Protocol for evaluating salt and alkaline tolerance in maize seedlings using a sand-bed germination assay

Summary

Here, we present a protocol to independently and concurrently evaluate salt and alkaline tolerance in maize at the seedling stage using a controlled sand-bed germination assay. We describe steps for preparing standardized stress solutions, establishing the sand-bed assay, applying stresses either independently or in combination, and measuring multiple phenotypic traits. Furthermore, we detail procedures for ranking germplasm using a comprehensive scoring system based on a membership function. This protocol enables reproducible, large-scale screening of maize germplasm at the seedling stage.

Graphical abstract

Highlights

• •Instructions for creating a stable sand-bed germination environment
• •Steps for screening independent and combined salt/alkaline stress tolerance
• •Guidance on phenotyping the germplasm for salt and alkaline stress tolerance
• •Procedures for ranking germplasm using multiple traits in a membership function

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Here, we present a protocol to independently and concurrently evaluate salt and alkaline tolerance in maize at the seedling stage using a controlled sand-bed germination assay. We describe steps for preparing standardized stress solutions, establishing the sand-bed assay, applying stresses either independently or in combination, and measuring multiple phenotypic traits. Furthermore, we detail procedures for ranking germplasm using a comprehensive scoring system based on a membership function. This protocol enables reproducible, large-scale screening of maize germplasm at the seedling stage.

Before you begin

Soil salinity and alkalinity constitute major abiotic stresses limiting global maize production.¹ The development of salt- and alkaline-tolerant maize varieties is crucial for the agricultural use of affected lands. Progress, however, has been hindered by the absence of precise and standardized phenotyping methods, which limits the identification and deployment of resilient germplasm. Critically, salt stress (neutral pH, dominated by NaCl/Na₂SO₄) and alkali stress (alkaline pH, dominated by Na₂CO₃/NaHCO₃) induce distinct physiological injuries, yet they are often confounded in screening. Traditional field-based evaluation is both time-consuming and susceptible to environmental variability, further impeding reproducible selection. To address these limitations, this protocol establishes a controlled, high-throughput system for the independent and parallel evaluation of salt and alkaline tolerance in maize at the seedling stage. The experimental protocol detailed herein entails the use of genetically purified maize seeds, clean quartz sand, germination boxes, and controlled-environment growth room facilities. Prior to setup, verify that all seed accessions have been genetically purified and stored under uniform conditions. Unless otherwise specified, all chemical solutions should be prepared fresh immediately before use.

Innovation

Discovery of a stable sand-bed system retains consistent moisture and stress levels, eliminating the need for daily watering

We present a phenotyping protocol designed to systematically differentiate between salt and alkali stress responses in maize seedlings. By employing a modular, standardized sand-bed system, we overcome key limitations of conventional soil-based and hydroponic assays, such as fluctuating ion concentrations and pH instability. The method ensures consistent moisture and stress distribution, eliminates environmental variability, and supports reproducible, high-throughput screening across large germplasm panels.

A tripartite stress regime enables independent assessment of salt stress, alkaline stress, and their interactions

A principal innovation lies in the tripartite stress regime, which facilitates the controlled application of salt (NaCl/Na₂SO₄) and alkali (Na₂CO₃/NaHCO₃) treatments either independently, in combination, or in sequence. This experimental framework empowers the precise assignment of physiological responses to ionic toxicity, elevated pH, or their interaction a mechanistic distinction that is challenging to achieve with conventional assays.

A buffered alkaline treatment using a NaHCO₃/Na₂CO₃ mixture ensures a stable target pH (e.g., 9.0) is maintained for the duration of the experiment

A key feature is a buffered alkaline treatment that locks in a target pH (e.g., 9.0), preventing the drift common in traditional assays. This creates a stable, realistic environment that accurately mirrors field alkaline stress.

The proposed framework employs a multi-trait scoring system that amalgamates key morphological and growth metrics, yielding a composite index for reliable germplasm comparison and ranking

A final innovation is a composite tolerance index, generated through membership function analysis, which synthesizes multiple seedling traits into a single robust score. This advances beyond single-trait screening, enabling reproducible, quantitative germplasm ranking. The integrated system provides a scalable and physiologically relevant tool for breeding and functional studies of saline-alkaline tolerance.

Preparations

Preparing maize seeds and establishing growth conditions

Timing: 1–2 days

• 1.Select genetically pure, uniform, and healthy and matured maize seeds from each accession to be tested (Figure 1A).
• 2.Store seeds in a dry, cool environment until use.
• 3.
  Prepare a controlled growth chamber or greenhouse with the following climatic conditions:

  • a.
    Temperature: 25 ± 1°C.
  • b.
    Relative humidity (RH): 65 ± 5%.
  • c.
    Photoperiod: 12 h light/12 h dark.
  • d.
    Light intensity: 250–350 μmol m^-2 s^-1.

