Phenotypic data related to seedling traits of hexaploid spring wheat panel evaluated under salinity stress

Abstract

Salt stress is a major abiotic stress affecting wheat at various developmental stages and significantly reduces grain yield. Developing salt resilient wheat cultivars alleviate the negative impacts of salt stress and helps in maintaining sustainable grain yield under salt stress. A study was undertaken to assess the response of various seedling traits in a genetically, phenotypically, and geographically diverse panel of 228 hexaploid spring wheat accessions using greenhouse lysimeter system with two irrigation treatments: control (electrical conductivity of irrigation water as deci-Siemens per meter., (EC_iw = 1.46 dSm⁻¹) and saline (EC_iw = 14 dSm⁻¹). Salt stress was given on 18 days old seedlings and the targeted salinity level (EC_iw = 14 dSm⁻¹) was achieved gradually over two days period, to overcome any osmotic shock. Data on various seedling traits [such as shoot height (SH; inches), root length (RL; inches), tiller number (TN), shoot weight (SW; grams), and root weight (RW; grams)] were collected after three weeks of salt treatment from control and salt stress environment. Shoot and root traits were used to calculate root length by shoot height (RL-by-SH) and root weight by shoot weight (RW-by-SW) ratios. Furthermore, the salt tolerance index (STI), was calculated for each trait by dividing trait values of each accession from salt-treated tanks by those from control tanks. Raw data was subjected to mixed linear analysis to derive best linear unbiased prediction (BLUP). BLUP values were also used for Pearson’s correlation coefficient analysis and principal component analysis (PCA), which gives intrinsic relationship among various seedling traits. Dataset presented here is a valuable source for identifying tolerant lines for salt stress environment. Moreover, researchers can utilize this information to identify potential genomic regions associated with salt stress tolerance and can be utilized in developing salt resilient wheat cultivars.

Specifications Table

Subject	Biology
Specific subject area	Abiotic stress; Plant breeding
Type of data	Tables and Figures
Data collection	Plants were harvested three weeks after the initiation of salt treatment from control and salt stress environments. Shoot height (SH; inch) was measured from the scutellar node to the tip of the topmost elongated leaf and root length (RL; inch) was measured from the scutellar node to the tip of the primary root. Shoot and roots were separated by cutting at the scutellar node, and the resulting samples were dried at 70 °C for 96 h to determine shoot weight (SW; g) and root weight (RW; g). Data on tiller number was collected by counting number of tillers from each plant and the mean tiller number was calculated. Shoot and root traits from control and salt stress treatments were used to calculate root length by shoot height (RL-by-SH) and root weight by shoot weight (RW-by-SW) ratios. Furthermore, the salt tolerance index (STI) was calculated for each trait by dividing trait values from salt-treated tanks by the that from control tanks.
Data source location	Phenotypic data was collected at the greenhouse lysimeters at the US salinity laboratory, Riverside, CA (33.973265 latitude and −117.321158 W longitude).
Data accessibility	Repository name: Mendeley Data Data identification number: 10.17632/88y7ppgx3w.2 Direct URL to data: https://data.mendeley.com/datasets/88y7ppgx3w/2
Related research article	S. Gudi, H. S. Gill, S. Collins, J. Singh, D. Sandhu, S. K. Sehgal, U. Gill, R. Gupta (2025) Association analysis identified superior haplotypes for improved salt stress tolerance in wheat (Triticum aestivum L.). Plant Stress. DOI: 10.1016/j.stress.2025.100900

1. Value of the Data

• •This dataset highlights the impact of salt stress on various seedling traits in a genetically, phenotypically, and geographically diverse panel of hexaploid spring wheat accessions.
• •Phenotypic data generated in this diversity panel can be utilized for genome-wide association studies (GWAS) to identify potential genomic regions associated with salt stress tolerance.
• •Researchers can select salt tolerant and salt sensitive lines to develop biparental or multiparent mapping populations, which are valuable resources for identifying salt tolerant quantitative trait loci (QTLs).
• •These observation helps researchers to select salt tolerant lines and utilize them in breeding program to develop salt resilient wheat cultivars.
• •This dataset plays key role in improving productivity of wheat under salt affected soils and regions where saline water is used for irrigation, thereby contributing to long-term agricultural sustainability and maintaining economic viability.

