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ContextHigh temperature during the flowering phase is a major constraint to stable rice productivity. Heat tolerance cannot be explained solely by yield, as yield is affected by several interacting traits. Therefore, an integrated multi-trait evaluation is essential to identify rice genotypes with superior performance under heat stress.ObjectiveThe research was designed to identify high-yielding, heat-tolerant rice genotypes and the key traits contributing to yield maintenance under heat stress.MethodsA total of 80 rice accessions, comprising 45 landraces and 35 improved cultivars, were evaluated across four field environments using an alpha-lattice design across two cropping seasons under normal and heat-stress conditions during 2024–2025. Twenty morphological, physiological, and phenological traits were recorded. Pooled analysis of variance, correlation analysis, path coefficient analysis, principal component analysis, cluster analysis, and Multi-Trait Genotype-Ideotype Distance Index (MGIDI) analysis were performed, while a trait-wise heat tolerance index was applied to examine genotype performance under heat stress conditions.ResultsPooled ANOVA revealed evident impacts of genotype, environment, and genotype × environment interaction for all traits, indicating substantial genetic variability and contrasting responses to heat stress. High temperature during the reproductive stage adversely affected reproductive and yield-associated traits, with the largest reductions noticed in single plant yield, panicle weight, number of filled grains per panicle, spikelet fertility, and productive tillers. Trait-wise, HTI showed considerable diversity among genotypes, with TKM 9, TRY 1, PR 128, TPS 5, and TRY 5 recording superior performance in single-plant yield. Correlation and path coefficient analyses highlighted harvest index, panicle weight, productive tillers, number of grains per panicle, and filled grains per panicle as major contributors for yield maintenance under stress, whereas delayed flowering and high leaf temperature were unfavorable. Principal component analysis revealed that reproductive efficiency and grain formation explained more variation in heat tolerance than vegetative vigor, while cluster analysis identified Cluster III as the most promising group with superior genotypes under heat stress. MGIDI-based ranking further recognized TRY 1, RNR 15048, TPS 5, Indhurani, and Anna R 4 as promising multi-trait genotypes.ConclusionsHeat tolerance in rice is governed by multiple interacting traits rather than yield alone. Integrating trait-based analysis with trait-wise HTI and MGIDI improves the identification of superior genotypes under field conditions. The identified genotypes and key traits offer valuable resources for breeding heat-tolerant rice under increasing temperature stress.

Background Sorghum has potential as a source of material for food, bioenergy, and animal feed, making it a worthy candidate for promotion. This cereal thrives in regions characterized by low moisture and dry conditions. To address the diminishing availability of arable dry land, it may be necessary to explore the cultivation of sorghum in tidal swamplands and sandy soils. Methods Twelve sorghum varieties were evaluated in tidal swamplands during the rainy and dry seasons, as well as in sandy soil during the dry season, using two levels of organic fertilizers to create six test environments. The experiments were arranged in a completely randomized block design with three replications. To choose sorghum varieties with features that closely resemble an idealized sorghum variety, the Multi-trait Genotype-Ideotype Distance Index (MGIDI) was utilized. Simultaneously, genotype plus genotype-environment interaction (GGE) biplots were employed to determine the best circumstances for choosing broadly adaptable varieties that exhibit desirable features, as well as to find varieties that thrive environmental contexts. Results Based on the MGIDI ranking on the average across environment, two varieties, i.e., Numbu and Kawali were selected. However selected varieties in each environment were differed due to significant variety-environment interaction. In terms of grain weight, the Soper 7 Agritan variety exhibits adaptability across diverse environments, while the Numbu variety likewise demonstrates versatility in various environmental conditions. When evaluating forage yield, several adaptable varieties have emerged. Tidal swamplands treated with a high application of organic fertilizer, as well as sandy soils, provide optimal environments for selecting broadly adaptable varieties that focus on both grain and forage yields. Conclusion Adaptable varieties differ for various groups of environments and different traits under consideration. Optimal environments for identifying broadly adaptable varieties varied by trait. The MGIDI proves to be a valuable tool for selecting varieties based on multiple traits. In parallel, the GGE biplots effectively identifies adaptable varieties based on individual traits.

