Screening of Sunflower Hybrids Using Physiological and Agronomic Traits

Abstract

Researching the photosynthetic activity of sunflower (Helianthus annuus L.) is essential for understanding how different genotypes respond to environmental conditions and utilise solar energy for growth and productivity. The objective of this study was to gain insight into and quantify the adaptation of ten sunflower hybrids during the flowering stage under field conditions. As part of an ongoing sunflower breeding programme, this research aimed to assess genotypic differences in photosynthetic performance and yield-related traits in response to variable environmental conditions. During the flowering stage, chlorophyll a fluorescence (ChlF) parameters revealed significant genotypic differences in energy fluxes, particularly in ABS/RC, DI₀/RC, ET₀/RC, and RE₀/RC. Those results indicate variability in light-harvesting efficiency and electron transport capacity. Although specific photochemical efficiency indicators (e.g., TR₀/RC, TR₀/ABS, ET₀/TR₀) showed slight variation, energy dissipation and photosystem I-related parameters differed significantly among hybrids. Leaf temperature and chlorophyll content also varied and showed moderate correlations with fluorescence-based indicators. Yield components (plant height, head diameter, and seed mass per head) displayed significant differences among sunflower hybrids, with notable opposite patterns between plant height and head size. Revealed strong relationships between photosynthetic performance (PI_TOTAL, RE₀/ABS) and yield traits, particularly plant height and number of seeds per head, were confirmed with correlation analysis. Principal Component Analysis (PCA) distinguished the hybrids into distinct groups. The analysis confirmed physiological and morphological variability among hybrids, enabling effective screening of genotypes for breeding purposes. Photosynthesis is a key physiological trait that directly influences biomass accumulation and seed yield, making it a critical parameter in evaluating the performance and adaptability of various sunflower genotypes. Thus, this study demonstrates the integrative value of combining ChlF, thermal, and agronomic traits for identifying high-performing sunflower hybrids under optimal field conditions.

1. Introduction

Sunflower is an annual plant with the Latin name Helianthus annuus L., which belongs to the Asteraceae family. Today’s domesticated sunflowers have common ancestors that originated from the same domestication centre in the middle interior latitudes of eastern North America [1]. More than 4000 years ago, Native Americans began cultivating sunflowers, marking the beginning of the domestication and breeding process of these flowers [2].

Plant breeding integrates both basic and applied sciences to develop economically valuable genotypes [3]. The concept of breeding or improving a plant species varies greatly depending on the specific plant species. It depends on the desires and needs of humanity, economic profitability, consumption of the product, and its intended use or market application [4]. The primary goals of plant breeding are to create genotypes with increased crop yield and quality, as well as resistance to environmental stresses [3]. This is achieved by generating genetic variability and creating new allele combinations through natural and artificial selection in order to create cultivars with new and beneficial properties [5]. Therefore, lines with more desirable allelic combinations are created by systematically producing plant populations through genetic separation and selection among populations. This is achieved by recurrent selection of heterozygous plants in cross-pollinated species, such as sunflowers, in contrast to pure lines that are preferred in self-pollinated species. Sunflowers and maize are among the most important crops developed through hybrid breeding techniques [6]. Sunflower is a diploid species with 2n = 34 chromosomes and a haploid genome size of approximately 3000 Mb [7]. Cross-pollination results in genotypes with diverse traits, which are then subject to material selection. For this reason, genotype testing is necessary to detect the response of the desired characteristics in different environmental conditions.

Global climate change poses a challenge for sunflower breeders, as it takes 10–15 years to develop sunflower hybrids. This means breeders must develop hybrids suited for both current and future environmental conditions. However, these selections are based on field trials conducted under current weather conditions [8]. Ahmed et al. [9] emphasise that the seed yield of sunflower represents a multifactorial trait governed by various morphological and physiological characteristics and is significantly affected by environmental variability.

Alongside soybeans, palm, and rapeseed, sunflower ranks among the world’s most important oilseed crops. Global production and harvested area have been steadily increasing, with regional fluctuations in cultivation trends reflecting the growing demand for oil production. According to FAOStat [10] in 2023, total world sunflower seed production was 58,574,867 tons, with the regional shares in sunflower seed production being as follows: Europe (72.0%), Asia (12.4%), the Americas (11.0%), Africa (4.4%), and Oceania (0.1%). The European Union accounts for approximately 15% of global sunflower production, with Croatia ranking second among EU countries in average seed yield per hectare based on long-term data from 1999 to 2018 [11]. They pointed out the need to increase sunflower crops in Croatian fields in the future.

Climate change, which has led to increasingly drier growing seasons, especially in summer with less precipitation, higher temperatures, and daily temperature maxima with a lack of precipitation [12,13], has contributed to the strategic importance of sunflower cultivation. Namely, with its morphological structure and appearance, the sunflower plant can withstand weather changes better than other crops. Still, its yield potential may be compromised by heat and water stress, which disrupts physiological and morphological processes and reduces achene yield [14]. Additive Main effects and Multiplicative Interaction (AMMI) analysis by Liović et al. [15] revealed that genotype × environment interaction (41.72%) had the greatest effect on seed yield, followed by genotype (20.75%) and environment (2.10%). Conversely, oil content was most influenced by genotype (52.82%), followed by environment (23.02%) and their interaction (17.27%).

Achieving stable and high yields under changing environmental conditions requires in-depth knowledge of crop biology, field experience, and precise implementation of agronomic practices [16]. Recently, many researchers have employed various approaches to detect the effects of changing weather conditions, which are the primary causes of plant stress. One widely used approach is chlorophyll fluorescence (ChlF) analysis, with which the authors gain insight into the structure and function of the photosynthetic apparatus using ChlF parameters. ChlF parameters have so far been used on sunflower plants when testing the effects of cadmium stress [17], tissue structure [18], phosphorus nutrition [19], the elevated temperature and light [20,21], drought and heat stress [22], herbicide effects [23], the relationship between ChlF parameters and yield components [24], exogenous application of selenium on sunflower [25], drought response [26,27], effects of NaCl Stress [28], salt-stress tolerance [29], etc.

According to Omonov et al. [30], exotic and local sunflower cultivars were compared for chlorophyll a, b, total pigment content, and carotenoids across budding, flowering, and ripening stages. The study confirmed high and significant variation among genotypes, with certain exotic lines showing higher chlorophyll content during flowering and ripening. Similarly, De la Mata et al. [31] observed that chlorophyll a and b content decreased under high irradiance (350 µmol photons m⁻² s⁻¹) during leaf ageing in sunflower, indicating accelerated senescence. On the contrary, the carotenoid content increased under high light, suggesting a protective response against photooxidative stress.