2. Background

Wheat (Triticum aestivum L.) is a globally important cereal crop, supplying food for nearly 30 % of the world population. Wheat production in recent years is affected by various environmental factors including drought, heat, salinity, and water logging [[1], [2], [3], [4]]. Salt stress affects wheat at various development stages including seedling, heading, flowering, and grain filling stages; however, the seedling stage is the most vulnerable among all [5,6]. Being a glycophytic, wheat can tolerate salt concentration up to 4–6 dSm⁻¹. Beyond this threshold, salt stress begins to impair seed germination, seedling establishment, anthesis, and grain filling, ultimately reduces grain yield up to 50–60 % [7,8]. Globally, 833 million hectares of land is affected by salinity and is expected to increase in future due to rising sea level, intrusion of sea water into farmlands, oceanic deposition, and elevation of water tables [9]. Moreover, excess utilization of inorganic fertilizers and brackish water for irrigation, further exacerbates salinity levels. Therefore, to overcome the negative impact of salt stress we generated an extensive data on globally diverse spring wheat panel for various seedling traits under control (EC_iw = 1.46 dSm⁻¹) and salt stress (EC_iw = 14 dSm⁻¹) environments. We also carried descriptive statistical analysis along with Pearson’s correlation coefficient and principal component analysis (PCA) to gain a deeper understanding of trait relationship.

3. Data Description

Present data article describes the phenotypic performance of 228 diverse panel of spring wheat accessions at seedling stage evaluated using greenhouse lysimeter system with two irrigation treatments: control (electrical conductivity of irrigation water as deci-Siemens per meter., (EC_iw = 1.46 dSm⁻¹) and saline (EC_iw = 14 dSm⁻¹) (Fig. 1). These germplasm lines were the part of exome sequenced panel [10,11] and were originally collected from Asia (55 accessions), Africa (47 accessions), America (67 accessions), Europe (37 accessions), Oceania (5 accessions), and Soviet Union (3 accessions). Whereas, the origins of the remaining 14 accessions were not known. Data on various seedling traits such as shoot height (SH; inches), root length (RL; inches), tiller number (TN), shoot weight (SW; grams), and root weight (RW; grams) were collected from control (EC_iw = 1.46 dSm⁻¹) and salt stress (EC_iw = 14 dSm⁻¹) environment after three weeks exposure to salinity in two replications. Raw data was used to calculate root length by shoot height (RL-by-SH) and root weight by shoot weight (RW-by-SW) ratios. Germplasm lines showed huge variation for studied traits (Table 1). Phenotypic variation was more under salt stress treatment than under control, as indicated by higher coefficient of variance (CV) observed in salt stress treatment. Under control, CV ranges from 6.3 % for SH to 39.04 % for RW-by-SW, whereas under salt stress, CV ranges from 6.59 % for SH to 49.35 % for RW-by-SW. Broad sense heritability, which represents the proportion of total phenotypic variation attributed to genetic factors, was higher under salt stress as compared to that under control treatment for all the traits except for RW. The broad sense heritability ranges from 69 % for RL to 91 % for SH under control, whereas it ranges from 72 % for RL to 94 % for SH under salinity. Moreover, salt stress imposed severe negative impact on various seedling traits, reducing shoot height by 17.76 %, root length by 15.59 %, tiller number by 43.49 %, shoot weight by 44.44 %, and root weight by 38.24 %. In contrast, ratios of RL-by-SH and RW-by-SW were increased by 6.06 and 27.08 %, respectively, under salt stress.

Fig. 1.

Experimental design showing the arrangement of sand filled tanks and sowing of genotypes within each sand tanks. Sand tanks in red colour font represent the salt stress tanks and one in black represent the normal water tanks.

Table 1.

Descriptive statistical analysis for seedling traits evaluated in wheat association panel. CV: coefficient of variance, RL-by-SH: ratio of root length by shoot height; RW-by-SW: ratio of root weight by shoot weight.

Descriptive statistics	Shoot height (SH; inch)	Root length (RL; inch)	Tiller number (TN)	Shoot weight (SW; gram)	Root weight (RW; gram)	RL-by-SH	RW-by-SW
Control
Mean	22.07	7.25	3.55	0.72	0.34	0.33	0.48
Range	11.69–29.08	4.79–9.66	1.43–7.94	0.22–1.71	0.08–1.17	0.23–0.57	0.21–1.08
CV ( %)	6.3	12.84	27.94	36.27	35.91	14.92	39.04
Heritability	0.91	0.69	0.83	0.74	0.81	0.72	0.58
Salinity
Mean	18.15	6.12	1.99	0.4	0.21	0.35	0.61
Range	8.91–25.59	3.66–8.74	1.15–3.83	0.06–1.08	0.06–0.68	0.21–0.59	0.19–2.7
CV ( %)	6.59	12.89	21.44	26.8	38.05	16.62	49.35
Heritability	0.94	0.72	0.85	0.86	0.78	0.74	0.81

