Evolution of water use efficiency, heat tolerance, and carbon isotope discrimination among Canadian spring wheat cultivars

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Chang, Pierre Hucl², and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8281694/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate projections predict reductions in crop-water availability and increases in the frequency of heatwaves across western Canada, posing major challenges to crop productivity and sustainability. Enhancing water use efficiency (WUE) and heat tolerance is therefore critical to improving yield stability and grain quality under future climatic conditions. In this study, 198 historical and modern Canadian spring wheat cultivars were evaluated for whole-plant and leaf-level WUE, heat tolerance, and carbon isotope discrimination (δ¹³C) to identify physiological traits associated with adaptation to water-limited environments. The δ¹³C was measured in flag leaves under water-deficient and high-temperature conditions. Leaf water potential (LWP), photosynthetically active radiation (PAR), chlorophyll fluorescence parameters (F₀, F V /F M , F M , F V ), quantum yield for heat dissipation of PSII (φDo), and relative electron transport rate (ETR) were measured across six growth stages. Significant genetic variation was observed in WUE and δ¹³C, with cultivars differing in their ability to produce biomass and grain per unit of water use. Whole-plant WUE and water use were negatively correlated with δ¹³C, indicating that genotypes with lower δ¹³C values tend to be more efficient in water use. Chlorophyll fluorescence traits varied markedly across growth stages: LWP, PAR, ETR, and F V /F M decreased, whereas F₀, F M , and φDo increased, from stem elongation to booting. Overall, low to moderate correlations among WUE, δ¹³C, biomass, and water use suggest limited genetic diversity for these traits within the tested germplasm. These findings provide valuable insights for breeding climate-resilient, water-use-efficient wheat cultivars to enhance sustainability in the Canadian Prairies. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Physiology Biological sciences/Plant sciences Genetic diversity spring wheat stable isotope abiotic stress tolerance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Wheat ( Triticum aestivum L.) is the largest cereal crop in the Canadian Prairies; however, seasonal variations in moisture and temperature are one of the factors affecting plant development, resulting in significant yield losses (Mapfumo et al., 2023 ). Climate projections by Environment Canada indicate increasing drought frequency, higher temperatures, and reduced water availability across the region, posing major threats to wheat productivity and sustainability (Environment Canada, 2019). In recent years, producers have frequently experienced severe moisture deficits during the growing season, resulting in recurrent drought and heat stress. These extreme conditions reduce yield and grain quality, and in severe cases, cause partial or complete crop failure, thereby reducing farm profitability (Tandzi & Mutengwa, 2020). According to Statistics Canada (2024), total wheat production declined by 38.5% due to moisture deficit, particularly in the 2021 and 2023 growing seasons. Zhao et al. (2020) reported an average wheat yield loss of 50–60% in a water-limited environment. High temperatures exacerbate yield losses by inhibiting photosynthesis, degrading chlorophyll, and disrupting key physiological processes, leading to extensive cellular damage (Mishr et al., 2021). Increases of only 3–4°C above the optimum during grain filling can reduce wheat yield by 10–50%, depending on the cultivar (Hussain et al., 2018; Nuttall et al., 2018). These challenges underscore the urgent need to develop high-yielding, resilient cultivars with enhanced drought and heat tolerance. Breeding such cultivars will enable more efficient utilization of available soil moisture, stabilize yields, and reduce production costs, contributing to sustainable wheat production in the Canadian Prairies. Crop water use efficiency (WUE) is a major trait for sustaining productivity in water-limited environments, where crop growth depends on the efficient use of available water resources (Durodola et al., 2025 ). WUE reflects how effectively a plant converts water into biomass or grain yield, balancing carbon assimilation with water loss through transpiration (Hai et al., 2022). Developing spring wheat cultivars with high WUE could enhance crop resilience, stabilize yields, and support sustainable production under increasingly variable climatic conditions. However, accurately quantifying WUE remains challenging because it integrates complex biological and physical processes that vary spatially and temporally, and are strongly influenced by environmental factors (Hoover et al., 2022; Harder et al., 2023). Various direct and indirect methods have been developed to estimate WUE (Vadez et al., 2023; Brendel, 2021 ), yet each approach has limitations in accuracy, scale dependency, and data requirements, leading to inconsistent results across environments. Traditionally, WUE has been estimated from leaf-level gas exchange measurements of photosynthesis and transpiration, assuming these are representative of whole-plant performance (Tarara et al., 2011; Tomás et al., 2012 ; Poni et al., 2009). In practice, WUE can be assessed at multiple scales, ranging from the leaf and whole-plant levels to population or field scales (biomass-based WUE), each capturing different physiological and ecological aspects of plant water use. Various direct and indirect methods have been developed to estimate WUE (Vadez et al., 2023; Brendel, 2021 ); however, each approach has inherent limitations related to accuracy, scale dependency, and data requirements, often leading to inconsistent results across environments. Traditionally, WUE has been estimated based on leaf-level gas exchange measurements, photosynthesis, and transpiration, assuming these are representative of whole-plant performance (Tarara et al., 2011; Tomás et al., 2012 ). At the leaf level, several physiological parameters have been proposed as indirect indicators of WUE and drought tolerance; among these, carbon isotope discrimination (δ 13 C) is particularly promising. δ¹³C provides a rapid and integrative measure of WUE by quantifying the ratio of ¹³C to ¹²C in plant tissue relative to atmospheric CO₂ (Richard, 2006; Farquhar et al., 1989 ; Ma et al., 2023). It reflects the balance between carbon assimilation and stomatal conductance, making it a reliable indicator of a plant’s capacity to use water efficiently. In wheat breeding programs, δ¹³C has been effectively used to identify genotypes with superior WUE and yield potential under drought conditions (Condon et al., 1987 , 2002 , 2004 ; Chen et al., 2012 , 2013 ). Under non-limiting water conditions, positive correlations between δ¹³C, grain yield, and biological yield have also been reported (Craufurd et al., 1991 ). Flexas et al. ( 2014 ) proposed that the ratio of mesophyll to stomatal conductance (gₘ/gₛ) represents a key determinant of CO₂ uptake efficiency, while respiration rate was identified as another major factor influencing WUE because increased respiration reduces net carbon assimilation (Gago et al., 2014). Other physiological traits, such as leaf water potential (LWP), have also been suggested as useful indicators for screening crops for drought tolerance and WUE (Gaosegelwe and Kirkham, 1990). The LWP reflects the plant's overall water status, and higher LWP values are associated with mechanisms to avoid dehydration. Gago et al. (2014) demonstrated that leaf-level WUE is a complex trait influenced by multiple physiological processes affecting photosynthesis and transpiration. At the whole-plant or population level, WUE is defined as the ratio of biomass or grain yield produced per unit of water consumed (Sinclair et al., 1984; Howell, 2001; Morison et al., 2008 ; Tang et al., 2014). This parameter is affected by genetic factors that influence either biomass production or transpiration efficiency. Substantial genetic variation for WUE has been reported in several crops, including barley (Paul et al., 2024; Wehner et al., 2016; Hubick & Farquhar, 1989), grapevine (Alsina et al., 2007 ; Tomas et al., 2012), cowpea (Ismail and Hall, 1992 ), peanut (Wright et al., 1994 ), sorghum (Donatelli et al., 1992 ), soybean (Mian et al., 1996; Hufstetler et al., 2007), cotton (Saranga et al., 2004; Fish and Earl, 2009 ), and wheat (Ehdaie and Waines, 1993 ; Van Den Boogaard et al., 1997 ; Siahpoosh et al., 2011). Genetic diversity is fundamental for improving WUE because it provides the basis for selecting genotypes with favorable combinations of water-use traits (Gao et al., 2024 ). Conventional breeding has relied on field-based evaluations of biomass production and grain yield under contrasting soil moisture conditions. At the same time, recent advances in high-throughput phenotyping have allowed precise measurements of WUE-related traits across large populations (Prey et al., 2020). However, previous studies on WUE and δ¹³C in wheat often involved limited numbers of genotypes, restricting the assessment of broader genetic diversity. Understanding the extent of genetic variation for WUE, δ¹³C, and heat tolerance among Canadian spring wheat cultivars is therefore essential for breeding climate-resilient varieties. The objectives of this study were to (a) assess genetic diversity and relationships for whole-plant WUE (WUE₍ WP ₎), δ¹³C, and related physiological traits associated with drought and heat tolerance among Canadian spring wheat cultivars registered between 1905 and 2018, and (b) determine the extent of genetic differentiation among breeding programs across Canada. 2. Materials and methods Plant material A spring wheat diversity panel consisting of 199 historical and modern Canadian cultivars registered in western Canada between 1905 and 2018 was used in this study (Table 1 ). These cultivars represent releases from more than eleven breeding programs across Canada. The panel encompasses a wide range of genetic backgrounds and end-use quality types, capturing the historical and contemporary genetic diversity of Canadian spring wheat. Cultivars were classified according to their official marketing classes, which are defined by functional characteristics such as grain color, hardness, kernel size, baking and milling quality, dough or gluten strength, grain protein concentration, and end-use (Canadian Grain Commission, https://www.grainscanada.gc.ca/en/grain-quality/grain-grading/wheat-class es.html). The classes are Canada Northern Hard Red (CNHR), Canada Prairie Spring Red (CPSR), Canada Prairie Spring White (CPSW), Canada Western Amber Durum (CWAD), Canada Western Extra Strong (CWES), Canada Western Hard White Spring (CWHWS), Canada Western Red Spring (CWRS), Canada Western Special Purpose (CWSP), and Canada Western Soft White Spring (CWSWS) (Supplementary Table S1 ). Among these, the CWRS class is the most widely cultivated, accounting for approximately 60% of Canada's total wheat production, followed by CWAD, CPSR, and CESRW (Cereals Canada, 2023). The inclusion of cultivars from these classes ensured comprehensive representation of genetic and phenotypic variation in agronomic and physiological traits related to water use efficiency, drought tolerance, and heat stress response. Table 1 The breeding program origins of 198 historical and modern Canadian spring wheat cultivars registered in western Canada between 1905 and 2018 used in the current study. CDC: Crop Development Centre; WPB: Wiersum Plant Breeding; UGG: United Grain Growers; CIMMYT: International Maize and Wheat Improvement Center; NDSU: North Dakota State University, USDA ID: United States Department of Agriculture. Breeding program Number of cultivars Cultivars Agriculture and Agri Food Canada 108 AAC AwesomeVB AAC Bailey AAC Brandon AAC Cabri AAC Cameron AAC Castle (HY2021) AAC Chiffon AAC Cirrus AAC Connery AAC Crossfield AAC Crusader AAC Current AAC Durafield AAC Elie AAC Entice AAC Foray AAC Iceberg AAC Innova AAC Jatharia AAC Penhold AAC Prevail AAC Proclaim AAC Raymore AAC Redwater AAC Ryley AAC Spitefire AAC Tenacious AAC Tradition AAC Viewfield AAC W1876 AAC Whitefox AC 2000 AC Abbey AC Andrew AC Cadillac AC Corinne AC Intrepid AC Meena AC Phil AC Snowbird AC Vista Alvena Burnside Canuck Carberry CDN Bison Conquer Cypress Enchant Fieldstar Garnet Glencross GoodeveVB Helios HY320 Kanata Kane Manitou Marquis Minnedosa MuchMore Napayo Neepawa Park Peace Pembina PT472 PT479 RL6077 Sadash Sinton Snowhite475 Snowstar Stettler Superb Unity Waskada Whitehawk Cardale AC Eatonia AC Barrie AC Cora AC Crystal AC Domain AC Elsa AC Foremost AC Karma AC Majestic AC Michael AC Minto AC Reed AC Splendor AC Taber Benito Biggar Bluesky Columbus Grandin Katepwa Lancer Laura Leader Lillian Pasqua Roblin Somerset Vesper Wildcat CDC / University of Saskatchewan 31 CDC Carbide CDC Abound CDC Alsask CDC Imagine CDC Kernen CDC Merlin CDC NRG003 CDC Osler CDC Stanley CDC Teal CDC Thrive CDC Utmost CDC Fortitude CDC Cordon CDC TERRAIN CDC VR Morris CDC Bradwell CDC Go CDC Hughes CDC Bounty CDC Plentiful CDC Primepurple CDC Rama CDC Titanium CDC Walrus CDC Whitewood Conway PT595 Kenyon Moats BW970 Pro University of Alberta 19 Alikat BW1039 BYT1411 Cutler Go Early GP168 PT771 PT778 PT780 Ellerslie Tracker Jake Coleman Thorsby RedNet Laser Parata Zealand BYT1419 University of Manitoba 2 Amazon Glenlea WPB / Plantomar 1 Pasteur 3 Cardale Infinity Lovitt AgriPro & UGG 3 5700PR 5701PR Invader Syngenta Canada Inc. 11 5604HR CL 5605HR CL GP112 SY087 SY 433 SY479 VB SY637 5702PR SY985 WR859 CL SY995 WFGD Co-op 1 WTF603 1 Harvest NDSU 2 Faller Prosper SK Wheat Pool 4 Prodigy McKenzie Oslo Journey CIMMYT 2 Pitic62 SAAR USDA ID 3 Owens Springfield Fielder Others 8 Bhishaj FL62R1 Red Bobs Sumai3 BW493 NRG007 SWS52 BW278 Growth conditions The experiment was conducted in a Conviron BioChamber LTRB growth room at the University of Alberta, Edmonton, Canada. Plants were grown under controlled conditions with a photoperiod of 18 h, light intensity of 1000 µmol m⁻² s⁻¹ (provided by sodium halide bulbs), day/night temperatures of 22/16°C, and relative humidity of 55%/70% (day/night). Plants were grown individually in 1-gallon pots filled with 1.9 kg of a soil mixture composed of field soil and peat moss (Promix BX) at a 1:3 (v/v) ratio. Before sowing, all pots were flushed with tap water and drained overnight to determine field capacity. Three seeds were sown per pot at 1.5 cm, and plants were thinned to one seedling per pot two weeks after emergence. To reduce direct soil evaporation, the soil surface was covered with a 2-cm layer of perlite. Plants were maintained at field capacity by daily water replenishment based on pot weight until the start of the drought and heat treatments. Each genotype was subjected to water-deficient (WD) treatments in two biological replicates. The WD and heat stress treatments were imposed for 7 days at the booting stage (BBCH 41–49) following the Zadoks scale (Zadoks et al., 1974 ). After the stress period, pots were re-watered to field capacity and maintained until grain maturity. Drought stress was induced by withholding irrigation. Once the target soil water content (SWC) was reached, plants were maintained under constant stress for 7 days by daily replenishment of the exact amount of water lost, determined gravimetrically. The SWC was calculated as: SWC = (pot weight − minimum pot weight) / (maximum pot weight − minimum pot weight) × 100). The SWC decreased from 70–90% (well-watered) to 10–15% (water-deficient). Soil moisture was also monitored using Soil Moisture Equipment Corp. (Santa Barbara, CA, USA) probes (20-mm stainless steel, 3-rod configuration). During the WD treatment, the temperature was increased from 22°C to 35°C for 7 days. Chlorophyll fluorescence parameters were measured before and after the treatments, and at the beginning and end of the stress period. Flag leaves that developed during the stress treatment were collected at maturity for carbon isotope discrimination analysis. Two control pots containing the same soil mixture and perlite, but without plants, were used to estimate soil surface evaporation using successive weight differences. At the end of the 7-day WD treatment (average soil water potential ≈ − 80 kPa), plants were maintained until maturity and harvested. Determination of WUE at the whole plant level Plant water consumed over the growth period until maturity was estimated from the sum of the daily water consumption determined by pot weight as follows: Whole Plant Water Consumption = (Pot Weight) Field capacity – (Pot Weight) Daily Weight At the plant maturity stage (BBCH 83), WUE was estimated as the ratio of total above-ground biomass accumulation to total water applied during the experiment, less water lost due to evaporation. WUE was determined as follows: WUE WP (g L -1 ) = (dry weight of final biomass) / total water consumed Determination of leaf water potential Leaf water potential was estimated using chlorophyll fluorescence parameters. Chlorophyll fluorescence in leaves was measured in the growth chamber using a portable photosynthesis yield analyzer (MINI-PAM, Walz, Effeltrich, Germany) and a pulse-amplitude-modulated (PAM) fluorometer (TEACHING-PAM, Walz, Effeltrich, Germany). The fluorometer was connected to a Leaf Clip Holder (2030-B, Walz) fitted with a microquantum sensor and a thermocouple for monitoring the leaf temperature and relative air humidity, respectively. These chlorophyll fluorescence parameters include ΦPSI and F V /F M = (F M – F 0 )/F M . The F V /F M ratio was used to assess stress tolerance under field conditions. This parameter measures the efficiency of excitation energy capture by open PSII reaction centres (Genty et al. 1989). Variable fluorescence (F V ) was calculated by subtracting F 0 from F M ( Table 2 ) . This parameter measures the efficiency of excitation energy capture by open PSII reaction centres (Genty et al. 1989) and represents the maximum capacity for light-dependent charge separation in PSII. The terms, formulas, and descriptions of the chlorophyll fluorescence parameters used in the study are presented in Table 2 . Table 2 Terms, formulas and description of the chlorophyll fluorescence parameters used in the study. Terms and Formulas Description PAR Photosynthetically active radiation F 0 Minimum yield of Chla fluorescence measured in a dark-adapted state F V = F M – F 0 Variable fluorescence F M Maximum yield of Chla fluorescence measured in a dark-adapted state F V /F M = ( F M – F 0 )/ F M Maximum quantum yield of photochemistry in PSII, measured in a dark-adapted state ETR = Φ P = ΔF/F M = (F M′ -F)/F M′ X (PPFD) or ETR = Y(II) × PAR × 0.84 × 0.5 Relative electron transport rate, is the product of the effective photochemical yield of PSII, Φ P = ΔF/F M′ = (F M′ -F)/F M′ and photosynthetic photon flux density (PPFD) (Genty et al., 1989; Geel et al., 1997; Kromkamp et al., 1998). Electron transport rate (ETR), estimated from chlorophyll fluorescence, is a widely-used indicator of photosynthetic activity. φDo = F 0 /F M Quantum yield (at t = 0) of energy dissipation. Quantum yield for heat dissipation of PSII LWP = F M /F 0 Leaf water potential (LWP) At the booting growth stage, water-deficit treatment was imposed for 7 days by withholding watering, followed by re-watering to restore the pots to field capacity. Drought stress levels were monitored with a soil moisture sensor. When the soil volumetric water content dropped to 5% (or at the wilting point), which occurred in three to four days after withholding water. The temperature was raised from 22°C to 30–35°C for 7 days. The chlorophyll fluorescence parameters were measured before and after the water deficit and temperature treatments, and at the beginning and end of the stress treatments, corresponding to six different growing points: stem elongation (BBCH 30–36 and BBCH 37–40), booting growth stage (BBCH41-44 and BBCH45-49), inflorescence emergence (BBCH50-59 and BBCH60-69). At the booting and inflorescence growth stages, measurements were taken in the centre of the flag leaf of each cultivar. Flag leaves of wheat, regarded in crop production as the ‘functional leaves’, are the main organs for photosynthesis, and contribute 45–58% of photosynthetic performance during the grain-filling stage (Duncan 1971 ; Khaliq et al. 2008 ). At the stem elongation growth stage, measurements were taken in the centre of the last completely unfolded leaf. Leaf water potential was calculated as follow: LWP = F M / F 0 (kPa) Where F M is the maximum yield of Chlorophyll a (Chla) fluorescence measured in a dark-adapted state, and F 0 is the minimum yield of Chla fluorescence measured in a dark-adapted state. kPa is the unit of pressure of leaf water potential in the International System of Units. Determination of carbon isotope discrimination (δ 13 C) in flag leaf For δ 13 C measurement, five flag leaves from each plant subjected to drought and heat stress were collected for δ 13 C determination. All flag leaves were harvested and bulked for the determination of δ 13 C. Leaf samples were air-dried and ground into powder. Subsamples of 2 mg were sent to the Analytical Service Laboratory, Faculty of Agricultural, Life and Environmental Sciences – Renewable Resources (University of Alberta, Edmonton, AB T6G 2J7, Canada) and analyzed for δ 13 C. The δ 13 C v VPDB plant was determined by flash combustion. There are two naturally occurring stable isotopes of both carbon 12 C (98.89%) and δ 13 C (1.11%). An aliquot of the sample was combusted in oxygen, and the carbon in the sample was converted to CO 2, which was separated by chromatography and then analyzed by continuous-flow IRMS. Working standards were calibrated against the International Reference scale (i.e. δ 13 C vs. VPDB). Raw data from the mass spectrometer was then referenced to VPDB using a linear regression calculated from the working standard results. Instrument used included a Thermo Delta V Advantage Isotope Ratio Mass Spectrometer (IRMS). Thermo Scientific Inc., Bremen, Germany 2016, Thermo FLASH HT Plus 2000 Organic Elemental Analyzer, ConFlo IV (for CF-IRMS). Farquhar and Richards ( 1984 ) defined Δ 13 C as: Δ 13 C (‰) \(\:=\frac{Ra-Rp}{Rp}\:\times\:1000=\frac{{\delta\:}\text{a}\:-\:{\delta\:}\text{p}}{1\:+\:{\delta\:}\text{p}}\) × 1000 (Eq. 1 ) where R a is the 13 C/ 12 C ratio of CO 2 in air, and R p is that of plant carbon. In the second form of Eq. 1, δ a is δ 13 C of CO 2 in air, and δ p is that of plant carbon. The δa – δp refers to the C isotope ratios of atmospheric CO 2 (–8‰) and plant tissue, respectively (Farquhar and Richards, 1984 ). The δ 13 C is defined with respect to a standard as: δ 13 C sample (‰) = \(\:\frac{{R}_{sample}-{R}_{std}}{{R}_{std}}\) Eq. 2 where δ 13 C sample is that of the sample of interest, R sample is its 13 C/ 12 C ratio, and R std is the 13 C/ 12 C ratio of a standard. The δ 13 C values were referenced to a Pee Dee Belemnite standard, which is the internationally accepted standard for expressing stable carbon isotope ratios, with a 13 C/ 12 C of 0.0112372 (Craig, 1957 ). In order to avoid working with very small numbers, Δ and δ 13 C sample are typically multiplied by 1000, and denoted as parts per thousand (‰). Data analysis Analyses of variance (ANOVA) were performed for WUE wp , biomass accumulation, and consumed water using SAS, version 10.0 (SAS Institute, Cary, NC). Pairwise Pearson correlation analyses were performed to quantify linear associations among continuous variables. Correlation coefficients ( r ) and corresponding p -values were computed, with statistical significance determined at α = 0.05. Pearson’s correlation coefficients were estimated with regression analysis. As all correlations were based on the combination of two continuous variables, model II regression was used to estimate the equation parameters (Sokal and Rohlf, 2000). All graphical outputs were produced using the ggplot2 package (v3.4.2). The Shannon Diversity Index was used to assess genetic diversity within the spring wheat panel. The Shannon Diversity Index was calculated as follows: $$\:{H}^{{\prime\:}}=-\sum\:\left(Pi*LnPi\right)$$ Where Σ: A Greek symbol that means “sum”, ln : Natural log, and \(\:Pi\) : The proportion of the entire panel made up of spring wheat cultivars i . The higher the value of H’ , the higher the diversity of the cultivars in the panel. The lower the value of H’ , the lower the diversity. A value of H’ = 0 indicates a panel that only has one cultivar. The Shannon diversity index typically ranges from 1.5 to 3.5 in genetic diversity studies, and rarely exceeds 4.5 (Kent et al., 1992). The maximum possible value depends on the number of cultivars in the panel. All diversity index analyses were performed using Microsoft Excel. A heatmap was generated using the pheatmap package based on z -score-standardized values across traits, employing hierarchical clustering with Euclidean distance and the complete linkage method. Principal component analysis (PCA) was conducted to explore multivariate relationships among traits after centering and scaling the data. The proportion of variance explained by each principal component was calculated to assess their relative contributions. 3. Results Variation in WUE WP , δ 13 C, biomass accumulation and water use per plant Significant differences ( P < 0.001) among cultivars were detected for WUE WP , with mean values ranging from 2.99 g L − 1 for the cultivar ‘Minnedosa’ to 7.81 g L − 1 for the cultivar ‘WR859’ (Table 3 ). The highest value of WUE WP was observed in the cultivars of Syngenta Canada Inc breeding program while the lowest was found in the AAFC breeding program. Biomass accumulation ranged from 9.50 g for ‘CDC Plentiful’ to 50.60 g for ‘Wildcat’ averaging 25.76 g (Table 3 ). The cultivar ‘ Wildcat ’ exhibited the highest total water use per plant, utilizing 12.34 L of water to produce 35.22 g of biomass. In contrast, ‘ CDC Plentiful ’ required only 2.28 L to produce 21.20 g of biomass per plant (Table 3 ). Table 3 Mean value, maximum, minimum, standard deviation (s), coefficient of variance (CV), and Shannon Diversity Index ( H’ ) of whole plant WUE, biomass, water use and δ 13 C measured in 198 historical and modern wheat cultivars under controlled environment. Studied Experiment 1 character Mean Max Min CV (%) ¶ LSD (0.05) Stdev Pr > F H' δ 13 C (‰) 26.37 29.33 24.28 - - 0.92 - 2.62 WUE wp (g L − 1 ) 4.17 5.71 3.07 9.48 0.79 0.43 < 0.001 1.88 Biomass (g) 16.10 23.85 9.50 12.13 3.89 3.36 < 0.001 1.57 Water use wp (L) 3.92 5.04 2.27 11.41 0.87 0.71 F H' δ 13 C (‰) 26.00 28.16 24.06 - - 0.80 - 2.49 WUE wp (g L − 1 ) 4.12 7.81 3.11 11.98 0.98 0.69 < .0001 2.02 Biomass (g) 35.24 50.60 24.80 12.33 8.68 0.32 F H' δ 13 C (‰) 26.18 29.33 24.06 - - 0.88 - 2.61 WUE wp (g L − 1 ) 4.15 7.81 3.07 11.44 0.94 0.57 < .0001 2.52 Biomass (g) 25.69 50.60 9.50 13.48 6.87 10.79 < .0001 2.52 Water use wp (L) 6.30 12.34 2.27 19.87 2.48 2.57 < .0001 1.98 The analysis revealed that WUE WP , δ¹³C, biomass accumulation, and water use per plant varied among breeding programs (Fig. 1 ). These differences may be attributed to factors such as the number of cultivars included or the intensity of selection within each program. Cultivars derived from Canterra, NDSU, USDA, and the group categorized as “Others” exhibited high genetic diversity for WUE WP (Fig. 1 ). Although, the cultivars with the highest WUE WP originated from the AAFC and University of Saskatchewan breeding programs, which also produced the largest number of cultivars, most of these lines exhibited relatively narrow genetic diversity. This limited diversity may constrain future genetic gains in WUE WP within these programs (Fig. 1 ), reflecting the complex genetic basis of this trait. Biomass accumulation and water use also varied significantly, indicating diverse physiological responses among cultivars both within and across breeding programs (Fig. 1 ). The δ¹³C values, ranging from 24.06‰ to 29.33‰, indicated substantial variation among cultivars and suggested the underlying genetic differences in photosynthetic processes or physiological mechanisms related to WUE (Fig. 1 ). No apparent differences were observed among Canadian wheat classes for WUE WP , δ¹³C, biomass accumulation, or total water use per plant, suggesting that these classes may share similar physiological responses and adaptative mechanisms under water limited and heat stress conditions (Fig. 2 ). The Shannon-Weaver diversity index ( H′ ) was used to compare phenotypic diversity among the studied traits. The lowest values were observed for WUE WP ( H’ = 1.88), biomass accumulation ( H’ = 1.57), and water use per plant ( H’ =1.43) in experiment 1, indicating that the cultivars exhibited a narrow range of genetic diversity for these traits. Plant health, slightly affected by excessive fertilizer application during the early growth stage, may be a cause of low or reduced genetic diversity. In experiment 2, the highest values of Shannon-Weaver diversity index for WUE WP ( H’ =2.02), biomass accumulation ( H’ = 2.29), and water use per plant ( H’ =2.15) indicated greater diversity in WUE WP , biomass accumulation, and water use per plant, suggesting a wider range of genetic diversity within the cultivars. The low level of diversity might indicate a narrow genetic base, and a small sample size contributed significantly to the low diversity index. The overall Shannon-Weaver diversity indices for the whole plant WUE WP , biomass accumulation, δ 13 C, and water use per plant ( H’ = 2.52; 2.52; 2.61 and 1.98, respectively) confirmed the existence of a moderate diversity among the spring wheat cultivars. The coefficient of variance has been repeatedly reported as a good estimator of genetic variability across different traits. Table 3 shows the mean values, maximum, minimum, standard deviation (stdev), and coefficient of variance (CV), and Shannon Diversity Index ( H’ ) for whole plant WUE WP , biomass accumulation, water use per plant among the cultivars in two experiments differed by their sets of cultivars. The CV values were moderate to high for the traits, ranging from 9.48–11.98% for WUE WP , 12.13–13.48% for biomass accumulation, and 11.41–19.87% for water use per plant (Table 3 ). The range of variation for WUE WP appears to be the lowest, while that of water use appears to be the highest, indicating greater dispersion within the values for those traits. The genetic variation observed for WUE WP among cultivars depends on the number of cultivars and the breeding program's origin. Frequency distribution of WUE WP , δ 13 C, biomass accumulation and water use per plant The 198 wheat cultivars were split into two experimental groups, each comprising 99 unique cultivars, due to the limited growth chamber space available. Both experimental groups were exposed to the same experimental conditions, allowing comparison of the cultivars' performance under similar environments. The cultivars tested in this study showed significant differences in several key traits, including WUE WP , biomass production efficiency, δ 13 C, and water use per plant (Table 3 ). The observed bimodal distribution for biomass accumulation and water use per plant indicated that the cultivars fell into two distinct groups, each with a different level of biomass accumulation and water use per plant, rather than being evenly distributed across a single range (Fig. 3 ). This may be due to variations in pest control (aphids) that affected plant health by creating uneven pest pressure, leading to slight differences in plant growth, biomass accumulation, and water use per plant. However, the unimodal distributions of WUE WP and δ 13 C showed a mode that represents the most common or typical value, indicating a single, dominant process or condition influencing these variables (Fig. 3 ). The clustering of data points around a single central tendency, with less variability, suggested that the factors affecting WUE WP and δ 13 C were relatively consistent and unaffected by slight differences in plant health, leading to unified and predictable patterns in the measured parameters among the cultivars studied. Trait correlation analysis Correlation analysis among the four traits (Fig. 3 ) demonstrated significant associations. Biomass accumulation and water use exhibited a very strong positive correlation (r = 0.95, p < 0.001 ), indicating that higher biomass is closely linked to greater water consumption. WUE WP (TRA1) showed a weak but significant positive correlation with biomass (r = 0.19, p < 0.01) and a negative correlation with δ 13 C (r = -0.14, p < 0.05 ) and water use (r = -0.18, p < 0.05 ). These results suggested that genotypes with higher WUE WP tend to use less water and exhibit slightly lower δ 13 C values, while biomass accumulation is primarily driven by water availability. Cluster Analysis of Wheat Cultivars Hierarchical clustering of 198 wheat cultivars based on four physiological traits, WUE WP , δ 13 C, biomass accumulation, and water use per plant, revealed distinct grouping patterns (Fig. 4 ). The heatmap indicates two major clusters, with sub-clusters differentiating genotypes exhibiting high biomass and water use from those with higher WUE and δ 13 C values. Cultivars with extreme trait values, particularly for biomass and water use, were concentrated in specific clusters, suggesting strong trait-based differentiation. These clusters represented groups of cultivars slightly affected by plant health during the experiment. Variation in leaf water potential (LWP) and chlorophyll fluorescence parameters To further characterize the physiological responses of the 198 spring wheat cultivars, we measured several chlorophyll fluorescence parameters, including LWP (F M /F 0 ), F M , F 0 , F V (F M – F 0 ), F V /F M [(F M – F 0 )/F M ], φDo (F 0 /F M ), relative electron transport rate (ETR), and photosynthetically active radiation (PAR) across six developmental stages: stem elongation (BBCH 30–36 and BBCH 37–40), booting (BBCH 41–44 and BBCH 45–49), and inflorescence emergence (BBCH 50–59 and BBCH 60–69). Box plots (Fig. 5 ) were used to illustrate the variation in physiological parameters across different growth stages. An increasing trend was observed in LWP, PAR, ETR, and the maximum quantum efficiency of PSII (F V /F M ) from the stem elongation to the booting stage. These parameters subsequently declined under water-deficient and heat-stressed conditions during the booting growth stage. A recovery phase was later observed, suggesting that the plants had adapted to the imposed stress (Fig. 5 ). In contrast, F O , F M , and the F O /F M ratio exhibited the opposite pattern, increasing from the stem elongation stage and then decreasing during the booting stage, indicating distinct regulatory responses of the photosynthetic apparatus under stress (Fig. 5 ). During the stem elongation stage, PAR showed the greatest genetic variation among growth stages, despite lower mean PAR values (Fig. 5 ). This suggests that, at this developmental phase, the spring wheat panel possesses a broader spectrum of genetic diversity influencing PAR, thereby providing a larger pool of allelic variation for selection to act upon. Variations in F V , F M , and ETR were similar across all growth stages (Fig. 5 ), whereas F V /F M showed a consistent trend at the stem elongation and booting stages under water deficit and elevated temperature conditions. Under these stress conditions, the cultivar ‘CDC Teal’ displayed the highest F V /F M , indicating superior tolerance, whereas ‘Super’ exhibited the lowest value, suggesting greater sensitivity to the combined stresses. The F V /F M reflects the maximum quantum efficiency of photosystem II (PSII), a critical determinant of photosynthetic performance, with higher values indicating reduced photoinhibition and better maintenance of photosynthetic function under stress. Notably, the relative ranking of cultivars shifted under stress, which may be attributed to evolutionary forces such as natural selection and genetic drift, leading to stochastic changes in allele frequencies. Overall, the values of the photosynthesis-related parameters F V /F M , PAR, LWP, ETR, and F V showed an upward trend from stem elongation to the booting growth stage, indicating strong photosynthetic activity before declining under stress, followed by slight recovery as the plants recovered from stress. On the other hand, the values of F O , F M , and φDo showed a downward trend, suggesting the plant's ability to absorb light energy and perform photochemistry was impaired. This may be due to a potential nutrient deficiency during the plant's stem elongation phase, a critical growth period where inadequate nutrients could lead to symptoms like slight yellowing observed on the leaf tip, which vary depending on the specific nutrient deficiency. Principal component analysis (PCA) Figure 6 presents a biplot integrating both cultivar and trait data, revealing patterns of phenotypic similarity and the relationships among traits across the evaluated wheat cultivars. The first principal component (PCA 1) accounted for 50.1% of the total variance, while the second component (PCA 2) explained an additional 28.1%, jointly capturing 78.2% of the overall variation in the dataset. The PCA 3 and PCA 4 contributed marginally (21.6% and 0.2%, respectively). Cultivars positioned closely together exhibited similar phenotypic profiles, suggesting comparable physiological or morphological responses. Conversely, cultivars plotted at opposite ends of the biplot displayed contrasting trait combinations, indicative of differing adaptive strategies. The strong loadings of specific traits along PCA 1 and PCA 2 axes highlight the major factors driving phenotypic differentiation, providing insights into the multivariate structure of cultivar performance. Overall, the PCA biplot underscores substantial phenotypic diversity and helps identify cultivars with favorable trait associations for potential use in breeding programs aimed at improving drought and heat resilience. 