Physiological and biochemical responses of hybrid maize (Zea mays L.) varieties grown under heat stress conditions

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Introduction

Corn (Zea mays L.) is an important summer-growing cereal grain, which is produced around the world as well as in Turkey, and is used for human consumption, feed for different animal groups, and as a raw material for bio-energy (Ge et al., 2012; Bassu et al., 2014). Approximately 1,102,164 billion tons of corn are produced worldwide, annually (FAO, 2020), providing 19.5% of the global calorie demand (Hussain et al., 2019a). In 2020, Turkey produced 6 million tons of corn, meeting 90% of the domestic market demand (TSI, 2021). Heat stress (‘HS’) is one of the most significant abiotic stresses as it causes a decrease in maize yield per unit area in many parts of the world. Global temperature is expected to increase by 1.5 °C due to an increase of greenhouse gases, especially CO2 and methane (CH4; IPCC, 2021). ‘HS’ significantly impacts the growth of agricultural crops, especially corn production (Chartzoulakis & Bertaki, 2015). Corn supply problems will arise with the simultaneous increase of ‘HS’ and the global population. Achieving optimum grain yield will no longer be possible if abiotic stresses significantly increase (Landman et al., 2018). Therefore, corn breeding programs, especially those focusing on the breeding of ‘HS’-tolerant corn varieties, should be carried out based on physiological and biochemical screening data of corn varieties.

Corn production has increased primarily by expanding agricultural production areas instead of increasing the corn yield per unit area (OECD/FAO, 2020). However, continuing to increase agricultural production areas is not sustainable because of emerging abiotic stress factors and urbanization (Santpoort, 2020). Optimum environmental conditions are needed in order to determine the genetic grain yield potential of different corn varieties. Extreme climate events, such as drought and ‘HS,’ remain a serious threat to the global food supply (FAO, 2018). ‘HS’ shortens the life cycle of corn (Stone, 2001), reduces light interception, increases respiration, and reduces photosynthesis and flexibility of the cell membrane (Crafts-Brander & Salvucci, 2002). Therefore, ‘HS’ severely constrains agricultural productivity, including corn yield (Rezaei et al., 2015). Significant corn grain yield losses have been reported due to temperatures above 35 °C (Tesfaye et al., 2017). Similarly, 26% grain yield loss has been reported under ‘HS’ conditions between 37 °C and 39 °C compared to control conditions (32–34 °C; Chukwudi, Kutu & Mavengahama, 2021). In one study, corn yield decreased linearly with each degree of temperature increase above 30 °C (Lobell et al., 2011), and Zhao et al. (2017) reported an approximate 7.4% loss in corn yield with each degree of temperature increase. Grain yield also significantly decreases when plants face ‘HS’ for a prolonged period of time (Schauberger et al., 2017). One study found that the radiation use efficiency of corn plants decreased significantly under ‘HS’ up to 36 °C, and dry matter accumulation and grain yield decreased due to low active nitrogen and carbon metabolism (Yang et al., 2019). ‘HS’ causes water loss in the leaves through stomatal openings (Sharma et al., 2015). In addition, ‘HS’ also causes a decrease in leaf water content and total water absorption rate, resulting in a serious decrease in grain yield (Ashraf & Hafeez, 2004). Corn grain yield is affected by the genetic characteristics of the seeds used and the conditions of the growing environment (Yang et al., 2020; Inyang et al., 2021). Although ‘HS’ causes a decrease in grain yield, Karmakar et al. (2016) also reported significant differences in the attributes of corn grown under two different temperatures.

The reproductive stage of corn plants between tasseling and silking is the most sensitive stage to temperatures fluctuations, and high temperatures accelerate the life cycle of tasseling (Siebers et al., 2017). Short- or long-term exposure to high temperature stress during corn growth periods (especially during the most critical tasseling stage) causes metabolic and/or morphological changes (Shim, Lee & Lee, 2017). Some corn genotypes mature earlier under ‘HS’ leading to slight yield losses (Adams, Cockshull & Cave, 2001). In addition, exposure to high temperatures adversely affects the gas exchange, leaf water content, photosynthesis activity and chlorophyll-a and b contents of corn plants (Pessarakli, 2007; Waqas et al., 2017). Neiff et al. (2016) also found that temperatures ranging from 33 °C to 36 °C during the pre-and post-flowering periods reduced chlorophyll content and CO2 exchange rate (17%), crop growth rate (17–29%), leaf water content (7–25%), and grain yield (10–45%). High ‘HS’ has detrimental impacts on macromolecules, cellular structures and membranes in corn due to the overproduction of reactive oxygen species (Hussain et al., 2018). In addition, ‘HS’ affects nutrient uptake, limits the functioning of various enzymes such as phenolics (Hussain et al., 2019b; Hasanuzzaman et al., 2020), and reduces corn plant cell membrane elasticity (Zhou et al., 2017).

Phytohormones regulate the biochemical processes of plant cells, which are ubiquitous to plant growth under ‘HS’ conditions (Sharma et al., 2019). The cell organelles of corn plants respond quickly to variations in ambient temperature, sometimes causing the alteration of biochemical and molecular pathways in all parts of the cells (Kosová et al., 2018). ‘HS’ conditions trigger a biochemical response in maize plants where phenolic compounds increase to work against oxidative stress in the cells (Ravazzolo et al., 2021). Phenolic compounds are biochemical materials involved in the vital activities of plants that reduce oxidative effects (Halliwell, 2006; Dong et al., 2014). The antioxidant effects and phenolic content of corn plants are higher than in oat, wheat, and rice (Adom & Liu, 2002).

High-tolerance corn lines and varieties should be bred to reduce the negative impacts of ‘HS’ on corn plants (Chukwudi, Kutu & Mavengahama, 2021). A more comprehensive understanding of the physiological and biochemical responses of corn plants to temperature extremes is needed to identify and breed corn varieties that can withstand extreme heat. Furthermore, determining which physiological and biochemical traits are correlated with corn grain yield will help create the selection criteria for future corn variety breeding programs. Therefore, this study was conducted to (i) determine the physiological and biochemical performances of candidate hybrid corn cultivars under natural ‘HS’ and control conditions, (ii) understand the response mechanisms of these corn cultivars, (iii) explain the relationship between GY and other physiological parameters, and (iv) reveal the physiological or biochemical parameters necessary to measure the stress and tolerance level of corn cultivars.

