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Article

13C Isotope Discrimination Variation in Guar [Cyamopsis tetragronoloba (L.) Taub.] Under Water-Deficit Conditions

by
Aurora Manley
1,
Waltram Ravelombola
1,2,*,
Curtis Adams
2,3,
Rajan Shrestha
2,
Philip Hinson
1 and
Calvin Trostle
2,4
1
Texas A&M AgriLife Research, 11708 Highway 70 South, Vernon, TX 76384, USA
2
Soil and Crop Sciences, Texas A&M University, 370 Olsen Blvd., College Station, TX 77843, USA
3
Columbia Plateau Conservation Research Center, USDA-ARS, 48037 Tubbs Ranch Road, Adams, OR 97810, USA
4
Texas A&M AgriLife Extension, 1102 East Drew Street, Lubbock, TX 79403, USA
*
Author to whom correspondence should be addressed.
Int. J. Plant Biol. 2025, 16(1), 31; https://doi.org/10.3390/ijpb16010031
Submission received: 18 December 2024 / Revised: 26 February 2025 / Accepted: 28 February 2025 / Published: 1 March 2025
(This article belongs to the Section Plant Response to Stresses)
Browse Figure
Versions Notes

Abstract

:
Guar is a legume cultivated for its high seed galactomannan content. India is the major guar producer globally and the U.S. has the largest guar market worldwide. Guar is drought-tolerant and suitable as a summer rotational crop in dryland farming systems. Studies have shown correlations between carbon δ13 isotope (C13) discrimination and water-use efficiency in other crops. The objective of this study was to assess the variation in carbon δ13 isotope discrimination among 30 guar accessions. Accessions were grown under greenhouse conditions in 3.79 L pots, including drought-stressed and well-watered treatments. For each accession, beginning at the V5–V8 growth stage, one pot was continuously irrigated, whereas irrigation was withheld from the other until wilting symptoms appeared after 50 days. Each treatment pair (well-watered/drought-stressed) was organized in a completely randomized design with three replications. Aboveground fresh and dry biomass data were collected, and the dry leaves were used for C13 isotope analysis. The results showed an increase in leaf C13 under drought stress. There were no differences among genotypes in C13 for well-watered plants (p = 0.63), but drought-stressed plants differed (p < 0.001). Significant positive correlations were identified between C13 under drought stress and the fresh (r = 0.70) and dry biomass (r = 0.68) of drought-stressed plants. These results demonstrate that C13 has potential as a criterion to identify drought-tolerant guar lines.

1. Introduction

Most crops are negatively affected by biotic and abiotic stresses during the growing season. Abiotic stresses are caused by drought, soil salinity, high soil aluminum levels, temperature, and other factors. Summer drought stress has a strong negative impact on crops due to high temperatures in combination with little rainfall. Low soil moisture stresses plants and reduces their productivity during the growing season [1]. Climate change impacts the frequency of drought occurrence, which is predicted to increase as the earth warms [1,2]. Agriculture uses most of the water on land [1], and drought stress is an increasing abiotic constraint affecting agriculture worldwide [3]. Thus, it is important for farmers to grow plant cultivars possessing drought tolerance. These options are available from plant breeders, who develop them by comparing new lines to the current cultivars and releasing those with improved performance capabilities. Differences among genotypes and cultivars are observed when tested for agronomic traits including drought tolerance in studies examining various crop species’ tolerance capabilities [4,5,6,7]. Drought causes greater yield loss in crops than most other factors [8]. Limited water resources affect about one-third of cultivable land. This lead breeders to focus on developing lines with broader adaptation abilities [9,10]. Studies have been conducted to assess guar drought tolerance, including those exploring the economic and environmental sustainability of guar farming, water management for seed production, drought-related gene expression, and how water stress impacts the guar yield [11,12,13,14]. Water stress also affects nutrient acquisition and allocation, yield, and the biomass production of guar [14,15].
Guar [Cyamopsis tetragonoloba (L.) Taub], often known as the cluster bean, is a crop plant in the Fabaceae family. It is grown in regions that experience drought, with about 95% of global production in India and Pakistan [16,17]. Annually, India produces 1.0 to 1.6 million tons of guar seeds, which accounts for 80% of the world’s guar production [18]. Guar is used for forage, green manure, and as a vegetable, and the seeds are used to extract guar gum for use as a gelling and thickening agent [19,20]. Guar is grown in parts of Oklahoma and Texas, USA as a dryland or catch crop [21]. In atmospheric CO2, two stable carbon isotopes (12C and 13C, 98.9% and 1.1%, respectively) naturally occur. Within plant tissues, the 13C isotope is less abundant than 12C, with levels comparable to their abundances in the atmosphere. During photosynthesis, when CO2 is taken up through the stomates, 13C is discriminated against, by Rubisco, via carbon fixation. The enzyme Rubisco and plants prefer the lighter 12C isotope to 13C. Isotope fractionation decreases as the CO2 concentration increases [22]. Carbon released from the plant during photorespiration has the same 13C value as newly fixed carbon. The isotopic composition of released CO2 in the leaf is the same as the average composition of CO2 entering the leaf during photosynthesis [23].
Labeling plants with highly enriched 13CO2 is more precise for assessing carbon allocation within plants [24]. These 13CO2 labeling studies allow for the direct quantification of both carbon fluxes and partitioning. When undergoing drought stress, plants allocate available CO2 elsewhere (i.e., roots), and the concentration of 13C increases during photosynthesis. The isotopic composition in leaves is dependent upon discrimination during CO2 fixation [25]. Both carbon isotope discrimination and water-use efficiency (WUE) are related to the ratio between the CO2 concentration in the leaf’s intercellular spaces and the air [26]. Values of 13CO2 discrimination tend to be expressed either by the carbon isotope ratio or carbon isotope discrimination [δ, negative values and ∆, positive values] [27]. The 13C/12C carbon isotopic ratio is determined using carbon dioxide from a fossil belemnite from the Pee Dee Formation (PDB) as a reference (R = 0.01124) [28]. Determining 13C levels is nondestructive and involves measuring the change in the 13C/12C ratio of the CO2 in the air as it passes through a leaf within a stirred cuvette [22]. If photosynthetic CO2 fixation discriminates against 13C, then the remaining CO2 should have a higher 13C concentration within the cuvette. The resulting discrimination is then calculated by measuring the concentration and isotopic composition of the CO2 in the air entering and leaving the cuvette with the following equation [22]: ∆ = ε(δo − δe)/(1 + δo-ε(δo − δe))
Carbon isotope discrimination decreases while water-use efficiency (WUE), the balance between plant biomass and water loss via transpiration, increases when available water is limited. This results in a more positive value for δ13C [29,30]. Irrigated plants have a more negative 13C value than drought-stressed plants as the stomata are more open [29]. When stomates close during drought, the internal CO2 concentration and photosynthetic rate are maintained while reducing water loss. Water loss is also reduced through premature leaf senescence. Both leaf senescence and decreased photosynthetic activity result in biomass and total yield loss [31]. During drought, water-use efficiency is increased after stomates close due to the rate of photosynthesis being less than that of transpiration [31]. Carbon isotope discrimination has been used as a method to identify plant germplasms with greater drought tolerance and those with more efficient water-use capabilities [25]. This analysis is quick and indirect, cost- and space-efficient, and can supply integrated information over long time periods [31]. Evaluation of the leaf water-use efficiency via gas exchange can be performed utilizing isotope mass spectrometry [32]. Carbon isotope discrimination has been utilized in sugar beet, sunflower, alfalfa, soybean, barley, etc. [33,34,35,36,37]. However, this has not been carried out in guar. Therefore, the objectives of the present study were to (1) evaluate the variation in carbon isotope discrimination (δ13C) among guar lines under drought stress, (2) investigate the relationship between carbon isotope discrimination and drought tolerance, and (3) distinguish guar lines with better potential drought adaptation.

