and most stable genotype, achieving the highest mean performance in the multi-trait analysis. Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(B) shows that genotype G-2, with PC1 scores near zero and along the center lines of the Y \u0026times; WAASB biplot, demonstrated the high mean performance and stability for grain yield across all environments. This finding aligns with previous research by Murphy et al. (2009)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, which supports the use of biplot analysis in revealing stable, high-performing genotypes across multiple traits and environments. Overall, these analytical tools collectively enhance the efficiency of genotype selection in MET, aiding breeders in identifying resilient and high-yielding varieties adapted to specific environmental conditions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eHeatmap analysis for genotypes on based yield and yield contributing traits across the environments\u003c/h3\u003e\n\u003cp\u003eA heatmap is a data visualization technique that represents the intensity of a phenomenon using color in two dimensions. The variation in color, either through hue or intensity, provides readers with clear visual insights into how the phenomenon is distributed or grouped across different environments. It highlights relative patterns of high-abundance features against a background of features with low abundance or absence. Heatmap analysis was conducted on yield-contributing traits and the yield performance of six lentil genotypes across four environments, offering a chromatic evaluation of these genotypes (Fig.\u0026nbsp;5). This analysis included the construction of double dendrograms. The first dendrogram, oriented horizontally, and arranged the yield and yield-contributing traits while the second dendrogram oriented vertically influencing the lentil genotypes and clustered at different caterogies (Fig.\u0026nbsp;5). The first dendrogram categorized the traits into three major groups viz, group (a) lined to traits SY, GY, FPP, DF, DM, BPP, and PPP, and the group (b) lined to trait PH and group (c) linked to traits HSW and Dsc indicating a degree of diversity among the genotypes. Similarly, the second dendrogram grouped the genotypes into five distinct clusters viz, group (a) consisting two genotypes G-2 and G-4 with better yield performance, straw yield and final plant stand; group (b) consisting one genotype G-3 with maximum days flowering and days to maturity, and the group (c) consisting the genotype G-6 with maximum pods per plant, branches per plant, plant height and days to flowering, group (d) consisting the genotype G-4 with better resistance against \u003cem\u003eStemphylium\u003c/em\u003e blight disease and group (e) consisting the genotype G-1 with maximum grain size. The clustering and characterization of these genotypes serve as crucial criteria for selecting and identifying the best materials for hybridization programs in plant breeding\u003csup\u003e43\u003c/sup\u003e. The heatmap analysis effectively illustrated the relationships among genotypes based on their morphological traits and comparative yield performance across different environment\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDisease reaction against root rot and\u003c/b\u003e \u003cb\u003eStemphylium\u003c/b\u003e \u003cb\u003eblight disease of lentil\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLentil genotypes were screened under natural field conditions to assess their response to root rot and \u003cem\u003eStemphylium\u003c/em\u003e blight diseases, which are globally recognized as the most devastating diseases of lentil, occurring at the seedling stage and from flowering initiation to the podding stage, respectively. The studied lentil genotypes exhibited significant differential responses to these diseases during the 2023-24 crop season (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the tested genotypes, BLX 05002-3 exhibited a resistant reaction, while two genotypes, BLX 05002-6 and the check variety BARI Masur-8, were found to be moderately resistant. Conversely, the genotypes BLX 09015 and LRIL 21-1-1-1-1 were categorized as susceptible, with LRIL 21-1-1-1-1-6 being the only genotype showing high susceptibility to root rot under natural epiphytic condition. These findings support with similar studies by Kharte et al. (2023)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, who screened 90 lentil genotypes under natural field conditions, and Mohammadi et al. (2012)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, who also evaluated 55 advanced lentil lines for resistance to Fusarium wilt caused by \u003cem\u003eFusarium oxysporum f. sp.\u003c/em\u003e under both natural field and greenhouse-controlled condition. The study suggests that the resistant genotypes, such as BLX 05002-3, BLX 05002-6, and BARI Masur-8, may produce higher levels of antifungal compounds, such as phenolics, compared to susceptible genotypes. This mechanism potentially provides resistance against root rot pathogens like \u003cem\u003eSclerotium rolfsii\u003c/em\u003e or \u003cem\u003eFusarium oxysporum f. sp.\u003c/em\u003e, as reported by Iftikhar et al. (2005)\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, Jamil et al. (1996) \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and Sahi et al. (2000) \u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRegarding susceptibility to \u003cem\u003eStemphylium\u003c/em\u003e blight, genotype G-3 exhibited the least infection (disease score: 2.25) with a moderately resistant reaction, followed by G-5 (2.50) and G-2 (2.75) across four environments. The highest disease rating score (3.75) was recorded in LRIL 21-1-1-1-1-6, indicating moderate susceptibility, followed by BLX 09015 (3.58) and BARI Masur-8 (3.25) under natural field conditions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar findings were reported by Aktar-Uz-Zaman et al. (2025)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e who screened 60 lentil genotypes against \u003cem\u003eStemphylium\u003c/em\u003e blight disease under natural epiphytic and artificial inoculum conditions during the 2019\u0026ndash;20 and 2020-21 crop seasons.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDisease reaction of the genotypes against root rot disease (based on 1\u0026ndash;9 disease rating scale) and \u003cem\u003eStemphylim\u003c/em\u003e blight disease (based on 0\u0026ndash;5 disease rating scale) at natural field condition during 2023-24.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eDisease rating score of root rot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eDisease rating scale of \u003cem\u003eStemphylium\u003c/em\u003e blight\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRating score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReaction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRating score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBLX 09015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.059a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.58ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBLX 05002-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.468d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.75c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLRIL 21-1-1-1-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.834c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.25d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLRIL 21-1-1-1-1-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.774a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.75a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBLX 05002-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.111b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.50cd\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMR\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBARI Masur-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.814b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.25b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e-\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.9680**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e-\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: **Significant at 0.05 level of probability.