Materials and Methods

Experimental site and climate characteristics

The field experiments were conducted at the Talat Demiroren research station of the South Eastern Anatolian Project Agricultural Research Institute, Sanliurfa Province, Turkey (36° 54′ 13″ N, 38° 55′ 03″E, altitude 378 m). The field studies were carried out in the 2020 and 2021 corn growing periods. The soil of the experimental field had a heavy clay textured, granular structure and the infiltration rate was within the optimum range. The organic matter content and pH levels were between 0.81% and 1.11% and 7.55, and 8.11, respectively.

The study area is located in a semi-arid climate with high temperatures and low relative humidity during the summer season. The corn growing periods in both 2020 and 2021 were drier and hotter compared to the historical temperature and precipitation averages. The amount of precipitation and duration of sunshine during the experiments verified that the region has a semi-arid climate close to the aridity line. The climate data of the study area recorded during the corn growing seasons of 2020 and 2021 are provided in Table 1.

Table 1:
Meteorological data of the study area.
Climate data, such as temperature and relative humidity, dramatically affected the performance of hybrid corn varieties.
Months Mean temperatures
(°C)
Mean maximum temperatures Mean relative humidity
(%)
Total rainfall (mm/m−2) Sunshine duration (hour)
2020 2021 ALY 2020 2021 ALY 2020 2021 ALY 2020 2021 ALY 2020 2021 ALY
May 22.8 25.7 22.3 31.0 36.7 28.8 47.7 33.0 38.9 0.0 0.0 29.2 10.1 10.5 10.0
June 28.3 28.5 28.1 36.5 38.2 34.7 35.9 33.1 35.1 0.0 0.2 7.3 11.9 12.1 12.1
July 33.2 32.7 32.0 41.6 43.0 38.8 33.4 32.3 33.4 0.0 0.0 4.2 13.0 12.8 12.3
August 30.7 31.3 31.6 40.7 42.0 38.4 34.2 39.3 31.6 0.0 0.0 5.6 12.2 12.6 11.3
September 28.7 30.4 27.2 38.3 40.1 34.0 45.6 38.5 30.2 0.2 0.0 7.6 10.0 10.3 10.0
October 22.1 23.3 20.6 31.8 32.3 27.1 56.2 46.5 43.7 0.0 0.0 31.4 7.1 6.9 7.9
November 13.1 14.0 13.1 19.9 18.9 18.8 70.0 64.2 65.9 76.4 11.5 51.7 6.4 6.6 6.0
December 9.0 11.2 7.6 15.7 17.0 12.0 68.5 65.2 69.9 8.6 16.4 80.2 4.4 5.0 4.2
Average 23.5 24.7 22.8 31.9 33.5 29.1 48.9 44.0 43.6 85.2 28.1 217.2 9.4 9.6 9.2
DOI: 10.7717/peerj.14141/table-1

Note:

ALY, Average for long years (1950–2021).

‘HS’ conditions were present between May 1 and September 1, while August 1 to December 1 had ‘C’ conditions, which are the optimum conditions. The ‘HS’ and ‘C’ conditions in the study area were determined using historical climate data. The temperature difference between the ‘HS’ and ‘C’ conditions was 4.77 °C in the first year of the study (2020), and increased to 6.65 °C in the second year (2021). Temperature differences were recorded both between years and corn growing conditions (GC). More extreme climate conditions occurred in the first year of the experiment.

The average relative humidity also differed between the experimental years and the ‘HS’ and ‘C’ conditions. In the first year of the study, the relative humidity during ‘HS’ conditions was 13.7% lower than under ‘C’ conditions, but 12.7% lower in the second year. The mean relative humidity values during ‘HS’ conditions in both experimental years were below 40%. Similar to the temperature and relative humidity values, the daily average maximum temperature values between the tasseling and silking periods during ‘HS’ conditions were higher than those recorded in ‘C’ conditions of both experimental years. The temperature exceeded 40 °C on some days during the 2021 growing season, especially under ‘HS’ conditions (Fig. 1).

Average relative humidity changes in heat stress (HS) and control (C) conditions and 10-day average maximum temperature values between tasseling and silking in both test years.

Figure 1: Average relative humidity changes in heat stress (HS) and control (C) conditions and 10-day average maximum temperature values between tasseling and silking in both test years.

Relative humidity <40% in HS conditions occurred as a secondary stress to the plants. Pollen fertility was adversely affected in the days between tasseling and silking due to the high-temperatures in HS conditions.

Plant material, experimental design, and cultural practices

Eleven candidate hybrid corn varieties from the national corn breeding program and two commercial hybrid corn varieties, used as controls, were used in this study. All included varieties were classified as having moderate maturity (FAO 650) and are widely grown in ‘HS’ areas and regions of the world. Some traits of the corn varieties used in the study are given in Table 2.

Table 2:
Characteristics of hybrid maize varieties used in the experiment.
Thirteen hybrid maize varieties (eleven from Turkey, two from the USA), with same ear type and colour and FAO maturity group, were used in the study.
Variety name Ear colour ve type Source FAO maturity group
ADA 17.1 Yellow dent Turkey 650
ADA 17.2 Yellow dent Turkey 650
ADA 17.3 Yellow dent Turkey 650
ADA 17.4 Yellow dent Turkey 650
ADA 17.5 Yellow dent Turkey 650
ADA 17.6 Yellow dent Turkey 650
ADA 17.7 Yellow dent Turkey 650
ADA 17.8 Yellow dent Turkey 650
SYM-305 Yellow dent Turkey 650
SYM-306 Yellow dent Turkey 650
SYM-307 Yellow dent Turkey 650
Check-1 Yellow dent USA 650
Check-2 Yellow dent USA 650
DOI: 10.7717/peerj.14141/table-2

This experiment used split plots in randomized blocks with three replications, with ‘HS’ and ‘C’ growing conditions applied to the main plots and the different corn varieties planted in the sub plots. The inter row and intra row spacings were 70 and 17 cm, respectively. Each plot had four rows, 5 m in length, and the area of each plot was 14 m2. Each plot had 115 to 120 corn plants. Fertilizer was applied in the seedbeds at a rate of 350 kg ha−1 in the form of diammonium phosphate (18%N-46%P2O5-0%K2O) during sowing (Kucuk, Cevheri & Mutlu, 2019). Nitrogen (350 kg N ha−1) was applied as urea (46% N) at the second and fourth weeks of sowing. Considering climate conditions and the water requirements of corn plants, a total of 660 and 600 mm irrigation water was applied in the ‘HS’ and ‘C’ conditions of the first year, respectively, and 710 and 630 mm irrigation water was applied in ‘HS’ and ‘C’ conditions of the second year, respectively, when the ‘HS’ was more serious. Corn cobs were harvested between August 25–29 and December 1–3 in the ‘HS’ and ‘C’ conditions of the first year, respectively, and between August 24–25 and November 28–30 in the second year. The harvest dates differed between the years due to changes in climate, and the cobs were harvested when the corn kernels had 24–25% moisture.