In field crops, numerous factors, such as plant density, fertilisation, water availability, and genotype, strongly influence yield components [32,33,34]. Miladinović et al. [35] confirmed that the flowering time is one of the most critical phenological growth stages in sunflower, as it directly influences yield formation, morphological development, and overall plant performance. The timing of flowering affects pollination efficiency, seed set, and the duration of the seed-filling period, all of which contribute to final yield. Additionally, environmental factors such as temperature and photoperiod interact with genetic factors to determine flowering time, making it a key trait for adaptation to diverse growing conditions.

The primary objective of this study was to gain insight into and quantify the adaptation of ten sunflower hybrids during the flowering stage. The study is part of a sunflower breeding programme that examines the effect of variable weather conditions in experimental fields to monitor the functioning of the photosynthetic apparatus and biomass traits.

2. Materials and Methods

2.1. Field Trial

The location of the experiment was the Tenja Experimental field of the Faculty of Agrobiotechnical Sciences in Osijek (Croatia). Ten different sunflower hybrids were sown in April 2020 at a depth of 4 cm. Rows were five metres long with a row spacing of 23 cm, and the spacing between rows was 70 cm. The experiment had three repetitions, and the design was a randomised complete block design (RBD). Maize (Zea mays L.) was the previous crop. A detailed soil chemical analysis was performed prior to sowing. The soil pH (in KCl) was 7.27, humus content was 2.56%, available phosphorus (P-Olsen) was 12.60 mg/kg, and available potassium (AL-K₂O) was 21.68 mg/100 g of soil. Fertilisation included both basal and pre-sowing applications. Basal fertilisation was performed using 250 kg/ha of NPK 0:20:30, while pre-sowing fertilisation involved 150 kg/ha of urea (46% N). All plant protection measures were carried out in accordance with professional standards and good agricultural practice, as described in Međimurec [36]. Harvest was in September 2020.

2.2. Weather Conditions

Weather conditions during the sunflower growing season were generally favourable for plant development (Figure 1a,b). Mean monthly temperature increased steadily from April, reaching a peak of approximately 23 °C in August, which is optimal for sunflower flowering and seed filling. Rainfall distribution showed notable variation across the months. The highest precipitation was recorded in August (over 100 mm), coinciding with peak temperatures, which may have facilitated seed filling and reduced heat stress. However, precipitation was relatively low in early spring (April), which could have potentially affected early vegetative growth if irrigation had not been provided. Overall, the combination of adequate rainfall in mid to late summer and optimal temperature profiles likely contributed positively to photosynthetic activity and yield formation in sunflowers.

2.3. Photosynthetic Activity

During the flowering stage (R5 according to Schneiter et al. [38]), physiological analyses were performed. A handy PEA device (Hansatech Instruments Ltd., King’s Lynn, Norfolk, UK) was used to determine chlorophyll a fluorescence (ChlF) parameters. Ten randomly chosen plants of each hybrid within a single plot were tested. During the measurement, a 3200 μmol m⁻² s⁻¹ pulse saturating red light was applied to sunflower leaves. Specially designed leaf clips were attached 30 min prior to measurement to induce dark adaptation, following the protocol described by Markulj Kulundžić et al. [39]. The clamps were positioned on a sufficiently developed leaf of ten plants per hybrid, located below the sunflower heads. After measurement, the data were analysed using the JIP test [40,41], and transients were recorded by measuring chlorophyll fluorescence over a time span from 50 microseconds (μs) to 1 second (s) after illumination began. This transient curve includes distinct steps labelled O, J, I, and P, which correspond to specific physiological events in photosystem II (PSII). The structural and functional parameters selected for this research are shown in Table S1.

After ChlF measurements, at the same leaf where ChlF measurements were performed, relative chlorophyll content was determined with a chlorophyll content metre (Hansatech Instruments Ltd., King’s Lynn, Norfolk, UK), and the leaf temperature with a Dual Focus Infrared Thermometer B+B Thermo-Technik GmbH, Donaueschingen, Germany). Those two analyses were conducted on ten leaves per hybrid. Three measurements were taken on each leaf, and the average value was calculated from these measurements. These values were then used to determine the analysis values. All measurements (ChlF, chlorophyll content and leaf temperature) were taken between 8:00 and 10:00 in the morning. During this period, the average air temperature was 23.5 °C, and the Photosynthetically Active Radiation (PAR) was around 854 µmol m⁻² s⁻¹.

During the experiment’s harvest, the following parameters were determined for ten plants per hybrid: plant height (PH), head diameter (HD), seed weight per head (SWH), number of head seeds (NHS), and 1000-seed weight (TSW).

2.4. Data Analysis

The collected data were put into the database and analysed using Statistica 12 to identify biometric differences among the sunflower hybrids. The ANOVA analysis was performed on all observed morphological parameters, and differences between means were determined using the LSD (least significant difference) test at p ≤ 0.05. Photosynthetic activity parameters and yield components were compared using Pearson’s pairwise correlation test, Principal Component analysis (PCA) and cluster analysis to evaluate the strength and significance of the relationships. Heatmap visualisation was generated using the Heatmapper [42] online tool.

3. Results and Discussion

3.1. Photosynthetic Activity Among Hybrids

Chlorophyll a fluorescence parameters observed in the leaves of ten sunflower hybrids at the flowering stage are shown in

Table 1. Specific energy fluxes (per reaction centre—RC) observed in the leaves of ten sunflower hybrids. The letters indicate a statistical difference at p ≤ 0.05 between hybrids.

