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Article

How Do Drought, Heat Stress, and Their Combination Impact Stem Reserve Mobilization in Wheat Genotypes?

by
Behrouz Vaezi
1,2,
Ahmad Arzani
2,* and
Thomas H. Roberts
3,4,*
1
Kohgiluyeh and Boyerahmad Agricultural and Natural Resources Research and Education Center, Dryland Agricultural Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Gachsaran 7589172050, Iran
2
Department of Agronomy and Plant Breeding, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran
3
School of Life and Environmental Sciences, Faculty of Science, University of Sydney, Camperdown, NSW 2006, Australia
4
Sydney Institute of Agriculture, University of Sydney, Eveleigh, NSW 2015, Australia
*
Authors to whom correspondence should be addressed.
Agronomy 2024, 14(8), 1867; https://doi.org/10.3390/agronomy14081867
Submission received: 17 July 2024 / Revised: 13 August 2024 / Accepted: 19 August 2024 / Published: 22 August 2024
(This article belongs to the Special Issue Crop Biology and Breeding under Environmental Stress)
Browse Figures
Versions Notes

Abstract

:
Drought and heat stresses represent the primary agricultural challenges in arid and semiarid regions globally. In wheat, among the most vulnerable stages to these stresses is the grain-filling process. This critical phase relies heavily on photosynthesis during the late growth stage and the mobilization of stem reserves. This study evaluated 60 spring wheat lines from the CIMMYT-Mexico Core Germplasm (CIMCOG) panel alongside four Iranian wheat cultivars under normal, drought, heat, and combined drought and heat stress conditions in two growing seasons. Several agronomic traits, including those associated with stem reserve mobilization, were assessed during the study. The combined analysis of variance revealed significant impacts of both independent and combined drought and heat stresses on the measured traits. Moreover, these stresses influenced the inter-relationships among the traits. High-yielding genotypes were identified through a combination of ranking and genotype and genotype by environment (GGE) biplot analysis. Among the top 40 genotypes, 21 were identified as environment-specific, while 19 remained common across at least two environments. Environmental dependence of grain yield responses to the sinks including stem reserve mobilization and spike reserve mobilization was found. Utilizing a machine learning algorithm, a regression tree analysis unveiled specific traits—including grain filling and canopy temperature—that contributed significantly to the high-yielding features of the identified genotypes under the various environmental conditions. These traits can serve as indirect selection criteria for enhancing yield under stressful conditions and can also be targeted for manipulation to improve wheat stress tolerance.

1. Introduction

Drought and heat stress frequently occur alone or in combination in many regions worldwide, greatly limiting the yield of small-grain cereals [1,2,3]. At the terminal growth stages, the combined stresses of drought and heat pose the most significant limitations in wheat production in arid and semi-arid countries [4,5]. Moreover, with the anticipated effects of global warming, these challenges are expected to exacerbate [3,6]. Therefore, the primary objective of wheat breeding in these regions is the development of cultivars that allocate a greater proportion of daily assimilates to grain production [7].
While both drought and heat stresses have been extensively studied individually, there has been little focus on their combined effects on grain yield in wheat [3,4,5,8,9]. Despite sharing some common plant responses to drought and heat stresses, each stress factor may lead to responses that are antagonistic to one another [10]. For example, when plants are exposed to heat stress, they often open their stomata to increase transpiration and consequently decrease leaf temperature. If drought stress is combined with heat stress, however, plants tend to keep their stomata closed to reduce water loss, which can result in higher leaf temperatures [11]. The combined effects of drought and heat stresses are of particular importance, especially considering their frequent occurrence during the terminal growth stages in wheat [1,12]. Research has shown that individual drought and combined drought and heat have a more pronounced impact on grain yield and quality compared to independent heat stress [1,13,14].
The reproductive growth stages (Zadoks scale) of wheat (Triticum aestivum L.) include booting (GS 4), heading (GS 5), flowering or anthesis (GS 6), milk development (GS 7), dough development (GS 8), and physiological maturity (GS 9), which are strongly impacted by drought and heat stresses [15]. Among these stages, the grain-filling period (R4 and R5) is crucial, as the post-anthesis duration of photosynthesis is a key determinant of grain yield [16]. Drought and heat stresses often result in a reduced rate and duration of grain-filling, leading to a decrease in total light interception due to the shortened life cycle. This can ultimately cause significant losses in grain weight [17]. This grain weight reduction and subsequent yield losses mainly arise from a reduction in both current photosynthesis and the availability of stored carbohydrates in the stem [18]. Drought and heat stresses substantially impair leaf photosynthetic capacity, thereby reducing the amount of available assimilates transported to the grain [19,20]. Therefore, under stress conditions, plants often store less stem reserves due to reduced photosynthesis. Since stem-stored carbohydrates play a significant role in contributing to grain yield, this reduction can have a notable impact on overall productivity [21,22].
Several studies support the impact of heat and drought stress on the mobilization of stem reserves in wheat genotypes. Research shows that during stress conditions, especially in the grain-filling stage, the use of stem reserves becomes crucial for maintaining grain yield [22]. Heat stress can cause premature degradation of stem reserves, leading to a decrease in carbohydrate supply to developing grains and ultimately reducing grain filling [23]. Additionally, drought stress has been linked to reduced photosynthetic activity, which triggers the mobilization of stem carbohydrates as a compensatory mechanism to support grain development [24]. There is genetic variability among wheat genotypes in their ability to effectively mobilize stem reserves under stress, with some genotypes maintaining higher reserves and improving yield stability [25]. Studies suggest that the timing and severity of stress significantly impact the extent of stem reserve mobilization, with severe drought hindering the ability to utilize reserves [24]. Furthermore, combined heat and drought stresses can worsen challenges for wheat plants, as the stresses work together to decrease photosynthetic efficiency and stem reserve mobilization. This emphasizes the importance of selecting genotypes that can withstand multiple abiotic stresses [23].
Integrating traits related to stem reserve mobilization into breeding programs could greatly enhance the resilience of wheat varieties in the face of changing climatic conditions [25,26]. lsamadany et al. (2023) evaluated the impact of drought and heat stress on a range of physiological and biochemical parameters in various wheat genotypes. They highlighted how these stresses can significantly reduce chlorophyll content and photosynthesis, ultimately affecting agronomic traits like grain weight and yield. Their results showed a correlation between stress conditions and reduced crop productivity [27]. Statkeviciute et al. (2022) found that combined heat and drought stress significantly decreased yield and quality components. Their research indicates that these stresses negatively impact photosynthesis and grain filling, which is essential for sustaining grain weight and overall yield [28]. Mahdavi et al. (2021) examined 64 wheat genotypes in the final growth stage that were exposed to two field temperatures caused by delayed planting in two consecutive growing seasons. They found that heat stress accelerates reproductive phases, shortens grain filling time, and ultimately leads to a decrease in the 1000-grain weight (TGW) and grain yield [29].
Little is known about the effects of combined drought and heat stress on the grain-filling period and stem reserve accumulation and mobilization in wheat. In addition, previous research often focused on a limited number of wheat cultivars or lines, which may not fully represent the genetic variation for drought and heat tolerance within wheat [30]. Studying the response of wheat to heat and drought stress conditions independently and in combination holds great promise for uncovering key principles of stress tolerance and enhancing yield stability. By elucidating how wheat responds to these stresses individually and in concert, we can gain valuable insights into strategies for improving stress tolerance and ensuring consistent crop yields.
In this study, we conducted field trials with four treatments: normal (control) conditions, drought, heat, and combined drought and heat using 64 diverse wheat genotypes over two growing seasons to meet the following objectives: (i) to evaluate the independent and combined effects of drought and high temperature stresses on agronomic, physiological, and phenological traits; (ii) to quantify the interrelationships among the studied traits, with a focus on grain yield and stem reserve mobilization; (iii) to identify suitable traits as selection criteria for identifying high-yielding wheat genotypes; (iv) to estimate genetic variation, heritability, and genotype × environment interactions (GEI); and (v) to model the major components underlying high-yielding wheat genotypes.