4. Discussion The experimental materials provided high-throughput data, which were analyzed to reveal genetic information related to WUE WP , δ 13 C, biomass accumulation, water use per plant, and chlorophyll fluorescence parameters. Thus far, there have been no relevant reports on the evolution of the whole plant and leaf WUE, heat tolerance, and δ 13 C among historical and modern Canadian spring wheat cultivars. This study used phylogenetic analysis, photosystem II (PSII) system, chlorophyll fluorescence capacity, Shannon diversity index, and coefficient of variation to explain how variations in whole plant and leaf WUE, heat tolerance, and δ 13 C can be used for the improvement of the cultivar resilience, which is important for breeding programs. The Shannon diversity index, which measures both the number and evenness of genotypes, revealed genetic diversity, while chlorophyll fluorescence parameters provided insights into cultivar performance at the physiological level. Biomass accumulation varied from 9.5 to 50.6 g per plant, and water use ranged from 2.27 to 12.34 L per plant. WUE WP ranged from 3.07 to 7.81 g L − 1 , while δ 13 C values ranged from 24.06‰ to 29.33‰. Despite substantial differences in biomass and water use among cultivars, WUE WP and δ 13 C exhibited relatively narrow and consistent ranges, indicating that the efficiency of water and carbon conversion into biomass is a stable trait. These findings suggested that WUE is cultivar-specific, reflecting both genetic potential and growth conditions, with some cultivars inherently more efficient at producing biomass per unit of water. Aggarwal & Sinha ( 1983 ) reported that the average WUE for wheat ranged from 3.34 to 4.70 g dry matter per kg of water used, indicating variability in efficiency depending on environmental conditions and management practices. Humphreys et al. (2004) observed that WUE in wheat could vary significantly, with values ranging from 0.5 to 13.8 g dry matter per kg of water used, depending on factors such as growth stage, cultivar, and water stress levels. These studies highlighted the variability in wheat water-use efficiency across environments and suggest potential for improvement in Canadian cultivars. The variation in genetic diversity across breeding programs reflects a combination of the germplasm used, the traits under selection, the environmental conditions targeted, and the intensity and methodology of selection. Programs with broader objectives and more diverse parental lines typically maintain greater diversity in physiological traits such as WUE, δ¹³C, biomass, and water use per plant. The AAFC breeding program contributed the largest number of cultivars to the panel; however, these cultivars exhibited a relatively narrow range of genetic variation for WUE WP . This suggests that, despite the greater sample size, the AAFC germplasm may share similar genetic backgrounds or selection histories for WUE-related traits. Similar patterns have been reported in other studies, where modern wheat breeding programs, despite releasing a large number of cultivars, often exhibit reduced genetic and phenotypic variation due to selection bottlenecks and the recurrent use of elite parental lines (Fu, 2015 ; Semagn et al., 2021; Cheng et al., 2024 ; Tanksley & McCouch, 1997; Sanchez et al., 2023; Louwaars et al., 2018; McCouch, 2004; Wang et al., 2025). The δ¹³C measurements, obtained from flag leaves exposed to heat and water stress, provided an integrated measure of photosynthetic and transpiration dynamics during stress exposure. The broad range observed (24.06‰–29.33‰) indicated substantial genotypic variation and suggested differences in stomatal regulation, mesophyll conductance, and photosynthetic capacity among cultivars. Such variation suggested that the plants exhibited differential physiological responses to drought and heat stress, reflecting differences in stomatal regulation, mesophyll conductance, and photosynthetic capacity. Plants with low δ¹³C values likely exhibit conservative water-use behavior and heat stress characterized by reduced stomatal conductance and enhanced WUE, whereas those with higher δ¹³C values may favor a more acquisitive strategy aimed at sustaining higher carbon assimilation rates despite greater water loss. These physiological trade-offs underscore the complexity of breeding for drought and heat tolerance, as the optimal balance between productivity and water conservation depends on target environment and breeding objectives (Rebetzke et al., 2002; Araus et al., 2012 ). Collectively, the variation in δ¹³C observed under stress conditions highlights the potential of δ¹³C as a reliable integrative trait for assessing genotypic differences in WUE and drought adaptation in wheat breeding. The negative relationship between WUE WP and δ¹³C, commonly reported in previous studies, may be due to physiological mechanisms that regulate δ¹³C during photosynthesis. During CO₂ fixation, plants preferentially assimilate the lighter carbon isotope (¹²C) over the heavier isotope (¹³C). When stomatal conductance is high and intercellular CO₂ concentration (C i ) approaches ambient levels, discrimination against ¹³C increases, resulting in higher δ¹³C values. Conversely, when stomatal conductance is reduced under stress conditions, C i declines, leading to lower discrimination and more negative δ¹³C values. As a result, lower δ¹³C values are generally associated with higher intrinsic WUE, reflecting a balance between maintaining photosynthetic carbon assimilation and minimizing water loss through transpiration (Farquhar et al., 1989 ; Condon et al., 2002 ). The phylogenetic tree showing differences in WUE WP , δ 13 C, biomass accumulation, and water use per plant among cultivars suggested that genetics contributed to determining these traits, and that selection for one trait may influence others. These differences indicated that the ability to use water efficiently was not uniform across cultivars, and the tree provides a visual representation of how these traits are distributed within a group of related plants based on their evolutionary history. These traits are interconnected, with δ 13 C serving as a proxy for WUE WP , which reflects how efficiently a plant uses water for biomass accumulation. Therefore, the discernible differences suggested that multiple factors interacted and influenced each other, indicating that the cultivars share a recent common ancestor. Canadian wheat cultivars were categorized according to official marketing classes, defined primarily by grain quality traits such as grain color, hardness, kernel size, baking and milling quality, dough or gluten strength, grain protein content, and intended end-use. Swe hypothesized that WUE WP , δ¹³C, biomass, and water use might vary across these classes due to underlying physiological differences. However, no significant differences were detected among classes for any of these traits, indicating a shared genetic background and parallel breeding histories. Because Canadian wheat breeding has historically emphasized grain yield, grain quality, and disease resistance, traits related to WUE WP have not been direct selection targets. Consequently, the classes exhibit similar physiological responses to water limitation and comparable photosynthetic adaptations to the temperate agroclimatic conditions of the Canadian Prairies. The Shannon diversity index values in the wheat panel ranged from 1.43 to 2.62, indicating low to medium diversity (1 = low, 2.5 = medium). The higher the values, the greater the diversity of traits (Petruccelli et al., 2013; Pan et al., 2015), indicating the genetic potential of spring wheat cultivars and the presence of desirable genes for improving WUE WP , drought tolerance, and biomass production. These results suggested a moderate level of diversity for traits related to WUE WP , δ¹³C, biomass accumulation, and water use per plant, consistent with previous reports in wheat (Liu et al., 2024; Gelelchaa & Kumsab, 2023). Although the observed genetic diversity among the evaluated cultivars may be adequate for specific breeding objectives, the effective improvement of WUE WP and associated physiological traits would likely require the incorporation of additional genetic variation. Expanding the breeding pool through the strategic introduction of international germplasm could strengthen the adaptive potential of Canadian spring wheat under increasingly variable climatic conditions. However, the continuing erosion of genetic diversity among modern, high-yielding varieties remains a critical concern, reinforcing the urgency of global and regional research initiatives aimed at broadening the genetic base and safeguarding long-term crop resilience (Fu & Somers, 2011 ; Fu et al., 2006 ; Fu, 2006 ; Fu & Sommers, 2009; Alachiotis & Pavlidis, 2016 ; Vitti et al., 2016). Among the chlorophyll fluorescence parameters, previous studies highlighted that the The maximum quantum efficiency of photosystem II (F V /F M ) is one of the most important and sensitive chlorophyll fluorescence parameters for assessing moisture and temperature stresses as it reflects the maximum efficiency of energy conversion in the photosynthetic system and provides an early and non-invasive indicator of plant stress by showing a decline in photosynthetic efficiency (Blum, 1986 ; Maxwell and Johnson, 2000; Sayed, 2003; Baker and Rosengvist, 2004; Salvatori et al ., 2014; Guo et al ., 2016). These parameters are indirectly related to WUE WP through their influence on photosynthetic capacity and stomatal regulation. The F V /F M , together with F M and ETR, was assessed across different growth stages of wheat cultivars under combined water deficit and heat stress. While the F V , F M , and ETR showed similar variations across stages, the F V /F M remained the most consistent and informative indicator. The cultivar ‘CDC Teal’ maintained the highest F V /F M , indicating superior tolerance, whereas ‘Superb’ displayed the lowest values, suggesting greater stress sensitivity. The F V /F M is a sensitive indicator of PSII photochemical efficiency and photosynthetic performance under stress. Higher F V /F M values reflect reduced photoinhibition and better maintenance of photosynthetic function (Maxwell & Johnson, 2000; Baker, 2008 ). Consistent with previous reports, cultivars with higher F V /F M under drought or heat stress demonstrate improved tolerance, maintaining photosynthetic activity and potentially supporting higher biomass accumulation (Kalaji et al., 2016; Farooq et al., 2014 ). Variation in F V /F M among cultivars under stress likely arises from genetic differences influencing PSII stability and efficiency. Evolutionary forces, including natural selection and genetic drift, can produce shifts in allele frequencies that affect stress-responsive traits. Selection may favor alleles enhancing stress tolerance, while drift can generate stochastic changes in trait distributions independent of fitness (Futuyma, 2013 ; Lynch & Walsh, 1998 ). These results highlight the importance of PSII efficiency in determining cultivar-specific responses to combined water deficit and heat stress. Understanding genetic variation in F V /F M can inform breeding programs aimed at improving abiotic stress resilience in cereal crops. The observed variations in photosynthetic and fluorescence parameters across growth stages reflect the differential physiological responses of wheat to combined water deficit and heat stress. The initial increase in LWP, PAR, ETR, and F V /F M from stem elongation to booting suggests an enhancement in photosynthetic performance and photochemical efficiency during early stress exposure, probably due to short-term acclimation mechanisms such as osmotic adjustment and activation of photoprotective pathways (Chaves, et al, 2009 ; Demmig-Adams & Adams, 1992 ; Munns & Tester, 2008 ). The subsequent decline in these parameters at the booting stage indicates that prolonged or intensified stress impaired photosynthetic electron transport and PSII efficiency, leading to a reduction in carbon assimilation capacity. Conversely, the increase in F 0 and F M followed by their decline suggests transient structural perturbations and subsequent partial recovery of PSII reaction centers, which are typical responses to photo-oxidative stress (Aro, et al, Andersson, 1993; Öquist & Huner, 2003 ). The decline in F V /F M and ETR at booting indicates photoinhibition and reduced photosystem II (PSII) efficiency, which are common responses to cumulative stress effects that impair the photosynthetic apparatus (Baker, 2008 ; Kalaji et al., 2016). The increase in F O under severe stress at booting could indicate damage to PSII reaction centers or dissociation of light-harvesting complexes, leading to a reduction in energy use efficiency (Maxwell & Johnson, 2000). These patterns align with reports that water deficit and high temperature synergistically exacerbate oxidative stress, leading to chlorophyll degradation, impaired electron transport, and photodamage (Havaux, 1993 ; Mathur et al., 2014 ). These patterns collectively imply that wheat plants exhibit dynamic adjustments of photochemical processes under stress, with the observed recovery phase reflecting the activation of adaptive mechanisms aimed at maintaining photosynthetic stability and resilience to environmental stress. The PCA results demonstrated differences among the studied wheat cultivars based on key physiological and agronomic traits such as WUE WP , δ 13 C, biomass accumulation and water use per plant, reflecting substantial phenotypic diversity within the panel. The high proportion of variance explained by the first two components (78.2%) indicated that a few major traits largely account for cultivar variability, consistent with previous findings in Canadian spring wheat populations (Fu & Somers, 2011 ; Mapfumo et al., 2023 ). The biplot revealed that biomass accumulation and water use were strongly aligned with PC1, indicating their major contribution to overall variability. The WUE WP and δ 13 C were more associated with PC2, suggesting these traits captured a different dimension of variation. Cultivars clustering near traits associated with higher biomass and WUE WP may possess adaptive advantages under water-limited conditions, whereas those aligned with higher δ¹³C values or reduced photosynthetic parameters may be more sensitive to stress. The observed trait associations highlighted potential trade-offs between productivity and water conservation, underlining the importance of selecting cultivars that balance these physiological dimensions for future breeding efforts under increasing drought and heat stress in the Canadian Prairies. 