Data collection

Leaf water potential (LWP, %)

The flag leaves of five randomly selected plants in each plot were collected during each of the experiment years between the tasseling and silking phenological periods and then quickly transferred to the laboratory in an ice container. The leaves were cut using a defined template to fit into the Petri dishes, and the wet weight (WW) of the leaf samples was measured using a precision balance. After weighing, the leaves were placed into the petri dishes filled with distilled water, submerging the cutting area, and kept in distilled water for 12 h, when the turgid weight of the leaves (TW) was measured. After determining the WW and TW, the leaves were kept in an oven at 70 °C for 24 h, and the dry weight (DW) was then determined. The leaf water potential (LWP) was calculated using the following equation (Bowman, 1989):

LWP(%)=(WWDW)/(TWDW)100

Chlorophyll – a (Chl-a, mg g−1)

Leaf samples were taken and transferred to the laboratory for the Chl-a analysis using the same methods that were used for the LWP analysis. Fresh leaf samples (0.1 g) in each plot were weighed on a precision scale, homogenized with 3 ml 80% (v/v) acetone in a mortar, and poured into Eppendorf tubes placed with white band filter paper and filtered. The filtered suspension was brought to 10 ml with 80% (v/v) acetone. Immediately after vortexing all Eppendorf tubes, the absorbance at 663 nm was determined using a spectrophotometer for Chl-a contents (Arnon, 1949).

Chl-a is a sub-pigment of chlorophyll. In this study, the Chl-a content was calculated using the following equation (Lichtenthaler & Wellburn, 1983):

Chlorophylla(Chla,mgg1)=ΔA663×12.70ΔA645×2.69/mgsampleweight

In the equation, ΔA663 × 12.70 and ΔA645 × 2.69 are optical absorptions in wavelengths determined by the spectrophotometer and multiplied by concentration (mg g−1).

Cell membrane damage (CMD, %)

After randomly selecting leaves, a 100 mg leaf sample from each selected leaf was weighed and the samples were washed three times with distilled water. The plant samples were transferred to test tubes containing 10 ml of deionized water. The tubes were then capped and kept in a water bath at 32 °C for 2 h. The electrical conductivity (EC) of the solution was measured with an EC meter and an EC1 value was determined. In the second phase, the samples were autoclaved at 121 °C for 20 min to destroy all cells and allow organic and inorganic ions to pass into the solution. After bringing the samples to room temperature, the EC of the solutions was measured, and an EC2 value was determined. The cell membrane damage (CMD, %) value was calculated using the following equation (Sairam, 1994):

CMD=[1(EC1/EC2)]×100

Total phenol content (TPC, μg g−1)

Flag leaves of five randomly selected plants were sampled from each plot between the tasseling and silking phenological periods. A 100 mg sample of each of the flag leaf samples was wrapped in aluminum foil and quickly transferred to the laboratory within a nitrogen tank at −198 °C, and then placed in a refrigerator at −20 °C (Azzouzi et al., 2021). A 100 g sample of plant tissues from each sample was then homogenized in 3 mL methanol (80%), and then more 80% methanol was added until the final volume of the homogenate reached 5 mL. From the prepared solutions, 76 µL of sample was taken and diluted with 684 µL of 80% methanol. The final volume of each of the tubes with plant tissues was 760 µL. For both the control samples and test samples, 120 µL of Folin-Ciocalteu reagent (1 N) was pipetted and incubated for 5 min at room temperature. Then, 320 µL of 20% Na2CO3 was pipetted, 800 µL of deionized water was added, and then the tubes with a final volume of 2,000 µL were vortexed. The absorbance values of the control samples and test plant samples were determined at 765 nm using a spectrophotometer (Singleton, Orthofer & Lamuela-Raventos, 1999).

Data analysis

A statistical analysis was carried out using JUMP (Version 13.2.0) statistical software. The differences in the traits between the growing conditions were tested using an analysis of variance (ANOVA). A statistical analysis was carried out for each experimental year, separately, as well as the 2-year combined data. The degrees of freedom and F ratio for all parameters were calculated in the combined-year data. The least significant difference (LSD) multiple comparison test, set at a 95% probability, was used where ANOVA indicated significant differences between the mean values for year, the growing conditions, and the plant varieties. The relationships between all traits examined in the study were determined using the Spearman’s correlation test.

Results

The difference in all parameters between year, growing conditions (GC), and corn varieties was statistically significant (P ≤ 0.01). The effect of GCxV interaction on DT, LWP, Chl-a and CMD was significant at the P ≤ 0.01 level, and it was significant for TPC at the P ≤ 0.05 level. In contrast, the interaction of GCxV had no significant effect on GY. The F ratio and degrees of freedom of variance for all parameters in the combined-year data are shown in Table 3.

Table 3:
Combined-year F ratios and degrees of freedom for each of the measured traits.
The combined-year F ratio, DF, CV, means, and level of probability values of each of the measured traits were calculated using the JUMP statistical software.
Source DF F ratio
DT GY LWP Chl-a CMD TPC
Y 1 122.658** 36.404** 113.664** 50.823** 113.565** 101.422**
R (Y) 4 4.224 0.601 3.345 0.858 2.604 5.994
GC 1 398.412** 127.874** 436.368** 197.986** 419.329** 270.099**
Y*GC 1 41.536** 0.035ns 0.167ns 0.006ns 0.022ns 8.958**
R*GC*Y 4 0.685 1.445 0.692 1.645 1.065 0.460
V 12 26.682** 60.344** 63.496** 35.901** 72.430** 92.354**
Y*V 12 1.625ns 0.327ns 0.996ns 0.943ns 1.165ns 0.714ns
GC*V 12 4.908** 1.346ns 2.149** 4.749** 7.277** 2.053*
Y*GC*V 12 1.022ns 0.465ns 2.753** 0.856ns 1.798* 1.305ns
Error 96
CV(%) 2.50 9.03 1.83 12.63 10.34 2.32
Mean 51.11 9,990.1 81.09 7.55 11.57 239.68
DOI: 10.7717/peerj.14141/table-3

Notes:

Significant at 0.05 level of probability.
Significant at 0.01 level of probability.

ns, not significant; DF, degrees of freedom; CV, coefficient of variation; R, repetition; Y, year; GC, growth condition; V, Variety.