Pearson’s correlation coefficient analysis showed significant correlation (P-value < 0.001) among different seedling traits (Fig. 2). Under control condition, most traits were positively associated with a strongest positive association observed between SW and RW (r = 0.65) (Figure 2a). In contrast, we also noticed a significant negative correlation for SH vs RL-by-SH (r = −0.71), SH vs RW-by-SW (r = −0.23), TN vs RW-by-SW (r = −0.25), and SW vs RW-by-SW (r = −0.2). No significant correlation found for SW vs RL-by-SH, TN vs RL-by-SH, and RL vs RW-by-SW. Under salt stress, we noticed a significant correlation among multiple traits with strongest positive association between SW and TN (r = 0.6) (Figure 2b). We also noticed a strong negative correlation for SH vs RL-by-SH (r = −0.81), SW vs RL-by-SH (r = −0.26), and RW-by-SW with SH (r = −0.56), RL (r = −0.24), TN (r = −0.38), and SW (r = −0.51). However, SH vs RW and TN vs RL-by-SH were not significantly associated under salt stress. Pearson’s correlation coefficient analysis based on STI showed a strongest positive association between RL and RL-by-SH (r = 0.66) and strong negative association between SH vs RL-by-SH (r = −0.80) (Fig. 2c).

Fig. 2.

Pearson’s correlation coefficient analysis for seedling traits under control (a) and salt stress (b) as well as for salt tolerance index (STI) (c).

Correlation studies also revealed a notable shift in the trait relationship under control and salt stress. For instance, SW vs RL-by-SH, RW vs RL-by-SH, and RL vs RW-by-SW showed no significant correlation under control treatment (Fig. 2a), whereas there was a significant correlation among these traits under salt stress treatment (Fig. 2b). Conversely, SH and RW were positively correlated under control condition but not under salt stress. Most interestingly, RL-by-SH and RW-by-SW showed weaker association with other traits under control condition, while these associations became markedly stronger under salt stress.

Principal component analysis (PCA) showed that first two PCs explained major portion of phenotypic variation under control (i.e., PC1: 38.4 % and PC2: 26.1 %), salt stress (i.e., PC1: 41.3 % and PC2: 28.1 %), and for STI (i.e., PC1: 37.5 % and PC2: 24.3 %) (Fig. 3). PCA biplots also supported the results of Pearson’s correlation coefficient, where most traits are strongly positively correlated under control, salt stress, and for STI.

Fig. 3.

Principal component analysis (PCA) for seedling traits: (a) scree plot showing the percentage of contribution by top seven PCs under control; (b) biplot showing the PCA loadings of seedling traits and genotypes to first two PCs under control; (c) scree plot showing the percentage of contribution by top seven PCs under salt stress; (d) biplot showing the PCA loadings of seedling traits and genotypes to first two PCs under salt stress; (e) scree plot showing the percentage of contribution by top seven PCs for salt tolerance index (STI); (f) biplot showing the PCA loadings of seedling traits and genotypes to first two PCs for salt tolerance index (STI).

4. Experimental Design, Materials and Methods

4.1. Experimental plan

A total of 228 hexaploid spring wheat germplasm lines were selected from the exome sequence panel for evaluation under salt stress, along with a check variety, GS-3. The experiment was carried out in greenhouse lysimeter sand tanks arranged in randomized complete block design (RCBD) with two replications. The experiment includes a total of 24 tanks, of which 12 were used for control treatment and 12 for salt treatment. Each sand tanks have the dimensions of 120 cm (L) x 60 cm (W) x 50 cm (D), which can fit 20 genotypes (i.e., 19 germplasm lines and one check variety) with 4-inch row distance. Check variety was repeated in all tanks and was used to account for microenvironmental variations resulting from differences in tank placement relative to cooling pads, sunlight exposure, and fan locations. Six seeds of each genotype were sown in one row in each replication and were thinned to three seedlings (spaced 1.5-in. apart) at the one-leaf stage (i.e., after 5–7 days after sowing). Plants were allowed to grow for 18 days by using irrigation water having basic micronutrients and macronutrients. The composition of irrigation water includes: 160 ppm Fe-DTPA (Sprint 330®), 1.2 µmol L⁻¹ ZnSO₄·7H₂O, 0.3 µmol L⁻¹ CuSO₄·5H₂O, 0.1 µmol L⁻¹ (NH₄)₆Mo₇O₂₄·4H₂O, 23 µmol L⁻¹ H₃BO₃, and 15 µmol L⁻¹ MnSO₄. Irrigation was done once a day by pumping water stored in 890 L reservoirs through PVC pipes, and the excess water was drained back into the reservoir.