Figure 1.

Schematic diagram of preparation of seeds and sand for the assay

(A) Select a specific number of corn seeds using a corn seed counter, ensuring they are mature, disease-free and uniform in shape and size.

(B) Wash the new sand 4–5 times.

(C) Use a magnet to absorb metallic impurities from the new sand.

(D) Sterilize and dry the sand at 140°C for 4 h.

(E) Schematic comparison between sand with 60% saturation moisture and sand with 100% saturation moisture, sand at 60% saturation moisture exhibits better aeration.

(F) Sand bed germination test setup for control and stressed plants.

Preparing quartz sand for the germination assay

Timing: 4–6 h (plus cooling time)

Note: The quartz sand can be reused for unlimited number of times after proper cleaning and sterilization.

• 4.Select quartz sand with a particle size of 26–40 mesh (0.43–0.60 mm).

Note: The total quartz sand required depends on the number of germplasm accessions to be tested.

• 5.
  Prepare the quartz sand for initial use by following three steps (Figures 1B and 1C).

  • a.
    Thoroughly rinse the sand with water at least three times to eliminate fine particles and dust.
  • b.
    Spread the rinsed sand in a metal tray to create a uniform layer approximately 0.5 cm thick.
  • c.
    Pass a strong magnet over the sand surface to extract metallic impurities and heavy metal residues.

CRITICAL: This step is essential for newly acquired sand to avoid unintended heavy metal stress on seedlings.

• 6.
  Sterilize the sand (Figure 1D).

  • a.
    Spread the rinsed sand evenly in a metal tray to create a layer approximately 1 cm in depth.
  • b.
    Sterilize it in an oven at 140°C temperature for 4 h.
  • c.
    Ensure the sand is completely cool before proceeding.
• 7.
  Determination of Saturated Water Content of Quartz Sand (Figure 1E).

  • a.
    Tare Container: Weigh a clean, dry container. Record mass as W₀.
  • b.
    Add Sand: Add ∼500 g of dry, sterilized sand. Weigh again and record total mass as W₁.
  • c.
    Hydrate: While stirring continuously, slowly add distilled water.
  • d.
    Saturate: Continue until the sand is fully saturated and signified by a glistening surface and a small pool of free water at the bottom. Weigh the saturated sand and record as W₂.

    Note: Avoid adding excess water beyond saturation. The goal is to reach maximum water-holding capacity without drainage.

  • e.
    Calculate saturated water content (SWC), on a gravimetric basis (g of water per g of dry sand):

SWC = (W₂ – W₀) / (W₁ – W₀)

Note: The gravimetric SWC value is used to calculate the volume of solution needed to prepare sand at a desired moisture level (e.g., 60% of saturation).

• 8.
  Reuse of sand

  • a.
    After each experiment, collect and rinse sand thoroughly with water until runoff is clear.
  • b.
    Sterilize again at 140°C for 4 h before reuse to prevent pathogen accumulation.

Note: Allow sterilized sand to cool to room temperature before mixing with water or stress solutions. This prevents inconsistencies in moisture content.

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Chemicals, peptides, and recombinant proteins
Sodium chloride (NaCl)	Sinopharm Chemical Reagent Co., Ltd	10019318
Sodium hydrogen carbonate (NaHCO3)	Sinopharm Chemical Reagent Co., Ltd	10018960
Sodium carbonate anhydrous (Na2CO3)	Sinopharm Chemical Reagent Co., Ltd	10019260
Sodium sulfate (Na2SO4)	Sinopharm Chemical Reagent Co., Ltd	10020518
dd H2O	Self-prepared	N/A
Experimental models: organisms/strains
Maize (Zea mays) inbred lines or varieties	Your Source/Collection	N/A
Software and algorithms
Microsoft Office 2019	Microsoft Corporation	https://www.microsoft.com
ImageJ	National Institutes of Health	https://imagej.nih.gov/ij/
GraphPad Prism 9	GraphPad Software lnc.	https://www.graphpad.com
IBM SPSS Statistics 26	IBM	http://www.ibm.com/products/spss-statistics
R Project for Statistical	The R Foundation	https://cran.r-project.org
Other
Quartz sand	N/A	26-40 mesh (0.425–0.6 mm)
Seed germination box (e.g., 33∗20∗18 cm)	Local supplier	N/A
Plastic wrap	N/A	N/A
Maize Seed Testing Instrument (scanner)	www.wseen.com	SC-G
Drying oven	Shanghai YiHeng Scientific Instruments Co., Ltd.	DHG-9240A
Electronic balance	Ningbo JINNUO Balance Instrument Co., Ltd.	JT1003D
Aluminum boxes (e.g., Ø 50 mm × H 30 mm)	N/A	N/A

Materials and equipment

100mM NaCl solution

Reagent	Final concentration	Amount
NaCl	100 mM	5.84 g
dd H2O	N/A	Up to 1000 mL
Total	N/A	1000 mL

Store at 15–25°C for up to 7 days.

