Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.

Skip to main content

Rooting for resilience: central metaxylem area as a breeding target for yield gain and resilience in wheat (Triticum aestivum L.)

Abstract

Background

To ensure food security amid unpredictable climatic conditions and depleting natural resources, larger and stable genetic gain have to be realised in wheat. Adapting to these challenges requires focus on both above-ground and below-ground traits. Root anatomy reveals the functional adaptations of the root system. Despite their potential, root anatomical traits remain underutilized but hold promise as breeding targets for developing efficient and resilient crops. Our study aims to identify highly plastic wheat genotypes with superior yield stability and robust root anatomical traits, enabling them to thrive under diverse and challenging environmental conditions. By leveraging advanced multi-trait stability indices and models, we seek to provide breeders with valuable insights for enhancing wheat resilience and productivity.

Results

In this study, 150 wheat genotypes were evaluated across three diverse environments for 10 root anatomical traits along with phenological observation and grain yield. The results show significant positive correlations between root traits, such as axial hydraulic conductance based on the central metaxylem area and total xylem area, with grain yield. This highlights the critical role of these less explored root traits in yield formation. Central metaxylem area was able to explain more than 14 per cent variation in yield over all the three environments. Although the polynomial equation did not significantly improve data fitness, it clearly indicates no sign of yield saturation at the highest CMXA levels. Modern tools like GGE and AMMI though highly effective in reducing the dimensions but do not effectively rank genotypes on the basis of different trait values simultaneously. Advanced models such as BLUP, WAASB, and multi-trait stability indices (MTSI, MGIDI, and FAI-BLUP) have the power to overcome the collinearity in different variables and use the trait values to identify superior genotypes. Genotypes such as G97 and G18 (both being derivative from the cross HDCSW18/CSW1), G112, G144 (both CIMMYT material) and G131 (31ESWYT135/CSW23) consistently exhibited high yield and stability and were picked up by all models. The study demonstrated a moderate coincidence index of 22.72% among these models, confirming the value of selected genotypes. Positive correlations between traits like axial hydraulic conductance and yield highlighted the importance of efficient water transport, nutrient exchange and hydraulic safety of crop.

Conclusion

Central metaxylem area based axial hydraulic conductance is explaining more than 14 per cent of variation in the yield across the environment and this along with whole root area and proper phenological adjustment can play key role in yield consolidation with high resilience under more likely uncertain production condition in the future. Three out of five genotypes consistently being picked by different stability models are derivative of HDCSW18, a variety released for conservation agriculture condition and with very strong root system and biomass. High biomass accumulation facilitated by early seeding of the genotypes with mild vernalisation requirement with high root central metaxylem area can sustain higher seed production under challenging climates and thus the findings contribute to strategies for improving wheat resilience.

Peer Review reports

Background

Rising populations and changing climates have heightened concerns about food security and increased freshwater demand [1, 2]. With limited scope for expanding cultivation areas, enhancing productivity per unit area is crucial [3]. However, current yield increases, estimated at 0.5-1% per year [4], fall short of the 2.4% annual growth needed to meet global demand [5]. Wheat, the world’s most widely adapted staple crop, is cultivated on over 220 million hectares, yielding approximately 789 million tonnes annually [6]. It provides 20% of the daily protein and caloric needs for humans and plays a vital role in the economies of developing nations.

Recent years have seen wheat production severely impacted by untimely rains, heatwaves, and droughts worldwide [7, 8]. Studies suggest that each 1 °C increase above normal temperatures can reduce global grain yields by 4.1–6.4% [9, 10]. Additionally, extreme climate scenarios predict crop losses of 7–23% [11]. In South Asia, wheat is grown as a winter crop, planted in October-December and harvested in March-May. Heatwaves and droughts during the flowering and grain-filling stages can significantly reduce final yields. To ensure food security amid unpredictable climatic conditions, prioritizing resilience and stability across varied environments is essential alongside enhancing yield potential.

Adapting to these challenges necessitates focusing on both above-ground and below-ground traits [12]. Roots, the first to sense drought and edaphic deficiencies, play a crucial role in sustainable productivity in variable environments. Root system architecture (RSA) and root anatomy are vital yet underexplored traits. Root anatomy reveals the functional adaptations of the root system, encompassing water and nutrient absorption, anchorage, storage, and growth. It underpins the entire plant’s physiology and performance. Improving root anatomy offers a multidisciplinary approach to addressing global challenges like food security, climate change, and environmental sustainability [13]. Despite their potential, root anatomical traits remain underutilized but hold promise as breeding targets for developing efficient and resilient crops [14,15,16].

Genotype-by-Environment Interaction (GEI) analysis is crucial for evaluating the performance of various genotypes across diverse environmental conditions. It helps identify genotypes that perform consistently across different environments or those specifically suited to particular conditions. The Multi Environmental-Testing (MET) method is the most effective tool for evaluating the adaptability and stability of genotypes across various locations. A thorough understanding of GEI and stability analysis assists breeders and farmers in selecting the best genotypes for specific environments, ultimately enhancing grain yield and crop productivity. MET also reduces the cost of genotype evaluations by eliminating redundant experimental replications in temporal and spatial yield trials. However, linear selection indexes often face issues with collinearity among traits, leading to biased regression coefficients and reduced selection efficiency [17]. Exploratory methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are helpful for dimensionality reduction and visualizing trait relationships in multi-environment research. Despite their advantages, these techniques struggle to effectively rank genotypes based on trait values. Multivariate techniques show high accuracy in addressing multicollinearity issues when handling multiple traits. To bridge the gaps in traditional breeding methods for stability analysis, Olivoto, Lúcio [18] developed a multi-trait analysis that overcomes the limitations of older methods in selecting stable genotypes.

Novel multivariate techniques such as the Multi-Trait Stability Index (MTSI) and Multi-Trait Genotype Ideotype Distance Index (MGIDI) have been employed to identify stable ideotypes with multiple traits suitable for broader environmental adaptation [19]. To address these issues, a linear mixed model (LMM)-based quantitative stability index, WAASB (Weighted Average of Absolute Scores) from the singular value decomposition (SVD) of the matrix of BLUPs), was developed to study GEI through biplots for identifying stable genotypes. In addition to this, to improve the efficiency of genotype selection, the WAASBY was developed to integrate mean performance with the WAASB stability score [20].

Multi-environment trial analysis often focuses on a single trait, primarily grain yield. However, considering multiple traits increases the reliability of recommended genotypes. MTSI and MGIDI have emerged as innovative techniques for selecting superior genotypes that perform well under varied environmental conditions while maintaining high yield stability and desirable traits. In this context, the current investigation proposes a framework that includes root anatomical traits alongside above-ground traits to identify stable and suitable ideotypes of wheat genotypes with stable yields in the central region of India. This framework utilizes MTSI and MGIDI indices for multifactorial multi-trait stability analysis, aiming to identify ideal high-yielding genotypes with consistent performance across diverse environmental conditions.

Materials and methods

Plant materials and growth conditions

The field experiment, conducted from November 2022 to April 2023, involved a diverse panel of 150 wheat genotypes from various pedigree sources at the wheat breeding laboratory, Cummings Lab, ICAR-Indian Agricultural Research Institute, New Delhi, India (Supplementary Table S1). The study utilized a partially replicated design, where one-third of the entries were replicated twice to ensure reliability in the evaluation. This design resulted in a total of 200 plots, providing a balance between resource efficiency and the ability to capture variability [21]. Each plot measured 5.4 m², with dimensions of 4.5 m in length and 1.2 m in width, and a 0.2 m space between plots. Each plot consisted of 6 rows, spaced 0.2 m apart. The experiment was conducted across three different environments, as detailed in the Table 1.

