Lead (Pb) and nickel (Ni) are serious soil pollutants that adversely affect plant growth and development and need to be removed through phytoremediation. The present study aimed to assess the morphological indices of Albizia lebbeck (L.) (Benth.) in relation to anatomical modifications for survival under both Pb and Ni stress. The seedlings of A. lebbeck were established and then subjected to four different concentrations, viz. 0 mM, 25 mM, 50 mM and 75 mM, of Pb and Ni for 14 days in two phases. Morphological traits such as shoot length (70.93%), fresh weight (79.27%), dry weight (83.9%), number of root hairs (65.7%), number of leaves per plant (67.4%) and number of leaflets per plant greatly reduced under Pb or Ni stress. Surprisingly, root length increased rather than decreased with the increase in Pb or Ni concentrations, along with an increase in leaflet width, leaflet length and leaflet area. Moreover, root cortical cell area, metaxylem area and phloem area decreased at 75 mM of Pb and Ni while epidermal thickness and cell area increased. Stem epidermal thickness, cell area and phloem area significantly decreased with the consistent increase in metaxylem area and cortical region thickness under both Pb and Ni stress. Leaf anatomical traits such as midrib thickness, abaxial epidermal thickness and stomatal density and adaxial epidermal thickness and stomatal area significantly increased with increasing Pb or Ni stress. Correlation analysis revealed close relations among morphological and anatomical traits (such as root length with cortical region thickness) for better plant survival under Pb or Ni stress, and a PCA-biplot further verified these correlation analyses. Cluster analyses demonstrated the associations among the morphological and anatomical traits based on different stress levels. Furthermore, we found that the longer exposure (from phase 1 to phase 2) of heavy metals stress is more dangerous for plant survival and can ultimately lead to plant death. Moreover, our results also confirmed that Ni is more harmful or dangerous to plants than Pb at high and moderate concentrations. The anatomical modifications ensured the survival of A. lebbeck in extreme heavy metals stress and therefore unlocked its potential to be used as a natural source of phytoremediation. We also recommend that the genetic potential of A. lebbeck associated with its survival under heavy metal stress be investigated.

Climate change has affected the food supply chain and raised serious food concerns for humans and animals worldwide. The present investigation aimed to assess the effect of environmental factors along with three different levels of cutting (i.e., cutting 1, 2, and 3 at the vegetative, budding, and flowering stages, respectively) and spacing (i.e., 21, 23, and 26 cm) on quinoa biomass and quality to select the most suitable accessions. This experiment was repeated for two years using a split–split plot experimental design. The cutting × genotype × year and cutting × space × genotype interactions were significant for most quinoa morphological traits (except for leaf area and intermodal distance), where the maximum growth in number of leaves/plant (NoL), plant height (PH), fresh weight (FW), number of branches/plant (Br), and dry weight (DW) were observed during the second growing season. Cutting and spacing levels also showed significant effects on morphological and quality traits of quinoa. Among the different levels of cutting and spacing, cutting level 3 and spacing level 2 were more effective across both years at gaining maximum biomass and quality traits such as crude fat (CF) and crude protein (CP). According to the MGIDI, only two accessions (R3 and R9) fared better in both growing seasons, and selected accessions had positive morphological and quality traits. There were moderately significant negative correlations between PH, NoL, LA, FW, and DW and anti-quality traits such as neutral detergent fiber (NDF) and acid detergent fiber (ADF), indicating that an increase in biomass decreased the concentrations of ADF and NDF in both stem and leaves. A comparison with oat accessions (G3 and G7) revealed that quinoa has higher CP and CF and lower NDF than oats in both stems and leaves (except for ADF). In conclusion, the combination of cutting level 3 and spacing level 2 (23 cm) is more suitable to obtain high-quality quinoa forage with maximum biomass production. Furthermore, the MGIDI is a useful tool for breeders to select genotypes based on their mean performance, stability, and desired traits.

Weed control is a major constraint to lentil production worldwide. Although post-emergence herbicides are commonly used to manage weeds, their application can reduce crop yield. Developing herbicide-tolerant lentil varieties through different breeding methods offers an effective solution to minimize such losses. In this study, 60 diverse lentil genotypes, developed through mutation, hybridization, and a combination of both, along with three standard checks, were evaluated against eight post-emergence herbicides to identify tolerant genotypes and determine which breeding method was most effective. The genotypes were planted in an augmented design and evaluated over two growing seasons. Abiotic stress indices were calculated, and data were analyzed using standard augmented design procedures. ANOVA was performed, and multivariate analyses such as PCA, hierarchical clustering, and the multi-trait genotype–ideotype distance index (MGIDI) were used to identify herbicide-tolerant genotypes. ANOVA results showed a highly significant genotype × treatment interaction for all measured traits, indicating that genotypes responded differently to the various herbicide treatments. PCA and clustering analyses revealed that most of the high-performing genotypes were mutants, a finding further confirmed by MGIDI, highlighting the effectiveness of mutation breeding for inducing herbicide tolerance. Based on MGIDI results, the best genotypes for each herbicide were G45 (T1), G32 (T2), G41 (T3), G30 (T4 and T5), G17 (T6), G9 (T7), and G4 (T8). These genotypes represent valuable sources of herbicide tolerance and can serve as donors in breeding programs, as controls in research studies, or as candidates for variety development following further adaptation trials.

Developing high-yielding and stable lentil genotypes is essential for sustaining productivity under increasingly variable climatic conditions. However, strong genotype × environment (G × E) interactions often complicate the identification of widely adapted genotypes. Although several stability models are available, each has distinct strengths and limitations, making it uncertain whether a single model or a combination of models provides the most reliable selection. Therefore, this study compared multiple stability models to identify high-yielding and stable lentil genotypes and to evaluate their effectiveness for genotype selection. A set of lentil genotypes was evaluated across four diverse environments using a randomized complete block design (RCBD). Adjusted mean data were analyzed using the Eberhart and Russell regression model, AMMI (Additive Main Effects and Multiplicative Interaction), BLUP (Best Linear Unbiased Prediction), and WAASBY (Weighted Average of Absolute Scores and Yield). Significant G × E interactions confirmed differential genotype responses across environments. The Eberhart and Russell model identified stable genotypes based on regression coefficients close to unity and minimal deviation from regression. AMMI and BLUP effectively partitioned G × E effects and distinguished genotypes with broad and specific adaptation. WAASBY integrated yield and stability into a single index and was particularly effective in identifying genotypes with superior performance and wide adaptability. Correlation analysis among stability indices and the Genotype Selection Index (GSI) further strengthened the comparison and integration of model outputs. Genotypes G4, G8, G9, and G3 consistently exhibited high yield and stable performance across environments, with G4 emerging as the most promising and widely adapted genotype. This study demonstrates that integrating regression, mixed model, and multivariate based stability analyses provides a more robust framework for selecting stable, high-yielding lentil genotypes than relying on a single method, thereby supporting breeding for diverse agro-ecological conditions in Pakistan.