Alkaline buffer solution (pH 8.9–9.1, 35.2 mM, NaHCO₃: Na₂CO₃ molar ratio = 10:1)

Reagent	Final concentration	Amount
NaHCO3	32 mM	2.69 g
Na2CO3	3.2 mM	0.34 g
dd H2O	N/A	Up to 1000 mL
Total	N/A	1000 mL

Prepare fresh daily.²

Note: The “35.2 mM” refers to the total carbonate/bicarbonate concentration (32 mM NaHCO₃ + 3.2 mM Na₂CO₃ = 35.2 mM total weak acid anions).

Combined saline-alkali solution (pH 8.3, 40 mM, NaCl: NaHCO₃: Na₂SO₄ molar ratio = 1:2:1)

Reagent	Final concentration	Amount
NaCl	10 mM	0.58 g
NaHCO3	20 mM	1.68 g
Na2SO4	10 mM	1.42 g
dd H2O	N/A	Up to 1000 mL
Total	N/A	1000 mL

Prepare fresh daily.

Note: 40 mM refers to the total concentration of all solutes (10 mM NaCl + 20 mM NaHCO₃ + 10 mM Na₂SO₄). When screening for the optimal salt-alkali tolerance treatment, proportionally adjust all other solutions based on the values provided in the table above.

Step-by-step method details

Sand-bed germination assay

Timing: 7 days

This step establishes the germination environment. The sand bed prevents seedling entanglement, ensures uniform moisture and stress exposure, and facilitates the accurate measurement of root and shoot lengths.³

• 1.
  Prepare moist sand

  • a.
    For each treatment (control or stress), mix 1000 g of dry sand with 174 mL of distilled water (for control) or stress solution (here salt/alkali stress).

    Note: The volume 174 mL is derived from the saturated water content (SWC) of quartz sand. As described in Preparation (Step 7), we determined the gravimetric SWC of sand to be approximately 0.29 g water/g dry sand. To achieve 60% saturation, the required water volume per 1000 g dry sand is: 1000 g × 0.29 × 0.60 = 174 mL.

  • b.
    Stir thoroughly to achieve uniform moisture (∼60% of saturated water content).

    Note: Hydrate the sand until it reaches 60–80% of its maximum water-holding capacity. Maintain this standard consistently across both control and stress treatments.

• 2.
  Set up germination boxes

  • a.
    Place 1250 g of moist sand into a germination box to form a 5 cm-thick base layer.
  • b.
    Level the sand surface using a flat card.
  • c.
    Arrange 30 seeds per accession in a 3 × 10 grid on the sand surface.

    Note: Germination box is a rigid plastic container with internal dimensions of approximately 33 cm (Length) × 20 cm (Width) × 18 cm (Height), equipped with removable plastic dividers (baffles) that allow four independent accessions to be placed in the same box without mixing (Figure 1F).

  • d.
    Cover seeds with 750 g of moist sand to form a 3 cm-thick overlay and level the surface.

    Note: We adopted the sand germination method by placing the seeds in the middle of the sand, which facilitates seed water absorption and germination.

  • e.
    Seal the box with transparent plastic wrap to prevent moisture loss.
  • f.
    Label each box carefully with accession and treatment information.

    Notes: Three independent biological replicates in separate boxes per accession per treatment are highly recommended. Under plastic wrap, the internal temperature remains very close to the chamber setting (±1°C) due to the thin wrap and the large thermal mass of the wet sand. Relative humidity in the headspace reaches near saturation (>90%) within the 7-days period. Importantly, this condition is consistent across all treatments (control and stresses) and does not confound comparisons, as all boxes are sealed uniformly. The plastic wrap is not opened at any point during germination and growth. The system is designed to be completely closed to eliminate manual intervention, which would otherwise introduce variability. With moisture content maintained at 60% saturation and the wrap sealed, stable moisture levels are preserved throughout the experiment. Thus, no replenishment is required for water and stress solution. While gas composition was not measured, the system yields normal seedlings per ISTA standards, confirming that hypoxia is not an issue.

    CRITICAL: Keep each biological replicate in a separate germination box with randomization.