2. Materials and Methods

2.1. Plant Materials

Thirty guar genotypes were included in this study (Table 1). Seven were advanced Texas A&M breeding lines, twenty were accessions from the USDA germplasm bank (Plant Genetic Resources Conservation Unit, Griffin, GA, USA), and three were commercially available guar cultivars in the U.S. (‘Lewis’, ‘Kinman’, and ‘Santa Cruz’) developed by Texas A&M University. USDA accessions were requested using the Germplasm Resources Information Network database (https://www.ars-grin.gov/; accessed on 3 March 2021). ‘Kinman’ was released in 1976 [38]. ‘Lewis’ and ‘Santa Cruz’ were released in 1985 [39,40].

2.2. Experimental Design and Treatments

This greenhouse study was conducted at the Texas A&M AgriLife Research & Extension Center in Vernon, TX, USA. Greenhouse day/night temperatures were maintained at 26 °C/21 °C, and the daylight length was about 14 h when the experiment was conducted. The guar seeds were sown on 10 February 2021. Eight seeds of each genotype were sown into each of six 3.8 L plastic pots filled with 380 g of LM-AP all-purpose potting soil (Lambert Peat Moss Inc., Quebec, QC, Canada). The composition of the soil medium consisted of 80–90% Canadian peat moss, horticultural perlite and vermiculite, calcitic limestone, and dolomitic limestone. All pots were watered with 1 L of water to allow sufficient moisture for seed germination. After germination and plant establishment, seedlings were thinned to one plant per pot. The six pots were separated into two treatments, well-watered and water-restricted, with three pots for each treatment combination (genotype X water). The pots were arranged in a randomized complete block design. Prior to the experiment starting, all plants were maintained in the same way. Then, once the experiment began, one set of plants were irrigated with 750 mL (provided sufficient soil moisture for guar growth) water every 7 to 9 days while the other set did not receive water, until the experiment ended, beginning at the V5–V8 growth stage until wilting symptoms appeared (~50 days after planting). The experiment was concluded at that time.

2.3. Data Collection

After stress treatment, aboveground plant biomass was harvested, placed into labeled paper bags, then weighed using a XS-410 scale (Denver Instrument Company, Denver, CO, USA) and recorded. The plant material was oven-dried at 60 °C until weight was constant and then weighed using the same scale. Sample weighing and grinding were conducted at the Texas A&M AgriLife Research and Extension center in Vernon, TX, USA. Dry leaf samples were placed into a 15 mL tubes, containing two stainless balls per tube, and ground to pass through a 2 mm sieve using a Geno Grinder set at 1500 rpm for 10 min. At least 0.4 g dry leaf samples from each pot were sent for carbon isotope analysis at the Texas A&M Stable Isotopes for Biosphere Science Laboratory. We placed 5 mg of each sample in a 96-well plate. Carbon isotope analysis was performed using an elemental analyzer interfaced with a continuous flow isotope ratio mass spectrometer. The δ13C (‰) was expressed relative to the international standard 13C/12C ratio of Vienna PeeDee Belemnite (V-PDB), obtained using the following formula: δ13C (‰) = 1000 * [(Rsample)/(Rstd − 1)], where Rsample and Rstd indicated the isotope ratios of the plant sample and standard, respectively [25].