Agronomic traits such as DT and GY significantly differed between year, GC, and corn varieties (Table 4). The highest DT among all growing conditions and plant varieties was 52.24 days in the first year of the study, while the highest DT value in the second year decreased by an average of 2 days to 49.97 days. The combined-year data indicated that the corn varieties flowered (tasseling) an average of 53.15 days under ‘C’ conditions, and an average of 49.06 days under ‘HS’ conditions. The DT levels in the ‘HS’ conditions of 2021 were lower than during the ‘C’ conditions of 2020. The ADA 17.3 corn variety stood out as the earliest variety in the experiment, and the SYM-307 corn variety was the latest variety. One of the most important findings of the study was recorded for the GY. The mean GY value in the first year was 10,426.09 kg ha−1, but this value decreased by 8.36% in 2021. The GY value in ‘HS’ conditions was 15.12% lower than in the ‘C’ conditions. The GY values of SYM-307 (12,608.9 kg ha−1) and ADA 17.4 (12,361.5 kg ha−1) candidate cultivars were higher than the control hybrid maize cultivars.

Table 4:
The effect of different growth conditions on the DT and GY of hybrid maize varieties.
The performances (DT and GY) of hybrid corn varieties under growing conditions in 2020 and 2021 were compared.
Days to 50% tassel (day)
Years 2020 2021 202–2021
GC/V HS C Mean HS C Mean HS C Mean
ADA 17.1 48.33jkl 55.67bcd 52.00cd 46.67 49.00 47.83ef 47.50l 52.33efg 49.92d
ADA 17.2 47.33l 56.00bc 51.67d 47.00 52.00 49.50de 47.17l 54.00bcd 50.58cd
ADA 17.3 44.33m 54.00de 49.17f 44.00 50.00 47.00f 44.17m 52.00e-h 48.08f
ADA 17.4 50.00hıj 53.00ef 51.50d 49.33 51.67 50.50bcd 49.67jk 52.33efg 51.00c
ADA 17.5 52.00fg 55.00cd 53.50b 51.00 53.33 52.17ab 51.50f-ı 54.17bc 52.83b
ADA 17.6 50.33ghı 57.00ab 53.67b 50.67 53.67 52.17ab 50.50ıj 55.33ab 52.92b
ADA 17.7 48.00kl 52.00fg 50.00ef 47.67 50.67 49.17de 47.83l 51.33f-ı 49.58de
ADA 17.8 51.33fgh 57.00ab 54.17b 50.00 52.67 51.33bc 50.67hıj 54.83ab 52.75b
SYM-305 49.33ıjk 53.00ef 51.17de 47.33 48.67 48.00ef 48.33kl 50.83hıj 49.58de
SYM-306 48.00kl 52.00fg 50.00ef 46.33 48.33 47.33f 47.17l 50.17ıj 48.67ef
SYM-307 53.00ef 58.00a 55.50a 52.33 54.33 53.33a 52.67def 56.17a 54.42a
Check-1 50.00hıj 56.00bc 53.00bc 49.33 50.67 50.00cd 49.67jk 53.33cde 51.50c
Check-2 52.00fg 55.67bcd 53.83b 50.00 52.67 51.33bc 51.00g-j 54.17bc 52.58b
Mean ± SE
r = 3
49.54 ± 0.65b 54.95 ± 0.54a 52.24 ± 0.52A 48.59 ± 0.64b 51.36 ± 0.55a 49.97 ± 0.57B 49.06 ± 0.63b 53.15 ± 0.50a 51.11
CV (%) 2.10 2.88 2.50
LSD (0.05) GC: 0.50**
V: 1.27**
GCxV: 1.80**
GC: 0.66**
V: 1.67**
GCxV: ns
Y: 0.41**
GC: 0.41**
V:1.04**
GCxV: 1.47**
Grain yield (kg ha−1)
ADA 17.1 9,364.7 10,982.3 10,173.5cd 9,046.3 10,313.0 9,679.7cd 9,205.5 10,647.7 9,926.6d
ADA 17.2 10,167.3 11,384.9 10,776.1bc 9,769.7 10,856.0 10,312.8cd 9,968.5 11,120.5 10,544.5bcd
ADA 17.3 9,738.0 11,560.5 10,649.2bc 8,259.0 11,280.0 9,769.5cd 8,998.5 11,420.2 10,209.4cd
ADA 17.4 11,483.5 14,400.7 12,942.1a 10,399.3 13,162.4 11,780.9a 10,941.4 13,781.5 12,361.5a
ADA 17.5 10,369.0 12,146.8 11,257.9bc 9,829.3 11,022.0 10,425.7cd 10,099.2 11,584.4 10,841.8bc
ADA 17.6 10,668.0 11,367.9 11,017.0bc 8,372.1 10,671.3 9,521.7d 9,520.0 11,019.6 10,269.8bcd
ADA 17.7 8,709.3 10,082.3 9,395.8de 8,004.0 8,417.7 8,210.8e 8,356.7 9,250.0 8,803.3e
ADA 17.8 8,528.7 9,347.9 8,938.3e 7,937.0 8,678.0 8,307.5e 8,232.8 9,012.9 8,622.9e
SYM-305 5,969.4 8,503.0 7,236.2f 5,613.7 7,517.4 6,565.5f 5,791.6 8,010.2 6,900.9f
SYM-306 5,276.1 7,256.9 6,266.5f 4,282.3 6,349.7 5,316.0g 4,779.2 6,803.3 5,791.2g
SYM-307 12,231.7 13,636.2 12,933.9a 11,636.3 12,931.3 12,283.8a 11,934.0 13,283.8 12,608.9a
Check-1 10,380.4 12,464.2 11,422.3b 9,752.3 11,337.0 10,544.7bc 10,066.4 11,900.6 10,983.5b
Check-2 11,855.5 13,203.1 12,529.3a 10,855.0 12,115.3 11,485.2ab 11,355.3 12,659.2 12,007.2a
Mean ± SE
r = 3
9,595.5 ± 578.9b 11,256.67 ± 569.2a 10,426.1 ± 567.4A 8,750.5 ± 568.0b 10,357.8 ± 572.1a 9,554.1 ± 559.8B 9,173.0 ± 568.4b 10,807.2 ± 567.4a 9,990.1
CV (%) 9.09 8.95 9.03
LSD (0.05) GC: 431.31**
V: 1099.63**
GCxV: ns
GC: 389.48**
V: 992.99**
GCxV: ns
Y: 286.86**
GC: 286.86**
V: 731.36**
GCxV: ns
DOI: 10.7717/peerj.14141/table-4

Notes:

Significant at 0.01 level of probability.