• 3.
  Germinate under controlled conditions

  • a.
    Place the germination boxes in greenhouse set to growth condition at 25 °C, 60% RH, and 12 h light/12 h dark.
  • b.
    Monitor daily for mold or contamination; remove affected seeds promptly.

Note: Management during seed germination: The temperature shall be controlled within a tolerance of ±1 °C.

• 4.
  Record germination data

  • a.
    Count germinated seeds daily for 7 days (coleoptile emergence as criterion) (Figure 2A).
  • b.
    Calculate germination percentage (GP), germination index (GI), and vigor index (VI) on day 7. The specific calculation formulas are as follows⁴:

    • i.
      GP= (Number of germinated seeds on Day 7/Total seeds planted) × 100%.
    • ii.
      GI = Σ (Gt/Dt), where Gt is the number of seeds germinated on day Dt.
    • iii.
      VI = GI × Average seedling dry weight.

Note: Take out the 7-days-old seedlings and process them for phenotypic measurement (refer to Step 8).

CRITICAL: Use the control treatment (ddH₂O) to assess baseline seed quality. Only accessions with a germination percentage of ≥80% under control conditions should proceed to stress treatments. For accessions with borderline germination (75%–80%), we recommend increasing the number of seeds per replicate to 40 and performing four biological replicates as a practical precaution, although this adjustment has not been statistically validated. Accessions with <75% control germination should be excluded from stress tolerance evaluation, as poor seed quality will confound any meaningful comparison. In such cases, obtain fresh seeds or omit the accession entirely.

Figure 2.

Schematic diagram of maize seedling data measurement process

(A) A seed was counted as germinated once it penetrated the sand layer.

(B) (C) Rinse the sand from the maize seedlings under running tap water.

(D) Seedlings of uniform length were selected for subsequent measurement and calculation after removing extreme phenotypes.

(E) The maize kernel, coleoptile, and radicle of each seedling were separated using forceps.

(F) For each replicate of a maize inbred line, the coleoptiles and radicles were separated, dried, and weighed to determine dry mass.

Defined salt and alkaline stress treatment assays

Timing: 7 days

Continue employing the sand bed germination system to apply separate salt stress, alkali stress, and combined salt-alkali stress treatments. Use the resultant data to provide a reference for evaluating maize resistance across distinct saline-alkali environments.

• 5.
  Salt Stress Treatment

  • a.
    Prepare moist sand with 100 mM NaCl solution as described in Step 1.
  • b.
    Set up germination boxes

    • i.
      Place 3600 g of salt-treated moist sand into each box to form a 5 cm-thick base layer and then level the surface.
    • ii.
      Place 30 seeds of each accession on the sand surface in evenly spaced rows. Use plastic baffles to physically separate different accessions within the same box.
    • iii.
      Cover seeds with another layer of 2200 g of salt-treated moist sand on top to form a 3 cm-thick overlay. Level the surface.
    • iv.
      Seal the germination boxes and label them clearly.
  • c.
    Grow under the same conditions as the control.
• 6.
  Alkali Stress Treatment

  • a.
    Prepare alkali-treated moist sand with alkali solution (pH 9.0) as described in Step 1.
  • b.
    Set up germination boxes as in Step 2. Using inter-sand germination as described above, sequentially place 3600 g of base sand (5 cm thick), seeds, and 2200 g of covering sand (3 cm thick) into each germination box.
  • c.
    Grow under the same conditions.
• 7.
  Combined Saline-Alkali Stress Treatment

  • a.
    Prepare moist sand with combined saline-alkali solution.
  • b.
    Set up germination boxes as in Step 2. Add 3600 g of base sand and 2200 g of sand to cover sequentially to each germination box.
  • c.
    Grow under the same conditions.
• 8.
  Control for each treatment

  • a.
    Prepare moist sand with distilled water. Distilled water (pH 7.3, within the ISTA-recommended range of 6.0–7.5) is used as the control. No pH adjustment or buffering is applied, as consistency across treatments and accessions is sufficient for comparative evaluation.

Note: Because the saline-alkali solution and the aqueous solution have different densities, the weight of the moist sand will differ after mixing for salt treated and moist sand treated with sterile water. Prepare a total of 5800 g of salt-treated moist sand for two layers (for bottom layer 3600 g and for covering seeds 2200 g) using stress treated moist as described in Step 1. The combined saline alkaline solution (NaCl:NaHCO₃:Na₂SO₄ = 1:2:1, total 40 mM, pH 8.3) was designed to replicate the ionic composition of typical the Yellow River Deltasaline-alkali soils in Shandong Province. The simplified 4:2:1 molar ratio (Na⁺:HCO₃^-:SO₄^2-) was adopted to ensure practical reproducibility. The pH of 8.3 corresponds to the soil pH measured at 10–20 cm depth in our sampling sites. For users in other regions, we recommend adjusting the ratio and concentration based on local soil analyses.