Hybrid	ABS/RC	DI0/RC	TR0/RC	ET0/RC	RE0/RC
Hybrid 1	1.91 ± 0.11 c	0.33 ± 0.06 c	1.58 ± 0.08 a	1.05 ± 0.08 bc	0.58 ± 0.05 de
Hybrid 2	1.92 ± 0.13 c	0.34 ± 0.07 c	1.58 ± 0.08 a	1.09 ± 0.05 abc	0.61 ± 0.05 cde
Hybrid 3	2.17 ± 0.21 a	0.41 ± 0.05 a	1.76 ± 0.17 a	1.10 ± 0.03 ab	0.62 ± 0.04 cd
Hybrid 4	2.03 ± 0.22 abc	0.35 ± 0.06 bc	1.69 ± 0.16 a	1.11 ± 0.06 ab	0.64 ± 0.05 bc
Hybrid 5	2.08 ± 0.18 abc	0.38 ± 0.05 abc	1.7 ± 0.13 a	1.09 ± 0.03 abc	0.65 ± 0.04 abc
Hybrid 6	2.1 ± 0.26 ab	0.4 ± 0.09 ab	1.7 ± 0.17 a	1.05 ± 0.05 bc	0.57 ± 0.02 e
Hybrid 7	2.09 ± 0.19 ab	0.41 ± 0.05 a	1.68 ± 0.15 a	1.13 ± 0.04 a	0.64 ± 0.05 bc
Hybrid 8	2.07 ± 0.19 abc	0.4 ± 0.07 a	1.66 ± 0.14 a	1.09 ± 0.05 ab	0.67 ± 0.05 ab
Hybrid 9	2.05 ± 0.2 abc	0.4 ± 0.07 a	1.64 ± 0.13 a	1.05 ± 0.1 bc	0.69 ± 0.06 a
Hybrid 10	1.95 ± 0.15 bc	0.36 ± 0.05 abc	1.58 ± 0.11 a	1.03 ± 0.12 c	0.64 ± 0.06 bc

ABS/RC—Absorption flux per active reaction centre (RC); DI₀/RC—Effective dissipation per active RC; TR₀/RC—Maximal trapping rate per RC; ET₀/RC—Electron transport per active RC; RE_-/RC—Electron flux reducing end electron acceptors at the PSI acceptor side per RC.

,

Table 2. Quantum efficiencies and probabilities observed in the leaves of ten sunflower hybrids. The letters indicate a statistical difference at p ≤ 0.05 between hybrids.

Hybrid	TR0/ABS	ET0/ABS	ET0/TR0	ΔR0	RE0/ABS
Hybrid 1	0.83 ± 0.02 a	0.66 ± 0.06 a	0.55 ± 0.05 a	0.56 ± 0.05 de	0.31 ± 0.03 bc
Hybrid 2	0.83 ± 0.02 a	0.69 ± 0.03 a	0.57 ± 0.02 a	0.56 ± 0.03 de	0.32 ± 0.02 ab
Hybrid 3	0.81 ± 0.01 a	0.63 ± 0.06 a	0.51 ± 0.05 a	0.57 ± 0.03 de	0.29 ± 0.02 cd
Hybrid 4	0.83 ± 0.01 a	0.66 ± 0.05 a	0.55 ± 0.05 a	0.58 ± 0.05 cde	0.31 ± 0.02 abc
Hybrid 5	0.82 ± 0.01 a	0.64 ± 0.05 a	0.53 ± 0.05 a	0.6 ± 0.04 bcd	0.32 ± 0.03 abc
Hybrid 6	0.81 ± 0.02 a	0.62 ± 0.05 a	0.51 ± 0.05 a	0.55 ± 0.02 e	0.28 ± 0.03 d
Hybrid 7	0.81 ± 0.02 a	0.67 ± 0.04 a	0.54 ± 0.03 a	0.57 ± 0.05 de	0.31 ± 0.04 abc
Hybrid 8	0.81 ± 0.02 a	0.66 ± 0.07 a	0.53 ± 0.06 a	0.62 ± 0.04 abc	0.33 ± 0.03 ab
Hybrid 9	0.8 ± 0.02 a	0.64 ± 0.09 a	0.52 ± 0.08 a	0.66 ± 0.06 a	0.34 ± 0.04 a
Hybrid 10	0.81 ± 0.02 a	0.65 ± 0.07 a	0.53 ± 0.06 a	0.63 ± 0.09 ab	0.33 ± 0.03 ab

TR₀/ABS—Maximum quantum yield of primary photochemistry; ET₀/ABS—Quantum yield for electron transport; ET₀/TR₀—Probability that an absorbed photon will enter the electron transport chain; electron transport efficiency; ΔR₀—Probability that an electron is transported from reduced PQ to the electron acceptor side of PSI; RE₀/ABS—Quantum yield of electron transport from Q_A⁻ to the PSI end electron acceptors.

,

Table 3. Phenomenological energy fluxes (per excited cross-section—CS) observed in the leaves of ten sunflower hybrids. The letters indicate a statistical difference at p ≤ 0.05 between hybrids.

Hybrid	ABS/CS0	DI0/CS0	TR0/CS0	ET0/CS0	RE0/CS0
Hybrid 1	537 ± 38.96 c	92.22 ± 17.9 e	444.68 ± 22.48 bc	294.03 ± 27.78 a	163.64 ± 18.94 de
Hybrid 2	558 ± 52.44 bc	98.1 ± 22.25 de	460.1 ± 32.93 bc	316.14 ± 26.48 a	177.96 ± 20.35 bcd
Hybrid 3	566 ± 43.17 bc	107.48 ± 10.58 abcd	458.02 ± 34.56 bc	290.13 ± 37.17 a	164.08 ± 20.66 de
Hybrid 4	561 ± 22.06 bc	95.33 ± 10.72 de	465.57 ± 15.07 abc	307.11 ± 29.63 a	175.79 ± 10.05 bcd
Hybrid 5	543 ± 24.74 c	99.59 ± 10.65 cde	443.08 ± 15.46 c	285.19 ± 23.76 a	171.51 ± 16.86 cde
Hybrid 6	565 ± 44.57 bc	105.97 ± 17.67 bcde	459.23 ± 30.35 bc	284.83 ± 21.06 a	155.68 ± 15.82 e
Hybrid 7	581 ± 32.57 ab	112.76 ± 9.56 abc	467.94 ± 30.06 ab	315.23 ± 21.05 a	179.79 ± 24.38 bcd
Hybrid 8	583 ± 58.18 ab	114.43 ± 21.78 ab	468.57 ± 38.55 ab	309.62 ± 35.5 a	190.32 ± 24.07 ab
Hybrid 9	610 ± 44.25 a	120.45 ± 18.67 a	489.85 ± 26.54 a	314.77 ± 38.43 a	205.74 ± 24.39 a
Hybrid 10	560 ± 24.03 bc	104.19 ± 11.56 bcde	455.92 ± 20.42 bc	296.33 ± 35.97 a	184.3 ± 16.14 bc

ABS/CS₀—Absorbed photon flux per excited cross-section (CS) of PSII at time 0; DI₀/CS₀—Dissipated energy flux per cross-section at time 0; TR₀/CS₀—Trapped energy flux per cross-section at time 0; ET₀/CS₀—Electron transport flux per cross-section at time 0; RE₀/CS₀—Electron flux reducing end acceptors at PSI per cross-section at time 0; RC/ABS—Density of RC on chlorophyll a basis.

and

Table 4. Relative ratios, derived parameters and performance indices observed in the leaves of ten sunflower hybrids. The letters indicate a statistical difference at p ≤ 0.05 between hybrids.

