Morphological Traits, Yield and Nutrient Uptake of Safflower Cultivars Under Water Stress Conditions

Document Type : Research Paper

Authors

1 Former student of Agroecology, Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Darab, Iran.

2 Assistant Professor, Department of Agroecology, Darab College of Agriculture and Natural Resources, Shiraz University, Shiraz, Iran.

3 Associate Professor of Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Darab, Iran.

4 Technisian of Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Darab, Iran.

Abstract

 
In order to investigate the genetic diversity of safflower cultivars based on yield, yield components, and mineral nutrient uptake under water stress conditions, a factorial experiment was conducted in a completely randomized design with three replicates at the research greenhouse of the Faculty of Agriculture and Natural Resources, Darab, Shiraz University. The experimental factors included three irrigation levels (normal irrigation, irrigation cut off at flowering stage, and irrigation cut off at head formation stage) and seven cultivars (Faraman, Sina, Omid, Padideh, Goldasht, Parnian, and Golmehr). The aim of this study was to evaluate the genetic variation among safflower cultivars in terms of grain yield, biological yield, plant height, 100 seed weight, number of seeds per head, head diameter, number of heads per plant under water stress conditions, as well as to assess mineral nutrient uptake from soil under such conditions. The results showed that the interaction between irrigation levels and cultivars had a highly significant effect (p ≤ 0.01) on all morphological traits, except for the number of secondary branches. The highest grain yield (31 g) was observed in the Parnian cultivar under normal irrigation. Seed yield of all cultivars decreased under water stress conditions, however, the highest yield (15.8 g per plant) in this condition related to Omid cultivar which also had the minimum variations among the cultivars. The results of this experiment showed that the highest genetic variation of cultivars appeared under normal irrigation conditions as well as high variations of nutrient uptakes.

Keywords


Background and Objectives

Water deficiency is a major abiotic stress that severely limits agricultural productivity, particularly in arid and semi-arid regions where crops like safflower are cultivated. Drought stress during critical growth stages can disrupt physiological processes, leading to substantial reductions in yield and alterations in nutrient acquisition. Understanding the genetic variation among cultivars in their response to water deficit is crucial for developing resilient crop varieties. The objectives of this study were to: i) investigate the genetic diversity of seven safflower cultivars under different irrigation regimes, ii) quantify the impact of water stress timing on grain yield, biomass, and morphological traits, and iii) assess how cultivar and irrigation interactions affect the uptake and concentration of key mineral nutrients (Cu, Fe, P, K) in the seeds.

Materials and Methods

A factorial experiment was conducted in a completely randomized design with three replicates at the research greenhouse of the Faculty of Agriculture and Natural Resources, Darab, Shiraz University. The experimental factors included three irrigation levels: normal irrigation, water stress imposed at the flowering stage, and water stress imposed at the head formation stage. These treatments were applied to seven distinct Iranian safflower cultivars: Faraman, Sina, Omid, Padideh, Goldasht, Parnian, and Golmehr. Standard agronomic practices were maintained for all groups except for the targeted water stress treatments. At physiological maturity, the plants were harvested, and data were collected on agronomic traits including grain yield, biomass, and the number of secondary branches. Additionally, seed samples were analyzed using appropriate laboratory techniques to determine the concentrations of copper, iron, phosphorus, and potassium.

Results

The statistical analysis revealed that the interaction between irrigation levels and cultivars had a highly significant effect (p ≤ 0.01) on nearly all measured morphological and agronomic traits. The only exception was the number of secondary branches, which was not significantly influenced by this interaction. In terms of yield, the Parnian cultivar demonstrated superior performance under optimal conditions, producing the highest grain yield of 31 grams per plant and the maximum biomass of 135 grams per plant. However, this cultivar was highly sensitive to water stress, suffering a drastic 58% reduction in grain yield and a 42% decrease in biomass when water was withheld at the flowering stage. The other cultivars exhibited varying degrees of tolerance, but none matched Parnian's yield under normal irrigation or its level of decline under stress. Regarding mineral nutrition, the irrigation-by-cultivar interaction also significantly (p ≤ 0.01) affected all seed nutrient uptake traits. The highest seed concentrations of copper, iron, phosphorus, and potassium were consistently recorded in the Parnian cultivar under normal irrigation. A clear pattern emerged where these optimal nutrient concentrations were significantly diminished across all cultivars when subjected to water stress, with the most severe reductions again occurring due to flowering-stage stress. This indicates that stress timing is critical, with the flowering stage being more detrimental to both yield and nutrient accumulation than the head formation stage.