2. Materials and Methods

2.1. Plant Materials and Field Trials

Sixty spring wheat lines from the CIMMYT-Mexico Core germplasm (CIMCOG) panel (Supplementary Table S1) and four Iranian wheat cultivars developed by the Dry-land Agricultural Research Institute of Iran, namely ‘Kohdasht’, ‘Zagros’, ‘Dehdasht’, and ‘Karim’ (genotype no. 61–64) were used in the field experiments. ‘Kohdasht,’ ‘Zagros,’ and ‘Dehdasht’ originated from CIMMYT, while ‘Karim’ is a locally developed cultivar. The CIMCOG panel, established in 2010 in consultation with wheat breeders and physiologists, comprises 60 elite wheat lines (59 T. aestivum and one T. turgidum ssp. durum). ‘CIRNO C2008’ is the only durum wheat cultivar included in the CIMCOG panel. These diverse lines were primarily selected to represent lines derived from the CIMMYT selections (from 1999 to 2009), including some ‘re-synthesized’ wheat, six older cultivars that represent landmarks in past genetic gains, and one high-yielding durum wheat.
The field experiments were conducted during two growing seasons (Season 1 = 2012–2013 and Season 2 = 2013–2014) in Gachsaran Agricultural Research Station (50°59′ N, 30°18′ E, and 668 m altitude) located in the southwestern warm and dry area of Iran. The air temperatures and other meteorological variables in each of the two growing seasons studied are presented in Supplementary Table S2.
The soil parameters were determined to be silty clay loam, with a pH of 7.0, organic matter content of less than 1%, and an average long-term rainfall of 434 mm. For each growing season, 64 wheat cultivars were planted across four experiments. A simple lattice design (8 × 8) with two replicates was employed for each experiment. Each experimental plot consisted of six rows, each 4.37 m in length, with a row spacing of 20 cm. The two experiments comprised normal (N1 and N2), terminal drought (D1 and D2), heat (H1 and H2) stress conditions, and combined terminal drought and heat (DH1 and DH2) stress. For the normal conditions and drought stress treatments, seeds were sown on standard planting dates of 5 December 2012 and 4 December 2013, respectively, for the first and second growing seasons.
For the terminal drought treatment, irrigation ceased when all plants reached the late heading stage (GS 59). For the H1 and H2 experiments, delayed sowing dates of 26 January 2013 and 25 January 2014, respectively, were employed for the first and second seasons. DH1 and DH2 involved delaying the planting date and applying the drought stress as implemented in the heat and drought experiments, respectively. In the N1, N2, H1, and H2 experiments, plants were regularly irrigated based on 50% management-allowed depletion (MAD) until the end of the experiments.