Table 1 Characteristics of the test environments

Measurement of root anatomical and yield traits

Root anatomical traits were measured and recorded on three randomly selected plants for every individual plot in the field. For measuring the root anatomical traits, free hand cross sections of seminal roots were prepared from the roots 2 cm below the seeds from which they originated at tillering stage using razor blades and stained with toluidine blue (0.0025% for 2 min) [22]. The cross sections were visualized with microscope (Nikon Eclipse Ni-U, Japan) and images were recorded with the help of a Nikon DS-Fi3 camera using NIS-Elements F v.4.60.00 image processing platform with appropriated scales for further processing. Root anatomical traits, such as the diameter of the central metaxylem, peripheral xylem, stele, cortex, and root, were measured in microns using cross-sectional images of seminal roots analysed with ImageJ software (http://imagej.nih.gov/ij). To ensure measurement reliability and minimize intra-observer error, all measurements were performed three times by the same observer, and the averages of these repeated measurements were used for subsequent analyses. The diameter were used to calculate the following traits by assumption of perfectly cylindrical elements [23] and methods were described in Table 2, anatomical features used in the study were depicted in the cross-sectional view of seminal root in Fig. 1.

Table 2 Description and formula for trait measurement
Fig. 1
figure 1

Cross sectional view of seminal root (Black roots depicting the seminal roots and Brown roots depicting the crown roots)

Axial conductance (Kx) was determined using a modified version of Hagen–Poiseuille’s equation for fluid flow through a bundle of ideal cylindrical pipes, following the method described by [15, 23]. Higher xylem conductance enhances drought adaptation by facilitating water extraction from deeper soil layers [24]. Here, ρ represents the fluid density (for distilled water, 1,000 kg m⁻³), η is the dynamic viscosity of the fluid (for distilled water, 10⁻⁹ MPa s), d is the diameter (m) of the ith pipe, and n denotes the number of pipes. Calculating axial conductance from anatomical root cross-sections yields a theoretical Kx, a technique frequently employed in several studies [25,26,27,28]. The number of days to 50% flowering (DF) was recorded for each plot, which corresponds to Zadok’s growth stage Z65 based on the two-digit code [29]. Grain yield was recorded as the total harvested grain per genotype and weighed on a laboratory precision digital scale. The weight of the grain was adjusted to 12% moisture content and expressed in g/plot.

Statistical analysis

Mean performance, variance and correlation analysis

Prior to analysis of variance the normality, homoscedasticity and homogeneity of residual variance was examined by the Shapiro-Wilk test, residual plots and Bartlett test, before combining the data of the three environments. The data analysis was conducted using R-Studio with base R version 4.4.1. Mean performance of the genotypes were performed using ge_details() function of metan package v.1.19 [30]. Subsequently, analysis of variance was performed to examine the differences between the genotypes, environments and their interactions. The estimation of components of variance, using model-based approach was performed using gamem_met() function in metan package v1.19 of R [30]. Significance was calculated using likelihood ratio test and results were depicted along with respective variance. Variance parameters were calculated using the following random effect model.

$$\:Y\:\sim\:\left(1\:\right|\:GEN)\:+\:(1\:|\:ENV/REP)\:+\:\left(1\:\right|\:GEN:ENV)$$

Where Y is a vector of response variable, GEN represents Genotypes, ENV represents environments, REP represents replications and GEN: ENV represents the interaction.

Pearson correlation coefficients were calculated to evaluate the association among the studied traits [31]. To examine the relationship between yield and key anatomical traits, a multiple linear regression analysis was conducted, with yield designated as the dependent variable. Initially, all measured traits were considered as potential predictors [32]. To address potential multicollinearity issues, a Variance Inflation Factor (VIF) analysis was performed. Predictors with VIF values exceeding the commonly accepted threshold of 5 were excluded to ensure the reliability and accuracy of the regression estimates in the final model [33, 34].

Multi-trait-based stability analysis

Multi-trait stability index (MTSI)

The MTSI incorporates WAASBY (mean performance and stability) values into the Fgp matrix through factor analysis. The WAASB is a quantitative genotypic stability measure, representing the Weighted Average of Absolute Scores from the singular value decomposition of the matrix of BLUPs for GEI effects generated by a linear mixed model.

$$\:{WAASB}_{i}\:=\:\frac{{\sum\:}_{k=1}^{P}|{\text{I}\text{P}\text{C}\text{A}}_{ik}-{EP}_{k}|\:}{{\sum\:}_{k=1}^{P}{EP}_{k}}$$

where, WAASBi is the weighted average of absolute scores for the ith genotype; IPCAik is the score of the ith genotype in the kth interaction principal component axis (IPCA); and EPk is the amount of variance explained by the kth IPCA.

The WAASBY index emphasis mean performance in addition to stability of genotypes [35, 36]. The genotypes were ranked based on the WAASBY scores, which was calculated using bootstrapping with n = 1000 iterations to ensure robust and reliable estimates. Subsequently, k-means clustering was applied to classify the genotypes into distinct groups based on their WAASBY scores, providing a clear differentiation of stability and performance profiles. The MTSI is calculated using the WAASBY index [37],

$$\:{MTSI}_{i}=\sqrt{{\sum\:}_{j=1}^{f}{({F}_{ij}\:-{F}_{j}\:)}^{2}}$$

Where, the MTSIi is the multi-trait stability index for the ith genotype, Fijrepresents the value of the factorial score for the ithgenotype and the jthfactor.; Fjrepresents the value of the factorial score for the jthfactor associated with the ideotype. Lower Multi-Trait Stability Index (MTSI) values indicate better outcomes, as they represent a closer proximity of a genotype to the ideotype, which exhibits the most desirable trait values. The waasb() and mtsi() functions from the metan package were used to compute the Multi-Trait Stability Index (MTSI) [30].

The strength and weakness of genotypes were assessed by calculating the proportion of the MTSI of the ith genotype explained by the jth factor (ωij) as follows.

$$\:{\omega\:}_{ij}=\frac{\sqrt{{D}_{ij}^{2}}}{{\sum\:}_{j=1}^{j}\sqrt{{D}_{ij}^{2}}}$$

Where, Dij is the distance between the ith genotype and ideal genotype for the jth factor.

Multi-trait genotype-ideotype distance index (MGIDI)

The MGIDI, calculated using the methodology of Olivoto and Nardino [19], involves four steps: (i) rescaling traits, (ii) factor analysis, (iii) designing an ideotype (with a rescaled value of 100 for all evaluated traits), and (iv) computing the MGIDI index.

$$\:{MGIDI}_{i}=\sqrt{{\sum\:}_{j=1}^{f}{({Y}_{ij}\:-{Y}_{j}\:)}^{2}}$$

where, MGIDIi represents the multi-trait genotype-ideotype distance index for the ith genotype, γij is the factorial score of the ith genotype in the jth factor (i = 1, 2, t; j = 1, 2, f), being t and f the number of genotypes and factors and γj is the jth score of the ideotype. The mgidi() function from the metan package was used to compute the Multi-Trait Genotype-Ideotype Distance Index (MGIDI) [30].

