Effects of Abiotic Factors on Photosynthetic Pigments (Chlorophylls A, B, and Carotenoids) in Pea Leaves Under Contrasting Hydrothermal Conditions in Northern Kazakhstan

Field studies took place in the Shortandinsky district of Akmola region, the village of Naukove, Republic of Kazakhstan, at the base of the Limited Liability Partnership “A.I. Baraev research and production centre for grain farming” [26] during 2022-2024. Field experiments were laid out on clean fallow. The soils of the experimental plot were represented by ordinary low-humus chernozems with fertility indicators (layer 0-20 cm): humus 4.2%, easily available nitrogen (N-NH₄⁺+N-NO₃⁻) 32.1 mg/100 g of soil, mobile phosphorus (P₂O₅) 24.5 mg/100 g, exchangeable potassium (K₂O) 35.7 mg/100 g, pH 6.8.

A total of 21 pea samples were studied, each having different ecological-geographical origins, of which 7 genotypes belonged to the tendrilled morphological leaf type and 14 to the leafed type. The selection included pea samples with clearly expressed morphotypes of leaf (tendrilled and leafed), different ecological-geographical origins, and stable agronomic characteristics, which ensured genetic diversity and adaptability to stress. Samples with an insufficient number of seeds, signs of diseases or damage, as well as those that did not correspond to the conditions of standardised cultivation, were excluded in order to avoid the influence of external factors on the research results.

Sowing was carried out at the optimal times for the region: the end of the second, the beginning of the third ten-day period of May. The sowing depth of seeds was 3-4 cm, row spacing was 15 cm. The experiment was duplicated (2 replications). The seed material was sown with a breeding, fractional, cone precision seeder with 7 sowing units (SSFK-7, Kazakhstan) on plots of 4 m². During the growing season, phenological monitoring was conducted to record the duration of interphase intervals and the total vegetation period. At the ripening stage, before harvesting, structural sheaves were collected from control plots. For harvesting from experimental plots, a breeding combine Wintersteiger Classic (Austria) was used.

The assessment of agroclimatic conditions during the research (2022-2024) was carried out based on daily meteorological observations obtained from the state meteorological station of Shortandy (Shortandinsky district, Akmola region, Kazakhstan). The data included: total precipitation (mm), average daily air temperature (℃), relative air humidity (%). The hydrothermal coefficient (HTC) was calculated according to Selyaninov’s Eq. (1):

$H T C=\frac{\sum P}{0.1 \times \sum T_{a c t}}$                (1)

where, ΣP – total precipitation (mm) for the vegetation season, ΣT_act – sum of average daily air temperatures >+10 ℃ for the same period, coefficient 0.1 – normalising multiplier according to Selyaninov’s method.

Threshold values for different drought categories are as follows: extreme drought occurs when the HTC is below 0.4, indicating severe moisture stress relative to temperature and evapotranspiration demands; moderate drought is defined by an HTC between 0.4 and 0.6, where water availability remains a limiting factor but not as severe as in extreme drought conditions; mild drought is characterised by an HTC between 0.6 and 0.8, signifying a moderate imbalance between precipitation and temperature, though crops can still survive with proper moisture management; and normal conditions are indicated by an HTC greater than 0.8, suggesting adequate moisture for optimal crop growth [27]. These thresholds are widely used to categorise drought intensity and guide agricultural management strategies.

The determination of chlorophyll a, b, and carotenoids was carried out by spectrophotometry, based on the ability of pigments to absorb light of a certain wavelength using a spectrophotometer Venta Lab T6 (Biogenetix, China). The quantitative assessment of chlorophyll content was carried out by the traditional method, which included extraction with acetone or ethanol followed by spectrophotometry or high-performance liquid chromatography, using the average sample method [28].

The spectrophotometric analysis was performed using 5 mL of 96% ethanol for each sample, with pigment extraction carried out at room temperature (25 ℃) for 30 minutes, ensuring periodic shaking to enhance extraction efficiency. The extraction was conducted in the dark to prevent light-induced degradation of the pigments. After extraction, the samples were centrifuged at 3000 rpm for 10 minutes to remove solid debris, and the supernatant was filtered through a 0.45 µm nylon filter to obtain a clear solution for analysis. The spectrophotometric measurements were conducted using a 1 cm cuvette, with 96% ethanol serving as the blank solvent. The instrument (Venta Lab T6) was calibrated using the blank solvent, and wavelength accuracy was verified using calibration standards for 470 nm (carotenoids), 646 nm (Chl b), and 663 nm (Chl a) to ensure precise readings. This protocol ensures accurate and reproducible measurements of photosynthetic pigments under controlled conditions.

For each sample at each growth phase, three independent measurements were carried out, which allowed the variability to be assessed and the reliability of the results to be improved. Leaf samples for the determination of photosynthetic pigments were taken at the phases: “seedlings”, “flowering”, and “ripening”.