2.2. Agro-Physiological Traits

Various field measurements were taken at different times throughout the experiments. To calculate the grain-filling period (GFP), the number of days to maturity was subtracted from the number of days to anthesis. The number of days from the sowing date until half of the plants had at least one extruded anther was recorded as the days to anthesis. The days to physiological maturity stage (GS93), also known as “days to maturity”, was defined as the number of days from the sowing date to the appearance of peduncle senescence in the entire plot. Plant height (PLH) was recorded at GS93 on 10 plants. The mean weight of three samples, each comprising 1000 randomly selected grains from every plot, was utilized to calculate the TGW. At GS93, the spikes located in the center of each plot, four rows excluding a 0.5-m border from the edges, were harvested to measure grain yield (GY). To assess the contribution of stem reserves to wheat grain yield in all experiments, 50 main tillers in each plot were color-tagged as spikes emerged. The main tillers were harvested at random from the soil surface when genotypes reached anthesis and at 10-day intervals after anthesis until maturity. Following each harvest, leaf blades were removed and the main tillers were divided into stem and spike. The samples were dried in an oven at 60 °C for 72 h and their dry weights were recorded. Finally, the magnitude of stem reserve mobilization (SRM) (g), the contribution of stem reserve mobilization to grain yield (CSRM) (%), spike reserve mobilization (PRM) (g), the contribution of spike reserve mobilization to grain yield (CPRM) (%), current photosynthesis (CP) (%), and contribution of current photosynthesis to grain yield (CCP) (%) were calculated as follows:
SRM = weight of stem at anthesis − weight of stem at maturity
CSRM = (SRM/grain yield) × 100
PRM = weight of spike after anthesis − weight of spike at maturity
CPRM = (PRM/grain yield) × 100
CP = grain yield − SRM
CCP = (CP/grain yield) × 100
Although both current photosynthesis (CP) and pre-anthesis assimilate contribute to grain yield, current photosynthesis is the primary source of carbohydrates for wheat grain development [31]. Notably, different environmental conditions result in varying levels of impairment in CP; our method is a sink-based approximation that estimates the trend of CP alteration. Canopy temperature (CT) (°C) was measured three times before flowering (GS18 to GS57) and three times after flowering (at anthesis and during the grain-filling period) using a portable infrared thermometer (Sixth Sense LT300 infrared thermometer, TTI instruments, Williston, VT, USA).

2.3. Statistical Analysis

A combined analysis of variance (ANOVA) was performed using the PROC GLM procedure of SAS software 9.3 (SAS Institute, Cary, NC, USA). Subsequently, Fisher’s LSD mean comparisons were conducted using SAS software 9.3 [32,33]. Broad-sense heritability for each trait in each environment was estimated, partitioning phenotypic variation into genetic and environmental components. The genetic variance was estimated by [(MSG − MSE)/r] where MSG represents the genotype’s mean square, MSE denotes the experimental error’s mean square, and (r) is the number of replications.
Principal component analysis was carried out using PROC FACTOR (method = prin) with Kaiser’s criterion, i.e., Eigenvalue ≥ 1.0 in SAS 9.3. A GGE biplot was generated based on the genotype-by-environment table of yield using GGE biplot software version 4.1 [34]. Rank-based analysis of the top ten high-yielding genotypes in each experiment was performed using STATISTICA 10.0.10 software (StatSoft Inc., Round Rock, TX, USA). To assign the major yield components in the identified high-yielding genotypes, the genotypes were classified based on all measured traits using regression trees with the Chi-square automatic interaction detection (CHAID) algorithm [35] in STATISTICA 10.0.10 software.

3. Results

3.1. Effects of Single and Combined Drought and Heat Stresses

The results of ANOVA showed that single drought and heat stress, as well as their combination, significantly affected all traits in the wheat genotypes (Table 1, Table 2 and Table 3). Wheat genotypes exhibited significant variation in the measured traits. Interaction effects were also statistically significant for several of the studied traits.
Mean comparisons revealed that GY declined significantly when plants were exposed to stress conditions. The highest GY (3.91 kg m2) was achieved in the N2 trial and the lowest (1.83 kg m2) in DH2 (Table 4 and Supplementary Tables S3 and S4). Overall, under delayed cultivation conditions (heat and combined stress trials), the wheat plants matured about 31 days earlier than those sown at the normal date (control and drought trials). Moreover, drought and combined stresses led to about a 4 and 3% decrease in GFP, respectively, compared to the controls. However, there was no significant difference in GFP between the control and heat-stress conditions. Plant height (PLH) was decreased by about 3.8, 16.8, and 16.5 cm in drought, heat, and combined stress, respectively.
Canopy temperature before (CT(B)) and after (CT(A)) anthesis exhibited slight variations among the trials on average (Supplementary Table S3). Spike reserve mobilization (PRM) showed a significant reduction in the stress treatments. Likewise, stem reserve mobilization (SRM) decreased, with a more pronounced reduction in heat and combined heat and drought conditions. The maximum magnitude of PRM (3.79 t ha−1) was recorded in N2, while the minimum (2.37 t ha−1) was observed in D2.
N1 exhibited the highest magnitude of PRM (7.76), whereas DH2 had the lowest (4.76) (Table 1). The highest CPRM (18.03%) was observed in the DH1 trial, while the lowest (9.66%) was recorded in the N2 trial. Unlike CPRM, CSRM only showed a significant increase under drought conditions (approximately 17% compared to controls). Coordinated with SRM, CSRM significantly decreased by about 28% and 44% under the heat and combined stresses, respectively. Furthermore, CP and CCP significantly decreased under drought stress, while the values for these parameters increased in the heat and combined stress conditions (Table 4).

3.2. Heritability and Variance Components

A varied range of heritability was observed for each trait across the trials (Table 4). High heritability estimates (0.73–0.93) were observed for GFP, PLH, and TGW in all environments. GY, SRM, and RWC showed moderate heritability estimates: 0.42, 0.56, and 0.43, respectively. Low heritability was estimated for the remaining traits.
The genotypic variance component (VG), represented in Table 4, was significant for GY, GFP, PLH, TGW, and SRM. Additionally, no significant VG values were observed for CT(A) and CT(B).