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMean performance and comparison of the genotypes\u003c/h3\u003e\n\u003cp\u003eAll genotypes exhibited significant variation across observed parameters, including final plant population (FPP) per square meter, days to 50% flowering, days to maturity (DM), plant height (PH), branches per plant (BPP), pods per plant (PPP), hundred seed weight (HSW), grain yield (GY) in kg per hectare, and straw yield (SY) in kg per hectare (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Plant populations ranged from 36.2 (G-1) to 232 (G-2), with an average of 117.12\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23. Genotypes G-1 and G-4 showed earlier flowering and maturity at 44.50 and 85.92 days, and 44.82 and 88.42 days, respectively. Plant height (PH) was lowest in G-1 at 27 cm and highest in G-2 at 43.4 cm, with a mean value of 34.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53 cm. BPP varied from 1.8 (G-1) to 4 (G-4), averaging 2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07. Genotype G-6 had the highest number of pods per plant (PPP) at 74.47, followed by G-2 (62.65) and G-5 (61.55), with an average of 56.92\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92; the lowest PPP was recorded in G-1 (40.84). The maximum grain yield (GY) was produced by G-5 at 1281.97 kg per hectare across all environments, followed by G-2 at 1259.04 kg per hectare, with an average of 1148.95\u0026thinsp;\u0026plusmn;\u0026thinsp;48 kg per hectare; the lowest yield was from G-1 (902.23 kg per hectare). HSW ranged from 1.8 (G-2) to 3.6 (G-1), with an average of 2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 across the four study locations in Bangladesh. The highest straw yield (SY) was recorded in G-5 at 1508.79 kg per hectare, followed closely by G-4 at 1500 kg per hectare, with an average SY of 1397.89\u0026thinsp;\u0026plusmn;\u0026thinsp;57.77 kg per hectare, while the lowest SY was recorded in G-1 at 1106.13 kg per hectare. According to Chowdhury et al. (2019)\u003csup\u003e51\u003c/sup\u003e, grain yield per plant correlates positively with primary branches per plant, pods per plant, hundred seed weight, and seeds per plant. Studies by Vanave et al. (2019)\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, Sharma et al. (2018)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, and Pandey et al. (2015)\u003csup\u003e54\u003c/sup\u003e further confirm significant positive correlations between yield per hectare and these yield-contributing traits, except for days to 50% flowering and days to maturity\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Plant breeders therefore prioritize these traits when selecting superior lentil genotypes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe mean performance of phenological duration, yield-contributing traits, and yield across six \u003cem\u003eLens culinaris\u003c/em\u003e Medik genotypes under four different environments during 2023-24.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSY\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e902.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1106.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG-2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1259.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1388.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e123.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1085.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1443.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1185.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1500.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e61.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1281.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1508.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1180.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1440.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1148.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1397.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e56.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStd. Dev\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e33.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e478.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e34.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.2 (G-1 in BSL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (G-3 in BSL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (G-1 in JOY)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (G-4 in RAJ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.8 (G-2 in BSL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.2 (G-3 in BSL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.8 (G-2 in BSL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e321 (G-1 in ISH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e386 (G-1 in ISH)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e223 (G-2 in ISH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (G-3 in RAJ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99 (G-6 in ISH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43.4 (G-2 in BSL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (G-4 in JOY)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e136 (G-6 in ISH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.6 (G-1 in JOY)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1980 (G-6 in RAJ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2250 (G-6 in RAJ)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinENV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBSL (60.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBSL (33.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJOY (84.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRAJ (31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJOY (2.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eJOY (32.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eISH (2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eISH (943.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eISH (1144.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaxENV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eISH (160.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRAJ (64.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eISH (95.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBSL (38.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRAJ (3.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eISH (107.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eJOY (2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRAJ (1625.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eRAJ (1930)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinGEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG-4 (96.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG-1 (44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG-1 (85.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG-4 (33.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eG-1 (2.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eG-1 (40.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eG-3 (2.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eG-1 (902.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eG-1 (1106.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaxGEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG-2 (140.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eG-6 (51.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG-3 (91.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG-6 (35.