The experimental layout was designed as a Randomised Complete Block Design (RCBD), with two replications per plot, where each replication represents a separate block. The plots were randomly assigned within each block to ensure randomisation of the experimental conditions. Each plot consisted of three randomly selected plants per genotype, and the leaves from these plants were pooled to account for plant-to-plant variability. Pigment sampling was averaged per plot to ensure consistency across experimental conditions, and the data from two replications were used for statistical analysis. Border rows were included to mitigate edge effects and maintain plot integrity. The two replications per plot helped in ensuring robust and reliable statistical analysis, allowing for accurate assessment of pigment content under the different experimental conditions.

A portion of plant material (0.2 g) was ground in a mortar to a homogeneous mass. Extraction and determination of chlorophyll and carotenoid contents were carried out in 96% ethyl alcohol in accordance with established recommendations [29]. The following calculation formulas were used.

Eq. (2) for the concentration of Chl a:

$\mathrm{C}_{\mathrm{Chl} \, \mathrm{a}}=12.21 \mathrm{D}_{663}-2.81 \mathrm{D}_{646}$                (2)

where, C_{Chl a}  – concentration of Chl a in extract (mg/L); D₆₆₃ – optical density of extract at wavelength 663 nm; D₆₄₆ – optical density of extract at wavelength 646 nm.

Eq. (3) for the concentration of Chl b:

$\mathrm{C}_{\mathrm{Chl} \mathrm{~b}}=20.13 \mathrm{D}_{646}-5.03 \mathrm{D}_{663}$             (3)

where, C_{Chl b} – concentration of Chl b in extract (mg/L); D₆₄₆ – optical density of extract at wavelength 646 nm; D₆₆₃ – optical density of extract at wavelength 663 nm.

Eq. (4) for the concentration of carotenoids [14]:

$\mathrm{C}_{\mathrm{car}}=\frac{\mathrm{D}_{470}-\left(\mathrm{C}_{\mathrm{Chl} \, \mathrm{a}} \times k_{\mathrm{Chl} \, \mathrm{a}}\right)-\left(\mathrm{C}_{\mathrm{Chl} \mathrm{~b}} \times k_{\mathrm{Chl} \mathrm{~b}}\right)}{k_{c a r}}$               (4)

where, C_car – concentration of carotenoids in extract (mg/L); D₄₇₀ – optical density of extract at wavelength 470 nm; C_{Chl a} – concentration of chlorophyll a (mg/L), calculated by Eq. (1); C_{Chl b} – concentration of chlorophyll b (mg/L), calculated by Eq. (3); $k_{\mathrm{Chl} \, \mathrm{a}}$, $k_{\mathrm{Chl} \mathrm{~b}}$, and $k_{\mathrm{car}}$ – specific coefficients for Chl a (11.64), Chl b (22.25), and carotenoids (4.69), respectively, derived for the 96% ethanol solvent system.

After determining the concentration of pigment in the extract, its content in the studied tissue was calculated taking into account the extract volume and the sample mass according to Eq. (5):

$\mathrm{F}=(\mathrm{V} \times \mathrm{C}) / \mathrm{P}$              (5)

where, F – pigment content in plant material (mg/g fresh mass); V – extract volume (L); C – pigment concentration (mg/L); P – mass of plant material portion (g). Additionally, the ratios of pigments Chl a/Chl b and (Chl a + b)/car were calculated.

In addition to the analysis of photosynthetic pigments, the yield of each genotype (g/m²) was measured after full ripening. To assess the relationship between pigment composition and productivity, Pearson’s correlation analysis was used (at p < 0.05). Sampling for pigment analysis and determination of yield was carried out on the same experimental plots, which allowed the influence of microclimatic differences to be avoided. The statistical processing of experimental data was carried out in the Statistica 6.0 software environment. The analysis of results included methods of descriptive statistics and Pearson’s correlation analysis (significance level p < 0.05). The strength of correlations was evaluated according to the standard scale: coefficient values r < 0.3 indicated a weak correlation, r = 0.3-0.7 indicated a moderate correlation, and r > 0.7 indicated a strong statistical relationship.

The obtained data on the dynamics of photosynthetic pigments in pea (Pisum sativum L.) under contrasting climatic scenarios of the Akmola region represented significant scientific interest in the context of plant adaptation to abiotic stress factors. These results demonstrated the complex interaction between the plant morphotype, vegetation phase, and external conditions, which found numerous parallels in modern research. A sharp decrease in Chl a content by 41-49% in the drought years 2022-2023 (0.51-0.68 mg/g compared to 1.03 mg/g in the 2022, which was relatively drier but slightly better than 2023) fully corresponded to the data of Farooq et al. [36], who described a similar decrease in Chl a concentration in pea under combined heat and water stress. Particularly noteworthy was the correlation revealed between Chl a content and yield in tendril morphotypes in 2022 (r = 0.840), which agreed with the study of Pandey et al. [37], who proved the relationship between the stability of the photosynthetic apparatus and pea productivity under drought conditions.