Multi-trait index based on factor analysis and ideotype-design (FAI-BLUP)

Once the ideotype is established, the spatial probability of each genotype is calculated by estimating its distance from the ideotype, assisting in genotype evaluation. The FAI-BLUP index is calculated as follows:

$$\:{P}_{ij}=\left[\frac{\left(\frac{1}{{d}_{ij}}\right)}{{\sum\:}_{i=1,j=1}^{i=n,j=m}\left(\frac{1}{{d}_{ij}}\right)}\right]$$

Where Pij represents the probability that the ith genotype (i = 1, 2,, n) is similar to the jth genotype (j = 1, 2,, m); dij represents the genotype-ideotype distance from the ith genotype to the jth ideotype according to the standardized average Euclidean distance [38]. The fai-blup() function from the metan package was used to compute the Multi-Trait Index Based on Factor Analysis and Ideotype-Design (FAI-BLUP) [30].

Selection differential (S) and genetic gain under selection (ΔG)

The selection differential, expressed as a percentage of the population mean, is computed for each trait.

$$\text{SD}\%=\frac{\left({X}_{s}-{X}_{o}\right)}{{X}_{O}}\times100$$

The percentage of genetic gain under selection (ΔG %) is calculated as follows:

$$\Delta\text{G}\%=\frac{\left({X}_{s}-{X}_{o}\right)}{{X}_{O}}\times\text{h}^2\times100$$

where Xo is the mean WAASBY index of the original population and Xs is the mean WAASBY index of the selected genotypes. Additionally, the coincidence_indexes() function was used to calculate the number of common genotypes selected across the various multi-trait indices.

Results

Mean performances, variance, correlations and regression analysis of traits across environments

The mean performances of 150 bread wheat genotypes across 3 diverse environments for 10 root anatomical traits, phenological traits and grain yield are given in Table 3. Notably, genotype G95 recorded the highest CMXA (81408.34 μm²) in Env 1, while genotype G55 showed the lowest CMXA (5260.15 μm²) in Env 3. Axial hydraulic conductance was highest in genotype G75 (2.34e-05 Kg s⁻¹ m MPa⁻¹) in Env 1, and lowest in genotype G90 (1.11e-06 Kg s⁻¹ m MPa⁻¹) in Env 3. Among the phenological traits, genotype G68 in Env 3 was identified as the earliest flowering genotype (68 DAS), whereas genotype G58 in Env 1 was the latest flowering (104 DAS). For grain yield, genotype G12 in Env 2 exhibited the highest yield (4284 g/plot), while genotype G104 in Env 3 had the lowest (1003 g/plot). Overall, Environment 1 supported superior mean performances for most traits, including total xylem area, CMXA, WRA, XYWRR, and DF, while Env 3 generally showed lower values for these traits.

Table 3 Mean performance of genotypes across three environments

The distribution of the residuals confirmed that they followed an approximately normal distribution. Additionally, the analysis showed homoscedasticity and homogeneity of variances (Supplementary Fig. 1). This ensured the validity of subsequent statistical analysis. The model-based analysis results indicate the presence of highly significant variation among the environments, genotypes and GEI for all the traits under study (Table 4). Significant variation observed in the traits paved the way for further delineation of the stability and mean performance of the genotypes and its selection.

Table 4 Components of variance and its contribution to traits

Results of Pearson correlation analysis among traits were illustrated in Fig. 2. Yield recorded a highly significant positive correlation with days to 50% flowering (r = 0.58), whole root area (r = 0.47), cortex area(r = 0.46), central meta xylem area (r = 0.39), axial hydraulic conductance (r = 0.38), total xylem area (r = 0.38), central meta xylem to total xylem area ratio (r = 0.30), xylem to whole root area ratio (r = 0.13) and cortex to stele ratio (r = 0.18). To further investigate the relationships between yield and key anatomical traits, a multiple linear regression analysis was performed with yield as the dependent variable. Initially, all measured traits were included as potential predictors. However, due to high collinearity, traits such as TXA, XYWRR, WRA and SA were excluded from the analysis. The final model included AHC, CMXA, CSR, CA, SWRR, CMXXYAR, and DF as predictors. Variance Inflation Factor (VIF) analysis was conducted to assess multicollinearity among the selected predictors. The VIF values ranged from 1.20 to 2.49, well below the commonly accepted threshold of 5, indicating that multicollinearity was not a concern in the final model. These results suggest that the chosen predictors reliably estimate yield without substantial multicollinearity effects (Supplementary Table S2). The model explained approximately 54.2% of the variability in YLD, as indicated by the R-squared value of 0.542. This demonstrates a moderate level of fit and highlights the significance of the selected predictors in explaining variations in yield. In addition to this, a simple linear regression analysis was conducted to assess the direct relationship between CMXA and YLD. The results showed a significant positive association (p = < 2e− 16, p < 0.001), with CMXA explaining 14.90% of the variation in YLD (adjusted R² = 0.149).

Fig. 2
figure 2

Correlation plot elucidating the association among the traits under study. The plot depicts the correlation coefficients and its significance ns p > = 0.05, *p < 0.05, ** p < 0.01, ***p < 0.001 respectively. TXA - Total xylem area, CMXA - Central meta xylem area, WRA - Whole root area, CMXXYAR - Central meta xylem to total xylem area ratio, XYWRR - Xylem to whole root ratio, SA - Stele area, SWRR - Stele to whole root ratio, CA - Cortex area, CSR - Cortex to stele ratio, AHC - Axial hydraulic conductance, DF– Days to 50% flowering, YLD - Yield

Multi-trait-based stability analysis

The initial phase involved conducting a stability analysis and obtaining WAASB for the GEI effect for each genotype. The WAASB and WAASBY indexes, which balance yield performance with stability, were employed to identify high-yielding and stable wheat genotypes and the results were depicted in the Y × WAASB biplot (Fig. 3). The genotypes G148, G12, G122, G121, G73, G97 were found to be having high yield performance along with high G-by-E interaction in Env1 and Env2. Genotypes such as G72, G104, G138 were found to be with low yield performance and having high G-by-E interaction. Genotypes such as G18, G22, and G74, located in the IV quadrant of the Y × WAASB biplot, were identified as stable genotypes with high mean performance, while genotypes G87, G26 and G17 located in III quadrant were identified as stable genotypes with low mean performance. The performance of these genotypes, based on mean performance and stability of other studied traits, is illustrated in Supplementary Fig. 2. Additionally, above average (blue) and below average genotypes (red) based on WAASBY values were presented in Fig. 4a. The heatmap (Fig. 4b) illustrates genotype clustering based on WAASBY index. Using k-means clustering, genotypes were classified into four distinct groups: red for unproductive and unstable, blue for productive but unstable, black for stable but unproductive, and green for productive and stable. The classification highlights the adaptability and performance consistency of genotypes under varying environmental conditions.