2.4. Statistical Analysis

Descriptive statistics and analysis of variance (ANOVA) for fresh and dry biomass and carbon isotope analysis were performed using JMP Genomics® 7 (SAS Institute, Inc., Cary, NC, USA). In addition, the following indices were computed, as previously described [41]:
  • FB_Index = 100 * (FB_stress/FB_non_stress);
  • DB_Index = 100 * (DB_stress/DB_non_stress);
  • CI_Index = 100 * (CI_stress/CI_non_stress).
In the above formulas, FB_Index, and DB_Index, CI_Index indicate drought tolerance indices for fresh biomass, dry biomass, and the carbon isotope, respectively. FB_stress, DB_stress, and CI_stress represent fresh biomass, dry biomass, and the carbon isotope under drought stress, respectively. FB_non_stress, DB_non_stress, and CI_non_stress corresponded to fresh biomass, dry biomass, and the carbon isotope under the well-watered condition, respectively. Mean separation analysis was conducted using protected LSD at α = 0.05 in JMP Genomics® 7. Pearson’s correlation analysis between parameters was also computed using JMP Genomics® 7.

3. Results

3.1. Biomass

Guar genotypes differed in fresh (p = 0.0001) and dry biomass (p = 0.0024) under water stress (Table 2). For well-watered plants, there were differences among genotypes for fresh biomass (p = 0.0226), but not for dry biomass (p = 0.1208). Genotypes differed in the fresh biomass index (p = 0.0032), but not for the dry biomass index (p = 0.3609) (Table 2). Table S1 shows the mean and standard deviation (n = 3) of each trait (13C, fresh biomass, and dry biomass) and indices for plants under each treatment. Under well-watered conditions, the average dry biomass was ~1.0 g versus 0.5 g under drought stress conditions. Index values for each line are in Table S1 for dry and fresh biomass weights. The closer to 100, the more related the trait is to drought tolerance as a selection criterion. Among the lines, PI288758 had the highest dry biomass index value (77.5), followed by PI288750, PI288408, and PI338870 (74.2, 73.3, and 70.0, respectively). ‘Lewis’ had the highest index value for fresh biomass of 90.0 followed by PI338870, PI288408, and PI288743 (84.9, 80.6, and 79.4, respectively). Overall, for some lines, dry biomass was a better criterion than fresh biomass weight, and vice versa for others (i.e., 50.0 DB_I and 90.0 FB_I for Lewis, but 42.9 DB_I and 28.8 FB_I for PI288754). A few could use both, and for others, no criterion was the best (i.e., 54.0 DB_I and 53.7 DB_I for TX3, and 22.2 DB_I and 17.7 FB_I for PI338863). Plants undergoing drought stress had an average dry biomass loss of 0.5 g (Table S2).

3.2. Carbon Isotope Discrimination

Genotypes differed in carbon isotope discrimination in the drought-stressed condition (p = 0.001), but not when well-watered (p = 0.63) (Table 2). Among all guar lines in Table S1, the carbon isotope levels (‰) for stressed plants averaged −30.1‰, with a minimum and maximum value of −31.5‰ (TX4) and −27.8‰ (PI288750), respectively. The average carbon isotope level for plants under well-watered conditions was −31.3‰, with a minimum and maximum of −32.9‰ (‘Lewis’) and −26.8‰ (PI338863), respectively. The genotypes did not differ in the 13C index (p = 0.2897) (Table 2). The average was 96.7%, with a minimum of 86.4% and maximum of 117.4%, and based on these values, we surmised that 13C is a good criterion for drought tolerance selection (Table S1).
The 13C values given in Table S2 result from (13C _W–13C _S). Negative values indicate a more positive 13C value during drought stress, while positive values indicate more negative values. The average difference is −1.2‰ (13C is more positive) between well-watered and drought-stressed plants.

3.3. Relationship Between 13C and Aboveground Plant Traits

Table 3 shows Pearson’s correlation coefficients between 13C, the fresh and dry aboveground biomass under each treatment, and indices for each (Figure 1). Strong positive correlations under drought conditions were found between 13C and dry biomass (r = 0.68), 13C and fresh biomass weight (r = 0.70), fresh and dry biomass weights (r = 0.82), and fresh biomass and the fresh biomass index (r = 0.65). Dry and fresh biomass weights with 13C (r = 0.61 and r = 0.74, respectively), and fresh biomass weight with the 13C index (r = 0.74) produced strong positive correlations for well-watered conditions. Strong negative correlations were found between the following under well-watered conditions: fresh biomass index and fresh biomass weight (r = −0.77), dry biomass index and 13C index, and fresh biomass index and 13C index (r = −0.78). Even though the 13C index had a strong positive correlation with the 13C of well-watered plants, it was negatively correlated with the 13C of drought-stressed plants. Table S2 compares the differences in fresh and dry biomass as well as 13C levels under each of the two treatments (well-watered and drought-stressed).