Salt stress begin 18 days after sowing by gradually increasing salinity of irrigation water to a target electrical conductivity (EC_iw) of 14 dSm⁻¹ over a two-day period. This gradual increase was implemented to avoid sudden osmotic shock to the plants. A mixed cation composition was used, maintaining the cation ratio of 1 Ca: 1.25 Mg: 0.25 Na, to mimic the natural composition of irrigation water. Chloride (Cl¯) was kept as the dominant anion. Moreover, to avoid the confounding effects from nutrient deficiency, similar nutrient composition was maintained in both the control and salinity treatments. The detailed composition of irrigation water used for control and salt treatment is given in Table 2. Salt stress was given for next three weeks.

Table 2.

Composition of irrigation water used for control and salt treatment. EC_iw: electrical conductivity of irrigation water; dSm⁻¹: deci-Siemens per meter.

Treatment	ECiw (dSm−1)	Ion concentration (mmolc L−1)
		Cl−	SO42−	NO3−	PO43−	Na+	K+	Ca2+	Mg2+
Control	1.46	1.41	1.44	5.39	1.5	1.88	6.59	3.35	2.1
Saline	14	128.35	26.69	5.39	1.5	106.88	6.59	29.61	23.1

4.2. Phenotypic data collection

Three weeks after salt stress treatment, plants were harvested to measure various seedling traits such as shoot height (SH), root length (RL), tiller number (TN), shoot weight (SW), root weight (RW). Shoot and roots of each plant were separated by cutting at the scutellar node. SH and RL were measured (in inches) from the scutellar node to the tip of the topmost elongated leaf and to the tip of the primary root, respectively. Then shoot and roots were dried at 70 °C for 96 h to measure SW and RW (in grams). Data on tiller number was collected by counting number of tillers from each plant and the mean tiller number was calculated. Shoot and root traits were used to derive root length by shoot height (RL-by-SH) and root weight by shoot weight (RW-by-SW) ratios. Moreover, the salt tolerance index (STI) was calculated for each trait by dividing trait values of each accession from salt-treated tanks by those from control tanks [3,12,13].

4.3. Statistical analysis for seedling traits

Phenotypic data was used to extract best linear unbiased prediction (BLUP). This was done using a mixed linear model built in META-R software [14]. META-R relies on LME4 package for deriving BLUP values by using the following model:

Y_in = µ + G_i + R_n + e_in

Where, Y_in is trait of interest, µ is the grand mean, G_i is the effect of i^th genotype, R_n is the effect of n^th replication, e_in is the error associated with i^th genotype and n^th replication.

Broad-sense heritability (h²_bs) was calculated by using the following formula:

$${h_{bs}}^2=\frac{{\sigma }^{2}g}{{\sigma }^{2}g+{\sigma }^{2}e/R}$$

Where, σ²_g is genetic variance, σ²_e is error variance, R is number of replications

Coefficient of variance (CV) was calculated by using the formula:

$$\text{Coefficient}\text{of}\text{variance}\left(\text{CV};\%\right)=\frac{\sigma }{\mu }\times 100$$

Where, σ is the standard deviation, µ is the mean.

To check the intrinsic relationship among seedling traits, we carried out Pearson’s correlation coefficient (r) analysis and principal component analysis (PCA) using "Corrplot" and “Factoextra” packages, respectively [15].

Limitations

This dataset includes the impact of salt stress on various seedling traits only. However, salt stress affects wheat crop at various developmental stages including flowering and grain filling stage. Future studies must focus on evaluating the impact of salt stress at later development stages also.

Ethics Statement

Authors have read and follow the ethical requirements for publication in Data in Brief and confirming that the current work does not involve human subjects, animal experiments, or any data collected from social media platforms

Credit Author Statement

Santosh Gudi: Conceptualization, Data analysis, Visualization, Writing- original draft preparation, Writing- review and editing. Harsimardeep S Gill: Data analysis. Serena Collins: Data collection. Jatinder Singh: Data analysis. Devinder Sandhu: Conceptualization, Experimental plan, Data collection, Writing- Review and editing. Sunish K Sehgal: Writing- review and editing. Upinder Gill: Writing- review and editing. and Rajeev Gupta: Conceptualization, Writing- review and editing, Supervision. All authors have read and agreed to this version of the manuscript.

Data Availability

• Mendeley DataDataset for seedling traits evaluated under salt stress in hexaploid spring wheat panel (Original data)