HS, heat stress condition; C, control condition; GC, growth condition; V, variety; r, replications; SE, Statistical Error; ns, not significant; CV, coefficient of variation.

The means indicated with the same letter in the same column and row are not significantly different according to the JUMP test at P ≤ 0.05.

Leaf water potential (LWP) is an important physiological parameter. The LWP value was 82.35% in the first year, and decreased by 3.06% in the second year. The LWP value also decreased by 5.92% in ‘HS’ conditions compared to ‘C’ conditions. The Check-2 corn variety had the highest LWP value (86.30%), and the SYM-307 and ADA 17.4 varieties, which stood out in terms of GY, were placed in a sub-statistical group (LWP, 84%). Chl-a was analyzed for year, corn variety, and GC. The Chl-a sub-pigment value was 8.09 mg g−1 in the first year, while this value decreased by approximately 1 mg g−1 in the second year. The combined-year average Chl-a value of the corn varieties was 6.47 mg g−1 in ‘HS’ conditions, which increased to 8.62 mg g−1 in ‘C’ conditions. The Chl-a pigment decreased by 24.94% in ‘HS’ conditions compared to ‘C’ conditions. The ADA 17.4 candidate variety also had the highest Chl-a value (Table 5).

Table 5:
The effect of different growth conditions on the physiological traits of hybrid maize varieties.
The Chl-a and LWP of hybrid corn varieties significantly differed between experiment years and growing conditions.
Leaf water potential (%)
Years 2020 2021 2020–2021
GC/V HS C Mean HS C Mean HS C Mean
ADA 17.1 77.01 83.44 80.23ef 75.99ı 79.52fg 77.76d 76.50mn 81.48e-h 78.99f
ADA 17.2 78.74 85.39 82.06d 75.59ıj 81.30ef 78.45d 77.17lmn 83.35bcd 80.26e
ADA 17.3 79.74 85.37 82.56d 77.45ghı 80.29f 78.87cd 78.59jkl 82.83cde 80.71de
ADA 17.4 81.88 88.78 85.33bc 80.30f 85.96bc 83.13a 81.09f-ı 87.37a 84.23b
ADA 17.5 81.22 85.91 83.57cd 79.33fgh 83.63d 81.48b 80.28g-j 84.77b 82.53c
ADA 17.6 80.37 84.03 82.20d 79.51fg 82.89de 81.20b 79.94hıj 83.46bc 81.70cd
ADA 17.7 78.97 84.96 81.96de 75.95ı 84.26cd 80.10bc 77.46lm 84.61b 81.03de
ADA 17.8 74.85 82.37 78.61fg 73.34jk 76.53ı 74.93e 74.09op 79.45ıjk 76.77g
SYM-305 73.70 79.03 76.37h 72.34k 77.10 74.72e 73.02p 78.07klm 75.54h
SYM-306 75.96 79.51 77.73gh 75.25ıj 76.23ı 75.74e 75.61no 77.87klm 76.74gh
SYM-307 84.48 87.99 86.24ab 80.89ef 86.30abc 83.60a 82.69c-f 87.15a 84.92b
Check-1 84.15 87.40 85.78b 79.30fgh 87.04ab 83.17a 81.72d-g 87.22a 84.47b
Check-2 86.74 89.23 87.99a 80.96ef 88.26a 84.61a 83.85bc 88.75a 86.30a
Mean ± SE
r = 3
79.83 ± 1.08b 84.88 ± 0.89a 82.35 ± 0.96A 77.40 ± 0.79b 82.26 ± 1.14a 79.83 ± 0.93B 78.62 ± 0.92b 83.57 ± 0.99a 81.09
CV (%) 1.91 1.73 1.83
LSD (0.05) GC: 0.72**
V: 1.83**
GCxV: ns
GC: 0.63**
V: 1.60**
GCxV: 2.26**
Y: 0.47**
GC: 0.47**
V: 1.20**
GCxV: 1.70**
Chlorophyll-a (mg g−1)
ADA 17.1 7.09ı-l 8.28f-ı 7.69c 6.01 7.48 6.74cd 6.55hıj 7.88d-g 7.21de
ADA 17.2 8.12ghı 12.26a 10.19b 6.73 10.19 8.46b 7.43fgh 11.22a 9.33b
ADA 17.3 6.50klm 6.71j-m 6.61d 4.91 5.67 5.29ef 5.71j 6.19ıj 5.95f
ADA 17.4 10.59bc 11.96ab 11.27a 8.65 11.53 10.09a 9.62b 11.7a 10.68a
ADA 17.5 6.37lm 8.39f-ı 7.38cd 5.80 8.41 7.11cd 6.08ıj 8.40c-f 7.24de
ADA 17.6 5.66mn 8.83e-h 7.24cd 5.33 7.62 6.48de 5.49j 8.23c-g 6.86e
ADA 17.7 6.14lmn 10.23cd 8.18c 5.88 8.23 7.05cd 6.01j 9.23bc 7.62de
ADA 17.8 6.14lmn 9.31c-g 7.73c 5.28 8.91 7.10cd 5.71j 9.11bc 7.41de
SYM-305 4.81n 8.04g-j 6.43d 3.74 6.27 5.01f 4.28k 7.16ghı 5.72f
SYM-306 3.26o 7.25ı-l 5.26e 2.33 5.09 3.71g 2.80l 6.17ıj 4.48g
SYM-307 9.59c-f 9.92cde 9.75b 7.58 9.35 8.46b 8.58b-e 9.63b 9.11b
Check-1 7.84h-k 8.14ghı 7.99c 7.29 8.53 7.91bc 7.57e-h 8.34c-f 7.95cd
Check-2 9.21d-h 9.77cde 9.49b 7.48 7.81 7.64bcd 8.34c-f 8.79bcd 8.57bc
Mean ± SE
r = 3
7.02 ± 0.56b 9.16 ± 0.45a 8.09 ± 0.46A 5.92 ± 0.47b 8.08 ± 0.49a 7.00 ± 0.46B 6.47 ± 0.51b 8.62 ± 0.45a 7.55
CV (%) 10.33 15.10 12.63
LSD (0.05) GC: 0.38**
V: 0.97**
GCxV: 1.37**
GC: 0.48**
V: 1.23**
GCxV: ns
Y: 0.30**
GC: 0.30**
V: 0.77**
GCxV: 1.09**
DOI: 10.7717/peerj.14141/table-5

Notes:

Significant at 0.01 level of probability.