3.3. Trait Association Analysis

As shown in Figure 1, the first two PCs explained approximately 51.4% and 55.26% of the total variation under N1 and N2 trials, respectively. The association of traits in each trial is illustrated in the PCA loading plot. Angles among vectors represent the magnitude and direction of corresponding correlations. Acute angles (<90°) represent a positive correlation, while wide obtuse angles (>90°) show a negative correlation. The cosine of angles determines the magnitude of the correlation. Additionally, the length of the vectors shows the extent of variability.
The results indicated that GFP was positively correlated with GY in both years. A negative relationship was observed between CT(A) and CT(B) with GY in both years (Figure 1). There was a positive correlation among CPRM, SRM, and PRM. The biplots clearly show a strong positive association between SRM and TGW under drought-stress conditions. In addition, PRM, CPRM, and GY are strongly correlated in both normal and drought stress conditions. Notably, SRM, PRM, CPRM, and CSRM are strongly associated with GY under heat stress and combined heat and drought stress conditions. In all seasons and growth conditions, a strong association was observed between CCP and GY, except in the second season under normal conditions.
Around 50.1% and 47.7% of the total variation was explained by the first two PCs under the D1 and D2 trials, respectively (Figure 2). The angles between GY, GFP, and TGW were less than 90°. In contrast, angles larger than 90° were observed between CT(A) and CT(B) vectors and GY in the D1 and D2 trials. The cosine of the angle between the vectors of any two traits approximates the correlation coefficient. Therefore, traits that were positively and significantly correlated were co-located on the PCA plot. For example, in the first season (D1), TGW, GFP, and GY co-located on the PCA plot, while in the second season (D2), CCP and GY co-located.
In the heat trials, 55.7% and 50.4% of the total variation was explained by the first two PCs under the H1 and H2 trials, respectively. GY was positively associated with GFP and PLH in both years (Figure 1). In contrast, there was a negative correlation between GY and CT(A) and CT(B) in heat stress conditions.
In the combined stress conditions, 59.3% and 57.0% of the total variation was explained by the first two PCs in Season 1 and Season 2, respectively (Figure 1). A positive association was observed between the GY vector and PLH vectors across the two years. However, no association was observed between GFP and GY in DH2.

3.4. Identification of High-Performance Genotypes

Genotypes were ranked based on the average yield in each environment over two years. The list of the top 10 high-yielding genotypes in each environment is shown in Table 5. Of the 40 assigned genotypes, 21 were specific to particular environments, while the remaining 19 genotypes were common across at least two environments (Table 5). A GGE biplot based on 2-year means and the distribution of identified high-yielding genotypes in each environment are shown in Figure 2. No considerable correlation existed between the GY of the control and stress trials. In contrast, a negative and high correlation was observed between drought stress and heat stress as well as drought stress and combined stress. A positive and moderately high correlation was also observed between heat and combined stress. The genotype × trait biplot indicated that genotype 46 was the only common high-yielding genotype among all environments. Moreover, genotypes 3, 5, 6, 22, 28, 36, 37, 46, 48, and 59 were specific to the normal environment. Genotypes 1, 3, 6, 8, 33, 34, 46, 49, 59, and 64 were also located in drought stress areas. Genotypes 6, 22, 24, 31, 39, 46, 54, 55, 62, and 63 were identified as heat stress-specific high-yielding genotypes. Additionally, genotypes 22, 31, 32, 38, 46, 47, 48, 50, 52, and 54 were specific to the combined stress environment.

3.5. Modeling of the Performance of High-Yielding Genotypes

To identify the 10 top genotypes in each environment based on the most important components underlying high grain yield, a regression tree of the two-year mean GY was constructed using a machine learning algorithm (Figure 3). In the control conditions, seven nodes were formed. PRM, CCP, and TGW were the most important traits in the 10 top genotypes (Figure 3A). For each of these traits, 7, 6, and 5 out of 10 superior genotypes contributed significantly to grain yield, respectively.
Under favorable conditions, reserve mobilization in wheat could potentially contribute to around 20% of grain dry weight [36]. Photosynthesis, especially in spikes, has a significant contribution to grain yield in wheat [37]. In drought conditions, five nodes were formed. Most of the high-yielding genotypes in drought conditions exhibited shorter GFP and lower PLH (Figure 3B). For each of the traits, 6 and 5 out of 10 genotypes contributed significantly to grain yield, respectively.
Reduction in phenological stages such as days to maturity and the grain-filling period allows for drought tolerance; hence, it is considered important when breeding for terminal drought stress tolerance [38]. In heat stress conditions, a total of seven nodes were formed and most of the high-yielding genotypes showed lower CT(A), CT(B), and GFP (Figure 3C). For CT(A), CT(B), and GFP in this environment, eight, six, and five out of 10 genotypes contributed significantly to grain yield, respectively [38,39,40].