4. Discussion

Drought stress changes cell growth and metabolism in meristematic regions, which leads to stunted growth due to reduced turgor pressure [42]. Plants experiencing water stress have a reduced cell water status, turgor and aggregate capacity, and cell growth and development. These result from stomatal closure, which can lead to wilting. Then, under extreme water stress, in hot dry environments, photosynthesis ceases, metabolism is disrupted, turgidity is lost, and cells die [43]. The stress can impact leaf, root, tiller and stem growth and development, panicle development, flower initiation, pollination, fertilization, seed development and yield, seed quality, and dry matter production [44]. Drought can also lead plants to become more susceptible to disease and insect feeding [42]. Resistance mechanisms, in plants, include escape, avoidance, and tolerance. Escape is characterized as speeding up flowering, while avoidance involves making it through stress with an increased water content to prevent tissue damage. Dropping older leaves and relocating resources helps with speeding up the flowering process for escape, while moving dry matter to roots for increased root growth to support additional water uptake aids in avoidance [31]. Tolerance is similar to avoidance, except the plants have a low internal water content while maintaining growth during the period they are under drought stress [34]. Smaller leaves, premature leaf senescence (older than younger leaves), and shorter plants result from drought stress as plants try to conserve water [45]. Stunted guar plants, smaller leaves, and leaf drop were observed among the lines evaluated in this study.
Soybeans have the highest sensitivity to drought, and this stress causes damage to soybean plants and significantly reduces the total chlorophyll content [46]. The biomass, seed yield, total number of pods on the main stem, and seed number per plant are reduced [47]. Additional studies of other crops reported height reductions due to the impact of drought stress [48]. Considering that, total plant biomass data were collected in this study. As expected, plants under drought stress had less total biomass than plants of the same genotype under well-watered conditions. Differences were observed among genotypes for biomass weight as well as index values. Similar findings were also reported in other crops [47]. This study shows a strong and positive correlation between plant biomass production and Carbon 13 under drought stress. This indicates that Carbon 13 can be used as trait to select for improved drought tolerance in guar.
When plants undergo drought stress, the rate of photosynthesis declines as the stomates close to reduce water loss and CO2 assimilation is reduced [49] Higher stomatal conductance increases CO2 diffusion into leaf tissue, which favors higher photosynthetic rates. Differences in stomatal conductance are linked to drought resistance variability among genotypes [50]. A plant with its stomates relatively closed while photosynthesis is active tends to have a less negative 13C value than one with relatively open stomates. It was previously reported that 13C values for C3 plants range from about −21 to −30‰ [51]. The observed 13C isotope values among the guar lines fall within this range. Another study found a greater presence of the 13C isotope in plants under water stress when evaluating bananas [52]. When evaluating faba beans’ potential for improving drought tolerance, much lower 13C discrimination values were found among water-stressed plants [53]. Levels of the 13C isotope in plants of different crops vary just as they did among the guar lines evaluated in this study. When under drought stress, the stomates close to conserve water, which results in CO2 scarcity in the chloroplasts and less intense discrimination [25]. With less discrimination, the amount of the 13C isotope increases and this value becomes less negative. Increased/less negative 13C isotope values were recorded from some lines in this study. Lines with the highest percent increase (represented by a negative number) of the 13C isotope include ‘Lewis’ (−13.3%), PI288408 (−10.4%), PI288743 (−11.7%), and PI338870 (−13.6%). Upon the conclusion of this study, all except five lines had relatively closed stomates due to drought stress (13C values were less negative). The 13C isotope index had a strong association with drought stress among the lines compared to both fresh and dry biomass weights. The 13C data collected between well-watered and drought-stressed plants are useful since five lines can already be eliminated, whose stomates stayed more open. Since drought stress affects plant development, it helps to consider the fresh biomass with 13C. Lines observed to have both an increased 13C isotope value and relatively low dry biomass loss included ‘Kinman’ (−3.77% 13C increase and −37.7% decrease in biomass), PI288408 (−10.4% 13C increase and −27.0% decrease in biomass), PI288750 (−5.1% 13C increase and −24.7% decrease in biomass), PI288758 (−8.4% 13C increase and −22.2% decrease in biomass), PI338870 (−13.6% 13C increase and −30.0% decrease in biomass), ‘Santa Cruz’ (−3.8% 13C increase and −32.4% decrease in biomass), TX3 (−3.5 13C increase and −41.4% decrease in biomass), and TX4 (−3.7 13C increase and −43.4% decrease in biomass). Avoiding PI338863 is strongly recommended due to a 17.5% decrease in 13C with a −77.8% decrease in biomass. This line’s stomates stayed open and a lot of dry biomass was lost as a result during drought stress.

5. Conclusions

Measurement of carbon isotope discrimination is a relatively fast and easy method for identifying guar lines with drought resistance capabilities. These data in conjunction with biomass data better identify which guar lines have the least biomass loss under drought. Among the guar genotypes studied, PI288408, PI288750, PI288758, PI338870, TX3, and TX4 stand as good lines to grow in drought conditions. The results also demonstrate that Carbon 13 can be used to assess drought tolerance and water-use efficiency in guar. Therefore, we conclude by recommending that Carbon 13 should be used as a key trait in guar breeding for an improved drought tolerance and water-use efficiency.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijpb16010031/s1, Table S1: Mean and standard deviation (n = 3) of each trait; Table S2: Difference between well-watered and drought-stressed plants.