Hybrid	RC/ABS	TR0/DI0	ET0/(TR0 − ET0)	ΔR0/(1 − ΔR0)	PIABS	PITOTAL
Hybrid 1	0.52 ± 0.03 a	4.95 ± 0.72 a	2.02 ± 0.45 a	1.29 ± 0.26 de	5.32 ± 1.6 a	6.74 ± 2.23 a
Hybrid 2	0.52 ± 0.04 a	4.83 ± 0.74 ab	2.21 ± 0.26 a	1.29 ± 0.15 de	5.6 ± 1.15 a	7.16 ± 1.41 a
Hybrid 3	0.52 ± 0.05 a	4.28 ± 0.27 c	1.78 ± 0.44 a	1.32 ± 0.15 cde	3.6 ± 1.21 a	4.63 ± 1.2 b
Hybrid 4	0.52 ± 0.06 a	4.93 ± 0.51 a	2 ± 0.48 a	1.38 ± 0.26 cde	5.13 ± 2.27 a	6.79 ± 2.45 a
Hybrid 5	0.52 ± 0.07 a	4.49 ± 0.38 abc	1.87 ± 0.43 a	1.54 ± 0.28 cd	4.17 ± 1.27 a	6.35 ± 2.02 ab
Hybrid 6	0.52 ± 0.08 a	4.41 ± 0.57 bc	1.69 ± 0.39 a	1.2 ± 0.09 e	3.82 ± 1.73 a	4.69 ± 2.41 b
Hybrid 7	0.52 ± 0.09 a	4.17 ± 0.41 c	2.13 ± 0.45 a	1.35 ± 0.26 cde	4.32 ± 1.12 a	5.96 ± 2.14 ab
Hybrid 8	0.52 ± 0.10 a	4.18 ± 0.52 c	2.05 ± 0.54 a	1.62 ± 0.27 bc	4.27 ± 1.57 a	6.79 ± 2.43 a
Hybrid 9	0.52 ± 0.11 a	4.13 ± 0.47 c	1.94 ± 0.59 a	2 ± 0.57 a	4.17 ±1.93 a	7.76 ± 2.8 a
Hybrid 10	0.52 ± 0.12 a	4.43 ± 0.53 bc	1.95 ± 0.52 a	1.9 ± 0.76 ab	4.63 ± 1.87 a	7.68 ± 1.73 a

TR₀/DI₀—Flux ratio trapping per dissipation; ET₀/(TR₀-ET₀)—Electron transport from Q_A⁻ to intersystem electron acceptors; PI_ABS—Performance index on an absorption basis; ΔR₀/(1 − ΔR₀)—Electron transport from PQH₂ to the final PSI acceptors; PI_TOTAL—Performance index for energy conservation from exciton to the reduction in PSI terminal acceptors.

.

The absorption flux per reaction centre (ABS/RC), which reflects the apparent antenna size, varied significantly among the tested sunflower hybrids (Table 1). These results indicate genotypic differences in hybrids in light-harvesting capacity. The highest ABS/RC was recorded in hybrid 3. In contrast, hybrids 1 and 2 exhibited the lowest ABS/RC values. In other crops, increased ABS/RC was associated with genotypes exposed to stress conditions [43,44,45]. The trend of hybrid behaviour in ABS/RC values was mirrored by changes in dissipated energy flux per RC (DI₀/RC), suggesting that increased energy absorption was accompanied by higher energy dissipation, and vice versa. Specifically, hybrids 1 and 2, which showed the lowest ABS/RC, also had the lowest DI₀/RC values, while hybrid 3 exhibited the highest values for both parameters. This parallel increase in ABS/RC and DI₀/RC likely reflects adjustment of the photosynthetic apparatus in hybrid 3. A substantial portion of the absorbed energy may not have been used for photochemistry but instead dissipated as heat or fluorescence. This pattern indicates activation of photoprotective mechanisms and possibly reduced energy use efficiency [46]. High ABS/RC may point to a relatively larger apparent antenna size or increased excitation pressure on PSII reaction centres. In contrast, elevated DI₀/RC suggests the engagement of non-photochemical quenching (NPQ) as a basal photoprotective process. NPQ dissipates excess excitation energy as heat, preventing photoinhibition even under moderate light levels [47]. Additionally, the xanthophyll cycle, particularly zeaxanthin-mediated energy dissipation, plays a central role in the thermal dissipation of excess photons as part of short-term photoprotection [48]. Such coordinated modulation of light absorption and energy dissipation may be characteristic of hybrid 3. It represents an energy allocation strategy that helps maintain PSII functional integrity during the early photoperiod, supporting sustained photosynthetic performance as light intensity rises later in the day. Similar correlations between these parameters have been documented in previous studies [49,50].

Interestingly, TR₀/RC (trapping flux leading to Q_A⁻), which reflects the efficiency of energy trapping at the PSII reaction centre, did not differ statistically among sunflower hybrids. This could indicate a conserved mechanism of primary photochemical charge separation across hybrids [50], despite differences in ABS/RC, ET₀/RC (electron transport flux beyond Q_A⁻) and RE₀/RC (probability that a chlorophyll molecule functions as an RC). Therefore, TR₀/RC alone is not a suitable parameter for discriminating among sunflower hybrids. Sunil et al. [51], who studied the effect of nitric oxide on PSII electron transport in pea mesophyll protoplasts treated with sodium nitroprusside (SNP) at different time points, also reported no significant differences in TR₀/RC. Similarly, Antunović Dunić et al. [52], examining the effect of drought on kale, reported that the combination of stable F₀ and stable TR₀/RC is a sign of constant trapping on active RCs. Conversely, numerous studies have shown that TR₀/RC can be extremely sensitive to stress conditions. Ghaffar et al. [53] found increased TR₀/RC in wheat under drought stress in susceptible cultivars, accompanied by reduced ET₀/RC. Franić et al. [54] observed increased TR₀/RC in maize genotypes exposed to cadmium, while in sunflower, increased TR₀/RC was found under elevated temperatures and high irradiation [20].

