Hasan AKAY, Elif ÖZTÜRK, İsmail SEZER, Mehmet Sait KİREMİT, Hakan ARSLAN, Bhaskara Anggarda Gathot SUBRATA

Mısır Fidelerinin Morfo-Fizyolojik Özelliklerinin Verime Doğrudan ve Dolaylı Etkilerinin Yapısal Eşitlik Modellemesinin Kısmi En Küçük Kare (SEM-PLS) Yaklaşımıyla Değerlendirilmesi

Environmental stress factors have a very complex effect on the growth and growth parameters of plants. Therefore, special analytical techniques such as SEM-PLS can better understand the between observational variables and abiotic stress factors. Therefore, the present study was aimed to evaluate the, directly and indirectly, effects of the growth and biochemical parameters of sweet corn seed on yield, which seed primed with different melatonin doses and grown under different soil salinity conditions using the SEM-PLS model. Seeds of sweet corn cultivar Vega F1 were soaked in 0, 50, 100, and 200 μM of melatonin solution for 24 h, and then primed seeds were cultivated under four (0.27, 5.45, 9.00, and 12.32 dSm-1) soil salinity conditions. The study results showed that melatonin directly and positively affected growth parameters (β = 0.502, p <0.05). In contrast, salinity directly and negatively affected growth parameters (β = -0.689, p <0.05). Also, melatonin had a mostly indirect effect (β = 0.623) on biochemical components compared to direct effect (β = -0.277). The indirect effect (β = -0.855) of salinity on biochemical components was more significant than its direct effect (β = 0.244). Finally, the SEM-PLS can be used as a significant tool for understanding the benefits of melatonin and salinity’s positive or negative effects through direct and indirect relationships with the mediating variables of growth parameters and biochemical, which are essential to optimize sweet corn yield.

Anahtar Kelimeler:

SEM-PLS, melatonin, tatlı mısır, verim, büyüme parametreleri

Evaluation of the Directly and Indirectly Effects of the Morpho-Physiological Traits of Sweet Corn Seedlings on Yield with Structural Equation Modeling Partial Least Square (SEM-PLS) Approach

Keywords:

SEM-PLS, melatonin, sweet corn, yield, growth parameters,

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Acosta-Motos, J., Ortuño, M., Bernal-Vicente, A., Diaz-Vivancos, P., Sanchez-Blanco, M., & Hernandez, J. (2017). Plant Responses to Salt Stress: Adaptive Mechanisms. Agronomy, 7(1), 18. https://doi.org/10.3390/agronomy7010018
Ali, M., Afzal, S., Parveen, A., Kamran, M., Javed, M. R., Abbasi, G. H., Malik, Z., Riaz, M., Ahmad, S., Chattha, M. S., Ali, M., Ali, Q., Uddin, M. Z., Rizwan, M., & Ali, S. (2021). Silicon mediated improvement in the growth and ion homeostasis by decreasing Na+ uptake in maize (Zea mays L.) cultivars exposed to salinity stress. Plant Physiology and Biochemistry, 158, 208–218. https://doi.org/10.1016/j.plaphy.2020.10.040
Anderson, J. C., Kellogg, J. L., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
Bahcesular, B., Yildirim, E. D., Karaçocuk, M., Kulak, M., & Karaman, S. (2020). Seed priming with melatonin effects on growth, essential oil compounds and antioxidant activity of basil (Ocimum basilicum L.) under salinity stress. Industrial Crops and Products, 146, 112165. https://doi.org/10.1016/j.indcrop.2020.112165
Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5), 815–824. https://doi.org/10.1016/J.PAID.2006.09.018
Cao, Q., Li, G., Cui, Z., Yang, F., Jiang, X., Diallo, L., & Kong, F. (2019). Seed Priming with Melatonin Improves the Seed Germination of Waxy Maize under Chilling Stress via Promoting the Antioxidant System and Starch Metabolism. Scientific Reports, 9(1), 1–12. https://doi.org/10.1038/s41598-019-51122-y
Chin, W. W. (1988). The Partial Least Squares Approach to Structural Equation Modeling. In G. A. Marcoulides (Ed.), Methodology for business and management (pp. 295–336). Lawrence Erlbaum Associates Publishers.
Dai, L., Li, J., Harmens, H., Zheng, X., & Zhang, C. (2020). Melatonin enhances drought resistance by regulating leaf stomatal behaviour, root growth and catalase activity in two contrasting rapeseed (Brassica napus L.) genotypes. Plant Physiology and Biochemistry, 149, 86–95. https://doi.org/10.1016/j.plaphy.2020.01.039
Erland, L. A. E., & Saxena, P. K. (2018). Melatonin in plant morphogenesis. In Vitro Cellular and Developmental Biology - Plant, 54(1), 3–24. https://doi.org/10.1007/s11627-017-9879-5
Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: an updated review. In Ecological Processes (Vol. 5, Issue 1, pp. 1–12). Springer Verlag. https://doi.org/10.1186/s13717-016-0063-3
Grace, J. B., Youngblood, A., & Scheiner, S. M. (2009). Structural equation modeling and ecological experiments. In Real World Ecology: Large-Scale and Long-Term Case Studies and Methods (pp. 19–45). Springer New York. https://doi.org/10.1007/978-0-387-77942-3_2
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hill, E. C., Renner, K. A., Sprague, C. L., & Fry, J. E. (2017). Structural Equation Modeling of Cover Crop Effects on Soil Nitrogen and Dry Bean. Agronomy Journal, 109(6), 2781–2788. https://doi.org/10.2134/agronj2016.12.0712
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Huang, M., Zhang, Z., Zhu, C., Zhai, Y., & Lu, P. (2019). Effect of biochar on sweet corn and soil salinity under conjunctive irrigation with brackish water in coastal saline soil. Scientia Horticulturae, 250, 405–413. https://doi.org/10.1016/j.scienta.2019.02.077
Isayenkov, S. V., & Maathuis, F. J. M. (2019). Plant salinity stress: Many unanswered questions remain. Frontiers in Plant Science, 10, 80. https://doi.org/10.3389/fpls.2019.00080
Jahan, M., Nassiri Mahallati, M., & Amiri, M. B. (2019). The effect of humic acid and water super absorbent polymer application on sesame in an ecological cropping system: a new employment of structural equation modeling in agriculture. Chemical and Biological Technologies in Agriculture, 6(1), 1–15. https://doi.org/10.1186/s40538-018-0131-2
Jiang, C., Cui, Q., Feng, K., Dafeng Xu, Li, C., Zheng, Q., & Górski, F. (2016). Melatonin improves antioxidant capacity and ion homeostasis and enhances salt tolerance in maize seedlings. Acta Physiol Plant, 38(82), 9. https://doi.org/10.1007/s11738-016-2101-2
Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (Todd D. Little (ed.); 4th ed.). The Guilford Press.
Lam, T. Y., & Maguire, D. A. (2012). Structural Equation Modeling: Theory and Applications in Forest Management. International Journal of Forestry Research, 2012, 1–16. https://doi.org/10.1155/2012/263953
Lamb, E. G., Shirtliffe, S. J., & May, W. E. (2011). Structural equation modeling in the plant sciences: An example using yield components in oat. Canadian Journal of Plant Science, 91(4), 603–619. https://doi.org/10.4141/cjps2010-035