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eG-6 (3.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eG-6 (74.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eG-1 (2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eG-5 (1281.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eG-5 (1508.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote: G-1(BLX 09015), G-2(BLX 05002-3), G-3(LRIL21-1-1-1-1), G-4(LRIL21-1-1-1-1-6), G-5(BLX 05002-6), G-6(BARI Masur-8), FPP final plant population per square meter, DF days to 50% flowering(days), DM days to maturity (days), PH plant height (cm), 3BPP branches per plant, PPP pods per plant, HSW hundred seed weight (g), GY yield (kg/ha), SY straw yield (kg/ha), DSc disease score, CV co-efficient of variation, SE. standard error, Std. Dev. standard deviation, Max. maximum, Min. minimum. ENV environment, GEN genotype with LSD test at p\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe primary goal of this multi-location study was to evaluate six lentil genotypes based on mean performance across a range of environments to select superior genotype. Multi-environment trials (MET) of lentil genotypes also provide valuable insights into genotype adaptability and stability in various environmental conditions. However, before a selected genotype can be proposed as a commercial variety, it is essential to assess its susceptibility to genotype-by-environment interaction (GEI). Based on multivariate statistical analyses using GGE, AMMI biplots and hierarchical clustering heatmap, the tested genotypes were grouped into four main categories. The first group includes genotypes with high stability and yield potential, notably genotype G-2 (BLX-05002-3), which showed strong adaptability across diverse environments. The second category consists of genotypes with high yield but lower stability, each performing best in specific environments. For instance, the genotypes G-6 (BARI Masur-8) and G-3 (LRIL-21-1-1-1-1) performed best in Rajshahi. The third category consisted the genotype G-4 (LRIL-21-1-1-1-1-6) performed better in Joydebpur. The four group consisted the genotype G-1 (BLX-09015), which exhibited lower yields across all test locations in terms of mean performance, stability, and yield. Considering the yield stability across the all environments the genotypes G-5 (BLX-05002-6) and G-2 (BLX-05002-3) were selected as suitable genotypes for targeted environments. The Multi-Trait Stability Index (MTSI) was also used to select a superior genotype with both high performance and stability across locations. As a result, genotype G-2 (BLX-05002-3) was identified as the superior genotype among the six studied, demonstrating high performance and stability across multiple traits, including days to flowering, days to maturity, plant height, pods per plant, 100-seed weight, resistance to \u003cem\u003eStemphylium\u003c/em\u003e blight, grain yield per hectare, and straw yield per hectare.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePlant Materials and Experimental sites\u003c/h2\u003e\u003cp\u003eSix heat-tolerant lentil genotypes viz; BLX 09015, BLX 05002-3, LRIL 21-1-1-1-1, LRIL-21-1-1-1-1-6, and BLX 05002-6 were used in this study. These genotypes were identified through field and laboratory screening for yield, yield-contributing traits, and physiological parameters associated with heat stress tolerance\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Among these genotypes, BLX 09015, BLX 05002-3, and BLX 05002-6 are advanced breeding lines developed by the Pulses Research Centre (PRC), BARI. The genotypes LRIL 21-1-1-1-1 and LRIL-21-1-1-1-1-6 were obtained from the International Centre for Agricultural Research in Dry Areas (ICARDA) in Morocco, as part of a research collaboration between ICARDA and BARI, following international guidelines and legislation for germplasm exchange. BARI Masur-8, a variety released by PRC, BARI, Ishurdi, Pabna, Bangladesh, was used as a check variety. The pedigree information for the plant materials is provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFour distinct environments were selected as hotspots for conducting this experiment: Barind, Rajshahi; Regional Agricultural Research Station, Bangladesh Agricultural Research Institute (BARI), Rahmatpur, Barishal; Pulses Research Centre, BARI, Ishurdi, Pabna; and Pulses Research Substation, BARI, Gazipur. These environments differ significantly due to variations in their Agro-Ecological Zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e), edaphic factors, and other soil characteristics \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e Weather data for each experimental site were recorded during the trial period and are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of genotypes with their pedigree and present status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSL. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eName of the genotypes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePedigree/Source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatus\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e01.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBLX 09015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePRC, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeat tolerant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e02.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBLX 05002-3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePRC, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeat tolerant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e03.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLRIL 21-1-1-1-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICARDA, Morocco\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGermplasm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e04.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLRIL-21-1-1-1-1-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICARDA, Morocco\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGermplasm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e05.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBLX 05002-6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePRC, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHeat tolerant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e06.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBARI Masur-8 (Check)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePRC, Bangladesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReleased variety\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBangladesh (Source: Bangladesh Agricultural Research Council).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSalient features of the experimental sites along with weather data (monthly average maximum, minimum temperature, relative humidity, sunshine hours in every day and total rainfall) during crop season 2023-24 at four locations in Bangladesh.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c13\" namest=\"c7\"\u003e\u003cp\u003eWeather variable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAEZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAltitude\u003c/p\u003e\u003cp\u003e(m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eGeographical Position\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSoil Texture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eSoil fertility level and soil pH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c13\" namest=\"c7\"\u003e\u003cp\u003eMonthly average maximum, minimum temperature, relative humidity, sunshine hours in very dayand at total rainfall (mm)/month during crop season 2023-24 at different locations\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWeather parameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNovember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDecember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eJanuary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eFebruary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eMarch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003eApril\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e2024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIshurdi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24◦030 N\u003c/p\u003e\u003cp\u003e89◦050 E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003cp\u003e7.