Fig. 3
figure 3

The Y × WAASB biplot depicts the performance of 150 wheat genotypes based on yield performance and stability across three environments

Fig. 4
figure 4

a) Classification of genotypes based on WAASBY mean (blue- above average genotypes, red- below average genotypes) b) Heatmap depicting the clustering of genotypes based on WAASBY scores (red - unproductive and unstable, blue - productive but unstable, black - stable but unproductive, and green - productive and stable)

Factor analysis results from MTSI evaluation of yield and its attributes across the three environments

Further analysis of these genotypes was conducted using MGIDI, MTSI, and FAI-BLUP to identify the most stable and high-performing genotypes and to compare the efficiency of these indices. By applying the Kaiser criterion, which retains factors with eigenvalues greater than 1, four factors were identified that together account for 69.4% of the total variance in the dataset, as assessed by the WAASB value of BLUP estimates. The weight assigned to the factors was based on the percentage of variation each factor explained out of the total explained variation. Table 5 summarizes the eigenvalues, explained variance, and factorial loadings from the MTSI factor analysis.

Table 5 Factor analysis results from MTSI evaluation of yield and its attributes across the three environments

The first factor (FA1) had an eigenvalue of 3.24 and accounted for 27.0% of the variance. The second factor (FA2) had an eigenvalue of 2.53, explaining 21.10% of the variance, while the third factor (FA3) showed an eigenvalue of 1.46, accounting for 12.10% of the total variance. The fourth factor (FA4) had an eigen value of 1.10 which accounts for 9.20% of variance. FA1 is primarily associated with total xylem area (-0.920), central meta xylem area (-0.938), central meta xylem to total xylem area ratio (-0.737) and xylem to whole root ratio (0.844). FA2 is strongly linked to whole root area (-0.914) and cortex area (-0.958). FA3 is contributed by stele to whole root ratio (0.826) and stele area (0.858). FA4 is contributed by axial hydraulic conductance (0.733) and days to 50% flowering (-0.674). Following varimax rotation, the average communality (h) was 0.69, with individual trait communalities spanning from 0.382 for yield to 0.941 for cortex area. These values represent the proportion of each trait’s variance explained by the extracted factors. The uniqueness values ranged from 0.059 to 0.618, indicating the variance in each trait that remains unexplained by the factors.

Genotypes selected by employing MTSI analysis

The genotype ranking based on the MTSI score is presented in Fig. 5a, with detailed information provided in Supplementary Table S3. The genotypes are prioritized based on their MTSI scores, with lower scores indicating greater value and suitability. The MTSI score integrates performance and stability across multiple traits, enabling the selection of genotypes that excel in balanced adaptability. Among the 150 evaluated genotypes, G49, G23, G64, G93, G97, G79, G41, G143, G94, G4, G117, G27, G65, G16, G40, G18, G112, G144, G131, G78, G15 and G134 with the lowest scores are identified as superior, reflecting their ability to meet multiple trait criteria effectively. This selection strategy ensures that the chosen genotypes demonstrate both stability and desirable trait expression, making them ideal for breeding programs.

Fig. 5
figure 5

a) Radar plot depicting the 22 genotypes selected (red dots) through MTSI (Multi Trait Stability Index) with 15% selection intensity. b) Radar plot depicting the contribution of factors to the MTSI index of selected genotypes

Figure 5b illustrates the strengths and weaknesses of selected genotypes as determined by each factor’s proportional contribution to the genotypes MTSI scores. Central positioning in the visualization denoted more significant contributors, while peripheral placement indicated less impactful factors. This illustration is further enhanced by the data provided in Supplementary Table S4, which offers a detailed summary of the contribution of each factor towards the selected genotypes. The results demonstrated that FA1 consisting of TXA, CMXA, CMXXYAR and XYWRR was found to be more impactful factor. Genotype G134 showed less strength towards FA1 compared with other selected genotypes. Genotypes G117, G15, G18, G4, G41, G78 and G79 were found to be less influenced by the factor FA2 contributed by YLD, WRA, CA and CSR. Factor FA3 contributed by traits such as SWRR and SA was found to be influencing most of the selected genotypes. Analysis of FA4, which includes AHC and DF, highlights the contribution of these traits for the selected genotypes. Genotypes G97, G144, G16, G27, G40, G49, G64, G78, G79, and G94 were found to be less influenced by FA4.

Response to selection by employing MTSI

Table 6 presents the predicted genetic gains under selection and the selection differentials for WAASB across all traits. Most traits exhibited positive selection differentials, with the exception of days to 50% flowering, which showed a negative differential of -3.55. The selection differentials (SD) for the WAASBY index of the studied traits varied from − 3.55 for days to 50% flowering to 13.40 for cortex area. The selection differential percentage (SD%) ranged from − 6.17% for days to 50% flowering to 23.00% for cortex area, with an average SD% of 13.68%. The genetic gain percentage (∆G%) had a mean of 5.70%, ranging from − 2.28% for days to 50% flowering to 11.50% for cortex area.

Table 6 Response to selection of all the traits by employing MTSI

Genotypes selected by employing MGIDI and FAI-BLUP models

In addition to the MTSI, several other multi-trait stability models such as MGIDI and FAI-BLUP were employed for selection of genotypes with 15% selection intensity, as illustrated in Fig. 6. The genotypes selected based on MTSI were G49, G23, G64, G93, G97, G79, G41, G143, G94, G4, G117, G27, G65, G16, G40, G18, G112, G144, G131, G78, G15 and G134 (Fig. 5a), while those selected based on MGIDI stability model were G124, G113, G129, G121, G25, G144, G52, G75, G112, G135, G18, G97, G3, G28, G73, G27, G127, G126, G123, G91, G111 and G131 (Fig. 6a). The genotypes selected through FAI-BLUP model were G124, G121, G129, G113, G25, G52, G112, G144, G135, G75, G18, G3, G97, G127, G122, G73, G28, G131, G126, G111, G109 and G91 (Fig. 6b).

Fig. 6
figure 6

a) Radar plot depicting the 22 genotypes selected (red dots) through MGIDI (Multi-Trait Genotype-Ideotype Distance Index) with 15% selection intensity. b) Radar plot depicting the 22 genotypes selected (red dots) through FAI-BLUP (Multi-Trait Index Based on Factor Analysis and Ideotype-Design) with 15% selection intensity

Consistency of genotypes selected across multiple multi-trait-based stability models

The genotypes common among the models were depicted in Venn-diagram (Fig. 7). The comparison of these models is demonstrated in coincidence index of 90.90% (20 common genotypes) between MGIDI and FAI-BLUP followed by coincidence index of 27.27% (6 common genotypes) was found between MTSI and MGIDI. Coincidence index of 27.27% (6 common genotypes) between MTSI and FAI-BLUP. The genotypes G97, G18, G112, G144 and G131 were found to be common among MTSI, MGIDI and FAI-BLUP models with coincidence index of 22.72% (5 common genotypes) (Fig. 7). This reflects the unique strengths and priorities of each model in evaluating stability and performance. Genotypes common across all three models demonstrate reliability as they meet stability and performance criteria across diverse evaluation methods.

Fig. 7
figure 7

Venn-diagram depicting the selected genotypes common among three different multi-trait stability models

Discussion

Wheat serves as the primary staple food for over half of the global population. The grain yield of wheat is influenced by various component traits of the plant. The role of root in above ground biomass accumulation through better resource uptake from the soil including major nutrient like N, P, and K have been repeatedly highlighted in a number of studies on spring wheat [39,40,41,42] and therefore understanding the genotypic and environmental interactions of root anatomical traits is crucial. This knowledge can aid in developing wheat crops that can endure adverse climatic conditions and ensure sustainable yield [40, 43, 44]. Crop breeders strive to exploit genotype-by-management interaction by selecting responsive genotypes that demonstrate high yield under favourable condition and comparatively stable yield under challenging production condition [2]. In the present study, we have simultaneously applied BLUP, WAASB, and three multi-trait stability models for better understanding of GEI for root anatomical traits.