Conclusion

This study conclusively demonstrates a significant genetic diversity among safflower cultivars in their physiological and agronomic response to water stress. The Parnian cultivar, while high-yielding under optimal irrigation, proved to be highly susceptible to drought, particularly when it occurred during the flowering stage. The findings highlight that the timing of water stress is a critical factor, with the flowering stage identified as the most sensitive period for irreversible damage to yield and nutrient partitioning. The strong interaction effect on seed mineral content further reveals that water stress not only reduces yield but also compromises the nutritional quality of the harvested crop. Therefore, selecting for drought tolerance specific to the flowering stage should be a key breeding objective. For cultivation in drought-prone environments, it is recommended to avoid highly sensitive cultivars like Parnian in favor of more stable, though potentially lower-yielding, alternatives under optimal conditions. Future research should focus on the physiological mechanisms conferring tolerance in the more resilient cultivars to inform marker-assisted breeding programs.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

The study was funded by Shiraz University.

Authorship contribution

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

Declaration of Generative AI and AI-assisted technologies in the writing process

Authors declare that they have not used any AI and AI assisted program and technologies in this paper.

Data availability statement

Data of this study are available by email to the corresponding author.

Acknowledgements

The authors would like to appreciate Shiraz University for financial support of this research.

The authors would like to thank anonymous reviewers for their valuable suggestions in manuscript revision. We also would like to thank chief editor of Iranian Journal of Soil and Water Research for his valuable comments during the publication of this paper.

Ethical considerations

The authors avoided data fabrication, falsification, and plagiarism, and any form of misconduct.

Conflict of interest

The authors declare no conflict of interest. 