5. Conclusions Whole-plant water-use efficiency is not always strongly correlated with δ 13 C, due to environmental conditions and genetic differences among cultivars. While a negative correlation between δ 13 C and WUE WP is often found, this relationship can be weak, leading to situations where δ 13 C may not be a reliable predictor of WUE WP . The spring wheat panel exhibited limited genetic diversity for WUE WP and high-temperature tolerance, suggesting that current Canadian spring wheat cultivars may not be resilient enough to withstand climate change-induced heat and drought stress, which can negatively impact yield and profitability in water-deficient environments. The weak correlation between WUE WP and δ 13 C could be due to low genetic diversity in the studied cultivars, as genetic variation is necessary to observe and exploit the theoretically linked relationship between WUE WP and δ 13 C for breeding purposes. The problem stemmed from the complex nature of these traits and the need to integrate novel genetic resources, such as those found in landraces, into elite Canadian wheat cultivars using advanced tools, including marker-assisted breeding and genomic selection. The PCA revealed phenotypic variations among cultivars, primarily driven by traits related to WUE WP , biomass, and δ¹³C. With 78.2% of the total variance explained by the first two components, the analysis highlighted key adaptive differences and identified cultivars combining superior WUE and stress resilience for targeted breeding in Canadian spring wheat. Chlorophyll fluorescence measurements revealed that water deficit and heat stress significantly reduce photosynthetic efficiency in plants, with the extent of reduction varying among growth stages and cultivars. Key parameters, such as F V /F M and the maximum quantum yield of Photosystem II, decrease under heat stress due to damage to the photosynthetic system. This decrease indicates impaired photosynthetic performance, which can be detected through chlorophyll fluorescence and used as a tool for screening and selecting cultivars under water-deficient and heat-stress conditions. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Funding This study was funded by Saskatchewan Wheat Development Commission, Manitoba Crop Alliance, and Western Grains Research Foundation (Grant No. 20220069). The University of Alberta internal project n umber: RES0060360 Author Contribution LJAC conceived the project, designed the experiments and wrote the article. MA and JZ reviewed and made critical revisions to the original project. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8281694","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":607771054,"identity":"46ebed24-56fb-455e-b1d0-8a2283b4d45c","order_by":0,"name":"Ludovic Joseph Anatole Capo-chichi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie2QsQrCMBCGrxQ6VbKeCPYVUgrFxwkU7KJScHEQKQh1Ete+hos4RgK61D2TVFxFOuoiRnQSCXVzyLfcEfLx3x2AwfCvWCkCUUX8pjRTgKeCdRUAyusqZLbflLd1xwtkVIpkdJiQ1D5WOgWLQeTPC/RXsktFXgwRuRNooyj0wpaVIQslo6KRMQTu6qej5PxSgjyuROPO0OOufdUq+E5RjUpJGVLuOtoUlBe1S4Z+XpwTkW9ZcymcsKNTyKKvLpZNPDKLl6dkzEh7Nz1JnQLgfj7Y+v/fFIPBYDB88ADu9kfiggM5zgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Alberta","correspondingAuthor":true,"prefix":"","firstName":"Ludovic","middleName":"Joseph Anatole","lastName":"Capo-chichi","suffix":""},{"id":607771055,"identity":"ec133334-2af6-4323-b3d5-f30235efe60c","order_by":1,"name":"Scott X. 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Histograms for WUE\u003csub\u003eWP\u003c/sub\u003e, δ\u003csup\u003e13\u003c/sup\u003eC, biomass accumulation, and water use per plant values measured are displayed along the diagonal. The frequency distribution of each variable is shown on the diagonal. On the bottom of the diagonal: the bivariate scatter plots with a fitted line are displayed. On the top of the diagonal: the values of the correlations plus the significance level as ‘ns’,*,** and *** are significance level 0.1, 0.05, 0.001 (not-significant, significant, very significant, and highly significant, respectively. The experiments were conducted in a BioChamber’s plant growth room, Conviron BioChamber LTRB Growth Room, at the University of Alberta in Edmonton, Alberta.\u0026nbsp; The values were measured on the whole plant in two replicates.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8281694/v1/a2c75e5f78bde7c5711dfb9c.jpeg"},{"id":105381027,"identity":"843b4775-bdc3-4041-aef5-a432e98bc37b","added_by":"auto","created_at":"2026-03-25 11:15:36","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":499955,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering heatmap for\u003cstrong\u003e \u003c/strong\u003eWUE\u003csub\u003eWP\u003c/sub\u003e, biomass accumulation, water use per plant of 198 spring wheat cultivars. Red and blue colors in the columns denote high and low values, respectively, for each trait, with color intensity associated to trait values.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8281694/v1/eafdd5a2c8439fb56bf859bd.jpeg"},{"id":105566489,"identity":"b0d2a36c-9a22-479d-af74-57fc40f1c266","added_by":"auto","created_at":"2026-03-27 12:56:31","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":419251,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots showing mean performances for the LWP and chlorophyll fluorescence parameters across six growth stages: stem elongation (BBCH 30-36 and BBCH 37-40), booting growth stage (BBCH 41-44 and BBCH 45-49), and inflorescence emergence (BBCH 50-59 and BBCH 60-69). (a) LWP, (b) PAR, (c) F\u003csub\u003e0\u003c/sub\u003e, (d) F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e, (e) \u003csub\u003eFM\u003c/sub\u003e, (f) F\u003csub\u003eV\u003c/sub\u003e, (g) φDo, (h) ETR. Plants were subjected to water-deficit and heat stress treatment for 7 days at the booting growth stage (BBCH 41-44 and BBCH 45-49).\u0026nbsp; The temperature was raised from 22°C to 35°C.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8281694/v1/c59c92d92ea21c0928a96169.jpeg"},{"id":105381024,"identity":"3a9a55ff-a829-4a91-9fba-15836d816bf4","added_by":"auto","created_at":"2026-03-25 11:15:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":374000,"visible":true,"origin":"","legend":"\u003cp\u003ePCA analysis of 198 Canadian spring wheat cultivars derived from various breeding programs. The x-axis and y-axis represent principal component 1 (PC 1) and principal component 2 (PC 2) with the proportions. Presented here are the following: WUE\u003csub\u003eWP\u003c/sub\u003e (TRA1), δ\u003csup\u003e13\u003c/sup\u003eC (TRA2), biomass (TRA3) and water use per plant (TRA4).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8281694/v1/e474aa2a34c8a6e266909a83.png"},{"id":105729877,"identity":"406b0156-34b8-4069-a9a1-45dca25dc24a","added_by":"auto","created_at":"2026-03-30 11:21:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4208039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8281694/v1/38a338a1-d898-4465-823d-b0c3b412a7b2.pdf"},{"id":105728014,"identity":"d5f441c4-901a-4f41-b210-b98bdc3e35c5","added_by":"auto","created_at":"2026-03-30 11:08:05","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":24682,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8281694/v1/76754c9efdfd013890846841.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evolution of water use efficiency, heat tolerance, and carbon isotope discrimination among Canadian spring wheat cultivars","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is the largest cereal crop in the Canadian Prairies; however, seasonal variations in moisture and temperature are one of the factors affecting plant development, resulting in significant yield losses (Mapfumo et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Climate projections by Environment Canada indicate increasing drought frequency, higher temperatures, and reduced water availability across the region, posing major threats to wheat productivity and sustainability (Environment Canada, 2019). In recent years, producers have frequently experienced severe moisture deficits during the growing season, resulting in recurrent drought and heat stress. These extreme conditions reduce yield and grain quality, and in severe cases, cause partial or complete crop failure, thereby reducing farm profitability (Tandzi \u0026amp; Mutengwa, 2020). According to Statistics Canada (2024), total wheat production declined by 38.5% due to moisture deficit, particularly in the 2021 and 2023 growing seasons. Zhao et al. (2020) reported an average wheat yield loss of 50\u0026ndash;60% in a water-limited environment. High temperatures exacerbate yield losses by inhibiting photosynthesis, degrading chlorophyll, and disrupting key physiological processes, leading to extensive cellular damage (Mishr et al., 2021). Increases of only 3\u0026ndash;4\u0026deg;C above the optimum during grain filling can reduce wheat yield by 10\u0026ndash;50%, depending on the cultivar (Hussain et al., 2018; Nuttall et al., 2018). These challenges underscore the urgent need to develop high-yielding, resilient cultivars with enhanced drought and heat tolerance. Breeding such cultivars will enable more efficient utilization of available soil moisture, stabilize yields, and reduce production costs, contributing to sustainable wheat production in the Canadian Prairies.\u003c/p\u003e \u003cp\u003eCrop water use efficiency (WUE) is a major trait for sustaining productivity in water-limited environments, where crop growth depends on the efficient use of available water resources (Durodola et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). WUE reflects how effectively a plant converts water into biomass or grain yield, balancing carbon assimilation with water loss through transpiration (Hai et al., 2022). Developing spring wheat cultivars with high WUE could enhance crop resilience, stabilize yields, and support sustainable production under increasingly variable climatic conditions. However, accurately quantifying WUE remains challenging because it integrates complex biological and physical processes that vary spatially and temporally, and are strongly influenced by environmental factors (Hoover et al., 2022; Harder et al., 2023).\u003c/p\u003e \u003cp\u003eVarious direct and indirect methods have been developed to estimate WUE (Vadez et al., 2023; Brendel, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), yet each approach has limitations in accuracy, scale dependency, and data requirements, leading to inconsistent results across environments. Traditionally, WUE has been estimated from leaf-level gas exchange measurements of photosynthesis and transpiration, assuming these are representative of whole-plant performance (Tarara et al., 2011; Tom\u0026aacute;s et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Poni et al., 2009). In practice, WUE can be assessed at multiple scales, ranging from the leaf and whole-plant levels to population or field scales (biomass-based WUE), each capturing different physiological and ecological aspects of plant water use. Various direct and indirect methods have been developed to estimate WUE (Vadez et al., 2023; Brendel, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); however, each approach has inherent limitations related to accuracy, scale dependency, and data requirements, often leading to inconsistent results across environments. Traditionally, WUE has been estimated based on leaf-level gas exchange measurements, photosynthesis, and transpiration, assuming these are representative of whole-plant performance (Tarara et al., 2011; Tom\u0026aacute;s et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the leaf level, several physiological parameters have been proposed as indirect indicators of WUE and drought tolerance; among these, carbon isotope discrimination (δ\u003csup\u003e13\u003c/sup\u003eC) is particularly promising. δ\u0026sup1;\u0026sup3;C provides a rapid and integrative measure of WUE by quantifying the ratio of \u0026sup1;\u0026sup3;C to \u0026sup1;\u0026sup2;C in plant tissue relative to atmospheric CO₂ (Richard, 2006; Farquhar et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Ma et al., 2023). It reflects the balance between carbon assimilation and stomatal conductance, making it a reliable indicator of a plant\u0026rsquo;s capacity to use water efficiently. In wheat breeding programs, δ\u0026sup1;\u0026sup3;C has been effectively used to identify genotypes with superior WUE and yield potential under drought conditions (Condon et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1987\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Under non-limiting water conditions, positive correlations between δ\u0026sup1;\u0026sup3;C, grain yield, and biological yield have also been reported (Craufurd et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFlexas et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) proposed that the ratio of mesophyll to stomatal conductance (gₘ/gₛ) represents a key determinant of CO₂ uptake efficiency, while respiration rate was identified as another major factor influencing WUE because increased respiration reduces net carbon assimilation (Gago et al., 2014). Other physiological traits, such as leaf water potential (LWP), have also been suggested as useful indicators for screening crops for drought tolerance and WUE (Gaosegelwe and Kirkham, 1990). The LWP reflects the plant's overall water status, and higher LWP values are associated with mechanisms to avoid dehydration. Gago et al. (2014) demonstrated that leaf-level WUE is a complex trait influenced by multiple physiological processes affecting photosynthesis and transpiration.\u003c/p\u003e \u003cp\u003eAt the whole-plant or population level, WUE is defined as the ratio of biomass or grain yield produced per unit of water consumed (Sinclair et al., 1984; Howell, 2001; Morison et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tang et al., 2014). This parameter is affected by genetic factors that influence either biomass production or transpiration efficiency. Substantial genetic variation for WUE has been reported in several crops, including barley (Paul et al., 2024; Wehner et al., 2016; Hubick \u0026amp; Farquhar, 1989), grapevine (Alsina et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tomas et al., 2012), cowpea (Ismail and Hall, \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), peanut (Wright et al., \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), sorghum (Donatelli et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), soybean (Mian et al., 1996; Hufstetler et al., 2007), cotton (Saranga et al., 2004; Fish and Earl, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and wheat (Ehdaie and Waines, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Van Den Boogaard et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Siahpoosh et al., 2011).\u003c/p\u003e \u003cp\u003eGenetic diversity is fundamental for improving WUE because it provides the basis for selecting genotypes with favorable combinations of water-use traits (Gao et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conventional breeding has relied on field-based evaluations of biomass production and grain yield under contrasting soil moisture conditions. At the same time, recent advances in high-throughput phenotyping have allowed precise measurements of WUE-related traits across large populations (Prey et al., 2020). However, previous studies on WUE and δ\u0026sup1;\u0026sup3;C in wheat often involved limited numbers of genotypes, restricting the assessment of broader genetic diversity.\u003c/p\u003e \u003cp\u003eUnderstanding the extent of genetic variation for WUE, δ\u0026sup1;\u0026sup3;C, and heat tolerance among Canadian spring wheat cultivars is therefore essential for breeding climate-resilient varieties. The objectives of this study were to (a) assess genetic diversity and relationships for whole-plant WUE (WUE₍\u003csub\u003eWP\u003c/sub\u003e₎), δ\u0026sup1;\u0026sup3;C, and related physiological traits associated with drought and heat tolerance among Canadian spring wheat cultivars registered between 1905 and 2018, and (b) determine the extent of genetic differentiation among breeding programs across Canada.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e \u003cb\u003ePlant material\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA spring wheat diversity panel consisting of 199 historical and modern Canadian cultivars registered in western Canada between 1905 and 2018 was used in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These cultivars represent releases from more than eleven breeding programs across Canada. The panel encompasses a wide range of genetic backgrounds and end-use quality types, capturing the historical and contemporary genetic diversity of Canadian spring wheat. Cultivars were classified according to their official marketing classes, which are defined by functional characteristics such as grain color, hardness, kernel size, baking and milling quality, dough or gluten strength, grain protein concentration, and end-use (Canadian Grain Commission, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grainscanada.gc.ca/en/grain-quality/grain-grading/wheat-class\u003c/span\u003e\u003cspan address=\"https://www.grainscanada.gc.ca/en/grain-quality/grain-grading/wheat-class\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e es.html). The classes are Canada Northern Hard Red (CNHR), Canada Prairie Spring Red (CPSR), Canada Prairie Spring White (CPSW), Canada Western Amber Durum (CWAD), Canada Western Extra Strong (CWES), Canada Western Hard White Spring (CWHWS), Canada Western Red Spring (CWRS), Canada Western Special Purpose (CWSP), and Canada Western Soft White Spring (CWSWS) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Among these, the CWRS class is the most widely cultivated, accounting for approximately 60% of Canada's total wheat production, followed by CWAD, CPSR, and CESRW (Cereals Canada, 2023). The inclusion of cultivars from these classes ensured comprehensive representation of genetic and phenotypic variation in agronomic and physiological traits related to water use efficiency, drought tolerance, and heat stress response.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe breeding program origins of 198 historical and modern Canadian spring wheat cultivars registered in western Canada between 1905 and 2018 used in the current study. CDC: Crop Development Centre; WPB: Wiersum Plant Breeding; UGG: United Grain Growers; CIMMYT: International Maize and Wheat Improvement Center; NDSU: North Dakota State University, USDA ID: United States Department of Agriculture.