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTmax (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\u003cp\u003eTmim (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\u003cp\u003eRH(%)\u003c/p\u003e\u003cp\u003eSunshine hours\u003c/p\u003e\u003cp\u003eRainfall(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.10\u003c/p\u003e\u003cp\u003e18.60\u003c/p\u003e\u003cp\u003e88.97\u003c/p\u003e\u003cp\u003e6.95\u003c/p\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26.10\u003c/p\u003e\u003cp\u003e14.00\u003c/p\u003e\u003cp\u003e90.16\u003c/p\u003e\u003cp\u003e6.70\u003c/p\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e22.20\u003c/p\u003e\u003cp\u003e10.80\u003c/p\u003e\u003cp\u003e89.18\u003c/p\u003e\u003cp\u003e5.12\u003c/p\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e27.40\u003c/p\u003e\u003cp\u003e13.80\u003c/p\u003e\u003cp\u003e82.10\u003c/p\u003e\u003cp\u003e7.22\u003c/p\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e31.90\u003c/p\u003e\u003cp\u003e17.80\u003c/p\u003e\u003cp\u003e66.61\u003c/p\u003e\u003cp\u003e7.69\u003c/p\u003e\u003cp\u003e27.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e39.20\u003c/p\u003e\u003cp\u003e24.50\u003c/p\u003e\u003cp\u003e63.57\u003c/p\u003e\u003cp\u003e8.37\u003c/p\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBarishal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22◦480 N\u003c/p\u003e\u003cp\u003e90◦370 E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003cp\u003e7.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTmax (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\u003cp\u003eTmim (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\u003cp\u003eRH(%)\u003c/p\u003e\u003cp\u003eSunshine hours\u003c/p\u003e\u003cp\u003eRainfall(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.11\u003c/p\u003e\u003cp\u003e16.09\u003c/p\u003e\u003cp\u003e59.37\u003c/p\u003e\u003cp\u003e4.84\u003c/p\u003e\u003cp\u003e80.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e25.29\u003c/p\u003e\u003cp\u003e16.23\u003c/p\u003e\u003cp\u003e77.20\u003c/p\u003e\u003cp\u003e5.01\u003c/p\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e22.36\u003c/p\u003e\u003cp\u003e12.97\u003c/p\u003e\u003cp\u003e76.54\u003c/p\u003e\u003cp\u003e4.39\u003c/p\u003e\u003cp\u003e11.oo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e27.45\u003c/p\u003e\u003cp\u003e16.71\u003c/p\u003e\u003cp\u003e73.77\u003c/p\u003e\u003cp\u003e5.77\u003c/p\u003e\u003cp\u003e54.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e31.40\u003c/p\u003e\u003cp\u003e20.10\u003c/p\u003e\u003cp\u003e73.79\u003c/p\u003e\u003cp\u003e6.54\u003c/p\u003e\u003cp\u003e21.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e34.79\u003c/p\u003e\u003cp\u003e26.61\u003c/p\u003e\u003cp\u003e75.07\u003c/p\u003e\u003cp\u003e7.42\u003c/p\u003e\u003cp\u003e24.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22◦460\u003csup\u003e0\u003c/sup\u003eN\u003c/p\u003e\u003cp\u003e90◦390\u003csup\u003e0\u003c/sup\u003e E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSCL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003cp\u003e7.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTmax (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\u003cp\u003eTmim (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\u003cp\u003eRH(%)\u003c/p\u003e\u003cp\u003eSunshine hours\u003c/p\u003e\u003cp\u003eRainfall(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31.17\u003c/p\u003e\u003cp\u003e19.88\u003c/p\u003e\u003cp\u003e79.50\u003c/p\u003e\u003cp\u003e7.92\u003c/p\u003e\u003cp\u003e28.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26.88\u003c/p\u003e\u003cp\u003e16.19\u003c/p\u003e\u003cp\u003e86.00\u003c/p\u003e\u003cp\u003e4.80\u003c/p\u003e\u003cp\u003e48.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.73\u003c/p\u003e\u003cp\u003e13.72\u003c/p\u003e\u003cp\u003e78.00\u003c/p\u003e\u003cp\u003e3.71\u003c/p\u003e\u003cp\u003e438.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e28.09\u003c/p\u003e\u003cp\u003e16.08\u003c/p\u003e\u003cp\u003e73.50\u003c/p\u003e\u003cp\u003e6.57\u003c/p\u003e\u003cp\u003e8.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e31.56\u003c/p\u003e\u003cp\u003e18.09\u003c/p\u003e\u003cp\u003e65.5\u003c/p\u003e\u003cp\u003e5.67\u003c/p\u003e\u003cp\u003e10.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e36.89\u003c/p\u003e\u003cp\u003e24.07\u003c/p\u003e\u003cp\u003e63.00\u003c/p\u003e\u003cp\u003e8.05\u003c/p\u003e\u003cp\u003e17.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBarind, Rajshahi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.170\u003csup\u003e0\u003c/sup\u003e N\u003c/p\u003e\u003cp\u003e89.140\u003csup\u003e0\u003c/sup\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVCL, terrace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTmax (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\u003cp\u003eTmim (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e\u003cp\u003eRH(%)\u003c/p\u003e\u003cp\u003eSunshine hours\u003c/p\u003e\u003cp\u003eRainfall(mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.34\u003c/p\u003e\u003cp\u003e18.72\u003c/p\u003e\u003cp\u003e90.47\u003c/p\u003e\u003cp\u003e6.91\u003c/p\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e26.25\u003c/p\u003e\u003cp\u003e18.72\u003c/p\u003e\u003cp\u003e90.24\u003c/p\u003e\u003cp\u003e5.53\u003c/p\u003e\u003cp\u003e37.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e21.99\u003c/p\u003e\u003cp\u003e10.78\u003c/p\u003e\u003cp\u003e88.92\u003c/p\u003e\u003cp\u003e3.35\u003c/p\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e27.44\u003c/p\u003e\u003cp\u003e14.11\u003c/p\u003e\u003cp\u003e81.19\u003c/p\u003e\u003cp\u003e6.45\u003c/p\u003e\u003cp\u003e14.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e71.79\u003c/p\u003e\u003cp\u003e17.90\u003c/p\u003e\u003cp\u003e75.98\u003c/p\u003e\u003cp\u003e7.23\u003c/p\u003e\u003cp\u003e23.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e39.07\u003c/p\u003e\u003cp\u003e24.00\u003c/p\u003e\u003cp\u003e66.45\u003c/p\u003e\u003cp\u003e8.19\u003c/p\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote: CL\u0026thinsp;=\u0026thinsp;Clay loam; SC\u0026thinsp;=\u0026thinsp;Silty clay; SL\u0026thinsp;=\u0026thinsp;Silty loam; SCL\u0026thinsp;=\u0026thinsp;Silty clay loam; VCL\u0026thinsp;=\u0026thinsp;Very clay loam; Tmax(\u003csup\u003e0\u003c/sup\u003eC)\u0026thinsp;=\u0026thinsp;Maximum Temperature in degree Celsius; Tmim (\u003csup\u003e0\u003c/sup\u003eC)\u0026thinsp;=\u0026thinsp;Minimum Temperature in degree Celsius; RH(%)\u0026thinsp;=\u0026thinsp;Relative Humidity in percentage, Rainfall(mm)\u0026thinsp;=\u0026thinsp;Rainfall in millimeter.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eTrial management and Seed sowing\u003c/h2\u003e\u003cp\u003eThe tested genotypes were arranged in a Randomized Complete Block Design with three replications. The study aimed to assess yield stability across different agro-ecological zones in Bangladesh. Seeds were sown at various times according to location (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Lentil seeds were hand-sown continuously in rows at a depth of 3 cm, at a rate of 30 kg ha⁻\u0026sup1;, with 30 cm between rows, and then covered with soil. Prior to sowing, seeds were treated with Provax-200 WP at 2.5 g per kg of seeds to control root rot disease. Each genotype was sown in eight rows, each 4 m long. Flood irrigation was applied immediately after sowing to ensure uniform germination and seedling establishment in the experimental plots. One spading and two hand weedings were carried out at 15, 25, and 35 days after sowing to keep the plots weed-free and support robust crop establishment. Additionally, three scheduled sprays of Rovral 50 WP at 1.0 mL per liter of water were applied at seven-day intervals, starting at the onset of flowering, to control \u003cem\u003eStemphylium\u003c/em\u003e blight, a major disease affecting lentils.