Notably, ET₀/RC and RE₀/RC exhibited significant variation among hybrids, with hybrid 7 showing the highest values and hybrid 10 the lowest. These parameters are vital indicators of the efficiency of electron flow beyond Q_A⁻ and the reduction in PSI end acceptors. Lower ET₀/RC in hybrid 10 and RE₀/RC in hybrid 6 likely reflect impaired electron transport and reduced availability of active PSII RCs, which could limit the plant’s photosynthetic potential and productivity. In contrast, the high ET₀/RC and RE₀/RC in hybrids 7 and 9, respectively, suggest a more effective conversion of excitation energy into electron transport, potentially contributing to higher photosynthetic efficiency under field conditions. Those parameters have been associated with stress-induced declines in photosynthetic performance in cereals [55,56].

According to the ANOVA results, the quantum efficiency parameters ET₀/ABS (quantum yield for electron transport), ET₀/TR₀ (probability that an absorbed photon will enter the electron transport chain, electron transport efficiency) and TR₀/ABS (maximum quantum yield of PSII photochemistry) did not differ significantly between sunflower hybrids (Table 2). This indicates that there is a conserved capacity for primary photochemical efficiency and linear electron flow beyond Q_A⁻ [26,50]. The TR₀/ABS ratio, widely regarded as a robust measure of PSII integrity, also showed no hybrid effect. Murchie and Lawson [46] report that the invariant TR₀/ABS values are consistent with its general stability under ambient and moderate stress conditions. Similarly, Cheng et al. [57] also established that the TR₀/ABS fluorescence intensity remained constant for both soybean cultivars throughout the 24 h period of salt stress, with no significant differences observed between the two cultivars. Furthermore, Umar et al. [50] did not find significant differences in ET₀/TR₀ among sunflower hybrids under combined drought and salinity stress. On the other hand, in a study conducted by Franić et al. [54], ET₀/ABS and ET₀/TR₀ showed statistically significant variations among the tested maize genotypes and cadmium treatments, suggesting different capacities of linear electron flow outside PSII among the hybrids.

In contrast, ΔR₀ (efficiency of electron transfer to final PSI acceptors) and RE₀/ABS (quantum yield of electron transport to PSI end acceptors) exhibited identical ranking patterns across hybrids. Coordination between these two parameters is often observed under stress conditions, where limitations of terminal PSI acceptors impose constraints on both [55]. A similar pattern of behaviour for these parameters was previously reported in sunflower by Markulj Kulundžić et al. [39].

The phenomenological energy fluxes per excited cross-section at t = 0 (CS₀), ABS/CS₀ (absorbed photon flux per excited cross-section (CS) of PSII), TR₀/CS₀ (trapped energy flux per cross-section), DI₀/CS₀ (dissipated energy flux per cross-section), ET₀/CS₀ (electron transport flux through PSII per excited cross-section), and RE₀/CS₀ (electron flux reducing end acceptors at PSI per cross-section at time 0) provides an integrated view of PSII activity across the entire sampled leaf surface, as opposed to the reaction centre parameters, specific fluxes that focus on individual active PSII units [40,58].

One-way ANOVA showed that only ET₀/CS₀ remained invariant among sunflower hybrids, whereas ABS/CS₀, TR₀/CS₀, DI₀/CS₀ and RE₀/CS₀ all differed significantly (Table 3). Fisher’s LSD post hoc comparisons revealed that hybrid 5 had the lowest ABS/CS₀ and TR₀/CS₀. In contrast, hybrid 9 exhibited the highest, suggesting substantial differences in light-harvesting capacities among genotypes. The fact that hybrid rankings for DI₀/CS₀ mirrored those for DI₀/RC and likewise for RE₀/CS₀ versus RE₀/RC demonstrates that energy dissipation and PSI acceptor reduction processes scale proportionally from individual reaction centres to the whole leaf cross-section [40,46].

Under various abiotic stress conditions, Faseela et al. [59] reported a consistent decrease in ET₀/CS₀ and RC/CS₀, along with increased DI₀/CS₀ in rice seedlings, particularly under PEG-induced osmotic stress. In contrast, results in this study obtained under non-stress conditions revealed no significant differences in ET₀/CS₀ among sunflower hybrids. This suggests a conserved efficiency of electron transport across hybrids under field-grown conditions. Kalaji et al. [58] showed that under shade conditions, the barley cultivar A. Aswad is an inefficient energy user as evidenced by reduced ET₀/CS₀, primarily lower ABS/CS₀ and TR₀/CS₀, along with increased DI₀/CS₀. In this study, although all sunflower hybrids were grown under optimal light, significant genotypic differences in ABS/CS₀, TR₀/CS₀, and DI₀/CS₀ suggest that underlying light-harvesting and energy dissipation capacities are genotype-specific traits, even in non-stressful environments.

The uniform scaling of DI₀/RC and DI₀/CS₀ across hybrids indicates consistent photoprotective regulation at both the reaction centre and cross-section levels [46]. Similarly, matching patterns of RE₀/CS₀ and RE₀/RC highlight that limitations at the PSI acceptor side (e.g., plastocyanin or ferredoxin pool size) impose equivalent constraints whether normalised per cross-section or reaction centre [55]. Furthermore, during the wheat tillering stage of growth and under drought stress conditions, lower ABS/CS₀, ET₀/CS₀ and DI₀/CS₀ values were recorded, along with higher PI_ABS values, which indicated greater drought tolerance of certain varieties. Conversely, cultivars with higher ABS/CS₀ and ET₀/CS₀ under stress conditions exhibited elevated DI₀/CS₀, negatively affecting yield, water-use efficiency, and stress resilience [60].

PI_ABS (performance index of PSII on absorption basis) integrates structural and functional aspects of PSII and includes RC/ABS (quantum yield for the reduction in end electron acceptors at the PSI acceptor side), TR₀/DI₀ (flux ratio trapping per dissipation), and ET₀/(TR₀ − ET₀) (electron transport further than primary acceptor Q_A), which reflects the efficiency of electron transport beyond the primary electron acceptor Q_A [61].