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreeding program\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cultivars\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c10\" namest=\"c3\"\u003e \u003cp\u003eCultivars\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture and Agri Food Canada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAC AwesomeVB\u003c/p\u003e \u003cp\u003eAAC Bailey\u003c/p\u003e \u003cp\u003eAAC Brandon\u003c/p\u003e \u003cp\u003eAAC Cabri\u003c/p\u003e \u003cp\u003eAAC Cameron\u003c/p\u003e \u003cp\u003eAAC Castle (HY2021)\u003c/p\u003e \u003cp\u003eAAC Chiffon\u003c/p\u003e \u003cp\u003eAAC Cirrus\u003c/p\u003e \u003cp\u003eAAC Connery\u003c/p\u003e \u003cp\u003eAAC Crossfield\u003c/p\u003e \u003cp\u003eAAC Crusader\u003c/p\u003e \u003cp\u003eAAC Current\u003c/p\u003e \u003cp\u003eAAC Durafield\u003c/p\u003e \u003cp\u003eAAC Elie\u003c/p\u003e \u003cp\u003eAAC Entice\u003c/p\u003e \u003cp\u003eAAC Foray\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAC Iceberg\u003c/p\u003e \u003cp\u003eAAC Innova\u003c/p\u003e \u003cp\u003eAAC Jatharia\u003c/p\u003e \u003cp\u003eAAC Penhold\u003c/p\u003e \u003cp\u003eAAC Prevail\u003c/p\u003e \u003cp\u003eAAC Proclaim\u003c/p\u003e \u003cp\u003eAAC Raymore\u003c/p\u003e \u003cp\u003eAAC Redwater\u003c/p\u003e \u003cp\u003eAAC Ryley\u003c/p\u003e \u003cp\u003eAAC Spitefire\u003c/p\u003e \u003cp\u003eAAC Tenacious\u003c/p\u003e \u003cp\u003eAAC Tradition\u003c/p\u003e \u003cp\u003eAAC Viewfield\u003c/p\u003e \u003cp\u003eAAC W1876\u003c/p\u003e \u003cp\u003eAAC Whitefox\u003c/p\u003e \u003cp\u003eAC 2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC Abbey\u003c/p\u003e \u003cp\u003eAC Andrew\u003c/p\u003e \u003cp\u003eAC Cadillac\u003c/p\u003e \u003cp\u003eAC Corinne\u003c/p\u003e \u003cp\u003eAC Intrepid\u003c/p\u003e \u003cp\u003eAC Meena\u003c/p\u003e \u003cp\u003eAC Phil\u003c/p\u003e \u003cp\u003eAC Snowbird\u003c/p\u003e \u003cp\u003eAC Vista\u003c/p\u003e \u003cp\u003eAlvena\u003c/p\u003e \u003cp\u003eBurnside\u003c/p\u003e \u003cp\u003eCanuck\u003c/p\u003e \u003cp\u003eCarberry\u003c/p\u003e \u003cp\u003eCDN Bison\u003c/p\u003e \u003cp\u003eConquer\u003c/p\u003e \u003cp\u003eCypress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnchant\u003c/p\u003e \u003cp\u003eFieldstar\u003c/p\u003e \u003cp\u003eGarnet\u003c/p\u003e \u003cp\u003eGlencross\u003c/p\u003e \u003cp\u003eGoodeveVB\u003c/p\u003e \u003cp\u003eHelios\u003c/p\u003e \u003cp\u003eHY320\u003c/p\u003e \u003cp\u003eKanata\u003c/p\u003e \u003cp\u003eKane\u003c/p\u003e \u003cp\u003eManitou\u003c/p\u003e \u003cp\u003eMarquis\u003c/p\u003e \u003cp\u003eMinnedosa\u003c/p\u003e \u003cp\u003eMuchMore\u003c/p\u003e \u003cp\u003eNapayo\u003c/p\u003e \u003cp\u003eNeepawa\u003c/p\u003e \u003cp\u003ePark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePeace\u003c/p\u003e \u003cp\u003ePembina\u003c/p\u003e \u003cp\u003ePT472\u003c/p\u003e \u003cp\u003ePT479\u003c/p\u003e \u003cp\u003eRL6077\u003c/p\u003e \u003cp\u003eSadash\u003c/p\u003e \u003cp\u003eSinton\u003c/p\u003e \u003cp\u003eSnowhite475\u003c/p\u003e \u003cp\u003eSnowstar\u003c/p\u003e \u003cp\u003eStettler\u003c/p\u003e \u003cp\u003eSuperb\u003c/p\u003e \u003cp\u003eUnity\u003c/p\u003e \u003cp\u003eWaskada\u003c/p\u003e \u003cp\u003eWhitehawk\u003c/p\u003e \u003cp\u003eCardale\u003c/p\u003e \u003cp\u003eAC Eatonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAC Barrie\u003c/p\u003e \u003cp\u003eAC Cora\u003c/p\u003e \u003cp\u003eAC Crystal\u003c/p\u003e \u003cp\u003eAC Domain\u003c/p\u003e \u003cp\u003eAC Elsa\u003c/p\u003e \u003cp\u003eAC Foremost\u003c/p\u003e \u003cp\u003eAC Karma\u003c/p\u003e \u003cp\u003eAC Majestic\u003c/p\u003e \u003cp\u003eAC Michael\u003c/p\u003e \u003cp\u003eAC Minto\u003c/p\u003e \u003cp\u003eAC Reed\u003c/p\u003e \u003cp\u003eAC Splendor\u003c/p\u003e \u003cp\u003eAC Taber\u003c/p\u003e \u003cp\u003eBenito\u003c/p\u003e \u003cp\u003eBiggar\u003c/p\u003e \u003cp\u003eBluesky\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eColumbus\u003c/p\u003e \u003cp\u003eGrandin\u003c/p\u003e \u003cp\u003eKatepwa\u003c/p\u003e \u003cp\u003eLancer\u003c/p\u003e \u003cp\u003eLaura\u003c/p\u003e \u003cp\u003eLeader\u003c/p\u003e \u003cp\u003eLillian\u003c/p\u003e \u003cp\u003ePasqua\u003c/p\u003e \u003cp\u003eRoblin\u003c/p\u003e \u003cp\u003eSomerset\u003c/p\u003e \u003cp\u003eVesper\u003c/p\u003e \u003cp\u003eWildcat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC / University of Saskatchewan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCDC Carbide\u003c/p\u003e \u003cp\u003eCDC Abound\u003c/p\u003e \u003cp\u003eCDC Alsask\u003c/p\u003e \u003cp\u003eCDC Imagine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDC Kernen\u003c/p\u003e \u003cp\u003eCDC Merlin\u003c/p\u003e \u003cp\u003eCDC NRG003\u003c/p\u003e \u003cp\u003eCDC Osler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCDC Stanley\u003c/p\u003e \u003cp\u003eCDC Teal\u003c/p\u003e \u003cp\u003eCDC Thrive\u003c/p\u003e \u003cp\u003eCDC Utmost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCDC Fortitude\u003c/p\u003e \u003cp\u003eCDC Cordon\u003c/p\u003e \u003cp\u003eCDC TERRAIN\u003c/p\u003e \u003cp\u003eCDC VR Morris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCDC Bradwell\u003c/p\u003e \u003cp\u003eCDC Go\u003c/p\u003e \u003cp\u003eCDC Hughes\u003c/p\u003e \u003cp\u003eCDC Bounty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCDC Plentiful\u003c/p\u003e \u003cp\u003eCDC Primepurple\u003c/p\u003e \u003cp\u003eCDC Rama\u003c/p\u003e \u003cp\u003eCDC Titanium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCDC Walrus\u003c/p\u003e \u003cp\u003eCDC Whitewood\u003c/p\u003e \u003cp\u003eConway\u003c/p\u003e \u003cp\u003ePT595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKenyon\u003c/p\u003e \u003cp\u003eMoats\u003c/p\u003e \u003cp\u003eBW970 Pro\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity of Alberta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlikat\u003c/p\u003e \u003cp\u003eBW1039\u003c/p\u003e \u003cp\u003eBYT1411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCutler\u003c/p\u003e \u003cp\u003eGo Early\u003c/p\u003e \u003cp\u003eGP168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePT771\u003c/p\u003e \u003cp\u003ePT778\u003c/p\u003e \u003cp\u003ePT780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEllerslie\u003c/p\u003e \u003cp\u003eTracker\u003c/p\u003e \u003cp\u003eJake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eColeman\u003c/p\u003e \u003cp\u003eThorsby\u003c/p\u003e \u003cp\u003eRedNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLaser\u003c/p\u003e \u003cp\u003eParata\u003c/p\u003e \u003cp\u003eZealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBYT1419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity of Manitoba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmazon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlenlea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWPB / Plantomar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePasteur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCardale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInfinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLovitt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriPro \u0026amp; UGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5700PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5701PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInvader\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyngenta Canada Inc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5604HR CL\u003c/p\u003e \u003cp\u003e5605HR CL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGP112\u003c/p\u003e \u003cp\u003eSY087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSY 433\u003c/p\u003e \u003cp\u003eSY479 VB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSY637\u003c/p\u003e \u003cp\u003e5702PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSY985\u003c/p\u003e \u003cp\u003eWR859 CL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSY995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFGD Co-op\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWTF603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHarvest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDSU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFaller\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProsper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSK Wheat Pool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProdigy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMcKenzie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOslo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJourney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIMMYT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePitic62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSAAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSDA ID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOwens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpringfield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFielder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBhishaj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFL62R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRed Bobs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSumai3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBW493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNRG007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSWS52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBW278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGrowth conditions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe experiment was conducted in a Conviron BioChamber LTRB growth room at the University of Alberta, Edmonton, Canada. Plants were grown under controlled conditions with a photoperiod of 18 h, light intensity of 1000 \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1; (provided by sodium halide bulbs), day/night temperatures of 22/16\u0026deg;C, and relative humidity of 55%/70% (day/night). Plants were grown individually in 1-gallon pots filled with 1.9 kg of a soil mixture composed of field soil and peat moss (Promix BX) at a 1:3 (v/v) ratio. Before sowing, all pots were flushed with tap water and drained overnight to determine field capacity. Three seeds were sown per pot at 1.5 cm, and plants were thinned to one seedling per pot two weeks after emergence. To reduce direct soil evaporation, the soil surface was covered with a 2-cm layer of perlite. Plants were maintained at field capacity by daily water replenishment based on pot weight until the start of the drought and heat treatments. Each genotype was subjected to water-deficient (WD) treatments in two biological replicates. The WD and heat stress treatments were imposed for 7 days at the booting stage (BBCH 41\u0026ndash;49) following the Zadoks scale (Zadoks et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). After the stress period, pots were re-watered to field capacity and maintained until grain maturity.\u003c/p\u003e \u003cp\u003eDrought stress was induced by withholding irrigation. Once the target soil water content (SWC) was reached, plants were maintained under constant stress for 7 days by daily replenishment of the exact amount of water lost, determined gravimetrically. The SWC was calculated as:\u003c/p\u003e \u003cp\u003eSWC = (pot weight\u0026thinsp;\u0026minus;\u0026thinsp;minimum pot weight) / (maximum pot weight\u0026thinsp;\u0026minus;\u0026thinsp;minimum pot weight) \u0026times; 100).\u003c/p\u003e \u003cp\u003eThe SWC decreased from 70\u0026ndash;90% (well-watered) to 10\u0026ndash;15% (water-deficient). Soil moisture was also monitored using Soil Moisture Equipment Corp. (Santa Barbara, CA, USA) probes (20-mm stainless steel, 3-rod configuration). During the WD treatment, the temperature was increased from 22\u0026deg;C to 35\u0026deg;C for 7 days. Chlorophyll fluorescence parameters were measured before and after the treatments, and at the beginning and end of the stress period. Flag leaves that developed during the stress treatment were collected at maturity for carbon isotope discrimination analysis. Two control pots containing the same soil mixture and perlite, but without plants, were used to estimate soil surface evaporation using successive weight differences. At the end of the 7-day WD treatment (average soil water potential\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;80 kPa), plants were maintained until maturity and harvested.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetermination of WUE at the whole plant level\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePlant water consumed over the growth period until maturity was estimated from the sum of the daily water consumption determined by pot weight as follows:\u003c/p\u003e \u003cp\u003eWhole Plant Water Consumption = (Pot Weight) \u003csub\u003eField capacity\u003c/sub\u003e \u0026ndash; (Pot Weight)\u003csub\u003eDaily Weight\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eAt the plant maturity stage (BBCH 83), WUE was estimated as the ratio of total above-ground biomass accumulation to total water applied during the experiment, less water lost due to evaporation. WUE was determined as follows:\u003c/p\u003e \u003cp\u003eWUE\u003csub\u003eWP\u003c/sub\u003e (g L\u003csup\u003e-1\u003c/sup\u003e) = (dry weight of final biomass) / total water consumed\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetermination of leaf water potential\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLeaf water potential was estimated using chlorophyll fluorescence parameters. Chlorophyll fluorescence in leaves was measured in the growth chamber using a portable photosynthesis yield analyzer (MINI-PAM, Walz, Effeltrich, Germany) and a pulse-amplitude-modulated (PAM) fluorometer (TEACHING-PAM, Walz, Effeltrich, Germany). The fluorometer was connected to a Leaf Clip Holder (2030-B, Walz) fitted with a microquantum sensor and a thermocouple for monitoring the leaf temperature and relative air humidity, respectively. These chlorophyll fluorescence parameters include ΦPSI and F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e = (F\u003csub\u003eM\u003c/sub\u003e \u0026ndash; F\u003csub\u003e0\u003c/sub\u003e)/F\u003csub\u003eM\u003c/sub\u003e. The F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e ratio was used to assess stress tolerance under field conditions. This parameter measures the efficiency of excitation energy capture by open PSII reaction centres (Genty et al. 1989). Variable fluorescence (F\u003csub\u003eV\u003c/sub\u003e) was calculated by subtracting F\u003csub\u003e0\u003c/sub\u003e from F\u003csub\u003eM\u003c/sub\u003e \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This parameter measures the efficiency of excitation energy capture by open PSII reaction centres (Genty et al. 1989) and represents the maximum capacity for light-dependent charge separation in PSII. The terms, formulas, and descriptions of the chlorophyll fluorescence parameters used in the study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTerms, formulas and description of the chlorophyll fluorescence parameters used in the study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerms and Formulas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhotosynthetically active radiation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum yield of Chla\u0026nbsp;fluorescence measured in a dark-adapted state\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003eV =\u003c/sub\u003e \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eM\u003c/em\u003e\u003c/sub\u003e\u0026ndash;\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable fluorescence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003eM\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum yield of Chla\u0026nbsp;fluorescence measured in a dark-adapted state\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM =\u003c/sub\u003e (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eM\u003c/em\u003e\u003c/sub\u003e\u0026ndash;\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e)/\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eM\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum quantum yield of photochemistry in PSII, measured in a dark-adapted state\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETR\u0026thinsp;=\u0026thinsp;Φ\u003csub\u003eP\u003c/sub\u003e\u0026nbsp;= ΔF/F\u003csub\u003eM\u003c/sub\u003e = (F\u003csub\u003eM\u0026prime;\u003c/sub\u003e-F)/F\u003csub\u003eM\u0026prime;\u003c/sub\u003e X (PPFD)\u003c/p\u003e \u003cp\u003eor\u003c/p\u003e \u003cp\u003eETR\u0026thinsp;=\u0026thinsp;Y(II) \u0026times; PAR \u0026times; 0.84 \u0026times; 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelative electron transport rate, is the product of the effective photochemical yield of PSII, Φ\u003csub\u003eP\u003c/sub\u003e\u0026nbsp;= ΔF/F\u003csub\u003eM\u0026prime;\u003c/sub\u003e = (F\u003csub\u003eM\u0026prime;\u003c/sub\u003e-F)/F\u003csub\u003eM\u0026prime;\u003c/sub\u003e and photosynthetic photon flux density (PPFD) (Genty et al., 1989; Geel et al., 1997; Kromkamp et al., 1998).\u003c/p\u003e \u003cp\u003eElectron transport rate (ETR), estimated from chlorophyll fluorescence, is a widely-used indicator of photosynthetic activity.