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSowing time of the different locations of the experiment during 2023-24\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDate of sowing\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBarind, Rajshahi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 December, 2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRARS, BARI, Rahmatpur, Barishal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e06 December, 2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRC, BARI, Ishurdi, Pabna\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 November, 2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRSS, BARI, Gazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 December, 2023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eFertilization\u003c/h2\u003e\u003cp\u003eThe recommended blanket doses of N, P, K, S, and B fertilizers were applied at rates of 21, 16, 17.5, 9.5, and 1.36 kg ha⁻\u0026sup1;, respectively, in the form of urea (46% N), triple super phosphate (TSP, 50% P₂O₅), muriate of potash (MP, 60% K₂O), gypsum (18% S), and boric acid (17% B)\u003csup\u003e56\u003c/sup\u003e. The full doses of all fertilizers were thoroughly incorporated into the soil as a basal application during the final land preparation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRoot rot and\u003c/b\u003e \u003cb\u003eStemphylium\u003c/b\u003e \u003cb\u003eblight disease scoring of lentil\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRoot rot and \u003cem\u003eStemphylium\u003c/em\u003e blight are among the most devastating diseases affecting lentil production in Bangladesh. Root rot typically occurs during the seedling stage, while \u003cem\u003eStemphylium\u003c/em\u003e blight infects plants from the flowering stage to the podding stage. Although the experiment was conducted across four locations, root rot disease data were collected from only two locations (Ishurdi and Barishal) due to the high severity of infection in these areas. The incidence of root rot was calculated based on the total number of infected plants relative to the total number of plants per square meter, using the following formula:\u003c/p\u003e\u003cp\u003eDisease incidence (%) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{N}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{i}\\text{n}\\text{f}\\text{e}\\text{c}\\text{t}\\text{e}\\text{d}\\:\\text{p}\\text{l}\\text{a}\\text{n}\\text{t}\\text{s}\\:}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{o}\\text{b}\\text{s}\\text{e}\\text{r}\\text{v}\\text{e}\\text{d}\\:\\text{p}\\text{l}\\text{a}\\text{n}\\text{t}\\text{s}}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe disease reaction score, indicating the resistance or susceptibility of each genotype, was determined using a modified 1\u0026ndash;9 disease rating scale based on the method described by Arya and Kushwaha (2019)\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e In contrast, \u003cem\u003eStemphylium\u003c/em\u003e blight disease scoring was conducted across four locations in the experimental plot, following the 0\u0026ndash;5 rating scale developed by Bakr and Ahmed (1992)\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRating scale for reaction of lentil genotypes against root rot disease.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRating scale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncidence percent of root rot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1% or less mortality of plants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResistant (R)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026ndash;10% mortality of plants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerately Resistant (MR)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u0026ndash;20% mortality of plants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerately Susceptible (MS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u0026ndash;50% mortality of plants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSusceptible (S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove 50% mortality of plants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHighly Susceptible (HS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRating scale for reaction of lentil genotypes against Stemphylium blight disease.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRating scale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncidence percent of root rot\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImmune\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFew scattered leaves infected and no twig blighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResistant (R)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026ndash;10% leaflets infected and/or scattered 1% twig blighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerately Resistant (MR)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u0026ndash;20% leaflets infected and/or 1\u0026ndash;5% twig blighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerately Susceptible (MS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u0026ndash;50% leaflets infected and/or 6\u0026ndash;10% twig blighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSusceptible (S)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove 50% leaflets infected /or more than 10% twig blighted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHighly Susceptible (HS)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eData collection and statistically analysis\u003c/h2\u003e\u003cp\u003eIn this study, data were collected for eight quantitative traits viz., final plant population per square meter (FPP m⁻\u0026sup2;), days to 50% flowering (DF), days to 80% maturity (DM), plant height in centimeters (PH cm), branches per plant (BPP), pods per plant (PPP), 100-seed weight in grams (HSW g), straw yield per hectare (SY kg ha⁻\u0026sup1;), disease score (DS) as a yield-contributing trait, and grain yield (GY kg ha⁻\u0026sup1;). Each entry was monitored from seedling to harvest and compared with the check varieties, with data recorded according to the classification and descriptors for \u003cem\u003eLens culinaris\u003c/em\u003e subspecies by IPGRI and ICARDA. Yield-related data were gathered from 10 randomly selected plants in the middle rows of each plot and replication, at various growth stages in the field and research station lab post-harvest. At maturity, individual lines were selected based on earliness, disease resistance, and yield potential. Grain yield was calculated from the entire plot and then converted to kilograms per hectare (kg ha⁻\u0026sup1;).\u003c/p\u003e\u003cp\u003eThe statistical analysis was done using R statistical computer-based program version R.4.4.1\u003csup\u003e59\u003c/sup\u003e. Stability were analyzed based on yield performance over locations using R software\u003csup\u003e59\u003c/sup\u003e. Superior genotypes were selected using Multritrait Stability Index (MSI) and Weighted average of the absolute scores (Y\u0026times;WAAB) which were estimated by R 4.4.1 version software based on the all yield and yield contributing traits of the tested genotypes of lentil over the locations\u003csup\u003e59\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec14\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeather data\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe weather data of the four growing locations were collected from the respective Meteorological Department, of that locations.