A significant positive correlation was observed between several traits—days to 50% flowering, central metaxylem area, axial hydraulic conductance, total xylem area, the ratio of central metaxylem to total xylem area, the ratio of xylem to whole root area, the cortex to stele ratio, and whole root area—with yield. This indicates that increasing these traits contributes to improved yield. The positive correlation between axial hydraulic conductance and yield underscores the importance of efficient water transport from the soil to the apical parts. This efficiency facilitates optimal nutrient exchange, sugar transport within the plant, and effective evaporative cooling of the plant’s internal environment through transpiration [15, 40, 45]. The maintenance of a continuous water column within crops is essential for their survival [46]. Any disruption in this column can result in embolism, ultimately compromising the hydraulic safety of the crop [15, 47]. This disruption reduces the crop’s ability to recover when exposed to intermittent drought conditions, caused by unfavourable situations [23]. Maintaining higher water transport efficiency under heat stress prevents thermal damage and supports photosynthesis. Research on winter wheat under combined drought and salinity stress shows that genotypes exhibiting higher hydraulic conductivity adapt better to such conditions [48]. High axial hydraulic conductance (AHC) is linked to efficient water transport mechanisms that enhance plant performance during drought [48, 49]. Genotypes with increased AHC demonstrate superior water uptake under stress, supported by adaptations in root architecture and aquaporin expression, which regulate water movement within plant tissues [50, 51]. Aquaporins, specialized water transport proteins, optimize root hydraulic conductance and improve plant performance under environmental stress [52]. Enhanced root hydraulic conductance mitigates water scarcity effects by sustaining physiological processes vital for growth and development during stress. AHC positively correlates with traits like stomatal conductance and transpiration rates, thereby aiding drought tolerance [49]. Variations in AHC among genotypes provide a biological basis for differential resilience, making breeding strategies targeting higher AHC promising for developing stress-resilient wheat varieties in climates where water availability is sufficient. In contrast, smaller xylem diameters in wheat grown in Australian drylands optimize water availability during grain-filling stages, ensuring better yield under terminal drought conditions [53]. Furthermore, lower root hydraulic conductivity has been shown to minimize water loss by inducing stomatal closure under stress [54]. This dynamic relationship is environment-specific, as crops with higher hydraulic conductance are more vulnerable to embolism in adverse conditions [40]. Mechanistic studies could involve manipulating root traits, such as xylem diameter and conductivity, to observe their direct effects on grain yield. Hendel et al. [15] explored this by using a segregating population derived from a cross between durum wheat and its direct progenitor, wild emmer wheat. Their study revealed that genotypes with contrasting axial conductance showed that lower axial conductance acted as a water conservation mechanism during grain filling, which contributed to an increase in grain size and yield. Evidence from such research highlights the correlation between hydraulic traits and yield under varying conditions; however, further controlled experiments are essential to establish causation. The positive correlation between days to 50% flowering (DF) and yield (YLD) indicates that genotypes which flower later would provide longer window for biomass to accumulate in vegetive phase which ultimately results in higher yield. This trait could be beneficial in environments with extended growing seasons [55]. Consequently, enhancing these traits plays a crucial role in promoting higher yield potential.

Simultaneous selection for grain yield and other root anatomical traits was conducted using three key multi-trait-based stability evaluation methods: MGIDI, MTSI, and FAI-BLUP. While traditional models such as the Eberhart-Russell Model and Shukla’s Stability Variance have been valuable in addressing stability and genotype performance, they primarily focus on linear regressions and variance components. These approaches may not fully account for the multidimensional nature of trait interactions, which is essential in modern breeding programs. In comparison, advanced models like WAASB, MTSI, MGIDI, and FAI-BLUP provide a more holistic evaluation framework, enabling better integration of genetic performance, stability, and environmental adaptability. WAASB balances performance and stability by incorporating GEI [37]. MTSI provides a straightforward tool for identifying consistent genotypes under environmental variations [56]. MGIDI evaluates multidimensional traits, aligning genotypes with ideal targets [57]. FAI-BLUP integrates genetic predictions and correlations, improving ideotype selection [58]. This enabled a comprehensive comparative analysis for selecting stable and high-performing genotypes. High-performing genotypes, such as G49, G94, G122, G64, G65, G16, and G37 consistently exhibited higher yield across different environments. The WAASB Scores integrates both performance and stability, offering a comprehensive evaluation of genotypes. The biplot effectively captures the interaction between genotype performance and GEI, making it a robust tool. While high WAASB scores generally indicate stronger GEI effects and reduced stability, genotypes with consistently high performance across favourable environments may still be considered stable [59]. WAASB accounts for both GEI variability and relative performance, allowing classification of genotypes as stable under specific conditions. This highlights its ability to balance environmental suitability with genotypic resilience. The model-based analysis of variance clearly indicates a substantial genetic variation for the traits under study. This genetic variation forms the foundation for further improvement of the genotypes through targeted selection. Results from our study demonstrates the presence of significant amount of genotypic-by-environmental interaction, which paves the way for deciphering GEI through further analysis to identify the better-performing genotypes.

BLUP based estimates helps in managing the influence of random effects and environmental factors, which often leads to heritability estimates deviated from its actual contribution [60]. The WAASBY biplot and heat maps offered significant insights into productivity and stability by categorizing genotypes into four distinct clusters [35, 57]. Positive and negative loadings in factor analysis represented the direction and strength of the relationship between the traits and the underlying factors. Among the four factors in our study factor FA1 and FA3 contributes more to the selected genotypes and FA4 contributes less to the selected genotypes. The presence of higher average communality value indicated that a significant portion of each trait’s variance was explained by the extracted factors. Traits such as TXA, CMXA, WRA and CA with higher communality values demonstrates that these traits contribute to be factor construction and its integration in selection process will result in improved yield performance. Traits with higher uniqueness values demonstrates that they have contributed less to the factor construction compared with other traits under study. The MTSI method plays an important role in crop improvement programs where multiple traits needed to be optimized concurrently, as it simplifies the decision-making process and enhances the efficiency of genotype selection [61].

In this study, the top 22 genotypes, representing a selection intensity of 15%, were identified based on their MTSI scores. A selection intensity of 15% is widely considered optimal in breeding programs, as it strikes a critical balance between achieving genetic gain and preserving genetic diversity [19]. This moderate intensity enhances the identification of superior genotypes while minimizing risks associated with high selection pressure, ensuring the genetic health of the population is maintained for sustainable breeding objectives [62, 63]. Genotype G40, with an MTSI score of 6.005, set the cut-off point at the final red circle, reflecting the selection pressure. Therefore, further investigation is necessary for the genotypes at this cut-off point, as detailed by Rahmati, Abdipour [64] and Kakanur Jagadeesha, Navathe [65] in wheat, Aruna, Sridhara [66] in green gram, Ghazvini, Pour-Aboughadareh [67] in barley and Subramani, Nalliappan [68] in fodder maize hybrids. Positive selection differentials were observed for most traits, highlighting the effectiveness of the selection process. These findings were found to be in accordance with Reddy, Singh [69] in wheat and Subramani, Nalliappan [68] in fodder maize hybrids. It was also observed that the negative selection differentials along with negative genetic gains for DF revealed a reduction in days to 50% flowering could be useful when breeding for earliness. Early flowering was often desirable for escaping terminal drought, a common challenge in wheat cultivation. Higher genetic gain of 11.02% for central meta xylem area (CMXA), followed by 10.56% for total xylem area (TXA) and 7.33% for stele area (SA) demonstrates that the presence of larger stele and xylem vessel aids in providing space for vascular bundles and efficient hydraulic transport in the crop respectively results with improved performance of the crop. Employing MTSI model result in average genetic gain of 5.70%, which showed the efficiency of employing several traits simultaneously in selection of better performing genotypes.