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eφDo\u0026thinsp;=\u0026thinsp;F\u003csub\u003e0\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuantum yield (at t\u0026thinsp;=\u0026thinsp;0) of energy dissipation. Quantum yield for heat dissipation of PSII\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLWP\u0026thinsp;=\u0026thinsp;F\u003csub\u003eM\u003c/sub\u003e/F\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeaf water potential (LWP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt the booting growth stage, water-deficit treatment was imposed for 7 days by withholding watering, followed by re-watering to restore the pots to field capacity. Drought stress levels were monitored with a soil moisture sensor. When the soil volumetric water content dropped to 5% (or at the wilting point), which occurred in three to four days after withholding water. The temperature was raised from 22\u0026deg;C to 30\u0026ndash;35\u0026deg;C for 7 days. The chlorophyll fluorescence parameters were measured before and after the water deficit and temperature treatments, and at the beginning and end of the stress treatments, corresponding to six different growing points: stem elongation (BBCH 30\u0026ndash;36 and BBCH 37\u0026ndash;40), booting growth stage (BBCH41-44 and BBCH45-49), inflorescence emergence (BBCH50-59 and BBCH60-69).\u003c/p\u003e \u003cp\u003eAt the booting and inflorescence growth stages, measurements were taken in the centre of the flag leaf of each cultivar. Flag leaves of wheat, regarded in crop production as the \u0026lsquo;functional leaves\u0026rsquo;, are the main organs for photosynthesis, and contribute 45\u0026ndash;58% of photosynthetic performance during the grain-filling stage (Duncan \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Khaliq et al. \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). At the stem elongation growth stage, measurements were taken in the centre of the last completely unfolded leaf. Leaf water potential was calculated as follow:\u003c/p\u003e \u003cp\u003eLWP\u0026thinsp;=\u0026thinsp;F\u003csub\u003eM\u003c/sub\u003e / F\u003csub\u003e0\u003c/sub\u003e (kPa)\u003c/p\u003e \u003cp\u003eWhere F\u003csub\u003eM\u003c/sub\u003e is the maximum yield of Chlorophyll a (Chla) fluorescence measured in a dark-adapted state, and F\u003csub\u003e0\u003c/sub\u003e is the minimum yield of Chla fluorescence measured in a dark-adapted state. kPa is the unit of pressure of leaf water potential in the International System of Units.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetermination of carbon isotope discrimination (δ\u003c/b\u003e \u003csup\u003e \u003cb\u003e13\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eC) in flag leaf\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor δ\u003csup\u003e13\u003c/sup\u003eC measurement, five flag leaves from each plant subjected to drought and heat stress were collected for δ\u003csup\u003e13\u003c/sup\u003eC determination. All flag leaves were harvested and bulked for the determination of δ\u003csup\u003e13\u003c/sup\u003eC. Leaf samples were air-dried and ground into powder. Subsamples of 2 mg were sent to the Analytical Service Laboratory, Faculty of Agricultural, Life and Environmental Sciences \u0026ndash; Renewable Resources (University of Alberta, Edmonton, AB T6G 2J7, Canada) and analyzed for δ\u003csup\u003e13\u003c/sup\u003eC. The δ\u003csup\u003e13\u003c/sup\u003eC v VPDB plant was determined by flash combustion. There are two naturally occurring stable isotopes of both carbon \u003csup\u003e12\u003c/sup\u003eC (98.89%) and δ\u003csup\u003e13\u003c/sup\u003eC (1.11%). An aliquot of the sample was combusted in oxygen, and the carbon in the sample was converted to CO\u003csub\u003e2,\u003c/sub\u003e which was separated by chromatography and then analyzed by continuous-flow IRMS. Working standards were calibrated against the International Reference scale (i.e. δ\u003csup\u003e13\u003c/sup\u003eC vs. VPDB). Raw data from the mass spectrometer was then referenced to VPDB using a linear regression calculated from the working standard results. Instrument used included a Thermo Delta V Advantage Isotope Ratio Mass Spectrometer (IRMS). Thermo Scientific Inc., Bremen, Germany 2016, Thermo FLASH HT Plus 2000 Organic Elemental Analyzer, ConFlo IV (for CF-IRMS).\u003c/p\u003e \u003cp\u003eFarquhar and Richards (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) defined Δ\u003csup\u003e13\u003c/sup\u003eC as:\u003c/p\u003e \u003cp\u003eΔ\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{Ra-Rp}{Rp}\\:\\times\\:1000=\\frac{{\\delta\\:}\\text{a}\\:-\\:{\\delta\\:}\\text{p}}{1\\:+\\:{\\delta\\:}\\text{p}}\\)\u003c/span\u003e\u003c/span\u003e \u0026times; 1000 (Eq.\u0026nbsp;\u003cb\u003e1\u003c/b\u003e)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eR\u003c/em\u003e\u003csub\u003ea\u003c/sub\u003e is the \u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC ratio of CO\u003csub\u003e2\u003c/sub\u003e in air, and \u003cem\u003eR\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e is that of plant carbon. In the second form of Eq.\u0026nbsp;1, δ\u003csub\u003ea\u003c/sub\u003e is δ\u003csup\u003e13\u003c/sup\u003eC of CO\u003csub\u003e2\u003c/sub\u003e in air, and δ\u003csub\u003ep\u003c/sub\u003e is that of plant carbon. The δa \u0026ndash; δp refers to the C isotope ratios of atmospheric CO\u003csub\u003e2\u003c/sub\u003e (\u0026ndash;8\u0026permil;) and plant tissue, respectively (Farquhar and Richards, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe δ\u003csup\u003e13\u003c/sup\u003eC is defined with respect to a standard as:\u003c/p\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC \u003csub\u003esample\u003c/sub\u003e (\u0026permil;) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{R}_{sample}-{R}_{std}}{{R}_{std}}\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003ewhere δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003esample\u003c/sub\u003e is that of the sample of interest, \u003cem\u003eR\u003c/em\u003e\u003csub\u003esample\u003c/sub\u003e is its \u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC ratio, and \u003cem\u003eR\u003c/em\u003e\u003csub\u003estd\u003c/sub\u003e is the \u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC ratio of a standard. The δ\u003csup\u003e13\u003c/sup\u003eC values were referenced to a Pee Dee Belemnite standard, which is the internationally accepted standard for expressing stable carbon isotope ratios, with a \u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC of 0.0112372 (Craig, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). In order to avoid working with very small numbers, Δ and δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003esample\u003c/sub\u003e are typically multiplied by 1000, and denoted as parts per thousand (\u0026permil;).\u003c/p\u003e \u003cp\u003e \u003cb\u003eData analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAnalyses of variance (ANOVA) were performed for WUE\u003csub\u003ewp\u003c/sub\u003e, biomass accumulation, and consumed water using SAS, version 10.0 (SAS Institute, Cary, NC). Pairwise Pearson correlation analyses were performed to quantify linear associations among continuous variables. Correlation coefficients (\u003cem\u003er\u003c/em\u003e) and corresponding \u003cem\u003ep\u003c/em\u003e-values were computed, with statistical significance determined at α\u0026thinsp;=\u0026thinsp;0.05. Pearson\u0026rsquo;s correlation coefficients were estimated with regression analysis. As all correlations were based on the combination of two continuous variables, model II regression was used to estimate the equation parameters (Sokal and Rohlf, 2000). All graphical outputs were produced using the ggplot2 package (v3.4.2).\u003c/p\u003e \u003cp\u003eThe Shannon Diversity Index was used to assess genetic diversity within the spring wheat panel. The Shannon Diversity Index was calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{H}^{{\\prime\\:}}=-\\sum\\:\\left(Pi*LnPi\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere Σ: A Greek symbol that means \u0026ldquo;sum\u0026rdquo;, \u003cem\u003eln\u003c/em\u003e: Natural log, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Pi\\)\u003c/span\u003e\u003c/span\u003e: The proportion of the entire panel made up of spring wheat cultivars \u003cem\u003ei\u003c/em\u003e. The higher the value of \u003cem\u003eH\u0026rsquo;\u003c/em\u003e, the higher the diversity of the cultivars in the panel. The lower the value of \u003cem\u003eH\u0026rsquo;\u003c/em\u003e, the lower the diversity. A value of \u003cem\u003eH\u0026rsquo;\u003c/em\u003e = 0 indicates a panel that only has one cultivar. The Shannon diversity index typically ranges from 1.5 to 3.5 in genetic diversity studies, and rarely exceeds 4.5 (Kent et al., 1992). The maximum possible value depends on the number of cultivars in the panel. All diversity index analyses were performed using Microsoft Excel.\u003c/p\u003e \u003cp\u003eA heatmap was generated using the pheatmap package based on \u003cem\u003ez\u003c/em\u003e-score-standardized values across traits, employing hierarchical clustering with Euclidean distance and the complete linkage method. Principal component analysis (PCA) was conducted to explore multivariate relationships among traits after centering and scaling the data. The proportion of variance explained by each principal component was calculated to assess their relative contributions.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cb\u003eVariation in WUE\u003c/b\u003e \u003csub\u003e \u003cb\u003eWP\u003c/b\u003e \u003c/sub\u003e, \u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e13\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC, biomass accumulation and water use per plant\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSignificant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among cultivars were detected for WUE\u003csub\u003eWP\u003c/sub\u003e, with mean values ranging from 2.99 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for the cultivar \u0026lsquo;Minnedosa\u0026rsquo; to 7.81 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for the cultivar \u0026lsquo;WR859\u0026rsquo; (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The highest value of WUE\u003csub\u003eWP\u003c/sub\u003e was observed in the cultivars of Syngenta Canada Inc breeding program while the lowest was found in the AAFC breeding program. Biomass accumulation ranged from 9.50 g for \u0026lsquo;CDC Plentiful\u0026rsquo; to 50.60 g for \u0026lsquo;Wildcat\u0026rsquo; averaging 25.76 g (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The cultivar \u003cem\u003e\u0026lsquo;\u003c/em\u003eWildcat\u003cem\u003e\u0026rsquo;\u003c/em\u003e exhibited the highest total water use per plant, utilizing 12.34 L of water to produce 35.22 g of biomass. In contrast, \u003cem\u003e\u0026lsquo;\u003c/em\u003eCDC Plentiful\u003cem\u003e\u0026rsquo;\u003c/em\u003e required only 2.28 L to produce 21.20 g of biomass per plant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean value, maximum, minimum, standard deviation (s), coefficient of variance (CV), and Shannon Diversity Index (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e) of whole plant WUE, biomass, water use and δ\u003csup\u003e13\u003c/sup\u003eC measured in 198 historical and modern wheat cultivars under controlled environment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudied\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eExperiment 1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echaracter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCV (%)\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLSD\u003csub\u003e(0.05)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePr\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eH'\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWUE\u003csub\u003ewp\u003c/sub\u003e (g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomass (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater use\u003csub\u003ewp\u003c/sub\u003e (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eExperiment 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLSD\u003csub\u003e(0.05)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePr\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eH'\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWUE\u003csub\u003ewp\u003c/sub\u003e (g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomass (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater use\u003csub\u003ewp\u003c/sub\u003e (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eCombined experiments 1 and 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLSD\u003csub\u003e(0.05)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStdev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePr\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eH'\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWUE\u003csub\u003ewp\u003c/sub\u003e (g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomass (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater use\u003csub\u003ewp\u003c/sub\u003e (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis revealed that WUE\u003csub\u003eWP\u003c/sub\u003e, δ\u0026sup1;\u0026sup3;C, biomass accumulation, and water use per plant varied among breeding programs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These differences may be attributed to factors such as the number of cultivars included or the intensity of selection within each program. Cultivars derived from Canterra, NDSU, USDA, and the group categorized as \u0026ldquo;Others\u0026rdquo; exhibited high genetic diversity for WUE\u003csub\u003eWP\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although, the cultivars with the highest WUE\u003csub\u003eWP\u003c/sub\u003e originated from the AAFC and University of Saskatchewan breeding programs, which also produced the largest number of cultivars, most of these lines exhibited relatively narrow genetic diversity. This limited diversity may constrain future genetic gains in WUE\u003csub\u003eWP\u003c/sub\u003e within these programs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), reflecting the complex genetic basis of this trait. Biomass accumulation and water use also varied significantly, indicating diverse physiological responses among cultivars both within and across breeding programs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The δ\u0026sup1;\u0026sup3;C values, ranging from 24.06\u0026permil; to 29.33\u0026permil;, indicated substantial variation among cultivars and suggested the underlying genetic differences in photosynthetic processes or physiological mechanisms related to WUE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No apparent differences were observed among Canadian wheat classes for WUE\u003csub\u003eWP\u003c/sub\u003e, δ\u0026sup1;\u0026sup3;C, biomass accumulation, or total water use per plant, suggesting that these classes may share similar physiological responses and adaptative mechanisms under water limited and heat stress conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Shannon-Weaver diversity index (\u003cem\u003eH\u0026prime;\u003c/em\u003e) was used to compare phenotypic diversity among the studied traits. The lowest values were observed for WUE\u003csub\u003eWP\u003c/sub\u003e (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e= 1.88), biomass accumulation (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e= 1.57), and water use per plant (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e=1.43) in experiment 1, indicating that the cultivars exhibited a narrow range of genetic diversity for these traits. Plant health, slightly affected by excessive fertilizer application during the early growth stage, may be a cause of low or reduced genetic diversity. In experiment 2, the highest values of Shannon-Weaver diversity index for WUE\u003csub\u003eWP\u003c/sub\u003e (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e=2.02), biomass accumulation (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e= 2.29), and water use per plant (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e=2.15) indicated greater diversity in WUE\u003csub\u003eWP\u003c/sub\u003e, biomass accumulation, and water use per plant, suggesting a wider range of genetic diversity within the cultivars. The low level of diversity might indicate a narrow genetic base, and a small sample size contributed significantly to the low diversity index. The overall Shannon-Weaver diversity indices for the whole plant WUE\u003csub\u003eWP\u003c/sub\u003e, biomass accumulation, δ\u003csup\u003e13\u003c/sup\u003eC, and water use per plant (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e = 2.52; 2.52; 2.61 and 1.98, respectively) confirmed the existence of a moderate diversity among the spring wheat cultivars.\u003c/p\u003e \u003cp\u003eThe coefficient of variance has been repeatedly reported as a good estimator of genetic variability across different traits. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the mean values, maximum, minimum, standard deviation (stdev), and coefficient of variance (CV), and Shannon Diversity Index (\u003cem\u003eH\u0026rsquo;\u003c/em\u003e) for whole plant WUE\u003csub\u003eWP\u003c/sub\u003e, biomass accumulation, water use per plant among the cultivars in two experiments differed by their sets of cultivars. The CV values were moderate to high for the traits, ranging from 9.48\u0026ndash;11.98% for WUE\u003csub\u003eWP\u003c/sub\u003e, 12.13\u0026ndash;13.48% for biomass accumulation, and 11.41\u0026ndash;19.87% for water use per plant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The range of variation for WUE\u003csub\u003eWP\u003c/sub\u003e appears to be the lowest, while that of water use appears to be the highest, indicating greater dispersion within the values for those traits. The genetic variation observed for WUE\u003csub\u003eWP\u003c/sub\u003e among cultivars depends on the number of cultivars and the breeding program's origin.