4. Discussion

This study demonstrated that drought, heat, and their combination significantly affected the measured agro-physiological traits in wheat. Grain yield decreased under all stress conditions, which may be attributed to impaired plant growth due to the disruption of metabolic and physiological functions. These include reduced absorption of water and nutrients, membrane dysfunction, and impairments to respiration, protein synthesis, and photosynthesis [41]. The severity of the effects on the wheat plants increased dramatically when drought and heat stresses were combined. It appears that a negative additional interaction between drought and heat stresses results in greater yield reduction [5,7].
Exposure to heat and combined stresses led to a disruption in phenological traits. Specifically, the length of the GFP was significantly shortened in the combined stress. Mahdavi et al. [29] used the same set of wheat germplasm as studied here to investigate responses to heat stress, observing slightly greater effects on GFP. Previous studies revealed that delayed sowing, resulting in high temperature during the reproductive and grain-filling stages, leads to shortened anthesis and maturity periods in wheat, aligning with our observation. Tahmasebi et al. (2014) reported a decrease of 18–20 days for maturity (DMA) in a recombinant inbred line (RIL) population under combined drought and heat stress [5]. Similarly, Pradhan et al. (2012) observed a reduction of 18–20 days in DMA under combined stress conditions [15]. Alteration in phenology is a significant factor contributing to yield reduction due to the diminished duration of light interception and plant photosynthesis [17]. Therefore, it can be inferred that both heat and drought, but especially their combination, diminish the yield capacity of genotypes by constraining their life cycle. Shortening the life cycle is one of the likely explanations for the observed reduction in grain weight in all the genotypes. Despite previous studies demonstrating that grain weight is more sensitive to drought than heat [5,15], the current study did not demonstrate a significant impact of these stresses.
Our study yielded complex and stress-specific results regarding grain-filling components. While we demonstrated a decrease in CP under drought stress, we observed elevated CP in response to heat and combined stress. Several studies revealed that CP and CCP are reduced under drought conditions due to concurrent demands of grain growth during grain filling and respiration, alongside a decrease in the photosynthetic capacity of the leaves [42,43]. How plants kept their photosynthetic capacity under heat and combined stress in this study may be surmised from the results for CP and CCP [44,45,46]. Ram et al. [40] illustrated that under drought and high-temperature stresses, there is earlier mobilization of non-structural reserve carbohydrates from the stem and leaf sheaths, providing a greater proportion of assimilates to the grain. Additionally, Golabadi et al. (2015) found that different environmental conditions, including drought stress, affected dry matter remobilization from various plant organs, such as the spike, stem, peduncle, and leaf sheath, as well as the current photosynthesis [47]. Furthermore, Morgun et al. (2019) showed that under drought conditions, wheat varieties exhibited decreased chlorophyll content, net CO2 assimilation rate, and transpiration rate in the flag leaf, while the activity of antioxidant enzymes increased [48]. In summary, these studies suggest that the maintenance of photosynthetic capacity under heat and combined stress may be attributed to the remobilization of stored reserves from the stems and sheaths, the regulation of antioxidant enzyme activity, and the contribution of ear photosynthesis, which can help support grain filling under stress conditions [47,48].
While no significant changes occurred in SRM during drought, its levels decreased under heat and the combined stresses. CSRM experienced a significant increase under drought stress but a significant decline under heat and combined stresses. The reduction in wheat SRM in response to drought stress has also been noted by Ehdaie et al. (2008) [42]. Conversely, some studies suggest that SRM capacity supports grain filling during heat stress [49]. These results underscore the variability in plant responses to different types of stress, often influenced by other environmental factors.
In cereal-growing regions, end-of-season drought, heat stress, and their combination pose a great concern, as they can lead to conditions causing photosynthetic damage and subsequent yield reduction [3,29]. The weak correlation observed in grain yield between control and stress conditions suggests that performance under non-stress conditions may not accurately predict yield under stress. However, the strong and significant correlation between plant performance in heat and combined stress conditions suggests that genotype performance in each condition can be relied upon for selection purposes.
The results of the ranking analysis of genotypes and GGE biplot analysis are in good agreement. According to the GGE biplot, an ideal genotype should exhibit a high PCA1 value and a PCA2 value close to zero [50]. Consequently, G46 emerges as the best genotype for all environments in the current study. The presence of common genotypes across different environments suggests their potential for durable yield under such environmental conditions [51].
The low genetic variation in canopy temperature observed in this study may be attributed to the strong dependency of CT measurements on macro/microenvironmental conditions. Low CT increases stress adaptation through root system function and the ability to absorb water from a deeper soil profile [40]. Both CTs in the vegetative (here CT(B)) and grain-filling period (here CT(A)) have strong associations with grain yield under drought and heat stress conditions [39,40]. Reduction in phenological stages, such as days to maturity and grain-filling period, allows for drought tolerance; hence, it is considered important when breeding for terminal drought stress tolerance [38].
Regression tree analysis has illuminated the primary components contributing to the high performance of the top 10 genotypes in each environmental condition. These components can serve as indirect criteria for yield improvement due to their correlation with yield [52]. In both control and combined stress conditions, the majority of the top genotypes exhibited lower canopy temperatures (represented as CT(A) and CT(B)). This finding aligns with a study on the independent and combined effects of drought and heat in the SeriM82/Babax population, where a lower canopy temperature was associated with higher grain yield [5]. The cooler canopy phenomenon explained over 60% of the yield variations in a wheat population, attributed to its association with dehydration avoidance and enhanced root functionality under drought and heat stresses [40].
While yield is influenced throughout the entire wheat growth cycle, the reproductive stage—particularly the grain-filling period—plays a more critical role, as it is heavily dependent on environmental factors. The findings of this study, as depicted in the relationships of the variables shown in biplots, also indicate that yield is constrained by sink strength during the effective grain-filling period. The prominence of the GFP (grain-filling period) in both normal and drought conditions underscores its potential as a selection criterion in these environments. Furthermore, our results reaffirm earlier findings that GFP serves as a critical turning point in response to individual stresses such as drought [53] and heat [29] in wheat. The strong association of SRM and GW under drought stress conditions underscores the crucial role of stem reserve mobilization in determining grain weight during water stress. PRM and CPRM showed a strong relationship with GY under normal and drought stress conditions, indicating a significant contribution of spike reserve mobilization to grain yield in these environments. Notably, SRM, PRM, CPRM, and CSRM demonstrate a strong correlation with GY under both heat stress and combined heat and drought stress conditions. Numerous studies have demonstrated that a higher-performing and stable photosynthesis system constitutes a major component of yield capacity under stressful conditions [29,54,55]. In general, CCP is depressed by stresses, as noted in a previous study on wheat [42]. Consequently, grain filling becomes more reliant on mobilized stem reserves, which may represent approximately 50% of the average accumulated dry matter in the grain [18,20,49]. The results of the current study reveal that the contribution of stem reserve mobilization to grain yield (CSRM) plays a significant role in ensuring yield stability under both heat stress and combined stress conditions. This finding contradicts that of Gurumurthy et al. (2023), who highlighted the significant role of CSRM only under drought-stress conditions. This contradiction may be justified by differences in genotypes and the severity of stresses between the two studies [22].