Afshar, M.H., Bulut, B., Duzenli, E., Amjad, M., & Yilmaz, M. (2022). Global spatiotemporal consistency between meteorological and soil moisture drought indices. Agric. For. Meteorol. 316, 108848.
Ahmed, K, Shahid, S., & Nawaz, N. (2018). Impacts of climate variability and change on seasonal drought characteristics of Pakistan. Atmospheric Research, 214, 364–374. 10.1016/j.atmosres.2018.08.020. 
Alexandersson, H. (1986) A homogeneity test applied to precipitation data. Journal of Climatology, 6(6), 661–675.
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
Bahrami, M., & Mahmoudi, M.R. (2020). Rainfall modelling using backward generalized estimating equations: a case study for Fasa plain, Iran. Meteorol Atmos Phys. 132, 771-779.
Bahrami, M., Bazrkar, S., & Zarei, A.R. (2019). Modeling, prediction and trend assessment of drought in Iran using standardized precipitation index. J Water Clim Change, 10(1), 181-196.
Barahooie, D., Hamidianpour, M., & Shoja, F. (2025). Identification of Spatiotemporal Drought Patterns in Southeastern Iran Using a Graphical Trend Analysis Approach. Physical Geography Research Quarterly, 57 (2), 77-98. http://doi.org/10.22059/jphgr.2025.398145.1007893  (In Persian)
Bazrafshan, J. (2017). Effect of air temperature on historical trend of long-term droughts in different climates of Iran. Water Resources Management, 31(14), 4683-4698.
Bickici Arikan, B., & Kahya, E. (2019). Homogeneity revisited: analysis of updated precipitation series in Turkey. Theoretical and Applied Climatology, 135(1), 211-220.
Bozorgzadeh, M., jahantigh, H., Rigi, M. & mohammadi, M. (2024). comprehensive assessment of drought severity with multi-indicator approach in saravan city-sistan and baluchistan province. ournal of Climate Change Research, 5(20), 19-32. (In Persian)
Dai, A. (2011). Drought under global warming: a review.Wiley Interdiscip. Rev Clim Chang. 2, 45–65. https://doi.org/10.1002/wcc.81  
Darroudi, H., Khosroshahi, M., & Shahabi, M. (2022). Investigating variations in climatic factors and drought trends in Sistan and Baluchestan Province. Desert Ecosystem Engineering, 10(32), 15-30. doi: 10.22052/deej.2021.10.32.11. (In Persian)
Dashtpagerdi, M. M., Kousari, M. R., Vagharfard, H., Ghonchepour, D., Hosseini, M. E., & Ahani, H. (2015). An investigation of drought magnitude trend during 1975–2005 in arid and semi-arid regions of Iran. Environmental earth sciences, 73(3), 1231-1244.
de Medeiros, F.J., Gomes, R.d.S., Coutinho, M.D.L., & Lima, K.C. (2022). Meteorological droughts and water resources: Historical and future perspectives for Rio Grande do Norte state, Northeast Brazil. Int. J. Climatol, 42, 6976–6995.
Deldarzehi, Z., Mahmoudi, P. & Khosravi, M. (2024). Arabian Sea’s Moisture Transfer Mechanisms in Pervasive Dry and Wet Periods of Iran. Geography and Environmental Planning35(1), 45-72. (In Persian)
Du, W., & Wang, G. (2013). Intra-event spatial correlations for cumulative absolute velocity, arias intensity, and spectral accelerations based on regional site conditions. Bull. Seismol. Soc. Am, 103, 1117–1129.
Fawen, L., Manjing, Z., Yong, Z., & Rengui, J. (2023). Influence of irrigation and groundwater on the propagation of meteorological drought to agricultural drought. Agric. Water Manag, 277, 108099.
Firoozi, F., Mahmoudi, P., Jahanshahi, S.M.A., Tavousi, T., Liu., Y., & Liang, Zh. (2020).  Modeling changes trend of time series of land surface temperature (LST) using satellite remote sensing productions (case study: Sistan plain in east of Iran). Arab J Geosci, 13, 367. https://doi.org/10.1007/s12517-020-05314-w
Golian, S., Mazdiyasni, O., & AghaKouchak, A. (2015). Trends in meteorological and agricultural droughts in Iran. Theoretical and Applied Climatology, 119(3), 679–688.
Guhathakurta, P., Menon, P., Mazumdar, A. B., & Sreejith, O. P. (2010). Changes in extreme rainfall events and flood risk in India during the last century. National Climatic Centre, Research Report, 3, 1-20.
Gurrapu, S., Chipanshi, A., Sauchyn, D., & Howard, A. (2014). Comparison of the SPI and SPEI on predicting drought conditions and streamflow in the Canadian prairies. In: 28th Conference on Hydrology and the 26th Conference on Climate Variability and Change. American Metereological Society, Georgia, p7.
Hoover, D.L., Hajek, O.L., Smith, M.D., Wilkins, K., Slette, I.J., & Knapp, A.K. (2022). Compound hydroclimatic extremes in a semi-arid grassland: Drought, deluge, and the carbon cycle. Glob. Chang. Biol, 28, 2611–2621.
IPCC. (2013). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Special Report of the Intergovernmental Panel on Climate Change (Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G.-K., Allen, S. K., Tignor, M. & Midgley, P. M., eds), Cambridge University Press, Cambridge, UK and New York, NY, USA.
Karimi, M., Khoshakhlagh, F., shamsi por, A. A. and noruzi, F. (2019). Arabian subtropical High Pressure circulation patterns in the middle troposphere and its relationship with Iran's Precipitation. Journal of Geography and Planning, 23(69), 233-255. (In Persian)