Author Contributions

Conceptualization, A.M. and W.R.; methodology, A.M. and W.R.; formal analysis, A.M., W.R., C.A. and C.T.; writing—original draft preparation, A.M.; writing—review and editing, A.M., W.R., C.A., R.S., P.H. and C.T.; funding acquisition, W.R., C.A. and C.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the USDA National Institute of Food and Agriculture (Award No. 2018-67019-27873), the USDA-ARS New and Industrial Crop Evaluation (Grant 58-6046-4-008), and the USDA National Institute of Food and Agriculture Hatch Project (Accession Number 1025956), the USDA Ogallala Aquifer Grant 230, the Texas Department of Agriculture Specialty Block Grant Award No. GSC2024075, the Texas A&M Advancing Discovery to Market Grant 2024, and the Texas A&M Institute for Advancing Health through Agriculture Grant 2024.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chenu, K.; Cooper, M.; Hammer, G.L.; Mathews, K.L.; Dreccer, M.F.; Chapman, S.C. Environment Characterization as an Aid to Wheat Improvement: Interpreting Genotype–Environment Interactions by Modelling Water-Deficit Patterns in North-Eastern Australia. J. Exp. Bot. 2011, 62, 1743–1755. [Google Scholar] [CrossRef] [PubMed]
  2. Dai, A. Increasing Drought under Global Warming in Observations and Models. Nat. Clim. Change 2012, 3, 52–58. [Google Scholar] [CrossRef]
  3. Grayson, M. Agriculture and Drought. Nature 2013, 501, S1. [Google Scholar] [CrossRef] [PubMed]
  4. Bota, J.; Flexas, J.; Medrano, H. Genetic Variability of Photosynthesis and Water Use in Balearic Grapevine Cultivars. Ann. Appl. Biol. 2001, 138, 353–361. [Google Scholar] [CrossRef]
  5. De Herralde, F.; Savé, R.; Biel, C. Differences in Drought Tolerance in Two Almond Cultivars: ‘Lauranne’ and ‘Masbovera’. Cah. Options Mediter. 2001, 56, 149–154. [Google Scholar]
  6. Klamkowski, K.; Treder, W. Response to Drought Stress of Three Strawberry Cultivars Grown under Greenhouse Conditions. J. Fruit Ornam. Res. 2008, 16, 179–188. [Google Scholar]
  7. Tari, I.; Laskay, G.; Takács, Z.; Poór, P. Response of Sorghum to Abiotic Stresses: A Review. J. Agron. Crop Sci. 2013, 199, 264–274. [Google Scholar] [CrossRef]
  8. Kramer, P. Drought, Stress, and the Origin of Adaptations. Plant Sci. 1981, 17, 7–20. [Google Scholar]
  9. Górny, A.G. Variation in Utilization Efficiency and Tolerance to Reduced Water and Nitrogen Supply among Wild and Cultivated Barleys. Euphytica 2001, 117, 59–66. [Google Scholar] [CrossRef]
  10. Boyer, J. Advances in Drought Tolerance in Plants. Adv. Agron. 1996, 56, 187–218. [Google Scholar]
  11. Gresta, F.; De Luca, A.I.; Strano, A.; Falcone, G.; Santonoceto, C.; Anastasi, U.; Gulisano, G. Economic and Environmental Sustainability Analysis of Guar (Cyamopsis tetragonoloba L.) Farming Process in a Mediterranean Area: Two Case Studies. Ital. J. Agron. 2014, 9, 20–24. [Google Scholar]
  12. Alexander, W.L.; Bucks, D.A.; Backhaus, R.A. Irrigation Water Management for Guar Seed Production. Agron. J. 1988, 80, 447–453. [Google Scholar] [CrossRef]
  13. Alshameri, A.; Al-Qurainy, F.; Gaafar, A.R.; Khan, S.; Nadeem, M.; Alansi, S.; Shaikhaldein, H.O.; Salih, A.M. Identification of Differentially Expressed Drought-Responsive Genes in Guar [Cyamopsis tetragonoloba (L.) Taub]. Int. J. Genom. 2020, 2020, 4147615. [Google Scholar] [CrossRef] [PubMed]
  14. Stafford, R.E.; McMichael, B.L. Effect of Water Stress on Yield Components in Guar. J. Agron. Crop Sci. 1991, 166, 63–68. [Google Scholar] [CrossRef]
  15. Lotter, D.; Valentine, A.J.; Archer Van Garderen, E.; Tadross, M. Physiological Responses of a Fynbos Legume, Aspalathus Linearis to Drought Stress. S. Afr. J. Bot. 2014, 94, 218–223. [Google Scholar] [CrossRef]
  16. Meftahizadeh, H.; Ghorbanpour, M.; Asareh, M.H. Comparison of Morphological and Phytochemical Characteristics in Guar (Cyamopsis tetragonoloba L.) Landraces and Cultivars under Different Sowing Dates in an Arid Environment. Ind. Crops Prod. 2019, 140, 111606. [Google Scholar] [CrossRef]
  17. Malhotra, M.; Sharma, D. Efficiency of Guar Seed Futures Market in India: An Empirical Study. J. Appl. Financ. 2013, 19, 45. [Google Scholar]
  18. Saeed, M.; Ul Hassan, F.; Shah, Q.A.; Arain, M.A.; Abd El-Hack, M.E.; Alagawany, M.; Dhama, K. Practical Application of Guar (Cyamopsis tetragonoloba L. Taub) Meal in Poultry Nutrition. Adv. Anim. Vet. Sci. 2017, 5, 491–499. [Google Scholar] [CrossRef]
  19. Singla, S.; Grover, K.; Angadi, S.V.; Schutte, B.; VanLeeuwen, D. Guar Stand Establishment, Physiology, and Yield Responses to Planting Date in Southern New Mexico. Agron. J. 2016, 108, 2289–2300. [Google Scholar] [CrossRef]
  20. Baath, G.S.; Northup, B.K.; Rocateli, A.C.; Gowda, P.H.; Neel, J.P.S. Forage Potential of Summer Annual Grain Legumes in the Southern Great Plains. Agron. J. 2018, 110, 2198–2210. [Google Scholar] [CrossRef]
  21. Abidi, N.; Liyanage, S.; Auld, D.; Imel, R.; Norman, L.; Grover, K.; Angadi, S.; Singla, S.; Trostle, C. Challenges and Opportunities for Increasing Guar Production in the United States to Support Unconventional Oil and Gas Production. Hydraul. Fract. Impacts Technol. 2015, 207–226. [Google Scholar]
  22. Evans, J.; Sharkey, T.; Berry, J.; Farquhar, G. Carbon Isotope Discrimination Measured Concurrently with Gas Exchange to Investigate CO2 Diffusion in Leaves of Higher Plants. Funct. Plant Biol. 1986, 13, 281–292. [Google Scholar] [CrossRef]
  23. Tabassum, T.; Farooq, M.; Ahmad, R.; Zohaib, A.; Wahid, A.; Shahid, M. Terminal Drought and Seed Priming Improves Drought Tolerance in Wheat. Physiol. Mol. Biol. Plants 2018, 24, 845–856. [Google Scholar] [CrossRef] [PubMed]