Phenotypic data collection and statistical analysis

Timing: 3 days

This step involves processing 7-days-old seedlings from both control and stress-treated groups. It includes measuring and calculating key traits that are influenced by salt, alkali and combined salt and alkali stresses.

• 9.
  Harvest and clean seedlings

  • a.
    Gently remove seedlings from sand and wash roots with distilled water (Figure 2B).
  • b.
    Select 15–20 uniform seedlings per accession (Figure 2).

Note: Avoid mixing seedlings from different accessions in the same germination box. When rinsing, handle seedlings gently to prevent root or shoot damage, as improper handling compromises measurement accuracy.

• 10.
  Seedling Length Measurement

  • a.
    Arrange the cleaned seedlings neatly and capture images of them using a scanner or camera.
  • b.
    Use ImageJ software to measure shoot length (from the seed’s scutellar node to the tip of its longest leaf) and primary root length (from base to tip).

Note: Alternatively, measure the length of each seedling directly using a ruler, record the data and calculate the average value.

• 11.
  Seedling Biomass Measurement

  • a.
    Separate roots and shoots of seedlings from each accession (Figure 2E).
  • b.
    Place the roots and shoots tissues into aluminum boxes separately, and label the boxes clearly.

    Note: Accurately record the number of seedlings for each accession.

    CRITICAL: The seeds remain attached to 7-days-old seedlings and must be removed prior to seedling biomass measurement.

  • c.
    Put the aluminum boxes into a constant-temperature oven and dry at 105 °C for 30 min, then at 80 °C to a constant weight (2–3 days) (Figure 2F).⁵

    Note: 100 °C continuous heat until constant weight can also be used for drying the samples as per the international standard AOAC 934.01 standard. The same drying protocol should be applied uniformly to all treatments (control, salt, alkali, and combined) and to all accessions within an experiment.

  • d.
    Weigh the dried samples immediately using an analytical balance (0.001 g precision).
  • e.
    Calculate root dry weight (RDW, g), shoot dry weight (SDW, g), and root-to-shoot ratio (RSR). RSR are calculated as follows:

RSR = (Root dry weight/Shoot dry weight) × 100%.

Note: Calculate the root or shoot biomass per seedling by dividing the total measured dry weight by the number of seedlings in the sample.

• 12.
  Perform statistical analysis

  • a.
    Process and collate the data from both the control and stress germination tests using Microsoft Excel 2019.
  • b.
    Calculate the coefficient of variation and other statistical parameters using software such as IBM SPSS Statistics 26.

Note: Conduct a minimum of three biological replicates for each accession and perform statistical analysis on the pooled replicated data.

Comprehensive evaluation of maize resistant germplasm

Timing: 2 h

The maize inbred lines were simultaneously evaluated under three parallel stress regimes: salt only (100 mM NaCl), alkali only (35.2 mM NaHCO₃/Na₂CO₃ buffer, pH 8.9), and combined saline-alkaline stress (40 mM mixture with a NaCl:NaHCO₃:Na₂SO₄ ratio of 1:2:1, pH 8.3), alongside a control treatment (ddH₂O). For each accession, relative phenotypic indices such as relative germination percentage, relative shoot length, and relative dry weight were calculated for each regime. Using the membership function method, separate comprehensive D values were subsequently derived for salt tolerance, alkali tolerance, and combined tolerance. These three D values can be directly compared to determine whether a given accession exhibits specific tolerance to salt (ionic toxicity), to alkali (high pH), or to both.

This step details the analysis pipeline for comprehensive tolerance evaluation.

• 13.
  Calculate relative indicators

  • a.
    For each trait, calculate the relative value by dividing trait value under stress/trait value under control.
  • b.
    Include:

    • Relative Germination Percentage (RGP)
    • Relative Germination Index (RGI)
    • Relative Shoot Length (RShL)
    • Relative Shoot Dry Weight (RSDW)
    • Relative Root Dry Weight (RRDW)
    • Relative Total Dry Weight (RTDW)
    • Relative Root-to-Shoot Ratio (RRSR)

Note: There are significant differences among different maize accessions, and relative indicators can better reflect their inherent salt-alkali tolerance.