5. Conclusions

Our study revealed that combined terminal drought and heat stress had a more severe impact on wheat growth and productivity compared to each stress independently. Furthermore, drought, heat, and the combined stresses affected the inter-relationships among measured traits differently, indicating the presence of genotype–environment interaction and suggesting complex and independent genetic control of drought, heat, and combined drought and heat tolerance. Our regression tree analysis helped decipher the major components contributing to the high-yielding capacity of wheat genotypes. Notably, the grain-filling period and canopy temperatures played important roles in protecting the yield of top wheat genotypes in stress environments. The environmental dependence of grain yield responses to the sinks (SRM and PRM) was another notable finding of this study.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy14081867/s1, Table S1. Origin and pedigree of CIMCOG wheat genotypes used in this study; Table S2. Overall means of the examined traits in four environmental conditions in (a) 2012–2013 and (b) 2013–2014 growing seasons; Table S3. The average grain yield of wheat genotypes in eight environmental conditions across two growing seasons (2011–2014 and 2014–2015); Table S4. Maximum (Max), minimum (Min), and mean values of air temperature (°C), rainfall amount (mm), and relative humidity (%) during growing season in Gachsaran station.

Author Contributions

Conceptualization, B.V. and A.A.; methodology, B.V.; software, B.V.; validation, B.V. and A.A.; formal analysis, B.V.; investigation, B.V.; resources, B.V.; data curation, B.V.; writing—original draft preparation, B.V.; writing—review and editing, A.A. and T.H.R.; visualization, B.V.; supervision, A.A.; project administration, B.V.; funding acquisition, B.V. and A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

All data relevant to the study are included in the article or uploaded as Supplementary Materials.

Acknowledgments

We thank Mostafa Haghpanah for his assistance in data analysis in preparing this manuscript.