HS, heat stress condition; C, control condition; GC, growth condition; V, variety; r, replications; SE, Statistical Error; ns, not significant; CV, coefficient of variation.

The means indicated with the same letter in the same column and row are not significantly different according to the JUMP test at P ≤ 0.05.

The cell membrane damage (CMD) value was significantly different between year, corn variety, and GC. Cell membrane damage was 12.60% in 2021, and 10.55% in 2020 when the growing conditions were better. The membrane damage was 9.61% in ‘C’ conditions and increased to 13.54% in ‘HS’ conditions. The lowest CMD values were obtained in the ADA 17.4 and SYM-307 candidate cultivars. The difference between the lowest and the highest CMD values was nearly two-fold. The TPC value in 2020 was 244.17 μg g−1, and a 3.67% decrease was recorded in 2021. The difference in TPC value between ‘HS’ and ‘C’ conditions was even higher. The mean TPC value of corn cultivars in ‘C’ conditions was 247.01 μg g−1; the TPC value decreased by 5.93% in ‘HS’ conditions. Candidate cultivar ADA 17.4 had the highest combined-year TPC value, indicating that it is genetically tolerant to stress conditions. In addition, the TPC difference between the ‘HS’ and ‘C’ conditions in the ADA 17.4 corn cultivar was 3.62%, while the differences in Check-1 and Check-2 cultivars between the two conditions were 7.18% and 6.17%, respectively. These results showed that the ADA 17.4 candidate corn cultivar maintains its stability between optimum and stress conditions (Table 6).

Table 6:
The effect of different growth conditions on the biochemical traits of hybrid maize varieties.
The biochemical traits of hybrid maize cultivars, such as CMD and TPC, differed under ‘HS’ and ‘C’ growth conditions.
Cell membrane damage (%)
Years 2020 2021 2020–2021
GC/V HS C Mean HS C Mean HS C Mean
ADA 17.1 8.77h-l 7.10lmn 7.94efg 11.66hıj 8.23nop 9.95fg 10.22hıj 7.67lmn 8.94f
ADA 17.2 10.66fgh 8.46jkl 9.56cd 13.21fgh 9.48lmn 11.35e 11.94fg 8.97jkl 10.46e
ADA 17.3 10.32g-k 6.30mno 8.31d-g 11.44h-k 8.59m-p 10.02fg 10.88gh 7.44mn 9.16f
ADA 17.4 8.25klm 6.21mno 7.23g 9.15mno 7.32p 8.24h 8.70klm 6.77n 7.73g
ADA 17.5 13.62bcd 10.68fgh 12.15b 15.93cde 12.22 14.08c 14.78c 11.45gh 13.11c
ADA 17.6 12.48def 9.43g-k 10.95bc 14.33efg 11.48h-k 12.90cd 13.40de 10.45 11.93d
ADA 17.7 13.25cde 8.50ı-l 10.88bc 16.48cd 10.21j-m 13.35c 14.87c 9.36ıjk 12.11d
ADA 17.8 15.18bc 13.57bcd 14.37a 19.21b 14.33efg 16.77b 17.20b 13.95cde 15.57b
SYM-305 19.37a 10.59f-ı 14.98a 22.67a 15.18de 18.93a 21.02a 12.89ef 16.95a
SYM-306 15.51b 14.06bcd 14.79a 17.40c 14.68def 16.04b 16.46b 14.37cd 15.41b
SYM-307 10.39f-j 5.21no 7.80fg 11.19ı-l 7.45op 9.32gh 10.79gh 6.33n 8.56fg
Check-1 11.41efg 7.22lmn 9.31de 12.55ghı 9.72k-n 11.14ef 11.98fg 8.47klm 10.23e
Check-2 13.30cde 4.56o 8.93def 14.20efg 9.15mno 11.68de 13.75cde 6.86n 10.30e
Mean ± SE
r = 3
12.50 ± 0.84a 8.61 ± 0.82b 10.55 ± 0.76B 14.57 ± 1.03a 10.62 ± 0.75b 12.60 ± 0.87A 13.54 ± 0.93a 9.61 ± 0.77b 11.57
CV(%) 12.16 8.76 10.34
LSD(0.05) GC: 0.58**
V: 1.49**
GCxV: 2.11**
GC: 0.50**
V: 1.28**
GCxV:1.81**
Y: 0.38**
GC: 0.38**
V: 0.97**
GCxV:1.37**
Total phenol content (μg g−1)
ADA 17.1 234.26 240.25 237.26e 226.41hıj 237.56efg 231.99de 230.33klm 238.91ıj 234.62e
ADA 17.2 236.63 252.38 244.51d 229.70ghı 241.52def 235.61d 233.17jkl 246.95gh 240.06d
ADA 17.3 240.88 258.55 249.72cd 220.65ıjk 250.22cd 235.44d 230.77klm 254.39def 242.58d
ADA 17.4 261.79 274.02 267.91a 258.40abc 265.74a 262.07a 260.10bcd 269.88a 264.99a
ADA 17.5 255.67 270.99 263.33ab 245.70de 261.67a 253.69b 250.69fg 266.33ab 258.51b
ADA 17.6 226.52 240.71 233.62ef 214.52kl 231.48gh 223.00f 220.52o 236.09ıjk 228.31fg
ADA 17.7 230.72 239.73 235.23ef 218.32jkl 235.70fgh 227.01ef 224.52mno 237.72ıj 231.12ef
ADA 17.8 226.52 233.40 229.96fg 211.28kl 229.22ghı 220.25fg 218.90op 231.31kl 225.11g
SYM-305 216.07 225.88 220.98h 210.02l 220.74ıjk 215.38gh 213.04pq 223.31no 218.18h
SYM-306 217.07 231.56 224.31gh 196.60m 226.30hıj 211.45h 206.83q 228.93lmn 217.88h
SYM-307 256.82 266.04 261.43b 247.41de 260.34ab 253.88b 252.11efg 263.19bc 257.65b
Check-1 247.56 256.54 252.05c 230.37ghı 258.37abc 244.37c 238.97ıj 257.46cde 248.21c
Check-2 245.85 262.01 253.93c 235.70fgh 251.22bcd 243.46c 240.78 256.62def 248.70c
Mean ± SE
r = 3
238.18 ± 4.12b 250.16 ± 4.40a 244.17 ± 4.23A 226.54 ± 4.79b 243.85 ± 4.16a 235.20 ± 4.37B 232.36 ± 4.41b 247.01 ± 4.25a 239.68
CV(%) 2.08 2.56 2.32
LSD(0.05) GC: 2.31**
V: 5.89**
GCxV: ns
GC: 2.74**
V: 6.98**
GCxV: 9.87**
Y: 1.77**
GC: 1.77**
V:4.51**
GCxV: 6.38*
DOI: 10.7717/peerj.14141/table-6

Notes:

Significant at 0.05 level of probability.
Significant at 0.01 level of probability.