  24. Simard, S.W.; Durall, D.M.; Jones, M.D. Carbon Allocation and Carbon Transfer between Betula Papyrifera and Pseudotsuga Menziesii Seedlings Using a 13C Pulse-Labeling Method. Plant Soil 1997, 191, 41–55. [Google Scholar] [CrossRef]
  25. Farquhar, G. Carbon Isotope Fractionation and Plant Water-Use Efficiency. Stable Isot. Ecol. Res. 1988, 12, 102–134. [Google Scholar]
  26. Farquhar, G.; O’Leary, M.; Berry, J. On the Relationship between Carbon Isotope Discrimination and the Intercellular Carbon Dioxide Concentration in Leaves. Aust. J. Plan 1982, 9, 121–137. [Google Scholar] [CrossRef]
  27. Rebetzke, G.J.; Condon, A.G.; Richards, R.A.; Farquhar, G.D. Selection for Reduced Carbon Isotope Discrimination Increases Aerial Biomass and Grain Yield of Rainfed Bread Wheat. Crop Sci. 2002, 42, 739–745. [Google Scholar] [CrossRef]
  28. Craig, H. Isotopic Standards for Carbon and Oxygen and Correction Factors for Mass-Spectrometric Analysis of Carbon Dioxide. Geochim. Cosmochim Acta 1957, 12, 133–149. [Google Scholar] [CrossRef]
  29. Hubick, K. Effects of Nitrogen Source and Water Limitation on Growth, Transpiration Efficiency and Carbon-Isotope Discrimination in Peanut Cultivars. Funct. Plant Biol. 1990, 17, 413–430. [Google Scholar] [CrossRef]
  30. Knight, J.D.; Livingston, N.J.; Van Kessel, C. Carbon Isotope Discrimination and Water-Use Efficiency of Six Crops Grown under Wet and Dryland Conditions. Plant Cell Env. 1994, 17, 173–179. [Google Scholar] [CrossRef]
  31. Rytter, R.M. Water Use Efficiency, Carbon Isotope Discrimination and Biomass Production of Two Sugar Beet Varieties Under Well-Watered and Dry Conditions. J. Agron. Crop Sci. 2005, 191, 426–438. [Google Scholar] [CrossRef]
  32. Pate, J.S.; Dawson, T.E. Assessing the Performance of Woody Plants in Uptake and Utilisation of Carbon, Water and Nutrients: Implications for Designing Agricultural Mimic Systems. Agrofor. Syst. 1999, 45, 245–275. [Google Scholar] [CrossRef]
  33. Bloch, D.; Hoffmann, C.M.; Märländer, B. Impact of Water Supply on Photosynthesis, Water Use and Carbon Isotope Discrimination of Sugar Beet Genotypes. Eur. J. Agron. 2006, 24, 218–225. [Google Scholar] [CrossRef]
  34. Canavar, O.; Gotz, K.; Ellmer, F.; Chmielewski, F.; Kaynak, M. Determination of the Relationship between Water Use Efficiency, Carbon Isotope Discrimination and Proline in Sunflower Genotypes under Drought Stress. Aust. J. Crop Sci. 2014, 8, 232–242. [Google Scholar]
  35. Moghaddam, A.; Raza, A.; Vollmann, J.; Ardakani, M.R.; Wanek, W.; Gollner, G.; Friedel, J.K. Carbon Isotope Discrimination and Water Use Efficiency Relationships of Alfalfa Genotypes under Irrigated and Rain-Fed Organic Farming. Eur. J. Agron. 2013, 50, 82–89. [Google Scholar] [CrossRef]
  36. Gitz, D.C.; Liu-Gitz, L.; Britz, S.J.; Sullivan, J.H. Ultraviolet-B Effects on Stomatal Density, Water-Use Efficiency, and Stable Carbon Isotope Discrimination in Four Glasshouse-Grown Soybean (Glyicine max) Cultivars. Env. Exp. Bot. 2005, 53, 343–355. [Google Scholar] [CrossRef]
  37. Adiredjo, A.L.; Navaud, O.; Muños, S.; Langlade, N.B.; Lamaze, T.; Grieu, P. Genetic Control of Water Use Efficiency and Leaf Carbon Isotope Discrimination in Sunflower (Helianthus annuus L.) Subjected to Two Drought Scenarios. PLoS ONE 2014, 9, e101218. [Google Scholar] [CrossRef]
  38. Stafford, R.E.; Kirby, J.S.; Kinman, M.L.; Lewis, C.R. Registration of Kinman and Esser Guar 1 (Reg. No. 4 to 5). Crop Sci. 1976, 16, 310. [Google Scholar]
  39. Ray, D.T.; Stafford, R.E. Registration of ‘Santa Cruz’ Guar. Crop Sci. 1985, 25, 1124–1125. [Google Scholar] [CrossRef]
  40. Stafford, R.E.; Ray, D.T. Registration of Lewis Guar. Crop Sci. 1985, 25, 365. [Google Scholar] [CrossRef]
  41. Saad, F.F.; El-Mohsen, A.A.A.; Abd, M.A.; Al-Soudan, I.H. Effective Selection Criteria for Evaluating Some Barley Crosses for Water Stress Tolerance. Adv. Agric. Biol. 2014, 2, 112–123. [Google Scholar] [CrossRef]
  42. Jaleel, C.; Manivannan, P.; Wahid, A. Drought Stress in Plants: A Review on Morphological Characteristics and Pigments Composition. Int. J. Agric. Biol. 2009, 11, 100–105. [Google Scholar]
  43. Bohnert, H.J.; Jensen, R.G. Strategies for Engineering Water-Stress Tolerance in Plants. Trends Biotechnol. 1996, 14, 89–97. [Google Scholar] [CrossRef]
  44. Jaleel, C.A.; Manivannan, P.; Sankar, B.; Kishorekumar, A.; Gopi, R.; Somasundaram, R.; Panneerselvam, R. Water Deficit Stress Mitigation by Calcium Chloride in Catharanthus Roseus: Effects on Oxidative Stress, Proline Metabolism and Indole Alkaloid Accumulation. Colloids Surf B Biointerfaces 2007, 60, 110–116. [Google Scholar] [CrossRef]
  45. Avramova, V.; Abdelgawad, H.; Zhang, Z.; Fotschki, B.; Casadevall, R.; Vergauwen, L.; Knapen, D.; Taleisnik, E.; Guisez, Y.; Asard, H.; et al. Drought Induces Distinct Growth Response, Protection, and Recovery Mechanisms in the Maize Leaf Growth Zone. Plant Physiol. 2015, 169, 1382–1396. [Google Scholar] [CrossRef] [PubMed]
  46. Zheng-lin, W.; You-zhi, Z. Effects of Exogenous Auxin on Physiological and Biochemical Characteristics of Soybean under PEG Simulated Drought Stress. Hubei Agric. Sci. 2019, 58, 16. [Google Scholar]
  47. Yordanov, I.; Velikova, V.; Tsonev, T. Plant Responses to Drought and Stress Tolerance. J. Plant Physiol. 2003, 2, 187–206. [Google Scholar]
  48. Specht, J.E.; Chase, K.; Macrander, M.; Graef, G.L.; Chung, J.; Markwell, J.P.; Germann, M.; Orf, J.H.; Lark, K.G. Soybean Response to Water: A QTL Analysis of Drought Tolerance. Crop Sci. 2001, 41, 493–509. [Google Scholar] [CrossRef]
  49. Arnau, G.; Monneveux, P.; This, D.; Alegre, L. Photosynthesis of Six Barley Genotypes as Affected by Water Stress. Photosynthetica 1998, 34, 67–76. [Google Scholar] [CrossRef]