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFrequency distribution of WUE\u003c/b\u003e \u003csub\u003e \u003cb\u003eWP\u003c/b\u003e \u003c/sub\u003e, \u003cb\u003eδ\u003c/b\u003e\u003csup\u003e\u003cb\u003e13\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eC, biomass accumulation and water use per plant\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe 198 wheat cultivars were split into two experimental groups, each comprising 99 unique cultivars, due to the limited growth chamber space available. Both experimental groups were exposed to the same experimental conditions, allowing comparison of the cultivars' performance under similar environments. The cultivars tested in this study showed significant differences in several key traits, including WUE\u003csub\u003eWP\u003c/sub\u003e, biomass production efficiency, δ\u003csup\u003e13\u003c/sup\u003eC, and water use per plant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The observed bimodal distribution for biomass accumulation and water use per plant indicated that the cultivars fell into two distinct groups, each with a different level of biomass accumulation and water use per plant, rather than being evenly distributed across a single range (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This may be due to variations in pest control (aphids) that affected plant health by creating uneven pest pressure, leading to slight differences in plant growth, biomass accumulation, and water use per plant. However, the unimodal distributions of WUE\u003csub\u003eWP\u003c/sub\u003e and δ\u003csup\u003e13\u003c/sup\u003eC showed a mode that represents the most common or typical value, indicating a single, dominant process or condition influencing these variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The clustering of data points around a single central tendency, with less variability, suggested that the factors affecting WUE\u003csub\u003eWP\u003c/sub\u003e and δ\u003csup\u003e13\u003c/sup\u003eC were relatively consistent and unaffected by slight differences in plant health, leading to unified and predictable patterns in the measured parameters among the cultivars studied.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTrait correlation analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis among the four traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrated significant associations. Biomass accumulation and water use exhibited a very strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.95, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e), indicating that higher biomass is closely linked to greater water consumption. WUE\u003csub\u003eWP\u003c/sub\u003e (TRA1) showed a weak but significant positive correlation with biomass (r\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and a negative correlation with δ\u003csup\u003e13\u003c/sup\u003eC (r = -0.14, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) and water use (r = -0.18, \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). These results suggested that genotypes with higher WUE\u003csub\u003eWP\u003c/sub\u003e tend to use less water and exhibit slightly lower δ\u003csup\u003e13\u003c/sup\u003eC values, while biomass accumulation is primarily driven by water availability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCluster Analysis of Wheat Cultivars\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHierarchical clustering of 198 wheat cultivars based on four physiological traits, WUE\u003csub\u003eWP\u003c/sub\u003e, δ\u003csup\u003e13\u003c/sup\u003eC, biomass accumulation, and water use per plant, revealed distinct grouping patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The heatmap indicates two major clusters, with sub-clusters differentiating genotypes exhibiting high biomass and water use from those with higher WUE and δ\u003csup\u003e13\u003c/sup\u003eC values. Cultivars with extreme trait values, particularly for biomass and water use, were concentrated in specific clusters, suggesting strong trait-based differentiation. These clusters represented groups of cultivars slightly affected by plant health during the experiment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eVariation in leaf water potential (LWP) and chlorophyll fluorescence parameters\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further characterize the physiological responses of the 198 spring wheat cultivars, we measured several chlorophyll fluorescence parameters, including LWP (F\u003csub\u003eM\u003c/sub\u003e/F\u003csub\u003e0\u003c/sub\u003e), F\u003csub\u003eM\u003c/sub\u003e, F\u003csub\u003e0\u003c/sub\u003e, F\u003csub\u003eV\u003c/sub\u003e (F\u003csub\u003eM\u003c/sub\u003e \u0026ndash; F\u003csub\u003e0\u003c/sub\u003e), F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e [(F\u003csub\u003eM\u003c/sub\u003e \u0026ndash; F\u003csub\u003e0\u003c/sub\u003e)/F\u003csub\u003eM\u003c/sub\u003e], φDo (F\u003csub\u003e0\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e), relative electron transport rate (ETR), and photosynthetically active radiation (PAR) across six developmental stages: stem elongation (BBCH 30\u0026ndash;36 and BBCH 37\u0026ndash;40), booting (BBCH 41\u0026ndash;44 and BBCH 45\u0026ndash;49), and inflorescence emergence (BBCH 50\u0026ndash;59 and BBCH 60\u0026ndash;69). Box plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) were used to illustrate the variation in physiological parameters across different growth stages. An increasing trend was observed in LWP, PAR, ETR, and the maximum quantum efficiency of PSII (F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e) from the stem elongation to the booting stage. These parameters subsequently declined under water-deficient and heat-stressed conditions during the booting growth stage. A recovery phase was later observed, suggesting that the plants had adapted to the imposed stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, F\u003csub\u003eO\u003c/sub\u003e, F\u003csub\u003eM\u003c/sub\u003e, and the F\u003csub\u003eO\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e ratio exhibited the opposite pattern, increasing from the stem elongation stage and then decreasing during the booting stage, indicating distinct regulatory responses of the photosynthetic apparatus under stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the stem elongation stage, PAR showed the greatest genetic variation among growth stages, despite lower mean PAR values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This suggests that, at this developmental phase, the spring wheat panel possesses a broader spectrum of genetic diversity influencing PAR, thereby providing a larger pool of allelic variation for selection to act upon. Variations in F\u003csub\u003eV\u003c/sub\u003e, F\u003csub\u003eM\u003c/sub\u003e, and ETR were similar across all growth stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), whereas F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e showed a consistent trend at the stem elongation and booting stages under water deficit and elevated temperature conditions. Under these stress conditions, the cultivar \u0026lsquo;CDC Teal\u0026rsquo; displayed the highest F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e, indicating superior tolerance, whereas \u0026lsquo;Super\u0026rsquo; exhibited the lowest value, suggesting greater sensitivity to the combined stresses. The F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e reflects the maximum quantum efficiency of photosystem II (PSII), a critical determinant of photosynthetic performance, with higher values indicating reduced photoinhibition and better maintenance of photosynthetic function under stress. Notably, the relative ranking of cultivars shifted under stress, which may be attributed to evolutionary forces such as natural selection and genetic drift, leading to stochastic changes in allele frequencies.\u003c/p\u003e \u003cp\u003eOverall, the values of the photosynthesis-related parameters F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e, PAR, LWP, ETR, and F\u003csub\u003eV\u003c/sub\u003e showed an upward trend from stem elongation to the booting growth stage, indicating strong photosynthetic activity before declining under stress, followed by slight recovery as the plants recovered from stress. On the other hand, the values of F\u003csub\u003eO\u003c/sub\u003e, F\u003csub\u003eM\u003c/sub\u003e, and φDo showed a downward trend, suggesting the plant's ability to absorb light energy and perform photochemistry was impaired. This may be due to a potential nutrient deficiency during the plant's stem elongation phase, a critical growth period where inadequate nutrients could lead to symptoms like slight yellowing observed on the leaf tip, which vary depending on the specific nutrient deficiency.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrincipal component analysis (PCA)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents a biplot integrating both cultivar and trait data, revealing patterns of phenotypic similarity and the relationships among traits across the evaluated wheat cultivars. The first principal component (PCA 1) accounted for 50.1% of the total variance, while the second component (PCA 2) explained an additional 28.1%, jointly capturing 78.2% of the overall variation in the dataset. The PCA 3 and PCA 4 contributed marginally (21.6% and 0.2%, respectively). Cultivars positioned closely together exhibited similar phenotypic profiles, suggesting comparable physiological or morphological responses. Conversely, cultivars plotted at opposite ends of the biplot displayed contrasting trait combinations, indicative of differing adaptive strategies. The strong loadings of specific traits along PCA 1 and PCA 2 axes highlight the major factors driving phenotypic differentiation, providing insights into the multivariate structure of cultivar performance. Overall, the PCA biplot underscores substantial phenotypic diversity and helps identify cultivars with favorable trait associations for potential use in breeding programs aimed at improving drought and heat resilience.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe experimental materials provided high-throughput data, which were analyzed to reveal genetic information related to WUE\u003csub\u003eWP\u003c/sub\u003e, δ\u003csup\u003e13\u003c/sup\u003eC, biomass accumulation, water use per plant, and chlorophyll fluorescence parameters. Thus far, there have been no relevant reports on the evolution of the whole plant and leaf WUE, heat tolerance, and δ\u003csup\u003e13\u003c/sup\u003eC among historical and modern Canadian spring wheat cultivars. This study used phylogenetic analysis, photosystem II (PSII) system, chlorophyll fluorescence capacity, Shannon diversity index, and coefficient of variation to explain how variations in whole plant and leaf WUE, heat tolerance, and δ\u003csup\u003e13\u003c/sup\u003eC can be used for the improvement of the cultivar resilience, which is important for breeding programs. The Shannon diversity index, which measures both the number and evenness of genotypes, revealed genetic diversity, while chlorophyll fluorescence parameters provided insights into cultivar performance at the physiological level.\u003c/p\u003e \u003cp\u003eBiomass accumulation varied from 9.5 to 50.6 g per plant, and water use ranged from 2.27 to 12.34 L per plant. WUE\u003csub\u003eWP\u003c/sub\u003e ranged from 3.07 to 7.81 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while δ\u003csup\u003e13\u003c/sup\u003eC values ranged from 24.06\u0026permil; to 29.33\u0026permil;. Despite substantial differences in biomass and water use among cultivars, WUE\u003csub\u003eWP\u003c/sub\u003e and δ\u003csup\u003e13\u003c/sup\u003eC exhibited relatively narrow and consistent ranges, indicating that the efficiency of water and carbon conversion into biomass is a stable trait. These findings suggested that WUE is cultivar-specific, reflecting both genetic potential and growth conditions, with some cultivars inherently more efficient at producing biomass per unit of water. Aggarwal \u0026amp; Sinha (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) reported that the average WUE for wheat ranged from 3.34 to 4.70 g dry matter per kg of water used, indicating variability in efficiency depending on environmental conditions and management practices. Humphreys et al. (2004) observed that WUE in wheat could vary significantly, with values ranging from 0.5 to 13.8 g dry matter per kg of water used, depending on factors such as growth stage, cultivar, and water stress levels. These studies highlighted the variability in wheat water-use efficiency across environments and suggest potential for improvement in Canadian cultivars.\u003c/p\u003e \u003cp\u003eThe variation in genetic diversity across breeding programs reflects a combination of the germplasm used, the traits under selection, the environmental conditions targeted, and the intensity and methodology of selection. Programs with broader objectives and more diverse parental lines typically maintain greater diversity in physiological traits such as WUE, δ\u0026sup1;\u0026sup3;C, biomass, and water use per plant. The AAFC breeding program contributed the largest number of cultivars to the panel; however, these cultivars exhibited a relatively narrow range of genetic variation for WUE\u003csub\u003eWP\u003c/sub\u003e. This suggests that, despite the greater sample size, the AAFC germplasm may share similar genetic backgrounds or selection histories for WUE-related traits. Similar patterns have been reported in other studies, where modern wheat breeding programs, despite releasing a large number of cultivars, often exhibit reduced genetic and phenotypic variation due to selection bottlenecks and the recurrent use of elite parental lines (Fu, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Semagn et al., 2021; Cheng et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tanksley \u0026amp; McCouch, 1997; Sanchez et al., 2023; Louwaars et al., 2018; McCouch, 2004; Wang et al., 2025).\u003c/p\u003e \u003cp\u003eThe δ\u0026sup1;\u0026sup3;C measurements, obtained from flag leaves exposed to heat and water stress, provided an integrated measure of photosynthetic and transpiration dynamics during stress exposure. The broad range observed (24.06\u0026permil;\u0026ndash;29.33\u0026permil;) indicated substantial genotypic variation and suggested differences in stomatal regulation, mesophyll conductance, and photosynthetic capacity among cultivars. Such variation suggested that the plants exhibited differential physiological responses to drought and heat stress, reflecting differences in stomatal regulation, mesophyll conductance, and photosynthetic capacity. Plants with low δ\u0026sup1;\u0026sup3;C values likely exhibit conservative water-use behavior and heat stress characterized by reduced stomatal conductance and enhanced WUE, whereas those with higher δ\u0026sup1;\u0026sup3;C values may favor a more acquisitive strategy aimed at sustaining higher carbon assimilation rates despite greater water loss. These physiological trade-offs underscore the complexity of breeding for drought and heat tolerance, as the optimal balance between productivity and water conservation depends on target environment and breeding objectives (Rebetzke et al., 2002; Araus et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Collectively, the variation in δ\u0026sup1;\u0026sup3;C observed under stress conditions highlights the potential of δ\u0026sup1;\u0026sup3;C as a reliable integrative trait for assessing genotypic differences in WUE and drought adaptation in wheat breeding.\u003c/p\u003e \u003cp\u003eThe negative relationship between WUE\u003csub\u003eWP\u003c/sub\u003e and δ\u0026sup1;\u0026sup3;C, commonly reported in previous studies, may be due to physiological mechanisms that regulate δ\u0026sup1;\u0026sup3;C during photosynthesis. During CO₂ fixation, plants preferentially assimilate the lighter carbon isotope (\u0026sup1;\u0026sup2;C) over the heavier isotope (\u0026sup1;\u0026sup3;C). When stomatal conductance is high and intercellular CO₂ concentration (C\u003csub\u003ei\u003c/sub\u003e) approaches ambient levels, discrimination against \u0026sup1;\u0026sup3;C increases, resulting in higher δ\u0026sup1;\u0026sup3;C values. Conversely, when stomatal conductance is reduced under stress conditions, C\u003csub\u003ei\u003c/sub\u003e declines, leading to lower discrimination and more negative δ\u0026sup1;\u0026sup3;C values. As a result, lower δ\u0026sup1;\u0026sup3;C values are generally associated with higher intrinsic WUE, reflecting a balance between maintaining photosynthetic carbon assimilation and minimizing water loss through transpiration (Farquhar et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Condon et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe phylogenetic tree showing differences in WUE\u003csub\u003eWP\u003c/sub\u003e, δ\u003csup\u003e13\u003c/sup\u003eC, biomass accumulation, and water use per plant among cultivars suggested that genetics contributed to determining these traits, and that selection for one trait may influence others. These differences indicated that the ability to use water efficiently was not uniform across cultivars, and the tree provides a visual representation of how these traits are distributed within a group of related plants based on their evolutionary history. These traits are interconnected, with δ\u003csup\u003e13\u003c/sup\u003eC serving as a proxy for WUE\u003csub\u003eWP\u003c/sub\u003e, which reflects how efficiently a plant uses water for biomass accumulation. Therefore, the discernible differences suggested that multiple factors interacted and influenced each other, indicating that the cultivars share a recent common ancestor.