One-way ANOVA revealed no significant differences among sunflower hybrids in RC/ABS, ET₀/(TR₀ − ET₀), or PI_ABS, indicating a uniform capacity for PSII reaction centre abundance and downstream electron transport efficiency. In contrast, TR₀/DI₀ significantly varied among genotypes (Table 4). Hybrids 1, 2, 4, and 5 showed the highest values of TR₀/DI_0, indicating more efficient energy trapping and reduced thermal dissipation. Opposite to those hybrids, hybrids 3, 7, 8, and 9 exhibited lower values, pointing to increased energy loss through non-photochemical quenching, potentially as a response to stress or reduced PSII efficiency [56]. These results imply that differences among sunflower hybrids are not primarily due to variations in PSII reaction centre density (RC/ABS), but rather to how efficiently absorbed energy is utilised. High TR₀/DI₀ values mark a favourable balance toward energy trapping, whereas low values indicate either structural inefficiencies or activation of protective mechanisms [40].

PI_TOTAL (performance index of electron flux to the final PSI electron acceptors) extends PI_ABS by incorporating ΔR₀/(1 − ΔR₀) (electron transport from plastoquinol (PQH₂) to final PSI acceptors) [62]. PI_TOTAL values were largely consistent across hybrids, with hybrids 9 and 10 showing the highest values, reflecting intact PSII and PSI connectivity. Uniform PI_TOTAL across most genotypes highlights the general resilience of the full electron transport chain, with only in hybrids 3 and 6 exhibiting impaired PSI level performance [41]. In previous research [39,63] on sunflower, it has been proven that PI_TOTAL and ΔR₀/(1 − ΔR₀) are very sensitive parameters that can be used to select materials.

3.2. Chlorophyll Content and Leaf Temperature

The observed variation in chlorophyll content and leaf temperature among sunflower hybrids reflects the interplay between photosynthetic pigment status and thermoregulation capacity. Chlorophyll in all its various forms is the primary photosynthetic pigment found in higher plants. Chlorophyll content represents a fundamental physiological parameter directly linked to the photosynthetic potential of plants, as it determines the capacity for light absorption and subsequent energy conversion in the photosystems [58]. Previous studies have shown that variations in chlorophyll concentration serve as an indicator of plant health, indicate the nutrient status of the plant, and are used to assess the impact of environmental factors on plants [64,65].

At the same time, leaf temperature is also an easily measurable physiological parameter that provides an indirect means of assessing plant transpiration and is closely related to water availability [66]. The environment influences leaf mass through energy flux and interactions between the plant and its environment. The energy received by the plant can be converted into heat within the plant, affecting its temperature, or it can be utilised in photochemical and thermochemical processes associated with metabolic and physiological activities [67]. Therefore, leaf temperature is closely linked to stomatal conductance and transpiration, thus indirectly reflecting the efficiency of photosynthetic cooling mechanisms and the overall water status [68].

In the present study, chlorophyll content and leaf temperature were measured across different sunflower hybrids to evaluate their physiological status regarding ChlF parameters (Figure 2a,b). Statistically significant differences were observed among hybrids for both traits. Hybrids 10, 5, 3, and 4 exhibited the lowest chlorophyll content, whereas hybrids 9, 2, and 1 demonstrated the highest chlorophyll content (Figure 2a). Interestingly, the lowest leaf temperatures were recorded in hybrids 1 and 2, while hybrid 8 had the highest leaf temperature (Figure 2b).

Similar coordinated changes in pigment concentration and leaf temperature have been documented in other crops. Water stress increased leaf temperature and decreased SPAD readings in both cotton and peanut, showing strong negative correlations between these traits in peanut [69]. Similarly, in sunflowers, Singh et al. [70] reported that water stress caused an increase in leaf temperature accompanied by a decline in chlorophyll content and yield attributes. Overall, Varalakshmi et al. [71] state that the positive associations between SPAD and key yield components suggest that chlorophyll content could serve as a useful indirect selection criterion in sunflower breeding for improved photosynthetic performance and productivity.

3.3. Sunflower Yield Components

The ANOVA analysis showed different influences of the sunflower yield components among hybrids (

Table 5. The ANOVA of sunflower yield components.

	Stem Height (cm)	Head Diameter (cm)	1000-Seed Weight (g)	No. Seed per Plant	Mass of Seeds per Head (g)
F Value	12.68	1.60	3.51	0.94	1.66
p	***	**	**	*	**
Mean	191.45	18.8	52.83	1380	75.94

ns—not significant; *—p ≤ 0.05; **—p ≤ 0.01; ***—p ≤ 0.001.

).

According to the results of the present study, the sunflower hybrids differed significantly (p ≤ 0.05) in all the yield components (Figure 3a–e).

On average, plants reached a height of 192 cm (Figure 3a), ranging from 169 cm (H3) to 208 cm (H1). The average head diameter across hybrids was 18.8 cm (Figure 3b), with values ranging from 17.7 cm (H1) to 20.1 cm (H3). The 1000-seed weight averaged 52 g (Figure 3c). The average number of seeds per head was 1380 (Figure 3d). The seed mass per head varied from 58 g (H8) to 85 g (H7), with a mean of 75 g (Figure 3e).

Sunflowers typically reach a plant height of 150–200 cm [58,72,73,74,75]. There were interesting findings for the hybrids 1 and 3 in the present study (Figure 3a). The hybrid 1 was the highest in the plant height (208 cm) and at the same time had the smallest head diameter (17.7 cm) among all hybrids. The opposite interaction was determined for the hybrid 3, which had the smallest stems (169 cm on average), but developed the largest heads with an average diameter of 20.1 cm (Figure 3b). Similarly, Kluza-Wieloch [73] states that the large plant height of sunflowers does not have a positive effect on their agronomic value. The author found that the shortest sunflower stems had the Wielkopolski genotype (124.2 cm), while the Coril genotype reached the greatest height at most growth stages (169.3 cm), followed by Frankasol (182.4 cm). In Uzbekistan, Ergasheva [76] found that the number of plants per unit area greatly affected the plant height during the growth stages. At a density of 80,000 plants ha⁻¹, plant height ranged from 213.6 cm (Buzuluk genotype) to 234.3 cm (Rodnik genotype). In China, Li et al. [77] found that sunflower plant height increased with higher irrigation levels. In 2016 and 2017, the tallest plants reached 221.2 cm and 220.2 cm, respectively, while head diameter ranged from 16 to 20 cm. Furthermore, plant height is highly influenced by climatic and soil conditions. Drought or nutrient-poor soils can significantly reduce plant height, whereas irrigation and reduced water stress have a markedly positive effect on its growth. Handayati and Sihombing [78] stated that increasing NPK doses positively influenced the head diameter, dry head weight, total seed per head, seed weight per head, and 100-seed mass. Authors found the highest seed number and seed mass per sunflower head in the treatment with NPK 150-75-75 kg ha⁻¹, demonstrating the beneficial effect of balanced fertilisation on sunflower yield. Hussain et al. [79] stated that nutrients as S and Zn are also essential for sunflower yield components (head diameter, number of seeds per head, and 1000 seed mass), which were strongly affected by the nutrients applied (S and Zn) and genotype of their 2-year study. According to Hladni et al. [80], the number of genes involved in the expression of a specific trait and the interrelationships among morphophysiological traits in sunflower breeding programmes are crucial. Specifically, head diameter and plant height are among the most important parameters for selecting genotypes. Authors stated that the sunflower plant height exhibited a mode of inheritance characterised by superdominance of the superior parent, while both dominance and superdominance of the better parent influenced head diameter. Ramos et al. [81] reported that due to reduced stature, dwarf sunflowers are less prone to lodging under adverse weather conditions, which can enhance yield stability. Moreover, their compact growth habit allows for optimised use of resources and may improve light interception efficiency in densely planted fields. Based on the research of Angadi and Entz [82], the factors responsible for reduced plant height in dwarf sunflower cultivars have not been fully elucidated, and their impact on root architecture and water uptake dynamics remains insufficiently understood.