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe data that has been generated in this study are attached in the supplementary files in the manuscript uploading system.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThis research has been financed by Bangladesh Agricultural Research Institute, Gazipur, Bangladesh. The authors would like to acknowledge the support of Bangladesh Agricultural Research Institute, Gazipur.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe publication of this article in Open Access mode and finding provided by the Regional Coordinator of South Asia and China, ICARDA, New Delhi. India.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e All authors have approved the manuscript and agree with its submission to Journal of Scientific Report\u0026rsquo;s.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\u003cbr\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchaefer, H. et al. Systematics, biogeography, and character evolution of the legume tribe Fabeae with special focus on the middle-Atlantic island lineages. \u003cem\u003eBMC Evol. Biol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 250 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZafar, M., Maqsood, M., Anser, M. R. \u0026amp; Ali, Z. Growth and yield of lentil as affected by phosphorus. \u003cem\u003eInt. J. Agric. Biol.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 98\u0026ndash;100 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNutritionvalue.org. Nutrition Value: Find Nutritional Value of a Product. Available online: April (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nutritionvalue.org/\u003c/span\u003e\u003cspan address=\"https://www.nutritionvalue.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed on 19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWebb, C. \u0026amp; Hawtin, G. Lentils. Commonwealth Bureau, Page Bros, Norwich (1981).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErskine, W. et al. Global production, supply and demand//The lentil: botany, production and uses. In: W Erskine (Editors), United Kingdom. pp. 4\u0026ndash;13 (1997).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaxena, M. C. \u0026amp; Wassimi, N. Effect of fertilizer application and inoculation on the performance of lentil and subsequent wheat crop. \u003cem\u003eLENS\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 52\u0026ndash;53 (1980).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuehlbauer, F. J., Summerfield, R. J., Kaiser, W. J., Boerboom, C. M. \u0026amp; Welsh-Maddux, M. M. Principles and practice of lentil production. \u003cem\u003eElectronic publication at\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ars.usda.gov/is/np/lentis/lentils.htm\u003c/span\u003e\u003cspan address=\"http://www.ars.usda.gov/is/np/lentis/lentils.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e FAOSTAT. World Food and Agriculture, Statistical Yearbook 2021, Rome. (1992). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4060/cb4477en\u003c/span\u003e\u003cspan address=\"10.4060/cb4477en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurner, N. C., Wright, G. C. \u0026amp; Siddique, K. H. M. Adaptation of rain legumes (Pulses) to water-limited environments. \u003cem\u003eAdvan Agron.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 193\u0026ndash;231 (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuehlbauer, F. J., Kaiser, W. J., Hannan, R. M. \u0026amp; Tullu, A. Lentil crop production manual (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAktar-Uz-Zaman, M. et al. Selection of lentil (\u003cem\u003eLens Culinaris\u003c/em\u003e (Medik.)) genotypes suitable for high-temperature conditions based on stress tolerance indices and Principal Component Analysis. \u003cem\u003eLife\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1719. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/life12111719\u003c/span\u003e\u003cspan address=\"10.3390/life12111719\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan, W. \u0026amp; Tinker, N. A. Biplot analysis of multi-environment trial data: Principles and applications. \u003cem\u003eCan. J. Plant. Sci.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e (3), 623\u0026ndash;645 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin, C. S., Binns, M. R. \u0026amp; Lefkovitch, L. P. Stability analysis: Where do we stand? \u003cem\u003eCrop Sci.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 894\u0026ndash;900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci1986.0011183X002600050012x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci1986.0011183X002600050012x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1986).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNavrood, F. F., Zakaria, R. A., Rad, M. M., Zare, N. \u0026amp; Ahrabi, M. M. Stability analysis of groundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) genotypes using AMMI and GGE biplot models and ideal genotype selection indicator. \u003cem\u003eIndian J. Genet. Plant. Breed.\u003c/em\u003e \u003cb\u003e83\u003c/b\u003e (4), 518\u0026ndash;525 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLal, C., Ajay, B. C. \u0026amp; Rupapara, K. V. AMMI and GGE models indicating seasonal variations as major source of variations for nodulation related characters in peanut. \u003cem\u003eIndian J. Genet. Plant. Breed.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e (2), 277\u0026ndash;288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31742/IJGPB.81.2.10\u003c/span\u003e\u003cspan address=\"10.31742/IJGPB.81.2.10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinde, A. S., Kamble, M. S. \u0026amp; Pawar, R. M. Stability analysis for pod yield and its component traits in groundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L). \u003cem\u003eAsian J. Bio Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 15\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15740/HAS/AJBS/12.1/15-20\u003c/span\u003e\u003cspan address=\"10.15740/HAS/AJBS/12.1/15-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReddy, P. S. et al. Athoni B. K. GGE biplot analysis for identification of ideal cultivars and testing locations of pearl millet (\u003cem\u003ePennisetum glaucum\u003c/em\u003e L.R. Br.) for peninsular India. \u003cem\u003eIndian J. Genet. Plant. Breed.\u003c/em\u003e \u003cb\u003e82\u003c/b\u003e (2), 167\u0026ndash;176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31742/IJGPB.82.2.5\u003c/span\u003e\u003cspan address=\"10.31742/IJGPB.82.2.5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCubukcu, P. et al. Stability analysis of some soybean genotypes using parametric and non-parametric methods in multienvironments. \u003cem\u003eTurk. J. Field Crops\u003c/em\u003e. \u003cb\u003e26\u003c/b\u003e, 262\u0026ndash;271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17557/tjfc.1033363\u003c/span\u003e\u003cspan address=\"10.17557/tjfc.1033363\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoksoy, A. T. et al. The parametric and non-parametric stability analysis for interpreting genotype by environment interaction of some soybean genotypes. \u003cem\u003eTurk. J. Field Crops\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e, 28\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17557/tjfc.562637\u003c/span\u003e\u003cspan address=\"10.17557/tjfc.562637\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWardofa, G. A. \u0026amp; Ararsa, A. D. Evaluation of grain yield stability analysis in bread wheat (Triticum aestivum L.) genotypes using parametricmethods. \u003cem\u003eAm. J. Life Sci.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 189\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11648/j.ajls.20200806.12\u003c/span\u003e\u003cspan address=\"10.11648/j.ajls.20200806.12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaya, Y. \u0026amp; Sahin, M. Non-parametric stability analyses of dough properties in wheat. \u003cem\u003eFood Sci. Technol.