• 14.
  Multi-Trait Assessment of Stress Tolerance in Maize Seedlings⁶

  • a.
    Calculate the membership function values for each trait:

    $$\mu \left({\mathrm{X}}_{\text{ik}}\right)=\left({\mathrm{X}}_{\text{ik}}-{\mathrm{X}}_{\text{ik}\min }\right)∕\left({\mathrm{X}}_{\text{ik}\max }-{\mathrm{X}}_{\text{ik}\min }\right)\left(\mathrm{k}=1,2,3\dots ..,\mathrm{n}\right)$$

    where, Xᵢ_k represents the observed value of trait k for inbred line i; Xᵢ_kmax and Xᵢ_kmᵢ_n denote the maximum and minimum observed values of trait k, respectively; μ(Xᵢ_k) is the membership function value;
  • b.
    Calculate weight for each trait based on coefficient of variation (CV),

    $${\mathrm{W}}_{\mathrm{k}}={\text{CV}}_{\mathrm{k}}∕{\mathrm{\Sigma }}_{\mathrm{k}=1}^{\mathrm{n}}{\text{CV}}_{\mathrm{k}}$$

    Where, W_k is the weight assigned to trait k; CV_k stands for the coefficient of variation of trait k;
  • c.
    Compute comprehensive evaluation value (D):

    $$D_i=\sum \left[\mu \left(X_{ik}\right)W_k\right]$$
    Dᵢ refers to the weighted comprehensive membership function value for inbred line i.
  • d.
    Rank all the maize accessions based on D values (using the seven indicators mentioned above).

    Note: The D value of the membership function integrates information from multiple stress tolerance indicators, thereby minimizing the error inherent in any single metric. This provides a more accurate assessment of salt tolerance, with a higher D value indicating greater stress tolerance in a maize accession.

Expected outcomes

Successful execution of this protocol will yield quantitative data for determining optimal stress tolerance levels and for identifying the best maize germplasm tolerant to salt stress, alkali stress or combined salt/alkali stress. Also, this protocol provides information on the number traits to be considered during screening of salt or alkali stress.

We describe below the typical results from the optimization phase, which validates the recommended conditions specified in the ‘materials and equipment’ section.

Determination of optimal salt stress concentration

To determine an appropriate salt stress level for screening, we evaluated eight maize inbred lines under three NaCl concentrations (85, 100, and 115 mM) using a sand-bed assay. Key relative indices namely Relative Germination Rate (RGR), Relative Germination Index (RGI), Relative Shoot Length (RSL), and Relative Dry Weight (RDW) were calculated. A representative dataset revealed that 85 mM NaCl produced excessively high indices with insufficient discriminatory power, while 115 mM caused severe, uniform growth suppression with overly low indices. In contrast, 100 mM NaCl induced significant divergence in RSL and RDW values compared to 85 mM, yielding clear phenotypic separation without excessive mortality. Consequently, 100 mM NaCl was selected as the optimal concentration for screening maize salt tolerance at the seedling stage (Figure 3).

Figure 3.

Determination of optimum salt concentration to screen salt tolerance at seedling stage in maize

Seedling growth performance of inbred lines was evaluated at three salt concentrations, (A) 85 mM (B) 100 mM (C) 115 mM. Seedling growth related traits under salt stress treatment were recorded. (D) relative germination rate, (E) relative germination index, (F) relative shoot length, (G) relative plant dry weight. Error bars represent the standard deviation (SD) from three biological replicates. Different lowercase letters (a, b) indicate statistically significant differences between treatments or genotypes at p < 0.05 (based on ANOVA followed by Tukey's HSD test).

Determination of optimal alkaline stress system and concentration

To establish a reproducible alkaline stress regime, we systematically optimized buffer composition and concentration. An initial system (NaHCO₃:Na₂CO₃ = 5:1, pH 9.5, 48–72 mM) inhibited seedling emergence across all lines. We therefore adjusted the buffer to a 10:1 molar ratio (pH 8.9). Screening concentrations from 33 mM to 66 mM showed that 55–66 mM caused severe phenotypic damage. Subsequent testing of a finer gradient (33–39.6 mM) identified 35.2 mM as optimal, providing measurable growth with clear differential responses among lines (Figure 4). Thus, we recommend 35.2 mM bicarbonate buffer (10:1 ratio, pH 8.9) as the standard alkaline stress condition.

Figure 4.

Determination of optimum alkaline concentration to screen alkali stress tolerance at seedling stage in maize

Seedling growth performance of inbred lines was evaluated at three alkaline concentrations, (A) 33 mM (B) 35.2 mM (C) 39.6 mM. Seedling growth related traits under salt stress treatment were recorded. (D) relative germination rate, (E) relative germination index, (F) relative shoot length, (G) relative plant dry weight. Error bars represent the standard deviation (SD) from three biological replicates. Different lowercase letters (a, b) indicate statistically significant differences between treatments or genotypes at p < 0.05 (based on ANOVA followed by Tukey's HSD test).