Chen et al. [83] reported that drought-induced reductions in leaf-level photosynthesis, through altered gas exchange and chlorophyll fluorescence, ultimately affected seed yield components, including seed number. Based on the principal component analysis of 21 sunflower black-hulled seed hybrids, Kanwal et al. [84] reported that quality parameters, such as oil and protein content, as well as linoleic acid content, and morphometric parameters, such as plant height, head diameter, achene length, thickness and weight, contribute about 35% to the seed yield.

3.4. Correlation Analysis of Sunflower Photosynthetic Activity and Yield Components

To investigate the relationship between photosynthetic activity and sunflower yield components, a Pearson correlation analysis and a heatmap were conducted (Table S1 and Figure 4). The analysis revealed several key associations between photosynthetic parameters and agronomic traits.

The strongest and highly significant positive correlation was found between plant height (PH) and PI_TOTAL (r = 0.809), indicating that higher overall photosynthetic performance is associated with taller plants. Other positive correlations were also observed with RE₀/ABS (r = 0.718) and ET₀/(TR₀ − ET₀) (r = 0.679). On the contrary, negative correlations were recorded between PH and ABS/RC (r = −0.665) and TR₀/RC (r = −0.735). Furthermore, head diameter (HD) was the only significant correlation found with ET₀/RC (r = 0.751), suggesting that efficient electron transport correlates with larger HD. Other correlations of sunflower head diameter with photosynthetic parameters were weak and not statistically significant.

The sunflower 1000-seed weight (TSW) showed no statistically significant correlations with any photosynthetic parameters, even though a mild (yet still non-significant) correlation was observed with ΔR₀ (r = 0.288).

On the other hand, the number of seeds per sunflower head (NHS) has multiple significant negative correlations with ΔR₀ (r = −0.773), ABS/CS₀ (r = −0.695), TR₀/CS₀ (r = −0.701), RE₀/CS₀ (r = −0.746), and ΔR₀/(1 − ΔR₀) (r = −0.780). These negative relationships suggest that increased energy flux or photochemical efficiency may coincide with fewer seeds per head. In the current study, additional attention was given to the chlorophyll content.

The chlorophyll content shows generally weak correlations with most photosynthetic and agronomic traits (Table S2, Figure 4), with none reaching strong statistical significance. However, moderate positive trends were detected for ET₀/ABS (r = 0.269) and ET₀/TR₀ (r = 0.239), suggesting that higher chlorophyll content might be associated with increased energy use efficiency in photosynthesis. A more notable positive correlation between the chlorophyll content and ABS/CS₀ (r = 0.451), DI₀/CS₀ (r = 0.263), and RE₀/CS₀ (r = 0.542), which points to a possible association between chlorophyll content and light absorption as well as energy re-emission at the chlorophyll fluorescence level. Still, these correlations do not reach high significance.

Leaf temperature also shows moderate correlations with other parameters. It correlates positively with DI₀/CS₀ (r = 0.718). In contrast, leaf temperature is negatively correlated with TR₀/ABS (r = −0.754) and TR₀/DI₀ (r = −0.759). Those results suggest that higher leaf temperature may be associated with reduced efficiency of energy transfer or electron transport during photosynthesis.

These findings are consistent with previous studies that emphasise the role of plant height, head diameter, and seed number as key yield-determining traits in sunflower. For instance, Göksoy and Turan [85] found strong positive correlations between seed yield and the number of seeds per head (r = 0.890), 1000-seed mass and seed yield, and sunflower head diameter. Rani et al. [86] examined 90 sunflower genotypes for correlation and path analysis of yield components. They found that 100-seed weight and head diameter had the most significant direct effects on seed yield per plant. The authors also emphasised that plant height and oil content showed positive direct influences, making them important selection criteria in sunflower breeding programmes. According to Sri et al. [87], among 22 hybrids, seed yield was significantly and positively correlated with plant height, head diameter, test weight, and volume weight. In contrast, path analysis confirmed that the plant height, head diameter, and test weight had the strongest direct contributions to the sunflower seed yield. Bonciu et al. [88] found that the key morphological traits, such as plant height, number of leaves, and head diameter of sunflower plants, are closely associated with productivity traits like seed number and weight. Authors found that plant height shows a very strong positive correlation with the number of leaves per plant and a moderate positive correlation with the head diameter and 1000-seed weight. Among six morphological traits, Hladni et al. [89] found that head diameter had the strongest negative direct effect on seed yield per plant in sunflower across two environments but still showed a significant positive correlation with yield. Plant height and total leaf area exhibited positive direct effects and were positively correlated with yield in both environments, suggesting that these traits are key for improving yield potential. Varalakshmi et al. [71] found that sunflower seed shows strong and significant positive correlations with several traits at both the genotypic and phenotypic levels (head diameter, seed weight, the number of seeds per head, plant height, the number of leaves per plant). For the SPAD index, the authors stated that SPAD values reflect chlorophyll content and indirectly photosynthetic capacity. They reported a moderate positive genotypic correlation between SPAD and plant height, suggesting that plants with higher chlorophyll content tend to grow taller, possibly due to better photosynthetic efficiency. SPAD values were also positively correlated with seed yield, although this correlation was not statistically significant. These findings support the results of this study, indicating that plant height and the number of seeds per head are the most important morphological traits.