\u003c/p\u003e \u003cp\u003eCanadian wheat cultivars were categorized according to official marketing classes, defined primarily by grain quality traits such as grain color, hardness, kernel size, baking and milling quality, dough or gluten strength, grain protein content, and intended end-use. Swe hypothesized that WUE\u003csub\u003eWP\u003c/sub\u003e, δ\u0026sup1;\u0026sup3;C, biomass, and water use might vary across these classes due to underlying physiological differences. However, no significant differences were detected among classes for any of these traits, indicating a shared genetic background and parallel breeding histories. Because Canadian wheat breeding has historically emphasized grain yield, grain quality, and disease resistance, traits related to WUE\u003csub\u003eWP\u003c/sub\u003e have not been direct selection targets. Consequently, the classes exhibit similar physiological responses to water limitation and comparable photosynthetic adaptations to the temperate agroclimatic conditions of the Canadian Prairies.\u003c/p\u003e \u003cp\u003eThe Shannon diversity index values in the wheat panel ranged from 1.43 to 2.62, indicating low to medium diversity (1\u0026thinsp;=\u0026thinsp;low, 2.5\u0026thinsp;=\u0026thinsp;medium). The higher the values, the greater the diversity of traits (Petruccelli et al., 2013; Pan et al., 2015), indicating the genetic potential of spring wheat cultivars and the presence of desirable genes for improving WUE\u003csub\u003eWP\u003c/sub\u003e, drought tolerance, and biomass production. These results suggested a moderate level of diversity for traits related to WUE\u003csub\u003eWP\u003c/sub\u003e, δ\u0026sup1;\u0026sup3;C, biomass accumulation, and water use per plant, consistent with previous reports in wheat (Liu et al., 2024; Gelelchaa \u0026amp; Kumsab, 2023). Although the observed genetic diversity among the evaluated cultivars may be adequate for specific breeding objectives, the effective improvement of WUE\u003csub\u003eWP\u003c/sub\u003e and associated physiological traits would likely require the incorporation of additional genetic variation. Expanding the breeding pool through the strategic introduction of international germplasm could strengthen the adaptive potential of Canadian spring wheat under increasingly variable climatic conditions. However, the continuing erosion of genetic diversity among modern, high-yielding varieties remains a critical concern, reinforcing the urgency of global and regional research initiatives aimed at broadening the genetic base and safeguarding long-term crop resilience (Fu \u0026amp; Somers, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Fu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fu, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fu \u0026amp; Sommers, 2009; Alachiotis \u0026amp; Pavlidis, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vitti et al., 2016).\u003c/p\u003e \u003cp\u003eAmong the chlorophyll fluorescence parameters, previous studies highlighted that the The maximum quantum efficiency of photosystem II (F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e) is one of the most important and sensitive chlorophyll fluorescence parameters for assessing moisture and temperature stresses as it reflects the maximum efficiency of energy conversion in the photosynthetic system and provides an early and non-invasive indicator of plant stress by showing a decline in photosynthetic efficiency (Blum, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Maxwell and Johnson, 2000; Sayed, 2003; Baker and Rosengvist, 2004; Salvatori et \u003cem\u003eal\u003c/em\u003e., 2014; Guo et \u003cem\u003eal\u003c/em\u003e., 2016). These parameters are indirectly related to WUE\u003csub\u003eWP\u003c/sub\u003e through their influence on photosynthetic capacity and stomatal regulation. The F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e, together with F\u003csub\u003eM\u003c/sub\u003e and ETR, was assessed across different growth stages of wheat cultivars under combined water deficit and heat stress. While the F\u003csub\u003eV\u003c/sub\u003e, F\u003csub\u003eM\u003c/sub\u003e, and ETR showed similar variations across stages, the F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e remained the most consistent and informative indicator. The cultivar \u0026lsquo;CDC Teal\u0026rsquo; maintained the highest F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e, indicating superior tolerance, whereas \u0026lsquo;Superb\u0026rsquo; displayed the lowest values, suggesting greater stress sensitivity.\u003c/p\u003e \u003cp\u003eThe F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e is a sensitive indicator of PSII photochemical efficiency and photosynthetic performance under stress. Higher F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e values reflect reduced photoinhibition and better maintenance of photosynthetic function (Maxwell \u0026amp; Johnson, 2000; Baker, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Consistent with previous reports, cultivars with higher F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e under drought or heat stress demonstrate improved tolerance, maintaining photosynthetic activity and potentially supporting higher biomass accumulation (Kalaji et al., 2016; Farooq et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Variation in F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e among cultivars under stress likely arises from genetic differences influencing PSII stability and efficiency. Evolutionary forces, including natural selection and genetic drift, can produce shifts in allele frequencies that affect stress-responsive traits. Selection may favor alleles enhancing stress tolerance, while drift can generate stochastic changes in trait distributions independent of fitness (Futuyma, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lynch \u0026amp; Walsh, \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese results highlight the importance of PSII efficiency in determining cultivar-specific responses to combined water deficit and heat stress. Understanding genetic variation in F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e can inform breeding programs aimed at improving abiotic stress resilience in cereal crops. The observed variations in photosynthetic and fluorescence parameters across growth stages reflect the differential physiological responses of wheat to combined water deficit and heat stress. The initial increase in LWP, PAR, ETR, and F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e from stem elongation to booting suggests an enhancement in photosynthetic performance and photochemical efficiency during early stress exposure, probably due to short-term acclimation mechanisms such as osmotic adjustment and activation of photoprotective pathways (Chaves, et al, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Demmig-Adams \u0026amp; Adams, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Munns \u0026amp; Tester, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The subsequent decline in these parameters at the booting stage indicates that prolonged or intensified stress impaired photosynthetic electron transport and PSII efficiency, leading to a reduction in carbon assimilation capacity. Conversely, the increase in F\u003csub\u003e0\u003c/sub\u003e and F\u003csub\u003eM\u003c/sub\u003e followed by their decline suggests transient structural perturbations and subsequent partial recovery of PSII reaction centers, which are typical responses to photo-oxidative stress (Aro, et al, Andersson, 1993; \u0026Ouml;quist \u0026amp; Huner, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The decline in F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e and ETR at booting indicates photoinhibition and reduced photosystem II (PSII) efficiency, which are common responses to cumulative stress effects that impair the photosynthetic apparatus (Baker, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kalaji et al., 2016). The increase in F\u003csub\u003eO\u003c/sub\u003e under severe stress at booting could indicate damage to PSII reaction centers or dissociation of light-harvesting complexes, leading to a reduction in energy use efficiency (Maxwell \u0026amp; Johnson, 2000). These patterns align with reports that water deficit and high temperature synergistically exacerbate oxidative stress, leading to chlorophyll degradation, impaired electron transport, and photodamage (Havaux, \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Mathur et al., \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These patterns collectively imply that wheat plants exhibit dynamic adjustments of photochemical processes under stress, with the observed recovery phase reflecting the activation of adaptive mechanisms aimed at maintaining photosynthetic stability and resilience to environmental stress.\u003c/p\u003e \u003cp\u003eThe PCA results demonstrated differences among the studied wheat cultivars based on key physiological and agronomic traits such as WUE\u003csub\u003eWP\u003c/sub\u003e, δ\u003csup\u003e13\u003c/sup\u003eC, biomass accumulation and water use per plant, reflecting substantial phenotypic diversity within the panel. The high proportion of variance explained by the first two components (78.2%) indicated that a few major traits largely account for cultivar variability, consistent with previous findings in Canadian spring wheat populations (Fu \u0026amp; Somers, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mapfumo et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The biplot revealed that biomass accumulation and water use were strongly aligned with PC1, indicating their major contribution to overall variability. The WUE\u003csub\u003eWP\u003c/sub\u003e and δ\u003csup\u003e13\u003c/sup\u003eC were more associated with PC2, suggesting these traits captured a different dimension of variation. Cultivars clustering near traits associated with higher biomass and WUE\u003csub\u003eWP\u003c/sub\u003e may possess adaptive advantages under water-limited conditions, whereas those aligned with higher δ\u0026sup1;\u0026sup3;C values or reduced photosynthetic parameters may be more sensitive to stress. The observed trait associations highlighted potential trade-offs between productivity and water conservation, underlining the importance of selecting cultivars that balance these physiological dimensions for future breeding efforts under increasing drought and heat stress in the Canadian Prairies.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWhole-plant water-use efficiency is not always strongly correlated with δ\u003csup\u003e13\u003c/sup\u003eC, due to environmental conditions and genetic differences among cultivars. While a negative correlation between δ\u003csup\u003e13\u003c/sup\u003eC and WUE\u003csub\u003eWP\u003c/sub\u003e is often found, this relationship can be weak, leading to situations where δ\u003csup\u003e13\u003c/sup\u003eC may not be a reliable predictor of WUE\u003csub\u003eWP\u003c/sub\u003e. The spring wheat panel exhibited limited genetic diversity for WUE\u003csub\u003eWP\u003c/sub\u003e and high-temperature tolerance, suggesting that current Canadian spring wheat cultivars may not be resilient enough to withstand climate change-induced heat and drought stress, which can negatively impact yield and profitability in water-deficient environments. The weak correlation between WUE\u003csub\u003eWP\u003c/sub\u003e and δ\u003csup\u003e13\u003c/sup\u003eC could be due to low genetic diversity in the studied cultivars, as genetic variation is necessary to observe and exploit the theoretically linked relationship between WUE\u003csub\u003eWP\u003c/sub\u003e and δ\u003csup\u003e13\u003c/sup\u003eC for breeding purposes. The problem stemmed from the complex nature of these traits and the need to integrate novel genetic resources, such as those found in landraces, into elite Canadian wheat cultivars using advanced tools, including marker-assisted breeding and genomic selection. The PCA revealed phenotypic variations among cultivars, primarily driven by traits related to WUE\u003csub\u003eWP\u003c/sub\u003e, biomass, and δ\u0026sup1;\u0026sup3;C. With 78.2% of the total variance explained by the first two components, the analysis highlighted key adaptive differences and identified cultivars combining superior WUE and stress resilience for targeted breeding in Canadian spring wheat.\u003c/p\u003e \u003cp\u003eChlorophyll fluorescence measurements revealed that water deficit and heat stress significantly reduce photosynthetic efficiency in plants, with the extent of reduction varying among growth stages and cultivars. Key parameters, such as F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e and the maximum quantum yield of Photosystem II, decrease under heat stress due to damage to the photosynthetic system. This decrease indicates impaired photosynthetic performance, which can be detected through chlorophyll fluorescence and used as a tool for screening and selecting cultivars under water-deficient and heat-stress conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003ePublisher\u0026rsquo;s note\u003c/h2\u003e \u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e \u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by Saskatchewan Wheat Development Commission, Manitoba Crop Alliance, and Western Grains Research Foundation (Grant No. 20220069). The University of Alberta internal project n umber: RES0060360\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLJAC conceived the project, designed the experiments and wrote the article. MA and JZ reviewed and made critical revisions to the original project. MI and PH provided the wheat genotypes and edited the manuscript. SC, AE, MH contributed to manuscript editing, critical revisions, and improvement of the discussion. GHR supervised the execution of the project and revised the article. All authors contributed to the article and approved of the final submitted version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe original contributions presented in the study are included in the Supplementary Material. Further inquiries can be directed to the corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAggarwal, P. K. \u0026amp; Sinha, S. K. Water stress and water-use efficiency in field-grown wheat: a comparison of its efficiency with that of C4 plants. \u003cem\u003eAgric. Meteorol.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 159\u0026ndash;167 (1983).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlachiotis, N. \u0026amp; Pavlidis, P. Scalable linkage-disequilibrium-based selective sweep detection: a performance guide. GigaScience (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlsina, M. 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Acta\u003c/em\u003e. \u003cb\u003e1143\u003c/b\u003e, 113\u0026ndash;134 (1993).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Genetic diversity, spring wheat, stable isotope, abiotic stress tolerance","lastPublishedDoi":"10.21203/rs.3.rs-8281694/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8281694/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate projections predict reductions in crop-water availability and increases in the frequency of heatwaves across western Canada, posing major challenges to crop productivity and sustainability. Enhancing water use efficiency (WUE) and heat tolerance is therefore critical to improving yield stability and grain quality under future climatic conditions. In this study, 198 historical and modern Canadian spring wheat cultivars were evaluated for whole-plant and leaf-level WUE, heat tolerance, and carbon isotope discrimination (δ\u0026sup1;\u0026sup3;C) to identify physiological traits associated with adaptation to water-limited environments. The δ\u0026sup1;\u0026sup3;C was measured in flag leaves under water-deficient and high-temperature conditions. Leaf water potential (LWP), photosynthetically active radiation (PAR), chlorophyll fluorescence parameters (F₀, F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e, F\u003csub\u003eM\u003c/sub\u003e, F\u003csub\u003eV\u003c/sub\u003e), quantum yield for heat dissipation of PSII (φDo), and relative electron transport rate (ETR) were measured across six growth stages. Significant genetic variation was observed in WUE and δ\u0026sup1;\u0026sup3;C, with cultivars differing in their ability to produce biomass and grain per unit of water use. Whole-plant WUE and water use were negatively correlated with δ\u0026sup1;\u0026sup3;C, indicating that genotypes with lower δ\u0026sup1;\u0026sup3;C values tend to be more efficient in water use. Chlorophyll fluorescence traits varied markedly across growth stages: LWP, PAR, ETR, and F\u003csub\u003eV\u003c/sub\u003e/F\u003csub\u003eM\u003c/sub\u003e decreased, whereas F₀, F\u003csub\u003eM\u003c/sub\u003e, and φDo increased, from stem elongation to booting. Overall, low to moderate correlations among WUE, δ\u0026sup1;\u0026sup3;C, biomass, and water use suggest limited genetic diversity for these traits within the tested germplasm. These findings provide valuable insights for breeding climate-resilient, water-use-efficient wheat cultivars to enhance sustainability in the Canadian Prairies.\u003c/p\u003e","manuscriptTitle":"Evolution of water use efficiency, heat tolerance, and carbon isotope discrimination among Canadian spring wheat cultivars","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 11:15:31","doi":"10.21203/rs.3.rs-8281694/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e3df534-05e4-4fa8-8495-f095b082a8bd","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64672669,"name":"Biological sciences/Ecology"},{"id":64672670,"name":"Earth and environmental sciences/Ecology"},{"id":64672671,"name":"Biological sciences/Physiology"},{"id":64672672,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-03-25T11:15:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 11:15:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8281694","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8281694","identity":"rs-8281694","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}