Determination of optimal mixed saline-alkaline stress concentration

For the combined stress treatment, we tested a saline-alkaline solution (NaCl:NaHCO₃:Na₂SO₄ = 1:2:1, pH 8.3) at total concentrations of 20, 40, and 60 mM (Figure 5). An analysis of germination and early seedling growth revealed that 40 mM provided sufficient stress to differentiate between tolerant and sensitive genotypes while avoiding total growth inhibition. Therefore, 40 mM was established as the appropriate concentration for combined stress screening.

Figure 5.

Determination of optimal mixed saline-alkaline stress concentration to screen saline-alkaline tolerance in maize at seedling stage

Seedling growth performance of inbred lines under different saline-alkaline concentrations, (A) 20 mM, (B) 40 mM, (C) 60 mM. Seedling growth related traits under mixed salt and alkaline stress treatment were recorded. (D) relative germination rate, (E) relative germination index, (F) relative shoot length, (G) relative plant dry weight. Error bars represent the standard deviation (SD) from three biological replicates. Different lowercase letters (a, b) indicate statistically significant differences between treatments or genotypes at p < 0.05 (based on ANOVA followed by Tukey's HSD test).

In summary, our optimization process confirms that the protocol effectively identifies stress levels with clear physiological relevance. The standardized treatments, 100 mM NaCl, 35.2 mM alkaline buffer (10:1 NaHCO₃:Na₂CO₃), and 40 mM mixed saline-alkaline solution support reproducible phenotyping. Consequently, maize germplasm can be consistently and reliably ranked via the integrated scoring framework detailed in ‘quantification and statistical analysis.’

The successful application of this protocol yields quantitative phenotypic data across control and stress conditions, enabling the ranking of maize germplasm via a comprehensive membership function score (D). Tolerant accessions, characterized by higher D values, exhibit superior germination rate, reduced growth inhibition, and stable biomass allocation under stress. The protocol’s discriminatory power is demonstrated by its clear discrimination between known tolerant and sensitive accessions. For example, in a practical application screening of 340 diverse maize accessions, the seven relative indices under salt stress were integrated into a D value for each entry via the membership function method (Tables S1 andS2). Subsequent cluster analysis classified the population into five distinct tolerance categories: highly salt-tolerant (17 accessions), salt-tolerant (63 accessions), moderately salt-tolerant (154 accessions), salt-sensitive (83 accessions), and highly salt-sensitive (23 accessions) (Figure 6 and Table 1). This outcome validates the protocol’s utility for robust, high-throughput classification and prioritization of maize germplasm based on seedling-stage tolerance.

Figure 6.

Cluster analysis to group the maize lines based on their level of tolerance

Cluster analysis categorized the germplasm in 5 distinct groups, high salt tolerant (HSS), salt tolerant (ST), moderate salt tolerant (MST), salt sensitive (SS), highly salt sensitive.

Table 1.

Clustering of maize inbreds based of ‘D’ values and variation of salt tolerance in each cluster

Cluster	No. of genotypes	Mean ‘D’ value	SD	‘D’ value Range
High Salt-Tolerant	17	0.65	0.03	0.62-0.69
Salt-Tolerant	63	0.56	0.03	0.52-0.61
Moderately Salt-Tolerant	154	0.45	0.03	0.40-0.51
Salt-Sensitive	83	0.33	0.05	0.25-0.39
Highly Salt-Sensitive	23	0.22	0.02	0.19-0.25

Quantification and statistical analysis

Construction method of cluster analysis plot

Calculate the D values for all germplasm materials, generate graphs using R packages, and the specific code is provided below. Finally, upload the graphs to iTOL (https://itol.embl.de/) for further optimization and polishing.

>getwd()

>install.packages("BiocManager")

>BiocManager::install("ggtree")

>df <- read.csv("input.csv", header = FALSE, row.names = 1)

>hc <- hclust(dist(df))

>plot(hc)

>library(ggtree); library(ape)

>ggtree(hc, layout = "circular") + geom_tiplab2(size = 2).

>write.tree(as.phylo(hc), file = "tree.nwk").

Limitations

While this protocol provides a controlled and reproducible system for screening salt and alkaline tolerance at the seedling stage, several limitations should be considered.

Higher setup complexity and material requirements: Preparing the sand (washing, sterilization, and moisture calculation) demands more time and effort than simply placing filter paper in Petri dishes.

No direct visualization of germination: In the sand system, seeds are covered with sand, making daily germination count dependent on careful observation or gentle probing. By contrast, filter paper allows direct, unobstructed visualization.