HS, heat stress condition; C, control condition; GC, growth condition; V, variety; r, replications; SE, Statistical Error; ns, not significant; CV, coefficient of variation.

The means indicated with the same letter in the same column and row are not significantly different according to the JUMP test at P ≤ 0.05.

Correlations

The lowest and highest correlation coefficients and significance levels of the traits are shown in Table 7. Significant (P ≤ 0.01) positive correlations were recorded between GY and TPC (r = 0.83), LWP (r = 0.78), Chl-a (r = 0.71), DT (r = 0.54), LWP and TPC (r = 0.77), Chl-a (r = 0.77), DT (r = 0.65), DT and Chl-a (r = 0.57), TPC (r = 0.53), and Chl-a and TPC (r = 0.67). Significant (P ≤ 0.01) negative correlations were observed between CMD and GY (r = −0.72), LWP (r = −0.71), TPC (r = −0.71), Chl-a (r = −0.64), and DT (r = −0.43). Damage to cell membranes caused a decrease in GY, TPC, and other physiological parameters. The lowest correlation coefficient was obtained between CMD and DT parameters, while the highest correlation coefficients were recorded between GY and TPC and LWP.

Table 7:
Correlation analysis between the traits investigated in the study.
High correlation coefficients and significant relationships were observed between grain yield and several physiological and biochemical parameters.
Traits By traits Correlation Count Lower 95% Upper 95% Signif prob (P) Correlation levels
Chl-a TPC 0.6697 156 0.5728 0.7481 <0.0001**
DT TPC 0.5302 156 0.4069 0.6344 <0.0001**
DT Chl-a 0.5712 156 0.4549 0.6683 <0.0001**
CMD TPC −0.7064 156 −0.7773 −0.6178 <0.0001**
CMD Chl-a −0.6383 156 −0.7229 −0.5348 <0.0001*
CMD DT −0.4254 156 −0.5460 −0.2875 <0.0001**
LWP TPC 0.7723 156 0.7001 0.8288 <0.0001**
LWP Chl-a 0.6768 156 0.5815 0.7537 <0.0001**
LWP DT 0.6498 156 0.5487 0.7322 <0.0001**
LWP CMD −0.7132 156 −0.7826 −0.6262 <0.0001**
GY TPC 0.8263 156 0.7690 0.8704 <0.0001**
GY Chl-a 0.7120 156 0.6247 0.7816 <0.0001**
GY DT 0.5441 156 0.4232 0.6460 <0.0001**
GY CMD −0.7163 156 −0.7850 −0.6300 <0.0001**
GY LWP 0.7794 156 0.7092 0.8344 <0.0001**
DOI: 10.7717/peerj.14141/table-7

Notes:

Significant at 0.05 level of probability.
Significant at 0.01 level of probability.

LWP, Leaf water potential (%); GY, Grain yield (kg ha−1); Chl-a: Chlorophyll-a (mg g−1); DT, Days to 50% tassel (day); CMD, Cell membrane damage (%); TPC, Total phenol content (μg g−1).

Discussion

Days to 50% tassel (day) and grain yield (kg ha−1)

The decrease in DT values during ‘HS’ may be attributed to the way the plants responded to the adverse environmental conditions; in order to produce grain under such harsh conditions, the plants may have accelerated their life cycles and tasseling stages (Wang et al., 2022). The disruptions to protein and chlorophyll synthesis, insufficient gas exchange through stomatal openings, and water loss through excessive leaf transpiration likely triggered early flowering (tasseling). Early flowering in the maize plants caused a decrease in other physiological parameters, especially GY. The stressed plants could not sufficiently complete their vegetative and morphological processes and experienced some problems at cellular level. Moderate correlations were seen between DT and other physiological parameters, especially GY. Similarly, Cao et al. (2008) observed that early flowering accelerated the tasseling process in maize when the temperature was above 35 °C. The male and female organs of the plants are damaged under ‘HS’ conditions. In addition, pollen germination, pollen tube growth, and viability of the ovules are inhibited, sterility is significantly increased, and grain yield is decreased with the increased ‘HS’ conditions (Maheswari et al., 2012). Lobell et al. (2011) found that a slight increase in temperature during flowering may cause a significant decrease in GY. In ‘HS’ conditions, the pollen fertility of maize plants likely decreases the tasseling period, which is the most sensitive period, disrupting the synchronization between tasseling and silking, resulting in decreased GY.

Previous studies have shown that both environmental conditions and the genetic characteristics of cultivars impact GY (Gao et al., 2021; Liu et al., 2021; Han et al., 2022). Environmental factors, such as high temperature and low relative humidity, adversely affect plant growth leading to yield losses because of the amount of water lost through evapotranspiration. Pollen viability decreases when daily temperatures are 40 °C and above during the reproductive stage (between tasseling and silking), decreasing GY. A correlation analysis indicated that an increase in physiological parameters such as LWP and Chl-a also increased GY, while an increase in CMD decreased GY. ‘HS’ conditions caused damage at the cellular level, deteriorated protein synthesis, disrupted photosynthesis and respiration, and decreased the carbohydrate reserves of the plants, chlorophyll amounts, and the rate of photosynthesis. Because of these changes, the plants could not grow properly, and GY significantly decreased. Yang et al. (2019) observed similar adverse effects on the protein and starch metabolism in leaves under serious ‘HS’ conditions leading to decreases in yield and quality. These GY decreases due to ‘HS’ conditions are also in line with the findings of Cairns et al. (2013). Similarly, Yang et al. (2020) reported a significant decrease in dry matter accumulation, tasseling day, chlorophyll content, and GY in maize under ‘HS’ conditions. The GY loss (22.9%) reported by Yang et al. (2020) was close to the GY loss recorded in this study (15%). High temperatures cause the denaturation of the chlorophyll sub-pigments involved in photosynthesis (Waraich et al., 2012; Mutlu, 2021) and deteriorate other major physiological processes (Mangani et al., 2019), shortening the grain filling time, and reducing the GY. In parallel with these findings, Siebers et al. (2017) found that temperatures over 35 °C inhibited ovary fertilization and the grain filling process of corn plants.