Background Commonly, several traits are assessed in agronomic experiments to better understand the factors under study. However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and post-hoc tests for mean comparison for each trait, which arouses the hypothesis that the benefits of a multi-trait framework analysis may have not been fully exploited in this area. Results In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments (e.g., one-way layout with few treatments and traits) or complex experiments (e.g., with a factorial treatment structure). We proposed an optional weighting process that makes the ranking of treatments that stands out in traits with higher weights more likely. Its application is illustrated using (1) simulated data and (2) real data from a strawberry experiment that aims to select better factor combinations (namely, cultivar, transplant origin, and substrate mixture) based on the desired performance of 22 phenological, productive, physiological, and qualitative traits. Our results show that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI ranked the Albion cultivar originated from Imported transplants and the Camarosa cultivar originated from National transplants as the better factor combinations. The substrates with burned rice husk as the main component (70%) showed satisfactory physical proprieties, providing higher water use efficiency. The strengths and weakness view provided by the MGIDI revealed that looking for an ideal treatment should direct the efforts on increasing fruit production of Albion transplants from Imported origin. On the other hand, this treatment has strengths related to productive precocity, total soluble solids, and flesh firmness. Conclusions Overall, this study opens the door to the use of MGIDI beyond the plant breeding context, providing a unique, practical, robust, and easy-to-handle multi-trait-based framework to analyze multivariate data. There is an exciting possibility for this to open up new avenues of research, mainly because using the MGIDI in future studies will dramatically reduce the number of tables/figures needed, serving as a powerful tool to guide researchers toward better treatment recommendations.

Guar, the most popular vegetable, is tolerant of drought and is a valuable industrial crop enormously grown across India, Pakistan, USA, and South Africa for pharmaceutically and cosmetically usable galactomannan (gum) content present in seed endosperm. Guar genotypes with productive traits which could perform better in differential environmental conditions are of utmost priority for genotype selection. This could be achieved by employing multivariate trait analysis. In this context, Multi-Trait Stability Index (MTSI) and Multi-Trait Genotype-Ideotype Distance Index (MGIDI) were employed for identifying high-performing genotypes exhibiting multiple traits. In the current investigation, 85 guar accessions growing in different seasons were assessed for 15 morphological traits. The results obtained by MTSI and MGIDI indexes revealed that, out of 85, only 13 genotypes performed better across and within the seasons, and, based on the coincidence index, only three genotypes (IC-415106, IC-420320, and IC-402301) were found stable with high seed production in multi-environmental conditions. View on strengths and weakness as described by the MGIDI reveals that breeders concentrated on developing genotype with desired traits, such as quality of the gum and seed yield. The strength of the ideal genotypes in the present work is mainly focused on high gum content, short crop cycle, and high seed yield possessing good biochemical traits. Thus, MTSI and MGIDI serve as a novel tool for desired genotype selection process simultaneously in plant breeding programs across multi-environments due to uniqueness and ease in interpreting data with minimal multicollinearity issues.

Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha -1 ) was consistent across five distinct rice genotypes. The genotype means varied from 5.03 to 4.73 t ha -1 depending on the environment. In AWD, all genotypes out performed in the CF system. With just a little interaction effect, the score was almost zero for several genotypes (E1, E2, E6, and E7 for the AWD technique, and E5, E6, E8, and E9 for the CF method) because they performed better in particular settings. The GGE biplot provided more evidence in support of the AMMI study results. The study's findings made it clear that the AMMI model provides a substantial amount of information when evaluating varietal performance across many environments. Out of the five accessions that were analyzed, one was found to be top-ranking by the multi-trait genotype ideotype distance index, meaning that it may be investigated for validation stability measures. The study's findings provide helpful information on the variety selection for the settings in which BRRI dhan47 and BRRI dhan29, respectively, performed effectively in AWD and CF systems. Plant breeders might use this knowledge to choose newer kinds and to design breeding initiatives. In conclusion, intermittent irrigation could be an effective adaptation technique for simultaneously saving water and mitigating GHG while maintaining high rice grain yields in rice cultivation systems.