Stress concentrations may need adjustment: The optimized concentrations (100 mM NaCl, 35.2 mM alkaline buffer, and 40 mM mixed stress) were determined using a subset of inbred lines. For germplasm exhibiting extreme sensitivity or tolerance, pilot testing is recommended. Further, the use of a fixed alkaline stress condition (pH 8.9, NaHCO₃/Na₂CO₃ buffer) may not represent the diverse pH and ionic compositions found in natural saline-alkaline soils. Researchers are encouraged to adjust stress levels or solution compositions based on local soil characteristics.

Field conditions might have slight differences: Although seedling-stage evaluations under controlled conditions show correlation with field performance in saline-alkaline soils, they may not fully capture the complex interactions and cumulative effects experienced by mature plants in the field. The protocol is most effective for assessing relative tolerance among genotypes rather than predicting absolute field resistance.

Seed quality may interfere with germination %: Seed quality strongly influences germination and early seedling performance under stress. Although relative indices can partially mitigate variation due to seed vigor, low-quality or long-stored seeds often exhibit disproportionately poor stress responses. We recommend using seeds with ≥80% germination under control conditions to ensure reliable and comparable results across genotypes.

Troubleshooting

Problem 1

Low or uneven germination rate under stress (Steps 4–7).

Potential solution

Reason: Poor seed viability or vigor. Solution: Ensure seed viability by pre-testing germination under control conditions. Use fresh stress solutions and maintain consistent sand moisture.

Problem 2

pH of alkaline solution drifts during experiment (Step 6)

Potential solution

The alkaline buffer solution is prepared fresh daily and adjusted to pH 8.9–9.1 prior to mixing with sand. Because the boxes remain sealed without replenishment, pH drift is minimal. Our validation further indicates that maize germination and early growth are unaffected by pH variations within the range of 8.2–9.2. Consequently, no in-experiment pH adjustment is required. If a user observes extreme pH deviation (e.g., >9.2), it likely stems from incorrect initial preparation; in such cases, the solution should be discarded and freshly prepared.

Reason: CO₂ absorption/loss from bicarbonate buffer. Solution: Prepare alkali solution fresh daily. Use a pH-stable buffer system or monitor and adjust pH every 24 h if necessary.

Problem 3

Seedling etiolation or abnormal growth (Step 3).

Potential solution

Reason: Insufficient light intensity in greenhouse. Solution: Verify light intensity settings. Avoid overcrowding of seedlings in germination boxes.

Problem 4

Fungal growth on sand or seedlings (Step 3).

Potential solution

Reason: Incomplete sand sterilization or high humidity. Solution: Sterilize sand by autoclaving or baking at 140°C for 4 h before use. Avoid over-wetting sand. Ensure greenhouse or growth chamber has adequate air circulation.

Problem 5

Roots are difficult to separate from sand without damage

Potential solution

Reason: Sand is too dry or too compacted. Solution: Ensure moisture content is correct (60% saturation). Gently rinse sand away under a slow stream of water.

Problem 6

High variability seedling length/biomass within a treatment replicate. (Steps 2, 9).

Potential solution

Reason: Uneven sand moisture, seed placement depth or seed quality. Solution: Standardize sand mixing procedure and seedling selection rigorously. Ensure seeds are placed on a leveled surface and covered uniformly.

Problem 7

Inconsistent results between biological replicates

Potential solution

Reason: Environmental gradients in growth chamber (temperature, light). Solution: Randomize the positions of germination boxes within the greenhouse daily or use a rotating tray. Perform at least 4 replicates and remove the replicates with large variation according to statistical methods.

Resource availability

Lead contact

Further information and requests for resources should be directed to the lead contact, Linmao Zhao (lmzhao@sdau.edu.cn).

Technical contact

Technical questions on executing this protocol should be directed to and will be answered by the technical contact, Jiaxin Yang (j18203567396@163.com).

Materials availability

The material used in this study is available upon request from the corresponding author.

Data and code availability

The data generated in this study are provided in Table S1. The code used in this study is detailed in the quantification and analysis section of the protocol.

Acknowledgments

This study was supported by the Key R&D Program of Shandong Province, China (grant no. 2025LZGC046) and the earmarked fund for Shandong Agriculture Research System (grant no. SDARS-02).

Author contributions

L.Z. and S.S.G. conceived the idea and designed the project. J.Y., Y.W., Y.Z., and J.X. performed all wet-lab experiments, recorded phenotypic data, and performed statistical analyses. S.S.G., L.Z., and J.Y. performed visualization, manuscript writing, review, and finalization of the manuscript. All authors carefully read the manuscript and finalized the final version of the manuscript.

Declaration of interests

The authors declare no competing interests.

Supplemental information

Table S1. Salt Stress tolerance indicators at the seedling stage across 340 maize inbred lines, related to step 13

Table S2. Descriptive statistics of the four variables (RGP, RShL, RSDW, and RRDW) (N = 340), related to step 13