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, 509\u0026ndash;515. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1590/1678-457X.6642\u003c/span\u003e\u003cspan address=\"10.1590/1678-457X.6642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShojaei, S. H. et al. Yield stability analysis of maize (Zea mays L.) hybrids using parametric and AMMI methods. \u003cem\u003eScientifica\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2021/5576691\u003c/span\u003e\u003cspan address=\"10.1155/2021/5576691\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021). Article ID 5576691.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasan, M. J. et al. Assessment of GGE, AMMI, Regression, and Its Deviation Model to Identify Stable Rice Hybrids in Bangladesh. \u003cem\u003ePlants\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 2336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants11182336\u003c/span\u003e\u003cspan address=\"10.3390/plants11182336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkter, A. et al. GGE Biplot analysis for yield stability in multi-environment trials of promising hybrid rice (\u003cem\u003eOryza sativa\u003c/em\u003e L). \u003cem\u003eBangladesh Rice J.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (1), 1\u0026ndash;8 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingamsetti, A. et al. Genotype \u0026times; environment interaction and selection of maize (\u003cem\u003eZea mays\u003c/em\u003e L.) hybrids across moisture regimes. \u003cem\u003eField Crops Res.\u003c/em\u003e, 270, 1 August 108224. (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIslam, M. R. et al. Yield stability and genotype environment interaction of water deficit stress tolerant mung bean (\u003cem\u003eVigna radiata\u003c/em\u003e L. Wilczak) genotypes of Bangladesh. \u003cem\u003eAgronomy\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (11), 2136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy11112136\u003c/span\u003e\u003cspan address=\"10.3390/agronomy11112136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan, W., Hunt, L. A., Sheng, Q. \u0026amp; Szlavnics, Z. Cultivar evaluation and mega-environment investigation based on the GGE biplot. \u003cem\u003eCrop Sci.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 597\u0026ndash;605 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan, W. \u0026amp; Tinker, N. A. Biplot analysis of multi-environment trial data: Principles and applications. \u003cem\u003eCan. J. Plant. Sci.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e (3), 623\u0026ndash;645 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChatterjee, S. et al. Identification of suitable genotypes of lentil (Lens culinaris) for improved adaptation to rice fallow areas of Gangetic Alluvial Zone. \u003cem\u003eIndian J. Agril Sci.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e (8), 862\u0026ndash;867. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.56093/ijas.v93i8.125345\u003c/span\u003e\u003cspan address=\"10.56093/ijas.v93i8.125345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan, W., Kang, M. S., Ma, B., Woods, S. \u0026amp; Cornelius, P. L. GGE biplot vs. AMMI analysis of genotype-by-environment data. \u003cem\u003eCrop Sci.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 643\u0026ndash;655 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan, W. \u0026amp; Kang, M. S. \u003cem\u003eGGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists\u003c/em\u003e p. 71 (CRC, 2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh, B. et al. Delineation of genotype-by-environment interactions for identification and validation of resistant genotypes in mungbean to root-knot nematode (\u003cem\u003eMeloidogyne incognita\u003c/em\u003e) using GGE biplot. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 4108 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGore, P. G. et al. Understanding G \u0026times; E interaction for nutritional and antinutritional factors in a diverse panel of \u003cem\u003eVigna stipulacea\u003c/em\u003e (Lam.) Kuntz germplasm tested over the locations. \u003cem\u003eFron Plant. Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 766645 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOladosu, Y. et al. Genotype \u0026times; Environment Interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia. \u003cem\u003eActa Agric. Sci. B Soil. Plant. Sci.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e, 590\u0026ndash;606 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarimizadeh, R. et al. F., GGE Biplot Analysis of Yield Stability in Multi-environment Trials of Lentil Genotypes under Rainfed Condition. \u003cem\u003eNot. Sc. Biol\u003c/em\u003e, 5(2), 256\u0026ndash;262 (2013). (2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15835/nsb.5.2.9067\u003c/span\u003e\u003cspan address=\"10.15835/nsb.5.2.9067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasan, M. J. et al. Assessment of GGE, AMMI, Regression, and Its Deviation Model to Identify Stable Rice Hybrids in Bangladesh. \u003cem\u003ePlants\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 2336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants11182336\u003c/span\u003e\u003cspan address=\"10.3390/plants11182336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkter, A. et al. GGE Biplot analysis for yield stability in multi-environment trials of promising hybrid rice (\u003cem\u003eOryza sativa\u003c/em\u003e L). \u003cem\u003eBangladesh Rice J.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (1), 1\u0026ndash;8 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNataraj, V. et al. WAASB-based stability analysis and simultaneous selection for grain yield and early maturity in soybean. \u003cem\u003eAgronomy J First published\u003c/em\u003e. 29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/agj2.20750\u003c/span\u003e\u003cspan address=\"10.1002/agj2.20750\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAuthrapun, J., Lertsuchatavanich, U. \u0026amp; Kang, D. Selection for improving field resistance to capsicum chlorosis virus and yield-related traits using selection indices in peanut Breeding. \u003cem\u003eActa Sci. Agric.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 22\u0026ndash;31 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSellami, M. H., Pulvento, C. \u0026amp; Lavini, A. Selection of suitable genotypes of lentil (\u003cem\u003eLens culinaris\u003c/em\u003e Medik.) under rainfed conditions in south italy using multi-trait stability index (MTSI). \u003cem\u003eAgronomy\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1807. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy11091807\u003c/span\u003e\u003cspan address=\"10.3390/agronomy11091807\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlivoto, T., L\u0026uacute;cio, A. D., da Silva, J. A., Sari, B. G. \u0026amp; Diel, M. I. Mean performance and stability in multi-environment trials II: Selection based on multiple traits. \u003cem\u003eJ. Agron.\u003c/em\u003e \u003cb\u003e111\u003c/b\u003e, 2961\u0026ndash;2969 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurphy, S. E. et al. Genotype \u0026times; Environment interaction and stability for iso favone content in soybean. \u003cem\u003eCrop Sci.\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, 1313\u0026ndash;1321 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSouza, E. \u0026amp; Sorrells, M. E. Relationships among 70 American oat germplasm. I. Cluster analysis using quantitative characters. \u003cem\u003eCrop Sci.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 599\u0026ndash;605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci1991.0011183X003100030010x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci1991.0011183X003100030010x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1991).