Soil salinity drastically hinders cotton productivity ( Gossypium hirsutum ), and fiber quality. The current study evaluated morpho-physiological and biochemical responses of fifty cotton genotypes under different salinity levels (control, 12 dS/m, and 17 dS/m) at the seedling stage. The experiment was performed in a factorial complete randomized design with three replications. Significant genotype × treatment interactions were observed for most traits, including shoot length (SL), root length (RL), fresh and dry shoot weight (FSW, DSW), fresh and dry root weight (FRW, DRW), total soluble protein (TSP), proline content, and antioxidant enzymes. Severe salinity stress reduces shoot length (SL) and root length (RL) along with notable decreases in biomass and altered biochemical responses, including increased antioxidant activities and proline content, indicating stress adaptation. Moreover, PCA and Pearson’s correlation analyses unveiled strong positive and negative correlations among studied attributes while MGIDI analyses assist in determining the salt-resilient cotton genotypes under applied treatments. The best-performing genotypes under control conditions were G 2 , G 8 , and G 12 , while G 7 , G 43 , and G 30 showed resilience under severe salinity stress. MGIDI effectively identified genotypes with outstanding salinity tolerance, such as G 2 , G 43 , G 40 , and G 26 , across all stress levels. This research assists in determining the salinity stress-tolerant cotton genotypes using morpho-physiological and biochemical parameters and MGIDI is used as a precise method for identifying salt-resilient cotton accessions.

Cotton ( Gossypium hirsutum L.) is a multipurpose crop. Abiotic stresses, especially extreme heat and drought, limit crop growth and thus reduce cotton yield by about 50%. In this study, 30 cotton genotypes were tested against low moisture stress in a pot experiment in triplicates along with control under wire house conditions. At the 3–4 leaf stage, different morpho-physiological and biochemical parameters were measured in order to select the low moisture stress-tolerant genotypes. For the selection of the best performing genotypes, Multi-Trait Genotype-Ideotype Distance Index (MGIDI) was used for the ranking of genotypes on the basis of multiple indices. For biochemical traits, 09 (TPC, TF, TSP, MDA, SOD, POD, CAT, APX, and Proline) out of 24 showed significant genotypic effects and were used for MGIDI. Eight genotypes (N-812 N-1296 N-696 N-377 N-121–896 N-T86, and N-3496) were observed to be best performing than others at 25% selection pressure (SI = 25%). For morpho-physiological traits, 14 out of 15 showed significant genotypic effects and used for MGIDI. Ten genotypes (N-1237 N-812 N-1296 N-696 N-9078 N-377 N-512 N-121 N-375, and N-896) were observed to be best performing at 35% selection pressure (SI = 35%). Six genotypes, i.e. N-812–1296 N-696 N-377 N-121, and N-896 were found common in both MGIDI analysis. In conclusion, three genotypes, i.e. N-696, N-896, and N-T86 proved to be most resilient to low moisture stress. Develop protocols, identified genotypes and markers that can be used for development of climate-smart cotton genotypes.