Li, H., Chang, J., Chen, H., Wang, Z., Gu, X., Wei, C., Zhang, Y., Ma, J., Yang, J., & Zhang, X. (2017). Exogenous Melatonin Confers Salt Stress Tolerance to Watermelon by Improving Photosynthesis and Redox Homeostasis. Frontiers in Plant Science, 8, 295. https://doi.org/10.3389/fpls.2017.00295
Li, X., Yu, B., Cui, Y., & Yin, Y. (2017). Melatonin application confers enhanced salt tolerance by regulating Na+ and Cl− accumulation in rice. Plant Growth Regulation, 83(3), 441–454. https://doi.org/10.1007/s10725-017-0310-3
Liang, D., Ni, Z., Xia, H., Xie, Y., Lv, X., Wang, J., Lin, L., Deng, Q., & Luo, X. (2019). Exogenous melatonin promotes biomass accumulation and photosynthesis of kiwifruit seedlings under drought stress. Scientia Horticulturae, 246, 34–43. https://doi.org/10.1016/j.scienta.2018.10.058
Liu, J., Shabala, S., Zhang, J., Ma, G., Chen, D., Shabala, L., Zeng, F., Chen, Z., Zhou, M., Venkataraman, G., & Zhao, Q. (2020). Melatonin improves rice salinity stress tolerance by NADPH oxidase‐dependent control of the plasma membrane K+ transporters and K+ homeostasis. Plant, Cell & Environment, 43(11), 2591–2605. https://doi.org/10.1111/pce.13759
Mbarki, S., Sytar, O., Cerda, A., Zivcak, M., Rastogi, A., He, X., Zoghlami, A., Abdelly, C., & Brestic, M. (2018). Strategies to mitigate the salt stress effects on photosynthetic apparatus and productivity of crop plants. In Salinity Responses and Tolerance in Plants, Volume 1: Targeting Sensory, Transport and Signaling Mechanisms (Vol. 1, pp. 85–136). Springer International Publishing. https://doi.org/10.1007/978-3-319-75671-4_4
Numan, M., Bashir, S., Khan, Y., Mumtaz, R., Shinwari, Z. K., Khan, A. L., Khan, A., & AL-Harrasi, A. (2018). Plant growth promoting bacteria as an alternative strategy for salt tolerance in plants: A review. Microbiological Research, 209, 21–32. https://doi.org/10.1016/j.micres.2018.02.003
Qin, H., Wang, J., Chen, X., Wang, F., Peng, P., Zhou, Y., Miao, Y., Zhang, Y., Gao, Y., Qi, Y., Zhou, J., & Huang, R. (2019). Rice Os DOF 15 contributes to ethylene‐inhibited primary root elongation under salt stress. New Phytologist, 223(2), 798–813. https://doi.org/10.1111/nph.15824
Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N., & Ray, S. (2018). Prediction-Oriented Model Selection in Partial Least Squares Path Modeling. Decision Sciences, 00. https://doi.org/10.1111/deci.12329
Shmueli, G., Ray, S., Velasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552–4564. https://doi.org/10.1016/j.jbusres.2016.03.049
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
Simlat, M., Szewczyk, A., & Ptak, A. (2020). Melatonin promotes seed germination under salinity and enhances the biosynthesis of steviol glycosides in Stevia rebaudiana Bertoni leaves. PLOS ONE, 15(3), e0230755. https://doi.org/10.1371/journal.pone.0230755
Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48(1), 159–205. https://doi.org/10.1016/j.csda.2004.03.005
Wang, H., Liang, L., Liu, S., An, T., Fang, Y., Xu, B., Zhang, S., Deng, X., Palta, J. A., Siddique, K. H. M., & Chen, Y. (2020). Maize genotypes with deep root systems tolerate salt stress better than those with shallow root systems during early growth. Journal of Agronomy and Crop Science, 206(6), 711–721. https://doi.org/10.1111/jac.12437
Wang, L. Y., Liu, J. L., Wang, W. X., & Sun, Y. (2016). Exogenous melatonin improves growth and photosynthetic capacity of cucumber under salinity-induced stress. PHOTOSYNTHETICA, 54(1), 19–27. https://doi.org/10.1007/s11099-015-0140-3
Wang, Q., An, B., Wei, Y., Reiter, R. J., Shi, H., Luo, H., & He, C. (2016). Melatonin Regulates Root Meristem by Repressing Auxin Synthesis and Polar Auxin Transport in Arabidopsis. Frontiers in Plant Science, 07, 1882. https://doi.org/10.3389/fpls.2016.01882
Weir, J. P. (2005). Quantifying Test-Retest Reliability Using The Intraclass Correlation Coefficient and The Sem. Journal of Strength and Conditioning Research, 19(1), 231–240. https://doi.org/10.1519/15184.1
Xiao, S., Liu, L., Wang, H., Li, D., Bai, Z., Zhang, Y., Sun, H., Zhang, K., & Li, C. (2019). Exogenous melatonin accelerates seed germination in cotton (Gossypium hirsutum L.). PLOS ONE, 14(6), e0216575. https://doi.org/10.1371/journal.pone.0216575
Yoon, Y., Kim, M., & Park, W. (2019). Foliar Accumulation of Melatonin Applied to the Roots of Maize (Zea mays) Seedlings. Biomolecules, 9(1), 26. https://doi.org/10.3390/biom9010026
Zhang, J., Zeng, B., Mao, Y., Kong, X., Wang, X., Yang, Y., Zhang, J., Xu, J., Rengel, Z., & Chen, Q. (2017). Melatonin alleviates aluminium toxicity through modulating antioxidative enzymes and enhancing organic acid anion exudation in soybean. Functional Plant Biology, 44(10), 961–968. https://doi.org/10.1071/FP17003