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVirga, G. et al. Agro-morphological characterization of sicilian chili pepper accessions for ornamental purposes. \u003cem\u003ePlants\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (10), 1400 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKharte, S. et al. Field Screening of Lentil Germplasm against Wilt of lentil caused by \u003cem\u003eFusarium oxysporum f. sp\u003c/em\u003e. lentis. \u003cem\u003eBiol. Forum-An Intern. J.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (1), 88\u0026ndash;91 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohammadi, N., Puralibaba, H., Mohammadi, E., Goltapeh, Ahari, A. B. \u0026amp; Sardrood, B. P. Advanced lentil lines screened for resistance to Fusarium oxysporum f. sp. lentis under greenhouse and field conditions. \u003cem\u003ePhytoparasitica\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 69\u0026ndash;76 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIftikhar, A., Khan, S., Sarwar, Alman, Haq, A. \u0026amp; Jabbar, A. Biochemistry of resistance in chickpea against wilt disease caused by \u003cem\u003eFusarium oxysporum\u003c/em\u003e f. sp. \u003cem\u003eciceris\u003c/em\u003e. \u003cem\u003ePakistan J. Bot.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 97\u0026ndash;104 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJamil, F. F., Bashir, N., Haq, I., Rath, J. \u0026amp; Barz, W. Accumulation of phytoalexins and other defense related compounds in chickpea against \u003cem\u003eAscochyta rabiei\u003c/em\u003e. \u003cem\u003ePakistan J. Phytopathol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 1\u0026ndash;4 (1996).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSahi, S. T., Randhawa, M. A., Sarwar, N. \u0026amp; Khan, S. M. Biochemical basis of resistance in lentil (\u003cem\u003eLens culinaris\u003c/em\u003e Medik.) against \u003cem\u003eAscochyta\u003c/em\u003e blight: 1. Phenols. \u003cem\u003ePakistan J. Biol. Sci.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 1141\u0026ndash;1143 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAktar-Uz-Zaman, M. et al. Screening of lentil genotypes against stemphylium blight disease and molecular identification of causal organism. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 32318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-025-15587-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-15587-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChowdhury, M. M., Haque, M. A., Malek, M. A., Rasel, M. \u0026amp; Ahamed, K. U. Genetic variability, correlation, and path coefficient analysis for yield and yield components of selected lentil (\u003cem\u003eLens culinaris\u003c/em\u003e M.) Genotypes. \u003cem\u003eFund Appl. Agril\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e (2), 769\u0026ndash;776. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5455/faa.21740\u003c/span\u003e\u003cspan address=\"10.5455/faa.21740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVanave, P. B. et al. Correlation and path analysis studies in lentil (\u003cem\u003eLens culinaris\u003c/em\u003e Medic). \u003cem\u003eElectron. J. Plant. Breed.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (3), 1322\u0026ndash;1328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5958/0975-928X.2019.00169.8\u003c/span\u003e\u003cspan address=\"10.5958/0975-928X.2019.00169.8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSharma, S. R., Singh, S., Gill, R. K., Kumar, R. \u0026amp; Parihar, A. K. Selection of promising genotypes of lentil (\u003cem\u003eLens culinaris\u003c/em\u003e Medik.) by deciphering genetic diversity and trait association. \u003cem\u003eLegume Res. Print. ISSN\u003c/em\u003e. 0250\u0026ndash;5371. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18805/LR-4056\u003c/span\u003e\u003cspan address=\"10.18805/LR-4056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018). /Online ISSN:0976\u0026thinsp;\u0026ndash;\u0026thinsp;0571.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandey, S., Bhatore, A. \u0026amp; Babbar, A. Studies on genetic variability, interrelationships association and path analysis in indigenous germplasm of Lentil in Madhya Pradesh, India. \u003cem\u003eElectron. J. Plant. Breed.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e (2), 592\u0026ndash;599 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSakthivel, G. et al. Genetic variability, correlation and path analysis in lentil germplasm (\u003cem\u003eLens culinaris\u003c/em\u003e Medik). \u003cem\u003ePharma Innov. J.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (6), 417\u0026ndash;420 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlam, M. J., Shahinuzzaman, M., Azam, M. G. \u0026amp; Islam, M. A. Ali, M. O. Production technology of BARI Masur-8. Leaflets (Bengali), Pulses Research Centre, BARI, Ishurdi, Pabna-6620 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArya, A. \u0026amp; Kushwaha, K. P. S. Management of Lentil Wilt through Host Resistance. \u003cem\u003eInt. J. Curr. Microbiol. Appl. Sci.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (03), 438\u0026ndash;444 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBakr, M. A. \u0026amp; Ahmed, F. Development of \u003cem\u003eStemphylium\u003c/em\u003e blight of lentil and its chemical control. \u003cem\u003eBangladesh J. Plant. Pathol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 39\u0026ndash;40 (1992).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeam, R. C. \u0026amp; Austria, U. R. L. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AMMI, GGE, Multitrait Stability Index, terminal heat stress, lentil","lastPublishedDoi":"10.21203/rs.3.rs-7736936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7736936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTerminal heat stress is a major abiotic constraint affecting lentil cultivation worldwide, presenting a significant challenge for plant breeders working to develop heat-tolerant genotypes. In this study, six heat-tolerant lentil genotypes BLX 09015, BLX 05002-3, LRIL-21-1-1-1-1, LRIL-21-1-1-1-1-6, BLX 05002-6 and a popular cultivated variety, BARI Mosur-8 (used as a check), were evaluated to identify more stable heat-tolerant genotypes across various agro-ecological zones in Bangladesh. Stability analysis was conducted based on grain yield data. The combined analysis of variance revealed significant differences among genotypes, environments, and genotype-environment interactions. Across the environment, BLX 05002-3 performed best in Barishal, BLX 05002-6 in Ishurdi, BARI Mosur-8 in the Barind region of Rajshahi, and LRIL-21-1-1-1-1-6 in Gazipur for improved yield. Among the four locations, Ishurdi proved to be the most stable for lentil cultivation, followed by Rajshahi, Barishal, and Gazipur, respectively. However, genotypes BLX 09015 and LRIL-21-1-1-1-1 showed instability in yield performance across the four environments. Overall, based on AMMI stability parameters, GGE biplots, Principal Component Analysis, Multitrait Stability Index (MTSI), and Y\u0026times;WAASB biplot the genotype BLX 05002-3 was identified as the most stable across environments for yield traits and grain yield under terminal heat stress conditions.\u003c/p\u003e","manuscriptTitle":"Assessing yield stability of heat-tolerant lentil genotypes across multiple hotspot regions in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 12:16:03","doi":"10.21203/rs.3.rs-7736936/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b3fa72fd-766d-495c-aa2d-0f24879dbd7c","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57332774,"name":"Biological sciences/Genetics"},{"id":57332775,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-11-19T12:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 12:16:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7736936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7736936","identity":"rs-7736936","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}