3.5. Principal Component Analysis (PCA) of Sunflower Photosynthetic Activity and Yield Components

Principal Components Analysis (PCA) was performed to reduce the dimensionality of the dataset and to identify the main components explaining the variation in photosynthetic parameters and yield traits of sunflower hybrids (Table S3 and Figure 5). The PCA identified four principal components with eigenvalues greater than 1, explaining a total of 74.93% of the variance in the dataset (Table S3). The first two components (PC1 and PC2) accounted for 54.38% of the variance and were selected for two-dimensional interpretation and visualisation of genotypic differences.

The first principal component (PC1) was strongly associated with variables indicative of photosynthetic efficiency and production potential, such as DI₀/CS₀, ABS/CS₀, R₀, RE₀/RC, and TR₀/CS₀. These ChlF parameters reflect the entire sampled leaf surface’s ability to absorb and utilise light energy effectively in photosynthesis. In contrast, variables such as TR₀/ABS, TR₀/DI₀, and PI_ABS showed strong negative loadings, suggesting lower energy conversion efficiency or higher dissipation losses in some hybrids.

The second principal component (PC2) revealed differences among hybrids in terms of energy distribution. TR₀/RC, ABS/RC, and DI₀/RC had positive loadings, which indicates high photosystem II activity. On the other hand, negative loadings for PI_TOTAL, PH, and RE₀/ABS suggest alternative energy flow strategies among hybrids, which may reflect inherent physiological differences rather than stress responses.

The results of this study demonstrate that PI_ABS, ET₀/TR₀, TR₀/ABS, and TR₀/DI₀ are the most informative and sensitive ChlF parameters for discriminating genotypic variation at the flowering stage. These parameters clustered in the same quadrant of the PCA biplot, indicating a strong intercorrelation. This shows that they accurately reflect the functional status and photochemical efficiency of PSII under the tested conditions. Their targeted application during the flowering stage phenotyping could enable a rapid, non-destructive and cost-effective assessment of photosynthetic performance, thus supporting the identification of superior genotypes before yield results are available. Incorporating these parameters into flowering stage screening protocols has the potential to increase selection accuracy, reduce the number of genotypes that will contribute to later stages of breeding, and ultimately improve the efficiency and genetic gain of sunflower breeding programmes.

The biplot (PC1 vs. PC2) further confirmed distinct clustering of hybrids based on dominant trait expression (Figure 5). Hybrids 3 and 6 were positioned in the upper right quadrant, associated with variables indicating high photosynthetic activity and efficiency, making them potential breeding material for high productivity. In contrast, hybrids 1 and 2 were located in the lower left quadrant, along with traits reflecting lower efficiency in energy transfer and absorption, suggesting a less competitive physiological profile.

Hybrids 4, 5, 7, 9, and 10 were located near the origin of the coordinate plane, showing balanced physiological profiles without extreme trait expressions, which may be desirable for broad adaptability. Hybrid 8 showed a positive contribution to PC1 but neutral values on PC2, suggesting solid photosynthetic performance with a distinct internal energy distribution pattern.

Principal component analysis has been widely used in sunflowers to characterise genetic diversity and facilitate hybrid screening. Sasikala et al. [90] reported that three principal components explained 71.45% of the total variance, identifying key traits like days to flowering and plant maturity. Zia Ullah et al. [91] found that four components accounted for 69.28% of variability, with 100-achene weight and yield per plant being major contributors. In early seedling stages, Zeinalzadeh-Tabrizi [92] showed that PC1 and PC2 together explained 80.93% of variability, enabling clear grouping of hybrids by seed vigour. These studies demonstrate the efficacy of PCA in sunflower hybrid evaluation. ChlF parameters and morphological traits in sunflower hybrids were previously studied and confirmed with PCA by Markulj Kulundžić et al. [21,39].

3.6. Cluster Analysis of Sunflower Hybrids

Cluster analysis revealed clear divisions of hybrids into groups that largely coincided with the PCA results. Using hierarchical clustering based on Euclidean distances and UPGMA connectivity, three main clusters of sunflower hybrids were identified (Figure 6). The first cluster included H1, H2, H3, H4, H5, H6 and H7. The connectivity of these hybrids indicates a high degree of similarity in their physiological and morphological traits. A second, smaller cluster was formed, containing hybrids H8 and H10, which differed moderately from the first group. In contrast, hybrid H9 positioned itself as a separate branch, indicating its unique physiological characteristics that distinguish it from all other tested hybrids.

This clustering pattern was corroborated by the PCA biplot (Figure 5 and Table S3), where H9 was clearly separated along PC1, driven by its divergent values for RE₀/CS₀, RE₀/ABS, and PI_TOTAL. Similarly, H8 and H10, which clustered together in UPGMA, were positioned close to each other in PCA space, suggesting that they share a comparable energy allocation strategy. The combined use of PCA and cluster analysis provided us with a multivariate framework for distinguishing hybrid differences at the flowering stage of sunflower.

4. Conclusions

This study provides valuable insights into the adaptive responses of sunflower hybrids during the flowering stage, a critical stage for yield formation. The significant variation observed among hybrids in photosynthetic efficiency, energy dissipation, and leaf-level physiological traits suggests that hybrid-specific mechanisms underlie light energy utilisation and thermal regulation under field conditions.

Photosynthetic efficiency parameters, TR₀/ABS and ET₀/TR₀, remained largely stable across hybrids, while parameters ABS/RC, DI₀/RC, and RE₀/RC, effectively differentiated hybrids based on their photochemical performance.

Differences in chlorophyll content and leaf temperature further supported the physiological divergence among hybrids. Those two parameters were found to be moderately associated with photosynthetic performance, suggesting their potential as indirect indicators of physiological status.

Yield component traits (plant height, head diameter, and number of seeds per head) also varied significantly. Several of them correlated strongly with chlorophyll fluorescence parameters, especially PI_TOTAL and RE₀/ABS. These findings support the role of photosynthetic efficiency as a predictor of agronomic performance.

Principal components analysis and cluster analysis confirmed the differentiation among sunflower hybrids, which enabled the classification of hybrids with superior or balanced photosynthetic and yield characteristics. Hybrids 3 and 6 proved to be physiologically superior, while hybrids 1 and 2 showed lower photosynthetic and agronomic performance.

Overall, it can be concluded that the integration of ChlF parameters, chlorophyll content, leaf temperature, and yield components offers a robust framework for evaluating hybrid adaptation under variable environmental conditions. Within the sunflower breeding programme, this approach will improve the efficiency of selecting materials aimed at improving photosynthetic function and yield stability.