Leaf water potential (%) and Chlorophyll-a (mg g−1)

The leaves are the plant organelles most affected by stress conditions (Killi et al., 2017), and they provide important information about the level of plant stress (Li et al., 2020). High temperatures cause water loss in the leaves, decrease the fluid of the cytoplasm organelle in plant cells, and decrease the flexibility of the cell membranes (Young, Wilen & Bonham-Smith, 2004; Li & Howell, 2021). The chloroplast organelles are also adversely affected by ‘HS’ conditions (Li & Zhang, 2022). Plant leaves wilt, shrink, and curl inward when the stress level is higher (Butler, Mueller & Huybers, 2018). Despite these protective reflexes, the water carried from the roots cannot compensate for the amount of water lost by the transpiration in the leaves (Zhu et al., 2019). Significant negative correlations were observed between LWP and CMD, while significant positive correlations were recorded between the remaining parameters. Flexibility of the cell membranes decreases under ‘HS’ conditions. An increase in ‘HS’ increases cell damage and electrolyte leakage, adversely affecting LWP. The water in the leaf, which is important to both food production and photosynthesis, also indirectly affects the GY of corn varieties. Alam et al. (2017) also found that ‘HS’ decreased leaf water content and increased leaf senescence, resulting in significant maize yield loss.

Chl-a pigments are also adversely affected by stress and undergo structural deterioration (Karim, Fracheboud & Stamp, 1999; Mohammed & Tarpley, 2010). Therefore, in addition to the defects in the photosynthesis mechanism seen with stress, leaves cannot sufficiently absorb the light energy to reduce carbon dioxide, sugar, and other plant substances. Sensitive corn varieties cannot tolerate high ‘HS.’ The formation of reactive oxygen species such as O2, OH and H2O2 in the cells of sensitive corn varieties causes damage to organelles such as chloroplasts and mitochondria in the plant cells (Guo, Zhou & Zhang, 2006). In parallel with these findings, many researchers have reported less photosynthesis and less assimilate accumulation in the leaves, shortened grain filling periods, and decreased corn grain yield due to dry matter loss under ‘HS’ conditions (Yan et al., 2017; Baiyeri et al., 2019). Low chlorophyll sub-pigments such as chlorophyll-a and b formation under ‘HS’ conditions also lowers GY (Yamori, Hikosaka & Way, 2014). Consistent with this study, Marchand et al. (2005) reported that the activity of photosystem II was significantly reduced or stopped under ‘HS,’ which disrupted the functioning of photosynthetic pigments such as chl-a and b.

Cell membrane damage (%) and total phenol content (µg g−1)

The CMD is the most practical and accurate indicator of the plant’s stress level at the cellular level. When the temperature rises significantly, reactive oxygen species accumulate in the cells of sensitive corn varieties, and these harmful oxygen species cause peroxidation in the membranes. A significant negative correlation was recorded between CMD and other parameters. Without water, the membranes lose flexibility and are damaged over time. Zhang et al. (2005) also reported that ‘HS’ increased membrane permeability and damaged mesophyll cells. In addition, decreases in other physiological and morphological characteristics, especially GY, were reported as the damage rate of membranes increased (Zhang et al., 2005). In contrast, resistant corn varieties have tolerance mechanisms such as the ability to make ionic and osmotic adjustments, membrane permeability adjustments, and these varieties can even protect and repair the proteins and cell membranes damaged by ‘HS’ (Vinocur & Altman, 2005).

The TPC accumulates more in plant tissues up to a certain stress level in ‘HS’ compared to ‘C’ conditions. The photosynthesis and gas exchange in the leaves were disrupted during ‘HS’ conditions due to a decrease in Chl-a pigment. The phenolic sub-pigments deteriorated and decreased accordingly. Environmental factors and the characteristics of the corn variety both significantly affect TPC. Intense ‘HS’ may adversely affect the structure of enzymes such as antioxidants and phenols, leaving plants vulnerable (Yamakawa et al., 2007). Consistent with these findings, Shahzad et al. (2011) reported that the total phenol content of corn plants decreased under high-temperature conditions.

Conclusions

The results of this study revealed that some physiological and biochemical reactions in corn leaves under stress conditions impact GY. High correlation coefficients were observed between GY and LWP, Chl-a, CMD, and TPC. The high temperatures and dry weather conditions experienced during 2021 ‘HS’ conditions compared to the ‘C’ conditions of 2020 caused the corn varieties to be more stressed. The performance assessment of maize varieties showed that the ADA 17.4, SYM-307, and Check-2 cultivars were more tolerant to stress, while the SYM-306 and SYM-305 varieties were more sensitive to ‘HS,’ and all other corn varieties were moderately sensitive to ‘HS.’ The ADA 17.4 and SYM-307 corn cultivars outperformed all other control and test corn cultivars. In addition to their physiological and biochemical tolerances, these cultivars are genetically tolerant to ‘HS.’ These results revealed that physiological parameters can be used to measure the stress and tolerance levels of plants during the tasseling period in the early stages of breeding. These physiological parameters could be used for a rapid selection of corn varieties in long-term breeding programs carried out using hundreds or even thousands of corn varieties. The CMD and LWP are easy and inexpensive traits to measure, providing a fast and effective selection opportunity at the beginning of breeding programs. This allows corn varieties to be classified as high tolerance, moderate tolerance, and sensitive corn varieties, allowing breeding programs to be created based on these classifications. Future studies should be carried out using a higher number of maize varieties, and more detailed physiological and biochemical analyses should be carried out to better understand the tolerance mechanisms of maize varieties under ‘HS’ conditions.

Supplemental Information

Raw data for 2020, 2021 and combined years.

Years and combined years were subjected to variance analysis at 6 parameters investigated in the study, furthermore, statistical groups were formed according to the LSD test. Correlation test was performed between all traits determined in the study.

DOI: 10.7717/peerj.14141/supp-1
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