  50. Blum, A.; Mayer, J.; Golan, G. Agronomic and Physiological Assessments of Genotypic Variation for Drought Resistance in Sorghum. Aust. J. Agric. Res. 1989, 40, 49–61. [Google Scholar] [CrossRef]
  51. Ehleringer, J.R. Carbon Isotope Ratios and Physiological Processes in Aridland Plants. Ecol. Stud. 1989, 68, 41–54. [Google Scholar]
  52. Kissel, E.; van Asten, P.; Swennen, R.; Lorenzen, J.; Carpentier, S.C. Transpiration Efficiency versus Growth: Exploring the Banana Biodiversity for Drought Tolerance. Sci. Hortic. 2015, 185, 175–182. [Google Scholar] [CrossRef]
  53. Khan, H.U.R.; Link, W.; Hocking, T.J.; Stoddard, F.L. Evaluation of Physiological Traits for Improving Drought Tolerance in Faba Bean (Vicia faba L.). Plant Soil 2007, 292, 205–217. [Google Scholar] [CrossRef]
Ijpb 16 00031 g001
Figure 1. Scatter plot matrix between variables. C13_S: Carbon 13 under drought stress, DB_S: dry biomass weight under drought stress, FB_S: fresh biomass weight under drought stress, C13_W: Carbon 13 under non-drought stress, DB_W: dry biomass weight under non-drought stress, FB_W: dry biomass weight under non-drought stress, C13_I: Carbon 13 index, DB_I: dry biomass index, and FB_I: fresh biomass index.
Figure 1. Scatter plot matrix between variables. C13_S: Carbon 13 under drought stress, DB_S: dry biomass weight under drought stress, FB_S: fresh biomass weight under drought stress, C13_W: Carbon 13 under non-drought stress, DB_W: dry biomass weight under non-drought stress, FB_W: dry biomass weight under non-drought stress, C13_I: Carbon 13 index, DB_I: dry biomass index, and FB_I: fresh biomass index.
Ijpb 16 00031 g001
Table 1. List of guar genotypes phenotyped for their carbon isotope and other aboveground traits for drought stress.
Table 1. List of guar genotypes phenotyped for their carbon isotope and other aboveground traits for drought stress.
Plant_IDOriginTaxonomy
TX1USACyamospsis tetragonoloba (L.) Taub.
TX2USACyamospsis tetragonoloba (L.) Taub.
TX3USACyamospsis tetragonoloba (L.) Taub.
TX4USACyamospsis tetragonoloba (L.) Taub.
TX5USACyamospsis tetragonoloba (L.) Taub.
TX6USACyamospsis tetragonoloba (L.) Taub.
TX7USACyamospsis tetragonoloba (L.) Taub.
“Kinman”USACyamospsis tetragonoloba (L.) Taub.
“Lewis”USACyamospsis tetragonoloba (L.) Taub.
“Santa Cruz”USACyamospsis tetragonoloba (L.) Taub.
PI 183400IndiaCyamospsis tetragonoloba (L.) Taub.
PI 275322IndiaCyamospsis tetragonoloba (L.) Taub.
PI 275323IndiaCyamospsis tetragonoloba (L.) Taub.
PI 288389IndiaCyamospsis tetragonoloba (L.) Taub.
PI 288408IndiaCyamospsis tetragonoloba (L.) Taub.
PI 288409IndiaCyamospsis tetragonoloba (L.) Taub.
PI 288425IndiaCyamospsis tetragonoloba (L.) Taub.
PI 288743IndiaCyamospsis tetragonoloba (L.) Taub.
PI 288750IndiaCyamospsis tetragonoloba (L.) Taub.
PI 288754IndiaCyamospsis tetragonoloba (L.) Taub.
PI 288758IndiaCyamospsis tetragonoloba (L.) Taub.
PI 288759IndiaCyamospsis tetragonoloba (L.) Taub.
PI 322775IndiaCyamospsis tetragonoloba (L.) Taub.
PI 323002IndiaCyamospsis tetragonoloba (L.) Taub.
PI 338796IndiaCyamospsis tetragonoloba (L.) Taub.
PI 338811IndiaCyamospsis tetragonoloba (L.) Taub.
PI 338863IndiaCyamospsis tetragonoloba (L.) Taub.
PI 338865IndiaCyamospsis tetragonoloba (L.) Taub.
PI 338870IndiaCyamospsis tetragonoloba (L.) Taub.
PI 340513IndiaCyamospsis tetragonoloba (L.) Taub.
Table 2. Analysis of variance (ANOVA) table for guar traits evaluated under drought stress and well-watered conditions.
Table 2. Analysis of variance (ANOVA) table for guar traits evaluated under drought stress and well-watered conditions.
Traits *SourceDFMean SquareF RatioProb > F
C13_S (‰) **Genotype263.163.020.001
Error381.05
C. Total64
DB_S (g) **Genotype260.053.660.0001
Error380.01
C. Total64
FB_S (g) **Genotype260.82.740.0024
Error380.29
C. Total64
C13_W (‰)Genotype263.210.880.63
Error383.66
C. Total64
DB_W (g)Genotype260.11.510.1208
Error380.06
C. Total64
FB_W (g)Genotype266.062.030.0226
Error382.98
C. Total64
C13_IGenotype2662.921.210.2897
Error3851.93
C. Total64
DB_IGenotype26315.251.130.3609
Error38279.46
C. Total64
FB_I **Genotype26703.342.640.0032
Error38266.9
C. Total64
* C13_S: Carbon 13 under drought stress, DB_S: dry biomass weight under drought stress, FB_S: fresh biomass weight under drought stress, C13_W: Carbon 13 under non-drought stress, DB_W: dry biomass weight under non-drought stress, FB_W: dry biomass weight under non-drought stress, C13_I: Carbon 13 index, DB_I: dry biomass index, and FB_I: fresh biomass index, ** significance at 0.05.
Table 3. Pearson’s correlation coefficients between Carbon 13 isotope discrimination and fresh and dry aboveground biomass under well-watered conditions and drought stress.
Table 3. Pearson’s correlation coefficients between Carbon 13 isotope discrimination and fresh and dry aboveground biomass under well-watered conditions and drought stress.
Traits **C13_SDB_SFB_SC13_WDB_WFB_WC13_IDB_IFB_I
C13_S−−−−−−−−−
DB_S0.68 *−−−−−−−−
FB_S0.70.82−−−−−−−
C13_W0−0.03−0.13−−−−−−
DB_W0.150.380.270.61−−−−−
FB_W−0.24−0.06−0.120.740.58−−−−
C13_I−0.59−0.41−0.510.810.420.74−−−
DB_I0.470.540.47−0.51−0.52−0.49−0.69−−
FB_I0.560.490.65−0.57−0.12−0.77−0.780.52−
* Bold indicates a significant correlation at p < 0.05. ** C13_S: Carbon 13 under drought stress, DB_S: dry biomass weight under drought stress, FB_S: fresh biomass weight under drought stress, C13_W: Carbon 13 under non-drought stress, DB_W: dry biomass weight under non-drought stress, FB_W: dry biomass weight under non-drought stress, C13_I: Carbon 13 index, DB_I: dry biomass index, and FB_I: fresh biomass index.
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MDPI and ACS Style