The coincidence analysis demonstrated a moderate consistency in genotype selection across various stability models, emphasizing the robustness of certain genotypes within the study, particularly when different models prioritize unique criteria for selection. Specifically, Genotypes like G97 and G18 (derived from the HDCSW18/CSW1 cross), as well as G112 and G144 (CIMMYT material), and G131 (31ESWYT135/CSW23), were consistently selected across the MTSI, MGIDI, and FAI-BLUP models. This consistency highlights their stability and potential for favourable performance. The genotypes that overlapped across different models displayed strong potential for stable and productive outcomes. The genotypes selected by all models demonstrated traits that enhanced their adaptability and performance, making them valuable genetic resources for pre-breeding and further breeding initiatives. The identification of unique genotypes by each model highlighted the importance of using multiple models to gain a comprehensive understanding of genotype performance and stability. The findings from this study will enable breeders to identify genotypes that consistently exhibit high yields and stable axial hydraulic conductance, thereby preventing hydraulic breakdown and ensuring reliable crop performance in unpredictable environments. This strategy is crucial for developing sustainable resilient wheat varieties that can thrive in challenging climatic conditions and ensures stable yield.

Conclusion

This study has successfully identified key genotypic traits and environmental interactions that contribute to higher yield and stability in wheat crops. By employing advanced models such as BLUP, WAASB, and multi-trait stability indices (MTSI, MGIDI, and FAI-BLUP), we demonstrated the effectiveness of these methods in selecting superior genotypes. The genotypes G97 and G18 (both being derivative from the cross HDCSW18/CSW1), G112, G144 (both CIMMYT material) and G131 (31ESWYT135/CSW23) emerged as consistently high-performing across diverse environments, showcasing their robustness and potential for breeding programs aimed at developing resilient wheat varieties. Our findings highlight the importance of comprehensive GEI analysis and multi-trait selection to enhance crop yield and adaptability. The positive correlations between root anatomical traits, axial hydraulic conductance, and yield underscore the critical role of efficient water transport and nutrient exchange in improving crop performance under variable climatic conditions. Additionally, scalable root measurement techniques like high-throughput phenotyping are critical for enabling their practical application in large-scale breeding trials. The identified genotypes exhibit strong adaptability and yield stability, and ongoing breeding initiatives could benefit from utilizing them as valuable genetic resources. Overall, this research provides a comprehensive framework for the development of wheat varieties that can sustainably thrive in challenging environments.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BLUP:

Best Linear Unbiased Prediction

FAI-BLUP:

Multi-Trait Index Based on Factor Analysis and Ideotype-Design

GEI:

Genotype-by-Environmental Interaction

∆G:

Genetic gain

MTSI:

Multi Trait Stability Index

MGIDI:

Multi-Trait Genotype-Ideotype Distance Index

SD:

Selection differential

WAASB:

Weighted Average of Absolute Scores

TXA:

Total xylem area

CMXA:

Central meta xylem area

WRA:

Whole root area

CMXXYAR:

Central meta xylem to total xylem area ratio

XYWRR:

Xylem to whole root ratio

SA:

Stele area

SWRR:

Stele to whole root ratio

CA:

Cortex area

CSR:

Cortex to stele ratio

AHC:

Axial hydraulic conductance

References

  1. Gleick PH. Moving to a sustainable future for water. Nat Water. 2023;1(6):486–7.

    Article  Google Scholar 

  2. Yadav R, Gaikwad KB, Bhattacharyya R. Breeding wheat for yield maximization under conservation agriculture. Indian J Genet Plant Breed. 2017;77(02):185–98.

    Article  CAS  Google Scholar 

  3. Rosegrant M, Agcaoili M. Global food demand, supply and price prospects to international food policy. Washington, DC. DC USA: Research Institute; 2010.

    Google Scholar 

  4. Yadav R, Gupta S, Gaikwad KB, Bainsla NK, Kumar M, Babu P, et al. Genetic gain in yield and associated changes in agronomic traits in wheat cultivars developed between 1900 and 2016 for irrigated ecosystems of Northwestern plain zone of India. Front Plant Sci. 2021;12:719394.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Crespo-Herrera L, Crossa J, Huerta-Espino J, Vargas M, Mondal S, Velu G, et al. Genetic gains for grain yield in CIMMYT’s semi-arid wheat yield trials grown in suboptimal environments. Crop Sci. 2018;58(5):1890–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. FAO. World food and Agriculture– Statistical yearbook 2024. Rome: FAO; 2024.

    Google Scholar 

  7. Langridge P, Alaux M, Almeida NF, Ammar K, Baum M, Bekkaoui F, et al. Meeting the challenges facing wheat production: the strategic research agenda of the global wheat initiative. Agronomy. 2022;12(11):2767.

    Article  CAS  Google Scholar 

  8. Yadav R, Gaikwad K, Bhattacharyya R, Bainsla NK, Kumar M, Yadav SS. Breeding new generation genotypes for conservation agriculture in maize-wheat cropping systems under climate change. In: Shyam Singh Yadav, Robert J. Redden, Jerry L. Hatfield, Andreas W. Ebert, Hunter D, editors. Food security and climate change. UK: John Wiley & Sons Ltd; 2019. p. 189–228.

  9. Zhao C, Liu B, Piao S, Wang X, Lobell DB, Huang Y et al. Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of sciences. 2017;114(35):9326-31.

  10. Asseng S, Ewert F, Martre P, Rötter RP, Lobell DB, Cammarano D, et al. Rising temperatures reduce global wheat production. Nat Clim Change. 2015;5(2):143–7.

    Article  Google Scholar 

  11. Rezaei EE, Webber H, Asseng S, Boote K, Durand JL, Ewert F, et al. Climate change impacts on crop yields. Nat Reviews Earth Environ. 2023;4(12):831–46.

    Article  Google Scholar 

  12. Bapela T, Shimelis H, Tsilo TJ, Mathew I. Genetic improvement of wheat for drought tolerance: progress, challenges and opportunities. Plants. 2022;11(10):1331.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lynch JP, Strock CF, Schneider HM, Sidhu JS, Ajmera I, Galindo-Castaneda T, et al. Correction to: root anatomy and soil resource capture. Plant Soil. 2022;475(1):669.

    Article  CAS  Google Scholar 

  14. Galindo-Castañeda T, Lynch JP, Six J, Hartmann M. Improving soil resource uptake by plants through capitalizing on synergies between root architecture and anatomy and root-Associated microorganisms. Front Plant Sci. 2022;13.

  15. Hendel E, Bacher H, Oksenberg A, Walia H, Schwartz N, Peleg Z. Deciphering the genetic basis of wheat seminal root anatomy uncovers ancestral axial conductance alleles. Plant Cell Environ. 2021;44(6):1921–34.