Evaluating sugar-related traits at different harvesting times is crucial for determining the best times to harvest in order to maximize yield and minimize losses from over-ripening. This study investigated the genetic and agronomic potential of 30 sugarcane ( Saccharum officinarum L.) genotypes across multiple growth stages (395–480 days after sowing, DAS) to optimize sugar yield (SY) and processing quality through advanced selection methodologies. By utilizing the Multi-Trait Genotype-Ideotype Distance Index (MGIDI) and Pesek–Baker Index (PBI), elite genotypes such as G09 and G07 were identified, showing superior sugar content (brix = 16.6–17.8%; commercial cane sugar, CCS = 9.13–10%) and stable SY (12.68–13.41 t ha⁻¹). Factor analysis revealed three latent drivers of productivity, explaining 88% of total variability. High heritability (> 80% for CCS and total recoverable sugar, TRS) and high genetic advance (> 15%) were recorded for sugar-related traits, particularly at the mid-late stages of growth (454–480 DAS). Regression analysis identified stem yield, polarization, and stalk diameter as primary contributors to SY, explaining 79% of the variance. Temporal trends confirmed progressive sucrose accumulation peaking at 480 DAS, with high genotypic coefficient of variation (GCV) in RS and CCS observed in the early stage of growth. The integration of MGIDI and PBI prioritized purity as an industrial-critical trait (PBI = 2.75). While G23 and G11 performed poorly, the genotypes G03 and G05 had high SY (> 13 t ha⁻¹). In conclusion, this study emphasizes the importance of using a trait-weighted- based selection index, temporal trait evaluation and multi-index selection in breeding programs of sugarcane to develop cultivars with synchronized high sugar content and yield stability.

Yield is a complex parameter of rice due to its polygonal nature, sometimes making it difficult to coat the selection process in the breeding program. In the current study, 34 elite rice genotypes were assessed to evaluate 3 locations for the selection of desirable rice cultivars suitable for multiple environments based on genetic diversity. In variance analysis, all genotypes have revealed significant variations (p ≤ 0.001) for all studied characters, signifying a broader sense of genetic variability for selection purposes. The higher phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were found for yield-associated characteristics such as the number of grains panicle−1 (GP), panicles hill−1 (PPH), and tillers hill−1 (TILL). All of the characters had higher heritability (greater than 60%) and higher genetic advance (greater than 20%), which pointed out non-additive gene action and suggested that selection would be effective. The most significant traits causing the genotype variants were identified via principal component analysis. In the findings of the cluster analysis, 34 elite lines were separated into 3 categories of clusters, with cluster II being chosen as the best one. The relationship matrix between each elite cultivar and traits was also determined utilizing a heatmap. Based on multi-trait genotype-ideotype distance index (MGIDI), genotypes Gen2, Gen4, Gen14, Gen22, and Gen30 in Satkhira; Gen2, Gen6, Gen7, Gen15, and Gen30 in Kushtia; and Gen10, Gen12, Gen26, Gen30, and Gen34 in Barishal were found to be the most promising genotypes. Upon validation, these genotypes can be suggested for commercial release or used as potential breeding material in crossing programs for the development of cultivars suitable for multiple environments under the future changing climate.

Water deficit stress triggers various physiological and biochemical changes in plants, substantially affecting both overall plant defense response and thus nutritional quality of tomatoes. The aim of this study was to assess the antioxidant defense response and nutritional quality of different tomato genotypes under water deficit stress. In this study, six tomato genotypes were used and subjected to water deficit stress by withholding water for eight days under glass house conditions. Various physiological parameters from leaves and biochemical parameters from tomato fruits were measured to check the effect of antioxidant defense response and nutritional value. Multi-trait genotype-ideotype distance index (MGIDI) was used for the selection of genotypes with improved defense response and nutritional value under water deficit stress condition. Results indicated that all physiological parameters declined under stress conditions compared to the control. Notably, NBH-362 demonstrated resilience to water deficit stress, improving both defense response and nutritional quality which is evident by an increase in proline (16.91%), reducing sugars (20.15%), total flavonoids (10.43%), superoxide dismutase (24.65%), peroxidase (14.7%), and total antioxidant capacity (29.9%), along with a decrease in total oxidant status (4.38%) under stress condition. Overall, the findings suggest that exposure to water deficit stress has the potential to enhance the nutritional quality of tomatoes. However, the degree of this enhancement is contingent upon the distinct genetic characteristics of various tomato genotypes. Furthermore, the promising genotype (NBH-362) identified in this study holds potential for future utilization in breeding programs.