Kendall, M.G. (1975). Rank Correlation Methods, 4th edition, Charles Griffin, London.
Keshavarz, A. (2025). Trends in Meteorological Drought in Iran Using the SPI Index and Mann-Kendall Test: A Comprehensive Review. Journal of Asian Geography, 4 (2), 79-83.
Kheyruri, Y., Nikaein, E., & Sharafati, A. (2023). Spatial monitoring of meteorological drought characteristics based on the NASA POWER precipitation product over various regions of Iran. Environ. Sci. Pollut. Res, 30, 43619–43640.
Kousari, M. R., Dastorani, M. T., Niazi, Y., Soheili, E., Hayatzadeh, M., & Chezgi, J. (2014). Trend detection of drought in arid and semi-arid regions of Iran based on implementation of reconnaissance drought index (RDI) and application of non-parametrical statistical method. Water resources management28(7), 1857-1872.
Liu, L., Liao, J., Chen, X., Zhou, G., Su, Y., Xiang, Z., ... & Shao, H. (2017). The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010). Remote Sensing of Environment, 199, 302-320.
Liu, Y., & Chen, J. (2021). Socioeconomic risk of droughts under a 2.0 C warmer climate: Assessment of population and GDP exposures to droughts in China. Int. J. Climatol. 41, E380–E391.
Lotfinasab Asal, S., Dost, G, A., & Khosroshahi, M. (2018). Assessment and application of geostatistics in identifying and analyzing drought characteristics of Jazmourian watershed. Watershed Manage Res. 1(18), 12-25.
Lotfirad, M., Esmaeili-Gisavandani, H., & Adib, A. (2022). Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran. Journal of Water and Climate Change13(2), 383-406.
Mahmoudi, P., Rigi, A., & Miri Kamak, M. (2019). A comparative study of precipitation-based drought indices with the aim of selecting the best index for drought monitoring in Iran: P. Mahmoudi et al. Theoretical and Applied Climatology, 137(3), 3123-3138.
Mahmoudi, P., Shirazi, S.A., Firoozi, F., Jahanshahi, S.M.A., & Mazhar, N. (2020). Detection of land cover changes in Balouchestan (shared between Iran, Pakistan, and Afghanistan) using the MODIS Land Cover Product. Arab. J. Geosci, 13, 1-14.
Mann, H.B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 245-259.
McKee, T.B., Doesken, N.J., & Kleist J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology: American Meteorological Society, 17(22), 179-183.
Mehdizadeh, S, Ahmadi, F, Mehr, AD, & Safari, MJS. (2020). Drought modeling using classic time series and hybrid wavelet-gene expression programming models. Journal of Hydrology, 587, Article 125017.10.1016/j.jhydrol.2020.125017.
Mirzavand, M., & Bagheri, R. (2020). The water crisis in Iran: development or destruction? World Water Policy, 6(1), 89-97.
Nouri, M., & Homaee, M. (2020). Drought trend, frequency and extremity across a wide range of climates over Iran. Meteorological Applications, 27(2), e1899.
Omidvar, K., Nabavizadeh, M., Rousta, I., & Olafsson, H. (2024). Remote sensing-based drought monitoring in Iran’s sistan and balouchestan province. Atmosphere15(10), 1211.
Pearson, K. (1897). Mathematical contributions to the theory of evolution. on a form of spurious correlation which may arise when indices are used in the measurement of organs. Proceedings of the royal society of London, 60 (359-367), 489-498.
Pettitt, A. N. (1979). A Non-Parametric Approach to the Change-Point Problem Journal of the Royal Statistical Society Series C (Applied Statistics), 28, 126–135. https://doi.org/10.2307/2346729  
Pourasghar, F., Ghaemi, H., Jahanbakhsh, S. & Sarisarraf, B. (2017). Variability of Precipitation in Southern Part of Iran and Linkage to Indian Ocean Sea Surface Temperature. Geography and Environmental Planning28(2), 145-166. (In Persian)
Qutbudin, I, Shiru, MS, Sharafati, A, Ahmed, K, Al-Ansari, N, Yaseen, ZM, Shahid, S, & Wang, X. (2019). Seasonal drought pattern changes due to climate variability: Case study in Afghanistan. Water, 11 (5), 1096.10.3390/w11051096. 
Raza, A., Mubarik, M.S., Sharif, R., Habib, M., Jabeen, W., Zhang, C., Chen, H., Chen, Z.H., Siddique, K.H., & Zhuang, W. (2023). Developing drought-smart, ready-to-grow future crops. Plant Genome. 16, e20279.
Saeidipou, M., Radmanesh, F., & Eslamian, S. (2019). Metreological drought monitoring using the multivariate index of SPEI (case study: Karun Basin). AUT J Civ Eng, 3, 85–92. https://doi.org/10.22060/ajce.2018.14740.5494
Saemian, P., Tourian, M.J., AghaKouchak, A., Madani, K., & Sneeuw, N. (2022). How much water did Iran lose over the last two decades? J Hydrology: Reg Stud, 41, 101095,
Salimi, H., Asadi, E., & Darbandi, S. (2021). Meteorological and hydrological drought monitoring using several drought indices. Applied Water Science, 11(2), 1-10.
Sen, P.K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379-1389.
Sharafi, S., & Ghaleni, M. M. (2022). Spatial assessment of drought features over different climates and seasons across Iran. Theoretical and Applied Climatology, 147(3), 941-957.