    Article  CAS  PubMed  Google Scholar 

  16. Lynch JP. Harnessing root architecture to address global challenges. Plant J. 2022;109(2):415–31.

    Article  CAS  PubMed  Google Scholar 

  17. Silva LA, Peixoto MA, Peixoto LA, Romero JV, Bhering LL. Multi-trait genomic selection indexes applied to identification of superior genotypes. Bragantia. 2021;80:e3621.

    Article  Google Scholar 

  18. Olivoto T, Lúcio AD, da Silva JA, Sari BG, Diel MI. Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agron J. 2019;111(6):2961–9.

    Article  Google Scholar 

  19. Olivoto T, Nardino M. MGIDI: toward an effective multivariate selection in biological experiments. Bioinformatics. 2021;37(10):1383–9.

    Article  CAS  PubMed  Google Scholar 

  20. Olivoto T, Lúcio AD, da Silva JA, Marchioro VS, de Souza VQ, Jost E. Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agron J. 2019;111(6):2949–60.

    Article  Google Scholar 

  21. dos Santos DP, Sermarini RA, dos Santos A, Demétrio CGB. Optimal designs in plant breeding experiments: A simulation study comparing Grid-Plot and partially replicated (p-Rep) design. Sugar Tech. 2024;26(2):387–95.

    Article  Google Scholar 

  22. O’Brien TP, Feder N, McCully ME. Polychromatic staining of plant cell walls by toluidine blue O. Protoplasma. 1964;59(2):368–73.

    Article  Google Scholar 

  23. Tyree MT, Ewers FW. The hydraulic architecture of trees and other Woody plants. New Phytol. 1991;119(3):345–60.

    Article  Google Scholar 

  24. Richards R, Passioura J. A breeding program to reduce the diameter of the major xylem vessel in the seminal roots of wheat and its effect on grain yield in rain-fed environments. Aust J Agric Res. 1989;40:943–50.

    Article  Google Scholar 

  25. Frensch J, Steudle E. Axial and radial hydraulic resistance to roots of maize (Zea Mays L). Plant Physiol. 1989;91(2):719–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Melchior W, Steudle E. Water transport in onion (Allium Cepa L.) roots (changes of axial and radial hydraulic conductivities during root development). Plant Physiol. 1993;101(4):1305–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Olson ME, Rosell JA. Vessel diameter–stem diameter scaling across Woody angiosperms and the ecological causes of xylem vessel diameter variation. New Phytol. 2013;197(4):1204–13.

    Article  PubMed  Google Scholar 

  28. Tyree MT, Zimmermann MH. Xylem structure and the ascent of Sap. Springer Science & Business Media; 2013.

  29. ZADOKS JC, CHANG TT, KONZAK CF. A decimal code for the growth stages of cereals. Weed Res. 1974;14(6):415–21.

    Article  Google Scholar 

  30. Olivoto T, Lúcio ADC. Metan: an R package for multi-environment trial analysis. Methods Ecol Evol. 2020;11(6):783–9.

    Article  Google Scholar 

  31. Schober P, Boer C, Schwarte LA. Correlation coefficients: appropriate use and interpretation. Anesth Analgesia. 2018;126(5):1763–8.

    Article  Google Scholar 

  32. Montgomery DC, Peck EA, Vining GG. Introduction to linear regression analysis. Wiley; 2021.

  33. O’brien RM. A caution regarding rules of thumb for variance inflation factors. Qual Quant. 2007;41:673–90.

    Article  Google Scholar 

  34. Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied linear statistical models: McGraw-hill; 2005.

  35. Nataraj V, Bhartiya A, Singh CP, Devi HN, Deshmukh MP, Verghese P, et al. WAASB-based stability analysis and simultaneous selection for grain yield and early maturity in soybean. Agron J. 2021;113(4):3089–99.

    Article  Google Scholar 

  36. Olivoto T, Lúcio ADC, da Silva JAG, Marchioro VS, de Souza VQ, Jost E. Mean performance and stability in Multi-Environment trials I: combining features of AMMI and BLUP techniques. Agron J. 2019;111(6):2949–60.

    Article  Google Scholar 

  37. Olivoto T, Lúcio ADC, da Silva JAG, Sari BG, Diel MI. Mean performance and stability in Multi-Environment trials II: selection based on multiple traits. Agron J. 2019;111(6):2961–9.

    Article  Google Scholar 

  38. Rocha JRdASdC, Machado JC, Carneiro PCS. Multitrait index based on factor analysis and ideotype-design: proposal and application on elephant grass breeding for bioenergy. GCB Bioenergy. 2018;10(1):52–60.

    Article  Google Scholar 

  39. Dharmateja P, Yadav R, Kumar M, Babu P, Jain N, Mandal PK, et al. Genome-wide association studies reveal putative QTLs for physiological traits under contrasting phosphorous conditions in wheat (Triticum aestivum L). Front Genet. 2022;13:984720.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Nirmalaruban R, Yadav R, Meda A, Babu P, Kumar M, Gaikwad KB. et al. Root traits: A key for breeding Climate-Smart wheat (Triticum aestivum). Plant Breeding. 2024. https://doi.org/10.1111/pbr.13248

  41. Ranjan R, Yadav R, Gaikwad K, Kumar M, Kumar N, Babu P, et al. Genetic variability for root traits and its role in adaptation under conservation agriculture in spring wheat. Indian J Genet Plant Breed. 2021;81(01):24–33.

    CAS  Google Scholar 

  42. Ranjan R, Yadav R, Gaikwad KB, Bainsla NK, Kumar M, Babu P, et al. Spring Wheat’s Ability to Utilize Nitrogen More Effectively Is Influenced by Root Phene Variation.Plants. 2023;12(5):1010.

  43. Coucheney E, Kätterer T, Meurer KH, Jarvis N. Improving the sustainability of arable cropping systems by modifying root traits: A modelling study for winter wheat. Eur J Soil Sci. 2024;75(4):e13524.

    Article  Google Scholar 

  44. Yadav R, Kumar M, Gaikwad K, Babu P, Kumar BN, Ansari R, et al. Exploiting Climate-Smart agriculture through breeding of Next-Generation high yielding genotypes of wheat under cropping system perspective. J Agricultural Phys. 2021;21(1):216–21.

    Google Scholar 

  45. Li X, Ingvordsen CH, Weiss M, Rebetzke GJ, Condon AG, James RA, et al. Deeper roots associated with cooler canopies, higher normalized difference vegetation index, and greater yield in three wheat populations grown on stored soil water. J Exp Bot. 2019;70(18):4963–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Ooeda H, Terashima I, Taneda H. Intra-specific trends of lumen and wall resistivities of vessels within the stem xylem vary among three Woody plants. Tree Physiol. 2018;38(2):223–31.

    Article  CAS  PubMed  Google Scholar 

  47. Santiago LS, De Guzman ME, Baraloto C, Vogenberg JE, Brodie M, Hérault B, et al. Coordination and trade-offs among hydraulic safety, efficiency and drought avoidance traits in Amazonian rainforest canopy tree species. New Phytol. 2018;218(3):1015–24.

    Article  PubMed  Google Scholar 

  48. Fu Y, Li P, Mounkaila Hamani AK, Wan S, Gao Y, Wang X. Effects of single and combined drought and salinity stress on the root morphological characteristics and root hydraulic conductivity of different winter wheat varieties. Plants. 2023;12(14):2694.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mao H, Jiang C, Tang C, Nie X, Du L, Liu Y, et al. Wheat adaptation to environmental stresses under climate change: molecular basis and genetic improvement. Mol Plant. 2023;16(10):1564–89.