Conflicts of Interest

All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

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Agronomy 14 01867 g001
Figure 1. Principal component analysis (PCA) of measured traits in normal, drought, heat, and combined heat and drought trials in two growing seasons. Trait abbreviations are the same as in Table 1.
Figure 1. Principal component analysis (PCA) of measured traits in normal, drought, heat, and combined heat and drought trials in two growing seasons. Trait abbreviations are the same as in Table 1.
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Agronomy 14 01867 g002
Figure 2. GGE biplot of genotype × environment interactions. Normal (N), drought (D), heat (H), and combined (DH) stress. The circles show the ten top genotypes under studied conditions. N: Agronomy 14 01867 i001, D: Agronomy 14 01867 i002, H: Agronomy 14 01867 i003, and DH: Agronomy 14 01867 i004.
Figure 2. GGE biplot of genotype × environment interactions. Normal (N), drought (D), heat (H), and combined (DH) stress. The circles show the ten top genotypes under studied conditions. N: Agronomy 14 01867 i001, D: Agronomy 14 01867 i002, H: Agronomy 14 01867 i003, and DH: Agronomy 14 01867 i004.
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Agronomy 14 01867 g003
Figure 3. Regression tree of yield components of the top 10 genotypes: (A): normal, (B): drought stress, (C): heat stress, and (D): combined heat and drought stress. The ordinal CHAID algorithm was used for analysis. Each rectangle represents its respective branch node. The attribute value interval is shown above the associated node. The node number, the percentage of genotypes located in each branch, and the variance of the corresponding traits are shown inside each node. Trait abbreviations are the same as in Table 1.
Figure 3. Regression tree of yield components of the top 10 genotypes: (A): normal, (B): drought stress, (C): heat stress, and (D): combined heat and drought stress. The ordinal CHAID algorithm was used for analysis. Each rectangle represents its respective branch node. The attribute value interval is shown above the associated node. The node number, the percentage of genotypes located in each branch, and the variance of the corresponding traits are shown inside each node. Trait abbreviations are the same as in Table 1.
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Table 1. Combined analysis of variance for agro-physiological traits of wheat genotypes grown in two environmental (normal and drought) conditions, replicated (R) twice per two conditions and in two growing seasons, and block (BL) eight blocks per two replicated in two conditions and two growing seasons.
Table 1. Combined analysis of variance for agro-physiological traits of wheat genotypes grown in two environmental (normal and drought) conditions, replicated (R) twice per two conditions and in two growing seasons, and block (BL) eight blocks per two replicated in two conditions and two growing seasons.
Source
of
Variation		dfMean Square
GYGFPPLHTGWCT(B)CT(A)PRMCPRMSRMCSRMCPCCP
Year (Y)128.20 **30,550.83 **1368.92 *39.33 ns17.93 *4963.50 **54.47 **125.616.53 ns82.750.04131.55
Environ (E)1128.32 **260.21 *1860.54 **2349.12 **21.92 *934.61 *58.05 *3597.59 **6.04 ns6291.33 **1416.28 **20,847.93 **
Y × E125.61 **229.78 *20.44 ns390.08 ns37.37 *438.28 ns71.55 **104.232.08 ns1029.3379.34238.62
R (Y × E)41.0216.4162.0777.272.0284.140.42.471.893.410.241.09
BL (Y × E × R)560.715.86106.3412.220.457.050.074.510.623.810.47.09
Genotype (G)630.50 ns15.65 *285.23 **53.84 **0.26 ns1.70 ns6.64 ns200.01 ns18.82 **398.64 ns34.27 ns682.99 ns
G × Y630.44 ns6.99 *45.85 **7.20.20 *0.9500.010.002 *0.0100.02
G × E630.225.1927.816.760.132.048.04 **234.41 **9.76 **411.30 **30.60 **722.06 **
G × Y × E630.33 **4.20 **23.727.55 **0.122.29 **00.010.0010.0100.02
Residual1960.152.3820.774.370.110.870.075.010.653.890.547.08
CV (%) 11/44.7456.21.683.029.8116.8810.84.459.466.26
R: replication, BL: block, df: degree of freedom, GY: Grain yield (t ha−1), GFP: Grain-filling period (day), PLH: Plant height (cm), TGW: Thousand-grain weight (g), CT(B): Canopy temperature before anthesis (°C), CT(A): CT after anthesis (°C), PRM: Spike reserve mobilization (t ha−1), CPRM: Percent contribution of stem reserve mobilization to grain yield (%), SRM: Stem reserve mobilization (g per m2), CSRM: Percent contribution of stem reserve mobilization to grain yield (%), CP: Current photosynthesis (g per m2), CCP: Percent contribution of current photosynthesis to grain yield (%). ns non-significant, * Significant at p < 5%, ** Significant at p < 1%.
Table 2. Combined analysis of variance for agro-physiological traits of wheat genotypes (G) grown in two environmental (normal and heat) conditions (E) and in two growing seasons (Y).
Table 2. Combined analysis of variance for agro-physiological traits of wheat genotypes (G) grown in two environmental (normal and heat) conditions (E) and in two growing seasons (Y).
Source
of
Variation		dfMean Square
GYGFPPLHTGWCT(B)CT(A)PRMCPRMSRMCSRMCPCCP
Year (Y)111.24 *10,621.5 **294.364572.07 **5564.99 **3228.61 **315.793.871996.73175.31 *49.07196.96
Environ (E)1220.61 **0.95 ns36,364.3 **2163.18 **89.58 **4333.98 **5239.68 **627.69 **16,695.35 **92.82 ns4514.28 *4981.39 **
Y × E112.98 *3200.0 **3443.0 *1730.19 **4.64 ns1041.93 **13.29 ns0.43 ns12.28 ns20.64 ns9.16 ns7.68 ns
R (Y × E)40.9712.85387.315.142.0910.492.611.30.027.57154.69172.27
BL (Y × E × R)560.747.4189.379.132.382.84.980.61.732.1163.3547.8
Genotype (G)630.52 ns16.02 *230.00 **53.06 **0.82 ns17.32 **157.23 ns13.54 *268.14 ns33.99 ns544.55 ns41.79 ns
G × Y630.38 ns8.55 **28.14 ns9.83 ns0.74 ns2.53 *0 ns0 ns0.01 ns0 ns0.01 ns33.34 ns
G × E630.41 ns5.29 ns26.25 ns8.79 ns0.79 *2.87 *212.94 **8.64 **273.27 **45.60 **733.63 **33.99
G × Y × E630.46 **4.02 ns27.85 ns9.09 **0.50 *1.63 ns0.01 ns0 ns0.01 ns0 ns0.02 ns31.81 ns
Residual1960.153.1623.115.050.351.635.550.622.191.9237.0238.5
CV (%) 12.35.335.6833.782.162.7417.0512.184.2214.1711.748.25
R: replicate, BL: block effect, df: degree of freedom, GY: Grain yield (t ha−1), GFP: Grain-filling period (day), PLH: Plant height (cm), TGW: Thousand-grain weight (g), CT(B): Canopy temperature before anthesis (°C), CT(A): CT after anthesis (°C), PRM: Spike reserve mobilization (t ha−1), CPRM: Percent contribution of stem reserve mobilization to grain yield (%), SRM: Stem reserve mobilization (g per m2), CSRM: Percent contribution of stem reserve mobilization to grain yield (%), CP: Current photosynthesis (g per m2), CCP: Percent contribution of current photosynthesis to grain yield (%). ns non-significant, * Significant at p < 5%, ** Significant at p < 1%.