Manley, A.; Ravelombola, W.; Adams, C.; Shrestha, R.; Hinson, P.; Trostle, C. 13C Isotope Discrimination Variation in Guar [Cyamopsis tetragronoloba (L.) Taub.] Under Water-Deficit Conditions. Int. J. Plant Biol. 2025, 16, 31. https://doi.org/10.3390/ijpb16010031

AMA Style

Manley A, Ravelombola W, Adams C, Shrestha R, Hinson P, Trostle C. 13C Isotope Discrimination Variation in Guar [Cyamopsis tetragronoloba (L.) Taub.] Under Water-Deficit Conditions. International Journal of Plant Biology. 2025; 16(1):31. https://doi.org/10.3390/ijpb16010031

Chicago/Turabian Style

Manley, Aurora, Waltram Ravelombola, Curtis Adams, Rajan Shrestha, Philip Hinson, and Calvin Trostle. 2025. "13C Isotope Discrimination Variation in Guar [Cyamopsis tetragronoloba (L.) Taub.] Under Water-Deficit Conditions" International Journal of Plant Biology 16, no. 1: 31. https://doi.org/10.3390/ijpb16010031

APA Style

Manley, A., Ravelombola, W., Adams, C., Shrestha, R., Hinson, P., & Trostle, C. (2025). 13C Isotope Discrimination Variation in Guar [Cyamopsis tetragronoloba (L.) Taub.] Under Water-Deficit Conditions. International Journal of Plant Biology, 16(1), 31. https://doi.org/10.3390/ijpb16010031

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