ANALYSING GROWTH PATTERNS OF SELECTED TURKISH OAT CULTIVARS USING SIGMOIDAL MODELS

Year 2022, Volume: 27 Issue: 1, 95 - 102, 20.06.2022

Onur Hocaoğlu

https://doi.org/10.17557/tjfc.1064138

Abstract

Increasing popularity of oat were accompanied with the introductions of many new cultivars for the last few decades. Aim of this study were to characterize the growth and developments of Kahraman, Küçükyayla, Yeniçeri, Sebat, Otağı and Diriliş oat cultivars using sigmoidal growth models. Growth data comprised of weekly observations of dry weights and growth stages with three samplings for two consecutive years. Results indicated that the growing season were the determining factor for the dry matter accumulation until the stem elongation stage since genotype differences became apparent only in the later stages. Sigmoidal growth models successfully fitted to the growth data and allowed for further evaluations. Goodness of fit statistics implied that Logistic, Logistic Power and Ratkowsky models were the best fitting growth models to explain dry matter accumulations of oat cultivars. Analysis also showed that Otağı, Yeniçeri and Sebat cultivars reached the highest dry matter accumulations. Point of inflections on the Logistic models indicated that Küçükyayla and Kahraman were the earliest cultivars in the Marmara region. Comparison of cultivars by using the growth models proved to be informative in terms of understanding the genotypic variation.

Keywords

dry matter accumulation, earliness, growth models, Marmara, oat, Turkey

References

• Buerstmayr, H., N., Krenn, U., Stephan, H., Grausgruber, E., Zechner. 2007. Agronomic performance and quality of oat (Avena sativa L.) genotypes of worldwide origin produced under Central European growing conditions. Field Crops Research, 101(3), 343-351.
• Diel, M. I., A. D. C. Lúcio, B. G. Sari, T. Olivoto, M. V. M. Pinheiro, D. K. Krysczum, P. J. Melo, D. Schmidt. 2020. Behavior of strawberry production with growth models: a multivariate approach. Acta Scientiarum. Agronomy, 43.
• Duan, A., J., Zhang, X., Zhang, C. He. 2015. Relationship between modelling accuracy and inflection point attributes of several equations while modelling stand diameter distributions. Plos one, 10(5), e0126831. doi:10.1371/journal.pone.0126831
• Gompertz, B. 1815. On the Nature of the Function Expressive of the Law of Human Mortality, and on a New Mode of Determining the Value of Life Contingencies. https://doi.org/10.1098/rspl.1815.0271
• Goshu, A. T., P. R. Koya. 2013. Derivation of inflection points of nonlinear regression curves-implications to statistics. Am J Theor Appl Stat, 2(6), 268-272. Hısır, Y., R., Kara, T. Dokuyucu. 2012. Evaluation of oat (Avena sativa L.) genotypes for grain yield and physiological traits. Žemdirbystė (Agriculture), 99(1), 55-60.
• Hocaoğlu, O., Y., Coşkun. 2018. Evaluation of dry matter accumulation in triticale by different sigmoidal growth models in west anatolia of Turkey. Genetika, 50(2), 561-574.
• Hocaoglu, O., M., Akcura. 2020. Decision making on nitrogen management of oat (Avena sativa) using grain yield potential and nitrogen use efficiency. Turkish Journal Of Field Crops, 25(2), 208-215.
• Hyams, D. G. 2010. CurveExpert Software, http://www.curveexpert.net.
• Hyams, D. G. 2020. Curve Expert Professional Documentation Release 2.7.3, https://www.curveexpert.net/support/documentation/.
• Karadavut, U. 2009. Non-linear models for growth curves of triticale plants under irrigation conditions. Turkish Journal of Field Crops, 14(2), 105-110.
• Keskin, I., B., Dag, V., Sariyel, M., Gokmen. 2009. Estimation of growth curve parameters in Konya Merino sheep. South African Journal of Animal Science, 39(2).
• Kuglitsch, F. G., A., Toreti, E., Xoplaki, P. M., Della‐Marta, C. S., Zerefos, M., Türkeş, J. Luterbacher. 2010. Heat wave changes in the eastern Mediterranean since 1960. Geophysical Research Letters, 37(4).
• Loskutov, I. G., E. K., Khlestkina. 2021. Wheat, barley, and oat breeding for health benefit components in grain. Plants, 10(1), 86.
• Önning, G., B., Åkesson, R., Öste, I., Lundquist. 1998. Effects of consumption of oat milk, soya milk, or cow’s milk on plasma lipids and antioxidative capacity in healthy subjects. Annals of nutrition and metabolism, 42(4), 211-220.
• Rasane, P., A., Jha, L., Sabikhi, A., Kumar, V. S., Unnikrishnan. 2015. Nutritional advantages of oats and opportunities for its processing as value added foods-a review. Journal of food science and technology, 52(2), 662-675.
• Saadi, S., M., Todorovic, L., Tanasijevic, L. S., Pereira, C., Pizzigalli, P. Lionello. 2015. Climate change and Mediterranean agriculture: Impacts on winter wheat and tomato crop evapotranspiration, irrigation requirements and yield. Agricultural water management, 147, 103-115.
• Tosh, S. M., N., Bordenave. 2020. Emerging science on benefits of whole grain oat and barley and their soluble dietary fibers for heart health, glycemic response, and gut microbiota. Nutrition reviews, 78(Supplement_1), 13-20.
• Tjørve, E. 2003. Shapes and functions of species–area curves: a review of possible models. Journal of Biogeography, 30(6), 827-835.
• Verhulst, Pierre-François. 1838. Notice sur la loi que la population poursuit dans son accroissement. Correspondance Mathématique et Physique. 10: 113–121. Retrieved 3 December 2014.
• Welch, R. W. (Ed.). 2012. The oat crop: production and utilization. Chapman & Hall, 2-6 Boundary Row, London, England.
• Wen, Y., K. Liu, H. Liu, H. Cao, H. Mao, X. Dong, Z. Yin. 2019. Comparison of nine growth curve models to describe growth of partridges (Alectoris chukar). Journal of Applied Animal Research. 47:1, 195-200, DOI: 10.1080/09712119.2019.1599900
• Zadoks, J. C., T. T., Chang and C. F., Konzak. 1974. A decimal code for the growth stages of cereals. Weed research, 14(6), 415-421.