Table 3. Combined analysis of variance for agro-physiological traits of wheat genotypes (G) grown in combined drought and heat stress conditions (E) and in two growing seasons (Y).
Table 3. Combined analysis of variance for agro-physiological traits of wheat genotypes (G) grown in combined drought and heat stress conditions (E) and in two growing seasons (Y).
Source
of
Variation		dfMean Square
GYGFPPLHTGWCT(B)CT(A)PRMCPRMSRMCSRMCPCCP
Year (Y)112.1816,256.3 **17.815704.46 **60.86109.96 **773.72 ns620.84 ns36.13 ns3.51 ns22.32 ns294.49 ns
Environ (E)1389.96 **156.4 ns34,735.7 **4754.34 **941.0 **752.6 **2935.70 **4870.85 **3323.16 **20,414.6 **383.8 **2950.6 *
Y × E113.99 ns1032.28 **2092.20 *2450.88 **37.6 ns2 ns59.68 ns12.25 ns716.78 ns1545 ns180.1 ns446.8 ns
R (Y × E)42.1833.62238.8978.379.067.410.61.870.980.740.432.94
BL (Y × E × R)561.077.7392.788.122.052.211.514.511.120.570.818.08
Genotype (G)630.64 ns19.44 *223.40 **56.56 **0.42 ns0.66 ns76.97 ns194.91 ns70.49 ns244.15 ns31.26 ns578.40 ns
G × Y630.64 **8.58 ns30.08 ns7.25 ns0.41 **0.64 ns0 ns0.01 ns0 ns0.01 ns0 ns0.02 ns
G × E630.32 **5.9 ns18.74 ns6.41 ns0.51 **0.59 ns60.24 **155.48 **71.05 **274.81 **36.74 **619.51 **
G × Y × E630.12 ns8.72 **22.12 ns9.27 **0.22 ns0.49 ns0 ns0.01 ns0 ns0.01 ns0 ns0.02 ns
Residual1960.152.6818.624.980.370.361.654.950.880.490.668.01
CV (%) 12.75.015.096.812.822.1514.1416.244.092.047.925.52
R: replicate, BL: block effect, df: degree of freedom, GY: Grain yield (t ha−1), GFP: Grain-filling period (day), PLH: Plant height (cm), TGW: Thousand-grain weight (g), CT(B): Canopy temperature before anthesis (°C), CT(A): CT after anthesis (°C), PRM: Spike reserve mobilization (t ha−1), CPRM: Percent contribution of stem reserve mobilization to grain yield (%), SRM: Stem reserve mobilization (g per m2), CSRM: Percent contribution of stem reserve mobilization to grain yield (%), CP: Current photosynthesis (g per m2), CCP: Percent contribution of current photosynthesis to grain yield (%). ns non-significant, * Significant at p < 5%, ** Significant at p < 1%.
Table 4. Mean and heritability of traits under four environmental conditions, with each in two growing seasons.
Table 4. Mean and heritability of traits under four environmental conditions, with each in two growing seasons.
TraitNormal Drought Heat Drought + Heat
Season 1 Season 2 Season 1 Season 2 Season 1 Season 2 Season 1 Season 2
VGMeanh2 VGMeanh2 VGMeanh2 VGMeanh2 VGMeanh2 VGMeanh2 VGMeanh2 VGMeanh2
GY0.203.890.71 0.023.910.14 0.052.440.41 0.153.360.64 0.032.900.12 0.232.280.81 0.322.470.74 0.051.830.52
GFP7.3240.320.83 1.6426.210.61 3.8740.230.76 1.6323.450.66 2.8335.400.50 0.9831.290.46 9.3236.370.83 1.727.940.66
PLH15.9991.240.46 47.6594.910.87 42.5387.830.80 53.690.70.85 34.2879.570.81 14.7772.870.46 23.3778.80.8 26.8974.400.74
TGW4.3436.980.79 9.7834.690.78 6.8730.960.82 8.6732.150.76 12.2136.550.82 2.0826.90.37 11.6135.270.85 4.4824.210.59
CT(B)0.0019.50.02 0.0020.40.01 0.0020.440 0.0620.280.51 0.1815.600.40 0.1524.320.58 0.0014.870 0.0627.760.58
CT(A)0.4623.420.79 0.2230.210.42 0.0025.190 0.0630.550.2 0.0024.450 0.130.860.29 0.0025.720 0.0032.760
PRM2.622.380.98 2.643.790.98 1.52.450.93 1.532.370.97 2.173.470.94 2.173.150.95 2.183.590.96 2.182.660.95
CPRM48.9511.570.91 49.979.660.91 68.7215.960.99 7.1115.890.97 51.1117.630.98 52.1216.380.99 47.9818.030.99 48.9915.520.98
SRM3.057.670.96 3.187.420.97 4.037.580.89 4.117.100.88 2.335.390.83 2.145.260.83 3.585.900.97 3.654.920.97
CSRM84.8238.980.99 86.4342.620.99 147.248.820.98 151.3546.790.98 51.1127.240.97 52.1231.500.97 60.4829.820.99 61.7515.520.92
CP11.239.810.96 11.59.040.97 7.685.960.98 7.746.500.98 9.2511.060.86 9.439.490.86 7.9610.360.97 8.311.960.97
CCP7.37168.990.96 172.248.690.95 242.0934.920.99 247.0537.30.96 146.7555.250.82 149.8354.360.82 175.4251.970.98 178.9355.360.99
VG: genetic variance, h2: broad-sense heritability, GY: Grain yield (t ha−1), GFP: Grain-filling period (day), PLH: Plant height (cm), TGW: Thousand-grain weight (g), CT(B): Canopy temperature before anthesis (°C), CT(A): CT after anthesis (°C), PRM: Spike reserve mobilization (t ha−1), CPRM: Percent Contribution of stem reserve mobilization to grain yield (%), SRM: Stem reserve mobilization (g per m2), CSRM: Percent contribution of stem reserve mobilization to grain yield (%), CP: Current photosynthesis (g per m2), CCP: Percent contribution of current photosynthesis to grain yield (%).
Table 5. The 10 top-yielding wheat genotypes, their grain yields and ranks under normal (N), drought (D), heat (H), and combined heat and drought (DH) conditions.
Table 5. The 10 top-yielding wheat genotypes, their grain yields and ranks under normal (N), drought (D), heat (H), and combined heat and drought (DH) conditions.
GenotypeNormalGenotypeDrought (D)GenotypeHeat (H)GenotypeD and H
Yield *RankYieldRankYieldRankYieldRank
64.9351333.6221633.7371462.9981
34.575283.5472623.4782312.8732
364.520363.5183243.2453542.8533
54.425433.4984313.2124472.834
284.363513.4335463.2005382.8225
464.3506343.4226393.1976322.7526
484.3277643.4187543.1907482.6437
224.2478593.382863.1778502.6408
594.2109463.3689553.1279362.6159
374.18810493.30010223.10310522.61310
* Yield represents grain yield per ha (t ha−1).
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Vaezi, B.; Arzani, A.; Roberts, T.H. How Do Drought, Heat Stress, and Their Combination Impact Stem Reserve Mobilization in Wheat Genotypes? Agronomy 2024, 14, 1867. https://doi.org/10.3390/agronomy14081867

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Vaezi B, Arzani A, Roberts TH. How Do Drought, Heat Stress, and Their Combination Impact Stem Reserve Mobilization in Wheat Genotypes? Agronomy. 2024; 14(8):1867. https://doi.org/10.3390/agronomy14081867

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Vaezi, Behrouz, Ahmad Arzani, and Thomas H. Roberts. 2024. "How Do Drought, Heat Stress, and Their Combination Impact Stem Reserve Mobilization in Wheat Genotypes?" Agronomy 14, no. 8: 1867. https://doi.org/10.3390/agronomy14081867

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