Siasar, H. & Salari, A. (2023). Predicting the probability of droughts using SPI drought index based on Markov chain model (Case study: Villages of Sistan and Baluchistan province). Rural Development Strategies, 10(3), 387-402. (In Persian)
Siasar, H., Salari, A., Bahrami, M., & Hamidifar, H. (2025). Integrating remote sensing and meteorological analysis for monitoring drought conditions in arid regions: a case study from Sistan and Baluchestan province, Iran. Theoretical and Applied Climatology, 156(5), 291.
So¨nmez, F.K., Koemuescue, A.U., Erkan, A., & Turgu, E. (2005). An analysis of spatial and temporal dimension of drought vulnerability in Turkey using the standardized precipitation index. Natural Hazards, 35, 243–264.
Svoboda, M., & Fuchs, B. (2017). Handbook of Drought Indicators and Indices Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev, 38, 55. https://doi.org/10.2307/210739
Tabari, H., Abghari, H., & Hosseinzadeh Talaee, P. J. H. P. (2012). Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran. Hydrological processes26(22), 3351-3361.
Talebi, M. (2023). Water crisis in Iran and its security consequences. J Hydraulic Struct, 8(4), 17-28.
Theil, H. (1950). A rank invariant method of linear and Polynomial regression analysis, Part3. Netherlands Akademic van Wettenschappen, Proceedings, 53, 1379-1412.
Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical review, 38(1), 55-94.
Torabinezhad, N., Zarrin, A. & Dadashi-Roudbari, A. (2023). Analysis of Different Types of Droughts and Their Characteristics in Iran Using the Standardized Precipitation Evapotranspiration Index (SPEI). Water and Soil, 37(3), 473-486. doi: 10.22067/jsw.2023.81322.1257 (In Persian)
Trenberth, KE., Dai, A., van der Schrier, G., Jones, PD., Barichivich, J., Briffa, KR., & Sheffield, J. (2014). Global warming and changes in drought. Nat Clim Chang, 4, 17–22. https://doi.org/10.1038/nclimate2067
Tsakiris, G., Pangalou, D., & Vangelis, H. (2007). Regional drought assessment based on the reconnaissance drought index (RDI). J Water Resour Manage, 21, 821–833. https://doi.org/10.1007/s11269- 006-9105-4
Ullah, I., Yuanjie, Z., Ali. S., & Rahman, G. (2020). Rainfall and drought variability in spatial and temporal context in Lop Nor region, South Xinjiang, China, during 1981–2018. Arabian Journal of Geosciences, 13, 1–13.
Valenzuela-Morales, G., Hernández-Téllez, M., Fonseca-Ortiz, C., Gómez-Albores, M., Esquivel-Ocadiz, A., Arévalo-Mejía, R., Mejía-Olivares, A., & Mastachi-Loza, C. (2023). Climatic and socioeconomic regionalization of the meteorological drought in Mexico using a predictive algorithm. Nat. Hazards, 117, 1381–1403.
Vicente-Serrano, S. M., Van der Schrier, G., Beguería, S., Azorin-Molina, C., & Lopez-Moreno, J. I. (2015). Contribution of precipitation and reference evapotranspiration to drought indices under different climates. Journal of Hydrology, 526, 42-54.
Vicente-Serrano, SM., Lopez-Moreno, J-I., Beguería, S., Lorenzo-Lacruz, J., Sanchez-Lorenzo, A., García-Ruiz, JM., Azorin-Molina, C., Morán- Tejeda, E., Revuelto, J., Trigo, R., Coelho, F., & Espejo, F. (2014). Evidence of increasing drought severity caused by temperature rise in southern Europe. Environ Res, Lett 9, 044001. https://doi.org/10.1088/1748-9326/9/4/044001
Visente Serrano, S.M., López-Moreno, J.I., Drummond, A., Gimeno, L., Nieto, R., Morán-Tejeda, E., & Zabalza, J. (2011). Effects of warming processes on droughts and water resources in the NW Iberian Peninsula, (1930-2006). Climate Research, 48, pp. 203-212.
Von Storch, H. (1999). Misuses of statistical analysis in climate research. In: Analysis of climate variability. Springer, pp 11-26.
Wang, T., Tu, X., Singh, V.P.; Chen, X., Lin, K., Zhou, Z., & Tan, Y.  (2023). Assessment of future socioeconomic drought based on CMIP6: Evolution, driving factors and propagation. J. Hydrol. 617, 129009.
Wu, H., Svoboda, M. D., Hayes, M. J., Wilhite, D. A., & Wen, F. (2007). Appropriate application of the standardized precipitation index in arid locations and dry seasons.
Xu, J., Zhou, G., Su, S., Cao, Q., & Tian, Z. (2022). The development of a rigorous model for bathymetric mapping from multispectral satellite-images. Remote Sens, 14, 2495.
Zarch, M. A. A., Sivakumar, B., & Sharma, A. (2015). Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI). Journal of hydrology526, 183-195.
Zhang, F., Cui, N., Guo, S., Yue, Q., Jiang, S., Zhu, B., & Yu, X. (2023). Irrigation strategy optimization in irrigation districts with seasonal agricultural drought in southwest China: A copula-based stochastic multi objective approach. Agric. Water Manag, 282, 108293.
Zhou, G., Lin, G., Liu, Z., Zhou, X., Li, W., Li, X., & Deng, R.  (2023a). An optical system for suppression of laser echo energy from the water surface on single-band bathymetric LiDAR. Opt. Lasers Eng, 163, 107468.
Zhou, G., Zhang, H., Xu, C., Zhou, X., Liu, Z., Zhao, D., Lin, J., & Wu, G. (2023b). A real-time data acquisition system for single-band bathymetric LiDAR. IEEE Trans. Geosci. Remote Sens, 61, 1–21.