An increased content of Chl b in tendril morphotypes (0.51 mg/g at the flowering stage) during the drought of 2023 confirmed the data of Wiśniewska et al. [38] on the adaptive role of this pigment under stress conditions. The authors established that Chl b contributed to optimising light absorption under reduced insolation, which fully corresponded to the conducted observations. However, the degree of Chl b decrease at the ripening stage in 2023 (to 0.15 mg/g) turned out to be more pronounced than in the studies of Jahan et al. [39] on tomato, which may indicate species-specific reactions. The 67% increase in carotenoids at the seedling stage in the wet 2024 year (0.3 mg/g) found confirmation in the works of Dhami and Cazzonelli [40], who described the activation of the synthesis of these pigments as a response to oxidative stress. This effect was especially pronounced in the early phases of ontogenesis, which fully confirmed the data obtained in this study. The role of carotenoids in reflecting oxidative stress is better understood when considering the carotenoids/chlorophyll ratio, as this index accounts for the balance between protective pigments and chlorophyll, offering accurate assessment of stress levels than absolute carotenoid content alone.

At the same time, the peak accumulation of carotenoids at the ripening stage (0.29 mg/g in 2022) agreed with the studies of Sarker et al. [41] on amaranth, but significantly differed from the results of Doğru and Çakirlar [42], who recorded maximum concentration already at the seedling stage. However, the absence of correlation between pigments and yield in these forms in 2024 contradicted the data of Balde et al. [43], who revealed a stable relationship between chlorophyll fluorescence and productivity in forest ecosystems.

The 27% increase in carotenoids at the flowering stage in the wet 2024 year fully matched expectations based on the work of Sun et al. [44], who described the antioxidant function of these pigments. However, the absence of a similar reaction in Al-Huqail et al. [45] on basil indicated a genus–species specificity of responses. A strong correlation between Chl a and Chl b in leafed forms in 2024 (r = 0.840) found theoretical confirmation in the study of Quian-Ulloa and Stange [46], where the coordination of chlorophyll biosynthesis under external factors was described in detail. This effect was less pronounced in tendril forms (r = 0.421), which may indicate different adaptive strategies of morphotypes.

The obtained data on the dynamics of pigments at different stages of ontogenesis found confirmation in modern literature. The increase in carotenoids from seedling to ripening, recorded in this study, fully agreed with the concept of Kolašinac et al. [47] on the ontogenetic dynamics of these pigments. At the same time, the revealed decrease in Chl b content at the ripening stage during the drought of 2023 found confirmation in the works of Sachdev et al. [48], who described the enhanced degradation of this pigment under oxidative stress. The obtained data on the role of chlorophyll b as an adaptive pigment in tendril morphotypes during drought found confirmation in the works of Moustakas et al. [49], where it was proven that changes in chlorophyll fluorescence were a sensitive indicator of early stress. Particularly noteworthy was the coincidence of results on the decrease of Chl a by 41-49% in drought years with the data of Moustaka and Moustakas [50], who revealed similar changes in fluorescence parameters in plants under abiotic stress.

The revealed increase in carotenoid content by 67% at the seedling stage under excess moisture fully corresponded to the studies of Hossain et al. [51] on amaranth, where salt stress caused a similar activation of the synthesis of these pigments. However, the degree of increase was higher in the studied case, which may indicate species-specificity of reactions.

At the same time, the current data on the protective role of carotenoids was confirmed by the works of Hamani et al. [52], who described the antioxidant function in cotton under salt stress. The decrease in Chl b content to 0.15 mg/g in the dry 2023 year found a partial analogue in the studies of Kalisz et al. [53] on lettuce, where metal nanoparticles caused similar changes in chlorophyll fluorescence. However, in this case, the effect was more pronounced, highlighting the intensity of climatic stress. The higher Chl a content in leafed forms (by 0.04 mg/g) agreed with the data of Trejo-Téllez et al. [54] on the positive effect of silicon on chlorophyll concentration in pepper, confirming the general patterns of photosynthetic apparatus formation under favourable conditions.

A comparative analysis revealed significant consistency of the main trends with the results of other researchers, confirming the universality of photosynthetic apparatus reactions to stress. At the same time, specific features such as morphotype-dependent Chl b dynamics and carotenoid synthesis intensity indicated the necessity of considering regional climatic conditions and genotypic characteristics. These observations formed the scientific basis for further conclusions regarding the adaptive potential of pea under climate change conditions.

The work was carried out within the framework of the Program-targeted financing of the Ministry of Agriculture of the Republic of Kazakhstan under the budget program 267, BR – 22885414 “Creation and introduction into production of highly productive varieties of leguminous crops based on modern biology methods, development of their varietal technology and primary seed production”.