    Article  CAS  PubMed  Google Scholar 

  50. Chaumont F, Tyerman SD. Aquaporins: highly regulated channels controlling plant water relations. Plant Physiol. 2014;164(4):1600–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Chen KP. Hydraulic resistance of a plant root to water-uptake: A slender-body theory. J Theor Biol. 2016;396:63–74.

    Article  PubMed  Google Scholar 

  52. Hachez C, Veselov D, Ye Q, Reinhardt H, Knipfer T, Fricke W, et al. Short-term control of maize cell and root water permeability through plasma membrane Aquaporin isoforms. Plant Cell Environ. 2012;35(1):185–98.

    Article  CAS  PubMed  Google Scholar 

  53. Richards R, Passioura J. A breeding program to reduce the diameter of the major xylem vessel in the seminal roots of wheat and its effect on grain yield in rain-fed environments. Aust J Agric Res. 1989;40(5):943–50.

    Article  Google Scholar 

  54. Vadez V, Kholova J, Medina S, Kakkera A, Anderberg H. Transpiration efficiency: new insights into an old story. J Exp Bot. 2014;65(21):6141–53.

    Article  CAS  PubMed  Google Scholar 

  55. Lateif M, Amani A. Comparison of yield and identification of effective traits on yield of some wheat genotypes in Baghlan Province. J Nat Sci Rev. 2024;2(Special Issue):407–18.

    Article  Google Scholar 

  56. Hussain T, Akram Z, Shabbir G, Manaf A, Ahmed M. Identification of drought tolerant Chickpea genotypes through multi trait stability index. Saudi J Biol Sci. 2021;28(12):6818–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Naveen A, Singh SP, Singhal T, Reddy S, Bhargavi H, Yadav S et al. Delineation of selection efficiency and coincidence of multi-trait-based models in a global germplasm collection of Pearl millet for a comprehensive assessment of stability and high performing genotypes. Genet Resour Crop Evol. 2024:1–17. https://doi.org/10.1007/s10722-024-02245-3

  58. de Carvalho JN, de Carvalho PA, Pio R, Barbosa MAG, Leão PCS. Multitrait selection in seedless grape hybrids in semiarid regions of Brazil. Crop Sci. 2023;63(4):2091–102.

    Article  Google Scholar 

  59. Dudhe MY, Jadhav MV, Sujatha M, Meena HP, Rajguru AB, Gahukar SJ, et al. WAASB-based stability analysis and validation of sources resistant to Plasmopara halstedii race-100 from the sunflower working germplasm for the semiarid regions of India. Genet Resour Crop Evol. 2024;71(4):1435–52.

    Article  Google Scholar 

  60. Piepho H, Möhring J, Melchinger A, Büchse A. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica. 2008;161(1):209–28.

    Article  Google Scholar 

  61. Benakanahalli NK, Sridhara S, Ramesh N, Olivoto T, Sreekantappa G, Tamam N, et al. A framework for identification of stable genotypes basedon MTSI and MGDII indexes: an example in Guar (Cymopsis Tetragonoloba L). Agronomy. 2021;11(6):1221.

    Article  Google Scholar 

  62. Sampaio Filho JS, Olivoto T, Campos MS. Oliveira EJd. Multi-trait selection in multi-environments for performance and stability in cassava genotypes. Front Plant Sci. 2023;14.

  63. Wang T, Ren J, Huang Q, Li J. Genetic parameters of growth and leaf traits and genetic gains with MGIDI in three Populus simonii × P. nigra families at two spacings. Front Plant Sci. 2024;15.

  64. Rahmati M, Abdipour M, Sepahvand M. Selection of durum wheat genotypes based on MGIDI and SIIG selection indexes. Plant Prod Genet. 2024;5(2):299–312.

    Google Scholar 

  65. Kakanur Jagadeesha Y, Navathe S, Krishnappa G, Ambati D, Baviskar V, Biradar S, et al. Multi-Environment analysis of nutritional and grain quality traits in relation to grain yield under drought and terminal heat stress in bread wheat and durum wheat. J Agron Crop Sci. 2024;210(5):e12763.

    Article  Google Scholar 

  66. Aruna K, Sridhara S, Sowjanya B, KL NK, Moussa IM, Elansary HO et al. Multi-trait stability index for identification of stable green gram (Vigna radiata (L.) Wilczek) genotypes with MYMV resistance. Heliyon. 2024;10(12):e32763.

  67. Ghazvini H, Pour-Aboughadareh A, Jasemi SS, Chaichi M, Tajali H, Bocianowski J. A framework for selection of high-yielding and drought-tolerant genotypes of barley: applying yield-based indices and multi-index selection models. J Crop Health. 2024;76(3):601–616.

  68. Subramani P, Nalliappan GK, Narayana M, Veerasamy R, Natesan S. Selection of superior and stable fodder maize hybrids using MGIDI and MTSI indices. Crop Breed Appl Biotechnol. 2024;24(4):e498624418.

    Article  Google Scholar 

  69. Reddy SS, Singh GM, Kumar U, Bhati P, Vishwakarma M, Navathe S, et al. Spatio-temporal evaluation of drought adaptation in wheat revealed NDVI and MTSI as powerful tools for selecting tolerant genotypes. Field Crops Res. 2024;311:109367.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are thankful to Division of Genetics and NAHEP CAAST Discovery centre, ICAR- IARI, New Delhi, for providing the necessary facilities and support for the smooth conductance of research. The first author acknowledges the UGC- JRF in Science, Humanities & Social Sciences fellowship received during the Ph.D. program.

Funding

This work was partially supported from the collaborative Project on “Application of Next-Generation Breeding, Genotyping and Digitalization Approaches for Improving the Genetic Gain in Indian Staple Crops” by the Indian Council of Agricultural Research (ICAR) and Bill and Melinda Gates Foundation (BMGF) [ICAR-IARI project code: 12–229].

Author information

Authors and Affiliations

Authors

Contributions

RN conceptualized the work and conducted the experiment; RY conceptualized and developed the experimental material; RN, RY curated and analyzed the data; AKM, AM and SS advised on data analyses; RN drafted the original manuscript; RN, RY, SS, AM, AKM, KBG, PB, MK, NKB, SKS and PKM reviewed and edited the manuscript; All the authors critically read and approved the manuscript.

Corresponding author

Correspondence to Rajbir Yadav.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

12870_2025_6523_MOESM1_ESM.pdf

Supplementary Material 1: Additional file 1Supplementary Figure 1: Representative residual plots of studied traits to depict the homoscedasticity. Supplementary Figure 2: Performance of 150 wheat genotypes based on mean performance and stability across three environments for traits. Supplementary Table S1: Pedigree details of the materials used for the study. Supplementary Table S2: Variance Inflation Factor values of predictors. Supplementary Table S3: MTSI index of genotypes. Supplementary Table S4: Contribution of factors towards selected genotypes

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nirmalaruban, R., Yadav, R., Sugumar, S. et al. Rooting for resilience: central metaxylem area as a breeding target for yield gain and resilience in wheat (Triticum aestivum L.). BMC Plant Biol 25, 493 (2025). https://doi.org/10.1186/s12870-025-06523-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12870-025-06523-9

Keywords